BMC Gastroenterol. 2025 Feb 14;25(1):80. doi: 10.1186/s12876-025-03682-9.

ABSTRACT

BACKGROUND: Intestinal failure (IF) is a broad term encompassing various conditions that hinder the body's ability to absorb nutrients for growth and maintenance. These conditions can significantly affect child's well-being, leading to physical limitations, psychological distress, and social isolation. We aimed to evaluate the available data on health-related quality of life (HRQoL) in pediatric patients with IF and without neurodevelopmental delay.

METHODS: For this systematic review and meta-analysis, we searched CINAHL, EMBASE, PsycINFO, PubMed, and Web of Science. All observational studies of pediatric patients (< 18 years) with IF which measured HRQOL and with evidence of absence of neurodevelopmental delay were included, without language or date restrictions, up to June 2024. We did separate random-effects meta-analyses for overall HRQOL and subgroup domains. Evidence from observational studies was synthesised as differences between standardised mean differences (SMDs) for all subgroup domains. Heterogeneity was assessed using the I² statistic and the Cochran Q test. The quality of the evidence was assessed with the Newcastle-Ottawa scale. This study is registered on PROSPERO, number CRD42024561812.

RESULTS: Of 491 records identified, 14 were eligible and data were available for 12 studies, all of which had a fair/good quality. The included studies involved a pooled sample of 510 participants (mean age = 7.0 ± 3.6 years). The analysis disclosed that compared to healthy children, pediatric patients with IF had lower overall quality of life in both child- and parent-report (Standardized Mean Difference [SMD]= -0.62; 95% CI [-0.80, -0.43]; p < 0.001, and SMD= -0.70; 95% CI [-1.11, -0.28]; p < 0.001, respectively), except for emotional and social domains (SMD[child] = -0.23; 95% CI [ -0.38, -0.08]; p = 0.001 Vs SMD[parent]= -0.23; 95% CI [ -0.60, 0.14]; p = 0.21, and SMD[child] = -0.40; 95% CI [ -0.70, -0.10]; p = 0.007 Vs SMD[parent]= -0.24; 95% CI [ -0.62, 0.14]; p = 0.21, respectively), where parents overestimate emotional and social HRQOL of their children.

CONCLUSIONS: This study highlights the significant impact of IF on well-being of pediatric patients. Targeted interventions addressing both physical and psychosocial needs are crucial to improve HRQOL in this population.

PMID:39953378 | DOI:10.1186/s12876-025-03682-9

Adv Med Sci. 2025 Feb 12:S1896-1126(25)00009-4. doi: 10.1016/j.advms.2025.01.009. Online ahead of print.

NO ABSTRACT

PMID:39952431 | DOI:10.1016/j.advms.2025.01.009

J Cheminform. 2025 Feb 14;17(1):20. doi: 10.1186/s13321-025-00968-8.

ABSTRACT

The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models hold promise in addressing this issue, their predictive reliability is limited by uncertain generalizability. Here, we present the ROASMI model, which enables reliable prediction of retention order within a well-defined application domain by coupling data-driven molecular representation and mechanistic insights. The generalizability of ROASMI is proven by 71 independent reversed-phase liquid chromatography (RPLC) datasets. The application of ROASMI to four real-world datasets demonstrates its advantages in distinguishing coexisting isomers with similar fragmentation patterns and in annotating detection peaks without informative spectra. ROASMI is flexible enough to be retrained with user-defined reference sets and is compatible with other MS/MS scorers, making further improvements in small-molecule identification.

PMID:39953609 | DOI:10.1186/s13321-025-00968-8

J Orthop Surg Res. 2025 Feb 14;20(1):161. doi: 10.1186/s13018-025-05564-y.

ABSTRACT

BACKGROUND: Traditional diagnostic tools for scoliosis screening necessitate a substantial number of specialized personnel and equipment, leading to inconvenience that can result in missed opportunities for early diagnosis and optimal treatment. We have developed a deep learning-based image segmentation model to enhance the efficiency of scoliosis screening.

METHODS: A total of 350 patients with scoliosis and 108 healthy subjects were included in this study. The dataset was created using their bare back images and standing full-length anteroposterior spinal X-rays. An attention mechanism was incorporated into the original U-Net architecture to build a Dual AttentionUNet model for image segmentation. The entire dataset was divided into the training (321 cases), validation (46 cases), and test (91 cases) sets in a 7:1:2 ratio. The training set was used to train the Dual AttentionUNet model, and the validation set was used to fine-tune hyperparameters and prevent overfitting during training. The performance of the model was evaluated in the test set. After automatic segmentation of the back contour, a back asymmetry index was calculated via computer vision algorithms to classify scoliosis into different severities. The accuracy of classifications was statistically compared to those of three clinical experts.

RESULTS: Following the segmentation of bare back images and the application of computer vision algorithms, the Dual AttentionUNet model achieved an accuracy, precision, and recall rate of over 90% in predicting severe scoliosis. Notably, the model achieved an AUC value of 0.93 in identifying whether the subjects had scoliosis, which was higher than the 0.92 achieved by the deputy chief physician. In identifying severe scoliosis, their AUC values were 0.95 and 0.96, respectively.

CONCLUSION: The Dual AttentionUNet model, based on only bare back images, achieved accuracy and precision comparable to clinical physicians in determining scoliosis severity. Radiation-free, cost-saving, easy-to-operate and noninvasive, this model provides a novel option for large-scale scoliosis screening.

PMID:39953540 | DOI:10.1186/s13018-025-05564-y

Int J Comput Assist Radiol Surg. 2025 Feb 14. doi: 10.1007/s11548-025-03330-3. Online ahead of print.

ABSTRACT

PURPOSE: Statistical shape models (SSMs) are widely used for morphological assessment of anatomical structures. However, a key limitation is the need for a clear relationship between the model's shape coefficients and clinically relevant anatomical parameters. To address this limitation, this paper proposes a novel deep learning-based anatomically parameterized SSM (DL-ANATSSM) by introducing a nonlinear relationship between anatomical parameters and bone shape information.

METHODS: Our approach utilizes a multilayer perceptron model trained on a synthetic femoral bone population to learn the nonlinear mapping between anatomical measurements and shape parameters. The trained model is then fine-tuned on a real bone dataset. We compare the performance of DL-ANATSSM with a linear ANATSSM generated using least-squares regression for baseline evaluation.

RESULTS: When applied to a previously unseen femoral bone dataset, DL-ANATSSM demonstrated superior performance in predicting 3D bone shape based on anatomical parameters compared to the linear baseline model. The impact of fine-tuning was also investigated, with results indicating improved model performance after this process.

CONCLUSION: The proposed DL-ANATSSM is therefore a more precise and interpretable SSM, which is directly controlled by clinically relevant parameters. The proposed method holds promise for applications in both morphometry analysis and patient-specific 3D model generation without preoperative images.

PMID:39953355 | DOI:10.1007/s11548-025-03330-3

J Imaging Inform Med. 2025 Feb 14. doi: 10.1007/s10278-025-01442-5. Online ahead of print.

ABSTRACT

This study aimed to develop a hybrid model combining radiomics and deep learning features derived from computed tomography (CT) images to classify histological subtypes of non-small cell lung cancer (NSCLC). We analyzed CT images and radiomics features from 235 patients with NSCLC, including 110 with adenocarcinoma (ADC) and 112 with squamous cell carcinoma (SCC). The dataset was split into a training set (75%) and a test set (25%). External validation was conducted using the NSCLC-Radiomics database, comprising 24 patients each with ADC and SCC. A total of 1409 radiomics and 8192 deep features underwent principal component analysis (PCA) and ℓ2,1-norm minimization for feature reduction and selection. The optimal feature sets for classification included 27 radiomics features, 20 deep features, and 55 combined features (30 deep and 25 radiomics). The average area under the receiver operating characteristic curve (AUC) for radiomics, deep, and combined features were 0.6568, 0.6689, and 0.7209, respectively, across the internal and external test sets. Corresponding average accuracies were 0.6013, 0.6376, and 0.6564. The combined model demonstrated superior performance in classifying NSCLC subtypes, achieving higher AUC and accuracy in both test datasets. These results suggest that the proposed hybrid approach could enhance the accuracy and reliability of NSCLC subtype classification.

PMID:39953259 | DOI:10.1007/s10278-025-01442-5

J Imaging Inform Med. 2025 Feb 14. doi: 10.1007/s10278-025-01407-8. Online ahead of print.

ABSTRACT

Accurate segmentation of ischemic stroke lesions is crucial for refining diagnosis, prognosis, and treatment planning. Manual identification is time-consuming and challenging, especially in urgent clinical scenarios. This paper presents an innovative deep learning-based system for automated segmentation of ischemic stroke lesions from Computed Tomography Perfusion (CTP) datasets. This paper introduces a deep learning-based system designed to segment ischemic stroke lesions from Computed Tomography Perfusion (CTP) datasets. The proposed approach integrates Edge Enhancing Diffusion (EED) filtering as a preprocessing step, acting as a form of hard attention to emphasize affected regions. Besides the Attention ResUnet (AttResUnet) architecture with a modified decoder path, incorporating spatial and channel attention mechanisms to capture long-range dependencies. The system was evaluated using the ISLES challenge 2018 dataset with a fivefold cross-validation approach. The proposed framework achieved a noteworthy average Dice Similarity Coefficient (DSC) score of 59%. This performance underscores the effectiveness of combining EED filtering with attention mechanisms in the AttResUnet architecture for accurate stroke lesion segmentation. The fold-wise analysis revealed consistent performance across different data subsets, with slight variations highlighting the model's generalizability. The proposed approach offers a reliable and generalizable tool for automated ischemic stroke lesion segmentation, potentially improving efficiency and accuracy in clinical settings.

PMID:39953256 | DOI:10.1007/s10278-025-01407-8

J Imaging Inform Med. 2025 Feb 14. doi: 10.1007/s10278-024-01287-4. Online ahead of print.

ABSTRACT

This study aims to investigate the impact of combining deep learning image reconstruction (DLIR) and metal artifacts reduction (MAR) algorithms on the quality of CT images with metal implants under different scanning conditions. Four images of the maxillofacial region in pigs were taken using different metal implants for evaluation. The scans were conducted at three different dose levels (CTDIvol: 20/10/5 mGy). The images were reconstructed using three different methods: filtered back projection (FBP), adaptive statistical iterative reconstruction with Veo at a 50% level (AV50), and DLIR at three levels (low, medium, and high). Regions of interest (ROIs) were identified in various tissues (near/far/reference fat, muscle, bone) both with and without metal implants and artifacts. Parameters such as standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and metal artifact index (MAI) were calculated. Additionally, two experienced radiologists evaluated the subjective image quality (IQ) using a 5-point Likert scale. (1) Both observers rated MAR generated significantly lower artifact scores than non-MAR in all types of tissues (P < 0.01), except for the far shadow and bloom in bone (phantoms 1, 3, 4) and the far bloom in muscle (phantom 3) without significant differences (P = 1.0). (2) Under the same scanning condition, DLIR at three levels produced a smaller SD than those of FBP and AV50 (P < 0.05). (3) Compared to FBP and AV50, DLIR denoted a better reduction of MAI and improvement of SNR and CNR (P < 0.05) for most tissues between the four phantoms. (4) Subjective overall IQ was superior with the increasement of DLIR level (P < 0.05) and both observers agreed that DLIR produced better artifact reductions compared with FBP and AV50. The combination of DLIR and MAR algorithms can enhance image quality, significantly reduce metal artifacts, and offer high clinical value.

PMID:39953255 | DOI:10.1007/s10278-024-01287-4

Eur Radiol. 2025 Feb 14. doi: 10.1007/s00330-025-11411-9. Online ahead of print.

ABSTRACT

BACKGROUND: While the CT-based method of detecting clinically significant portal hypertension (CSPH) emerged as a noninvasive alternative for evaluating CSPH, its predictive ability for post-hepatectomy outcomes is unknown. Therefore, this study aimed to evaluate the impact of CT-based CSPH on outcomes following hepatectomy for hepatocellular carcinoma (HCC).

METHODS: This retrospective single-center study included patients with advanced chronic liver disease (ACLD) who underwent hepatectomy for very early or early-stage HCC between January 2017 and December 2018. CSPH was assessed using CT-based criteria, which included splenomegaly determined by deep learning-based spleen volume measurements with personalized reference thresholds, and the presence of gastroesophageal varices (GEV), spontaneous portosystemic shunt or ascites. Logistic regression and competing risk analyses were used to identify factors associated with severe post-hepatectomy liver failure (PHLF), hepatic decompensation, and liver-related death or transplantation. The predictive performance of existing models for PHLF was compared using both CT-based and conventional CSPH criteria (endoscopic GEV or splenomegaly with thrombocytopenia).

RESULTS: Among 593 patients (460 men; mean age 57.9 ± 9.3 years), 41 (6.9%) developed severe PHLF. The median follow-up period was 62 months. CT-based CSPH independently predicted severe PHLF (OR 7.672 [95% CI 3.209-18.346]), hepatic decompensation (subdistribution hazard ratio (sHR) 4.518 [1.868-10.929]), and liver-related death or transplantation (sHR 2.756 [1.315-5.773]). When integrated into existing models, CT-based CSPH outperformed conventional CSPH in predicting severe PHLF (AUC 0.724 vs. 0.694 for EASL algorithm (p = 0.036) and 0.854 vs. 0.830 for Wang's model (p = 0.011)).

CONCLUSIONS: CT-based CSPH is a strong predictor of poor post-hepatectomy outcomes in HCC patients with ACLD, offering a noninvasive surgical risk assessment tool.

KEY POINTS: Question Can CT-based detection of clinically significant portal hypertension (CSPH) serve as a noninvasive predictor of post-hepatectomy outcomes in hepatocellular carcinoma (HCC) patients? Findings CT-based CSPH independently predicted severe post-hepatectomy liver failure, hepatic decompensation, and liver-related death or transplantation, outperforming conventional CSPH criteria in predictive performance. Clinical relevance CT-based CSPH offers a noninvasive and effective tool for surgical risk assessment in HCC patients, potentially improving the selection of candidates for hepatectomy and optimizing patient outcomes.

PMID:39953152 | DOI:10.1007/s00330-025-11411-9

Eur Radiol. 2025 Feb 14. doi: 10.1007/s00330-025-11445-z. Online ahead of print.

ABSTRACT

OBJECTIVES: This study evaluated the effect of enhancing a GPT-4 model with retrieval-augmented generation on its ability to diagnose and classify traumatic injuries based on radiology reports.

MATERIALS AND METHODS: In this prospective proof-of-concept study, we used retrieval-augmented generation as a zero-shot learning approach to provide expert knowledge from the RadioGraphics top ten reading list for trauma radiology to the GPT-4 model, creating the context-aware TraumaCB. Radiological report findings of 50 traumatic injuries were independently generated by two radiologists. The performance of the TraumaCB compared to the generic GPT-4 was evaluated by three board-certified radiologists, assessing the accuracy and trustworthiness of the chatbot responses in the 100 reports created.

RESULTS: The TraumaCB achieved 100% correct diagnoses, 96% correct classification, and 87% correct grading, outperforming the generic GPT-4 with 93% correct diagnoses, 70% correct classification, and 48% correct grading. TraumaCB sources consistently achieved a median rating of 5.0 for explanation and trust. Challenges encountered mainly involved traumatic injuries lacking widely accepted classification systems.

CONCLUSION: Augmenting a commercial GPT-4 model with retrieval-augmented generation improves its diagnostic and classification capabilities, positioning it as a valuable tool for efficiently assessing traumatic injuries across various anatomical regions in trauma radiology.

KEY POINTS: Question Retrieval-augmented generation has the potential to enhance generic chatbots with task-specific knowledge of emergency radiology. Findings The TraumaCB excelled in accuracy, particularly in injury classification and grading, and provided explanations along with the sources used, increasing transparency and facilitating verification. Clinical relevance The TraumaCB provides accurate, fast, and transparent access to trauma radiology classifications, potentially increasing the efficiency of image interpretation in emergency departments and enabling customized reports based on local or individual preferences.

PMID:39953150 | DOI:10.1007/s00330-025-11445-z

Sci Rep. 2025 Feb 14;15(1):5449. doi: 10.1038/s41598-025-88551-x.

ABSTRACT

Opportunistic computed tomography (CT) scans, which can assess relevant bones of interest, offer a potential solution for identifying osteoporotic individuals. However, it has been well documented that image protocol parameters, such as reconstruction kernel, impact the quantitative analysis of volumetric bone mineral density (vBMD) from CT scans. The purpose of this study was to investigate the impact that CT reconstruction kernels have on quantitative results for vBMD from clinical CT scans using phantom and internal calibration. 45 clinical CT scans were reconstructed using the standard kernel and seven alternative kernels: soft, chest, detail, edge, bone, bone plus and lung [GE HealthCare]. Two methods of image calibration, internal and phantom, were used to calibrate the scans. The total hip and fourth lumbar vertebra (L4) were extracted from the scans via deep learning segmentation. Integral vBMD was calculated based on both calibration techniques from CT scans reconstructed with the eight kernels. Linear regression and Bland-Altman analyses were used to determine the coefficient of determination [Formula: see text] and to quantify the agreement between the different kernels. Differences between the reconstruction kernels were determined using paired t tests, and mean differences from the standard were computed. Using internal calibration, the smoothest kernel (soft) yielded a mean difference of -0.95 mg/cc (-0.33%) compared to the reference standard at the L4 vertebra and 2.07 mg/cc (0.51%) at the left femur. The sharpest kernel (lung) yielded a mean difference of 25.36 mg/cc (9.63%) at the L4 vertebra and -25.10 mg/cc (-5.98%) at the left femur. Alternatively, using phantom calibration soft yielded higher mean differences than internal calibration at both locations, with mean differences of 1.21 mg/cc (0.42%) at the L4 vertebra and 2.53 mg/cc (0.65%) at the left femur. The most error-prone results stemmed from the use of the lung kernel, as this kernel displayed a mean difference of -21.90 mg/cc (-7.38%) and -17.24 mg/cc (-4.34%) at the L4 vertebra and femur, respectively. These results indicate when performing opportunistic CT analysis, errors due to interchanging smoothing kernels soft, chest and detail are negligible, but that interchanging between sharpening kernels (lung, bone, bone plus, edge) results in large errors that can significantly impact vBMD measures for osteoporosis screening and diagnosis.

PMID:39953113 | DOI:10.1038/s41598-025-88551-x

Transl Res. 2025 Feb 12:S1931-5244(25)00023-4. doi: 10.1016/j.trsl.2025.02.002. Online ahead of print.

ABSTRACT

Mitochondrial dysfunction is closely associated with idiopathic pulmonary fibrosis (IPF). However, the causal association between mitochondria-related genes and IPF remains to be determined. We obtained gene expression, protein abundance, and methylation quantitative trait locus data for mitochondria-related genes from previous studies. Genome-wide association study data for patients with IPF were obtained from the FinnGen study. A two-sample Mendelian randomisation analysis was conducted to assess the association between mitochondria-related genes and IPF. Furthermore, we conducted validation of expression differences utilizing transcriptome data derived from the BLM-induced pulmonary fibrosis mouse model (n=15). Concurrently, multiphoton imaging was utilized to quantify collagen contents and structural assessment. The direction of causality was verified using the Steiger test, and colocalisation analysis was used to better validate causality. Single-cell data were used to explore the localisation and expression of positive genes across different cell types. The study identified significant associations between mitochondria-related genes and IPF, with POLG and NDUFB10 classified as Grade 1; LYRM4, NBR1, and ACSF3 as Grade 2; MCL1, GFER, MFN2, IVD, and SLC25A35 as Grade 3; and METAP1D and MTX1 as Grade 4. Single-cell analysis showed elevated expression of NBR1, MCL1, and MTX1 in pulmonary myofibroblasts of IPF. This study elucidated the causal effects of mitochondria-related genes on IPF, underscoring their significance in pathogenesis. These findings contribute to an improved understanding of the mechanisms underlying IPF, offering new potential therapeutic targets for interventions.

PMID:39952317 | DOI:10.1016/j.trsl.2025.02.002

J Vasc Access. 2025 Feb 14:11297298251320269. doi: 10.1177/11297298251320269. Online ahead of print.

ABSTRACT

BACKGROUND: This study investigates the relationship between national catheter use among hemodialysis (HD) patients and kidney transplantation (KTX) activity, exploring the hypothesis that higher KTX activity may be associated with increased catheter usage. The rationale is based on the idea that shorter waiting times for transplants in high-activity countries could make central venous catheters (CVCs) more favorable as a temporary bridge to transplantation compared to arteriovenous fistulas or grafts which require longer maturation times.

METHODS: Nine national dialysis and transplant registries (Argentina, Australia, Austria, New Zealand, Portugal, Scotland, Sweden, USA, Turkey) were included in this analysis. The included descriptive analysis of baseline information from included countries, followed by crude association analyses using correlation and regression analyses to explore the relationship between CVC usage and kidney transplants per million inhabitants, considering relevant confounders. Adjusted analyses were performed to account for these confounders, providing a more nuanced understanding of the relationship.

RESULTS: Data from nine different national registries was analyzed. CVC use and KTX activity had a weak to moderate positive correlation (r = 0.23, 95% CI: 0.07, 0.39). In all included countries CVC use increased over time. Adjusting for temporal patterns, country-specific factors, and the proportion of female HD patients, there was still strong evidence for a moderate increase of CVCs among prevalent HD patients with increasing KTX activity.

CONCLUSION: Higher national KTX activity is associated with a moderate increase in CVCs among prevalent HD patients.

PMID:39953649 | DOI:10.1177/11297298251320269

Sci Rep. 2025 Feb 14;15(1):5512. doi: 10.1038/s41598-025-89783-7.

ABSTRACT

The early sequence of respiratory failure events after the onset of sepsis is still unknown. We hypothesize that the lung should signal through circulating extracellular vesicles (EVs) when it is affected by a systemic inflammatory response. Blood samples were obtained from septic patients with (n = 5) and without acute respiratory distress syndrome (ARDS) (n = 13) at 24 h of intensive care unit admission and 3 days later at Sírio-Libanês Hospital. Pulmonary-originated sepsis was not considered. The characteristics of the plasma-isolated EVs were compatible with exosomes. 48 miRNAs were evaluated by real-time PCR. Comparing all samples from patients with sepsis + ARDS to sepsis only, 9 miRNAs are transported in smaller amounts: miR-766 (-35.7, p = 0.002), miR-127 (-23.8, p = 0.001), miR-340 (-13.5, p = 0.006), miR-29b (-12.8, p = 0.001), miR-744 (-7.1, p = 0.05), miR-618 (-4.0, p = 0.02), miR-598 (-3.8, p = 0.035), miR-1260 (-2.5, p = 0.035); and miR-885-5p is expressed at higher levels (9.5; p = 0.028). In paired samples, the set of altered miRNAs is generally different (p < 0.05) between sepsis + ARDS (miR-1183,-1267,-1290,-17,-192,-199a-3p,-25,-485-3p,-518d,-720) or sepsis only (miR-148a,-193a-5p,-199a-3p,-222,-25,-340,744). Bioinformatic analysis showed that when sepsis is associated with ARDS, those differentially expressed miRNAs potentially target messenger RNAs from the Glycoprotein VI/GP6 signaling pathway. Circulating EV-miRNA cargo could be potential biomarkers of lung inflammation during sepsis in patients requiring mechanical ventilation.

PMID:39953195 | DOI:10.1038/s41598-025-89783-7

Nat Rev Rheumatol. 2025 Feb 14. doi: 10.1038/s41584-024-01217-2. Online ahead of print.

ABSTRACT

Systemic sclerosis (SSc) remains a challenging and enigmatic systemic autoimmune disease, owing to its complex pathogenesis, clinical and molecular heterogeneity, and the lack of effective disease-modifying treatments. Despite a century of research in SSc, the interconnections among microvascular dysfunction, autoimmune phenomena and tissue fibrosis in SSc remain unclear. The absence of validated biomarkers and reliable animal models complicates diagnosis and treatment, contributing to high morbidity and mortality. Advances in the past 5 years, such as single-cell RNA sequencing, next-generation sequencing, spatial biology, transcriptomics, genomics, proteomics, metabolomics, microbiome profiling and artificial intelligence, offer new avenues for identifying the early pathogenetic events that, once treated, could change the clinical history of SSc. Collaborative global efforts to integrate these approaches are crucial to developing a comprehensive, mechanistic understanding and enabling personalized therapies. Challenges include disease classification, clinical heterogeneity and the establishment of robust biomarkers for disease activity and progression. Innovative clinical trial designs and patient-centred approaches are essential for developing effective treatments. Emerging therapies, including cell-based and fibroblast-targeting treatments, show promise. Global cooperation, standardized protocols and interdisciplinary research are vital for advancing SSc research and improving patient outcomes. The integration of advanced research techniques holds the potential for important breakthroughs in the diagnosis, treatment and care of individuals with SSc.

PMID:39953141 | DOI:10.1038/s41584-024-01217-2

Nat Commun. 2025 Feb 14;16(1):1643. doi: 10.1038/s41467-025-56901-y.

ABSTRACT

C4 photosynthesis exemplifies convergent evolution of complex traits. Herein, we construct chromosome-scale genome assemblies and perform multi-omics analysis for five Flaveria species, which represent evolutionary stages from C3 to C4 photosynthesis. Chromosome-scale genome sequence analyses reveal a gradual increase in genome size during the evolution of C4 photosynthesis attributed to the expansion of transposable elements. Systematic annotation of genes encoding C4 enzymes and transporters identify additional copies of three C4 enzyme genes through retrotranspositions in C4 species. C4 genes exhibit elevated mRNA and protein abundances, reduced protein-to-RNA ratios, and comparable translation efficiencies in C4 species, highlighting a critical role of transcriptional regulation in C4 evolution. Furthermore, we observe an increased abundance of ethylene response factor (ERF) transcription factors and cognate cis-regulatory elements associated with C4 genes regulation. Altogether, our study provides valuable genomic resources for the Flaveria genus and sheds lights on evolutionary and regulatory mechanisms underlying C4 photosynthesis.

PMID:39952962 | DOI:10.1038/s41467-025-56901-y

Pharmacol Rev. 2025 Jan;77(1):100001. doi: 10.1124/pharmrev.123.001140. Epub 2024 Nov 22.

ABSTRACT

For most vertebrates, bitter perception plays a critical role in the detection of potentially harmful substances in food items. The detection of bitter compounds is facilitated by specialized receptors located in the taste buds of the oral cavity. This work focuses on these receptors, including their sensitivities, structure-function relationships, agonists, and antagonists. The existence of numerous bitter taste receptor variants in the human population and the fact that several of them profoundly affect individual perceptions of bitter tastes are discussed as well. Moreover, the identification of bitter taste receptors in numerous tissues outside the oral cavity and their multiple proposed roles in these tissues are described briefly. Although this work is mainly focused on human bitter taste receptors, it is imperative to compare human bitter taste with bitter taste of other animals to understand which forces might have shaped the evolution of bitter taste receptors and their functions and to distinguish apparently typical human features from rather general ones. For readers who are not very familiar with the gustatory system, short descriptions of taste anatomy, signal transduction, and oral bitter taste receptor expression are included in the beginning of this article. SIGNIFICANCE STATEMENT: Apart from their role as sensors for potentially harmful substances in the oral cavity, the numerous additional roles of bitter taste receptors in tissues outside the gustatory system have recently received much attention. For careful assessment of their functions inside and outside the taste system, a solid knowledge of the specific and general pharmacological features of these receptors and the growing toolbox available for studying them is imperative and provided in this work.

PMID:39952694 | DOI:10.1124/pharmrev.123.001140

Annu Rev Plant Biol. 2025 Feb 14. doi: 10.1146/annurev-arplant-083123-050311. Online ahead of print.

ABSTRACT

The target of rapamycin (TOR) is a central regulator of growth, development, and stress adaptation in plants. This review delves into the molecular intricacies of TOR signaling, highlighting its conservation and specificity across eukaryotic lineages. We explore the molecular architecture of TOR complexes, their regulation by a myriad of upstream signals, and their consequential impacts on plant physiology. The roles of TOR in orchestrating nutrient sensing, hormonal cues, and environmental signals are highlighted, illustrating its pivotal function in modulating plant growth and development. Furthermore, we examine the impact of TOR on plant responses to various biotic and abiotic stresses, underscoring its potential as a target for agricultural improvements. This synthesis of current knowledge on plant TOR signaling sheds light on the complex interplay between growth promotion and stress adaptation, offering a foundation for future research and applications in plant biology.

PMID:39952681 | DOI:10.1146/annurev-arplant-083123-050311

Adv Med Sci. 2025 Feb 12:S1896-1126(25)00009-4. doi: 10.1016/j.advms.2025.01.009. Online ahead of print.

NO ABSTRACT

PMID:39952431 | DOI:10.1016/j.advms.2025.01.009

J Adv Res. 2025 Feb 12:S2090-1232(25)00111-0. doi: 10.1016/j.jare.2025.02.013. Online ahead of print.

ABSTRACT

BACKGROUND: Despite notable advancements in AML therapy in recent years, a substantial proportion of patients remain refractory or at high risk of recurrence with limited efficacy. Therefore, it's urgent to develop novel drugs for treating AML.

METHODS: The small molecule drug library was utilized to screen for drugs that elicit the inflammatory death of AML cells. Cell viability, cell morphological analysis, western blotting, and RNA-seq were used to determine the pathway of Mebendazole (MBD)-induced AML cell death. Cell cycle analysis, protein expression profiling, molecular docking, western blotting and lentivirus overexpression were used to analyze the target protein of MBD in AML cells. The anti-AML activity of MBD in vivo was evaluated using tumor xenograft models constructed by AML cell lines and patient-derived primary AML cells.

RESULTS: In this study, we have identified Mebendazole (MBD), a conventional anthelmintic drug known for its low toxicity and cost, as a potent agent that exerts significant anti-AML effects in vitro. Furthermore, we have observed its inhibitory effects on the invasion of AML cell lines and primary AML cells in xenograft mouse models, while noting its negligible toxic side effects in normal mice in vivo. Mechanically, MBD inhibits the cell cycle in G2/M phase by inhibiting tubulin α1A (TUBA1A) and promotes ZBP-1 mediated PANoptosis in AML cells. Our results confirm that MBD exerts anti-AML activity in preclinical models.

CONCLUSION: These results highlight the remarkable clinical translational potential of MBD, providing new potential medicine for AML patients. In addition, TUBA1A can be used potential novel therapeutic target in tumors with abnormal TUBA1A expression.

PMID:39952321 | DOI:10.1016/j.jare.2025.02.013