Loop-mediated isothermal amplification (LAMP) is a novel method for nucleic acid detection known for its isothermal properties, high efficiency, sensitivity, and specificity. LAMP employs 4 to 6 primers targeting 6 to 8 regions of the desired sequence, allowing for amplification at temperatures between 60 and 65°C and the production of up to 10⁹ copies within a single hour. The product can be monitored by various methods such as turbidimetry, fluorometry, and colorimetry. However, it faces limitations such as the risk of non-specific amplification, challenges in primer design, unsuitability for short gene sequences, and difficulty in multiplexing. Recent advancements in polymerase and primer design have enhanced the speed and convenience of the LAMP reaction. Additionally, integrating LAMP with technologies like rolling circle amplification (RCA), recombinase polymerase amplification (RPA), and CRISPR-Cas systems has enhanced its efficiency. The combination of LAMP with various biosensors has enabled real-time analysis, broadening its application in point-of-care testing (POCT). Microfluidic technology has further facilitated the automation and miniaturization of LAMP assays, allowing for the simultaneous detection of multiple targets and preventing contamination. This review highlights advancements in LAMP, focusing on primer design, polymerase engineering, and its integration with other technologies. Continuous improvements and integration of LAMP with complementary technologies have significantly enhanced its diagnostic capabilities, making it a robust tool for rapid, sensitive, and specific nucleic acid detection with promising implications for healthcare, agriculture, and environmental monitoring.

Background: Available literature has reported the association of Helicobacter pylori (H pylori) infection with inflammatory bowel disease (IBD) in adults. However, only a few studies have addressed the disease in children. Aim: To ascertain the correlation of H pylori infection with IBD among children. Methods: The aim of this systematic review and meta-analysis is to assess the association between H pylori infection and IBD in children. We searched databases including Cochrane, EMBASE, Google Scholar, PubMed, Medline, and Web of Science to select relevant studies. Ultimately, based on predetermined inclusion criteria, we included 6 studies that met the requirements. Review Manager and Stata software were used to extract and analyze the data from the relevant studies. In the methods, we employed both qualitative and quantitative approaches for comprehensive analysis. Qualitative analysis involved describing study designs, sample characteristics, and results, while quantitative analysis involved statistical tests such as calculating pooled risk ratios and 95% confidence intervals to evaluate the association between H pylori infection and IBD in children. Lastly, by combining the results of the individual studies, our objective is to provide a comprehensive understanding of the relationship between H pylori infection and IBD in children. Results: In totality, we involved 2236 participants that were recruited in 6 studies. We detected no significant difference in H pylori prevalence (9.8% vs 12.7%, P = .12) by comparing the children IBD group to controls. Among the IBD children, we estimated odds ratio (OR) of H pylori infection to 0.62 [(95% confidence interval (CI) of 0.34–1.12)]. In children suffering from ulcerative colitis (UC) and Crohn disease (CD), the H pylori infection rates were higher than in those with IBD-unclassified (IBDU).When analyzed stratified by disease of study design, In CD group [OR = 1.42, 95% CI: 0.72–2.80)] (I ² = 0%, P = .64). but no significant difference in CD group. Conclusions: No correlation was found between H pylori infection and the occurrence of IBD in children.

BACKGROUND Video capsule endoscopy (VCE) is a noninvasive technique used to examine small bowel abnormalities in both adults and children. However, manual review of VCE images is time-consuming and labor-intensive, making it crucial to develop deep learning methods to assist in image analysis. AIM To employ deep learning models for the automatic classification of small bowel lesions using pediatric VCE images. METHODS We retrospectively analyzed VCE images from 162 pediatric patients who underwent VCE between January 2021 and December 2023 at the Children's Hospital of Nanjing Medical University. A total of 2298 high-resolution images were extracted, including normal mucosa and lesions (erosions/erythema, ulcers, and polyps). The images were split into training and test datasets in a 4:1 ratio. Four deep learning models: DenseNet121, Visual geometry group-16, ResNet50, and vision transformer were trained using 5-fold cross-validation, with hyperparameters adjusted for optimal classification performance. The models were evaluated based on accuracy, precision, recall, F1-score, and area under the receiver operating curve (AU-ROC). Lesion visualization was performed using gradient-weighted class activation mapping. RESULTS Abdominal pain was the most common indication for VCE, accounting for 62% of cases, followed by diarrhea, vomiting, and gastrointestinal bleeding. Abnormal lesions were detected in 93 children, with 38 diagnosed with inflammatory bowel disease. Among the deep learning models, DenseNet121 and ResNet50 demonstrated excellent classification performance, achieving accuracies of 90.6% [95% confidence interval (CI): 89.2-92.0] and 90.5% (95%CI: 89.9-91.2), respectively. The AU-ROC values for these models were 93.7% (95%CI: 92.9-94.5) for DenseNet121 and 93.4% (95%CI: 93.1-93.8) for ResNet50. CONCLUSION Our deep learning-based diagnostic tool developed in this study effectively classified lesions in pediatric VCE images, contributing to more accurate diagnoses and increased diagnostic efficiency.

Original Article Artificial intelligence-powered early identification of refractory constipation in children Yi-Hsuan Huang, Ruixuan Wan, Yan Yang, Yu Jin, Qian Lin, Zhifeng Liu, Yan Lu

Background: Children with intractable functional constipation (IFC) who are refractory to traditional pharmacological intervention develop severe symptoms that can persist even in adulthood, resulting in a substantial deterioration in their quality of life. In order to better manage IFC patients, efficient subtyping of IFC into its three subtypes, normal transit constipation (NTC), outlet obstruction constipation (OOC), and slow transit constipation (STC), at early stages is crucial. With advancements in technology, machine learning can classify IFC early through the use of validated questionnaires and the different serum concentrations of gastrointestinal motility-related hormones.Method: A hundred and one children with IFC and 50 controls were enrolled in this study. Three supervised machine-learning methods, support vector machine, random forest, and light gradient boosting machine (LGBM), were used to classify children with IFC into the three subtypes based on their symptom severity, self-efficacy, and quality of life which were quantified using certified questionnaires and their serum concentrations of the gastrointestinal hormones evaluated with enzyme-linked immunosorbent assay. The accuracy of machine learning subtyping was evaluated with respect to radiopaque markers.Results: Of 101 IFC patients, 37 had NTC, 49 had OOC, and 15 had STC. The variables significant for IFC subtype classification, according to SelectKBest, were stool frequency, the satisfaction domain of the Patient Assessment of Constipation Quality of Life questionnaire (PAC-QOL), the emotional self-efficacy for Functional Constipation questionnaire (SEFCQ), motilin serum concentration, and vasoactive intestinal peptide serum concentration. Among the three models, the LGBM model demonstrated an accuracy of 83.8%, a precision of 84.5%, a recall of 83.6%, a f1-score of 83.4%, and an area under the receiver operating characteristic curve (AUROC) of 0.89 in discriminating IFC subtypes.Conclusion: Using clinical characteristics measured by certified questionnaires and serum concentrations of the gastrointestinal hormones, machine learning can efficiently classify pediatric IFC into its three subtypes. Of the three models tested, the LGBM model is the most accurate model for the classification of IFC, with an accuracy of 83.8%, demonstrating that machine learning is an efficient tool for the management of IFC in children.

Introduction: Roxadustat, an inhibitor of hypoxia-inducible factor prolyl hydroxylase, has therapeutic potential for colitis but is limited clinically by poor water solubility and rapid gastrointestinal absorption. This study aimed to develop a pH-responsive, intestinal-targeted nanocarrier to enhance its oral bioavailability and anti-colitis efficacy. Methods: Roxadustat-loaded liposomes (ROX-LP) were prepared via thin-film hydration with DSPG-PEG2000, cholesterol, and sodium cholate, then coated with Kollicoat MAE 100 P to form ROX-MAE@LP. The formulation was evaluated for physicochemical properties, in vitro release, cellular uptake, pharmacokinetics, and in vivo therapeutic efficacy. Results: ROX-MAE@LP showed excellent encapsulation and uniform spherical morphology, with pH-dependent release (markedly increased at pH 6.8 and 7.4). Enhanced cellular uptake was observed in Caco-2 cells under intestinal pH-mimicking conditions. In rats, the oral bioavailability of ROX-LP (150.32±9.22%) and ROX-MAE@LP (255.36±12.82%) was improved compared with roxadustat suspension, and ROX-MAE@LP had a prolonged mean residence time (14.66±1.97 h). In a murine dextran sulfate sodium-induced colitis model, ROX-MAE@LP alleviated inflammation, downregulated pro-inflammatory cytokines, and promoted mucosal repair, outperforming ROX-LP and free roxadustat. Discussion: The pH-responsive coating and intestinal-targeted property of ROX-MAE@LP effectively address the poor solubility and rapid absorption of roxadustat, enhancing local intestinal concentration and therapeutic effect in colitis, showing high potential for oral inflammatory bowel disease therapy. Conclusion: ROX-MAE@LP is an effective intestine-targeted delivery system that improves roxadustat’s solubility, bioavailability and therapeutic outcomes in inflammatory bowel disease, while offering additional options for the development of novel roxadustat-based preparations and pHresponsive nanocarriers.