J Eval Clin Pract. 2025 Feb;31(1):e70010. doi: 10.1111/jep.70010.

ABSTRACT

RATIONALE: Despite growing emphasis among healthcare decision-makers on patient perspectives and real-world outcomes to inform care and access decisions, understanding of patient journey experiences in rare diseases remains limited due to data collection and evaluation challenges.

AIMS AND OBJECTIVES: This systematic literature review (SLR) assessed study designs, methodologies, and outcomes reported in real-world investigations of rare disease patient journeys.

METHODS: Searches in PubMed and Google Scholar targeted English-language publications and congress proceedings from 1 January 2014, to 30 April 2024, including rare disease patients, caregivers, or healthcare providers. Keywords included 'Journey', 'Path', or 'Odyssey'. Two reviewers independently assessed eligibility and abstracted data. Descriptive analyses and quality assessments were conducted.

RESULTS: Thirty-one studies met inclusion criteria, with 296,548 participants spanning over 600 rare diseases. Most studies used prospective observational (61%) and cross-sectional (26%) designs and were conducted in Europe (45%). Interviews (39%) and surveys (29%) were common methodologies. Patients (87%) were the primary research focus, compared to caregivers (32%) or providers (10%). The most studied journey stages were 'Pre-diagnosis/Screening' (97%) and 'Diagnosis' (84%), while 'Disease Awareness' (16%) and 'Treatment Adherence' (6%) were less common. Across 164 outcomes reported, frequent outcomes included 'Healthcare Resource Utilization' (94%), 'Symptoms' (74%), and 'Time-to-Diagnosis' (71%). Fewer studies reported 'Costs' (19%), 'Caregiver/Family Burden' (16%), and 'Productivity' (13%). Time-to-diagnosis averaged 11.8 years and a median of 6.1 years. All but one study (97%) was rated low or very low quality due to observational designs.

CONCLUSION: Most rare disease patient journey evidence focuses on 'Pre-diagnosis/Screening' and 'Diagnosis' stages using qualitative methods and surveys. While symptoms, time-to-diagnosis, and resource utilization were commonly reported, evidence gaps included treatment adherence, caregiver burden and productivity. Longitudinal assessments to collect real-world care and treatment burden outcomes, including caregiver perspectives, can enhance both clinician and policy decision-making for individuals living with rare diseases.

PMID:39960234 | DOI:10.1111/jep.70010

Mo Med. 2025 Jan-Feb;122(1):53-59.

ABSTRACT

As the rate of diagnosis for rare disease increases, so does the need to develop scalable solutions to address patient community needs. Drawing upon our experiences in rare intellectual and developmental disability research, advocacy, and treatment, we present two examples of how collaboration between patient groups, clinicians, and investigators at Washington University in St. Louis have generated invaluable resources to accelerate toward treatments. These successful partnerships serve as models for building research and clinical infrastructure for rare diseases.

PMID:39958601 | PMC:PMC11827657

Pharmacogenet Genomics. 2025 Feb 12. doi: 10.1097/FPC.0000000000000562. Online ahead of print.

ABSTRACT

In Advancing Clinical Therapeutics Globally protocol A5372, a pharmacokinetic study of dolutegravir with 1-month of daily rifapentine/isoniazid, twice-daily dolutegravir offset the induction effects of rifapentine on plasma dolutegravir trough concentrations (Ctrough). Here, we characterize the impact on dolutegravir Ctrough of UGT1A1, AADAC, and NAT2 polymorphisms that affect dolutegravir, rifapentine, and isoniazid, respectively. People with HIV receiving dolutegravir-based antiretroviral therapy with an indication to treat latent tuberculosis underwent pharmacokinetic sampling during dolutegravir 50 mg once daily alone, and on day 28 of dolutegravir 50 mg twice daily with rifapentine/isoniazid. Multivariable linear regression models characterized genetic associations with dolutegravir Ctrough. Among 30 participants evaluable for genetic associations, median (Q1, Q3) day 0 dolutegravir Ctrough was 1745 (1099, 2694) ng/ml, and day 28 was 2146 (1412, 2484) ng/ml. Day 28 Ctrough was higher with UGT1A1 rs887829 TT [geometric mean ratio (GMR) = 1.65; 90% confidence interval (CI): 0.97-2.78] and CT (GMR = 1.38; 90% CI: 1.02-1.86) than with CC, and was higher with AADAC rs1803155 GG (GMR = 1.79; 90% CI: 1.09-2.93) and AG (GMR = 1.48; 90% CI: 1.14-1.90) than with AA. Median day 28 Ctrough ranged from 1205 (1063, 1897) ng/ml with 4 total UGT1A1 and AADAC risk alleles, to 3882 and 3717 ng/ml with only one risk allele. Individuals with concomitant AADAC slow metabolizer and UGT1A1 normal metabolizer genotypes may be at greater risk for clinically significant drug-drug interactions between rifapentine/isoniazid and dolutegravir.

PMID:39960813 | DOI:10.1097/FPC.0000000000000562

Clin Pharmacol Ther. 2025 Feb 17. doi: 10.1002/cpt.3599. Online ahead of print.

ABSTRACT

In the past decade, pharmacogenomic (PGx) testing to predict drug response have emerged into clinical care. Clinical decision support (CDS) has and continues to play a key role in educating prescribers and facilitating the integration of pharmacogenomic results into routine clinical practice. The Epic Genomics module, an add-on to Epic's base clinical software, allows for storage of structured genomic data and provides electronic heath record tools designed with PGx CDS implementation in mind. In early 2022, the University of Florida Health deployed the Genomics module. This tutorial outlines the steps taken by the University of Florida Health Precision Medicine Program to implement Epic's Genomic Module at University of Florida Health and identifies key factors for a successful implementation.

PMID:39960348 | DOI:10.1002/cpt.3599

Elife. 2025 Feb 17;13:RP95952. doi: 10.7554/eLife.95952.

ABSTRACT

Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense-mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high-throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD in human cells. This screen implicated disruption of kinase SMG1's phosphorylation of UPF1 as a potential disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from human and murine truncating mutations in vitro and murine cells in vivo. Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable human leukocyte antigens (HLA) class I-associated peptides from NMD-downregulated proteins on the surface of human cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which NMD plays a role, including cancer and inherited diseases.

PMID:39960487 | DOI:10.7554/eLife.95952

Pediatr Pulmonol. 2025 Feb;60(2):e70000. doi: 10.1002/ppul.70000.

ABSTRACT

BACKGROUND: The COVID-19 pandemic ushered widespread adoption of telehealth (TH) by cystic fibrosis (CF) centers in the USA. TH was initially described as well-accepted by both clinicians and patients. As we move past the unusual circumstances of the pandemic, the sustainability of TH remains untested. This study sought to test the durability of clinician perceptions of TH post-pandemic.

METHODS: This is a cross-sectional, survey study of clinicians at seven US CF centers. We refined a previously disseminated survey initially designed to assess clinician perceptions of TH in 2020. Survey results were analyzed using descriptive statistics and current responses were compared to prior results.

RESULTS: Clinician perceptions surrounding TH remain high but have changed over time with 75% now endorsing satisfaction (90% in 2020, p = 0.02). The most cited barriers were technology limitations (68%) and limited in-person assessments (66%). We found a significant decrease in concern over missing in-person assessments compared to 2020. Benefits of TH included convenience for patients and families (100%) and reduction in missed days of school or work (100%). In total, 83% of current respondents felt TH should remain part of routine CF care. A majority indicated certain patient characteristics increased their preference to conduct at least one TH visit per year.

CONCLUSIONS: Despite restoration of full access to in-person care, clinicians caring for pwCF continue to use TH across the surveyed CF centers post-pandemic. Respondents continue to view TH favorably. Further study is needed to understand for which patient and clinical scenarios TH is most appropriate.

PMID:39960328 | DOI:10.1002/ppul.70000

Invest Radiol. 2025 Feb 18. doi: 10.1097/RLI.0000000000001162. Online ahead of print.

ABSTRACT

OBJECTIVES: Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences.

METHODS: Initial BCA segmentations (10 body regions and 4 body parts) were generated by mapping CT segmentations from body and organ analysis (BOA) model to synthetic MR images created using an in-house trained CycleGAN. In total, 30 synthetic data pairs were used to train an initial nnU-Net V2 in 3D, and this preliminary model was then applied to segment 120 real T2-weighted MRI sequences from 120 patients (46% female) with a median age of 56 (interquartile range, 17.75), generating early segmentation proposals. These proposals were refined by human annotators, and nnU-Net V2 2D and 3D models were trained using 5-fold cross-validation on this optimized dataset of real MR images. Performance was evaluated using Sørensen-Dice, Surface Dice, and Hausdorff Distance metrics including 95% confidence intervals for cross-validation and ensemble models.

RESULTS: The 3D ensemble segmentation model achieved the highest Dice scores for the body region classes: bone 0.926 (95% confidence interval [CI], 0.914-0.937), muscle 0.968 (95% CI, 0.961-0.975), subcutaneous fat 0.98 (95% CI, 0.971-0.986), nervous system 0.973 (95% CI, 0.965-0.98), thoracic cavity 0.978 (95% CI, 0.969-0.984), abdominal cavity 0.989 (95% CI, 0.986-0.991), mediastinum 0.92 (95% CI, 0.901-0.936), pericardium 0.945 (95% CI, 0.924-0.96), brain 0.966 (95% CI, 0.927-0.989), and glands 0.905 (95% CI, 0.886-0.921). Furthermore, body part 2D ensemble model reached the highest Dice scores for all labels: arms 0.952 (95% CI, 0.937-0.965), head + neck 0.965 (95% CI, 0.953-0.976), legs 0.978 (95% CI, 0.968-0.988), and torso 0.99 (95% CI, 0.988-0.991). The overall average Dice across body parts (2D = 0.971, 3D = 0.969, P = ns) and body regions (2D = 0.935, 3D = 0.955, P < 0.001) ensemble models indicates stable performance across all classes.

CONCLUSIONS: The presented approach facilitates efficient and automated extraction of BCA parameters from T2-weighted MRI sequences, providing precise and detailed body composition information across various regions and body parts.

PMID:39961134 | DOI:10.1097/RLI.0000000000001162

Invest Radiol. 2025 Feb 18. doi: 10.1097/RLI.0000000000001166. Online ahead of print.

ABSTRACT

OBJECTIVES: Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this study was to test the benefit of a deep learning-based contrast signal amplification in true single-dose T1-weighted (T-SD) images creating artificial double-dose (A-DD) images for metastasis detection in brain magnetic resonance imaging.

MATERIALS AND METHODS: In this prospective, multicenter study, a deep learning-based method originally trained on noncontrast, low-dose, and T-SD brain images was applied to T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8 years; 23 women) acquired externally between November 2022 and June 2023. Four readers with different levels of experience independently reviewed T-SD and A-DD images for metastases with 4 weeks between readings. A reference reader reviewed additionally acquired true double-dose images to determine any metastases present. Performances were compared using Mid-p McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.

RESULTS: All readers found more metastases using A-DD images. The 2 experienced neuroradiologists achieved the same level of sensitivity using T-SD images (62 of 91 metastases, 68.1%). While the increase in sensitivity using A-DD images was only descriptive for 1 of them (A-DD: 65 of 91 metastases, +3.3%, P = 0.424), the second neuroradiologist benefited significantly with a sensitivity increase of 12.1% (73 of 91 metastases, P = 0.008). The 2 less experienced readers (1 resident and 1 fellow) both found significantly more metastases on A-DD images (resident, T-SD: 61.5%, A-DD: 68.1%, P = 0.039; fellow, T-SD: 58.2%, A-DD: 70.3%, P = 0.008). They were therefore able to use A-DD images to increase their sensitivity to the neuroradiologists' initial level on regular T-SD images. False-positive findings did not differ significantly between sequences. However, readers showed descriptively more false-positive findings on A-DD images. The benefit in sensitivity particularly applied to metastases ≤5 mm (5.7%-17.3% increase in sensitivity).

CONCLUSIONS: A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.

PMID:39961132 | DOI:10.1097/RLI.0000000000001166

Medicine (Baltimore). 2025 Feb 14;104(7):e41560. doi: 10.1097/MD.0000000000041560.

ABSTRACT

Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires considerable expertise, time, and effort. Using a 2-stage classification strategy, we sought to examine the effectiveness of deep learning in detecting and classifying glomeruli into 4 groups, namely normal, abnormal, sclerotic, and glomerulitis, as a potential biopsy triage system focused on transplant rejection. We used the U-Net model to build a glomeruli detection model using 137 kidney biopsy slides obtained from 80 kidney transplant patients. The median age of the patients was 52 (19-74) years, with 65% being men and 35% women. MobileNetV2 and VGG16 models were compared using a 2-stage classification strategy. In the first classification step, the models classified glomeruli into sclerotic and nonsclerotic glomeruli. In the second classification step, the nonsclerotic glomeruli from the first step were classified as normal, abnormal, or glomerulitis. The U-Net model achieved satisfactory detection (Dice coefficient = 0.90), and the MobileNetV2 model was the best for the 2 classification steps, with F1 scores of 0.85, 0.91, 0.98, and 0.92 for normal, abnormal, sclerotic, and glomerulitis, respectively. The 2-stage classification strategy identifies sclerotic glomeruli and abnormal glomeruli relative to permeable glomeruli and quantifies glomerulitis with significant accuracy while avoiding bias from abnormal glomeruli that do not have glomerulitis, providing valuable diagnostic information.

PMID:39960931 | DOI:10.1097/MD.0000000000041560

Medicine (Baltimore). 2025 Feb 14;104(7):e41530. doi: 10.1097/MD.0000000000041530.

ABSTRACT

This study reports the first steps toward establishing a computer vision system to help caregivers of bedridden patients detect pressure ulcers (PUs) early. While many previous studies have focused on using convolutional neural networks (CNNs) to elevate stages, hardware constraints have presented challenges related to model training and overreliance on medical opinions. This study aimed to develop a tool to classify PU stages using a Vision Transformer model to process actual PU photos. To do so, we used a retrospective observational design involving the analysis of 395 images of different PU stages that were accurately labeled by nursing specialists and doctors from 3 hospitals. In the pressure ulcer cluster vision transformer (PUC-ViT) model classifies the PU stage with a mean ROC curve value of 0.936, indicating a model accuracy of 97.76% and F1 score of 95.46%. We found that a PUC-ViT model showed higher accuracy than conventional models incorporating CNNs, and both effectively reduced computational complexity and achieved low floating point operations per second. Furthermore, we used internet of things technologies to propose a model that allows anyone to analyze input images even at low computing power. Based on the high accuracy of our proposed model, we confirm that it enables community caregivers to detect PUs early, facilitating medical referral.

PMID:39960905 | DOI:10.1097/MD.0000000000041530

Anal Chem. 2025 Feb 17. doi: 10.1021/acs.analchem.4c06001. Online ahead of print.

ABSTRACT

The integration of paper-based microfluidics with deep learning represents a pivotal trend in enhancing diagnostic capabilities. This paper introduces a new approach to improve the performance of a paper-based microfluidic enzyme-linked immunosorbent assay (ELISA) by training the temporal sequence colorimetric data rather than static data conventionally, using deep learning. Traditional deep learning-assisted ELISA analysis methods usually rely on a single snapshot of the reaction at its end, which limits the further improvement of sensitivity and specificity (or accuracy for combined evaluation), as it misses dynamic changes in the reaction over time. In this work, we developed a temporal sequence-enhanced paper analytical device (TimePAD) that captures continuous video data of the ELISA reaction, which contains the dynamic colorimetric changes. With the YOLOv8 deep learning alogrithm and the Rabbit IgG as the model for ELISA assay, we can use the initial 20 min signal instead of waiting for 30 min for full reaction, achieving a 33% reduction in the turnaround time. Moreover, the overall accuracy at 20 min is 94.1%, which is slightly improvement to the 93.5% using a traditional single snapshot method at 30 min. This method not only accelerates result interpretation but also enhances the overall efficiency of diagnostics, making it particularly valuable for time-sensitive point-of-care testing applications. Lastly, to demonstrate its real-world use, we expanded to the disease biomarker cTnI detection and obtained accuracy of 98.1% within only 10 min, compared to 25 min with 97.8% accuracy in traditional methods.

PMID:39960863 | DOI:10.1021/acs.analchem.4c06001

J Synchrotron Radiat. 2025 Mar 1. doi: 10.1107/S1600577525000359. Online ahead of print.

ABSTRACT

Computed tomography (CT) using synchrotron radiation is a powerful technique that, compared with laboratory CT techniques, boosts high spatial and temporal resolution while also providing access to a range of contrast-formation mechanisms. The acquired projection data are typically processed by a computational pipeline composed of multiple stages. Artifacts introduced during data acquisition can propagate through the pipeline and degrade image quality in the reconstructed images. Recently, deep learning has shown significant promise in enhancing image quality for images representing scientific data. This success has driven increasing adoption of deep learning techniques in CT imaging. Various approaches have been proposed to incorporate deep learning into computational pipelines, but each has limitations in addressing artifacts effectively and efficiently in synchrotron CT, either in properly addressing the specific artifacts or in computational efficiency. Recognizing these challenges, we introduce a novel method that incorporates separate deep learning models at each stage of the tomography pipeline - projection, sinogram and reconstruction - to address specific artifacts locally in a data-driven way. Our approach includes bypass connections that feed both the outputs from previous stages and raw data to subsequent stages, minimizing the risk of error propagation. Extensive evaluations on both simulated and real-world datasets illustrate that our approach effectively reduces artifacts and outperforms comparison methods.

PMID:39960472 | DOI:10.1107/S1600577525000359

J Craniofac Surg. 2025 Feb 17. doi: 10.1097/SCS.0000000000011143. Online ahead of print.

ABSTRACT

Accurate virtual orbital reconstruction is crucial for preoperative planning. Traditional methods, such as the mirroring technique, are unsuitable for orbital defects involving both sides of the midline and are time-consuming and labor-intensive. This study introduces a modified 3D U-Net+++ architecture for orbital defects reconstruction, aiming to enhance precision and automation. The model was trained and tested with 300 synthetic defects from cranial spiral CT scans. The method was validated in 15 clinical cases of orbital fractures and evaluated using quantitative metrics, visual assessments, and a 5-point Likert scale, by 3 surgeons. For synthetic defect reconstruction, the network achieved a 95% Hausdorff distance (HD95) of<2.0 mm, an average symmetric surface distance (ASSD) of ∼0.02 mm, a surface Dice similarity coefficient (Surface DSC)>0.94, a peak signal-to-noise ratio (PSNR)>35 dB, and a structural similarity index (SSIM)>0.98, outperforming the compared state-of-the-art networks. For clinical cases, the average 5-point Likert scale scores for structural integrity, edge consistency, and overall morphology were>4, with no significant difference between unilateral and bilateral/trans-midline defects. For clinical unilateral defect reconstruction, the HD95 was ∼2.5 mm, ASSD<0.02 mm, Surface DSC>0.91, PSNR>30 dB, and SSIM>0.99. The automatic reconstruction process took ∼10 seconds per case. In conclusion, this method offers a precise and highly automated solution for orbital defect reconstruction, particularly for bilateral and trans-midline defects. We anticipate that this method will significantly assist future clinical practice.

PMID:39960444 | DOI:10.1097/SCS.0000000000011143

Med Phys. 2025 Feb 17. doi: 10.1002/mp.17704. Online ahead of print.

ABSTRACT

BACKGROUND: Plan review is critical for safely delivering radiation dose to a patient under radiotherapy and mainly performed by medical physicist in routine clinical practice. Recently, the deep-learning models have been used to assist this manual process. As black-box models the reason for their predictions are unknown. Thus, it is important to improve the model interpretability to make them more reliable for clinical deployment.

PURPOSE: To alleviate this issue, a deep generative model, adversarial autoencoder networks (AAE), was employed to automatically detect anomalies in intensity-modulated radiotherapy plans.

METHODS: The typical plan parameters (collimator position, gantry angle, monitor unit, etc.) were collected to form a feature vector for the training sample. The reconstruction error was the difference between the output and input of the model. Based on the distribution of reconstruction errors of the training samples, a detection threshold was determined. For a test plan, its reconstruction error obtained by the learned model was compared with the threshold to determine its category (anomaly or regular). The model was tested with four network settings. It was also compared with the vanilla AE and the other six classic models. The area under receiver operating characteristic curve (AUC) along with other statistical metrics was employed for evaluation.

RESULTS: The AAE model achieved the highest accuracy (AUC = 0.997). The AUCs of the other seven classic methods are 0.935 (AE), 0.981 (K-means), 0.896 (principle component analysis), 0.978 (one-class support vector machine), 0.934 (local outlier factor), and 0.944 (hierarchical density-based spatial clustering of applications with noise), and 0.882 (isolation forest). This indicates that AAE model could detect more anomalous plans with less false positive rate.

CONCLUSIONS: The AAE model can effectively detect anomaly in radiotherapy plans for lung cancer patients. Comparing with the vanialla AE and other classic detection models, the AAE model is more accurate and transparent. The proposed AAE model can improve the interpretability of the results for radiotherapy plan review.

PMID:39960256 | DOI:10.1002/mp.17704

J Diabetes Sci Technol. 2025 Feb 17:19322968251317521. doi: 10.1177/19322968251317521. Online ahead of print.

ABSTRACT

INTRODUCTION: Early detection of diabetic foot complications is essential for effective management and prevention of complications. Artificial intelligence (AI) technology based on digital image analysis offers a promising noninvasive method for diabetic foot screening. This systematic review aims to identify a study on the development of an AI model for diabetic foot screening using digital image analysis.

METHODOLOGY: The review scrutinized articles published between 2018 and 2023, sourced from PubMed, ProQuest, and ScienceDirect. The keyword-based search resulted in 2214 relevant articles and nine articles that met the inclusion criteria. The article quality assessment was done through Quality Assessment of Diagnostic Accuracy Studies (QUADAS). Data were extracted and analyzed using NVivo.

RESULTS: Thermal imagery or foot thermogram was the main data source, with plantar temperature distribution patterns as an important indicator. Deep learning methods, specifically artificial neural networks (ANNs) and convolutional neural networks (CNNs), are the most commonly used methods. The highest performance is demonstrated by the ANN model with MATLAB's Image Processing Toolbox that is able to classify each type of macula with 97.5% accuracy. The findings show the great potential of AI in improving the accuracy and efficiency of diabetic foot screening.

CONCLUSION: This research provides important insights into the development of AI in digital image-based diabetic foot screening. Future studies need to focus on evaluating clinical applicability, including ethical aspects and patient data security, as well as developing more comprehensive data sets.

PMID:39960227 | DOI:10.1177/19322968251317521

Hepatology. 2025 Mar 1;81(3):755-756. doi: 10.1097/HEP.0000000000001220. Epub 2025 Feb 17.

NO ABSTRACT

PMID:39960202 | DOI:10.1097/HEP.0000000000001220

Minerva Med. 2025 Feb 17. doi: 10.23736/S0026-4806.25.09643-0. Online ahead of print.

ABSTRACT

BACKGROUND: Early diagnosis of idiopathic pulmonary fibrosis (IPF) is fundamental to slow disease progression; multidisciplinary teams (MDTs) play a central role in posing the final diagnosis of IPF, thus aiming to improve patient outcomes. However, the practical implementation of MDTs in clinical real-life settings may be hindered by the lack of local expertise or time constraints, with the diagnosis being made without the support of complementary professional health care figures. This study aims to evaluate the impact of MDT meetings on the latency between the symptom onset and the final diagnosis of IPF.

METHODS: Patients referred to a regional center for IPF between January 2019 and August 2019 were included. The length of time to pose a definite diagnosis by means of MDT evaluation was compared with that of patients diagnosed elsewhere (no MDT evaluation) in an observational case-control investigation.

RESULTS: Among 24 IPF patients, those evaluated by MDT (M/F: 14/2, age: 69.8±8.2 yrs) showed a time interval from the first outpatient visit to the definite diagnosis of 3±2.3 months; on the other hand, patients in the control group (M/F: 7/1, age: 76.9±7.7 yrs) showed a time interval of 12.8±9.4 months (P=0.02). The time elapsed between the onset of symptoms and the definite diagnosis was 11.1±5.3 months for patients evaluated within the MDT, compared to 33.8±21.5 months for the control group (P=0.02).

CONCLUSIONS: These exploratory findings confirm the essential role of the MDT in the early diagnosis of IPF, thus discouraging the acquisition of diagnosis solely on individual basis. The current findings highlight the need for the implementation of MDTs in clinical practice to optimize patient care.

PMID:39960753 | DOI:10.23736/S0026-4806.25.09643-0

Funct Plant Biol. 2025 Feb;52:FP24051. doi: 10.1071/FP24051.

ABSTRACT

We applied a systems biology approach to gain a deep insight into the regulatory mechanisms of barley (Hordeum vulgare ) under drought and waterlogging stress conditions. To identify informative models related to stress conditions, we constructed meta-analysis and two distinct weighted gene co-expression networks. We then performed module trait association analyses. Additionally, we conducted functional enrichment analysis of significant modules to shed light on the biological performance of underlying genes in the two contrasting stresses. In the next step, we inferred the gene regulatory networks between top hub genes of significant modules, kinases, and transcription factors (TFs) using a machine learning algorithm. Our results showed that at power=10, the scale-free topology fitting index (R2) was higher than 0.8 and the connectivity mean became stable. We identified 31 co-expressed gene modules in barley, with 13 and 14 modules demonstrating significant associations with drought and waterlogging stress, respectively. Functional enrichment analysis indicated that these stress-responsive modules are involved in critical processes, including ADP-rybosylation factors (ARF) protein signal transduction, ethylene-induced autophagy, and phosphoric ester hydrolase activity. Specific TFs and kinases, such as C2C2-GATA, HB-BELL, and MADS-MIKC, were identified as key regulators under these stress conditions. Furthermore, certain TFs and kinases established unique connections with hub genes in response to waterlogging and drought conditions. These findings enhance our understanding of the molecular networks that modulate barley's response to drought and waterlogging stresses, offering insights into the regulatory mechanisms essential for stress adaptation.

PMID:39960829 | DOI:10.1071/FP24051

Elife. 2025 Feb 17;12:RP87930. doi: 10.7554/eLife.87930.

ABSTRACT

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

PMID:39960760 | DOI:10.7554/eLife.87930

J Occup Health. 2025 Feb 17:uiaf013. doi: 10.1093/joccuh/uiaf013. Online ahead of print.

ABSTRACT

The novel properties and functions of nanomaterials have naturally alerted the toxicologists to the fact that such materials may also have novel effects on the human body and living organisms. In particular, materials with high stability or biopersisteny have been shown to have a tendency to accumulate in the body, leading to chronic toxicity including carcinogenicity. However, at the early stages of toxicity research, the information is often limited to the effects of short-term exposure studies, and findings on chronic effects are very much delayed. In this context, it was rather exceptional that studies on multiwall carbon nanotubes (MWCNTs) have started with the verification of their potential to induce mesothelioma. This toxicological endpoint was expected on the basis of existing knowledge of asbestos and asbestos-like fiber particles. This movement has led to the achievement of the original mission of the "Modern Toxicology", which is "to achieve a win-win situation where both industrial promotion and safety assurance are ensured by communicating and sharing toxicity information to developers and consumers at a stage before mass production and consumption begins, that is, before massive exposure of the general public begins". Inaccurate toxicity assessments of asbestos in the 1980s and 1990s allowed its spread to our living environment, which is difficult to decontaminate, and the damage still continues to this day. However, the case described here could be an example of realizing the proposition that 'nanomaterials, the flagship of high technology, must not repeat the same mistakes.'

PMID:39960454 | DOI:10.1093/joccuh/uiaf013