Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective To explore the effect of influenza vaccination on the prevention of respiratory tract infection in preschool children. Methods The clinical data of 400 preschool children (1-6 years old) who were diagnosed with respiratory tract infection for the first time in department of pediatrics of Xi'an Third Hospital and second department of respiratory medicine of Xi'an Children's Hospital were retrospectively analyzed from January 2023 to December 2023, including acute bronchitis, upper respiratory tract infection and pneumonia. According to the actual influenza vaccination status, the patients were divided into vaccination group (n=210) and non-vaccination group (n=190). The incidence of respiratory tract infection was compared between both groups. The fever duration, average course of disease, hospitalization rate, clinical symptoms scores (fever, cough, nasal congestion, sore throat), inflammation indicators [C-reactive protein (CRP), white blood cell count (WBC), neutrophil percentage (NE%)] and recurrence rate after 6 months of follow-up were compared. Results The incidence of respiratory tract infection in the vaccination group was significantly lower than that in the non-vaccination group (21.43% vs 43.16%, P<0.05), and the hospitalization rate was significantly lower compared with that in the non-vaccination group (P<0.05). The scores of fever, cough, nasal congestion and sore throat were lower in the vaccination group than those in the non-vaccination group (P<0.05), and the CRP, WBC and NE% were significantly lower compared to the non-vaccination group (P<0.05). After 6 months of follow-up, the recurrence rate in the vaccination group was 11.11% (5/45), which was significantly lower than 26.83% (22/82) in the non-vaccination group (χ²=0.038, P=4.288<0.05). Conclusion Influenza vaccination can effectively reduce the incidence of respiratory tract infection in preschool children, relieve the symptoms and shorten the disease course after infection. Its preventive effect on influenza is particularly significant, suggesting the importance of strengthening influenza vaccination in preschool children.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

OBJECTIVES: To evaluate the effectiveness of single-balloon and double-balloon enteroscopy in diagnosing pediatric small bowel diseases and assess the diagnostic efficacy of computed tomography enterography (CTE) for small bowel diseases using enteroscopy as the reference standard. METHODS: Clinical data from 576 children who underwent enteroscopy at Hunan Children's Hospital between January 2017 and December 2023 were retrospectively collected. The children were categorized based on enteroscopy type into the single-balloon enteroscopy (SBE) group (n=457) and double-balloon enteroscopy (DBE) group (n=119), and the clinical data were compared between the two groups. The sensitivity and specificity of CTE for diagnosing small bowel diseases were evaluated using enteroscopy results as the standard. RESULTS: Among the 576 children, small bowel lesions were detected by enteroscopy in 274 children (47.6%).There was no significant difference in lesion detection rates or complication rates between the SBE and DBE groups (P>0.05), but the DBE group had deeper insertion, longer procedure time, and higher complete small bowel examination rate (P<0.05). The complication rate during enteroscopy was 4.3% (25/576), with 18 cases (3.1%) of mild complications and 7 cases (1.2%) of severe complications, which improved with symptomatic treatment, surgical, or endoscopic intervention. Among the 412 children who underwent CTE, the sensitivity and specificity for diagnosing small bowel diseases were 44.4% and 71.3%, respectively. CONCLUSIONS SBE and DBE have similar diagnostic efficacy for pediatric small bowel diseases, but DBE is preferred for suspected deep small bowel lesions and comprehensive small bowel examination. Enteroscopy in children demonstrates relatively good overall safety. CTE demonstrates relatively low sensitivity but comparatively high specificity for diagnosing small bowel diseases.
Retrospective Studies ; Treatment Outcome ; Double-Balloon Enteroscopy/statistics & numerical data* ; Single-Balloon Enteroscopy/statistics & numerical data* ; Humans ; Male ; Female ; Child ; Operative Time ; Tomography, X-Ray Computed/statistics & numerical data* ; Sensitivity and Specificity ; Intestine, Small/surgery* ; Intestinal Diseases/surgery*

Enamel demineralization, the formation of white spot lesions, is a common issue in clinical orthodontic treatment. The appearance of white spot lesions not only affects the texture and health of dental hard tissues but also impacts the health and aesthetics of teeth after orthodontic treatment. The prevention, diagnosis, and treatment of white spot lesions that occur throughout the orthodontic treatment process involve multiple dental specialties. This expert consensus will focus on providing guiding opinions on the management and prevention of white spot lesions during orthodontic treatment, advocating for proactive prevention, early detection, timely treatment, scientific follow-up, and multidisciplinary management of white spot lesions throughout the orthodontic process, thereby maintaining the dental health of patients during orthodontic treatment.
Humans ; Consensus ; Dental Caries/etiology* ; Dental Enamel/pathology* ; Tooth Demineralization/etiology* ; Tooth Remineralization

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

Objective:To investigate the reasons for the weak expression of RHCE gene in a patient whose mimicking anti-Ce combined with anti-Jkb caused cross-matching non-combination.Methods:ABO,Rh,and Kidd blood group antigens were identified by test tube method and capillary centrifugation.Antibody screening and antibody specificity identification were performed using saline,polybrene and antiglobulin in tri-media association with multispectral cells.RHCE gene sequencing and haploid analysis were performed by multiplex PCR technique and RHCE protein modeling was performed using Swiss-Model.Results:The serum of the patient contained anti-Ce mimicking autoantibodies along with anti-Jkb antibodies.c.48G＞C,c.150C＞T,c.178C＞A,c.201A＞G,c.203A＞G,and c.307C＞T mutations were detected in the RHCE triple-molecule sequencing.A 109 bp insertion sequence was found in intron 2,with fragment loss from intron 5-8.The Rh-group genotype was DCe/DCe,and phenotype was CCDee.Conclusion:Genotyping techniques can assist in deducing the molecular mechanisms of some weakly expressed RhC,c,E,and e in patients'sera to aid in the identification of difficult antibodies and thus ensure the safety of patients'blood transfusion.

ObjectiveThe traditional Chinese medicine Strychnos nux-vomica L. (SN) has the clinical effect of reducing swelling and relieving pain; however, SN is toxic due to its alkaloid components. Little is known about the endogenous metabolic changes induced by SN toxicity in rats and their potential effects on the metabolic dysregulation of intestinal microbiota. Therefore, toxicological investigation of SN is of great significance to its safety assessment. In this study, the toxic mechanisms of SN were explored using a combination of metabonomics and 16S rRNA gene sequencing. MethodsThe toxic dose, intensity, and target organ of SN were determined in rats using acute, cumulative, and subacute toxicity tests. UHPLC-MS was used to analyze the serum, liver, and renal samples of rats after intragastric SN administration. The decision tree and K Nearest Neighbor (KNN) model were established based on the bootstrap aggregation (bagging) algorithm to classify the omics data. After samples were extracted from rat feces, the high-throughput sequencing platform was used to analyze the 16S rRNA V3-V4 region of bacteria. ResultsThe bagging algorithm improved the accuracy of sample classification. Twelve biomarkers were identified, where their metabolic dysregulation may be responsible for SN toxicity in vivo. Several types of bacteria such as Bacteroidetes, Anaerostipes, Oscillospira and Bilophila, were demonstrated to be closely related to physiological indices of renal and liver function, indicating that SN-induced liver and kidney damage may be related to the disturbance of these intestinal bacteria. ConclusionThe toxicity mechanism of SN was revealed in vivo, which provides a scientific basis for the safe and rational clinical use of SN.

Objective To establish a high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)method for simultaneous determination of meropenem,linezolid,voriconazole and posaconazole in children with small volume of plasma,and to use the method in children therapeutic drug monitoring(TDM).Methods The plasma samples were precipitated protein by methanol.And then,the analytes were gradient eluted on an EVO-C18 column by HPLC-MS/MS with mobile phase consisted of water(0.1％formic acid)-acetonitrile(0.1％formic acid)at the flow rate of 0.5 mL·min 1.Ions were monitored in the multiple reaction monitoring(MRM)mode,using positive ion electrospray ionization(ESI).Results Meropenem had a good liner relationship in 0.5-64.0 μg·mL-1(r=0.999 2),0.2-25.6 μg·mL-1 for linezolid and voriconazole(r=0.999 2,r=0.999 9),and 0.1-12.8 μg·mL-1 for posaconazole(r=0.998 9).Accuracy,precision,matrix effect and stability studies all met the requirements.This method was fully verified,and applied to determine the plasma antimicrobial concentrations of 49 pediatric patients.Conclusion The method is simple,rapid,sensitive and suitable for children's plasma concentration monitoring of four antimicrobials.

Aim To evaluate the hypolipidemic effect of the total phenylpropanoid glycosides extracted from Ligustrum robustum (Roxb.) Blume (LRTPG) on hyperlipidemic golden hamsters and explore its regulatory effect on intestinal flora. Methods Sixty hamsters were randomly divided into a control group, a model group, a positive drug group, LRTPG-L group, LRTPG-M group, and LRTPG-H group. After the successful induction of the model by high-fat diet, the animals were continuously administered for four weeks, and their blood lipids and liver lipids were detected. The formed feces from the colorectal region of the hamsters in the control group, model group and LRTPG-H group were collected for 16S rDNA sequencing. Results LRTPG reduced serum TG, TC, LDL-C and liver TG, TC concentrations significantly in hyperlipidemic hamsters. The results of the intestinal microbiota sequencing showed that compared to the control group, LRTPG significantly decreased the relative abundance of the phylum Firmicutes and increased the relative abundance of the phylum Bacteroidetes and Verrucomicrobia (P < 0.01) at the phylum level. At the family level, LRTPG significantly increased the relative abundance of Christensenellaceae, Peptococcaceae, and Verrucomicrobiaceae (P < 0.05 or P < 0.01). At the genus level, LRTPG significantly increased the relative abundance of Oscillospira, Oscillibacter, Flavonifractor and Akkermansiaceae (P < 0.05 or P < 0.01). These changes in the flora were beneficial to the hypolipidemic effect of LRTPG. Conclusion LRTPG may exert its hypolipidemic effect by improving the intestinal flora disorder caused by a high-fat diet in golden hamsters.