J Exp Biol. 2025 Feb 26:jeb.250110. doi: 10.1242/jeb.250110. Online ahead of print.

ABSTRACT

All ionoregulating marine fishes examined to date utilize seawater-type ionocytes expressing the apical Cl- channel, cystic fibrosis transmembrane conductance regulator (Cftr) to secrete Cl-. We performed transcriptomic, molecular, and functional studies to identify Cl- transporters in the seawater-type ionocytes of sea lamprey (Petromyzon marinus). Gill cftr expression was minimal or undetectable in larvae and post-metamorphic juveniles. We identified other Cl- transporters highly expressed in the gills and/or upregulated following metamorphosis and further investigated two candidates that stood out in our analysis, a Ca2+-activated Cl- channel, anoctamin 1 (ano1), and the Clc chloride channel family member 2 (clcn2). Of these, ano1 was expressed 10-100 times more than clcn2 in the gills; moreover, ano1 was upregulated during seawater acclimation, while clcn2 was not. Using an antibody raised against sea lamprey Ano1, we did not detect Ano1 in the gills of larvae, found elevated levels in juveniles and observed a 4-fold increase in juveniles after seawater acclimation. Ano1 was localized to seawater-type branchial ionocytes but, surprisingly, was localized to the basolateral membrane. In vivo pharmacological inhibition experiments demonstrated that a DIDS-sensitive mechanism was critical to the maintenance of osmoregulatory homeostasis in seawater- but not freshwater-acclimated sea lamprey. Taken together, our results provide evidence of a Cftr-independent mechanism for branchial Cl- secretion in sea lamprey that leverages Ano1-expressing ionocytes. Once further characterized, the Cftr-independent, Ano1-rich ionocytes of sea lamprey could reveal novel strategies for branchial Cl- secretion, whether by Ano1 or some other Cl- transporter, not previously known in ionoregulating marine organisms.

PMID:40007443 | DOI:10.1242/jeb.250110

Viruses. 2025 Jan 29;17(2):189. doi: 10.3390/v17020189.

ABSTRACT

Pseudomonas aeruginosa, an opportunistic pathogen, causes various biofilm-associated infections like pneumonia, infections in cystic fibrosis patients, and urinary tract and burn infections with high morbidity and mortality, as well as low treatment efficacy due to the extremely wide spread of isolates with multidrug resistance. Here, we report the new bacteriophage Pseudomonas phage Ka2 isolated from a tributary stream of Lake Baikal and belonging to the Pbunavirus genus. Transmission electron microscopy resolved that Pseudomonas phage Ka2 has a capsid of 57 ± 9 nm and a contractile and inflexible tail of 115 ± 10 nm in the non-contracted state. The genome consists of 66,310 bp with a GC content of 55% and contains 96 coding sequences. Among them, 52 encode proteins have known functions, and none of them are potentially associated with lysogeny. The bacteriophage lyses 21 of 30 P. aeruginosa clinical isolates and decreases the MIC of amikacin, gentamicin, and cefepime up to 16-fold and the MIC of colistin up to 32-fold. When treating the biofilms with Ka2, the biomass was reduced by twice, and up to a 32-fold decrease in the antibiotics MBC against biofilm-embedded cells was achieved by the combination of Ka2 with cefepime for the PAO1 strain, along with a decrease of up to 16-fold with either amikacin or colistin for clinical isolates. Taken together, these data characterize the new Pseudomonas phage Ka2 as a promising tool for the combined treatment of infections associated with P. aeruginosa biofilms.

PMID:40006944 | DOI:10.3390/v17020189

Pharmaceutics. 2025 Feb 6;17(2):200. doi: 10.3390/pharmaceutics17020200.

ABSTRACT

Background: The combination of ivacaftor, tezacaftor and elexacaftor (ETI) is approved for patients with cystic fibrosis (CF) aged two years and older and at least one F508del mutation in the CFTR gene. Variability in ETI treatment response has been repeatedly reported, and its reasons are unclear and understudied. Objectives: We present a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the rapid and simultaneous quantification of ETI in plasma, dried plasma spots (DPS), and whole blood volumetric absorptive microsampling (VAMS). Methods: The method utilizes a rapid extraction protocol with 200 μL methanol after the addition of deuterated internal standards. Chromatographic separation was achieved using a reversed-phase Hypersil Gold aQ column (Thermo Fisher Scientific). The method was validated according to ICH (International Council on Harmonisation) guidelines M10 for bioanalytical method validation, demonstrating linearity in the concentration range 0.020-12.000 µg/mL. It was also proved accurate and reproducible with no matrix effect. This method was applied to anonymized samples from patients undergoing ETI treatment: eight plasma and DPS and five VAMS samples were analyzed. Results: ETI concentrations measured in plasma and DPS were interchangeable, whereas ETI concentrations in VAMS were lower than in plasma, as expected for molecules with high plasma protein binding (99%). A correction factor based on the hematocrit value was used to calculate the equivalent plasma concentration from VAMS concentrations. Conclusions: This method is suitable for pharmacokinetic (PK) studies and could facilitate the centralization of samples to specialized laboratories, supporting multicenter studies.

PMID:40006567 | DOI:10.3390/pharmaceutics17020200

Pharmaceuticals (Basel). 2025 Feb 7;18(2):225. doi: 10.3390/ph18020225.

ABSTRACT

Background/Objectives: Mycobacterium abscessus (MAB) is a highly resilient pathogen that causes difficult-to-treat pulmonary infections, particularly in individuals with cystic fibrosis (CF) and other underlying conditions. Its ability to form robust biofilms within the CF lung environment is a major factor contributing to its resistance to antibiotics and evasion of the host immune response, making conventional treatments largely ineffective. These biofilms, encased in an extracellular matrix, enhance drug tolerance and facilitate metabolic adaptations in hypoxic conditions, driving the bacteria into a persistent, non-replicative state that further exacerbates antimicrobial resistance. Treatment options remain limited, with multidrug regimens showing low success rates, highlighting the urgent need for more effective therapeutic strategies. Methods: In this study, we employed artificial sputum media to simulate the CF lung environment and conducted high-throughput screening of 24,000 compounds from diverse chemical libraries to identify inhibitors of MAB biofilm formation, using the Crystal Violet (CV) assay. Results: The screen established 17 hits with ≥30% biofilm inhibitory activity in mycobacteria. Six of these compounds inhibited MAB biofilm formation by over 60%, disrupted established biofilms by ≥40%, and significantly impaired bacterial viability within the biofilms, as confirmed by reduced CFU counts. In conformational assays, select compounds showed potent inhibitory activity in biofilms formed by clinical isolates of both MAB and Mycobacterium avium subsp. hominissuis (MAH). Key compounds, including ethacridine, phenothiazine, and fluorene derivatives, demonstrated potent activity against pre- and post-biofilm conditions, enhanced antibiotic efficacy, and reduced intracellular bacterial loads in macrophages. Conclusions: This study results underscore the potential of these compounds to target biofilm-associated resistance mechanisms, making them valuable candidates for use as adjuncts to existing therapies. These findings also emphasize the need for further investigations, including the initiation of a medicinal chemistry campaign to leverage structure-activity relationship studies and optimize the biological activity of these underexplored class of compounds against nontuberculous mycobacterial (NTM) strains.

PMID:40006039 | DOI:10.3390/ph18020225

Pharmaceuticals (Basel). 2025 Jan 27;18(2):175. doi: 10.3390/ph18020175.

ABSTRACT

This manuscript provides a comprehensive review of advancements in dry powder inhaler (DPI) technology for pulmonary and systemic drug delivery, focusing on proteins, peptides, nucleic acids, and small molecules. Innovations in spray-drying (SD), spray freeze-drying (SFD), and nanocarrier engineering have led to enhanced stability, bioactivity, and aerosol performance. Studies reveal the critical role of excipients, particle morphology, and device design in optimizing deposition and therapeutic efficacy. Applications include asthma, cystic fibrosis, tuberculosis (TB), and lung cancer, with emerging platforms such as ternary formulations and siRNA-loaded systems demonstrating significant clinical potential. Challenges such as stability, scalability, and patient adherence are addressed through novel strategies, including Quality by Design (QbD) approaches and advanced imaging tools. This work outlines pathways for future innovation in pulmonary drug delivery.

PMID:40005989 | DOI:10.3390/ph18020175

J Clin Med. 2025 Feb 18;14(4):1362. doi: 10.3390/jcm14041362.

ABSTRACT

The Hungarian Pancreatic Study Group (HPSG) was established with the aim of advancing pancreatology. Our summary outlines the methodologies, key results, and future directions of the HPSG. Methodological elements included, the formation of strategic national and international collaborations, the establishment of patient registries and biobanks, and a strong focus on education and guideline development. Key results encompassed, pioneering research on pancreatic ductal function and the role of cystic fibrosis transmembrane conductance regulator (CFTR) in inflammation, significant advancements in understanding acute and chronic pancreatitis, and the execution of numerous clinical trials to explore new therapeutic approaches. Despite challenges, such as securing funding and translating research into clinical practice, the HPSG's commitment to patient care and scientific innovation has been unwavering. The group aims to deepen research into pancreatic cancer and chronic pancreatitis, conduct more randomized controlled trials (RCTs), and expand its efforts internationally by involving global staff and patients. The authors hope that this summary inspires others to undertake similar initiatives and contribute to the global advancement of medical research and patient care in pancreatology.

PMID:40004893 | DOI:10.3390/jcm14041362

Children (Basel). 2025 Jan 28;12(2):157. doi: 10.3390/children12020157.

ABSTRACT

BACKGROUND/OBJECTIVES: Cystic fibrosis (CF) is a life-limiting genetic disorder affecting multiple organ systems. This study compared clinical outcomes, hospitalization rates, and survival between children and adolescents with CF who received CFTR modulator therapies (ivacaftor, lumacaftor, tezacaftor, and elexacaftor).

METHODS: A retrospective cohort study was conducted using data from the TriNetX global collaborative network. Patients with CF aged 2-12 years (children) and 13-18 years (adolescents) who received CFTR modulator therapies were included. The propensity score matching balanced baseline characteristics between the two age groups.

RESULTS: After propensity score matching, 946 patients per group were analyzed. The incidence of respiratory failure (3.81% vs. 1.06%, p < 0.001) and respiratory infections (62.7% vs. 57.5%, p = 0.021) were significantly higher in adolescents compared to children. Adolescents had a higher risk of respiratory failure (HR = 3.6, 95% CI = 1.79-7.21, p < 0.001) and respiratory infections (HR = 1.09, 95% CI = 1.01-1.17, p < 0.001). Adolescents also had a higher hospitalization rate (29.6% vs. 20.3%, p < 0.001), with a 47% higher risk (HR = 1.47, 95% CI = 1.22-1.77, p = 0.001), more hospital visits per person (8.8 vs. 3.7, p = 0.004), and longer hospital stays (32.7 vs. 20.4 days, p = 0.006). Mortality rates were similar between the groups (1.58% vs. 1.26%, p = 0.56).

CONCLUSIONS: CF patients who initiated CFTR modulator therapies during adolescence had a higher incidence of respiratory failure, respiratory infections, hospitalization rates, and healthcare resource utilization compared to those who started therapy in childhood, despite similar mortality rates. These findings highlight the importance of the early initiation of CFTR modulator therapies.

PMID:40003259 | DOI:10.3390/children12020157

Diagnostics (Basel). 2025 Feb 16;15(4):474. doi: 10.3390/diagnostics15040474.

ABSTRACT

Hyperpolarized xenon-129 MRI (129XeMRI) has emerged as a powerful tool in the identification, evaluation, and assessment of disease endotyping and in response to interventions for a myriad of pulmonary diseases. Growing investigative efforts ranging from basic science to application in translational research have employed 129XeMRI in the evaluation of pulmonary conditions such as chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, and cystic fibrosis (CF). The novel feature of 129XeMRI is its ability to generate anatomic and physiologic readouts of the lung with resolution from the whole lung down to the lobar level. Additional advantages include being non-invasive and non-radioactive, and utilizing an inexpensive and ubiquitous noble gas as an inhalation contrast agent: xenon-129. In this review, we outline the clinical advances provided by 129XeMRI among common pulmonary diseases with high healthcare burdens in recent decades.

PMID:40002625 | DOI:10.3390/diagnostics15040474

Antibiotics (Basel). 2025 Feb 11;14(2):177. doi: 10.3390/antibiotics14020177.

ABSTRACT

Background/Objectives: The pulmonary administration of antibiotics can be advantageous in treating pulmonary infections by promoting high intrapulmonary drug concentrations with reduced systemic exposure. However, limited benefits have been observed for pulmonary administration versus other administration routes due to its rapid clearance from the lung. Here, the effects of structural modifications on the epithelial permeability and antibacterial potency of a third-generation cephalosporin were investigated to improve the understanding of drug properties that promote intrapulmonary retention and how they may impact efficacy. Methods: Ceftazidime was modified by attaching 18 hydrophobic, hydrophilic, and mucus-binding motifs to the carboxylic acid distant from the beta-lactam by amidation. Epithelial permeability was investigated by drug transport assays using human bronchial epithelial air-liquid interface cultures. Antibacterial potency was determined by microtiter MIC assays with B. pseudomallei, P. aeruginosa, E. coli, and S. aureus. Results: A 40-50% reduction in the transepithelial transport rate was exhibited by two PEGylated ceftazidime analogs (mPEG8- and PEG5-pyrimidin-2-amine-ceftazidime) and n-butyl-ceftazidime. An increase in the transport rate was exhibited by four analogs bearing small and hydrophobic or negatively charged motifs (n-heptane-, phenyl ethyl-, glutamic acid-, and 4-propylthiophenyl boronic acid-ceftazidime). The antibacterial potency was reduced by ≥10-fold for most ceftazidime analogs against B. pseudomallei, P. aeruginosa, and E. coli but was retained by seven ceftazidime analogs primarily bearing hydrophobic motifs against S. aureus. Conclusions: The covalent conjugation of PEGs with MW > 300 Da reduced the epithelial permeability of ceftazidime, but these modifications severely reduced antibacterial activity. To improve the pulmonary retention of antibiotics with low membrane permeability, this work suggests future molecular engineering studies to explore high-molecular-weight prodrug strategies.

PMID:40001420 | DOI:10.3390/antibiotics14020177

Antibiotics (Basel). 2025 Feb 8;14(2):169. doi: 10.3390/antibiotics14020169.

ABSTRACT

Background/Objectives: Pulmonary delivered tobramycin (TOB) is a standard treatment for Pseudomonas aeruginosa lung infections, that, along with Staphylococcus aureus, is one of the most common bacteria causing recurring infections in CF patients. However, the only available formulation on the market containing tobramycin, TOBI®, is sold at a price that makes the access to the treatment difficult. Therefore, this work focuses on the development and characterization of an ionic complex between a polyelectrolyte, hyaluronic acid (HA) and its salt, sodium hyaluronate (NaHA), and TOB to be formulated as an inhalable dry powder. Methods: The solid state complex obtained by spray drying technique was physicochemically characterized by infrared spectroscopy, thermal analysis and X-ray diffraction, confirming an ionic interaction for both complexes. Results: The powder density, geometric size, and morphology along with the aerodynamic performance showed suitable properties for the powder formulations to reach the deep lung. Moisture uptake was found to be low, with the complex HA-TOB remaining physicochemically unchanged, while the NaHA-TOB required significant protection against humidity. The biopharmaceutical in vitro experiments showed a rapid dissolution which can have a positively impact in reducing side effects, while the drug release study demonstrated a reversible polyelectrolyte-drug interaction. Microbiological experiments against P. aeruginosa and S. aureus showed improved bacterial growth inhibition and bactericidal efficacy, as well as better inhibition and eradication of biofilms when compared with to TOB. Conclusions: A simple polyelectrolyte-drug complex technique represents a promising strategy for the development of antimicrobial dry powder formulations for pulmonary delivery in the treatment of cystic fibrosis (CF) lung infections.

PMID:40001413 | DOI:10.3390/antibiotics14020169

Antibiotics (Basel). 2025 Feb 7;14(2):165. doi: 10.3390/antibiotics14020165.

ABSTRACT

The lack of available treatments in pediatric non-cystic fibrosis (non-CF) bronchiectasis is a major concern, especially in the context of the increasing disease burden due to better detection rates with advanced imaging techniques. Recurrent infections in these patients are the main cause of deterioration, leading to impaired lung function and increasing the risk of morbidity and mortality. Since pediatric non-CF bronchiectasis with early recognition and appropriate treatment can be reversible, optimal management is an issue of growing significance. The use of inhaled antibiotics as a treatment option, although a standard of care for CF patients, has been poorly studied in patients with non-CF bronchiectasis, especially in children. In this review, we present the current data on the potential use of inhaled antibiotics in the treatment of non-CF bronchiectasis and assess their safety and efficacy profile, focusing mainly on children. We conclude that inhaled antibiotics as an adjuvant maintenance treatment option could be tried in a subgroup of patients with frequent exacerbations and recent or chronic Pseudomonas aeruginosa infection as they appear to have beneficial effects on exacerbation rate and bacterial load with minimal safety concerns. However, the level of evidence in children is extremely low; therefore, further research is needed on the validity of this recommendation.

PMID:40001409 | DOI:10.3390/antibiotics14020165

Antimicrob Resist Infect Control. 2025 Feb 25;14(1):17. doi: 10.1186/s13756-025-01527-4.

ABSTRACT

BACKGROUND/INTRODUCTION: Antipseudomonal antibiotics are frequently used in patients admitted to hospitals. Many of these substances are classified as a reserve or watch status by the WHO. Inappropriate risk assessment of invasive detection of P. aeruginosa (PAE) can be a reason for overuse of antipseudomonal antibiotics. Therefore it is important to define relevant and specific risk factors for invasive PAE detection.

OBJECTIVE: The objective of this study was to identify risk factors for invasive detection of PAE in patients upon hospital admission.

METHODS: All patients 18 years of age and older with a detection of PAE and/or Enterobacterales in clinical samples taken within 48 h of admission to one of the hospitals of Charité Universitätsmedizin Berlin between 2015 and 2020 were included into this retrospective cohort study.

RESULTS: Overall, we included a total of 27,710 patients. In 3,764 (13.6%) patients PAE was detected in clinical samples taken within 48 h after admission. The most frequently detected Enterobacterales was E. coli in 14.142 (51%) patients followed by Klebsiella spp. in 4.432 (16%) patients. Multivariable regression analysis identified that prior colonisation with a multi drug resistant PAE or detection of a PAE in clinical samples during a previous hospitalisation increased the risk for invasive detection of PAE (OR 39.41; 95% CI 28.54-54.39) and OR 7.87 (95% CI 6.60-9.38) respectively. Admission to a specialised ward for patients with cystic fibrosis was associated with an increased risk (OR 26.99; 95% CI 20.48-35.54). Presence of chronic pulmonary disease (OR 2.05; 95% CI 1.85-2.26), hemiplegia (OR 2.16; 95% CI 1.90-2.45) and male gender (OR 1.60; 95% CI 1.46-1.75) were associated with a modest increase in risk for presence of PAE.

CONCLUSION: Patients with a prior detection of P. aeruginosa or admission to a cystic fibrosis ward had the highest risk for invasive detection of P. aeruginosa. Adherence to specific risk scores based on local risk factors could help to optimize prescription of anti-pseudomonal antibiotics that categorized as reserve and watch.

PMID:40001254 | DOI:10.1186/s13756-025-01527-4

Nat Mach Intell. 2025;7(2):232-243. doi: 10.1038/s42256-024-00971-y. Epub 2025 Jan 28.

ABSTRACT

Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein-Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.

PMID:40008296 | PMC:PMC11847706 | DOI:10.1038/s42256-024-00971-y

Phys Imaging Radiat Oncol. 2025 Feb 1;33:100719. doi: 10.1016/j.phro.2025.100719. eCollection 2025 Jan.

ABSTRACT

Synthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-inspired DL model, this study found mean dose differences between a deformed planning CT (dCT) and sCT were 0.2 % (D98 %). Three Dimensional Gamma analysis showed a mean of 90.4 % at 1 %/1mm. This study showed accurate sCTs (dose) can be generated from routinely available T2 spin echo sequences without the need for additional specialist sequences.

PMID:40008279 | PMC:PMC11851199 | DOI:10.1016/j.phro.2025.100719

Ther Adv Drug Saf. 2025 Feb 24;16:20420986251321704. doi: 10.1177/20420986251321704. eCollection 2025.

ABSTRACT

Artificial intelligence (AI) is transforming medication research and development, giving clinicians new treatment options. Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. Furthermore, generative AI is accelerating the development and re-engineering of medicinal molecules to cater to both common and rare diseases. Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered "black boxes," making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. Despite these obstacles, AI-driven drug discovery has substantially reduced development times and costs, expediting the process and financial risks of bringing new medicines to market. In the future, AI is expected to continue to impact pharmaceutical innovation positively, making life-saving drug discoveries faster, more efficient, and more widespread.

PMID:40008227 | PMC:PMC11851753 | DOI:10.1177/20420986251321704

JAMIA Open. 2025 Jan 31;8(1):ooaf007. doi: 10.1093/jamiaopen/ooaf007. eCollection 2025 Feb.

ABSTRACT

OBJECTIVES: The automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.

MATERIALS AND METHODS: A corpus of 6170 tweets about COVID-19 vaccination, posted between March 1, 2020 and January 4, 2022, was manually annotated by native speakers. Traditional predictive models were compared with deep learning models to ascertain a baseline performance for the detection of stance in Spanish tweets. The evaluation focused on the ability of multilingual and language-specific embeddings to contextualize the topic of those short texts adequately.

RESULTS: The BERT-Multi+BiLSTM combination yielded the best results (macroaveraged F1 and Matthews correlation coefficient scores of 0.86 and 0.79, respectively; interpolated area under the receiver operating curve [AUC] of 0.95 for tweets against vaccination and 0.85 in favor of vaccination and a score of 0.97 for tweets containing no stance information), closely followed by the BETO+BiLSTM and RoBERTa BNE-LSTM Spanish models and the term frequency-inverse document frequency+SVM model (average AUC decrease of 0.01). The main differentiating factor among these models was the ability to predict tweets against vaccination.

DISCUSSION: The BERT Multi+BILSTM model outperformed the other models in terms of per class prediction capacity. The main assumption is that language-specific embeddings do not outperform multilingual embeddings or TF-IDF features because of the context of the topic. The inherent context of BERT or RoBERTa embeddings is general. So, these embeddings are not familiar with the slang commonly used on Twitter and, more specifically, during the pandemic.

CONCLUSION: The best performing model detects tweet stance with performance high enough to ensure its usefulness for public health applications, namely awareness campaigns, misinformation detection and other early intervention and prevention actions seeking to improve an individual's well-being based on autoreported experiences and opinions. The dataset and code of the study are available on GitHub.

PMID:40008184 | PMC:PMC11854073 | DOI:10.1093/jamiaopen/ooaf007

Front Public Health. 2025 Feb 11;13:1505825. doi: 10.3389/fpubh.2025.1505825. eCollection 2025.

ABSTRACT

INTRODUCTION: Parents' negative parenting style is an important cause of anxiety, depression, and suicide among university students. Given the widespread use of social media, microblogs offer a new and promising way for non-invasive, large-scale assessment of parenting styles of students' parents.

METHODS: In this study, we have two main objectives: (1) investigating the correlation between students' microblog discourses and parents' parenting styles and (2) devising a method to predict students' parenting styles from their microblog discourses. We analyzed 111,258 posts from 575 university students using frequency analysis to examine differences in the usage of topical and emotional word across different parenting styles. Informed by these insights, we developed an effective parenting style assessment method, including a correlation injection module.

RESULTS: Experimental results on the 575 students show that our method outperforms all the baseline NLP methods (including ChatGPT-4), achieving good assessment performance by reducing MSE by 14% to 0.12.

DISCUSSION: Our study provides a pioneering microblog-based parenting style assessment tool and constructs a dataset, merging insights from psychology and computational science. On the one hand, our study advances the understanding of how parenting styles are reflected in the linguistic and emotional expressions of students on microblogs. On the other hand, our study provides an assisting tool that could be used by healthcare institutions to identify students' parenting styles. It facilitates the identification of suicide risk factors among microblog student users, and enables timely interventions to prevent suicides, which enhances human wellbeing and saves lives.

PMID:40008146 | PMC:PMC11850274 | DOI:10.3389/fpubh.2025.1505825

Front Pharmacol. 2025 Feb 11;16:1525029. doi: 10.3389/fphar.2025.1525029. eCollection 2025.

ABSTRACT

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

PMID:40008124 | PMC:PMC11850324 | DOI:10.3389/fphar.2025.1525029

J Oral Biol Craniofac Res. 2025 Mar-Apr;15(2):215-220. doi: 10.1016/j.jobcr.2025.01.016. Epub 2025 Feb 8.

ABSTRACT

BACKGROUND: Dental implants are the most accepted prosthetic alternative for missing teeth. With growing demands, several manufacturers have entered the market and produce a variety of implant brands creating a challenge for clinicians to identify the implant when the necessity arises. Currently, radiographs are the only tools for implant identification which is inherently a complex process, hence the need for implant identification technique. Artificial intelligence capable of analysing images in a radiograph and predicting implant type is an efficient tool. The study evaluated an advanced deep learning technique, DEtection TRanformer for implant identification.

METHODS: A transformer-based deep learning technique, DEtection TRanformer was trained to identify implants in radiographs. A dataset of 1138 images consisting of five implant types captured from periapical and panoramic radiographs was chosen for the study. After augmentation, a dataset of 1744 images was secured and then split into training, validation and test datasets for the model. The model was trained and evaluated for its performance.

RESULTS: The model achieved an overall precision of 0.83 and a recall score of 0.89. The model achieved an F1-score of 0.82 indicating a strong balance between recall and precision. The Precision-Recall Curve, with an AUC of 0.96, showed that the model performed well across various thresholds. The training and validation graphs showed a consistent decrease in the loss functions across classes.

CONCLUSION: The model showed high performance on the training data, though it faced challenges with unseen validation data. High precision, recall and F1 score indicate the model's potential for implant identification. Optimizing this model for a balance between accuracy and efficiency will be necessary for real-time medical imaging applications.

PMID:40008072 | PMC:PMC11849603 | DOI:10.1016/j.jobcr.2025.01.016

Front Hum Neurosci. 2025 Feb 11;19:1498297. doi: 10.3389/fnhum.2025.1498297. eCollection 2025.

ABSTRACT

The rapid expansion of dialectally unique Arabic material on social media and the internet highlights how important it is to categorize dialects accurately to maximize a variety of Natural Language Processing (NLP) applications. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications. Recent advances in deep learning (DL) models have shown promise in overcoming potential challenges in identifying Arabic dialects. In this paper, we propose a novel stacking model based on two transformer models, i.e., Bert-Base-Arabertv02 and Dialectal-Arabic-XLM-R-Base, to enhance the classification of dialectal Arabic. The proposed model consists of two levels, including base models and meta-learners. In the proposed model, Level 1 generates class probabilities from two transformer models for training and testing sets, which are then used in Level 2 to train and evaluate a meta-learner. The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. The results show that the stacking model combination of two models archives outperformance over single-model approaches due to capturing a broader range of linguistic features, which leads to better generalization across different forms of Arabic. The proposed model is evaluated based on the performance of IADD and Shami. For Shami, the Stacking-Transformer achieves the highest performance in all rates compared to other models with 89.73 accuracy, 89.596 precision, 89.73 recall, and 89.574 F1-score. For IADD, the Stacking-Transformer achieves the highest performance in all rates compared to other models with 93.062 accuracy, 93.368 precision, 93.062 recall, and 93.184 F1 score. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications.

PMID:40007884 | PMC:PMC11850318 | DOI:10.3389/fnhum.2025.1498297