Ther Drug Monit. 2024 Nov 12. doi: 10.1097/FTD.0000000000001267. Online ahead of print.

ABSTRACT

BACKGROUND: Ciprofloxacin (CIP) is effective against many Gram-negative pathogens and penetrates well into respiratory secretions and pulmonary tissues, thus making it useful for treating respiratory infections in patients with cystic fibrosis (CF).

METHODS: A 13-year-old patient with severe CF and an acute respiratory exacerbation from multidrug-resistant Pseudomonas aeruginosa was treated with 700 mg of CIP every 8 hours. Bronchial secretions confirmed that P. aeruginosa was sensitive to high doses of CIP. Pharmacokinetic monitoring using 2 blood samples estimated the AUC24 at 50 hours*mg/L. This led to an increase in CIP dosage to 850 mg three time a day (TID), then to 1000 mg TID, and finally to 1200 mg every 6 hours.

RESULTS: CIP pharmacokinetics can vary significantly, particularly in patients with CF due to increased clearance, ultimately resulting in shorter half-lives and higher risks of therapeutic failure and resistance. Therapeutic drug monitoring helps when adjusting dosages to maintain effective blood concentrations.

CONCLUSIONS: This case underscores the role of therapeutic drug monitoring in optimizing CIP dosing for patients with CF and highlights the necessity for close collaboration between clinicians and pharmacologists to ensure effective antibiotic exposure.

PMID:39933065 | DOI:10.1097/FTD.0000000000001267

Acta Med Port. 2025 Feb 3;38(2):117-118. doi: 10.20344/amp.21948. Epub 2025 Feb 3.

NO ABSTRACT

PMID:39932840 | DOI:10.20344/amp.21948

Biomed Phys Eng Express. 2025 Feb 11. doi: 10.1088/2057-1976/adb494. Online ahead of print.

ABSTRACT

Abstract

Objectives: Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (EDLA) to improve breast cancer subtype prediction using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods: A dataset of 344 patients with histologically confirmed breast cancer was divided into training (n=200), validation (n=60), and testing (n=62) cohorts. RAE-Net, built on ResNet-50 with Multi-Head Attention (MHA) fusion and Multi-Layer Perceptron (MLP) mechanisms, combines radiomic and deep learning features for subtype prediction. The EDLA module adds uncertainty estimation to enhance classification reliability.

Results: RAE-Net with the MFF module achieved a mean accuracy of 0.83 and a Macro-F1 score of 0.78, outperforming traditional radiomics models (accuracy: 0.79, Macro-F1: 0.75) and standalone deep learning models (accuracy: 0.80, Macro-F1: 0.76). With an EDLA uncertainty threshold of 0.2, performance improved significantly, with accuracy reaching 0.97 and Macro-F1 at 0.92. RAE-Net also outperformed ResGANet, increasing accuracy by 0.5% and improving AUC. Compared to HIFUSE, RAE-Net reduced parameters and computational cost by 90%, with only a 5.7% increase in computation time.

Conclusions: RAE-Net integrates feature fusion and uncertainty estimation to predict breast cancer subtypes from DCE-MRI. The model achieves high accuracy while maintaining computational efficiency, demonstrating its potential for clinical use as a reliable and resource-efficient diagnostic tool.

PMID:39933196 | DOI:10.1088/2057-1976/adb494

Biomed Phys Eng Express. 2025 Feb 11. doi: 10.1088/2057-1976/adb495. Online ahead of print.

ABSTRACT

Despite significant progress in diagnosis and treatment, breast cancer remains a formidable health challenge, emphasizing the continuous need for research. This simulation study uses polarized Monte Carlo approach to identify and locate breast cancer. The tissue model Mueller matrix derived from polarized Monte Carlo simulations provides enhanced contrast for better comprehension of tissue structures. This study explicitly targets tumour regions found at the tissue surface, a possible scenario in thick tissue sections obtained after surgical removal of breast tissue lumps. We use a convolutional neural network for the identification and localization of tumours. Nine distinct spatial positions, defined relative to the point of illumination, allow the identification of the tumour even if it is outside the directly illuminated area. A system incorporating deep learning techniques automates processes and enables real-time diagnosis. This research paper aims to showcase the concurrent detection of the tumour's existence and position by utilizing a Convolutional Neural Network (CNN) implemented on depolarized index images derived from polarized Monte Carlo simulations. The classification accuracy achieved by the CNN model stands at 96%, showcasing its optimal performance. The model is also tested with images obtained from in-vitro tissue models, which yielded 100% classification accuracy on a selected subset of spatial positions.

PMID:39933195 | DOI:10.1088/2057-1976/adb495

Biomed Phys Eng Express. 2025 Feb 11. doi: 10.1088/2057-1976/adb493. Online ahead of print.

ABSTRACT

In recent years, deep learning (DL)-based automatic view classification of 2D transthoracic echocardiography (TTE) has demonstrated strong performance, but has not fully addressed key clinical requirements such as view coverage, classification accuracy, inference delay, and the need for thorough exploration of performance in real-world clinical settings. We proposed a clinical requirement-driven DL framework, TTESlowFast, for accurate and efficient video-level TTE view classification. This framework is based on the SlowFast architecture and incorporates both a sampling balance strategy and a data augmentation strategy to address class imbalance and the limited availability of labeled TTE videos, respectively. TTESlowFast achieved an overall accuracy of 0.9881, precision of 0.9870, recall of 0.9867, and F1 score of 0.9867 on the test set. After field deployment, the model's overall accuracy, precision, recall, and F1 score for view classification were 0.9607, 0.9586, 0.9499, and 0.9530, respectively. The inference time for processing a single TTE video was (105.0 ± 50.1) ms on a desktop GPU (NVIDIA RTX 3060) and (186.0 ± 5.2) ms on an edge computing device (Jetson Orin Nano), which basically meets the clinical demand for immediate processing following image acquisition. The TTESlowFast framework proposed in this study demonstrates effective performance in TTE view classification with low inference delay, making it well-suited for various medical scenarios and showing significant potential for practical application.

PMID:39933194 | DOI:10.1088/2057-1976/adb493

Proc Natl Acad Sci U S A. 2025 Feb 18;122(7):e2420498122. doi: 10.1073/pnas.2420498122. Epub 2025 Feb 11.

ABSTRACT

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1 to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artifacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation <10%. Coupled with deep learning, explainable AI, and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management.

PMID:39932995 | DOI:10.1073/pnas.2420498122

Bioinformatics. 2025 Feb 11:btaf069. doi: 10.1093/bioinformatics/btaf069. Online ahead of print.

ABSTRACT

MOTIVATION: The widespread use of antibiotics has led to the emergence of resistant pathogens. Antimicrobial peptides (AMPs) combat bacterial infections by disrupting the integrity of cell membranes, making it challenging for bacteria to develop resistance. Consequently, AMPs offer a promising solution to addressing antibiotic resistance. However, the limited availability of natural AMPs cannot meet the growing demand. While deep learning technologies have advanced AMP generation, conventional models often lack stability and may introduce unforeseen side effects.

RESULTS: This study presents a novel denoising VAE-based model guided by desirable physicochemical properties for AMPs generation. The model integrates key features (e.g., molecular weight, isoelectric point, hydrophobicity, etc.), and employs position encoding along with a Transformer architecture to enhance generation accuracy. A customized loss function, combining reconstruction loss, KL divergence, and property preserving loss, ensures effective model training. Additionally, the model incorporates a denoising mechanism, enabling it to learn from perturbed inputs, thus maintaining performance under limited training data. Experimental results demonstrate that the proposed model can generate AMPs with desirable functional properties, offering a viable approach for AMP design and analysis, which ultimately contributes to the fight against antibiotic resistance.

AVAILABILITY AND IMPLEMENTATION: The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711).

CONTACT AND SUPPLEMENTARY INFORMATION: wzzhao@ccnu.edu.cn, and Supplementary materials are available at Bioinformatics online.

PMID:39932977 | DOI:10.1093/bioinformatics/btaf069

PLoS One. 2025 Feb 11;20(2):e0316543. doi: 10.1371/journal.pone.0316543. eCollection 2025.

ABSTRACT

Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However, with the evolution of glioma, tumours form that invade healthy tissues in the brain, leading to neurological impairment, seizures, hormonal dysregulation, and venous thromboembolism. Medical tests, including medical resonance imaging (MRI), computed tomography (CT) scans, biopsy, and electroencephalograms are used for early detection of glioma. However, these tests are expensive and may cause irritation and allergic reactions due to ionizing radiation. The deep learning models are highly optimal for disease prediction, however, the challenge associated with it is the requirement for substantial memory and storage to amalgamate the patient's information at a centralized location. Additionally, it also has patient data-privacy concerns leading to anonymous information generalization, regulatory compliance issues, and data leakage challenges. Therefore, in the proposed work, a distributed and privacy-preserved horizontal federated learning-based malignant glioma disease detection model has been developed by employing 5 and 10 different clients' architectures in independent and identically distributed (IID) and non-IID distributions. Initially, for developing this model, the collection of the MRI scans of non-tumour and glioma tumours has been done, which are further pre-processed by performing data balancing and image resizing. The configuration and development of the pre-trained MobileNetV2 base model have been performed, which is then applied to the federated learning(FL) framework. The configurations of this model have been kept as 0.001, Adam, 32, 10, 10, FedAVG, and 10 for learning rate, optimizer, batch size, local epochs, global epochs, aggregation, and rounds, respectively. The proposed model has provided the most prominent accuracy with 5 clients' architecture as 99.76% and 99.71% for IID and non-IID distributions, respectively. These outcomes demonstrate that the model is highly optimized and generalizes the improved outcomes when compared to the state-of-the-art models.

PMID:39932966 | DOI:10.1371/journal.pone.0316543

Dentomaxillofac Radiol. 2025 Feb 11:twae069. doi: 10.1093/dmfr/twae069. Online ahead of print.

ABSTRACT

OBJECTIVES: This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in CBCT data to provide a reliable and efficient support-tool for dental implant treatment planning.

METHODS: A dataset of 90 cone beam computed tomography (CBCT) scans was annotated as ground truth for mandibular canal segmentation. The dataset was split into training (n = 69), validation (n = 1), and testing (n = 20) subsets. A deep learning model based on a hierarchical convolutional neural network architecture was developed and trained. The model's performance was evaluated using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD), and average symmetric surface distance (ASSD). Qualitative assessment was performed by two experienced dental imaging practitioners who evaluated the segmentation quality in terms of trust and safety on a 5-point Likert scale at three mandibular locations per side.

RESULTS: The trained model achieved a mean DSC of 0.77 ± 0.09, HD of 1.66 ± 0.86 mm, and ASSD of 0.31 ± 0.15 mm on the testing subset. Qualitative assessment showed no significant difference between the deep learning-based segmentations and ground truth in terms of trust and safety across all investigated locations (p > 0.05).

CONCLUSIONS: The proposed deep learning-based segmentation technique exhibits sufficient accuracy for the reliable identification of mandibular canals in CBCT scans. This automated approach could streamline the pre-operative planning process for dental implant placement, reducing the risk of neurovascular complications and enhancing patient safety.

PMID:39932925 | DOI:10.1093/dmfr/twae069

PLoS One. 2025 Feb 11;20(2):e0314327. doi: 10.1371/journal.pone.0314327. eCollection 2025.

ABSTRACT

In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the temperatures in a city can vary from year to year due to various unclear reasons. In this paper, we utilized three distinct statistical techniques to analyze annual data drifting at various stations. These techniques calculate the P values for each station by comparing data from five years (2014-2018) to identify data drifting phenomena. To find out the data drifting scenario those statistical techniques and calculate the P value from those techniques to measure the data drifting in specific locations. From those statistical techniques, the highest drifting stations can be identified from the previous year's datasets To identify data drifting and highlight areas with significant drift, we utilized meteorological air quality and weather data in this study. We proposed two models that consider the characteristics of data drifting for PM2.5 prediction and compared them with various deep learning models, such as Long Short-Term Memory (LSTM) and its variants, for predictions from the next hour to the 64th hour. Our proposed models significantly outperform traditional neural networks. Additionally, we introduced a wrapped loss function incorporated into a model, resulting in more accurate results compared to those using the original loss function alone and prediction has been evaluated by RMSE, MAE and MAPE metrics. The proposed Front-loaded connection model(FLC) and Back-loaded connection model (BLC) solve the data drifting issue and the wrap loss function also help alleviate the data drifting problem with model training and works for the neural network models to achieve more accurate results. Eventually, the experimental results have shown that the proposed model performance enhanced from 24.1% -16%, 12%-8.3% respectively at 1h-24h, 32h-64h with compared to baselines BILSTM model, by 24.6% -11.8%, 10%-10.2% respectively at 1h-24h, 32h-64h compared to CNN model in hourly PM2.5 predictions.

PMID:39932913 | DOI:10.1371/journal.pone.0314327

Cereb Cortex. 2025 Feb 5;35(2):bhae486. doi: 10.1093/cercor/bhae486.

ABSTRACT

The prefrontal cortex regulates emotions and is influenced by serotonin. Rodents lacking the serotonin transporter (5-HTT) show increased anxiety and changes in excitatory and inhibitory cell markers in the prefrontal cortex. However, these observations are constrained by limitations in brain representation and cell segmentation, as standard immunohistochemistry is inadequate to consider volume variations in regions of interest. We utilized the deep learning network of the StarDist method in combination with novel open-source methods for automated cell counts in a wide range of prefrontal cortex subregions. We found that 5-HTT knockout rats displayed increased anxiety and diminished relative numbers of subclass excitatory VGluT2+ and activated ΔFosB+ cells in the infralimbic and prelimbic cortices and of inhibitory GAD67+ cells in the prelimbic cortex. Anxiety levels and ΔFosB cell counts were positively correlated in wild-type, but not in knockout, rats. In conclusion, we present a novel method to quantify whole brain subregions of multilabeled cells in animal models and demonstrate reduced excitatory and inhibitory neuronal marker expression in prefrontal cortex subregions of 5-HTT knockout rats.

PMID:39932853 | DOI:10.1093/cercor/bhae486

Proc Natl Acad Sci U S A. 2025 Feb 18;122(7):e2421623122. doi: 10.1073/pnas.2421623122. Epub 2025 Feb 11.

ABSTRACT

Acidic glycans are essential for the biology of multicellular eukaryotes. To utilize them, microbial life including symbionts and pathogens has evolved polysaccharide lyases (PL) that cleave their 1,4 glycosidic linkages via a β-elimination mechanism. PL family 33 (PL33) enzymes have the unusual ability to target a diverse range of glycosaminoglycans (GAGs), as well as the bacterial polymer, gellan gum. In order to gain more detailed insight into PL33 activities we recombinantly expressed 10 PL33 members derived from all major environments and further elucidated the detailed biochemical and biophysical properties of five, showing that their substrate specificity is conferred by variations in tunnel length and topography. The key amino acids involved in catalysis and substrate interactions were identified, and employing a combination of complementary biochemical, structural, and modeling approaches, we show that the tunnel topography is induced by substrate binding to the glycan. Structural and bioinformatic analyses revealed that these features are conserved across several lyase families as well as in mammalian GAG epimerases.

PMID:39932998 | DOI:10.1073/pnas.2421623122

PLoS Biol. 2025 Feb 11;23(2):e3003033. doi: 10.1371/journal.pbio.3003033. Online ahead of print.

ABSTRACT

Rodent and human data implicate the hippocampus in the arbitration of approach-avoidance conflict (AAC), which arises when an organism is confronted with a stimulus associated simultaneously with reward and punishment. Yet, the precise contributions of this structure are underexplored, particularly with respect to the decision-making processes involved. We assessed humans with hippocampal damage and matched neurologically healthy controls on a computerized AAC paradigm in which participants first learned whether individual visual images were associated with the reward or loss of game points and were then asked to approach or avoid pairs of stimuli with non-conflicting or conflicting valences. To assess hippocampal involvement more broadly in response conflict, we also administered a Stroop and a Go/No-go task. On the AAC paradigm, following similar learning outcomes in individuals with hippocampal damage and matched controls, both participant groups approached positive and negative image pairs at the same rate but critically, those with hippocampal damage approached conflict pairs more often than controls. Choice and response AAC data were interrogated using the hierarchical drift diffusion model, which revealed that, compared to controls, individuals with hippocampal damage were more biased towards approach, required less evidence to make a decision during conflict trials, and were slower to accumulate evidence towards avoidance when confronted with conflicting image pairs. No significant differences were found between groups in performance accuracy or response time on the response conflict tasks. Taken together, these findings demonstrate the importance of the hippocampus to the evidence accumulation processes supporting value-based decision-making under motivational conflict.

PMID:39932954 | DOI:10.1371/journal.pbio.3003033

Dialogues Clin Neurosci. 2025 Dec;27(1):10-19. doi: 10.1080/19585969.2025.2463443. Epub 2025 Feb 11.

ABSTRACT

BACKGROUND: Psychotropic medications are critical in managing severe mental illnesses (SMI) such as schizophrenia, major depressive disorder (MDD) and bipolar disorder. However, these treatments often lead to adverse side effects that can impair patients' quality of life (QoL) and affect treatment adherence.

OBJECTIVE: This study aims to investigate the specific side effects of psychotropic treatments that contribute to a decline in QoL among patients with SMI, independently of treatment adherence.

METHODS: We conducted a cross-sectional study with 1248 patients diagnosed with SMI, recruited from a university psychiatric unit in Marseille, France. QoL was assessed using the Schizophrenia Quality of Life Scale (SQoL-18), and side effects were measured using the UKU Side Effect Rating Scale. Treatment adherence was evaluated using the Medication Adherence Rating Scale (MARS). Statistical analyses included Pearson correlations and multiple linear regression models to identify predictors of QoL.

RESULTS: The study found that side effects, as identified by the UKU scores, could significantly predict a reduction in QoL across multiple domains, including multiple dimensions of QoL and the overall QoL index, independent of treatment adherence. Patients on antipsychotics and benzodiazepines reported higher levels of adverse side effects, which correlated with lower QoL scores. An increase in the number of psychotropic treatment classes was also associated with a significant decline in QoL (p < 0.001).

CONCLUSION: Managing psychic side effects and minimising polypharmacy are critical to improving QoL in patients with SMI. Clinicians should consider these factors when developing personalised treatment strategies to enhance patient outcomes.

PMID:39933032 | DOI:10.1080/19585969.2025.2463443

Cancer Immunol Immunother. 2025 Feb 11;74(3):104. doi: 10.1007/s00262-025-03955-y.

ABSTRACT

The response rates of PD-1/PD-L1 blockade in cancer immunotherapy are relatively low, necessitating the development of novel immune checkpoint inhibitors. Compared with other immune checkpoints, VISTA interacts with its ligand PSGL-1 only under acidic conditions in the tumor microenvironment to suppress the function of CD8+ T cells. On the other hand, drug repurposing offers advantages such as time efficiency and high safety. However, the development of VISTA/PSGL-1 inhibitor based on drug repurposing is still infancy. Here, by screening a library of marketed drugs, we identified Chidamide had a strong binding affinity toward VISTA (KD = 5 nM) and blocked VISTA/PSGL-1 under acidic conditions, thereby significantly enhancing the function of CD8+ T cells and inhibiting the tumor growth in immunocompetent murine CT26 tumor model. This study represents the first discovery of Chidamide as VISTA/PSGL-1 blocker for cancer immunotherapy.

PMID:39932560 | DOI:10.1007/s00262-025-03955-y

Curr Drug Discov Technol. 2025 Feb 10. doi: 10.2174/0115701638364199250123062248. Online ahead of print.

ABSTRACT

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the in-creasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polyphar-macology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based vir-tual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided as-sistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effec-tive drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the de-scriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

PMID:39931986 | DOI:10.2174/0115701638364199250123062248

Curr Opin Pediatr. 2025 Feb 11. doi: 10.1097/MOP.0000000000001448. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: Neonatal and infantile epilepsies represent a diverse group of disorders with significant neurodevelopmental impact, necessitating early diagnosis, and tailored treatment. Recent advancements in genetic research, phenotyping, and therapeutic development have reshaped the understanding and management of these conditions, making this review both timely and relevant.

RECENT FINDINGS: Next-generation sequencing has emerged as a cornerstone for diagnosing neonatal and infantile epilepsies, offering high diagnostic yields and enabling identification of etiology-specific phenotypes. Precision therapies, including sodium channel blockers, ganaxolone, and mammalian target of rapamycin (mTOR) inhibitors, target specific molecular mechanisms. Early initiation of treatment in conditions with a high risk of progressing to epilepsy, like vigabatrin in tuberous sclerosis complex, lower the incidence of infantile spasms and improve developmental outcomes. Drug repurposing has also provided effective options, such as fenfluramine in Dravet syndrome, with promising outcomes. Gene-based therapies, including antisense oligonucleotides and gene replacement, represent the new frontier for addressing the root causes of these disorders.

SUMMARY: The integration of genetic and molecular advancements is transforming the management of neonatal and infantile epilepsies, fostering precision-driven care. Continued research and innovation are essential to refine these strategies, optimize patient outcomes, and establish new standards of care.

PMID:39931929 | DOI:10.1097/MOP.0000000000001448

J Int Med Res. 2025 Feb;53(2):3000605251315355. doi: 10.1177/03000605251315355.

ABSTRACT

Linezolid, a synthetic oxazolidinone antibiotic, is used to treat gram-positive bacterial infections, including methicillin-resistant Staphylococcus aureus. Despite its efficacy, linezolid can cause serotonin syndrome, a potentially fatal condition associated with excessive serotonin activity in the brain. This narrative review examined the pharmacological mechanisms of this interaction, particularly linezolid's mild monoamine oxidase-inhibitory activity, which can trigger serotonin syndrome in combination with serotonergic drugs. Serotonin syndrome causes cognitive, autonomic, and somatic symptoms ranging from mild (tremors, diarrhea) to severe (hyperthermia, seizures, multiorgan failure). The Hunter Serotonin Toxicity Criteria have superior sensitivity and specificity over the Sternbach Criteria for diagnosis. Clinical evidence indicates that although the incidence of linezolid-induced serotonin syndrome is low, the risk justifies careful monitoring and risk assessment. This review emphasizes enhanced pharmacovigilance and standardized reporting criteria to better capture and analyze data on linezolid-induced serotonin syndrome. Assessments of the pharmacological mechanisms, large-scale clinical trials, and cohort studies are essential to elucidate risk factors and outcomes. Developing comprehensive clinical guidelines and education programs for healthcare providers is crucial to improve linezolid's safety profile. Exploring pharmacogenomic approaches and alternative therapies with lower serotonin syndrome risks is recommended to enhance patient outcomes while maintaining linezolid's efficacy in treating severe bacterial infections.

PMID:39932284 | DOI:10.1177/03000605251315355

J Endourol. 2025 Feb 11. doi: 10.1089/end.2024.0666. Online ahead of print.

ABSTRACT

Introduction: Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and without deep learning reconstruction (DLR) and metal artifact reduction (MAR) in the presence of metal hip prostheses. Methods: Ten urinary system combinations with 4 to 6 mm ureteral stones were implanted into a cadaver with bilateral hip prostheses. Each set was scanned under two different radiation doses (conventional dose [CD] = 115 mAs and ultra-low dose [ULD] = 6.0 mAs). Two scans were obtained for each dose as follows: one with and another without DLR and MAR. Two blinded radiologists ranked each image in terms of artifact, image noise, image sharpness, overall quality, and diagnostic confidence. Stone detection accuracy at each setting was calculated. Results: ULD with DLR and MAR improved subjective image quality in all five domains (p < 0.05) compared with ULD. In addition, the subjective image quality for ULD with DLR and MAR was greater than the subjective image quality for CD in all five domains (p < 0.05). Stone detection accuracy of ULD improved with the application of DLR and MAR (p < 0.05). Stone detection accuracy of ULD with DLR and MAR was similar to CD (p > 0.25). Conclusions: DLR with MAR may allow the application of low-dose CT protocols in patients with hip prostheses. Application of DLR and MAR to ULD provided a stone detection accuracy comparable with CD, reduced radiation exposure by 94.8%, and improved subjective image quality.

PMID:39932744 | DOI:10.1089/end.2024.0666

Med Biol Eng Comput. 2025 Feb 11. doi: 10.1007/s11517-025-03300-6. Online ahead of print.

ABSTRACT

Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic insights for clinical practice. Existing deep learning-based methods often focus on extracting the primary information from individual modalities while ignoring the correlation of information distribution across different modalities, which leads to insufficient fusion of image details and color information. To address this problem, a diffusion-driven MMIF method is proposed to leverage the information distribution relationship among multi-modality images in the latent space. To better preserve the complementary information from different modalities, a local and global network (LAGN) is suggested. Additionally, a loss strategy is designed to establish robust constraints among diffusion-generated images, original images, and fused images. This strategy supervises the training process and prevents information loss in fused images. The experimental results demonstrate that the proposed method surpasses state-of-the-art image fusion methods in terms of unsupervised metrics on three datasets: MRI/CT, MRI/PET, and MRI/SPECT images. The proposed method successfully captures rich details and color information. Furthermore, 16 doctors and medical students were invited to evaluate the effectiveness of our method in assisting clinical diagnosis and treatment.

PMID:39932643 | DOI:10.1007/s11517-025-03300-6