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.

Background::Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods::This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Na?ve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.Results::The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. Conclusions::Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Objective:To explore the effects of repeated transcranial magnetic stimulation(rTMS)at different stimulating sessions on upper limb motor function and brain functional connectivity in stroke patients,targeting on stimu-late the contralateral premotor cortex(PMC). Method:Sixty patients with upper limb motor dysfunction after ischemic stroke were randomly assigned to 6-week rTMS group,4-week rTMS group,2-week rTMS group or control group,each with 15 participants.Up-per limb Brunnstrom stage,Fugl-Meyer assessment for upper extremity(FMA-UE)and Wolf motor function test(WMFT)were assessed before and after treatment to evaluate behavioral outcomes.The functional connec-tivity was analyzed by resting state functional magnetic resonance imaging(rs-fMRI)with the contralateral PMC as the seed point. Result:After treatment,the FMA-UE and WMFT scores of the 2-week rTMS group were significantly differ-ent from those of the 4-week and 6-week rTMS groups(P＜0.05),but there was no significant difference be-tween the 4-week rTMS group and the 6-week rTMS group(P＞0.05).Functional connectivity analysis using contralateral PMC as a seed point showed that functional connections were enhanced between contralateral PMC and ipsilateral anterior central gyrus,as well as the contralateral middle temporal gyrus and precuneus af-ter rTMS treatment(P＜0.05). Conclusion:The 4-week rTMS group has the best duration-benefit compared with the 2-week rTMS group and the 6-week rTMS group.The probable mechanism was related to that rTMS treatment could reduce the ef-fect of interhemispheric inhibition on motor cortex,and then enhance the cortico-cortical functional connectivi-ty in the contralateral hemisphere effectively,resulting in the recovery of upper limb motor function.

Physical resilience is closely related to the health and function of the elderly. This paper reviews the assessment methods of physical resilience in the elderly, focusing on the application of stress test, Physical Resilience Instrument for Older Adults (PRIFOR), physical resilience trajectory, difference in expected recovery and frailties mismatch in the assessment of physical resilience, so as to provide reference for promoting healthy aging from the perspective of physical resilience.

The effect of porcine SIRT5 on replication of foot and mouth disease virus type O(FMDV-O)and the underlying regulatory mechanism were investigated.Western blot and RT-qPCR analyses were employed to monitor expression of endoge-nous SIRT5 in PK-15 cells infected with FMDV-O.Three pairs of SIRT5-specific siRNAs were synthesized.Changes to SIRT5 and FMDV-O protein and transcript levels,in addition to virus copy numbers,were measured by western blot and RT-qPCR analyses.PK-15 cells were transfected with a eukaryotic SIRT5 expression plasmid.Western blot and RT-qPCR analyses were used to explore the impact of SIRT5 overexpression on FMDV-O replication.Meanwhile,RT-qPCR analysis was used to detect the effect of SIRT5 overexpression on the mRNA expression levels of type I interferon-stimulated genes induced by SeV and FMDV-O.The results showed that expression of SIRT5 was up-regulated in PK-15 cells infected with FMDV-O and siRNA interfered with SIRT5 to inhibit FMDV-O replication.SIRT5 overexpression promoted FMDV-O replication.SIRT5 over-expression decreased mRNA expression levels of interferon-stimulated genes induced by SeV and FMDV-O.These results suggest that FMDV-O infection stimulated expression of SIRT5 in PK-15 cells,while SIRT5 promoted FMDV-O rep-lication by inhibiting production of type I interferon-stimula-ted genes.These findings provide a reference to further ex-plore the mechanism underlying the ability of porcine SIRT5 to promote FMDV-O replication.

Graft-versus-host disease(GVHD)reduces the clinical effect and life quality of patients after allogeneic hematopoietic stem cell transplantation(HSCT).Especially for steroid-resistant GVHD,it becomes essential to explore new prevention and treatment strategies.Rapamycin has shown certain clinical advantages in the prevention and treatment of acute and chronic GVHD by inhibiting the mTOR signal pathway.Furthermore,rapamycin exhibits the ability to regulate cell subsets,including T cells,B cell,dendritic cells and myeloid-derived suppressor cells,which elucidates the mechanism on effective preventing and treating GVHD.This article reviewed the roles of mTOR inhibitor-rapamycin on GVHD,and discussed how to optimize the usage of rapamycin.

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.

Objective To observe the effect of Danshen injection (DSI) on pulmonary fibrosis in silicosis mice, and to analyze the differential metabolic pathway on pulmonary fibrosis in silicosis using DSI by urine metabolomics. Methods The specific pathogen free C57BL/6J mice were randomly divided into control group, silicosis model group, DSI prevention group and DSI treatment group. The mice in the last three groups were given 1 mL silica suspension with a mass concentration of 50 g/L by the one-time non-exposed tracheal method, and the mice in the control group were not given any treatment. Subsequently, mice in the DSI prevention group and the DSI treatment group were given intraperitoneal injection of DSI with a dose of 5 mL/kg body weight from 24 hours after exposure to dust and from the 29th day after exposure to dust, respectively, once per day until the 56th day after exposure. Mice in the other two groups were not treated. After DSI intervention, the lung histopathological changes of mice in all groups were evaluated. The components of mouse urine metabolites were analyzed using ultra-high performance liquid chromatography-quadrupole-time-of-fight mass spectrometry method. Human Metabolome Database was used to screen the potential differential metabolites (DMs). The related metabolic pathways were analyzed using MetaboAnanlyst 5.0 Web analytics platform. Results The result of hematoxylin-eosin staining and Van Gieson staining of mouse lung tissues showed that the pulmonary alveolar structure destroyed, typical fibrotic nodules appeared, collagen fiber deposition increased, and clumpy accumulation in the silicosis model group, compared with the control group. Compared with the silicosis model group, the degree of pulmonary alveolar inflammation and fibrosis in the lung tissues of mice in the DSI prevention group was obviously reduced to close to the control group, while pulmonary alveolar inflammation and fibrosis in the lung tissues of mice in the DSI treatment group were also reduced, although the outcome was not as good as that in the DSI prevention group. The result of urine metabolomics analysis identified four DMs in the model group and control group, seven DMs were identified in the DSI prevention group and silicosis model group, seven DMs were identified in the DSI treatment group and silicosis model group. A total of three DMs pathways related to pulmonary fibrosis in silicosis model group and the protective effect of DSI prevention group were identified, including D-arginine and D-ornithine metabolism, folic acid biosynthesis and metabolism, pantothenate and succinyl coenzyme A biosynthesis pathways (all P<0.01). Conclusion DSI treatment in any time point can interfere the process of pulmonary fibrosis in the silicosis mice, while the interference is more effective in the DSI group treated right after dust-exposure. DSI interferes with the urinary metabolism pathway of silicosis mice, and the D-arginine and D-ornithine metabolism, folic acid biosynthesis and metabolism, pantothenate and succinyl coenzyme A biosynthesis pathways may participate in the inhibiting process of early pulmonary fibrosis in silicosis mice by DSI.