Anal Chem. 2025 Jan 30. doi: 10.1021/acs.analchem.4c06057. Online ahead of print.

ABSTRACT

Off-axis integrated cavity output spectroscopy (OA-ICOS) allows the laser to be reflected multiple times inside the cavity, increasing the effective absorption path length and thus improving sensitivity. However, OA-ICOS systems are affected by various types of noise, and traditional filtering methods offer low processing efficiency and perform limited feature extraction. Deep learning models enable us to extract important features from large-scale, complex spectral data and analyze them efficiently and accurately. We propose a carbon dioxide (CO2) sensor operating in the near-infrared spectral region (1.602 μm) based on OA-ICOS and deep learning models. A radiofrequency (RF) noise source is employed to reduce the cavity-mode noise in OA-ICOS and thus improve the signal-to-noise ratio (SNR). A time-series-based neural network, known as the informer, is employed for filtering CO2 spectral time series. After filtering, spectral features are directly extracted from the filtered spectral data and CO2 concentrations are predicted using a multilayer perceptron (MLP) model. Our results showed that the SNR attained using informer filtering approximately double those obtained using traditional filtering methods (Savitzky-Golay filtering, Kalman filtering, and wavelet threshold). The linear correlation coefficient (R2) between measured concentrations and standard concentrations was increased from 79.74% (obtained by using the absorption-peak-fitting method) to 98.52% (obtained by using the proposed MLP model). Moreover, the detection limit of the CO2 sensor using the MLP model reached 1.38 ppm at 224.4 s, a 3.79-fold improvement compared to that obtained by using the absorption-peak-fitting method. Our results demonstrate the feasibility of integrating deep learning methods in the field of spectroscopy-based sensing and provide a promising approach for spectral data processing.

PMID:39882837 | DOI:10.1021/acs.analchem.4c06057

Pulmonology. 2025 Dec 31;31(1):2424637. doi: 10.1080/25310429.2024.2424637. Epub 2024 Nov 6.

NO ABSTRACT

PMID:39883507 | DOI:10.1080/25310429.2024.2424637

Tree Physiol. 2025 Jan 30:tpaf016. doi: 10.1093/treephys/tpaf016. Online ahead of print.

ABSTRACT

Ink disease caused by the hemibiotrophic root pathogen Phytophthora cinnamomi (Pc) is devastating for the European chestnut (Castanea sativa), unlike Asian chestnuts and interspecific hybrids which are resistant to Pc. The role that hormone responses play for Pc resistance remains little understood, especially regarding the temporal regulation of hormone responses. We explored the relationship between changes in tree health and physiology and alterations in leaf and root phytohormones and primary and secondary metabolites during compatible and incompatible Castanea spp.-Pc interactions. Susceptible (S) C. sativa and resistant (R) C. sativa x C. crenata ramets were inoculated with Pc in roots and assessed for disease progression, leaf physiology and biochemistry at 1, 3, 5 and 8 days after inoculation (dai). In S chestnuts, Pc increasingly deteriorated the leaf physiological functioning by decreasing leaf CO2 assimilation, stomatal conductance, transpiration rate, chlorophylls content and the maximum quantum yield of the photosystem II over time, triggering aerial symptoms (leaf wilting and chlorosis) 8 dai. Pc had little impact on the leaf physiological functioning of R chestnuts which remained asymptomatic. In roots of S chestnuts, Pc transiently induced jasmonates signaling (5 dai) while impairing root carbohydrates metabolism over time. In leaves, a transient antioxidant burst (5 dai) followed by abscisic acid (ABA) accumulation (8 dai) was observed. R chestnuts responded to Pc by up-regulating root salicylic acid (SA) at early (1 dai) and late (8 dai) infection stages, in an antagonistic crosstalk with root ABA. Overall, the results pinpoint an important role of SA in mediating the resistant response of chestnuts to Pc, but also show that the specific hormone pathways induced by Pc are genotype dependent. The study also highlights that the dynamic nature of hormone responses over time must be considered when elucidating hormone-regulated responses to Pc.

PMID:39883087 | DOI:10.1093/treephys/tpaf016

Arterioscler Thromb Vasc Biol. 2025 Jan 30. doi: 10.1161/ATVBAHA.124.321527. Online ahead of print.

ABSTRACT

BACKGROUND: Marfan syndrome (MFS) is an inherited disorder caused by mutations in the FBN1 gene encoding fibrillin-1, a matrix component of extracellular microfibrils. The main cause of morbidity and mortality in MFS is thoracic aortic aneurysm and dissection, but the underlying mechanisms remain undetermined.

METHODS: To elucidate the role of endothelial XOR (xanthine oxidoreductase)-derived reactive oxygen species in aortic aneurysm progression, we inhibited in vivo function of XOR either by endothelial cell (EC)-specific disruption of the Xdh gene or by systemic administration of an XOR inhibitor febuxostat in MFS mice harboring the Fbn1 missense mutation p.(Cys1041Gly). We assessed the aberrant activation of mechanosensitive signaling in the ascending aorta of Fbn1C1041G/+ mice. Further analysis of human aortic ECs investigated the mechanisms by which mechanical stress upregulates XOR expression.

RESULTS: We found a significant increase in reactive oxygen species generation in the ascending aorta of patients with MFS and Fbn1C1041G/+ mice, which was associated with a significant increase in protein expression and enzymatic activity of XOR protein in aortic ECs. Genetic disruption of Xdh in ECs or treatment with febuxostat significantly suppressed aortic aneurysm progression and improved perivascular infiltration of macrophages. Mechanistically, mechanosensitive signaling involving FAK (focal adhesion kinase)-p38 MAPK (p38 mitogen-activated protein kinase) and Egr-1 (early growth response-1) was aberrantly activated in the ascending aorta of Fbn1C1041G/+ mice, and mechanical stress on human aortic ECs upregulated XOR expression through Egr-1 upregulation. Consistently, EC-specific knockout of XOR or systemic administration of febuxostat in Fbn1C1041G/+ mice suppressed reactive oxygen species generation, FAK-p38 MAPK activation, and Egr-1 upregulation.

CONCLUSIONS: Aberrant activation of mechanosensitive signaling in vascular ECs triggered endothelial XOR activation and reactive oxygen species generation, which contributes to the progression of aortic aneurysms in MFS. These findings highlight a drug repositioning approach using a uric acid-lowering drug febuxostat as a potential therapy for MFS.

PMID:39882602 | DOI:10.1161/ATVBAHA.124.321527

Int J Gen Med. 2025 Jan 25;18:391-402. doi: 10.2147/IJGM.S490196. eCollection 2025.

ABSTRACT

BACKGROUND: Bronchiectasis, a respiratory ailment, significantly impacts the life expectancy of individuals. This study aimed to explore the prevalence of multidrug-resistant organisms (MDROs) among bronchiectasis patients, the resistance patterns within various antibiotic classes, and the associated factors with these organisms.

METHODS: A retrospective observational analysis was conducted on adult bronchiectasis patients attending clinics at Jordan University Hospital. The diagnosis of bronchiectasis was established through lung Computerized Tomography (CT) scans and clinical symptom assessment.

RESULTS: The study encompassed 235 patients, revealing a notably higher occurrence of MDROs among non-cystic fibrosis patients compared to their counterparts (P-value=0.001). Additionally, MDROs showed significant associations with the usage of inhaled beta agonists, anti-cholinergics, corticosteroids, and inhaled antibiotics (P-value<0.050). Patients with MDROs experienced a significantly elevated mean number of hospitalizations, exacerbations, and antibiotic courses compared to their counterparts (P-value<0.050). Moreover, those with MDROs exhibited a higher incidence of requiring O2 device support and faced an increased risk of mortality (P-value<0.050).

CONCLUSION: The observational nature of our study limits the associations in our study. However, we provided evidence that it is imperative for clinicians to assess their bronchiectasis patients for MDRO risk factors, facilitating appropriate initial antibiotic selection. Nevertheless, the validation of MDRO risk factors necessitates further exploration through larger studies with extended follow-up periods.

PMID:39881954 | PMC:PMC11776428 | DOI:10.2147/IJGM.S490196

Case Rep Anesthesiol. 2025 Jan 21;2025:6388254. doi: 10.1155/cria/6388254. eCollection 2025.

ABSTRACT

Recent advancements in therapeutics and risk reduction in the management of cystic fibrosis have increased the life expectancy of cystic fibrosis patients to the fifth decade of life. As the life expectancy of cystic fibrosis patients has increased, more cystic fibrosis patients have opted to pursue pregnancy. Normal pregnancy is accompanied by physiological changes that affect anesthetic management. These normal physiological changes, combined with the pathological manifestations of cystic fibrosis, create a unique set of anesthetic challenges. Here, we report on the management and clinical course of a 37-year-old parturient with cystic fibrosis, focusing on the anesthetic approach.

PMID:39881848 | PMC:PMC11774568 | DOI:10.1155/cria/6388254

Sovrem Tekhnologii Med. 2024;16(4):38-44. doi: 10.17691/stm2024.16.4.04. Epub 2024 Aug 30.

ABSTRACT

The aim of the study was to search for the associations of benign unconjugated hyperbilirubinemia phenotype with rs1799945 (H63D), rs1800562 (C282Y), rs1800730 (S65C) mutations of HFE gene, rs113993960 (ΔF508) of CFTR gene, rs28929474 (PIZ), rs17580 (PIS) mutations of SERPINA1 gene.

MATERIAL AND METHODS: The study design is case-control. The group with Gilbert's syndrome (GS) phenotype (n=414; mean age - 36.7±15.9 years; 49.8% men) was formed by gastroenterologists, and included the individuals with unconjugated hyperbilirubinemia who underwent a standard clinical examination. The individuals with known causes of unconjugated hyperbilirubinemia were excluded from the group. The control group (n=429; mean age - 38.5±14.3 years; 52.2% men) was a random sampling from DNA banks of MONICA project participants, the screening of young people aged 25-44 and a one-time study of schoolchildren in Novosibirsk (Russia). DNA was isolated by phenol-chloroform extraction or by the express method (PROBA-RAPID-GENETIKA; DNA-Technology, Moscow, Russia) from venous blood. Genotyping of groups by nucleotide sequence rs1799945 (H63D), rs1800562 (C282Y), rs1800730 (S65C) of HFE gene, rs113993960 (ΔF508) of CFTR gene, rs28929474 (PIZ), rs17580 (PIS) of SERPINA1 gene was performed by polymerase chain reaction followed by the analysis of fragment length polymorphism on a polyacrylamide gel.

RESULTS: According to the genotypes and alleles of the variants rs1799945 (H63D), rs1800562 (C282Y), rs1800730 (S65C) of HFE gene, rs113993960 (ΔF508) of CFTR gene, rs28929474 (PIZ), rs17580 (PIS) of SERPINA1 gene, no statistically significant differences were found between the GS group and the control group (p>0.05).

CONCLUSION: Nucleotide sequence variants rs1799945 (H63D), rs1800562 (C282Y), rs1800730 (S65C) of HFE gene, rs113993960 (ΔF508) of CFTR gene, rs28929474 (PIZ), rs17580 (PIS) of SERPINA1 gene, or their combinations with rs3064744 of UGT1A1 gene were found to have no association with GS.

PMID:39881831 | PMC:PMC11773142 | DOI:10.17691/stm2024.16.4.04

J Chem Theory Comput. 2025 Jan 30. doi: 10.1021/acs.jctc.4c01602. Online ahead of print.

ABSTRACT

Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by the scarcity of experimental data. Here, we report the development of pKa ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment pKa database (PKAD-3) enriched with highly shifted pKa's. KaML-CBtree significantly outperforms the current state of the art in predicting pKa values and ionization states across all six titratable amino acids, notably achieving accurate predictions for deprotonated cysteines and lysines─a blind spot in previous models. The superior performance of KaMLs is achieved in part through several innovations, including the separate treatment of acid and base, data augmentation using AlphaFold structures, and model pretraining on a theoretical pKa database. We also introduce the classification of protonation states as a metric for evaluating pKa prediction models. A meta-feature analysis suggests a possible reason for the lightweight tree model to outperform the more complex deep learning GAT. We release an end-to-end pKa predictor based on KaML-CBtree and the new PKAD-3 database, which facilitates a variety of applications and provides the foundation for further advances in protein electrostatic research.

PMID:39882632 | DOI:10.1021/acs.jctc.4c01602

Front Med (Lausanne). 2025 Jan 15;11:1505692. doi: 10.3389/fmed.2024.1505692. eCollection 2024.

ABSTRACT

INTRODUCTION: In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence.

METHODS: Following a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics.

RESULTS: A total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT.

CONCLUSION: This study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.

PMID:39882522 | PMC:PMC11775008 | DOI:10.3389/fmed.2024.1505692

Heliyon. 2025 Jan 8;11(2):e41802. doi: 10.1016/j.heliyon.2025.e41802. eCollection 2025 Jan 30.

ABSTRACT

Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions. Therefore, there is a need to estimate uncertainty in the model's predictions, which cannot be estimated by classical DL models alone. To address this issue, Bayesian DL methods have been proposed, and their use is increasing in the field. In this paper, we developed a straightforward architecture for the classification of DR using a Convolutional Neural Network (CNN) model. We then applied the Bayesian CNN twice, once using Variational Inference (VI) and once using Monte Carlo dropout (MC-dropout) methods, to the same CNN architecture. This allowed us to gain the posterior predictive distributions for each of them. The performance of the proposed models was evaluated on two benchmark datasets, namely APTOS 2019 and Messidor-2. Experimental findings demonstrated that the proposed models surpassed other state-of-the-art models, achieving a test accuracy of 94.70 % and 77.00 % for CNN, 94.00 % and 86.00 % for BCNN-VI, and 93.30 % and 81.00 % for BCNN-MC-dropout on the APTOS dataset and Messidor-2 dataset, respectively. Finally, we computed the entropy and standard deviation on the obtained predictive distribution to quantify the model uncertainty. This research highlights the potential benefits of using Bayesian DL methods in medical image analysis to improve the accuracy and reliability of diagnosing disease and treatment.

PMID:39882466 | PMC:PMC11774831 | DOI:10.1016/j.heliyon.2025.e41802

Front Neurol. 2025 Jan 15;15:1510522. doi: 10.3389/fneur.2024.1510522. eCollection 2024.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

METHODS: We searched for records related to MRI studies in AD patients from 2004 to 2023 in the Web of Science Core Collection (WoSCC) database. CiteSpace was applied to analyze institutions, references and keywords. VOSviewer was used for the analysis of countries, authors and journals.

RESULTS: A total of 13,659 articles were obtained in this study. The number of published articles showed overall exponential growth from 2004 to 2023. The top country and institution were the United States and the University of California System, accounting for 40.30% and 9.88% of the total studies, respectively. Jack CR from the United States was the most productive author. The most productive journal was the Journal of Alzheimers Disease. Keyword burst analysis revealed that "machine learning" and "deep learning" were the keywords that frequently appeared in the past 6 years. Timeline views of the references revealed that "#0 tau pathology" and "#1 deep learning" are currently the latest research focuses.

CONCLUSION: This study provides an in-depth overview of publications on MRI studies in AD. The United States is the leading country in this field with a concentration of highly productive researchers and high-level institutions. The current research hotspot is deep learning, which is being applied to develop noninvasive diagnosis and safer treatment of AD.

PMID:39882364 | PMC:PMC11774745 | DOI:10.3389/fneur.2024.1510522

Digit Discov. 2025 Jan 24. doi: 10.1039/d4dd00219a. Online ahead of print.

ABSTRACT

Plastic pollution, particularly microplastics (MPs), poses a significant global threat to ecosystems and human health, necessitating innovative remediation strategies. Biocompatible and biodegradable plastic-binding peptides (PBPs) offer a potential solution through targeted adsorption and subsequent MP detection or removal from the environment. A challenge in discovering plastic-binding peptides is the vast combinatorial space of possible peptides (i.e., over 1015 for 12-mer peptides), which far exceeds the sample sizes typically reachable by experiments or biophysics-based computational methods. One step towards addressing this issue is to train deep learning models on experimental or biophysical datasets, permitting faster and cheaper evaluations of peptides. However, deep learning predictions are not always accurate, which could waste time and money due to synthesizing and evaluating false positives. Here, we resolve this issue by combining biophysical modeling data from Peptide Binder Design (PepBD) algorithm, the predictive power and uncertainty quantification of evidential deep learning, and metaheuristic search methods to identify high-affinity PBPs for several common plastics. Molecular dynamics simulations show that the discovered PBPs have greater median adsorption free energies for polyethylene (5%), polypropylene (18%), and polystyrene (34%) relative to PBPs previously designed by PepBD. The impact of including uncertainty quantification in peptide design is demonstrated by the increasing improvement in the median adsorption free energy with decreasing uncertainty. This robust framework accelerates peptide discovery, paving the way for effective, bio-inspired solutions to MP remediation.

PMID:39882101 | PMC:PMC11771220 | DOI:10.1039/d4dd00219a

Front Endocrinol (Lausanne). 2025 Jan 14;16:1513929. doi: 10.3389/fendo.2025.1513929. eCollection 2025.

NO ABSTRACT

PMID:39882100 | PMC:PMC11772191 | DOI:10.3389/fendo.2025.1513929

Sovrem Tekhnologii Med. 2024;16(4):5-13. doi: 10.17691/stm2024.16.4.01. Epub 2024 Aug 30.

ABSTRACT

The aim of the study is to develop a method for diagnosing fungal keratitis based on the analysis of photographs of the anterior segment of the eye using deep learning algorithms with subsequent evaluation of sensitivity and specificity of the method on a test data set in comparison with the results of practicing ophthalmologists.

MATERIALS AND METHODS: The study has included the stages of data acquisition, image pre-training and markup, selection of training approach and neural network architecture, training with input data augmentation, validation with hyperparameter correction, evaluation of algorithm performance on a test sample, and determination of sensitivity and specificity of fungal keratitis detection by practicing doctors. A total of 274 anterior segment images were used, including 130 photographs of the eyes affected by fungal keratitis and 144 photographs illustrating normal eyes, keratitis of other etiologies, and various anterior segment pathologies. Photographs taken after the treatment onset, illustrations of keratitis of mixed etiology and corneal perforation were excluded from the study. Images of the training sample were marked up using the VGG Image Annotator web application and then used to train the YOLOv8 convolutional neural network. Images from the test data set were also offered to practicing ophthalmologists to determine the diagnostic accuracy of fungal keratitis.

RESULTS: The sensitivity of the model was 56.0%, the specificity level reached 96.1%, and the proportion of correct answers of the algorithm was 76.5%. The accuracy of image recognition by practicing ophthalmologists was 50.0%, specificity - 41.7%, sensitivity - 57.7%.

CONCLUSION: The study showed the high potential of deep learning algorithms in the diagnosis of fungal keratitis and its advantages in accuracy compared to expert judgment in the absence of metadata. The use of computer vision technologies may find application as a complementary diagnostic method in decision making in complex cases and in telemedicine care settings. Further research is required to compare the developed model with alternative approaches, to expand and standardize databases.

PMID:39881837 | PMC:PMC11773139 | DOI:10.17691/stm2024.16.4.01

Health Inf Sci Syst. 2025 Jan 27;13(1):19. doi: 10.1007/s13755-025-00339-5. eCollection 2025 Dec.

ABSTRACT

Coronary artery disease (CAD) remains the leading cause of death globally, highlighting the critical need for accurate diagnostic tools in medical imaging. Traditional segmentation methods for coronary angiograms often struggle with vessel discontinuity and inaccuracies, impeding effective diagnosis and treatment planning. To address these challenges, we developed the Local Adaptive Segmentation Framework (LASF), enhancing the YOLOv8 architecture with dilation and erosion algorithms to improve the continuity and precision of vascular image segmentation. We further enriched the ARCADE dataset by meticulously annotating both proximal and distal vascular segments, thus broadening the dataset's applicability for training robust segmentation models. Our comparative analyses reveal that LASF outperforms well-known models such as UNet and DeepLabV3Plus, demonstrating superior metrics in precision, recall, and F1-score across various testing scenarios. These enhancements ensure more reliable and accurate segmentation, critical for clinical applications. LASF represents a significant advancement in the segmentation of vascular images within coronary angiograms. By effectively addressing the common issues of vessel discontinuity and segmentation accuracy, LASF stands to improve the clinical management of CAD, offering a promising tool for enhancing diagnostic accuracy and patient outcomes in medical settings.

PMID:39881813 | PMC:PMC11772642 | DOI:10.1007/s13755-025-00339-5

Front Med (Lausanne). 2025 Jan 15;11:1526531. doi: 10.3389/fmed.2024.1526531. eCollection 2024.

ABSTRACT

BACKGROUND: There is a paucity of real-world data on patients with interstitial lung diseases (ILDs) that are progressive, other than idiopathic pulmonary fibrosis (IPF), including treatment patterns and attitudes toward treatment. This study aimed to investigate the diagnosis, clinical characteristics, treatment paradigm and current decision-making practices of IPF and progressive pulmonary fibrosis (PPF) in a Japanese real-world setting.

METHODS: Data were drawn from the Adelphi Real World PPF-ILD Disease Specific Programme™, a cross-sectional survey with retrospective data collection of pulmonologists and rheumatologists in Japan from April to October 2022. Physicians provided data for up to 12 consecutive patients with a physician-confirmed diagnosis of progressive ILD; patients were also invited to complete patient self-completion forms. Analyses were descriptive.

RESULTS: A total of 63 physicians (43 pulmonologists and 20 rheumatologists) provided data on 312 patients with PPF and 70 patients with IPF. Patients had a mean (standard deviation [SD]) age at survey date of 68.0 (11.6) years, 43.5% were female, 50.3% were former smokers and 18.1% were employed full time. For breathlessness, 26.5% of patients had Grade 2 physician-reported breathlessness; this was 16.7% when reported by patients themselves. A total of 81.4% of patients were currently receiving treatment for ILD. Mean (SD) duration of current treatment was 1.5 (1.4) years. Slowing disease progression was the primary reason influencing physicians' choice of current ILD treatment (48.5%). A total of 16.0% had never been treated (most frequent physician-reported reason: disease was manageable without treatment, 55.7%) and 2.6% had treatment discontinued (most frequent reason: patient request, 70.0%). Physicians reported 82.3% of patients as fully compliant with their treatment regimen. As reported by patients themselves (n = 53), 49.1% never and 37.7% rarely missed a dose.

CONCLUSION: This analysis of real-world data from Japan provides insights into the clinical profile of patients with IPF and PPF in Japan, and highlights differences between physicians and patients in perception of symptom severity and attitudes to treatment.

PMID:39882519 | PMC:PMC11775758 | DOI:10.3389/fmed.2024.1526531

J R Soc Interface. 2025 Jan;22(222):20240404. doi: 10.1098/rsif.2024.0404. Epub 2025 Jan 30.

ABSTRACT

Bond graphs provide an energy-based methodology for modelling complex systems hierarchically; at the moment, the method allows biological systems with both chemical and electrical subsystems to be modelled. Herein, the bond graph approach is extended to include chemomechanical transduction thus extending the range of biological systems to be modelled. Actin filament polymerization and force generation is used as an example of chemomechanical transduction, and it is shown that the TF (transformer) bond graph component provides a practical, and conceptually simple, alternative to the Brownian ratchet approach of Peskin, Odell, Oster and Mogilner. Furthermore, it is shown that the bond graph approach leads to the same equation as the Brownian ratchet approach in the simplest case. The approach is illustrated by showing that flexibility and non-normal incidence can be modelled by simply adding additional bond graph components and that compliance leads to non-convexity of the force-velocity curve. Energy flows are fundamental to life; for this reason, the energy-based approach is utilized to investigate the power transmission by the actin filament and its corresponding efficiency. The bond graph model is fitted to experimental data by adjusting the model physical parameters.

PMID:39881657 | DOI:10.1098/rsif.2024.0404

Ther Adv Drug Saf. 2025 Jan 29;16:20420986241310685. doi: 10.1177/20420986241310685. eCollection 2025.

ABSTRACT

In 2019, intranasal esketamine gained approval as a promising therapy for those individuals grappling with treatment-resistant depression. Both clinical trials and real-world studies have underscored its efficacy in alleviating and remitting depressive symptoms, with sustained benefits observed for nearly 4.5 years. As the S-enantiomer of ketamine, esketamine's dosing guidelines and strict medical supervision stem from prior research on ketamine's use in depression and history as a recreational drug. Despite initial concerns, long-term clinical studies have not documented instances of abuse, misuse, addiction or withdrawal, and the same was found in case reports or subsamples of high-risk populations with comorbidities such as substance use disorder or alcohol use disorder. Esketamine has proven to be safe and well tolerated without fostering new-onset substance use in vulnerable groups. Real-world studies reinforced these observations, reporting no adverse events (AEs) related to pharmacological interactions of esketamine with any other substance, and no new-onset drug or alcohol misuse, craving, misuse or diversion of use. Reports of esketamine craving remain rare, with only one case report documented in 2022. Most drug-related AEs reported in pharmacovigilance databases are those identified in the product's technical data sheet and with known reported frequency. More importantly, no register of illicit acquisition of esketamine or its tampering for obtaining ketamine or other altered products was found in our search. Overall, our review confirms esketamine's safety across diverse patient populations, reassuring its responsible use and the scarcity of reports of abuse or misuse since its introduction to the market.

PMID:39882342 | PMC:PMC11776012 | DOI:10.1177/20420986241310685

Front Pharmacol. 2025 Jan 15;15:1523136. doi: 10.3389/fphar.2024.1523136. eCollection 2024.

ABSTRACT

OBJECTIVE: Although there are certain drug categories associated with heart failure (HF), most of the associated risks are unclear. We investigated the top drugs associated with HF and acute HF (AHF) reported in the FDA Adverse Event Reporting System (FAERS).

METHODS: We reviewed publicly available FAERS databases from 2004 to 2023. Using the search terms "cardiac failure" or "cardiac failure acute" and classifying cases by drug name, we processed and analyzed drug reports related to HF or AHF.

RESULTS: From 2004 to 2023, 17,379,609 adverse drug events were reported by FAERS, of which 240,050 (1.38%) were reported as HF. Among those with HF, the male-to-female ratio was 0.94% and 52.37% were >65 years old; 46.2% were from the United States. There were 5,971 patients with AHF. We identified 38 drugs and 13 drug classes with a potential high risk of causing HF, and 41 drugs and 19 drug classes were associated with AHF. The median onset times of HF and AHF were 83 days (IQR: 11-416) and 49 days (IQR: 8-259), respectively. The Weibull shape parameter (WSP) test showed early failure-type profile characteristics.

CONCLUSION: This study highlights key drugs associated with drug-induced HF and AHF, emphasizing the importance of early risk assessment and close monitoring, particularly during the initial stages of treatment. These findings contribute to a better understanding of drug-induced HF and provide a basis for future research on its underlying mechanisms.

PMID:39881876 | PMC:PMC11775474 | DOI:10.3389/fphar.2024.1523136

Int Med Case Rep J. 2025 Jan 25;18:163-171. doi: 10.2147/IMCRJ.S488796. eCollection 2025.

ABSTRACT

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely administered in all age groups due to their effectiveness in reducing fever, relieving pain, and reducing inflammation. However, they have also been identified as the second most common cause of drug-induced hypersensitivity reactions, after beta-lactam antibiotics. Adverse reactions to NSAIDs can range from expected pharmacological side effects such as gastritis to severe allergies, including anaphylaxis. It is important to distinguish true hypersensitivity reactions from other side effects to ensure proper management and patient safety. Four patients aged 35-60 years were treated with NSAIDs for pain management and subsequently developed hypersensitivity reactions to NSAIDs such as ketorolac, ketoprofen, and diclofenac sodium in the type of allergic reactions such as NSAIDs-induced urticaria/angioedema (NIUA). This case series provides valuable insights into the clinical presentations and potential mechanisms of NSAID hypersensitivity in the documented cases in one of the hospitals in Indonesia. It highlights important areas for future investigation, including the need for larger, controlled studies to better understand incidence, risk factors, and generalizability to broader populations.

PMID:39881781 | PMC:PMC11776529 | DOI:10.2147/IMCRJ.S488796