ObjectiveTo understand the infection characteristics of multidrug-resistant organisms (MDROs) in hospitalized patients in a tertiary sentinel hospital in Shanghai, so as to provide an evidence for the development of targeted prevention and control measures. MethodsData of MDROs strains and corresponding medical records of some hospitalized patients in a hospital in Shanghai from 2021 to 2023 were collected, together with an analysis of the basic information, clinical treatment, underlying diseases and sources of sample collection. ResultsA total of 134 strains of MDROs isolated from hospitalized patients in this hospital were collected from 2021 to 2023 , including 63 strains of methicillin-resistant Staphylococcus aureus (MRSA), 57 strains of carbapenem-resistant Acinetobacter baumannii (CRAB), and 14 strains of carbapenem-resistant Klebsiella pneumoniae (CRKP). Of the 134 strains, 30 strains were found in 2021, 47 strains in 2022 and 57 strains in 2023. The male-to-female ratio of patients was 2.05∶1, with the highest percentage (70.90%) in the age group of 60‒<90 years. The primary diagnosis was mainly respiratory disease, with lung and respiratory tract as the cheif infection sites. There was no statistically significant difference in the distribution of strains between different genders and infection sites (P>0.05). However, the differences in the distribution of strains between different ages and primary diagnosis were statistically significant (P<0.05). Patients who were admitted to the intensive care unit (ICU), had urinary tract intubation, were not artery or vein intubated, were not on a ventilator, were not using immunosuppresants or hormones, and were not applying radiotherapy or chemotherapy were in the majority. There was no statistically significant difference in the distribution of strains for whether received radiotherapy or chemotherapy or not (P>0.05), while the differences in the distribution of strains with ICU admission history, urinary tract intubation, artery or vein intubation, ventilator use, and immunosuppresants or hormones use or not were statistically significant (all P<0.05). The type of specimen was mainly sputum, the hospitalized ward was mainly comprehensive ICU, the sampling time was mainly in the first quarter throughout the year, the number of underlying diseases was mainly between 1 to 2 kinds, the application of antibiotics ≥4 kinds, and those who didn’t receive any surgery recently accounted for the most. There were statistically significant differences in the distribution of strains between different specimen types, wards occupied and history of ICU stay (P<0.05), but no statistically significant difference in the distribution of strains between different sampling times, number of underlying diseases and types of antibiotics applied (P>0.05). ConclusionThe situation of prevention and control on MDROs in this hospital is still serious. Focus should be placed on high-risk factors’ and infection monitoring and preventive measures should be strengthened to reduce the incidence rate of MDROs infection.

A targeted metabolomics study was conducted on the bile acid profiles in the liver and feces of rats induced by a high-fat diet and intervened by ginsenoside Rb_1, along with the detection of FXR pathway gene expression in the liver, to explore and clarify its mechanism of action. The content of biochemical indicators in the serum were detected using an automatic biochemical analyzer. Hematoxylin and eosin(HE) staining and oil red O staining were used to detect pathological changes and lipid deposition in the liver. RT-PCR was used to detect the mRNA expression of FXR, small heterodimer partner(SHP), cholesterol 7 alpha-hydroxylase(CYP7A1), and sterol regulatory element-binding protein-1c(SREBP-1c) in the liver. Targeted bile acid metabolomics technology was employed to analyze changes in bile acid profiles in liver tissue and feces, and a correlation analysis was performed between key genes such as FXR, SHP, CYP7A1, SREBP-1c and differential bile acid metabolites. The results showed that ginsenoside Rb_1 significantly reduced the levels of total cholesterol(TC), triglycerides(TG), low-density lipoprotein cholesterol(LDL-C), and high-density lipoprotein cholesterol(HDL-C) in the serum, alleviated the large fat vacuoles and lipid deposition in the liver, increased the expression of FXR mRNA in the liver, and decreased the expression of SREBP-1c mRNA. The expression of CYP7A1 and SHP mRNA was increased, but the differences were not statistically significant. Targeted bile acid metabolomics showed that ginsenoside Rb_1 could restore the levels of 9 bile acids in the liver and 8 bile acids in the feces. Ginsenoside Rb_1 also increased the percentage of taurocholic acid(TCA) in the liver(56.78%) and the percentage of 12-ketolithocholic acid(12-KLCA) in the feces(26.10%). Pathway enrichment analysis revealed two pathways involved in bile acid metabolism: primary bile acid biosynthesis and taurine and hypotaurine metabolism. Correlation analysis showed that FXR, SHP, CYP7A1, and SREBP-1c were positively correlated with multiple differential bile acids. These results suggest that ginsenoside Rb_1 may intervene in lipid metabolism disorders induced by a high-fat diet by regulating the FXR pathway and modulating bile acid profiles in the liver and feces.
Animals ; Bile Acids and Salts/metabolism* ; Rats ; Ginsenosides/pharmacology* ; Male ; Receptors, Cytoplasmic and Nuclear/genetics* ; Liver/drug effects* ; Diet, High-Fat/adverse effects* ; Metabolomics ; Rats, Sprague-Dawley ; Feces/chemistry* ; Cholesterol 7-alpha-Hydroxylase/metabolism* ; Sterol Regulatory Element Binding Protein 1/genetics* ; Humans

Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Humans ; Electroencephalography/methods* ; Meditation ; Calibration ; Neural Networks, Computer ; Brain/physiology* ; Rest/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

Current studies on electroencephalogram (EEG) emotion recognition primarily concentrate on discrete stimulus paradigms under controlled laboratory settings, which cannot adequately represent the dynamic transition characteristics of emotional states during multi-context interactions. To address this issue, this paper proposes a novel method for emotion transition recognition that leverages a cross-modal feature fusion and global perception network (CFGPN). Firstly, an experimental paradigm encompassing six types of emotion transition scenarios was designed, and EEG and eye movement data were simultaneously collected from 20 participants, each annotated with dynamic continuous emotion labels. Subsequently, deep canonical correlation analysis integrated with a cross-modal attention mechanism was employed to fuse features from EEG and eye movement signals, resulting in multimodal feature vectors enriched with highly discriminative emotional information. These vectors are then input into a parallel hybrid architecture that combines convolutional neural networks (CNNs) and Transformers. The CNN is employed to capture local time-series features, whereas the Transformer leverages its robust global perception capabilities to effectively model long-range temporal dependencies, enabling accurate dynamic emotion transition recognition. The results demonstrate that the proposed method achieves the lowest mean square error in both valence and arousal recognition tasks on the dynamic emotion transition dataset and a classic multimodal emotion dataset. It exhibits superior recognition accuracy and stability when compared with five existing unimodal and six multimodal deep learning models. The approach enhances both adaptability and robustness in recognizing emotional state transitions in real-world scenarios, showing promising potential for applications in the field of biomedical engineering.
Humans ; Emotions/physiology* ; Electroencephalography ; Neural Networks, Computer ; Eye Movements ; Perception

The three-dimensional (3D) printed bone tissue repair guide scaffold is considered a promising method for treating bone defect repair. In this experiment, chitosan (CS), sodium alginate (SA), and mineralized collagen (MC) were combined and 3D printed to form scaffolds. The experimental results showed that the printability of the scaffold was improved with the increase of chitosan concentration. Infrared spectroscopy analysis confirmed that the scaffold formed a cross-linked network through electrostatic interaction between chitosan and sodium alginate under acidic conditions, and X-ray diffraction results showed the presence of characteristic peaks of hydroxyapatite, indicating the incorporation of mineralized collagen into the scaffold system. In the in vitro collagen release experiments, a weakly alkaline environment was found to accelerate the release rate of collagen, and the release amount increased significantly with a lower concentration of chitosan. Cell experiments showed that scaffolds loaded with mineralized collagen could significantly promote cell proliferation activity and alkaline phosphatase expression. The subcutaneous implantation experiment further verified the biocompatibility of the material, and the implantation of printed scaffolds did not cause significant inflammatory reactions. Histological analysis showed no abnormal pathological changes in the surrounding tissues. Therefore, incorporating mineralized collagen into sodium alginate/chitosan scaffolds is believed to be a new tissue engineering and regeneration strategy for achieving enhanced osteogenic differentiation through the slow release of collagen.
Chitosan/chemistry* ; Alginates/chemistry* ; Tissue Scaffolds/chemistry* ; Printing, Three-Dimensional ; Osteogenesis ; Collagen/chemistry* ; Cell Differentiation ; Animals ; Tissue Engineering/methods* ; Cell Proliferation ; Biocompatible Materials ; Glucuronic Acid/chemistry* ; Hexuronic Acids/chemistry*

OBJECTIVES: To assess the preliminary efficacy and safety of a dose-intensified C5VD regimen (cisplatin, 5-fluorouracil, vincristine, and doxorubicin) in children with locally advanced hepatoblastoma. METHODS: This prospective study enrolled 24 children with newly diagnosed, locally advanced hepatoblastoma who received the dose-intensified C5VD regimen at Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, and Shanghai Children's Hospital between January 2020 and December 2023. Clinical characteristics, treatment outcomes, and chemotherapy-related toxicities were analyzed. RESULTS: Of the 24 patients, 13 were male and 11 were female, with a median age at diagnosis of 18.7 months (range: 3.5-79.4 months). All patients achieved complete macroscopic resection of hepatic lesions without liver transplantation. Serum alpha-fetoprotein levels decreased significantly after two chemotherapy cycles. During a median follow-up of 38.4 months (range: 15.8-50.7 months), all patients maintained continuous complete remission, with 3-year event-free survival and overall survival rates of 100%. Across 144 chemotherapy cycles, the incidence rates of grade 3-4 neutropenia, thrombocytopenia, and infections were 97%, 77%, and 71%, respectively; no treatment-related deaths occurred. Notably, 5 patients (21%) developed Brock grade ≥3 hearing loss, of whom 1 required a hearing aid. CONCLUSIONS The dose-intensified C5VD regimen demonstrates significant efficacy with an overall favorable safety profile in the treatment of newly diagnosed, locally advanced pediatric hepatoblastoma. Grade 3-4 myelosuppression and infection are the predominant toxicities. However, high‑dose cisplatin-induced ototoxicity remains a concern, highlighting the need for improved otoprotective strategies.
Humans ; Hepatoblastoma/pathology* ; Male ; Female ; Infant ; Liver Neoplasms/pathology* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Child, Preschool ; Prospective Studies ; Doxorubicin/adverse effects* ; Child ; Cisplatin/adverse effects* ; Vincristine/adverse effects* ; Fluorouracil/adverse effects*

Computer-assisted methods for pathological image analysis can improve doctor's efficiency of image reading and diagnostic accuracy, effectively addressing the shortage of pathology diagnostic manpower. With the rapid development of artificial intelligence and digital pathology, deep learning technology has spurred a wealth of research in the field of histopathology. This article reviews the various applications of deep learning in digital pathological image analysis, such as pathological image segmentation, cancer auxiliary diagnosis, and cancer prognosis prediction, and discusses the challenges and solutions in its application. Furthermore, it predicts future trends in deep learning for pathological image analysis and proposes potential research directions.
Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods* ; Artificial Intelligence ; Neoplasms

Alzheimer's disease (AD) is characterized by cognitive and functional deterioration, with pathological features such as amyloid-beta (Aβ) aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein. Despite a thorough investigation, current treatments targeting the reduction of Aβ production, promotion of its clearance, and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations, posing a substantial obstacle in the development of drugs for AD. Recently, artificial intelligence (AI), computational biology (CB), and systems biology (SB) have emerged as promising methodologies in AD research. Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns, thereby enriching our comprehension of AD pathology. This paper provides a comprehensive examination of the utilization of AI, CB, and SB in the diagnosis of AD, including the use of imaging omics for early detection, drug discovery methods such as lecanemab, and complementary therapies like phototherapy. This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.