Sci Rep. 2025 Feb 6;15(1):4470. doi: 10.1038/s41598-025-88843-2.

ABSTRACT

With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. This manuscript presents an Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities. The AAIFLF-PPCD approach aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities. Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. The simulated outcome of the AAIFLF-PPCD technique is evaluated using a benchmark dataset. The performance validation of the AAIFLF-PPCD technique exhibited a superior accuracy value of 99.47% over existing models under diverse measures.

PMID:39915579 | DOI:10.1038/s41598-025-88843-2

Sci Rep. 2025 Feb 7;15(1):4569. doi: 10.1038/s41598-025-87290-3.

ABSTRACT

Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impairment. The standard treatment involves anti-VEGF (Vascular Endothelial Growth Factor) therapy, but approximately 36% of patients do not respond adequately, highlighting the need for more precise predictive models to guide treatment. This study aims to develop a Hybrid Deep Learning model to predict treatment responses in DME patients undergoing anti-VEGF therapy, thereby improving the accuracy of treatment planning and minimizing the unnecessary use of costly anti-VEGF agents. The model integrates both Optical Coherence Tomography (OCT) images and clinical data from 181 patients, including key parameters such as serum VEGFR-2 concentration and the duration of DME. The architecture combines convolutional neural networks (CNNs) for image data with multi-layer perceptron (MLP) for tabular clinical data, allowing for a comprehensive analysis of both data types. These pathways converge into a unified predictive framework designed to enhance the model's accuracy. This study utilized a Hybrid Deep Learning model that achieved an 85% accuracy, with additional metrics including precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC) confirming its robustness and reliability. The findings suggest that the model accurately predicts patient responses to anti-VEGF therapy, paving the way for more personalized and targeted treatment strategies. This approach has the potential to enhance patient outcomes and minimize unnecessary administration of anti-VEGF agents, thereby optimizing therapeutic interventions in ophthalmology.

PMID:39915516 | DOI:10.1038/s41598-025-87290-3

Nat Commun. 2025 Feb 7;16(1):1425. doi: 10.1038/s41467-025-56784-z.

ABSTRACT

Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 13261 pre-operative computed computed tomography (CT) volumes of 4557 patients. Two multi-phase convolutional neural networks are developed to predict the malignancy and aggressiveness of renal masses. The first diagnostic model designed to predict the malignancy of renal masses achieves area under the curve (AUC) of 0.871 in the prospective test set. This model surpasses the average performance of seven seasoned radiologists. The second diagnostic model differentiating aggressive from indolent tumors has AUC of 0.783 in the prospective test set. Both models outperform corresponding radiomics models and the nephrometry score nomogram. Here we show that the deep learning models can non-invasively predict the likelihood of malignant and aggressive pathology of a renal mass based on preoperative multi-phase CT images.

PMID:39915478 | DOI:10.1038/s41467-025-56784-z

Light Sci Appl. 2025 Feb 7;14(1):70. doi: 10.1038/s41377-024-01721-w.

ABSTRACT

To reveal the fundamental aspects hidden behind a variety of transient events in mechanics, physics, and biology, the highly desired ability to acquire three-dimensional (3D) images with ultrafast temporal resolution has been long sought. As one of the most commonly employed 3D sensing techniques, fringe projection profilometry (FPP) reconstructs the depth of a scene from stereo images taken with sequentially structured illuminations. However, the imaging speed of current FPP methods is generally capped at several kHz, which is limited by the projector-camera hardware and the number of fringe patterns required for phase retrieval and unwrapping. Here we report a novel learning-based ultrafast 3D imaging technique, termed single-shot super-resolved FPP (SSSR-FPP), which enables ultrafast 3D imaging at 100,000 Hz. SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while "regenerating" the lost spatial resolution through deep learning. To demonstrate the high spatio-temporal resolution of SSSR-FPP, we present 3D videography of several transient scenes, including rotating turbofan blades, exploding building blocks, and the reciprocating motion of a steam engine, etc., which were previously challenging or even impossible to capture with conventional methods. Experimental results establish SSSR-FPP as a significant step forward in the field of 3D optical sensing, offering new insights into a broad spectrum of dynamic processes across various scientific disciplines.

PMID:39915449 | DOI:10.1038/s41377-024-01721-w

Biofabrication. 2025 Feb 6. doi: 10.1088/1758-5090/adb338. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a lethal lung disease of unknown etiology. Macrophages are implicated in the fibrotic process, but exhibit remarkable plasticity in the activated immune environment in vivo, presenting significant challenges as therapeutic targets. To explore the influence of macrophages on IPF and develop macrophage-targeted therapies, we engineered a micro-lung chip with a lung epithelium-interstitium tissue unit to establish a controlled immune environment containing only macrophages. We discovered that macrophages exacerbated inflammation and fibrosis by comparing microchips treated with bleomycin in the presence and absence of macrophages. Based on the duration of bleomycin treatment, we established pathological models corresponding to inflammation and fibrosis stages. Transcriptome analysis revealed that activation of the PI3K-AKT signaling pathway facilitates the transition from inflammation to fibrosis. However, LY294002, a PI3K inhibitor, not only suppressed fibrosis and decreased the accumulation of M2 macrophages but also intensified the severity of inflammation. These findings suggest that macrophages play a pivotal role in the potential development at the tissue level. The micro-lung chip cocultured with macrophages holds significant potential for exploring the pathological progression of IPF and elucidating the mechanisms of anti-fibrotic drugs.

PMID:39914008 | DOI:10.1088/1758-5090/adb338

Sci Rep. 2025 Feb 6;15(1):4480. doi: 10.1038/s41598-025-88325-5.

ABSTRACT

Circadian rhythms, intrinsic 24-h cycles regulating physiological processes, are crucial for skin homeostasis. Disruptions in these rhythms are linked to various skin disorders and impaired barrier function. Circadian rhythms can be modulated by botanical compounds, which hold therapeutic potential. However, the effect of cinnamomi cortex (CC), an anti-inflammatory, antioxidant, and antimicrobial agent, on the circadian rhythm of keratinocytes remains unclear. This study aimed to examine the effects of CC extract and its 18 individual components on the circadian rhythm of HaCaT, an immortalized human keratinocyte line. CC extract and its bioactive components epicatechin (EC) and linalool (LO) significantly enhanced the circadian amplitude without altering the period. Gene expression analysis revealed that CC extract, EC, and LO altered the mRNA and protein levels of clock genes in a time-dependent manner. During molecular docking simulations, both EC and LO exhibited strong binding affinities for RORA, a key nuclear receptor involved in circadian regulation. Enhanced BMAL1 promoter activity following EC and LO treatments corroborated these findings. Furthermore, EC and LO demonstrated significant antioxidant activities, as evidenced by reduced reactive oxygen species levels and increased expression of antioxidant enzymes. EC and LO also upregulated skin barrier-related and ceramide synthesis genes and modulated the expression of cellular longevity-promoting genes. In conclusion, CC extract, particularly the components EC and LO, modulated circadian rhythms, reduced oxidative stress, and enhanced skin barrier function in keratinocytes. These findings highlight the potential of CC extract and its components as novel dermatological treatments to improve skin health and combat aging.

PMID:39915616 | DOI:10.1038/s41598-025-88325-5

Cancer Gene Ther. 2025 Feb 6. doi: 10.1038/s41417-025-00874-z. Online ahead of print.

ABSTRACT

PARP-inhibitors (PARPi) are an integral part of ovarian cancer treatment. However, overcoming acquired PARPi resistance or increasing the benefit of PARPi in patients without homologous recombination deficiency (HRD) remains an unmet clinical need. We sought to identify genetic modulators of PARPi response, guiding pharmacological PARPi sensitization. CRISPR-Cas9 mediated loss-of-function screen with a focused sgRNA library revealed that DNA-demethylases JMJD1B/JMJD1C, targetable by the small inhibitor methylstat, promote PARPi resistance. Methylstat synergistically interacted with olaparib, and (re-)sensitized ovarian cancer cells to PARPi treatment, surpassing the efficacy of common demethylase inhibitors. Genetic knockout of JMJD1B and/or JMJD1C phenocopied the effect of methylstat in an additive manner. Validation studies revealed methylstat to be a universal PARPi-sensitizing drug, effective, regardless of PARPi resistance status or BRCA1 mutational background. Methylstat modulated clonal cancer dynamics by mitigating positive selection of PARPi-resistant or BRCA1-proficient cells under olaparib treatment. Using a model of PARPi-induced cellular toxicity, we showed that methylstat impairs cellular DNA repair, indicated by an increased susceptibility of ovarian cancer cells to olaparib-induced DNA double strand breaks after methylstat exposure. This study proposes the histone demethylase inhibitor methylstat as an epigenetic drug for overcoming PARPi-resistance or for increasing efficacy of PARPi beyond HRD in ovarian cancer patients.

PMID:39915607 | DOI:10.1038/s41417-025-00874-z

Sci Rep. 2025 Feb 6;15(1):4464. doi: 10.1038/s41598-025-86893-0.

ABSTRACT

Second harmonic generation (SHG) microscopy is a powerful tool for imaging collagen and other noncentrosymmetric fibrillar structures in biological tissue. Polarimetric SHG measurements provide ultrastructural information about the fibrillar organization in a focal volume (voxel). We present a reduced nonlinear polarimetry method named double Stokes polarimetry (DSP) for quick characterization of chiral C 6 symmetry fibers without data fitting that simplifies and speeds up the polarimetric analysis. The method is based on double Stokes-Mueller polarimetry and uses linear and circular incident and outgoing polarization states. The analytical expressions of DSP polarimetric parameters are defined in terms of conventional SHG Stokes vector components. A complex chiral susceptibility (CCS) model is assumed to derive expressions of ultrastructural parameters consisting of the magnitude and phase of molecular complex-valued chiral susceptibility ratio, real-valued achiral ratio, and fiber orientation in a voxel. The ultrastructural parameters are expressed in terms of directly measurable DSP polarimetric parameters. DSP is validated with rat tail tendons sectioned at different orientations. DSP can be applied to investigate the origin of chiral complex-valued susceptibility of collagen, to study modifications of collagen in cancerous tissue, and to map ultrastructural parameters of large areas for whole-slide histopathology.

PMID:39915558 | DOI:10.1038/s41598-025-86893-0

Nat Commun. 2025 Feb 6;16(1):1402. doi: 10.1038/s41467-025-56659-3.

ABSTRACT

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8 T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

PMID:39915487 | DOI:10.1038/s41467-025-56659-3

Mol Cell Proteomics. 2025 Feb 4:100914. doi: 10.1016/j.mcpro.2025.100914. Online ahead of print.

ABSTRACT

Noncanonical micropeptides or called novel microproteins, i.e., polypeptides mostly under 10 kDa, are encoded by genomic sequences that have been previously annotated as noncoding but now known as small open reading frames (sORFs). The recent identification of microproteins encoded by sORFs has provided evidence that many sORFs encode functional microproteins that play crucial roles in various biological processes. T cell activation is a critical biological process for adaptive immune response. Understanding key players in this process will allow us to decipher the complex mechanisms as well as develop immunotherapy for treating a wide range of diseases. Although there have been extensive studies on canonical proteins in T cell activation, the novel microproteins in T cells and their roles have been uncharted water to date. Nascent proteins are defined as newly synthesized polypeptides emerged during the translation of mRNA. In this study, we combined nascent proteomics and quantitative proteomics to identify 411 novel microproteins in primary human T cells, including 83 nascent microproteins. We activated the T cell function with either PMA/Ionomycin (distal activation) or CD3/CD28 activating antibodies (proximal activation), and obtained a comprehensive canonical protein and microprotein profiles to pinpoint common and distinct differentially expressed proteins under these two activation conditions. After experimental testing, three microproteins numbered T1, T2 and T3 were found to be functional in regulating T cell activation. Bioinformatic and proteomic analyses suggested that T1 was functional related to immune as negative feedback to T cell activation. Our study not only established an integrated approach to uncover and elucidate novel microproteins but also highlight the significant role of microproteins in regulating T cell activation.

PMID:39914663 | DOI:10.1016/j.mcpro.2025.100914

Cell Genom. 2025 Jan 29:100768. doi: 10.1016/j.xgen.2025.100768. Online ahead of print.

ABSTRACT

Emerging research underscores the gut microbiome's impact on the nervous system via the microbiota-gut-brain axis, yet comprehensive insights remain limited. Using a CHD8-haploinsufficient model for autism spectrum disorder (ASD), we explored host-gut microbiota interactions by constructing a single-cell transcriptome atlas of brain and intestinal tissues in wild-type and mutant mice across three developmental stages. CHD8 haploinsufficiency caused delayed development of radial glial precursors and excitatory neural progenitors in the E14.5 brain, inflammation in the adult brain, immunodeficiency, and abnormal intestinal development. Selective CHD8 knockdown in intestinal epithelial cells generated Chd8ΔIEC mice, which exhibited normal sociability but impaired social novelty recognition. Probiotic intervention with Lactobacillus murinus selectively rescued social deficits in Chd8ΔIEC mice, with single-cell transcriptome analysis revealing underlying mechanisms. This study provides a detailed single-cell transcriptomic dataset of ASD-related neural and intestinal changes, advancing our understanding of the gut-brain axis and offering potential therapeutic strategies for ASD.

PMID:39914389 | DOI:10.1016/j.xgen.2025.100768

Cell Genom. 2025 Jan 28:100765. doi: 10.1016/j.xgen.2025.100765. Online ahead of print.

ABSTRACT

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells.

PMID:39914387 | DOI:10.1016/j.xgen.2025.100765

Science. 2025 Feb 6:eadq2634. doi: 10.1126/science.adq2634. Online ahead of print.

ABSTRACT

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

PMID:39913643 | DOI:10.1126/science.adq2634

PLoS One. 2025 Feb 6;20(2):e0314982. doi: 10.1371/journal.pone.0314982. eCollection 2025.

ABSTRACT

Structural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV detection, short-read sequencing remains essential due to practical and cost considerations, as well as the need to analyze existing short-read datasets. Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. In this study, we evaluated the efficacy of six advanced SV detection algorithms, including the commercial software DRAGEN, using the GIAB v0.6 Tier 1 benchmark and HGSVC2 cell lines. We employed both individual and combination strategies, with systematic assessments of recall, precision, and F1 scores. Our results demonstrate that the union combination approach enhanced detection capabilities, surpassing single algorithms in identifying deletions and insertions, and delivered comparable recall and F1 scores to the commercial software DRAGEN. Interestingly, expanding the number of algorithms from three to five in the combination did not enhance performance, highlighting the efficiency of a well-chosen ensemble over a larger algorithmic pool.

PMID:39913463 | DOI:10.1371/journal.pone.0314982

STAR Protoc. 2025 Feb 4;6(1):103625. doi: 10.1016/j.xpro.2025.103625. Online ahead of print.

ABSTRACT

Spatially resolved transcriptomics (SRT) data contain intricate noise due to the diffusion of transcripts caused by tissue fixation, permeabilization, and cell lysis during the experiment. Here, we present a protocol for denoising SRT data using SpotGF, an optimal transport-based gene filtering algorithm, without modifying the raw gene expression. We describe steps for data preparation, SpotGF score calculation, filtering threshold determination, denoised data generation, and visualization. Our protocol enhances SRT quality and improves the performance of downstream analyses. For complete details on the use and execution of this protocol, please refer to Du et al.1.

PMID:39913289 | DOI:10.1016/j.xpro.2025.103625

Farm Hosp. 2025 Feb 5:S1130-6343(24)00169-7. doi: 10.1016/j.farma.2024.10.009. Online ahead of print.

ABSTRACT

INTRODUCTION: Older patients are more susceptible to medication use, and physiological changes resulting from aging and organic dysfunctions presented by critically-ill patients may alter the pharmacokinetic or pharmacodynamic behavior. Thus, critically-ill older people present greater vulnerability to the occurrence of pharmacotherapeutic problems.

OBJECTIVE: To evaluate pharmacotherapy and the development of potential adverse drug reactions (ADRs) in older patients admitted to an intensive care unit (ICU).

METHOD: A cohort study was conducted in an ICU for adults of a Brazilian University Hospital during a 12-month period. The patients' pharmacotherapy was evaluated daily, considering the occurrence of ADRs and drug-drug interactions (DDIs), the use of potentially inappropriate medications (PIMs) for older people, and the pharmacotherapy anticholinergic burden (ACB). A trigger tool was used for active search of ADRs, with subsequent causality evaluation. PIM use was evaluated by means of the Beers criteria and the STOPP/START criteria. The ABC scale was employed to estimate ACB. The Micromedex® and Drugs.com® medication databases were employed to evaluate the DDIs.

RESULTS: The sample of this study consisted of 41 patients, with a mean age of 66.8 years old (±5.2). The 22 triggers used assisted in identifying 15 potential ADRs, and 26.8% of the patients developed them. The mean estimated ACB score was 3.0 (±1.8), and the patients used 3.1 (±1.4) and 3.3 (±1.6) PIMs according to the Beers and the STOPP criteria, respectively. A total of 672 DDIs were identified, with a mean of 16.8 (±9.5) DDIs/patient during ICU hospitalization. Our findings show an association between occurrence of ADRs in the ICU and polypharmacy (p = 0.03) and DDIs (p = 0.007), corroborating efforts for rational medication use as a preventive strategy.

CONCLUSIONS: Using tools to evaluate the pharmacotherapy for older people in intensive care can assist in the recognition and prevention of pharmacotherapeutic problems, with emphasis on the identification of ADRs through the observation of triggers and subsequent causality analysis.

PMID:39915129 | DOI:10.1016/j.farma.2024.10.009

J Nucl Med. 2025 Feb 6:jnumed.124.268318. doi: 10.2967/jnumed.124.268318. Online ahead of print.

ABSTRACT

Prostate-specific membrane antigen (PSMA) is highly expressed in most prostate cancers (PCs). PET and CT imaging studies using 68Ga-labeled PSMA ligands demonstrated the specific localization of 68Ga in PC lesions and distant metastatic lesions. [68Ga]Ga-PSMA-R2 (68Ga-PSMA-R2) is a PSMA-targeted PET/CT radiotracer with potential diagnostic applications. Methods: PROfind (NCT03490032) was a phase 1/2, open-label, multicenter study of administration of 3 MBq/kg of 68Ga-PSMA-R2 (from >150 to ≤250 MBq) in patients with biochemical recurrence (BCR) or metastatic PC (mPC). Participants underwent baseline conventional imaging (CT/MRI or bone scan) and PET/CT. Whole-body PET/CT imaging sequences were obtained between 20 min and 4 h after injection. Primary endpoints were safety and tolerability; secondary endpoints included biodistribution, potential lesion identification, pharmacokinetics, and dosimetry. Potential lesions were identified by 2 masked expert panels; a third panel evaluated the identified lesions. Results: Six patients with BCR were enrolled into phase 1, and 24 patients with BCR or mPC (n = 12 each) into phase 2. Thirteen treatment-emergent adverse events were reported, including 1 serious adverse event (ileus), unrelated to drug administration. All adverse events were mild or moderate and deemed not related to 68Ga-PSMA-R2. Peak blood concentration of 68Ga-PSMA-R2 was typically observed approximately 5 min after injection, steadily decreasing over 6 h. Mean absorbed radiation dose was highest in the urinary bladder wall (0.120 mGy/MBq) and kidney (0.061 mGy/MBq). No other organ mean absorbed radiation dose exceeded 0.020 mGy/MBq. Mean absorbed radiation doses in the salivary and lacrimal glands were 0.016 and 0.008 mGy/MBq, respectively. Mean total body absorbed radiation dose was 0.014 mGy/MBq. Mean effective total body dose was 0.015 mSv/MBq (range, 0.012-0.018 mSv/MBq). 68Ga-PSMA-R2 PET/CT detected 85 lesions in 22 participants at 1 h after injection and 103 lesions in 22 participants at 2 h after injection. Conventional imaging detected 49 lesions in 8 participants with mPC but none in participants with BCR. Conclusion: 68Ga-PSMA-R2 was well tolerated, with no drug-related treatment-emergent adverse events. Safety and preliminary imaging performance data support further development of 68Ga-PSMA-R2 as a diagnostic agent in patients with PC.

PMID:39915126 | DOI:10.2967/jnumed.124.268318

Eur J Hosp Pharm. 2025 Feb 6:ejhpharm-2024-004445. doi: 10.1136/ejhpharm-2024-004445. Online ahead of print.

ABSTRACT

Cytomegalovirus (CMV) is a common herpesvirus that can cause severe infections in immunocompromised patients. Standard treatments, such as ganciclovir and valganciclovir, are usually effective, but refractory CMV requires alternatives like foscarnet, cidofovir, or immunotherapies. New treatments, such as maribavir, have shown promise for refractory cases. This report discusses a woman in her 50s with sarcoidosis, previously treated with infliximab, leflunomide, and hydroxychloroquine, who developed refractory CMV. Initial treatment with ganciclovir and intravenous immunoglobulin (IVIG) was discontinued due to severe pancytopenia, leading to the initiation of foscarnet. Despite this, CMV viremia persisted, leading to off-label use of maribavir, which reduced the viral load with mild gastrointestinal side effects. The patient also developed haemophagocytic syndrome, complicating her condition.Unfortunately, she succumbed to an opportunistic infection, leaving the complete efficacy of maribavir unconfirmed. This case highlights the potential of novel antiviral agents and underscores the need for further research on refractory CMV management.

PMID:39915049 | DOI:10.1136/ejhpharm-2024-004445

PLoS One. 2025 Feb 6;20(2):e0317680. doi: 10.1371/journal.pone.0317680. eCollection 2025.

ABSTRACT

BACKGROUND: Drug-induced autoimmune-like hepatitis (DI-ALH) is a potentially life-threatening condition that can lead to acute liver failure and necessitate liver transplantation. While the association between certain drugs and DI-ALH has been documented, a comprehensive analysis of drug-related signals in a large, real-world pharmacovigilance database is lacking. This study aimed to systematically identify drugs linked to DI-ALH by analyzing adverse event reports from the U.S. Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) database.

METHODS: We searched the FAERS database for the term "autoimmune hepatitis" and extracted DI-ALH reports from the first quarter of 2004 to the first quarter of 2024. Positive signal drugs were identified using Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). To confirm a significant drug-adverse event association, each method had to meet predefined thresholds: for PRR and ROR, values were considered significant if the lower 95% confidence interval (CI) was greater than 1 and at least three reports were identified; for BCPNN, an Information Component (IC025) greater than 0 indicated a signal; for EBGM, a value greater than 2 for the lower 95% confidence interval (EBGM05) was used to denote a positive signal.

RESULTS: A total of 5,723 DI-ALH reports were extracted from the FAERS database. Disproportionality analysis identified 50 drugs with strong associations to DI-ALH, with biologics, statins, antibiotics, and antiviral drugs representing the most common categories. Among these, nitrofurantoin (ROR 94.79, CI 78.53-114.41), minocycline (ROR 77.82, CI 65.09-93.05), and nivolumab (ROR 47.12, CI 15.06-147.39) exhibited the strongest signals. Additionally, several previously unreported drugs, including mesalazine, aldesleukin, onasemnogene abeparvovec-xioi, and nefazodone, were identified as having strong associations with DI-ALH. These findings were consistent across all four signal detection methods, further validating the robustness of the associations.

CONCLUSION: This study provides a comprehensive assessment of drugs associated with DI-ALH through a rigorous analysis of the FAERS database using multiple signal detection methods. By identifying both well-known and previously underreported drugs, this study contributes to a more complete understanding of drug-induced liver injury. The findings have important implications for pharmacovigilance strategies and clinical risk assessment. However, limitations inherent in the FAERS database, such as underreporting and the potential for reporting bias, should be considered. Further clinical validation is warranted to confirm these associations.

PMID:39913410 | DOI:10.1371/journal.pone.0317680

Future Oncol. 2025 Feb 6:1-8. doi: 10.1080/14796694.2025.2461446. Online ahead of print.

ABSTRACT

PURPOSE: A phase I study of FOLFIRINOX3-bevacizumab (bFOLFIRINOX3)defined the RP2D for irinotecan at 70 mg/m² and showed promising activity. This phase II trial aimed to evaluate the efficacy of bFOLFIRINOX-3 in chemorefractory metastatic colorectal cancer (mCRC).

METHODS: In phase II, chemorefractory mCRC were enrolled. The regimen tested consisted of bevacizumab (5 mg/kg), folinic acid(400 mg/m²), 5-fluorouracil (2400 mg/m² for 46 h), oxaliplatin (85 mg/m²) and irinotecan (70 mg/m² administered before and after infusion of 5-fluorouracil). The primary endpoint was efficacy defined by 2-month progression-free survival(PFS). Secondary endpoints included objective response, median PFS, overall survival (OS) and toxicity.

RESULTS: 32 patients were enrolled (October 2018 to December 2022); median age 62.5 years (range 32-78). The majority had been treated with several previous lines of chemotherapy (median 3, range [1-8]). Median follow up was 12 months (range [1.5-12]). Two-month PFS was 96.9%. Best objective response rate (ORR) was 28.1%. Median PFS was 9.4 months (95%CI [6.9;11.5]) and median OS was not reached (95% [11.6; NR]). Grade 3 adverse events occurred in 81.2%; mostly diarrhea (37.5%) and neutropenia (12.5%). Grade 3 diarrhea consistently resolved after irinotecan dose reduction. The most common drug-related adverse events (all grades) were diarrhea (96.9%), fatigue (68.8%), nausea (68.7%), anemia (56.3%), peripheral neuropathy (53.4%) and thrombopenia (40.6%).

CONCLUSION: The combination of bFOLFIRINOX-3 yielded 2-month PFS of 96.9% and best ORR of 28.1%, and was well tolerated. These results are promising in chemotherapy refractory mCRC and provide a rationale for future randomized phase III trials.

CLINICAL TRIAL REGISTRATION: NCT03795311 (clinicaltrials.gov).

PMID:39913183 | DOI:10.1080/14796694.2025.2461446