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.

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.

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.

OBJECTIVE To explore the in vitro and in vivo inhibitory effects and mechanism of metformin on the malignant biological behavior of esophageal squamous cell carcinoma （ESCC） cells by the hypoxia inducible factor-1α （HIF-1α）/interleukin-8 （IL-8） signaling pathway. METHODS Human ESCC TE1 cells were assigned into blank group， metformin low-， medium-， and high-dose groups （0.5， 1， 2 mmol/L）， IDF-11774 （HIF-1α inhibitor） group （20 μmol/L）， and high-dose metformin+HIF-1α activator dimethyloxalylglycine （DMOG） group. After 24 h treatment， cell proliferation [measured by the positive rate of 5-ethynyl- 2′-deoxyuridine （EdU） and optical density at 450 nm （OD450 value）]， apoptosis， invasion and migration as well as mRNA expressions of proliferating cell nuclear antigen （PCNA）， Bcl-2 interacting mediator of cell death （Bim）， migration and invasion enhancer 1 （MIEN1）， and matrix metalloproteinase-9 （MMP-9）， and protein expressions of HIF-1α and IL-8 in the cells were detected. The xenograft tumor model of nude mice was established. Thirty nude mice were randomly divided into blank group， metformin low-， medium-， and high-dose groups （i.g. administration of metformin 62.5， 125， 250 mg/kg+i.p. administration of equal volume of normal saline）， IDF-11774 group （i.g. administration of 50 mg/kg IDF-11774+i.p. administration of equal volume of normal saline） and high-dose metformin+DMOG group （i.g. administration of metformin 250 mg/kg+i.p. administration of DMOG 250 mg/kg）， with 5 mice in each group. They were given relevant medicine， once a day， for 4 consecutive weeks； the mass and volume of the tumor and protein expressions of HIF-1α and IL-8 in the tumor tissue were determined. RESULTS The EdU positive rate， OD450 value， cell invasion number， scratch healing rate， mRNA expressions of PCNA， MIEN1 and MMP-9， protein expressions of HIF-1α and IL-8， as well as the mass and volume of transplanted tumors and protein expressions of HIF-1α and IL-8 in tumor tissues were decreased by metformin in concentration/dose-dependent manner （P＜0.05）. Additionally，metformin increased the apoptosis rate and mRNA expression of Bim in cells （P＜0.05）. The trend of changes in corresponding indicators in the IDF-11774 group was consistent with that in the metformin groups， whereas DMOG could significantly attenuate the aforementioned effects of high-concentration/high-dose metformin （P＜0.05）. CONCLUSIONS Metformin can inhibit the proliferation， invasion， migration of TE1 cells， and tumor growth of nude mice， and induce cell apoptosis， the mechanism of which may be related to the inhibition of HIF-1α/IL-8 signaling pathway.

This study aims to investigate the scientificity and efficacy of the compatibility of Jinlingzi San from pharmacokinetics. Liquid chromatography-tandem mass spectrometry(LC-MS/MS) was utilized to determine the plasma concentrations of the active components: toosendanin, tetrahydropalmatine A, and tetrahydropalmatine B at various time points following the gavage of Jinlingzi San and its single medicines in rats. Subsequently, WinNonlin was employed to calculate pertinent pharmacokinetic parameters. The pharmacokinetic parameters in rat plasma were compared between the single medicines and the compound formula of Jinlingzi San. It was discovered that the area under the curve(AUC_(all)) and peak concentrations(C_(max)) of tetrahydropalmatine A, and tetrahydropalmatine B were significantly elevated in the compound formula group compared with the single medicine groups. Conversely, the AUC_(all )and C_(max) of toosendanin notably decreased. Furthermore, the compound formula group had longer mean residence time(MRT) and lower apparent clearance(CL/F) of all three active ingredients than the single medicine groups(P<0.05). These findings indicated that Jinlingzi San enhanced the absorption of tetrahydropalmatine A and tetrahydropalmatine B in vivo, facilitating their pharmacological actions. Concurrently, it inhibited the absorption of toosendanin, thereby preventing potential toxic reactions. Moreover, the compatibility prolonged the residence time of the active ingredients in the body. This study provides a reference for exploring the compatibility rationality of Jinlingzi San.
Animals ; Rats ; Tandem Mass Spectrometry/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Chromatography, Liquid/methods* ; Berberine Alkaloids/blood* ; Liquid Chromatography-Mass Spectrometry

Jiuwei Xifeng Granules have become a Chinese patent medicine in the market. Because the formula contains Os Draconis, a top-level protected fossil of ancient organisms, the formula was to be improved by replacing Os Draconis with Ostreae Concha. To evaluate whether the improved formula has the same effectiveness and safety as the original formula, a randomized, double-blind, parallel-controlled, equivalence clinical trial was conducted. This study enrolled 288 tic disorder(TD) of children and assigned them into two groups in 1∶1. The treatment group and control group took the modified formula and original formula, respectively. The treatment lasted for 6 weeks, and follow-up visits were conducted at weeks 2, 4, and 6. The primary efficacy endpoint was the difference in Yale global tic severity scale(YGTSS)-total tic severity(TTS) score from baseline after 6 weeks of treatment. The results showed that after 6 weeks of treatment, the declines in YGTSS-TSS score showed no statistically significant difference between the two groups. The difference in YGTSS-TSS score(treatment group-control group) and the 95%CI of the full analysis set(FAS) were-0.17[-1.42, 1.08] and those of per-protocol set(PPS) were 0.29[-0.97, 1.56], which were within the equivalence boundary [-3, 3]. The equivalence test was therefore concluded. The two groups showed no significant differences in the secondary efficacy endpoints of effective rate for TD, total score and factor scores of YGTSS, clinical global impressions-severity(CGI-S) score, traditional Chinese medicine(TCM) response rate, or symptom disappearance rate, and thus a complete evidence chain with the primary outcome was formed. A total of 6 adverse reactions were reported, including 4(2.82%) cases in the treatment group and 2(1.41%) cases in the control group, which showed no statistically significant difference between the two groups. No serious suspected unexpected adverse reactions were reported, and no laboratory test results indicated serious clinically significant abnormalities. The results support the replacement of Os Draconis by Ostreae Concha in the original formula, and the efficacy and safety of the modified formula are consistent with those of the original formula.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Male ; Double-Blind Method ; Drugs, Chinese Herbal/therapeutic use* ; Tic Disorders/drug therapy* ; Treatment Outcome

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

BACKGROUND: Spinal cord injury (SCI) survivors often experience constipation, which contributes to a reduced sense of well-being and a lower quality of life. Acupressure offers a non-pharmacological and non-invasive alternative therapy for treating constipation. OBJECTIVE: This study examined the effects of home-based acupressure on constipation and subjective well-being among SCI survivors. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: This randomized controlled trial randomly assigned 80 adults from Hong Kong with SCI to two study groups. Using a video demonstration filmed by a registered traditional Chinese medicine practitioner, the intervention group performed home-based acupressure (self-administered or caregiver-assisted) twice daily, 15 min/session, for 10 consecutive days. The control group performed manual light touching of the abdomen with the same frequency and duration as the intervention group. Both groups received defecation education through a structured booklet. MAIN OUTCOMES MEASURES: The primary outcome was constipation severity. Secondary outcomes included bowel habits, psychological well-being, and quality of life. Focus group interviews were conducted after the intervention to collect subjective feedback from participants. RESULTS: Significant group-by-time interaction effects on constipation severity (P = 0.005) and quality of life (P = 0.001) revealed that home-based acupressure produced better results than the control. These treatment effects persisted at the one-month follow-up and continued to have a large effect size (Cohen's d > 0.8). Compared to the control group, the acupressure group also had improvements in anxiety (Cohen's d = 0.69) and depression (Cohen's d = 0.72) at the end of the intervention period. Three qualitative categories were identified from the focus group interviews: improvements in bowel function and management; reduced psychological distress following relief from constipation; and acceptability of home-based acupressure. CONCLUSION: Acupressure effectively relieves constipation, enhances psychological well-being, and improves quality of life in people with SCI. These data provide novel evidence supporting the use of home-based acupressure as an acceptable and effective therapy for treating constipation after SCI. TRIAL REGISTRATION ClinicalTrials.gov (NCT05558657). Please cite this article as: Li MQ, Li Y, Lam W, Yeung WF, Ho YS, Li JY, Sun TC, Yuen S, Hu YL, Yorke J. Home-based acupressure for managing constipation and subjective well-being in spinal cord injury survivors: A randomized controlled trial. J Integr Med. 2025; 23(6):660-669.
Humans ; Acupressure/methods* ; Constipation/psychology* ; Male ; Female ; Spinal Cord Injuries/complications* ; Middle Aged ; Adult ; Quality of Life ; Aged