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Background: Chronic obstructive pulmonary disease (COPD) is a widespread inflammatory disease with a high mortality rate. Long noncoding RNAs play important roles in pulmonary diseases and are potential targets for inflammation intervention. Methods: The expression of small nucleolar RNA host gene 6 (SNHG6) in mouse lung epithelial cell line MLE12 with or without cigarette smoke extract (CSE) treatment was first detected using quantitative reverse‐transcription PCR. ELISA was used to evaluate the release of inflammatory cytokines (TNF‐α, IL‐1β, and IL‐6). The binding site of miR‐182‐5p with SNHG6 was predicted by using miRanda, which was verified by double luciferase reporter assay. Results: Here, we revealed that SNHG6 was upregulated in CS‐exposed MLE12 alveolar epithelial cells and lungs from COPD‐model mice. SNHG6 silencing weakened CS‐induced inflammation in MLE12 cells and mouse lungs. Mechanistic investigations revealed that SNHG6 could upregulate IκBα kinase through sponging the microRNA miR‐182‐5p, followed by activated NF‐κB signaling. The suppressive effects of SNHG6 silencing on CS‐induced inflammation were blocked by an miR‐182‐5p inhibitor. Conclusion: Overall, our findings suggested that SNHG6 regulates CS‐induced inflammation in COPD by activating NF‐κB signaling, thereby offering a novel potential target for COPD treatment. [ABSTRACT FROM AUTHOR]

Objectives To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. Methods We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. Results AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P  <  .05). Conclusion The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. Advances in knowledge This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE. [ABSTRACT FROM AUTHOR]

Background: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.Methods: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.Results: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.Conclusion: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients. [ABSTRACT FROM AUTHOR]

Assessing the effectiveness of nanomedicines involves evaluating the drug content at the target site. Currently, most research focuses on monitoring the signal responses from loaded drugs, neglecting the changes caused by the nanohosts. Here, we propose a strategy to quantitatively evaluate the content of loaded drugs by detecting the signal variations resulting from the alterations in the microenvironment of the nanohosts. Specifically, hyperpolarized (HP) 129 Xe atoms are employed as probes to sense the nanohosts' environment and generate a specific magnetic resonance (MR) signal that indicates their accessibility. The introduction of drugs reduces the available space in the nanohosts, leading to a crowded microenvironment that hinders the access of the 129 Xe atoms. By employing 129 Xe atoms as a signal source to detect the alterations in the microenvironment, we constructed a three-dimensional (3D) map that indicated the concentration of the nanohosts and established a linear relationship to quantitatively measure the drug content within the nanohosts based on the corresponding MR signals. Using the developed strategy, we successfully quantified the uptake of the nanohosts and drugs in living cells through HP 129 Xe MR imaging. Overall, the proposed HP 129 Xe atom-sensing approach can be used to monitor alterations in the microenvironment of nanohosts induced by loaded drugs and provides a new perspective for the quantitative evaluation of drug presence in various nanomedicines.

Background: The concentrations of linezolid, its optimal regimen and the associated side effects in elderly patients remain unclear., Methods: In this multicentre, prospective study, elderly patients receiving linezolid at four tertiary hospitals in Beijing between May 2021 and December 2022 were included. Linezolid concentrations and haematological toxicity were monitored dynamically. Risk factors for linezolid overexposure and moderate-to-severe linezolid-induced thrombocytopenia (M/S LIT) were analysed, and a predictive model of M/S LIT was developed., Results: A total of 860 linezolid concentrations were measured in 313 patients. The median trough concentrations of linezolid were 24.4 (15.3, 35.8) mg/L at 36-72 h and 26.1 (17.0, 38.1) mg/L at 5-10 days (P = 0.132). Severe linezolid exposure was independently associated with age, estimated glomerular filtration rate (eGFR) and the worst SOFA score (SOFA1), and we further recommended dose regimens for elderly patients based on these findings. The incidences of linezolid-induced thrombocytopenia(LIT) and M/S LIT were 73.5% and 47.6%, respectively. M/S LIT was independently correlated with treatment duration, average trough concentration (TDMa), baseline platelet count, eGFR and baseline SOFA score (SOFA0). The developed nomogram predicted M/S LIT with an area under the curve of 0.767 (95% CI 0.715-0.820), a sensitivity of 71.1% and a specificity of 73.2%., Conclusions: Linezolid trough concentrations increased dramatically in the elderly, by about 10 mg/L in patients aged 65-80 years, followed by a further increase of 10 mg/L for every 10 years of age. Therapeutic drug monitoring is recommended in elderly patients receiving linezolid. The developed nomogram may predict M/S LIT and guide dosage adjustments of linezolid. Clinical trial registration number: ChiCTR2100045707., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality., (© 2024 Institute of Physics and Engineering in Medicine.)