Development and validation of a nomogram for preoperative prediction of immature teratoma in children with teratoma: a retrospective, multicenter, diagnostic study.

Background: Teratomas are the most common germ cell tumors in children, and histologically classified as mature teratomas (MTs) and immature teratomas (ITs). Preoperative IT identification can affect the surgical approach, the type of procedure, and future possible reproductive health. However, there is no complete diagnostic criterion for ITs nowadays. We aimed to establish and validate a nomogram based on clinical and computed tomography (CT) features for preoperative prediction of ITs in children. Methods: We retrospectively reviewed 519 teratoma patients from hospital I for training (n=364) and validation (n=155), and 113 patients from hospital II for external validation. Univariate and multivariate logistic regression analyses were performed on the training set to screen risk factors, including alpha-fetoprotein (AFP), age, gender, tumor site, size, tumor composition, calcification and fat. Then, a nomogram was established based on identified risk factors and validated on the validation set. The performance of the nomogram was evaluated in terms of discrimination, calibration and the clinical usefulness. Results: Multivariate logistic regression showed that tumor composition, AFP, age, calcification and fat were independent risk factors for preoperative prediction of IT. The area under the receiver operating characteristic (ROC) curves (AUCs) for the nomogram on the training set, internal and external validation set were 0.92 (0.88-0.96), 0.91 (0.84-0.97) and 0.92 (0.86-0.97), respectively. The model demonstrated sensitivity of 80%, specificity of 90% at the cut-off value of 0.262. Whatever the set, the calibration curve indicated good calibration. Decision curve analysis (DCA) curves demonstrated that the nomogram had greater net benefits than either the treat-all tactics or the treat-none tactics within a large scope of threshold. Conclusions: The nomogram established based on clinical and CT findings had the favorable accuracy for the preoperative prediction of IT, and may help in clinical decision-making and risk stratification.

Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients.

Background: Nephron sparing nephrectomy may not reduce the prognosis of nephroblastoma in the absence of involvement of the renal capsule, sinus vessels, and lymph nodes, However, there is no accurate preoperative noninvasive evaluation method at present. Materials and methods: 105 nephroblastoma patients underwent contrast-enhanced CT scan between 2013 and 2020 in our hospital were retrospectively collected, including 59 cases with localized stage and 46 cases with non-localized stage, and then were divided into training cohort (n= 73) and validation cohort (n= 32) according to the order of CT scanning time. After lesion segmentation and data preprocessing, radiomic features were extracted from each volume of interest. The multi-step procedure including Pearson correlation analysis and sequential forward floating selection was performed to produce radiomic signature. Prediction model was constructed using the radiomic signature and Logistic Regression classifier for predicting the localized nephroblastoma in the training cohort. Finally, the model performance was validated in the validation cohort. Results: A total of 1652 radiomic features have been extracted, from which TOP 10 features were selected as the radiomic signature. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity of the prediction model were 0.796, 0.795, 0.732 and 0.875 for the training cohort respectively, and 0.710, 0.719, 0.611 and 0.857 for the validation cohort respectively. The result comparison with prediction models composed of different machine learning classifiers and different parameters also manifest the effectiveness of our radiomic model. Conclusion: A logistic regression model based on radiomic features extracted from preoperative CT images had good ability to noninvasively predict nephroblastoma without renal capsule, sinus vessel, and lymph node involvement.

Pyruvate and lactate based hydrogel film inhibits UV radiation-induced skin inflammation and oxidative stress.

Solar skin damage is one of the most common diseases among outdoor workers. An important cause for the damage is the ultraviolet and infrared rays in sunlight, which are absorbed by the skin in large amounts, leading to severe skin inflammation and oxidative stress. Therefore, physical prevention by shielding the light from harmful wavelengths can be an effective method of skin protection from radiation. However, for existing skin lesions, prompt treatment is essential to avoid the aggravation of the injury and promote repair. Therefore, to improve the therapeutic effect on sun-damaged skin, we attempted to design a system with a dual purpose of eliminating toxic free radicals and modulating tissue inflammatory response. Here, we designed and synthesized a poly-acryloyl lysine (P-Ac-Lys) and polyvinyl alcohol-dihydroxyphenylalanine (PVA-DOPA) composite hydrogel (PAL@PVA-DOPA Hydrogel) loaded with lactate and pyruvate, that exhibites a good free radical scavenging activity and an excellent ability to modulate the inflammatory response. Experimental results showe that this hydrogel film could effectively reduce the UV-induced skin inflammation response, alleviate pathological damage and promote the recovery of the damaged skin.

Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children.

Purpose: To develop and validate a machine learning-based CT radiomics method for preoperatively predicting the stages (stage I and non-stage I) of Wilms tumor (WT) in pediatric patients. Methods: A total of 118 patients with WT, who underwent contrast-enhanced computed tomography (CT) scans in our center between 2014 and 2021, were studied retrospectively and divided into two groups: stage I and non-stage I disease. Patients were randomly divided into training cohorts (n = 94) and test cohorts (n = 24). A total of 1,781 radiomic features from seven feature classes were extracted from preoperative portal venous-phase images of abdominal CT. Synthetic Minority Over-Sampling Technique (SMOTE) was used to handle imbalanced datasets, followed by a t-test and Least Absolute Shrinkage and Selection Operator (LASSO) regularization for feature selection. Support Vector Machine (SVM) was deployed using the selected informative features to develop the predicting model. The performance of the model was evaluated according to its accuracy, sensitivity, and specificity. The receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC) was also arranged to assess the model performance. Results: The SVM model was fitted with 15 radiomic features obtained by t-test and LASSO concerning WT staging in the training dataset and demonstrated favorable performance in the testing dataset. Cross-validated AUC on the training dataset was 0.79 with a 95 percent confidence interval (CI) of 0.773-0.815 and a coefficient of variation of 3.76%, while AUC on the test dataset was 0.81, and accuracy, sensitivity, and specificity were 0.79, 0.87, and 0.69, respectively. Conclusions: The machine learning model of SVM based on radiomic features extracted from CT images accurately predicted WT stage I and non-stage I disease in pediatric patients preoperatively, which provided a rapid and non-invasive way for investigation of WT stages.

[Progress in Precision Medicine of Radiomics in Abdominal Tumors Practice].

With the advent of the era of artificial intelligence, as an emerging technology, radiomics can extract a large amount of quantitative information describing the physiological condition and phenotypic characteristics of tumors with high throughput from the massive data of CT, MRI and other imaging tomography, and analyze these high-dimensional imaging omics features containing disease pathophysiological information can be used to accurately determine tumor differentiation, staging, and predict tumor behavior, which has broad application prospects. This article aims to introduce the technical principles of radiomics and its abdominal tumor application status, and to prospect its application prospects in pediatric abdominal neuroblastoma.

Mesenchymal stem cell-derived exosome miR-542-3p suppresses inflammation and prevents cerebral infarction.

BACKGROUND: Cerebral infarction ranks as the second leading cause of disability and death globally, and inflammatory response of glial cells is the main cause of brain damage during cerebral infarction. METHODS: Studies have shown that mesenchymal stem cells (MSCs) can secrete exosomes and contribute to cerebral disease. Here, we would explore the function of MSC-derived exosome in cerebral infarction. RESULTS: Microarray indicated a decrease of miR-542-3p and an increase of Toll-Like Receptor 4 (TLR4) in middle cerebral artery occlusion (MCAO) mice comparing with sham mice. And luciferase and RIP analysis indicated a binding of miR-542-3p and TLR4. Then, we injected AAV9-miR-542-3p into paracele of sham or MCAO mice. Functional analysis showed that AAV9-miR-542-3p inhibited infarction area and the number of degenerating neurons and suppressed inflammatory factors' expression and inflammatory cell infiltration. As well, transfection of miR-542-3p mimics into HA1800 cells underwent oxygen and glucose deprivation (OGD). Similarly, overexpression of miR-542-3p alleviated OGD induced cell apoptosis, ROS, and activation of inflammation response. Moreover, miR-542-3p could be packaged into MSCs and secreted into HA1800 cells. The extractive exosome-miR-21-3p treatment relieved MCAO- or OGD-induced cerebral injury and inflammation through targeting TLR4. CONCLUSION: These results confirmed that MSC-derived exosome miR-542-3p prevented ischemia-induced glial cell inflammatory response via inhibiting TLR4. These results suggest possible therapeutic strategies for using exosome delivery of miR-542-3p to cure cerebral ischemic injury.

Imaging features of pharyngeal hairy polyps in infants.

BACKGROUND: Hairy polyps are the most common congenital deformity of the oral-nasopharynx/oral cavity in infants, which may lead to severe complications in the newborns. However, few studies have been published with respect to their radiological features, and most are case reports. OBJECTIVE: This study aimed to analyze radiological features of the oral­nasopharyngeal hairy polyps and to identify the radiological features with the highest diagnostic value. MATERIALS AND METHODS: From 2014 to 2019, pediatric cases pathologically diagnosed as hairy polyps and having received radiological examination at the Children's Hospital Affiliated to Zhejiang University were retrospectively analyzed. Radiological evaluations were conducted on tumor size, location, morphology, density or signal features as well as the enhancement pattern. RESULTS: A total of six infants were recruited. Clinical features observed in these cases included choking on milk with cyanosis, intermittent dyspnea, oropharyngeal mass, and snoring. Lesions were derived from the left tubal torus in three out of six cases, from the left lateral aspect of soft palate in one, from the left lateral pharyngeal wall in one, and from the right tubal torus in one. They were shown as pedicled polyp-like well-circumscribed mass with fat density or signal as well as a stalk on CT or MRI. In the contrast-enhanced scan, the fat components were not enhanced, while the stalk was mildly enhanced. CONCLUSION: Hairy polyps had typical radiological features. Hairy polyps should be considered for pedicled polyp-like well-circumscribed mass in the oral-nasopharynx of infants with fat density or signal as well as a stalk.

Cross electro-nape-acupuncture ameliorates cerebral hemorrhage-induced brain damage by inhibiting necroptosis.

BACKGROUND: Receptor interacting protein kinase 1 (RIPK1)-mediated cell death, including apoptosis and necroptosis, belongs to programmed cell death. It has been reported that RIPK1-mediated necroptosis exists in lesions of cerebral hemorrhage (CH). Electroacupuncture, a treatment derived from traditional Chinese medicine, could improve neurological impairment in patients with brain injury. AIM: To investigate the protective role of cross electro-nape acupuncture (CENA) in CH, and clarify the potential mechanism. METHODS: CH rat models were established, and CENA was applied to the experimental rats. Neurological functions and encephaledema were then measured. Necrotic cells in the brain of rats with CH were evaluated by propidium iodide staining. Necroptosis was assessed by immunofluorescence. Activation of the necroptosis-related pathway was detected by western blot. Extraction of brain tissue, cerebrospinal fluid and serum samples was conducted to measure the expression and secretion of inflammatory cytokines by quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. RESULTS: The necroptotic marker p-MLKL was detectable in the brains of rats with CH. Next, we found that CENA could ameliorate neurological functions in rat models of CH. Moreover, the upregulation of RIPK1-mediated necroptosis-related molecules in the brains of rats with CH were inhibited by CENA. Further investigation revealed that CENA partially blocked the interaction between RIPK1 and RIPK3. Finally, in vivo assays showed that CENA decreased the expression of the inflammatory cytokines tumor necrosis factor-α, interleukin-6 and interleukin-8 in CH rat models. CONCLUSION: These findings revealed that CENA exerts a protective role in CH models by inhibiting RIPK1-mediated necroptosis.

Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system.

BACKGROUND: Bone age can reflect the true growth and development status of a child; thus, it plays a critical role in evaluating growth and endocrine disorders. This study established and validated an optimized Tanner-Whitehouse 3 artificial intelligence (TW3-AI) bone age assessment (BAA) system based on a convolutional neural network (CNN). METHODS: A data set of 9,059 clinical radiographs of the left hand was obtained from the picture archives and communication systems (PACS) between January 2012 and December 2016. Among these, 8,005/9,059 (88%) samples were treated as the training set for model implementation, 804/9,059 (9%) samples as the validation set for parameters optimization, and the remaining 250/9,059 (3%) samples were used to verify the accuracy and reliability of the model compared to that of 4 experienced endocrinologists and 2 experienced radiologists. The overall variation of TW3-metacarpophalangeal, radius, ulna and short bones (RUS) and TW3-Carpal bone score, as well as each bone (13 RUS + 7 Carpal) between reviewers and the AI, were compared by Bland-Altman (BA) chart and Kappa test, respectively. Furthermore, the time consumption between the model and reviewers was also compared. RESULTS: The performance of TW3-AI model was highly consistent with the reviewers' overall estimation, and the root mean square (RMS) was 0.50 years. The accuracy of the BAA of the TW3-AI model was better than the estimate of the reviewers. Further analysis revealed that human interpretations of the male capitate, hamate, the first distal and fifth middle phalanx and female capitate, the trapezoid, and the third and fifth middle phalanx, were most inconsistent. The average image processing time was 1.5±0.2 s in the TW3-AI model, which was significantly shorter than manual interpretation. CONCLUSIONS: The diagnostic performance of CNN-based TW3 BAA was accurate and timesaving, and possesses better stability compared to diagnostics made by experienced experts.

miR-124 suppresses glioblastoma growth and potentiates chemosensitivity by inhibiting AURKA.

Glioblastoma (GBM) accounts for about half of all malignant brain cancers. Although the treatment strategies for glioblastoma develop rapidly, a considerable number of patients could not benefit from temozolomide (TMZ)-based chemotherapy. Here, we revealed a miR-124-AURKA axis that regulated glioblastoma growth and chemosensitivity. Mechanistically, AURKA was up-regulated in glioblastoma tissues and associated with poor overall survival. While overexpression of AURKA enhanced tumor growth, genetic or pharmacological inhibition of AURKA led to growth-inhibitory and chemopotentiating effects in glioblastoma. AURKA was further identified as a target of miR-124. Furthermore, our data showed that miR-124 down-regulated AURKA expression and subsequently suppressed cell growth. Re-expression of AURKA significantly rescued miR124-mediated proliferation repression and chemosensitivity. In conclusion, our results demonstrated that miR-124 inhibited glioblastoma growth and potentiated chemosensitivity by targeting AURKA, which may represent promising targets and rational therapeutic options for glioblastoma.

Pediatric Isolated Sinonasal Schwannoma: A New Case Report and Literature Review.

Schwannomas of the paranasal sinus are uncommon. Less than 4% of schwannomas involve the nasal cavity and paranasal sinuses, even less in the pediatric age group. A case of schwannoma arising in maxillary sinus in a 2.5-year-old Chinese boy is reported. The basis for discussion of this case is the exceptional rarity of sinonasal schwannoma in pediatric patients.