Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.

Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results: For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion: The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Biomimetic Intrafibrillar Mineralization of Type I Collagen with Intermediate Precursors-loaded Mesoporous Carriers.

Limited continuous replenishment of the mineralization medium is a restriction for in-situ solution-based remineralization of hypomineralized body tissues. Here, we report a process that generated amine-functionalized mesoporous silica nanoparticles for sustained release of biomimetic analog-stabilized amorphous calcium phosphate precursors. Both two-dimensional and three-dimensional collagen models can be intrafibrillarly mineralized with these released fluidic intermediate precursors. This represents an important advance in the translation of biomineralization concepts into regimes for in-situ remineralization of bone and teeth.

A review of the bioactivity of hydraulic calcium silicate cements.

OBJECTIVES: In tissue regeneration research, the term "bioactivity" was initially used to describe the resistance to removal of a biomaterial from host tissues after intraosseous implantation. Hydraulic calcium silicate cements (HCSCs) are putatively accepted as bioactive materials, as exemplified by the increasing number of publications reporting that these cements produce an apatite-rich surface layer after they contact simulated body fluids. METHODS: In this review, the same definitions employed for establishing in vitro and in vivo bioactivity in glass-ceramics, and the proposed mechanisms involved in these phenomena are used as blueprints for investigating whether HCSCs are bioactive. RESULTS: The literature abounds with evidence that HCSCs exhibit in vitro bioactivity; however, there is a general lack of stringent methodologies for characterizing the calcium phosphate phases precipitated on HCSCs. Although in vivo bioactivity has been demonstrated for some HCSCs, a fibrous connective tissue layer is frequently identified along the bone-cement interface that is reminiscent of the responses observed in bioinert materials, without accompanying clarifications to account for such observations. CONCLUSIONS: As bone-bonding is not predictably achieved, there is insufficient scientific evidence to substantiate that HCSCs are indeed bioactive. Objective appraisal criteria should be developed for more accurately defining the bioactivity profiles of HCSCs designed for clinical use.