Anisotropic MXene@polydopamine- and Dialdehyde Carboxymethyl Cellulose-Modified Collagen Aerogel Supported Form-Stable Phase Change Composites for Light-To-Heat Conversion and Energy Storage.

As a renewable alternative heat source, the inherently intermittent feature of solar energy needs to be coordinated by reliable energy conversion and storage systems for utilizing the most abundant solar energy. Phase change materials (PCMs) are supposed to be advanced mediums for storing a great deal of heat generated by solar light. However, PCMs cannot effectively absorb and utilize solar energy due to leakage, low photothermal conversion efficiency, and poor thermal conductivity. Herein, we developed a collagen-based aerogel modified by dialdehyde carboxymethyl cellulose and polydopamine-modified two-dimensional transition-metal carbide/nitride (MXene@PDA) through bidirectional freeze-drying technology for supporting PCMs, which exhibited anisotropy in structure and properties. In particular, the thermal conductivity of the aerogel was 0.0871 W/(m·K) in the axial direction and 0.0504 W/(m·K) in the radial direction, demonstrating its anisotropic thermal insulation performance. Moreover, the final aerogel composite PCMs had been obtained via impregnating the obtained aerogel supporting matrix into polyethylene glycol (PEG) and hydrophobic treatment of polydimethylsiloxane, which exhibited outstanding solar-thermal conversion ability, good thermal storage capacity, advanced leakage-proof property, and antifouling performance. The loading rate of PEG was as high as 92.2%, and the melting enthalpy was 132.6 J/g. Most importantly, the water contact angle was evaluated to be 156.8°, indicating its superior antifouling performance. This material has intensive application prospects in the fields of solar energy collection, conversion, and storage.

Terminal Chemical Modifications of crRNAs Enable Improvement in the Performance of CRISPR-Cas for Point-of-Care Nucleic Acid Detection.

CRISPR-Cas systems, harnessing their precise nucleic acid recognition via CRISPR RNA (crRNA), offer promise for the accurate testing of nucleic acids in the field. However, the inherent susceptibility of crRNA to degradation poses challenges for accurate detection in low-resource settings. Here, we utilized the chemically modified crRNA for the CRISPR-Cas-based assay (CM-CRISPR). We found that the extension and chemical modification to crRNA significantly enhanced the trans-cleavage activity of LbCas12a. The chemically modified crRNA was resistant to degradation, and CM-CRISPR showed superior detection capability in complex environments. CM-CRISPR could be combined with recombinase polymerase amplification (RPA) and applied in a droplet digital platform, enabling attomolar-level sensitivity. We also developed a portable and automated device for a digital CRISPR assay, which is amenable to point-of-care testing (POCT). The extraction-free procedure was integrated with this assay to streamline the workflow, and clinical samples were successfully detected. This work finds a simple and efficient way to improve the performance of CRISPR-Cas and develops a portable platform for POCT, representing a significant advance toward practical applications of CRISPR-based diagnostics.

A Spectral Detection Method Based on Integrated and Partition Modeling for Trace Copper in High-Concentration Zinc Solution.

In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid.

Development and Validation of a Contrast-Enhanced US VI-RADS for Evaluating Muscle Invasion in Bladder Cancer.

Background Contrast-enhanced US (CEUS) can be used preoperatively for evaluating muscle invasion in bladder cancer, which is important for determining appropriate treatment. However, diagnostic criteria for assessing this at CEUS have not been standardized. Purpose To develop and validate a CEUS Vesical Imaging Reporting and Data System (VI-RADS) for evaluating muscle invasion in bladder cancer. Materials and Methods This single-center prospective study consecutively enrolled patients with suspected bladder cancer. Participants underwent transabdominal or intracavity CEUS between July 2021 and May 2023. Participants were divided into a training set and a validation set at a 2:1 ratio based on the chronologic order of enrollment. The training set was used to identify major imaging features to include in CEUS VI-RADS, and the likelihood of muscle invasion per category was determined using a pathologic reference standard. The optimal VI-RADS category cutoff for muscle invasion was determined with use of the maximum Youden index. The validation set was assessed by novice and expert readers and used to validate the diagnostic performance and interreader agreement of the developed system. Results Overall, 126 participants (median age, 64 years [IQR, 57-71 years]; 107 male) and 67 participants (median age, 64 years [IQR, 56-69 years]; 49 male) were included in the training and validation set, respectively. In the training set, the optimal CEUS VI-RADS category cutoff for muscle invasion was VI-RADS 4 or higher (Youden index, 0.77). In the validation set, CEUS VI-RADS achieved good performance for both novice and expert readers (area under the receiver operating characteristic curve, 0.80 [95% CI: 0.70, 0.90] vs 0.88 [95% CI: 0.80, 0.97]; P = .09). The interreader agreement regarding the evaluation of CEUS VI-RADS category was 0.77 (95% CI: 0.65, 0.85) for novice readers, 0.87 (95% CI: 0.79, 0.92) for expert readers, and 0.78 (95% CI: 0.70, 0.84) for all readers. Conclusion The developed CEUS VI-RADS showed good performance and interreader agreement for the assessment of muscle invasion in bladder cancer. Chinese Clinical Trial Registry no. ChiCTR2100049435 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Morrell in this issue.

Self-healing cellulose nanocrystals/fluorinated polyacrylate with double dynamic interactions of coumarin groups and multiple hydrogen bonds.

In this work, self-healing cellulose nanocrystals/fluorinated polyacrylate with dual dynamic networks of photoreversible crosslinking network and high-density hydrogen bonds was prepared by Pickering emulsion polymerization. The main work was to study the effects of 7-(2-methacryloyloxy)-4-methylcoumarin (CMA) and 2-ureido-4[1H]-pyrimidinone methyl methacrylate (UPyMA) monomer dosage on emulsion polymerization and latex film properties. The monomer conversion increased first and then decreased as the CMA and UPyMA monomer dosage increased, while a reverse trend was noted for the particle size and particle size distribution. Incorporating UPyMA allowed the rapid formation of hydrogen bonds at the crosslinking sites, which increased the interaction force between the healing surfaces. Besides, reversible photocrosslinking reaction of coumarin groups provided another support for self-healing performance. Moreover, the influence of self-healing temperature, self-healing time and UV irradiation on the self-healing ability was also systematically investigated The tensile strength of the prepared cellulose nanocrystals/fluorinated polyacrylate latex film exhibited a self-healing efficiency of 91.4 % under 365 nm UV irradiation and 80 °C for 12 h. The latex film had excellent thermal stability as was shown by TG and DTG analyses. The outstanding self-healing capability of latex film was attributed to the reversible photodimerization of coumarin groups and multiple hydrogen bonds. In addition, the water-oil repellent and mechanical properties of the latex films were improved as the CMA and UPyMA monomer dosage increased.

CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.

In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification and 97.69% and 98.05% in ternary classification, and on the SEED dataset, we achieved an accuracy of 98.63%, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

CATM: A Multi-Feature-Based Cross-Scale Attentional Convolutional EEG Emotion Recognition Model.

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency-space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency-space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence-arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence-arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.

Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images.

RATIONALE AND OBJECTIVES: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer. MATERIALS AND METHODS: A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DLUS and DLMRI, respectively), a multimodal deep learning (DLMRI+US+Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DLUS, DLMRI, combined bimodal (DLMRI+US), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves. RESULTS: A total of 588 patients with breast cancer participated in this study. The DLMRI+US+Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness. CONCLUSION: The DLMRI+US＋Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer.

Real-world data on ALK rearrangement test in Chinese advanced non-small cell lung cancer (RATICAL): a nationwide multicenter retrospective study.

BACKGROUND: Anaplastic lymphoma kinase (ALK) test in advanced non-small cell lung cancer (NSCLC) can help physicians provide target therapies for patients harboring ALK gene rearrangement. This study aimed to investigate the real-world test patterns and positive rates of ALK gene rearrangements in advanced NSCLC. METHODS: In this real-world study (ChiCTR2000030266), patients with advanced NSCLC who underwent an ALK rearrangement test in 30 medical centers in China between October 1, 2018 and December 31, 2019 were retrospectively analyzed. Interpretation training was conducted before the study was initiated. Quality controls were performed at participating centers using immunohistochemistry (IHC)-VENTANA-D5F3. The positive ALK gene rearrangement rate and consistency rate were calculated. The associated clinicopathological characteristics of ALK gene rearrangement were investigated as well. RESULTS: The overall ALK gene rearrangement rate was 6.7% in 23,689 patients with advanced NSCLC and 8.2% in 17,436 patients with advanced lung adenocarcinoma. The quality control analysis of IHC-VENTANA-D5F3 revealed an intra-hospital consistency rate of 98.2% (879/895) and an inter-hospital consistency rate of 99.2% (646/651). IHC-VENTANA-D5F3 was used in 53.6%, real-time polymerase chain reaction (RT-PCR) in 25.4%, next-generation sequencing (NGS) in 18.3%, and fluorescence in-situ hybridization (FISH) in 15.9% in the adenocarcinoma subgroup. For specimens tested with multiple methods, the consistency rates confirmed by IHC-VENTANA-D5F3 were 98.0% (822/839) for FISH, 98.7% (1,222/1,238) for NGS, and 91.3% (146/160) for RT-PCR. The overall ALK gene rearrangement rates were higher in females, patients of ≤ 35 years old, never smokers, tumor cellularity of > 50, and metastatic specimens used for testing in the total NSCLC population and adenocarcinoma subgroup (all P < 0.05). CONCLUSIONS: This study highlights the real-world variability and challenges of ALK test in advanced NSCLC, demonstrating a predominant use of IHC-VENTANA-D5F3 with high consistency and distinct clinicopathological features in ALK-positive patients. These findings underscore the need for a consensus on optimal test practices and support the development of refined ALK test strategies to enhance diagnostic accuracy and therapeutic decision-making in NSCLC.

A simple numerical simulation model can elucidate the key factors for designing a miniaturized ion trap mass spectrometer.

BACKGROUND: Miniature ion trap mass spectrometer enables mass-to-charge ratio analysis of ions via quadrupole field in a low vacuum environment. It plays an important role in on-site detection due to its portability and specificity. In order to gain a deeper understanding of the analysis mechanism of miniature ion trap mass spectrometers, a quadrupole MS ion trajectory numerical simulation model (QITNS) is established in this paper for ions trajectory calculation under the action of quadrupole field, exciting field and neutral gas molecule collision. Compared with the existing methods, the model in this paper is simpler and more direct, which effectively explored the effects of dipole excitation and quadrupole excitation on ion manipulation under high background pressure. RESULTS: The simulation results demonstrate that high RF amplitude, low auxiliary AC amplitude and quadrupole excitation can effectively improve the isolation resolution. Besides, it clarified the difference between the analysis mechanism of ion trap mass spectrometers under high background pressure (above 13.332 Pa) and absolute vacuum conditions. The relevant results are consistent with the conclusions of previous experiments and other theories, proving the applicability and accuracy of the proposed calculation model and solution method. SIGNIFICANCE: This research bears the guiding significance for further understanding the mechanism of quadrupole mass spectrometry as well as designing and developing miniature mass spectrometers.

The Role of TRAIL Signaling in Cancer: Searching for New Therapeutic Strategies.

Cancer continues to pose a significant threat to global health, with its status as a leading cause of death remaining unchallenged. Within the realm of cancer research, the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) stands out as a critical player, having been identified in the 1990s as the tenth member of the TNF family. This review examines the pivotal role of TRAIL in cancer biology, focusing on its ability to induce apoptosis in malignant cells through both endogenous and exogenous pathways. We provide an in-depth analysis of TRAIL's intracellular signaling and intercellular communication, underscoring its potential as a selective anticancer agent. Additionally, the review explores TRAIL's capacity to reshape the tumor microenvironment, thereby influencing cancer progression and response to therapy. With an eye towards future developments, we discuss the prospects of harnessing TRAIL's capabilities for the creation of tailored, precision-based cancer treatments, aiming to enhance efficacy and improve patient survival rates.

The Emerging Role of Ferroptosis in EBV-Associated Cancer: Implications for Cancer Therapy.

Ferroptosis is a novel and iron-dependent form of programmed cell death, which has been implicated in the pathogenesis of various human cancers. EBV is a well-recognized oncogenic virus that controls multiple signaling pathways within the host cell, including ferroptosis signaling. Recent studies show that inducing ferroptosis could be an efficient therapeutic strategy for EBV-associated tumors. This review will firstly describe the mechanism of ferroptosis, then summarize EBV infection and EBV-associated tumors, as well as the crosstalk between EBV infection and the ferroptosis signaling pathway, and finally discuss the role and potential application of ferroptosis-related reagents in EBV-associated tumors.

Dual-Mode Fluorescent/Intelligent Lateral Flow Immunoassay Based on Machine Learning Algorithm for Ultrasensitive Analysis of Chloroacetamide Herbicides.

Given the harmful effect of pesticide residues, it is essential to develop portable and accurate biosensors for the analysis of pesticides in agricultural products. In this paper, we demonstrated a dual-mode fluorescent/intelligent (DM-f/DM-i) lateral flow immunoassay (LFIA) for chloroacetamide herbicides, which utilized horseradish peroxidase-IgG conjugated time-resolved fluorescent nanoparticle probes as both a signal label and amplification tool. With the newly developed LFIA in the DM-f mode, the limits of detection (LODs) were 0.08 ng/mL of acetochlor, 0.29 ng/mL of metolachlor, 0.51 ng/mL of Propisochlor, and 0.13 ng/mL of their mixture. In the DM-i mode, machine learning (ML) algorithms were used for image segmentation, feature extraction, and correlation analysis to obtain multivariate fitted equations, which had high reliability in the regression model with R2 of 0.95 in the range of 2 × 102-2 × 105 pg/mL. Importantly, the practical applicability was successfully validated by determining chloroacetamide herbicides in the corn sample with good recovery rates (85.4 to 109.3%) that correlate well with the regression model. The newly developed dual-mode LFIA with reduced detection time (12 min) holds great potential for pesticide monitoring in equipment-limited environments using a portable test strip reader and laboratory conditions using ML algorithms.

Barbed arrow-like structure membrane with ultra-high rectification coefficient enables ultra-fast, highly-sensitive lateral-flow assay of cTnI.

Acute myocardial infarction (AMI) has become a public health disease threatening public life safety due to its high mortality. The lateral-flow assay (LFA) of a typical cardiac biomarker, troponin I (cTnI), is essential for the timely warnings of AMI. However, it is a challenge to achieve an ultra-fast and highly-sensitive assay for cTnI (hs-cTnI) using current LFA, due to the limited performance of chromatographic membranes. Here, we propose a barbed arrow-like structure membrane (BAS Mem), which enables the unidirectional, fast flow and low-residual of liquid. The liquid is rectified through the forces generated by the sidewalls of the barbed arrow-like grooves. The rectification coefficient of liquid flow on BAS Mem is 14.5 (highest to date). Using BAS Mem to replace the conventional chromatographic membrane, we prepare batches of lateral-flow strips and achieve LFA of cTnI within 240 s, with a limit of detection of 1.97 ng mL-1. The lateral-flow strips exhibit a specificity of 100%, a sensitivity of 93.3% in detecting 25 samples of suspected AMI patients. The lateral-flow strips show great performance in providing reliable results for clinical diagnosis, with the potential to provide early warnings for AMI.

Finite element analysis of a novel anatomical plate in posterolateral plateau fractures.

Objective: This study aims to analyze the biomechanical characteristics of posterolateral plateau fractures fixed by a novel anatomical plate using finite element analysis. Methods: A three-dimensional digital model of the full length of right tibiofibula was obtained by CT scanning. A posterolateral tibial plateau fracture model was then created. The acquired fracture model was assembled with 4 groups of internal fixations: Group A, novel anatomical plate; Group B, straight buttress plate; Group C, oblique T-shaped locking plate; Group D, two lag screws. Axial loads of 500, 1,000 and 1,500 N perpendicular to the horizontal plane were used to simulate the stress on the lateral plateau of a 65 kg person standing, walking and fast running. Results: Vertical displacements of the posterolateral fragments in each of the four groups gradually increased under loads from 500 N to 1,500 N. The maximum displacement of the fracture fragment in four groups were all located on the lateral side of the proximal part, and the displacement gradually decreased from the proximal part to the distal end. The maximum displacement values under the axial load of 1,500 N was in the following order: novel anatomical plate (1.2365 mm) < oblique T-shaped locking plate (1.314 mm) < two lag screws (1.3747 mm) < straight buttress plate (1.3932 mm). As the axial load increased, the stress value of the different internal fixation models gradually increased. The stress behavior of the same internal fixation model under different loads was similar. The maximum stress value under the axial load of 1,500 N was in the following order: novel anatomical plate (114.63 MPa) < oblique T-shaped locking plate (277.17 MPa) < two lag screws (236.75 MPa) < straight buttress plate (136.2 MPa). Conclusion: The patients with posterolateral plateau fractures fixed with a novel anatomical plate in standing, walking and fast running can achieve satisfactory biomechanical results, which lays the foundation for future applications. At the same time, clinical fracture types are often diverse and accompanied by damage to the soft tissue. Therefore, the ideal surgical approach and appropriate internal fixation must be selected based on the patient's injury condition.

DECODE: Contamination-Free Digital CRISPR Platform for Point-of-Care Detection of Viral DNA/RNA.

Rapid and precise nucleic acid testing at the point-of-care (POC) is essential for effective screening and management of infectious diseases. Current polymerase-based molecular diagnostics often suffer from potential cross-contamination issues, particularly in POC settings. Here, we introduce DECODE, a contamination-free nucleic acid detection platform integrating digital microfluidics (DMF) for nucleic acid extraction and a digital CRISPR amplification-free assay for pathogen detection. The digital CRISPR assay demonstrates sensitivity, detecting target DNA and RNA in the reaction mixture at concentrations of 10 and 5 copies/µL, respectively. Leveraging DMF-extracted samples enhances the performance of the digital CRISPR amplification-free assay. DECODE offers a sample-to-result workflow of 75 min using compact devices. Validation studies using clinical samples confirm DECODE's robust performance, achieving 100% sensitivity and specificity in detecting HPV18 from cervical epithelial cells and influenza A from nasal swabs. DECODE represents a versatile, contamination-free detection platform poised to enhance integrated public health surveillance efforts.

Exogenous iron caused osteocyte apoptosis, increased RANKL production, and stimulated bone resorption through oxidative stress in a murine model.

Iron overload is a risk factor for osteoporosis due to its oxidative toxicity. Previous studies have demonstrated that an excessive amount of iron increases osteocyte apoptosis and receptor activator of nuclear factor κ-B ligand (RANKL) production, which stimulates osteoclast differentiation in vitro. However, the effects of exogenous iron supplementation-induced iron overload on osteocytes in vivo and its role in iron overload-induced bone loss are unknown. This work aimed to develop an iron overloaded murine model of C57BL/6 mice by intraperitoneal administration of iron dextran for two months. The iron levels in various organs, bone, and serum, as well as the microstructure and strength of bone, apoptosis of osteocytes, oxidative stress in bone tissue, and bone formation and resorption, were assessed. The results showed that 2 months of exogenous iron supplementation significantly increased iron levels in the liver, spleen, kidney, bone tissue, and serum. Iron overload negatively affected bone microstructure and strength. Osteocyte apoptosis and empty lacunae rate were elevated by exogenous iron. Iron overload upregulated RANKL expression but had no significant impact on osteoprotegerin (OPG) and sclerostin levels. Static and dynamic histologic analyses and serum biochemical assay showed that iron overload increased bone resorption without significantly affecting bone formation. Exogenous iron promoted oxidative stress in osteocytes in vivo and in vitro. Additional supplementation of iron chelator (deferoxamine) or N-acetyl-l-cysteine (NAC) partially alleviated bone loss, osteocyte apoptosis, osteoclast formation, and oxidative stress due to iron overload. These findings, in line with prior in vitro studies, suggest that exogenous iron supplementation induces osteoclastogenesis and osteoporosis by promoting osteocyte apoptosis and RANKL production via oxidative stress.

Early onset stage III diabetic nephropathy in a child with Prader-Willi syndrome treated with dulaglutide: a case report.

Background: Prader-Willi syndrome (PWS) is a multisystem genetic disorder caused by chromosomal imprinting gene defects, with approximately 70% of cases resulting from paternal deletion of the chromosomal region 15. The main clinical features include severe infantile hypotonia, early-onset childhood obesity, hyperphagia, and underdeveloped external genitalia. As individuals with PWS age, they may exhibit irritability, social dysfunction, impaired gonadal development, and metabolic syndrome. Previous literature places the prevalence of type 2 diabetes mellitus (T2DM) in PWS at approximately 7-24%. Oxytocin is a neuropeptide secreted by the paraventricular (PVN) and supraoptic (SON) nuclei of the hypothalamus and regulates energy metabolism, which is involved in PWS. Due to age limitations, very few patients progress to diabetic nephropathy during childhood, and reports of typical diabetic nephropathy in PWS during childhood are extremely rare. Dulaglutide is a glucagon-like peptide-1 (GLP-1) receptor agonist which can be used in the treatment of T2DM. Case Description: This article reports a case of a child with PWS complicated by stage III diabetic nephropathy, providing a retrospective analysis of the diagnosis and treatment process, as well as a review of domestic and international literature, to enhance understanding of this condition. And this article provides a treatment idea for PWS patients with diabetic nephropathy. Conclusions: It is very important to enhance understanding of PWS. And we offer new diagnostic and possible therapeutic approaches for pediatric patients with diabetic nephropathy.

Radiomics-Based Machine Learning Classification Strategy for Characterization of Hepatocellular Carcinoma on Contrast-Enhanced Ultrasound in High-Risk Patients with LI-RADS Category M Nodules.

Objective Accurate differentiation within the LI-RADS category M (LR-M) between hepatocellular carcinoma (HCC) and non-HCC malignancies (mainly intrahepatic cholangiocarcinoma [CCA] and combined hepatocellular and cholangiocarcinoma [cHCC-CCA]) is an area of active investigation. We aimed to use radiomics-based machine learning classification strategy for differentiating HCC from CCA and cHCC-CCA on contrast-enhanced ultrasound (CEUS) images in high-risk patients with LR-M nodules. Methods A total of 159 high-risk patients with LR-M nodules (69 HCC and 90 CCA/cHCC-CCA) who underwent CEUS within 1 month before pathologic confirmation from January 2006 to December 2019 were retrospectively included (111 patients for training set and 48 for test set). The training set was used to build models, while the test set was used to compare models. For each observation, six CEUS images captured at predetermined time points (T1, peak enhancement after contrast injection; T2, 30 seconds; T3, 45 seconds; T4, 60 seconds; T5, 1-2 minutes; and T6, 2-3 minutes) were collected for tumor segmentation and selection of radiomics features, which included seven types of features: first-order statistics, shape (2D), gray-level co-occurrence matrix, gray-level size zone matrix, gray-level run length matrix, neighboring gray tone difference matrix, and gray-level dependence matrix. Clinical data and key radiomics features were employed to develop the clinical model, radiomics signature (RS), and combined RS-clinical (RS-C) model. The RS and RS-C model were built using the machine learning framework. The diagnostic performance of these three models was calculated and compared. Results Alpha-fetoprotein (AFP), CA19-9, enhancement pattern, and time of washout were included as independent factors for clinical model (all p < 0.05). Both the RS and RS-C model performed better than the clinical model in the test set (area under the curve [AUC] of 0.698 [0.571-0.812] for clinical model, 0.903 [0.830-0.970] for RS, and 0.912 [0.838-0.977] for the RS-C model; both p < 0.05). Conclusions Radiomics-based machine learning classifiers may be competent for differentiating HCC from CCA and cHCC-CCA in high-risk patients with LR-M nodules.