ObjectiveTo investigate the epidemiological and clinical characteristics of human bocavirus (HBoV) in hospitalized children with acute lower respiratory tract infection (ALRTI) at a single-center children’s hospital in Shanghai, thereby providing evidence for the diagnosis, treatment, and prevention of HBoV infection. MethodsA retrospective study was conducted on 19 537 hospitalized children with ALRTI at Shanghai Children’s Hospital from January 2021 to December 2023. Multiplex polymerase chain reaction (PCR) combined with capillary electrophoresis was used to detect HBoV and 12 other common respiratory viruses /atypical pathogens. The positive detection rate, demographic characteristics (sex, age), temporal distribution (year, season) of HBoV, as well as the clinical characteristics of severe and non-severe pneumonia were analyzed. ResultsThe overall HBoV-positive rate was 2.57% (503/19 537), with 59.44% (299/503) being single infections and 40.56% (204/503) being co-infections. The positive detection rate was significantly higher in boys than that in girls (2.78% vs 2.33%, χ²=3.88, P=0.049). The highest infection rate was observed in toddlers, followed by infants (χ²=379.57, P<0.001). The positive rate peaked in 2021 and reached its lowest point in 2023 (χ²=45.49, P<0.001), with epidemics mainly prevalent in summer and autumn. The main clinical symptoms were cough (90.06%, 453/503), fever (75.94%, 382/503), and wheezing (39.96%, 201/503). Children with severe pneumonia showed a higher incidence of wheezing compared with the non-severe group (P<0.001), while underlying diseases and co-infections had no significant association with disease severity (P>0.05). ConclusionHBoV was an important pathogen of ALRTI in children, predominantly affecting infants and toddlers, with higher susceptibility in boys and seasonal peaks in autumn and summer. The main clinical manifestations included cough, fever, and wheezing, with wheezing being more prevalent in children with severe pneumonia.

Background: Visceral white adipose tissue (vWAT) inflammation is a critical pathology of obesity-caused heart damage and is closely associated with adipocyte endoplasmic reticulum (ER) dysfunction. Serine (or cysteine) peptidase inhibitor, clade A, member 3C (Serpina3c) has been identified as an adipokine with anti-vWAT inflammatory effects. However, it remains unclear whether Serpina3c deficiency promotion of vWAT inflammation involves adipocyte ER dysfunction and whether it further contributes to heart damage in obesity. Methods: Wild type and Serpina3c knockout (Serpina3c–/–) mice were fed a high-fat diet (HFD) for 12 weeks. An adeno-associated virus (AAV) was injected locally into epididymal white adipose tissue (eWAT) of Serpina3c–/– mice to induce eWAT-adipocyte- specific overexpression of Serpina3c (AAV-Serpina3c) or knockdown of hypoxia-inducible factor 1α (AAV-shHIF1α). In vitro experiments were performed in 3T3-L1 adipocytes. Results: Serpina3c–/– mice exhibited more severe eWAT, serum and heart inflammation after HFD feeding. Consistently, these adverse phenotypes were mitigated in AAV-Serpina3c and AAV-shHIF1α mice. Mechanistically, ER oxidoreductase 1α (Ero1α) and protein disulfide isomerase (PDI) family members PDIA3 and PDIA4 were found to be target genes of HIF1α. In the obese mice, Serpina3c deficiency caused adipocyte more hypertrophy, and activated HIF1α-Ero1α/PDI mediated ER overoxidation and ER stress in eWAT. Subsequently, this led to increased adipocyte apoptosis and chemokine production and decreased adiponectin expression, which promoted macrophage infiltration and M1 polarization in eWAT, thus exacerbating eWAT inflammation and ultimately facilitating serum and distal heart inflammation. Conclusion These findings indicate that Serpina3c is a significant regulator of adipocyte ER redox homeostasis, thus highlighting Serpina3c as a potential therapeutic target for obesity-related eWAT inflammation and heart damage.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

ObjectiveThis study aimed to investigate whether dexmedetomidine （Dex） alleviates stress-induced mammary tumor progression in mice by inhibiting the TLR4/NF‑κB signaling pathway， and to examine its effects on anxiety‑depression‑like behaviors and stress hormone levels. MethodsAn orthotopic EMT6 mammary tumor model was established in mice， and chronic mild stress （CMS） was applied to induce anxiety‑depression‑like states. The mice were divided into six groups： naive， tumor， CMS， CMS+tumor， CMS+tumor+Dex， and CMS+tumor+Vehicle groups. Behavioral changes were evaluated using the open field test and tail suspension test. Plasma stress hormone levels were measured by high‑performance liquid chromatography. Western blot and RT‑qPCR were performed to detect the expression of TLR4， p‑NF‑κB p65， p‑IκBα， and inflammatory cytokines （IL‑6， IL‑1β， TNF‑α） in tumor tissues. In vitro， EMT6 cells were treated with cortisol to mimic the stress-related microenvironment， and the effects of Dex on the TLR4/NF‑κB pathway were assessed by CCK‑8， Western blot， and RT-qPCR. ResultsTumor‑bearing mice exhibited significant anxiety‑depression‑like behaviors （P＜0.05）， along with markedly elevated plasma levels of norepinephrine， epinephrine， and cortisol （P＜0.05）. Chronic stress further aggravated these behavioral abnormalities and promoted tumor growth （P＜0.05）. Dexmedetomidine intervention significantly improved anxiety‑depression‑like behaviors （P＜0.05） and lowered peripheral stress hormone levels （P＜0.05） in stress‑exposed tumor‑bearing mice. In vitro experiments demonstrated that cortisol treatment activated the TLR4/NF‑κB pathway （P＜0.001） and upregulated the expression of inflammatory cytokines （P＜0.05 in EMT6 cells， whereas dexmedetomidine effectively suppressed this activation （P＜0.001）. In the in vivo model， dexmedetomidine treatment downregulated the expression of TLR4， p‑NF‑κB p65， p‑IκBα， and pro‑inflammatory cytokines in tumor tissues （P＜0.001） and significantly inhibited tumor growth （P＜0.05）. ConclusionDexmedetomidine inhibits the TLR4/NF‑κB signaling pathway， alleviates stress‑induced inflammatory responses， improves anxiety- and depression‑ like behaviors， reduces stress hormone levels， and suppresses mammary tumor progression. These findings provide an experimental basis for understanding anxiety- and depression- associated breast tumor progression and suggest that Dexmedetomidine and the TLR4/NF-κB pathway may serve as potential therapeutic targets.

Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

Objective To investigate the epidemiological characteristics of Mycoplasma pneumoniae(MP)infection among pediatric inpatients with respiratory tract infections(RTIs)at Children's Hospital of Hebei Province,providing evidence for clinical diagnosis and treatment.Methods A retrospective analysis was conducted on 79 546 children hospitalized for RTIs between January 2016 and February 2024.Nasopharyngeal aspirates or deep sputum samples were collected,and polymerase chain reaction(PCR)was used to detect nucleic acids of 13 respiratory pathogens,including MP and adenovirus.The epidemiological trends across different years,seasons,genders,and age groups were analyzed.Results Among the 79 546 enrolled cases(male:47 437,59.6%;female:32 109,40.4%),the MP-positive rate was 17.7%(14 106/79 546),peaking in 2023(28.8%)and reaching the lowest in 2021(7.0%).Except for the period from July 2020 to March 2021 with exceptionally low MP positivity,epidemic peaks consistently occurred between August and February or March of the following year.Seasonal analysis revealed significantly higher MP positivity in autumn and winter compared to spring(P<0.001).Female children exhibited a higher MP-positive rate(20.1%,6444/32 109)than males(16.2%,7662/47 437)(P<0.001).The MP-positive rate increased with age:infancy(3.2%,849/26 741),toddlerhood(9.3%,1935/20 763),preschool(21.2%,3918/18 448),and school-age(54.5%,7404/13 594)(P<0.001).Co-infections with other respiratory pathogens were observed in 37.3%(5264/14 106)of MP-positive cases,with human rhinovirus(HRV)being the most frequent co-pathogen(43.3%,2279/5264 of mixed infections).Conclusion MP is a major pathogen of RTIs in hospitalized children at Children's Hospital of Hebei Province.Infections occur year-round but predominantly in autumn,with higher susceptibility in females and school-age children.Targeted preventive measures should be implemented during peak seasons to mitigate transmission risks.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

AIM To investigate the effects of Shuyu Pills combined with evolimus on the epithelial mesenchymal transformation of triple negative breast cancer cells 4T1 and MDA-MB-231 induced by TGF-β1.METHODS The 4T1 and MDA-MB-231 cells were divided into the blank group and the induction group to induce the epithelial mesenchymal transformation with TGF-β1 cytokine treatment,followed by the assignment into the model group,the Shuyu Pills group,the everolimus group and the Shuyu Pills combined with everolimus group.CCK8 method,plate cloning method,cell scratch test and Transewll test were used to detect the proliferation,cloning formation,migration and invasion ability of the cells whose expressions of E-cadherin,N-cadherin,Vimentin,MMP9,MMP2 and pathway proteins PTEN,PI3K,Akt and mTOR were detected by Western blot.RESULTS Compared with the blank group,the induction group displayed a cell morphological change from epithelioid to stromal,decreased expression of E-cadherin protein(P＜0.01);and increased protein expressions of N-cadherin and Vimentin(P＜0.05).Compared with the model group,each group intervened with the medicine displayed decreased proliferation,clone formation,migration and invasion ability of both kinds of cells(P＜0.01);increased protein expressions of PTEN and E-cadherin(P＜0.05,P＜0.01);and decreased protein expressions of PI3K,Akt,mTOR,N-cadherin,Vimentin,MMP9 and MMP2(P＜0.05,P＜0.01);and more significantly in the Shuyu Pills combined with evolimus group(P＜0.05,P＜0.01).CONCLUSION With a more ideal effect than the single uses in inhibiting the TGF-β1-induced epithelial mesenchymal transformation of triple-negative breast cancer cells,the combination use of Shuyu Pills and everolimus may work through the regulation of PI3K/Akt/mTOR signaling pathway.

Objective To systematically evaluate the research progress of risk prediction models for periprosthetic joint infection(PJI)after total joint arthroplasty(TJA),analyze the limitations of current researches,and propose optimized suggestions.Methods Chinese and English databases such as PubMed,Embase,Web of Science,Co-chrane Library,SinoMed,Wanfang Database,VIP Database,and CNKI were retrieved systematically.The re-trieved period was from the establishment of each database to August 31,2024.Two researchers independently screened literatures and extracted data according to the CHARMS checklist,and the risk of bias in the included studies was evaluated by the PROBAST tool.Results A total of 14 studies were included in this study,involving 17 prediction models.The most common predictors included history of diabetes mellitus,obesity(body mass index[BMI]≥30 kg/m2),advanced age(≥65 years old),history of traumatic fracture,and prolonged operation time(≥2 hours).All of the included studies had high risks of bias,mainly study subject selection bias(such as single-center sample)and statistical analysis bias(such as unadjusted confounding factors).Conclusion Most of the cur-rently published risk prediction models for PJA after TJA have good predictive performance,however,there are sig-nificant limitations in the research design,especially in the insufficient control of bias risk.Future research needs to focus on improving methodological design,including adoption of prospective multi-center studies,definition of standardized predictive variables,and sufficient adjustment of confounding factors.