OBJECTIVE To explore the potential mechanism of ketamine-induced cognitive impairment in mice based on metabolomics.METHODS Male C57BL/6 mice were randomly divided into control group and ketamine group(25 mg/kg),with 12 mice in each group.Each group of mice was intraperitoneally injected with normal saline or corresponding drugs,4 times a day,for 10 consecutive days.On the last 2 days of drug administration,the cognitive behavior was evaluated by Y maze and novel object recognition test,and the histopathological changes in the prefrontal cortex(PFC)were observed.Ultra-high performance liquid chromatography-tandem mass spectrometry technology was used to analyze the changes of metabolites in PFC,screen for differential metabolites,and perform pathway enrichment analysis.RESULTS Compared with the control group,the morphology of PFC neurons in the ketamine group of mice was inconsistent.There were cavities around the nucleus,and the number of deeply stained cells increased.The mean optical density value of the Nissl staining positive area was significantly reduced,and the alternation rate and discrimination index were significantly reduced(P＜0.05 or P＜0.01).In the PFC tissue samples of mice of the two groups,there were a total of 114 differential metabolites,including 73 up-regulated and 41 down-regulated metabolites,including glutamine,succinic acid,ketoglutarate,and choline,etc.The differential metabolites mentioned above were mainly enriched in metabolism of alanine,aspartate and glutamate,metabolism of arginine and proline,γ aminobutyric acid synapses,pyrimidine metabolism,cholinergic synapses pathways,etc.CONCLUSIONS Ketamine can induce cognitive impairment in mice.Its neurotoxicity is related to abnormal synaptic transmission and energy metabolism,and neuroimmune regulation disorders.

The cellular and molecular components of the tumor immune microenvironment (TIME) and their information exchange processes significantly influence the trends of anti-tumor immunity. In recent years, numerous studies have begun to evaluate TIME in the context of previous cancer treatment strategies. This review will systematically summarize the compositional characteristics of TIME and, based on this foundation, explore the impact of current cancer therapies on the remodeling of TIME, aiming to provide new insights for the development of innovative immune combination therapies that can convert TIME into an anti-tumor profile.
Tumor Microenvironment/immunology* ; Humans ; Neoplasms/therapy* ; Immunotherapy/methods* ; Animals

Objective It aims to explore the impact of Diagnosis-Intervention Packet (DIP)refinement management on the operational performance of a dental specialty hospital,providing empirical evidence for further optimization of hospital management.Methods Operational performance data from a dental specialty hospital in Shenzhen,Guangdong Province,were collected from January 2022 to June 2024.An interrupted time series analysis was employed to assess the impact of DIP refinement management on hospital operational performance. Results Following the implementation of DIP refined management,the hospital's per capital output significantly increased by 4058.64 yuan(P<0.001)and continued to rise by 1292.44 yuan monthly(P<0.001).The proportion of consumables exhibited a slight decline,with a monthly decrease of 0.78 percentage point (P=0.04).The readmission rate significantly decreased by 1.13 percentage point (P=0.03),with a continuous monthly reduction of 0.15 percentage point (P=0.04).Patient satisfaction showed a slight upward trend,with a monthly increase of 0.61 percentage point (P=0.04).Additionally,changes in the cost-effectiveness ratio,return on assets,drug ratio,average length of stay,bed occupancy rate,cure rate,and the number of medical insurance violations were not statistically significant (P>0.05).Conclusion DIP refined management demonstrated significant economic benefits and improvements in service quality within the specialized dental hospital,though its impact on service efficiency was limited.Future efforts should focus on enhancing data-driven management decisions,optimizing personalized treatment pathways,and establishing patient feedback mechanisms to steadily improve operational performance.

Purpose To explore the clinical value of quantitative assessment of renal perfusion using ultrasound contrast imaging for the auxiliary diagnosis of type 2 diabetic nephropathy.Materials and Methods This prospective study was conducted from May 2017 to December 2019 at the First Medical Center of Chinese PLA General Hospital.A total of 41 patients with type 2 diabetes and renal function abnormalities,who were scheduled for renal biopsy,underwent contrast-enhanced ultrasound.Differences in contrast imaging parameters,including time to peak in the renal cortex,peak enhancement,mean transit time local,and area under the curve between diabetic nephropathy and focal segmental glomerulosclerosis were compared,and the correlation between imaging parameters and pathological results was analyzed.Results Among 41 patients,30 cases were diagnosed as diabetic nephropathy,and 11 cases were diagnosed as focal segmental glomerulosclerosis.The peak enhancement and area under the curve in the diabetic nephropathy group were significantly lower than those in the focal segmental glomerulosclerosis group[peak enhancement:3 837.16(2 449.16,5 929.16)vs.8 508.00(4 334.88,21 201.00),Z=-2.766,P=0.006;area under the curve:0.14±0.05 vs.0.19±0.05,t=-3.135,P=0.003].In the diabetic nephropathy group,peak enhancement showed a negative correlation with the global glomerulosclerosis rate(r=-0.489,P=0.006).Conclusion Contrast-enhanced ultrasound can quantitatively evaluate renal perfusion and has certain clinical value in assisting the diagnosis of type 2 diabetic nephropathy.

Objective It aims to explore the impact of Diagnosis-Intervention Packet (DIP)refinement management on the operational performance of a dental specialty hospital,providing empirical evidence for further optimization of hospital management.Methods Operational performance data from a dental specialty hospital in Shenzhen,Guangdong Province,were collected from January 2022 to June 2024.An interrupted time series analysis was employed to assess the impact of DIP refinement management on hospital operational performance. Results Following the implementation of DIP refined management,the hospital's per capital output significantly increased by 4058.64 yuan(P<0.001)and continued to rise by 1292.44 yuan monthly(P<0.001).The proportion of consumables exhibited a slight decline,with a monthly decrease of 0.78 percentage point (P=0.04).The readmission rate significantly decreased by 1.13 percentage point (P=0.03),with a continuous monthly reduction of 0.15 percentage point (P=0.04).Patient satisfaction showed a slight upward trend,with a monthly increase of 0.61 percentage point (P=0.04).Additionally,changes in the cost-effectiveness ratio,return on assets,drug ratio,average length of stay,bed occupancy rate,cure rate,and the number of medical insurance violations were not statistically significant (P>0.05).Conclusion DIP refined management demonstrated significant economic benefits and improvements in service quality within the specialized dental hospital,though its impact on service efficiency was limited.Future efforts should focus on enhancing data-driven management decisions,optimizing personalized treatment pathways,and establishing patient feedback mechanisms to steadily improve operational performance.

Anxiety disorders are the most prevalent mental health condition.More and more studies have found that interoceptive sensitivity, such as the sensitivity of the body to a series of physiological activities such as heart rate, respiration, blood pressure and so on, is closely related to the susceptibility to anxiety disorders.So, understanding the role of interoception in the occurrence of anxiety disorders plays an important role in understanding the pathogenesis of the disease and guiding treatment.This article reviewed the pathogenesis of anxiety disorders, the interaction between increased interoceptive sensitivity and abnormal activation of the amygdala, which results in pathological anxiety, the insula also regulates emotional responses by regulating interoception.It is also associated with genes and neurotransmitters, which may be important biomarkers of anxiety disorders.At the same time, interoception is also associated with genes and neurotransmitters, which may also be important biomarkers of anxiety disorders.In terms of treatment, we can use respiratory therapy to regulate respiratory perception, apply rTMS to stimulate the relevant network of interoception, and use electroencephalography to reflect interoception biofeedback and other treatment methods to improve interoception, to alleviate anxiety symptoms.In conclusion, the abnormal sensitivity of interoception plays an important role in the occurrence of anxiety disorders.Currently, there are many therapeutic methods for the treatment of anxiety disorders based on interoception, but the relevant therapeutic mechanism is unclear.Therefore, future research needs to explore the mechanism of interoception in anxiety and explore the mechanism of related treatment.

Objective:To quantitatively evaluate the left ventricular circumferential and longitudinal strain after percutaneous coronary intervention(PCI)in elderly patients with acute myocardial infarction(AMI)using speckle-tracking imaging(STI)on echocardiography.Methods:A prospective case-control study was conducted on 47 elderly patients diagnosed with ST-elevation AMI and undergoing percutaneous coronary interference(PCI)in our hospital from August 2017 to June 2020 as PCI-study group.The 35 normal subjects matched for age and sex were as a normal-control group.The longitudinal peak systolic strain(LPSS)and circumferential peak systolic strain(CPSS)were measured using STI at one week and three months after PCI in the two groups.Results:The values of LPSS and CPSS were apical segment > middle segment > basal segment, which was the similar between LPSS and CPSS.Compared with normal-control group, AMI-PCI group showed that CPSS and LPSS in each segment were significantly reduced at 1 week and 3 months after operation.Compared with the control group, all the CPSS and LPSS values were significantly decreased in AMI group at one week after PCI(-12.3±2.7)% vs.(-22.5±1.7)%( t=19.62, P<0.01); (-12.9±3.2)% vs.(-23.1±2.6)%( t=15.43, P<0.01). Both LPSS and CPSS values were improved at a certain extent at three months after PCI compared with AMI group at one week after PCI.The complete CPSS and LPSS values were significantly increased in AMI group at three months after PCI compared with one week after PCI(-16.8±2.6)% vs.(-12.3±2.7)%, ( t=8.23, P<0.01); (-17.0±3.3)% vs.(-12.9±3.2)%( t=6.11, P<0.01). But, there were still significant differences compared with the NC group(-16.8±2.6)% vs.(-22.5±1.7)%( t=11.29, P<0.01); (-17.0±3.3)% vs.(-23.1±2.6)%( t=9.04, P<0.01). Conclusions:The longitudinal and circumferential strain of left ventricle were severely damaged in elderly patients with AMI.The speckle-tracking imaging technique can be used to quantitatively evaluate the left ventricular strain and its improved situation after PCI in elderly AMI patients.

Objective:To establish a model for predicting the mental health status of nurses based on machine learning algorithm.Methods:In February 2020, the nurses from Shantou Central Hospital and Cancer Hospital of Shantou University Medical College were recruited by convenience sampling, investigated using the Self-reporting Inventory, Coping Style Questionnaire, Social Support Rating Scale and Work Attitude Scale. Mental health status was treated as a dichotomous variable, and candidate predictors were screened out by univariate and multivariate Logistic regression analysis. The subjects were randomly divided into a training set (80%) and a test set (20%). Then five prediction models of nursing staff mental health status were constructed using the five machine learning methods (Logistic Regression, Artificial Neural Network, C5.0 Decision Tree, Bayesian Network and Support Vector Machine), verified and compared to screen out the model with the highest predictive efficiency.Results:A total of 415 nurses were enrolled, and the positive detection rate of mental health symptoms was 20.48%. According to univariate and multiple Logistic regression analysis, candidate predictors were work attitude ( OR=1.098, 95% CI 1.028-1.174), self-accusation ( OR=7.703, 95% CI 2.014-29.468), problem-solving( OR=0.131, 95% CI 0.025-0.686), the number of night shifts per month ( OR=0.204, 95% CI 0.073-0.573)and support availability ( OR=0.830, 95% CI 0.701-0.984). The accuracy of prediction of Logistic Regression, Artificial Neural Network, C5.0 Decision Tree, Bayesian Network and Support Vector Machine were 84.21%, 85.53%, 82.89%, 78.95%, 84.21%. The area under the ROC curve was 0.801, 0.825, 0.777, 0.583, 0.774. Artificial Neural Network was significantly more effective than Logistic regression, C5.0 Decision Tree, Bayesian Network and Support Vector Machine (DeLong test, P<0.05). Conclusions:The machine learning based predictive models for nursing staff mental health status has higher predictive value, which can be applied into nursing staff mental health screening decisions to accurately grasp its dynamic changes, early identification of high-risk mental health abnormalities and early intervention. Work attitude, self-accusation, problem-solving, the number of night shifts per month and support availability was predictors to construct predictive models.

Objective:To understand the mental health of nurses, and to explore the influence of general information, coping styles, social support and work adaptation barriers on the mental health of nurses, and to initially construct a prediction model of clinical nurses' mental health.Methods:In February 2020, convenience sampling was used to select 374 clinical nurses from two hospitals in a city as the research objects. All nurses were investigated with the self-designed general information questionnaire, Symptom Checklist 90 (SCL-90) , Coping Style Questionnaire, Social Support Rating Scale (SSRS) and Work Adjustment Disorder Scale. The mental health was processed as binary data, and the multi-layer perceptron in the neural network was used to construct a prediction model of the clinical nurses' mental health, and the model was verified and analyzed.Results:Among 374 clinical nurses, the scores of SCL-90, Coping Style Questionnaire, SSRS and Work Adjustment Disorder Scale were (123.06±41.70) , (27.90±12.14) , (38.84±8.46) and (9.72±6.35) respectively. Correlation analysis results showed that working years, self-blame, fantasy, avoidance, rationalization and work adjustment disorder were positively correlated with mental health ( P<0.05) ; whether they were authorized strength, problem solving, asking for help, subjective support, support utilization were negatively correlated with mental health ( P<0.05) . The above variables related to mental health were incorporated into the prediction model, and 374 samples were divided into training samples (177) , test samples (81) and persistence samples (116) . The results showed that the modeling accuracy of training, testing and persistence samples were 79.7%, 86.4% and 81.0%, respectively, and the area under the receiver operating characteristic curve was 0.810. The variable which normalization importance was 100% in the prediction model was the work adjustment disorder. Conclusions:The prediction model of clinical nurses' mental health constructed in this study has good accuracy, and the application of neural network model is relatively simple, which can be used as an effective method to evaluate the mental health of clinical nurses.

BACKGROUND/AIMS: This study investigated the protection provided by gabexate mesylate thermo-sensitive in-situ gel (GMTI) against grade III pancreatic trauma in rats. METHODS: A grade III pancreatic trauma model with main pancreatic duct dividing was established, and the pancreas anatomical diagram, ascites, and serum biochemical indices, including amylase, lipase, C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor-α (TNF-α), were examined. The pancreas was sliced and stained with hematoxylin eosin and subjected to terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining. RESULTS: Ascites, serum amylase, lipase, CRP, IL-6, and TNF-α levels were significantly increased in the pancreas trauma (PT) groups with prolonged trauma time and were significantly decreased after GMTI treatment. The morphological structure of the pancreas was loose, the acinus was significantly damaged, the nuclei were irregular and hyperchromatic, and there was inflammatory cell invasion in the PT group compared to the control. After GMTI treatment, the morphological structure of the pancreas was restored, and the damaged acinus and inflammatory cell invasion were decreased compared to the PT group. Moreover, the cell apoptosis index was significantly increased in the PT group and restored to the same levels as the control group after GMTI treatment. CONCLUSIONS: GMTI, a novel formulation and drug delivery method, exhibited specific effective protection against PT with acute pancreatitis therapy and has potential value as a minimally invasive adjuvant therapy for PT with acute pancreatitis.
Amylases ; Animals ; Apoptosis ; Ascites ; C-Reactive Protein ; DNA Nucleotidylexotransferase ; Eosine Yellowish-(YS) ; Gabexate* ; Hematoxylin ; Interleukin-6 ; Lipase ; Methods ; Necrosis ; Pancreas ; Pancreatic Ducts ; Pancreatitis ; Rats*