Background Lead is a typical persistent environmental pollutant that can accumulate in bones for decades. During pregnancy, alterations in calcium metabolism promote the mobilization of bone lead, resulting in secondary exposure; however, the mechanisms by which pregnancy-associated bone lead mobilization affects maternal renal function remain unclear. Objective To investigate the role of mitochondrial dysfunction in pregnancy-related bone lead mobilization-induced renal injury. Methods Newly weaned female Wistar rats were randomly assigned to a control or a lead-exposed group administered either 0.05% sodium acetate or 0.05% lead acetate in drinking water. Following a 4-week lead exposure and a 4-week washout period, the females were co-housed with healthy age-matched males for mating. Rats were sacrificed at early (gestational day 3) and late (gestational day 17) pregnancystages, respectively. Renal histopathology was assessed using hematoxylin and eosin staining staining. Mitochondria-related indicators, including oxidative stress, inflammatory responses, and energy metabolism, were measured. Differential metabolites were identified using serum metabolomics. Results Renal injury in the lead-exposed pregnant rats progressed in a time-dependent manner, characterized by degeneration of proximal tubular epithelial cells, glomerular hyaline changes, and interstitial inflammatory cell infiltration. Repeated measures ANOVA indicated a significant interaction between the treatment factor (lead exposure) and the temporal factor (gestational stage) on renal injury (P<0.001). Further analysis of mitochondrial function-related indicators in late-pregnancy renal tissue revealed that the lead exposure group exhibited significantly increased levels of malondialdehyde (MDA) and reactive oxygen species (ROS) (P<0.05), accompanied by a reduction in superoxide dismutase (SOD) and reduced glutathione (GSH) activities (P<0.05); regarding inflammatory markers, levels of interleukin-18 (IL-18) and interleukin-1β (IL-1β) were elevated (P<0.01), whereas interleukin-33 (IL-33) was decreased in the lead-exposed group (P<0.05); energy metabolism-related indicators, including adenosine triphosphate (ATP) level, Na⁺-K⁺-ATPase and Ca²⁺-Mg²⁺-ATPase activities, and mitochondrial respiratory chain complexes I, III, and V activities, were significantly reduced (P<0.05) in the lead-exposed gorup. The typical differential metabolite N-methylisoleucine, identified through serum metabolomics analysis, was negatively correlated with blood lead levels, kidney injury scores, and IL-1β, while positively correlated with catalase (CAT) activity and Ca²⁺-Mg²⁺-ATPase. Conclusions Mitochondrial dysfunction may play a critical role in renal injury induced by bone lead mobilization during late gestation.

ObjectiveTo explore the mechanism of Hei Xiaoyaosan in improving the cognitive function in Alzheimer's disease （AD） from cell apoptosis mediated by the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/nuclear factor kappa B （NF-κB） signaling pathway. MethodsFour-month-old SD male rats were randomly assigned into a blank group， a sham group， a model group， a donepezil hydrochloride （0.45 mg·kg^-1） group， and high-， medium-， and low-dose （15.30， 7.65， and 3.82 g·kg^-1， respectively） Hei Xiaoyaosan groups， with 10 rats in each group. The sham group received bilateral hippocampal injection of 1 μL normal saline， while the other groups received bilateral hippocampal injection of 1 μL beta-amyloid 1-42 （Aβ_1-42） solution for the modeling of AD. Rats were administrated with corresponding agents once a day for 42 consecutive days. The Morris water maze test was carried out to assess the learning and memory abilities of rats. Hematoxylin-eosin staining was employed to observe pathological changes in the hippocampus of rats. Enzyme-linked immunosorbent assay was employed to measure the levels of cysteinyl aspartate-specific proteinase-3 （Caspase-3）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax）. Western blot was employed to determine the protein levels of PI3K， Akt， and NF-κB. A cell model of AD was established by co-culturing Aβ_1-42 and PC12 cells in vitro. Cell viability and apoptosis were detected by the cell-counting kit 8 （CCK-8） assay and flow cytometry （FC）， respectively. ResultsAnimal experiments showed that compared with the blank group， the model group had a prolonged escape latency （P<0.01）， a reduced number of crossing platforms （P<0.01）， disarrangement and a reduced number of hippocampal neurons， up-regulated expression of Bax and Caspase-3， down-regulated expression of Bcl-2 （P<0.01）， decreased p-PI3K/PI3K and p-Akt/Akt levels， and an increased p-NF-κB/NF-κB level （P<0.01）. Compared with the model group， donepezil hydrochloride and high- and medium-dose Hei Xiaoyaosan shortened the escape latency and increased the number of crossing platforms （P<0.05， P<0.01）， improved the arrangement and increased the number of hippocampal neurons， down-regulated the expression levels of Bax and Caspase-3， up-reguated the expression level of Bcl-2 （P<0.05， P<0.01）， increased the p-PI3K/PI3K and p-Akt/Akt levels （P<0.05， P<0.01）， and reduced the p-NF-κB/NF-κB level （P<0.05， P<0.01）. Cell experiments showed that compared with the blank group， the model group exhibited an increased apoptosis rate （P<0.01）. Compared with the model group， the serum containing Hei Xiaoyaosan at various doses improved the cell viability （P<0.01）， and the serum containing Hei Xiaoyaosan at the high dose decreased the cell apoptosis （P<0.01）. ConclusionHei Xiaoyaosan may improve the learning and memory abilities of AD model rats by regulating cell apoptosis， while increasing the vitality and reducing the apoptosis rate of AD model cells via the PI3K/Akt/NF-κB signaling pathway.

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.

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

Objective To investigate the mechanism by which angiopoietin-like protein 8(ANGPTL8)regulates vascular smooth muscle cell(VSMCs)apoptosis.Methods An in vitro abdominal aortic aneurysm cell model was established by stimulating human VSMCs(HUSMCs)with angiotensin Ⅱ(AngⅡ).Stable ANGPTL8 knockdown and over-expression VSMC cell strains were generated using lentiviral transfection.TUNEL staining was used to de-tect apoptosis.Western blot analysis was performed to measure the protein expression of ANGPTL8,caspase9,caspase3,Bcl-2,Bax,p53,and PUMA,while RT-qPCR was used to assess mRNA expression of ANGPTL8,Bcl-2 and Bax.Results AngⅡ significantly induced ANGPTL8 expression in HVSMCs in a time-and dose-de-pendent manner(P＜0.05).ANGPTL8 knockdown significantly reduced the expression of apoptosis-related proteins caspase9,caspase3,and Bax,while increased the expression of the anti-apoptotic protein Bcl-2(P＜0.05).Con-versely,ANGPTL8 over-expression markedly induced HVSMCs apoptosis,which was significantly suppressed by treatment with the p53 pathway inhibitor pifithrin-α(PFT-α).Conclusions ANGPTL8 may promote VSMC apop-tosis by activation of p53 signaling pathway.

Fermented traditional Chinese medicine(TCM) has a long history of medicinal use, such as Sojae Semen Praeparatum, Arisaema Cum Bile, Pinelliae Rhizoma Fermentata, red yeast rice, and Jianqu. Fermentation technology was recorded in the earliest TCM work, Shen Nong's Classic of the Materia Medica. Microorganisms are essential components of the fermentation process. However, the contamination of fermented TCM by toxigenic fungi and mycotoxins due to unstandardized fermentation processes seriously affects the quality of TCM and poses a threat to the life and health of consumers. In this paper, the characteristics, microbial composition, and mycotoxin profile of fermented TCM are systematically summarized to provide a theoretical basis for its quality and safety control.
Fermentation ; Mycotoxins/analysis* ; Drugs, Chinese Herbal/analysis* ; Fungi/classification* ; Bacteria/genetics* ; Drug Contamination ; Medicine, Chinese Traditional

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