DHA-enriched phosphatidylserine alleviates bisphenol A-induced liver injury through regulating glycerophospholipid metabolism and the SIRT1-AMPK pathway.

To investigate the alleviating effect and mechanism of the docosahexaenoic acid-enriched phosphatidylserine (DHA-PS) on bisphenol A (BPA)-induced liver injury in mice, the murine liver injury model was established by gavage of BPA (5 mg/kg) or co-administration of BPA and DHA-PS (50 mg/kg or 100 mg/kg) for 6 weeks. The results showed that after administration of 100 mg/kg DHA-PS, the liver index, serum levels of AST, ALT, TC, TG, NEFA, and LDL-C in mice were significantly decreased, while HDL-C was significantly increased. The LPS, IL-6, IL-1ß, TNF-α, and MDA levels in liver tissues were effectively down-regulated, and IL-10, SOD, GSH-Px, and CAT levels were effectively up-regulated. The H&E and Oil Red O staining results showed that liver damage was notably repaired and lipid deposition was notably reduced after DHA-PS administration. Furthermore, metabolomics and immunohistochemical studies revealed that DHA-PS mainly regulates glycerophospholipid metabolism and the SIRT1-AMPK pathway to improve metabolic disorders of the liver caused by BPA. Therefore, DHA-PS could potentially alleviate BPA-induced murine liver injury through suppressing inflammation and oxidative stress, and modulating lipid metabolism disorders.

Mitigating environmental pollution in China: Unlocking the potential for high-quality innovation.

The nexus between environmental pollution (EP) and technological innovation is crucial for achieving sustainable development. However, existing literature has paid less attention to the new form of high-quality innovation (HI) in environmental management. This paper uses panel data from 31 Chinese provinces from 2008 to 2020, employing the two-stage least squares method to investigate the relationship between HI and EP. The empirical results reveal that HI can effectively reduce the EP, which holds after multiple robustness tests, and this effect is more obvious in southern China. Meanwhile, HI drives clean and efficient energy transition and decreases EP. Moreover, increased environmental regulation weakens the influence of HI on EP. The major contributions of this study are constructing an HI index including innovation, human capital, and government support and examining its influence on EP in China. The findings encourage government to implement policies of innovation-driven transformation, energy conservation and emissions reduction.

Integrated transcriptome and metabolome analysis reveals the molecular responses of Pardosa pseudoannulata to hypoxic environments.

Terrestrial organisms are likely to face hypoxic stress during natural disasters such as floods or landslides, which can lead to inevitable hypoxic conditions for those commonly residing within soil. Pardosa pseudoannulata often inhabits soil crevices and has been extensively studied, yet research on its response to hypoxic stress remains unclear. Therefore, we investigated the adaptive strategies of Pardosa pseudoannulata under hypoxic stress using metabolomics and transcriptomics approaches. The results indicated that under hypoxic stress, metabolites related to energy and antioxidants such as ATP, D-glucose 6-phosphate, flavin adenine dinucleotide (FAD), and reduced L-glutathione were significantly differentially expressed. Pathways such as the citric acid (TCA) cycle and oxidative phosphorylation were significantly enriched. Transcriptome analysis and related assessments also revealed a significant enrichment of pathways associated with energy metabolism, suggesting that Pardosa pseudoannulata primarily copes with hypoxic environments by modulating energy metabolism and antioxidant-related substances.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

3,4,5-tri-O-caffeoylquinic acid attenuates influenza A virus induced inflammation through Toll-like receptor 3/7 activated signaling pathway.

BACKGROUND: 3,4,5-tri-O-caffeoylquinic acid (3,4,5-TCQA), a natural polyphenolic acid, has been shown to be effective against influenza A virus (IAV) infection. Although it was found to inhibit the neuraminidase of IAV, it may also perturb other cellular functions, as polyphenolic acids have shown antioxidant, anti-inflammatory and other activities. PURPOSE: This study aimed to investigate the effect of 3,4,5-TCQA at a cell level, which is critical for protecting host cell from IAV infection. STUDY DESIGN AND METHODS: We explored the effect of 3,4,5-TCQA on H292 cells infected or un-infected with Pr8 IAV. The major genes and related pathway were identified through RNA sequencing. The pathway was confirmed by qRT-PCR and western blot analysis. The anti-inflammatory activity was evaluated using nitric oxide measurement assay. RESULTS: We showed that 3,4,5-TCQA downregulated the immune response in H292 cells, and reduced the cytokine production in Pr8-infected cells, through Toll-like receptor (TLR) signaling pathway. In addition, 3,4,5-TCQA showed anti-inflammatory activity in LPS-activated RAW264.7 cells. CONCLUSION: Collectively, our results indicated that 3,4,5-TCQA suppressed inflammation caused by IAV infection through TLR3/7 signaling pathway. This provides a new insight into the antiviral mechanism of 3,4,5-TCQA.

Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna.

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

Smart-Adhesive, Breathable and Waterproof Fibrous Electronic Skins.

For the need of direct contact with the skin, electronic skins (E-skins) should not only fulfill electric functions, but also ensure comfort during wearing, including permeability, waterproofness, and easy removal. Herein, the study has developed a self-adhesive, detach-on-demand, breathable, and waterproof E-skin (PDSC) for motion sensing and wearable comfort by electrospinning styrene-isoprene block copolymer rubber with carbon black nanosheets as the sensing layer and liner copolymers of N, N-dimethylacrylamide, n-octadecyl acrylate and lauryl methacrylate as the adhesive layer. The high elasticity and microfiber network structure endow the PDSC with good sensitivity and high linearity for strain sensing. The hydrophobic and crystallizable adhesive layer ensures robust, waterproof, and detaching-on-demand skin adhesion. Meanwhile, the fiber structure enables the PDSC good air and water permeability. The integration of electric and wearable functions endows the PDSC with great potential for motion sensing during human activities as both the sensing and wearable performances.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 µM, with Oxyberberine showing promising effects even at 10 µM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Advances in the interaction of glycolytic reprogramming with lactylation.

Lactylation is a novel post-translational modification (PTM) involving proteins that is induced by lactate accumulation. Histone lysine lactylation alters chromatin spatial configuration, influencing gene transcription and regulating the expression of associated genes. This modification plays a crucial role as an epigenetic regulatory factor in the progression of various diseases. Glycolytic reprogramming is one of the most extensively studied forms of metabolic reprogramming, recognized as a key hallmark of cancer cells. It is characterized by an increase in glycolysis and the inhibition of the tricarboxylic acid (TCA) cycle, accompanied by significant lactate production and accumulation. The two processes are closely linked by lactate, which interacts in various physiological and pathological processes. On the one hand, lactylation levels generally correlate positively with the extent of glycolytic reprogramming, being directly influenced by the lactate concentration produced during glycolytic reprogramming. On the other hand, lactylation can also regulate glycolytic pathways by affecting the transcription and structural functions of essential glycolytic enzymes. This review comprehensively outlines the mechanisms of lactylation and glycolytic reprogramming and their interactions in tumor progression, immunity, and inflammation, with the aim of elucidating the relationship between glycolytic reprogramming and lactylation.

In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method.

Estrogen-related receptor α (ERRα) is considered a very promising target for treating metabolic diseases such as type 2 diabetes. Development of a prediction model to quickly identify potential ERRα agonists can significantly reduce the time spent on virtual screening. In this study, 298 ERRα agonists and numerous nonagonists were collected from various sources to build a new dataset of ERRα agonists. Then a total of 90 models were built using a combination of different algorithms, molecular characterization methods, and data sampling techniques. The consensus model with optimal performance was also validated on the test set (AUC=0.876, BA=0.816) and external validation set (AUC=0.867, BA=0.777) based on five selected baseline models. Furthermore, the model's applicability domain and privileged substructures were examined, and the feature importance was analyzed using the SHAP method to help interpret the model. Based on the above, it's hoped that our publicly accessible data, models, codes, and analytical techniques will prove valuable in quick screening and rational designing more novel and potent ERRα agonists.

Hydrogen-Bonded Mesoporous Frameworks with Tunable Pore Sizes and Architectures from Nanocluster Assembly Units.

Construction of mesoporous frameworks by noncovalent bonding still remains a great challenge. Here, we report a micelle-directed nanocluster modular self-assembly approach to synthesize a novel type of two-dimensional (2-D) hydrogen-bonded mesoporous frameworks (HMFs) for the first time based on nanoscale cluster units (1.0-3.0 nm in size). In this 2-D structure, a mesoporous cluster plate with ∼100 nm in thickness and several micrometers in size can be stably formed into uniform hexagonal arrays. Meanwhile, such a porous plate consists of several (3-4) dozens of layers of ultrathin mesoporous cluster nanosheets. The size of the mesopores can be precisely controlled from 11.6 to 18.5 nm by utilizing the amphiphilic diblock copolymer micelles with tunable block lengths. Additionally, the pore configuration of the HMFs can be changed from spherical to cylindrical by manipulating the concentration of the micelles. As a general approach, various new HMFs have been achieved successfully via a modular self-assembly of nanoclusters with switchable configurations (nanoring, Keggin-type, and cubane-like) and components (titanium-oxo, polyoxometalate, and organometallic clusters). As a demonstration, the titanium-oxo cluster-based HMFs show efficient photocatalytic activity for hydrogen evolution (3.6 mmol g-1h-1), with a conversion rate about 2 times higher than that of the unassembled titanium-oxo clusters (1.5 mmol g-1h-1). This demonstrates that HMFs exhibited enhanced photocatalytic activity compared with unassembled titanium-oxo clusters units.

Long range topography by dispersion unmatched spectral-domain interferometry based on virtually imaged phased array modes.

We propose to realize a long range topography by dispersion unmatched spectral-domain interferometry based on virtually imaged phased array (VIPA) modes. By filtering the continuous spectrum of a supercontinuum source through a side-entrance Fabry-Perot etalon configured at two input angles, two groups of VIPA modes are generated. A method based on unmatched dispersion is proposed for non-aliasing reconstruction of the true depth from the interference spectrum under-sampled at two groups of VIPA modes. With the high spectral resolution provided by the VIPA modes instead of the grating-based spectrometer, only a 10 dB falloff in sensitivity over a range of 10 mm was demonstrated. The feasibility of the proposed method was confirmed by topography of a sample of gauge blocks and a model of three-dimensional (3D) printed tooth. The occlusal surface of the tooth model was further quantitatively evaluated, demonstrating its potential application in long range 3D topography.

Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio in patients with type 2 diabetes at different stages of diabetic retinopathy.

AIM: To investigate systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) levels in patients with type 2 diabetes at different stages of diabetic retinopathy (DR). METHODS: This retrospective study included 141 patients with type 2 diabetes mellitus (DM): 45 without diabetic retinopathy (NDR), 47 with non-proliferative diabetic retinopathy (NPDR), and 49 with proliferative diabetic retinopathy (PDR). Complete blood counts were obtained, and NLR, PLR, and SII were calculated. The study analysed the ability of inflammatory markers to predict DR using receiver operating characteristic (ROC) curves. The relationships between DR stages and SII, PLR, and NLP were assessed using multivariate logistic regression. RESULTS: The average NLR, PLR, and SII were higher in the PDR group than in the NPDR group (P=0.011, 0.043, 0.009, respectively); higher in the NPDR group than in the NDR group (P<0.001 for all); and higher in the PDR group than in the NDR group (P<0.001 for all). In the ROC curve analysis, the NLR, PLR, and SII were significant predictors of DR (P<0.001 for all). The highest area under the curve (AUC) was for the PLR (0.929 for PLR, 0.925 for SII, and 0.821 for NLR). Multivariate regression analysis indicated that NLR, PLR, and SII were statistically significantly positive and independent predictors for the DR stages in patients with DM [odds ratio (OR)=1.122, 95% confidence interval (CI): 0.200-2.043, P<0.05; OR=0.038, 95%CI: 0.018-0.058, P<0.05; OR=0.007, 95%CI: 0.001-0.01, P<0.05, respectively). CONCLUSION: The NLR, PLR, and SII may be used as predictors of DR.

Status and influencing factors of nurses' burnout: A cross-sectional study during COVID-19 regular prevention and control in Jiangsu Province, China.

Background: Chinese nurses working with immense stress may have issues with burnout during COVID-19 regular prevention and control. There were a few studies investigating status of burnout and associated factors among Chinese nurses. However, the relationships remained unclear. Objectives: To investigate status and associated factors of nurses' burnout during COVID-19 regular prevention and control. Methods: 784 nurses completed questionnaires including demographics, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Insomnia Severity Index, Impact of Event Scale-revised, Perceived Social Support Scale, Connor-Davidson Resilience Scale, General Self-efficacy Scale and Maslach Burnout Inventory. Results: 310 (39.5%), 393 (50.1%) and 576 (73.5%) of respondents were at high risk of emotional exhaustion (EE), depersonalization (DP) and reduced personal accomplishment (PA). The risk of EE, DP and reduced PA were moderate, high and high. Nurses with intermediate and senior professional rank and title and worked >40 h every week had lower scores in EE. Those worked in low-risk department reported lower scores in PA. Anxiety, post-traumatic stress disorder (PTSD), self-efficacy and social support were influencing factors of EE and DP, while social support and resilience were associated factors of PA. Conclusion: Chinese nurses' burnout during COVID-19 regular prevention and control was serious. Professional rank and title, working unit, weekly working hours, anxiety, PTSD, self-efficacy, social support and resilience were associated factors of burnout.

Treponema pallidum recombinant protein Tp0768 enhances the ability of HUVECs to promote neutrophil chemotaxis through TLR2/ER stress signaling pathway.

Neutrophils are essential cells involved in inflammation. However, the specific mechanism of neutrophil chemotaxis induced by Treponema Pallidum (T. pallidum) remains unknow. In this study, human umbilical vein endothelial cells (HUVECs) were utilized as target cells to investigate the expression levels of chemokines when stimulated with different concentrations of Tp0768(also known as TpN44.5 or TmpA, a T. pallidum infection dependent antigen). The results indicated that Tp0768 treatment enhanced neutrophil chemotaxis in HUVECs, which was closely associated with the expression levels of CXCL1(C-X-C Motif Chemokine Ligand 1), CXCL2(C-X-C Motif Chemokine Ligand 2), and CXCL8(C-X-C Motif Chemokine Ligand 8, also known as interleukin-8). At the same time, the results show that Toll Like Receptor 2 (TLR2) signaling pathway is activated and endoplasmic reticulum stress (ER stress) occurs. Furthermore, the findings revealed that the use of protein kinase RNA-like endoplasmic reticulum kinase (PERK) and Immunoglobulin-Regulated Enhancer 1 (IRE1) inhibitors reduced the expression levels of CXCL1, CXCL2, and CXCL8. Additionally, inhibiting TLR2 significantly decreased the expression levels of ER stress-related proteins (PERK and IRE1), CXCL1, CXCL2, and CXCL8. Consequently, neutrophil chemotaxis was significantly inhibited after treatment with TLR2, PERK, and IRE1 inhibitors. These findings shed light on the role of Tp0768 in enhancing neutrophil chemotaxis in endothelial cells, providing a foundation for further exploration of syphilis pathogenesis and offering a new direction for the diagnosis and treatment of T. pallidum infection.

Polar Bloch points in strained ferroelectric films.

Topological domain structures have drawn great attention as they have potential applications in future electronic devices. As an important concept linking the quantum and classical magnetism, a magnetic Bloch point, predicted in 1960s but not observed directly so far, is a singular point around which magnetization vectors orient to nearly all directions. Here we show polar Bloch points in tensile-strained ultrathin ferroelectric PbTiO3 films, which are alternatively visualized by phase-field simulations and aberration-corrected scanning transmission electron microscopic imaging. The phase-field simulations indicate local steady-state negative capacitance around the Bloch points. The observation of polar Bloch points and their emergent properties consequently implies novel applications in future integrated circuits and low power electronic devices.

Functional Connectivity Differences in the Resting-state of the Amygdala in Alcohol-dependent Patients with Depression.

RATIONALE AND OBJECTIVES: The mechanism of comorbidity between alcohol dependence and depressive disorders are not well understood. This study investigated differences in the brain function of alcohol-dependent patients with and without depression by performing functional connectivity analysis using resting-state functional magnetic resonance imaging. MATERIALS AND METHODS: A total of 29 alcohol-dependent patients with depression, 31 alcohol-dependent patients without depression and 31 healthy control subjects were included in this study. The resting-state functional connectivity between the amygdala and the whole brain was compared among the three groups. Additionally, we examined the correlation between functional connectivity values in significantly different brain regions and levels of alcohol dependence and depression. RESULTS: The resting-state functional connectivity between the left amygdala and the right caudate nucleus was decreased in alcohol-dependent patients. Additionally, the resting-state functional connectivity of the right amygdala with the right caudate nucleus, right transverse temporal gyrus, right temporal pole: superior temporal gyrus were also decreased. In alcohol-dependent patients with depression, not only was functional connectivity between the above brain regions significantly decreased, but so was functional connectivity between the right amygdala and the left middle temporal gyrus. Also, there was no significant correlation between the resting-state functional connectivity values in statistically significant brain regions and the levels of alcohol dependence and depression. CONCLUSION: The impairment of the functional connectivity of the amygdala with caudate nucleus and partial temporal lobe may be involved in the neural mechanism of alcohol dependence comorbidity depressive disorders.

Paroxysmal sympathetic hyperexcitability after brain injury: A clinical analysis of case series.

BACKGROUND: Paroxysmal sympathetic hyperexcitability (PSH) is a group of complex syndromes with various etiologies. Previous studies were limited to the description of traumatic brain injury (TBI), and the description of PSH after other types of brain injury was rare. We explored the clinical features, treatment, and prognosis of PSH after various types of brain injuries. METHODS: Patients admitted to the neurosurgery intensive care unit with PSH after brain injury from July 2019 to December 2022 were included. Demographic data, clinical manifestations, drug therapy, and disease prognosis were retrospectively collected and analyzed. RESULTS: Fifteen male and 9 female patients with PSH after brain injury were selected. TBI was most likely to cause PSH (66.7%), followed by spontaneous intracerebral hemorrhage (25%). Glasgow coma scale scores of 19 patients (79.2%) were lower than 8 and 14 patients (58.3%) underwent tracheotomy. Electroencephalogram monitoring was performed in 12 individuals, none of which showed epileptic waves. Clinical symptom scale showed mild symptoms in 17 cases (70.8%). Almost all patients were administered a combination of drugs. After follow-up, most patients had a poor prognosis and 2 (8.3%) died after discharge. CONCLUSION: The etiology of PSH is complex. TBI may be the most common cause of PSH. Non-TBI may also be an important cause of PSH. Therefore, early identification, prevention and diagnosis are helpful for determining the prognosis and outcome of the disease.