Anal Chem. 2025 Mar 14. doi: 10.1021/acs.analchem.4c05707. Online ahead of print.

ABSTRACT

In this paper, we propose a new spectral data mechanism called LIBS pseudohyperspectral data cube. This mechanism allows for the utilization of multidimensional information from laser-induced plasma, transforming 1-D LIBS spectra into 3-D data cube. Specifically, two additional dimensions are introduced to capture spectral variations information, allowing more features to be learned during pretraining. Proposed mechanism can make the LIBS system more robust when handling unstable spectra acquired onsite, and can also allow LIBS take full advantage of deep learning algorithms. In the context of nuclear power plants, traditional LIBS classification faces significant challenges due to unstable spectra, which reduce the accuracy of classifying similar or tiny extreme condition materials. By combining deep learning algorithms with LIBS pseudohyperspectral data cube, we can capture spectral and other dimensional features to enhance classification accuracy. Experimental results show that, compared to traditional 1-D data processing, the new method significantly improves the classification accuracy of unstable spectra. Moreover, by incorporating an attention mechanism, the model can adaptively adjust the weights of different features, further improving classification accuracy to over 99%. Visualizing the attention mechanism's weight matrix allows us to identify the importance of different features in classification. Additionally, t-SNE visualizations demonstrate the clustering of different categories in the feature space, further validating the performance of the new method. We believe this data cube mechanism offers an effective new approach for applying deep learning algorithms and enhancing data dimensionality in the LIBS field.

PMID:40085530 | DOI:10.1021/acs.analchem.4c05707

IEEE Trans Med Imaging. 2025 Mar 14;PP. doi: 10.1109/TMI.2025.3551514. Online ahead of print.

ABSTRACT

Deep learning models have been widely investigated for computing and analyzing brain images across various downstream tasks such as disease diagnosis and age regression. Most existing models are tailored for specific tasks and diseases, posing a challenge in developing a foundation model for diverse tasks. This paper proposes a Dense Transformer Foundation Model with Mixture of Experts (DenseFormer-MoE), which integrates dense convolutional network, Vision Transformer and Mixture of Experts (MoE) to progressively learn and consolidate local and global features from T1-weighted magnetic resonance images (sMRI) for multiple tasks including diagnosing multiple brain diseases and predicting brain age. First, a foundation model is built by combining the vision Transformer with Densenet, which are pre-trained with Masked Autoencoder and self-supervised learning to enhance the generalization of feature representations. Then, to mitigate optimization conflicts in multi-task learning, MoE is designed to dynamically select the most appropriate experts for each task. Finally, our method is evaluated on multiple renowned brain imaging datasets including UK Biobank (UKB), Alzheimer's Disease Neuroimaging Initiative (ADNI), and Parkinson's Progression Markers Initiative (PPMI). Experimental results and comparison demonstrate that our method achieves promising performances for prediction of brain age and diagnosis of brain diseases.

PMID:40085471 | DOI:10.1109/TMI.2025.3551514

IEEE Trans Biomed Eng. 2025 Mar 14;PP. doi: 10.1109/TBME.2025.3551260. Online ahead of print.

ABSTRACT

Microwave-induced thermoacoustic tomography (TAT) is a hybrid imaging technique that combines microwave excitation with ultrasound detection to create detailed images of biological tissue. Most TAT systems require a costly amplification system (or a sophisticated high-power microwave source), which limits the wide adoption of this imaging modality. We have developed a rotating single-element thermoacoustic tomography (RTAT) system using a low-cost amplifier that has been optimized in terms of microwave signal pulse width and antenna placement. The optimized system, enhanced with signal averaging, advanced signal processing, and a deep learning computational core, successfully produced adequate-quality images. The system has been characterized in terms of spatial resolution, imaging depth, acquisition speed, and multispectral capabilities utilizing tissue-like phantoms, ex-vivo specimens and in-vivo imaging. We believe our low-cost, portable system expands accessibility for the research community, empowering more groups to explore thermoacoustic imaging. It supports the development of advanced signal processing algorithms to optimize both low-power and even high-power TAT systems, accelerating the clinical adoption of this promising imaging modality.

PMID:40085469 | DOI:10.1109/TBME.2025.3551260

IEEE Trans Neural Netw Learn Syst. 2025 Mar 14;PP. doi: 10.1109/TNNLS.2025.3538335. Online ahead of print.

ABSTRACT

Motor imagery (MI), an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning (DL) algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a noniterative leaky integrate-and-fire (NiLIF) neuron model, overcoming the gradient issues in traditional SNNs that use iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed noniterative SNN with attention (NiSNN-A) model on two MI EEG datasets, OpenBMI and BCIC IV 2a. Experimental results demonstrate that: 1) our model outperforms other SNN models by achieving higher accuracy and 2) our model increases energy efficiency compared with the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy.

PMID:40085464 | DOI:10.1109/TNNLS.2025.3538335

IEEE Trans Neural Netw Learn Syst. 2025 Mar 14;PP. doi: 10.1109/TNNLS.2025.3545967. Online ahead of print.

ABSTRACT

Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), molecular dynamics, and dynamical systems. Although traditional numerical methods like the finite difference/element method are widely used, their computational inefficiency, due to the large number of iterations required, has long been a challenge. Recently, deep learning (DL) has emerged as a promising alternative for solving PDEs, offering new paradigms beyond conventional methods. Despite the growing interest in techniques like physics-informed neural networks (PINNs), a systematic review of the diverse neural network (NN) approaches for PDEs is still missing. This survey fills that gap by categorizing and reviewing the current progress of deep NNs (DNNs) for PDEs. Unlike previous reviews focused on specific methods like PINNs, we offer a broader taxonomy and analyze applications across scientific, engineering, and medical fields. We also provide a historical overview, key challenges, and future trends, aiming to serve both researchers and practitioners with insights into how DNNs can be effectively applied to solve PDEs.

PMID:40085460 | DOI:10.1109/TNNLS.2025.3545967

IEEE J Biomed Health Inform. 2025 Mar 14;PP. doi: 10.1109/JBHI.2025.3551365. Online ahead of print.

ABSTRACT

Immune thrombocytopenia (ITP) is a typically self-limiting and immune-mediated bleeding disorder in children. Approximately 20% of children with ITP experience chronicity, leading to reduced quality of life and increased treatment burden. The accurate prediction of chronicity would enable clinicians to make personalized treatment plans at an early stage. However, due to the self-limiting nature of ITP and the scarcity of available children patients, the data presents two prominent issues: small data and imbalanced class, which are unfavorable for effectively training a deep learning model. To handle these issues concurrently, we proposed a novel method that integrates contrastive learning with the Transformer. First, we adopt the FT-Transformer as our backbone, which allows our model to flexibly process heterogeneous tabular data. Second, we amplify and balance the original data via random masking and oversampling, respectively. Lastly, we build contrastive pairs according to the latent representations generated by the FT-Transformer encoder, such that the amplified and oversampled synthetic data can be utilized thoroughly. The experimental results on real-world ITP children data show that our proposal outperforms the state-of-the-art methods, and demonstrate the significant advantages of dealing with insufficient and imbalanced problems.

PMID:40085458 | DOI:10.1109/JBHI.2025.3551365

Sci Adv. 2025 Mar 14;11(11):eads6004. doi: 10.1126/sciadv.ads6004. Epub 2025 Mar 14.

ABSTRACT

NALCN (sodium leak channel, nonselective) is vital for regulating electrical activity in neurons and other excitable cells, and mutations in the channel or its auxiliary proteins lead to severe neurodevelopmental disorders. Here, we show that the neuronal SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) complex proteins syntaxin and SNAP25 (synaptosome-associated protein 25), which enable synaptic transmission in the nervous system, inhibit the activity of the NALCN channel complex in both heterologous systems and primary neurons. The existence of this interaction suggests that the neurotransmitter release machinery can regulate electrical signaling directly and therefore modulate the threshold for its own activity. We further find that reduction of NALCN currents is sufficient to promote cell survival in syntaxin-depleted cells. This suggests that disinhibited NALCN may cause the puzzling phenomenon of rapid neuronal cell death in the absence of syntaxin. This interaction could offer opportunities for future drug development against genetic diseases linked to both NALCN- and SNARE protein-containing complexes.

PMID:40085699 | DOI:10.1126/sciadv.ads6004

Int J Syst Evol Microbiol. 2025 Mar;75(3). doi: 10.1099/ijsem.0.006716.

ABSTRACT

Streptomyces strains DSM 116494T and DSM 116496T were isolated from sediment samples of the River Oker in Braunschweig, Germany, and subjected to a polyphasic taxonomic study and genome mining for specialized secondary metabolites. Phenotypic, genetic and genomic data confirmed the assignment of these strains to the Streptomyces genus. Pairwise 16S rRNA gene sequence similarity values between the strains and validly named Streptomyces species reached 99.5 and 99.7% for strains DSM 116494T and DSM 116496T, respectively. Genome-based phylogeny demonstrated that Streptomyces pilosus and Streptomyces griseoflavus species were the close relatives to strain DSM 116494T, while Streptomyces vinaceus species was the nearest neighbour to strain DSM 116496T. Digital DNA-DNA hybridization and average nucleotide identity comparisons of the genomic sequence of the strains and their close phylogenomic relatives revealed that values were below the determined threshold of 70 and 95-96% for prokaryotic species demarcation, respectively. The strains were distinguished from their close neighbours based on biochemical, chemotaxonomic and enzymatic data. Given these results, the strains merit being affiliated to novel species within the genus Streptomyces, for which the names Streptomyces okerensis sp. nov. (=OG2.3T=DSM 116494T=KCTC 59408T) and Streptomyces stoeckheimensis sp. nov. (=OG3.14T=DSM 116496T=KCTC 59410T) are proposed. Strains DSM 116494T and DSM 116496T harboured several biosynthetic gene clusters encoding potentially novel antimicrobial and anticancer compounds. Crude extracts of strains DSM 116494T and DSM 116496T inhibited the growth of Gram-negative bacteria (Escherichia coli ΔtolC, Proteus vulgaris) and a multi-drug-resistant Gram-positive, Staphylococcus aureus.

PMID:40085491 | DOI:10.1099/ijsem.0.006716

Neurol Sci. 2025 Mar 14. doi: 10.1007/s10072-025-08114-w. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative disorder primarily characterized by motor impairment, resulting from the accumulation of α-synuclein and neuronal cell death in the substantia nigra of the midbrain. Emerging evidence suggests that α-synuclein aggregation may originate in the enteric nervous system (ENS) and subsequently propagate to the brain via the vagus nerve. Clinical observations, such as prodromal gastrointestinal dysfunction in PD patients and the increased incidence of PD among individuals with inflammatory bowel disease, support the hypothesis that abnormal intestinal inflammation may contribute to the onset of motor dysfunction and neuropathology in PD. This review examines recent findings on the interplay between intestinal immune cells and α-synuclein aggregation within the framework of gut-originated PD pathogenesis. It begins by discussing evidence linking dysbiosis and intestinal inflammation to α-synuclein aggregation in the ENS. Additionally, it explores the potential role of intestinal immune cells in influencing enteric neurons and α-synuclein aggregation, furthering the understanding of PD development.

PMID:40085320 | DOI:10.1007/s10072-025-08114-w

Front Public Health. 2025 Feb 27;13:1417771. doi: 10.3389/fpubh.2025.1417771. eCollection 2025.

ABSTRACT

Burnout among physicians has gained increasing attention in recent years. This issue arises not only from stressful working conditions and individual factors but also from the correlation between burnout and physicians' tolerance of uncertainty. This association could be particularly important in the context of rare diseases, which inherently present greater uncertainty. To date, no studies have explored this topic. Our exploratory study aimed to investigate the associations between uncertainty and burnout scores among physicians while considering secondary factors associated with rare diseases and COVID-related stress. Although not the primary focus, we included COVID-related stress due to its impact during the ongoing pandemic. We conducted an online survey using the Physicians' Reaction to Uncertainty Scale (PRU) and the Oldenburg Burnout Inventory (OLBI). Experience with rare diseases was quantified by assessing the weekly working hours devoted to patients with such conditions. We conducted a path analysis, initially using a fully recursive model and subsequently eliminating non-significant paths. 128 physicians (n = 73 female) participated in the survey, with 31% of them displaying significant burnout scores. Notably, significant associations were found between the PRU subscale anxiety and both dimensions of burnout, as well as between the PRU subscale disclosure to patients and the burnout dimension of exhaustion. COVID-related stress was also significantly associated with exhaustion, while experience with rare diseases was significantly associated with disengagement. No correlation was observed between experience with rare diseases and uncertainty scores. The model demonstrated an excellent fit (RMSEA = 0.055). Our results show that physician burnout is a pressing issue and confirm the association between anxiety due to uncertainty and increased burnout scores.

PMID:40084205 | PMC:PMC11903758 | DOI:10.3389/fpubh.2025.1417771

Lancet Reg Health Southeast Asia. 2025 Feb 26;34:100555. doi: 10.1016/j.lansea.2025.100555. eCollection 2025 Mar.

ABSTRACT

Leprosy is effectively treated with multi-drug therapy (MDT), a regimen containing three antibiotic drugs, including dapsone - a sulfone drug associated with potentially life-threatening adverse drug reactions. Specifically, dapsone hypersensitivity syndrome (DHS), linked to HLA-B∗13:01 polymorphism, and hemolytic anemia associated with glucose-6-phosphate dehydrogenase deficiency (G6PDd). Both of these pharmacogenetic polymorphisms can be prevented through diagnostic screening before MDT initiation averting potential complications. However, in leprosy-endemic areas like Indonesia, access to these tests often remains inaccessible due to high costs and limited laboratory capacity. Additionally, alternative dapsone-sparing treatment regimens are usually unavailable or unaffordable, restraining individuals onto suboptimal dual-therapy with rifampicin and clofazimine, which has uncertain efficacy. We raise concerns regarding the safety of dapsone-containing MDT without routine pharmacogenetic screening and the unavailability of alternative regimens. We call for action to address persisting global health inequities in care delivery, ensuring all individuals receive the safest and most effective leprosy treatment options.

PMID:40084155 | PMC:PMC11905890 | DOI:10.1016/j.lansea.2025.100555

Front Pediatr. 2025 Feb 27;13:1543136. doi: 10.3389/fped.2025.1543136. eCollection 2025.

ABSTRACT

IMPORTANCE: Gaucher disease (GD) is a rare lysosomal storage disorder with limited treatment options for pediatric patients. Oral substrate reduction therapy (SRT) with eliglustat offers a potential alternative, particularly for those with barriers to enzyme replacement therapy (ERT).

OBJECTIVE: Evaluate the safety and efficacy of eliglustat SRT in pediatric patients with type 1 Gaucher disease (GD1), both as initial therapy and as a switch from intravenous ERT.

DESIGN: A prospective case series was conducted from 2017 to 2024.

SETTING: Yale's National Gaucher Disease Treatment Center, New Haven, CT, United States.

PARTICIPANTS: Fourteen pediatric GD1 patients with significant barriers to receiving ERT.

INTERVENTION: Eliglustat SRT was dosed pharmacogenomically based on CYP2D6 metabolizer status.

PRIMARY OUTCOMES AND MEASURES: Primary outcomes included safety and efficacy in reversing indicators of disease activity. Secondary outcomes involved changes in patient and parent-reported quality of life, assessed using PROMIS questionnaires.

RESULTS: Eliglustat was initiated at a mean age of 12.5 years (range: 6-17 years) and administered for a mean duration of 3.6 years (range: 1-7 years). All patients remained on treatment and exhibited sustained reductions in glucosylsphingosine (GlcSph) levels compared to baseline (p = 0.005). Other disease indicators demonstrated corresponding improvements. Adverse effects were limited to transient gastroesophageal reflux in 3/14 patients (21%). Serial electrocardiograms (EKGs) were normal. Growth and developmental milestones were appropriate for age in all patients. Patients and their parents reported a global improvement in quality of life.

CONCLUSIONS: Eliglustat demonstrated significant clinical benefits in pediatric GD1 patients, as evidenced by reductions in GlcSph levels and other disease indicators. The therapy showed a favorable safety profile comparable to that observed in adults. These findings suggest eliglustat is a promising therapeutic option for pediatric GD1 patients, providing an effective alternative to ERT.

PMID:40083427 | PMC:PMC11903696 | DOI:10.3389/fped.2025.1543136

BMC Cardiovasc Disord. 2025 Mar 13;25(1):179. doi: 10.1186/s12872-025-04611-0.

ABSTRACT

BACKGROUND: To systematically evaluate the effect of ABCG2 c.421 C > A (rs2231142) single nucleotide polymorphism (SNP) on the lipid-modulating efficacy of rosuvastatin (RST).

METHODS: Searches were conducted using the Wan Fang database, Web of Science, Embase, PubMed, Cochrane Library, and China Journal Full Text Database. The time frame for the search was from the database's creation to September 1, 2024. The RevMan 5.4 software was used to conduct a meta-analysis after the literature was filtered based on the inclusion and exclusion criteria, and pertinent data was extracted following methodological quality evaluation.

RESULTS: A total of 7 studies, including 1347 patients, were included. Meta-analysis showed that in a dominant model of inheritance, RST had a significant effect on low-density lipoprotein cholesterol (LDL-C) [MD = -7.23, 95% CI (-8.71, -5.75), P < 0.05], total cholesterol (TC) [MD = -7.15, 95% CI (-8.71, -5.75), P < 0.05], and triglyceride (TG) [MD = -7.34, 95% CI (-10.88, -3.80), P < 0.05] in patients harboring an A allele decreased significantly more than CC, but there was no significant difference in the change of high-density lipoprotein cholesterol (HDL-C) [MD = -2.22, 95% CI (-19.87, 15.43), P = 0.81]. The results of the sensitivity analysis suggested that all outcome indicators were stable. However, this study's small sample size may be heterogeneous, and more large-sample, multi-center studies are needed for future validation.

CONCLUSIONS: The ABCG2 c.421 C > A (rs2231142) SNP significantly affected the lipid-modulating efficacy of RST, especially the down-regulation of LDL-C, TC, and TG.

PMID:40082758 | DOI:10.1186/s12872-025-04611-0

Nat Comput Sci. 2025 Mar 13. doi: 10.1038/s43588-025-00777-x. Online ahead of print.

ABSTRACT

The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.

PMID:40082701 | DOI:10.1038/s43588-025-00777-x

J Cancer Res Clin Oncol. 2025 Mar 13;151(3):109. doi: 10.1007/s00432-025-06160-7.

ABSTRACT

INTRODUCTION: Telomerase reverse transcriptase (TERT) is a catalytic subunit of telomerase that maintains genome stability by maintaining telomere length (TL). The massive proliferation of donor cells in the recipient's body for engraftment results in accelerated telomere shortening. Genetic variability within the TERT gene affects telomerase activity, and was shown to influence of haematopoietic stem cell transplantation (HSCT) outcome. In the present study, we aimed to analyse the effect of recipient and donor TL and TERT single nucleotide polymorphism (SNP) on the occurrence of post-HSCT complications.

METHODS: Our study included 120 recipient-donor pairs. TERT promoter (TERTp) SNP (rs2853669) SNP variant was detected with the use of the LightSNiP typing assay employing real-time polymerase chain reaction (PCR) amplifications. Telomere length measurements were performed using qPCR test kits (ScienCell's Absolute Human Telomere Length Quantification qPCR Assay Kit [AHTLQ], Carlsbad, CA, USA).

RESULTS: The presence of TERTp rs2853669 T allele in the recipient was associated with a higher risk for acute graft-versus-host-disease (aGvHD) manifestation (p = 0.046) and a significantly shorter aGvHD-free survival (p = 0.041). The latter association was further confirmed in a Cox proportional hazards model (p = 0.043). However, no statistically significant association between telomere length and post-transplant complications was observed. Furthermore, we found that shorter TL characterized donors of patients with late complete chimerism at 180 day after HSCT (p = 0.011).

CONCLUSION: Our results suggest that recipient allele TERTp rs2853669 T is a marker of unfavourable outcome in the context of aGvHD. Shorter TL in donors could be associated with later achievement of complete chimerism.

PMID:40082305 | DOI:10.1007/s00432-025-06160-7

Turk J Pediatr. 2025 Feb 20;67(1):22-30. doi: 10.24953/turkjpediatr.2025.4680.

ABSTRACT

BACKGROUND: Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) variants are essential for determining eligibility for CFTR modulator drugs (CFTRms). In contrast to Europe and the USA, the treatment eligibility profile of cystic fibrosis (CF) patients in Türkiye is not known. In this study we aimed to determine the eligibility of CF patients in Türkiye for the CFTRms.

METHODS: The Cystic Fibrosis Registry of Türkiye (CFrT) data was used to determine the age of patients in the year 2021 and the genetic variants they were carrying. Age- and CFTR-variant appropriate modulator therapies were determined using the Vertex® algorithm.

RESULTS: Among a total of 1930 registered patients, CTFR gene analysis was performed on a total of 1841 (95.4%) patients. Mutations were detected in one allele in 10.7% (198 patients), and in both alleles in 79% (1455 patients) of patients. A total of 855 patients (51.7% for whom at least 1 mutation was detected) were eligible for the drugs. The most appropriate drug among genotyped patients was found to be elexacaftor/tezacaftor/ivacaftor for 486 patients (26.4%), followed by ivacaftor for 327 patients (17.7%) and lumacaftor/ivacaftor for 42 patients (2%).

CONCLUSIONS: Only half of patients registered in CFrT were eligible for CFTRms, which is a significant difference from the CFTR variant profile seen in USA and Europe. However, access to treatment is hampered for some patients whose genes are not analysed. Further studies in CF populations, where rare mutations are relatively more common, will contribute to the field of CFTR modulator treatments for such rare mutations.

PMID:40084730 | DOI:10.24953/turkjpediatr.2025.4680

Exp Ther Med. 2025 Feb 25;29(4):85. doi: 10.3892/etm.2025.12835. eCollection 2025 Apr.

ABSTRACT

The coronavirus disease-19 (COVID-19) pandemic has been a very significant health issue in the period between 2020 and 2023, forcing research to characterize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences and to develop novel therapeutic approaches. Interleukin-6 (IL-6) and IL-8 are considered significant therapeutic targets for COVID-19 and emerging evidence has suggested that microRNAs (miRNAs/miRs) serve a key role in regulating these genes. MiRNAs are short, 19-25 nucleotides in length, non-coding RNAs that regulate gene expression at the post-transcriptional level through the sequence-selective recognition of the 3'-untranslated region (3'-UTR) of the regulated mRNAs, eventually repressing translation, commonly, via mRNA degradation. For example, among several miRNAs involved in the regulation of the COVID-19 'cytokine storm', miR-93-5p can inhibit IL-8 gene expression by directly targeting the 3'-UTR of IL-8 mRNA. In addition, miR-93-5p can regulate Toll-like receptor-4 (TLR4) and interleukin-1 receptor-associated kinase 4 (IRAK4) expression, thus affecting the nuclear factor-κB (NF-κB) pathway and the expression of NF-κB-regulated genes, such as IL-6, IL-1β and other hyper-expressed genes during the COVID-19 'cytokine storm'. In the present study, the results provided preliminary evidence suggesting that the miR-93-5p-based miRNA therapeutics could be combined with the anti-inflammatory aged garlic extract (AGE) to more effectively inhibit IL-8 gene expression. The human bronchial epithelial IB3-1 cell line was employed as experimental model system. IB3-1 cells were stimulated with the BNT162b2 COVID-19 vaccine and transfected with pre-miR-93-5p in the absence or in the presence of AGE, to verify the inhibitory effects on the BNT162b2-induced expression of the IL-8 gene. The accumulation of IL-8 mRNA was assessed by RT-qPCR; the release of IL-8 protein was determined by Bio-Plex assay. In addition, the possible applications of TLR4/NF-κB inhibitory agents (such as miR-93-5p and AGE) for treating human pathologies at a hyperinflammatory state, such as COVID-19, cystic fibrosis and other respiratory diseases, were summarized.

PMID:40084194 | PMC:PMC11904878 | DOI:10.3892/etm.2025.12835

BMC Infect Dis. 2025 Mar 13;25(1):358. doi: 10.1186/s12879-025-10736-6.

ABSTRACT

This systematic review evaluates the efficacy and safety of COVID-19 vaccines in individuals with cystic fibrosis (CF). A systematic search of major databases conducted between December 2019 and January 2024 identified eight cohort studies comprising 1,361 CF patients. Studies without subgroup analyses specific to CF patients were excluded, which may have limited the generalizability of findings, particularly for CF lung transplant recipients. COVID-19 vaccines generally induced robust serological responses following the second and third doses, although reduced antibody levels were observed in lung transplant recipients. Factors influencing humoral response included prior SARS-CoV-2 infection, age, inhaled corticosteroid use, and immunosuppressive therapy. Vaccination-related adverse events were predominantly mild. Although breakthrough infections were reported, severe COVID-19 outcomes were infrequent among vaccinated CF patients. The evidence supports the immunogenicity and safety of COVID-19 vaccines in the CF patients. However, individualized vaccination strategies may be necessary for CF lung transplant recipients and those on immunosuppressive therapies. Further research is essential to optimize vaccination strategies and to identify risk factors associated with breakthrough infections in this high-risk population.

PMID:40082759 | DOI:10.1186/s12879-025-10736-6

Cell Death Differ. 2025 Mar 13. doi: 10.1038/s41418-025-01474-y. Online ahead of print.

ABSTRACT

The plasma concentrations of acyl CoA binding protein (ACBP) encoded by the gene diazepam binding inhibitor (DBI) are increased in patients with severe osteoarthritis (OA). Here, we show that knee OA induces a surge in plasma ACBP/DBI in mice subjected to surgical destabilization of one hind limb. Knockout of the Dbi gene or intraperitoneal (i.p.) injection of a monoclonal antibody (mAb) neutralizing ACBP/DBI attenuates OA progression in this model, supporting a pathogenic role for ACBP/DBI in OA. Furthermore, anti-ACBP/DBI mAb was also effective against OA after its intraarticular (i.a.) injection, as monitored by sonography, revealing the capacity of ACBP/DBI to locally reduce knee inflammation over time. In addition, i.a. anti-ACBP/DBI mAb improved functional outcomes, as indicated by the reduced weight imbalance caused by OA. At the anatomopathological level, i.a. anti-ACBP/DBI mAb mitigated histological signs of joint destruction and synovial inflammation. Of note, i.a. anti-ACBP/DBI mAb blunted the OA-induced surge of plasma ACBP/DBI, as well as that of other inflammatory factors including interleukin-1α, interleukin-33, and tumor necrosis factor. These findings are potentially translatable to OA patients because joints from OA patients express both ACBP/DBI and its receptor GABAARγ2. Moreover, a novel mAb against ACBP/DBI recognizing an epitope conserved between human and mouse ACBP/DBI demonstrated similar efficacy in mitigating OA as an anti-mouse ACBP/DBI-only mAb. In conclusion, ACBP/DBI might constitute a promising therapeutic target for the treatment of OA.

PMID:40082721 | DOI:10.1038/s41418-025-01474-y

Pain. 2025 Mar 12. doi: 10.1097/j.pain.0000000000003587. Online ahead of print.

ABSTRACT

Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.

PMID:40084983 | DOI:10.1097/j.pain.0000000000003587