PLoS Comput Biol. 2025 Apr 2;21(4):e1012876. doi: 10.1371/journal.pcbi.1012876. Online ahead of print.

ABSTRACT

Computational approaches for drug repurposing for viral diseases have mainly focused on a small number of antivirals that directly target pathogens (virus centric therapies). In this work, we combine ideas from collaborative filtering and network medicine for making predictions on a much larger set of drugs that could be repurposed for host centric therapies, that are aimed at interfering with host cell factors required by a pathogen. Our idea is to create matrices quantifying the perturbation that drugs and viruses induce on human protein interaction networks. Then, we decompose these matrices to learn embeddings of drugs, viruses, and proteins in a low dimensional space. Predictions of host-centric antivirals are obtained by taking the dot product between the corresponding drug and virus representations. Our approach is general and can be applied systematically to any compound with known targets and any virus whose host proteins are known. We show that our predictions have high accuracy and that the embeddings contain meaningful biological information that may provide insights into the underlying biology of viral infections. Our approach can integrate different types of information, does not rely on known drug-virus associations and can be applied to new viral diseases and drugs.

PMID:40173200 | DOI:10.1371/journal.pcbi.1012876

PLoS Pathog. 2025 Apr 2;21(4):e1013021. doi: 10.1371/journal.ppat.1013021. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa is an opportunistic pathogen responsible for airway infections in immunocompromised individuals, including those with cystic fibrosis (CF). P. aeruginosa has a type III secretion system (T3SS) that translocates effectors into host cells. ExoS is a T3SS effector with ADP ribosyltransferase (ADPRT) activity. ExoS ADPRT activity promotes P. aeruginosa virulence by inhibiting phagocytosis and limiting oxidative burst in neutrophils. The P. aeruginosa T3SS also translocates flagellin, which can activate the NLRC4 inflammasome, resulting in: 1) gasdermin-D pores, release of IL-1β and pyroptosis; and 2) histone 3 citrullination (CitH3), nuclear DNA decondensation and expansion into the neutrophil cytosol with incomplete NET extrusion. However, studies with P. aeruginosa PAO1 indicate that ExoS ADPRT activity inhibits the NLRC4 inflammasome in neutrophils. Here, we identified an ExoS+ CF clinical isolate of P. aeruginosa with a hyperactive T3SS. Variants of the hyperactive T3SS mutant or PAO1 were used to infect neutrophils from C57BL/6 mice that were wildtype or engineered to have a CF genotype or defects in inflammasome assembly. Responses to NLRC4 inflammasome assembly or ExoS ADPRT activity were assayed and found to be similar for C57BL/6 or CF neutrophils. ExoS ADPRT activity in the hyperactive T3SS mutant regulated inflammasome, nuclear DNA decondensation and incomplete NET extrusion responses, like PAO1, but promoted enhanced CitH3 and plasma membrane rupture (PMR). Glycine supplementation inhibited PMR by the hyperactive T3SS mutant, suggesting ninjurin-1 is required for this process. These results identify enhanced neutrophil PMR as a pathogenic activity of ExoS ADPRT in hypervirulent P. aeruginosa.

PMID:40173191 | DOI:10.1371/journal.ppat.1013021

Invest Radiol. 2025 Apr 2. doi: 10.1097/RLI.0000000000001182. Online ahead of print.

ABSTRACT

OBJECTIVES: Respiratory motion poses a significant challenge for clinical workflows in diagnostic imaging and radiation therapy. Many technologies such as motion artifact reduction and tumor tracking have been developed to compensate for its effect. To assess these technologies, respiratory motion phantoms (RMPs) are required as preclinical testing environments, for instance, in computed tomography (CT). However, current CT RMPs are highly simplified and do not exhibit realistic tissue structures or deformation patterns. With the rise of more complex motion compensation technologies such as deep learning-based algorithms, there is a need for more realistic RMPs. This work introduces PixelPrint4D, a 3D printing method for fabricating lifelike, patient-specific deformable lung phantoms for CT imaging.

MATERIALS AND METHODS: A 4DCT dataset of a lung cancer patient was acquired. The volumetric image data of the right lung at end inhalation was converted into 3D printer instructions using the previously developed PixelPrint software. A flexible 3D printing material was used to replicate variable densities voxel-by-voxel within the phantom. The accuracy of the phantom was assessed by acquiring CT scans of the phantom at rest, and under various levels of compression. These phantom images were then compiled into a pseudo-4DCT dataset and compared to the reference patient 4DCT images. Metrics used to assess the phantom structural accuracy included mean attenuation errors, 2-sample 2-sided Kolmogorov-Smirnov (KS) test on histograms, and structural similarity index (SSIM). The phantom deformation properties were assessed by calculating displacement errors of the tumor and throughout the full lung volume, attenuation change errors, and Jacobian errors, as well as the relationship between Jacobian and attenuation changes.

RESULTS: The phantom closely replicated patient lung structures, textures, and attenuation profiles. SSIM was measured as 0.93 between the patient and phantom lung, suggesting a high level of structural accuracy. Furthermore, it exhibited realistic nonrigid deformation patterns. The mean tumor motion errors in the phantom were ≤0.7 ± 0.6 mm in each orthogonal direction. Finally, the relationship between attenuation and local volume changes in the phantom had a strong correlation with that of the patient, with analysis of covariance yielding P = 0.83 and f = 0.04, suggesting no significant difference between the phantom and patient.

CONCLUSIONS: PixelPrint4D facilitates the creation of highly realistic RMPs, exceeding the capabilities of existing models to provide enhanced testing environments for a wide range of emerging CT technologies.

PMID:40173424 | DOI:10.1097/RLI.0000000000001182

Aesthet Surg J. 2025 Apr 2:sjaf047. doi: 10.1093/asj/sjaf047. Online ahead of print.

ABSTRACT

BACKGROUND: Breast Implant Illness (BII) is a spectrum of symptoms some people attribute to breast implants. While causality remains unproven, patient interest has grown significantly. Understanding patient perceptions of BII on social media is crucial as these platforms increasingly influence healthcare decisions.

OBJECTIVES: The purpose of this study is to analyze patient perceptions and emotional responses to BII on social media using RoBERTa, a natural processing model trained on 124 million X posts.

METHODS: Posts mentioning BII from 2014-2023 were analyzed using two NLP models: one for sentiment (positive/negative) and another for emotions (fear, sadness, anger, disgust, neutral, surprise, and joy). Posts were then classified by their highest-scoring emotion. Results were compared over across 2014-2018 and 2019-2023, with correlation analysis (Pearson correlation coefficient) between published implant explantation and augmentation data.

RESULTS: Analysis of 6,099 posts over 10 years showed 75.4% were negative, with monthly averages of 50.85 peaking at 213 in March 2019. Fear and neutral emotions dominated, representing 35.9% and 35.6% respectively. The strongest emotions were neutral and fear, with an average score of 0.293 and 0.286 per post, respectively. Fear scores increased from 0.219 (2014-2018) to 0.303 (2019-2023). Strong positive correlations (r>0.70) existed between annual explantation rates/explantation-to-augmentation ratios and total, negative, neutral, and fear posts.

CONCLUSIONS: BII discourse on X peaked in 2019, characterized predominantly by negative sentiment and fear. The strong correlation between fear/negative-based posts and explantation rates suggests social media discourse significantly influences patient decisions regarding breast implant removal.

PMID:40173420 | DOI:10.1093/asj/sjaf047

PLoS One. 2025 Apr 2;20(4):e0319078. doi: 10.1371/journal.pone.0319078. eCollection 2025.

ABSTRACT

Hepatitis is a widespread inflammatory condition of the liver, presenting a formidable global health challenge. Accurate and timely detection of hepatitis is crucial for effective patient management, yet existing methods exhibit limitations that underscore the need for innovative approaches. Early-stage detection of hepatitis is now possible with the recent adoption of machine learning and deep learning approaches. With this in mind, the study investigates the use of traditional machine learning models, specifically classifiers such as logistic regression, support vector machines (SVM), decision trees, random forest, multilayer perceptron (MLP), and other models, to predict hepatitis infections. After extensive data preprocessing including outlier detection, dataset balancing, and feature engineering, we evaluated the performance of these models. We explored three modeling approaches: machine learning with default hyperparameters, hyperparameter-tuned models using GridSearchCV, and ensemble modeling techniques. The SVM model demonstrated outstanding performance, achieving 99.25% accuracy and a perfect AUC score of 1.00 with consistency in other metrics with 99.27% precision, and 99.24% for both recall and F1-measure. The MLP and Random Forest proved to be in pace with the superior performance of SVM exhibiting an accuracy of 99.00%. To ensure robustness, we employed a 5-fold cross-validation technique. For deeper insight into model interpretability and validation, we employed an explainability analysis of our best-performed models to identify the most effective feature for hepatitis detection. Our proposed model, particularly SVM, exhibits better prediction performance regarding different performance metrics compared to existing literature.

PMID:40173410 | DOI:10.1371/journal.pone.0319078

Sci Adv. 2025 Apr 4;11(14):eads5185. doi: 10.1126/sciadv.ads5185. Epub 2025 Apr 2.

ABSTRACT

Atlantic and Benguela Niño events substantially affect the tropical Atlantic region, with far-reaching consequences on local marine ecosystems, African climates, and El Niño Southern Oscillation. While accurate forecasts of these events are invaluable, state-of-the-art dynamic forecasting systems have shown limited predictive capabilities. Thus, the extent to which the tropical Atlantic variability is predictable remains an open question. This study explores the potential of deep learning in this context. Using a simple convolutional neural network architecture, we show that Atlantic/Benguela Niños can be predicted up to 3 to 4 months ahead. Our model excels in forecasting peak-season events with remarkable accuracy extending lead time to 5 months. Detailed analysis reveals our model's ability to exploit known physical precursors, such as long-wave ocean dynamics, for accurate predictions of these events. This study challenges the perception that the tropical Atlantic is unpredictable and highlights deep learning's potential to advance our understanding and forecasting of critical climate events.

PMID:40173237 | DOI:10.1126/sciadv.ads5185

Sci Adv. 2025 Apr 4;11(14):eadp0558. doi: 10.1126/sciadv.adp0558. Epub 2025 Apr 2.

ABSTRACT

Historical records of climate fields are often sparse because of missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we use a recently introduced deep learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach, we are able to realistically reconstruct large and irregular areas of missing data and to reproduce known historical events, such as strong El Niño or La Niña events, with very little given information. Our method outperforms the widely used statistical kriging method, as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

PMID:40173235 | DOI:10.1126/sciadv.adp0558

Global Spine J. 2025 Apr 2:21925682251332556. doi: 10.1177/21925682251332556. Online ahead of print.

ABSTRACT

Study DesignRetrospective cohort.ObjectiveDespite numerous studies highlighting patient comorbidities and surgical factors in postoperative success, the role of social determinants of health (SDH) in anterior cervical discectomy and fusion (ACDF) outcomes remains unexplored. This study evaluates the predictive impact of SDH on 90-day readmission and health utilization (HU) in ACDF patients using machine learning (ML).MethodsWe analyzed 3127 ACDF patients (2003-2023) from a multisite academic center, incorporating over 35 clinical and demographic variables. SDH characteristics were assessed using the Social Vulnerability Index. Primary outcomes included 90-day readmission and postoperative HU. ML models were developed and validated by the area under the curve (AUC) for readmission and mean absolute error (MAE) for HU. Feature importance analysis identified key predictors.ResultsBalanced Random Forest (AUC = 0.75) best predicted 90-day readmission, with length of stay, Elixhauser score, and Medicare status as top predictors. Among SDH factors, minority status & language, household composition & disability, socioeconomic status, and housing type & transportation were influential. Support Vector Regression (MAE = 1.96) best predicted HU, with perioperative duration, socioeconomic status, and minority status & language as key predictors.ConclusionsFindings highlight SDH's role in ACDF outcomes, suggesting the value of stratifying for interventions such as targeted resource allocation, language-concordant care, and tailored follow-up. While reliance on a single healthcare system and proxy SDH measures are limitations, this is the first study to apply ML to assess SDH in ACDF patients. Further validation with direct patient-reported SDH data is needed to refine predictive models.

PMID:40173192 | DOI:10.1177/21925682251332556

IEEE Trans Pattern Anal Mach Intell. 2025 Apr 2;PP. doi: 10.1109/TPAMI.2025.3557245. Online ahead of print.

ABSTRACT

This paper introduces a one-stage deep uncalibrated photometric stereo (UPS) network, namely Fourier Uncalibrated Photometric Stereo Network (FUPS-Net), for non-Lambertian objects under unknown light directions. It departs from traditional two-stage methods that first explicitly learn lighting information and then estimate surface normals. Two-stage methods were deployed because the interplay of lighting with shading cues presents challenges for directly estimating surface normals without explicit lighting information. However, these two-stage networks are disjointed and separately trained so that the error in explicit light calibration will propagate to the second stage and cannot be eliminated. In contrast, the proposed FUPS-Net utilizes an embedded Fourier transform network to implicitly learn lighting features by decomposing inputs, rather than employing a disjointed light estimation network. Our approach is motivated from observations in the Fourier domain of photometric stereo images: lighting information is mainly encoded in amplitudes, while geometry information is mainly associated with phases. Leveraging this property, our method "decomposes" geometry and lighting in the Fourier domain as guidance, via the proposed Fourier Embedding Extraction (FEE) block and Fourier Embedding Aggregation (FEA) block, which generate lighting and geometry features for the FUPS-Net to implicitly resolve the geometry-lighting ambiguity. Furthermore, we propose a Frequency-Spatial Weighted (FSW) block that assigns weights to combine features extracted from the frequency domain and those from the spatial domain for enhancing surface reconstructions. FUPS-Net overcomes the limitations of two-stage UPS methods, offering better training stability, a concise end-to-end structure, and avoiding accumulated errors in disjointed networks. Experimental results on synthetic and real datasets demonstrate the superior performance of our approach, and its simpler training setup, potentially paving the way for a new strategy in deep learning-based UPS methods.

PMID:40173071 | DOI:10.1109/TPAMI.2025.3557245

Respir Investig. 2025 Apr 1;63(3):431-437. doi: 10.1016/j.resinv.2025.03.016. Online ahead of print.

ABSTRACT

BACKGROUND: The effect of combining high fraction of inspired oxygen (FIO2) and high flow through a high-flow nasal cannula (HFNC) on exercise tolerance in chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) remains unclear.

METHODS: This prospective, single-blind, randomized, crossover study included patients with COPD (n = 25) and IPF (n = 25). The patients performed a 6-min walking test (6 MWT) while attached to a battery-supplied portable HFNC device under the following four conditions: FIO2 set to a minimum percutaneous oxygen saturation (SpO2) of 86-88 % during 6 MWT with a flow rate of 10 L/min (LOLF) or 50 L/min (LOHF); and FIO2 set to a minimum SpO2 of 92-94 % with a flow rate of 10 L/min (HOLF) or 50 L/min (HOHF).

RESULTS: In both groups, the 6-min walking distance (6 MWD) was significantly greater for HOHF than for LOLF (COPD: 323.2 ± 77.6 m vs. 268.6 ± 87.3 m, respectively, p < 0.0001 and IPF: 406 ± 50.7 m vs. 372.3 ± 50.9 m, respectively, p < 0.0001). In the analysis of the interaction effects for the 6 MWD, the combination of high FIO2 and high flow resulted in an additional 15.9-m extension of the 6 MWD (95 % confidence interval: 0.34-31.5; p = 0.050). The interaction between IPF and high-flow was -14.0 m, suggesting a less pronounced extension effect compared with COPD (95 % confidence interval: -29.5-1.6; p = 0.085).

CONCLUSION: The combination of high FIO2 and high flow through an HFNC may improve exercise tolerance in patients with COPD and IPF.

PMID:40174242 | DOI:10.1016/j.resinv.2025.03.016

Plant Physiol. 2025 Apr 2:kiaf126. doi: 10.1093/plphys/kiaf126. Online ahead of print.

ABSTRACT

Potato (Solanum tuberosum) is highly water and space efficient but susceptible to abiotic stresses such as heat, drought, and flooding, which are severely exacerbated by climate change. Our understanding of crop acclimation to abiotic stress, however, remains limited. Here, we present a comprehensive molecular and physiological high-throughput profiling of potato (Solanum tuberosum, cv. Désirée) under heat, drought, and waterlogging applied as single stresses or in combinations designed to mimic realistic future scenarios. Stress responses were monitored via daily phenotyping and multi-omics analyses of leaf samples comprising proteomics, targeted transcriptomics, metabolomics, and hormonomics at several timepoints during and after stress treatments. Additionally, critical metabolites of tuber samples were analyzed at the end of the stress period. We performed integrative multi-omics data analysis using a bioinformatic pipeline that we established based on machine learning and knowledge networks. Waterlogging produced the most immediate and dramatic effects on potato plants, interestingly activating ABA responses similar to drought stress. In addition, we observed distinct stress signatures at multiple molecular levels in response to heat or drought and to a combination of both. In response to all treatments, we found a downregulation of photosynthesis at different molecular levels, an accumulation of minor amino acids, and diverse stress-induced hormones. Our integrative multi-omics analysis provides global insights into plant stress responses, facilitating improved breeding strategies toward climate-adapted potato varieties.

PMID:40173380 | DOI:10.1093/plphys/kiaf126

Sci Adv. 2025 Apr 4;11(14):eado0173. doi: 10.1126/sciadv.ado0173. Epub 2025 Apr 2.

ABSTRACT

Understanding the heterogeneity of fibroblasts depends on decoding the complexity of cell subtypes, their origin, distribution, and interactions with other cells. Here, we integrated 249,156 fibroblasts from 73 studies across 10 tissues to present a single-cell atlas of fibroblasts. We provided a high-resolution classification of 18 fibroblast subtypes. In particular, we revealed a previously undescribed cell population, TSPAN8+ chromatin remodeling fibroblasts, characterized by high expression of genes with functions related to histone modification and chromatin remodeling. Moreover, TSPAN8+ chromatin remodeling fibroblasts were detectable in spatial transcriptome data and multiplexed immunofluorescence assays. Compared with other fibroblast subtypes, TSPAN8+ chromatin remodeling fibroblasts exhibited higher scores in cell differentiation and resident fibroblast, mainly interacting with endothelial cells and T cells through ligand VEGFA and receptor F2R, and their presence was associated with poor prognosis. Our analyses comprehensively defined the shared and specific characteristics of fibroblast subtypes across tissues and provided a user-friendly data portal, Fibroblast Atlas.

PMID:40173240 | DOI:10.1126/sciadv.ado0173

Neurodegener Dis Manag. 2025 Apr 2:1-3. doi: 10.1080/17582024.2025.2488230. Online ahead of print.

NO ABSTRACT

PMID:40173153 | DOI:10.1080/17582024.2025.2488230

PLoS Biol. 2025 Apr 2;23(4):e3003072. doi: 10.1371/journal.pbio.3003072. eCollection 2025 Apr.

ABSTRACT

An alga that abandoned photosynthesis? This Primer explores a PLOS Biology study showing that a single horizontal gene transfer event allowed the diatom Nitzschia sing1 to evolve a complete enzymatic machinery to break down alginate from brown algae, unlocking a new ecological niche.

PMID:40173128 | DOI:10.1371/journal.pbio.3003072

J Agric Food Chem. 2025 Apr 2. doi: 10.1021/acs.jafc.4c12671. Online ahead of print.

ABSTRACT

Methanol is an ideal one-carbon (C1) feedstock for bioconversion into multicarbon value-added compounds. Biocatalytic approaches to methanol conversion provide sustainable and environmentally friendly alternatives to conventional methods. This process is facilitated by methanol-oxidizing enzymes, including alcohol oxidase (AOx). Here, we report an AOx from Pestalotiopsis fici (PfAOx) with the highest methanol oxidation activity and efficient heterologous expression compared to other AOxs. To investigate the bioconversion of a multicarbon compound (C4 chemical, 2-keto-4-hydroxybutyrate, 2-KHB) from cost-effective methanol, we developed a one-pot enzyme system including PfAOx and pyruvate aldolase from Deinococcus radiodurans (DrADL) with catalase from Bos taurus (BtCAT, commercially available enzyme) to remove toxic H2O2. The optimal reaction conditions for 2-KHB production using PfAOx, DrADL, and BtCAT were determined as pH 8.0, 35 °C, 0.9 mg mL-1 PfAOx, 0.3 mg mL-1 DrADL, 1.5 mg mL-1 BtCAT, 150 mM methanol, 100 mM pyruvate, and 5 mM Mg2+ with shaking at 200 rpm. Under these reaction conditions, 88.8 mM (10.4 g L-1) of 2-KHB was produced for 75 min, representing a 74.0-fold higher yield compared to previously reported 2-KHB production systems from methanol and pyruvate. This study demonstrates a promising multi-enzyme cascade approach for the bioconversion of methanol into valuable compounds.

PMID:40173089 | DOI:10.1021/acs.jafc.4c12671

J Med Virol. 2025 Apr;97(4):e70319. doi: 10.1002/jmv.70319.

ABSTRACT

Papaverine (PV) has been previously identified as a promising candidate in SARS-CoV-2 repurposing screens. In this study, we further investigated both its antiviral and immunomodulatory properties. PV displayed antiviral efficacy against SARS-CoV-2 and influenza A viruses H1N1 and H5N1 in single infection as well as in co-infection. We demonstrated PV's activity against various SARS-CoV-2 variants and identified its action at the post-entry stage of the viral life cycle. Notably, treatment of air-liquid interface (ALI) cultures of primary bronchial epithelial cells with PV significantly inhibited SARS-CoV-2 levels. Additionally, PV was found to attenuate interferon (IFN) signaling independently of viral infection. Mechanistically, PV decreased the activation of the IFN-stimulated response element following stimulation with all three IFN types by suppressing STAT1 and STAT2 phosphorylation and nuclear translocation. Furthermore, the combination of PV with approved COVID-19 therapeutics molnupiravir and remdesivir demonstrated synergistic effects. Given its immunomodulatory effects and clinical availability, PV shows promising potential as a component for combination therapy against COVID-19.

PMID:40171981 | DOI:10.1002/jmv.70319

Pediatr Blood Cancer. 2025 Apr 2:e31697. doi: 10.1002/pbc.31697. Online ahead of print.

ABSTRACT

Parents of children with complex vascular anomalies (VAs) struggle to locate credible information. We explored whether their perceptions of the adequacy of VA-related information were associated with caregiver burden, anxiety, child health, and their ability to navigate the healthcare system and seek information. We also examined how their perceptions of clinician knowledge and communication affect their perceptions of information adequacy. A total of 86 parents completed our online survey. Perceived information adequacy was associated with lower anxiety, greater ability to navigate the healthcare system, greater clinician knowledge, and better clinician communication. These data identify important communication barriers that future research studies should address.

PMID:40172173 | DOI:10.1002/pbc.31697

Allergy. 2025 Apr 2. doi: 10.1111/all.16544. Online ahead of print.

NO ABSTRACT

PMID:40171941 | DOI:10.1111/all.16544

Am J Health Syst Pharm. 2025 Apr 2:zxaf071. doi: 10.1093/ajhp/zxaf071. Online ahead of print.

ABSTRACT

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE: A customized Epic scoring tool for monitoring medications requiring renal dose adjustment utilizing Epic Bugsy and a custom renal function trend scoring column was developed and implemented in June 2023 at UT Southwestern Medical Center (UTSW) to replace the manual review and intervention (i-Vent) documentation process.

METHODS: This retrospective, observational cohort study evaluated pharmacist interventions and antimicrobial dosing before and after implementation of the UTSW renal clinical pharmacist responsibility (CPR) dose adjustment tool. Adult patients (aged 18 years or older) requiring renal dose adjustment were included. The preintervention group included patients admitted between July 1 and August 31, 2022, whereas the postintervention group included patients admitted from July 1 through August 31, 2023. Patients exempt from the institutional automatic adult renal dosing guideline (ie, those with cystic fibrosis, solid organ transplantation, or bone marrow transplantation) or actively receiving renal replacement therapy during the index encounter were excluded.

RESULTS: In a comparable 2-month timespan, implementation of the renal CPR dose adjustment tool resulted in a 68.2% increase in the number of renal dosing interventions completed (P < 0.0001), a 47.2% reduction in the number of unique alerts requiring pharmacist review (P < 0.0001), and an increase in the proportion of actionable interventions per alert requiring review from 11.1% before implementation to 39.4% after implementation (P < 0.0001). Pharmacist satisfaction with the renal monitoring workflow also improved with implementation.

CONCLUSION: In a comparable 2-month timespan, implementation of the renal CPR dose adjustment tool at UTSW resulted iin improvements in interventions completed, a reduction in alerts requiring review, an increased total duration that selected antimicrobials were dosed appropriately, and improved pharmacist satisfaction.

PMID:40172577 | DOI:10.1093/ajhp/zxaf071

Curr Opin Pediatr. 2025 Mar 27. doi: 10.1097/MOP.0000000000001458. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: The field of cystic fibrosis is experiencing dramatic changes, as the translation of a massive body of scientific knowledge accumulated from the day of the cloning of the CFTR gene has led to the identification of effective therapies to correct the basic defect. This has also allowed care providers and people with cystic fibrosis in low-income and middle-income countries (LMICs) to become more knowledgeable and proficient in cystic fibrosis cares.

RECENT FINDINGS: This review focuses on two main aspects highly relevant to understand the current status of cystic fibrosis in LMICs: The recognition of the universal occurrence of cystic fibrosis, as well as the varying incidence in different regions of the world, and the collaborative international efforts for dissemination of best care practices as an attempt to close gaps in care.

SUMMARY: As the field continues to change rapidly, multiple international efforts are attempting to close gaps and disparities clearly apparent between affluent countries and LMICs. However, these efforts are seriously hampered by limited access to effective therapies and most dramatically to CFTR modulator drugs.

PMID:40172290 | DOI:10.1097/MOP.0000000000001458