Objective: To investigate the inhibitory mechanism of dl-3-n-butylphthalide (NBP) on neuronal apoptosis after intracerebral hemorrhage (ICH) through the mitogen and stress activated protein kinase1-cAMP-response element binding protein(MSK1-CREB)signaling axis. Methods: A rat model of ICH was established using collagenase type Ⅶ injection into the caudate nucleus. Groups were set up as follows: control, sham operation(Sham), ICH+NBP and ICH+ normal saline (NS). Neurological function was assessed using the modified neurological severity score (mNSS). Brain water content was measured by the wet-dry weight method. Expression levels of Caspase-3, B-cell lymphoma-2(Bcl-2), cAMP-response element binding protein (CREB), and mitogen and stress activated protein kinase1 (MSK1) in the perihematoma tissue were detected by Western blotting and immunofluorescence analysis. Results: NBP treatment significantly improved neurological deficits after ICH in rats, alleviated brain edema, and inhibited the expression of Caspase-3 while increasing the expression of Bcl-2, CREB, and MSK1. Conclusion: NBP inhibits neuronal apoptosis after ICH in rats through the MSK1-CREB signaling axis, a theoretical basis for the application of NBP in the treatment of ICH.
Data mining (DM) is the process of extracting potentially valuable information and knowledge from large amounts of incomplete, noisy, fuzzy, and random real-world application data. This technology integrates artificial intelligence, databases, statistics and other multidisciplinary content, providing excellent convenience for the innovation and development of diseases management-related research. In recent years, the application of DM technology to diabetes management has achieved great results in the early prevention of diabetes, the prediction of diabetes complications and the prognosis of diabetes. However, the application of data mining technology in the field of diabetes is still in the exploratory stage in China, so this paper aims to make a detailed review of the classification of data mining technology and the application and progress of data mining technology in diabetes screening, diabetes complications prediction and improvement of the management quality of diabetes patients, so that medical staffs can have a clear and intuitive understanding of the application of data mining technology in diabetes management, and find the existing problems in the application of data mining technology in diabetes management. This paper will provide evidences for the fine management of healthcare professionals to facilitate the production of research results that are beneficial to patients and healthcare providers.
Objective: To investigate the effect of breast milk with bacteria on intestinal microbiota of 6-month-old infants based on 16S rRNA sequencing technology. Methods: A total of 12 postpartum women and their infants were included in this study. The 6-month-old postpartum milk and 6-month-old infant fecal samples were collected. According to the 6-month-old postpartum breast milk with or without bacteria, the breast milk was divided into sterile breast milk group and bacterial breast milk group, and the diversity and species difference of intestinal microbiota of 6-month-old infants fed by sterile breast milk and bacterial breast milk were compared. Results: In 6-month-old postpartum breast milk samples of 12 cases, microbiota was found in 7 cases, and the detection rate of microbiota in breast milk samples was 58.33%. There was no significant difference in α and β diversity between 6-month-old infants fed with bacterial breast milk and 6-month-old infants fed by sterile breast milk. The relative abundance of Clostridium_innocuum, unidentified_Erysipelotrichaceae, Erysipelotrichia, Erysipelotrichales and Erysipelotrichaceae in intestinal microbiota of 6-month-old infants fed by bacterial breast milk was significantly higher than that of 6-month-old infants fed by sterile breast milk (P<0.05). The relative abundance of Lactobacillaceae and Lactobacillus in intestinal microbiota of infants fed by sterile breast milk was significantly higher than that of infants fed with by bacterial breast milk (P<0.05). Conclusion: The 6-month-old postpartum breast milk may without bacteria. The presence or absence of microbiota in breast milk may not change the diversity of intestinal microbiota of 6-month-old infants, but it can change the structure of intestinal microbiota of 6-month-old infants.
Objective: To investigate the change of epicardial fat thickness (EFT) in patients with systemic lupus erythematosus (SLE) and its correlation with cardiac ultrasound parameters and traditional cardiovascular risk factors. Methods: SLE patients admitted to Rheumatology and Immunology Department of the First Affiliated Hospital of Jinan University from October 2019 to October 2021 were included as the case group, and healthy subjects in the same period were included as the control group. EFT was measured in both groups. SLE patients were divided into two subgroups according to whether EFT was thickened. The differences in inflammatory indicators, cardiac ultrasound parameters and traditional cardiovascular risk factors between the two subgroups were compared. Logistic regression analysis was used to explore the risk factors for EFT thickening. Results: The EFT of SLE patients was significantly thicker than that of the control group [4.40(2.60)mm vs. 3.10(1.60)mm, P<0.001]. EFT thickening occurred in 42.0% (76/131) of SLE patients and 9.5% (9/95) of healthy controls. The age, body mass index (BMI), left atrial diameter (LAD), right ventricular inner diameter (RVD), interventricular septal thickness (IVST), left ventricular posterior wall thickness (LVPWT) and carotid intima-media thickness (CIMT) in the EFT thickening group were significantly higher than those in the non-thickening group. EFT was positively correlated with age, disease course, BMI, triglyceride, low-density lipoprotein, LAD, RVD, IVST, LVPWT and CIMT. Logistic regression analysis demonstrated that increased LVPWT was a risk factor for EFT thickening. Conclusion: EFT thickening in SLE patients is associated with changes in cardiac structural or functional parameters, suggesting that further screening for cardiovascular risk factors should be conducted in SLE patients with increased EFT and abnormal cardiac structure or functional parameters.
Objective: To investigate the incidence and cause of false positive reaction about hepatitis C virus antibody detection in maintenance hemodialysis outpatients, in order to correctly deal with the antibody test reports and the doctor-patient communication problems. Methods: The hepatitis C virus antibody and RNA in 279 maintenance hemodialysis outpatients of General Hospital of Southern Theater Command of PLA were analyzed by cluster sampling, cross-sectional investigation and retrospective study from December 2019 to December 2022. Results: It was found that 81 patients were false positive for hepatitis C virus antibody,which accounted to 29.03%; S/CO values between 1.0 and 5.0 was 98.77%, and between 5.0 and 10.0 was 1.23%. All cases were reviewed by HCV-RNA three times,and were negative. Different detection methods and reporting procedures were chosen at the department of laboratory and the department of blood transfusion medicine,and the results were significantly different(P<0.01). Conclusion: The S/CO values of false positive reactions about hepatitis C virus antibody detection are mostly in the gray zone range and are related to the detection strategy and reporting procedure.
Objective: The aim of this study was to investigate the predictive value of blood lactate, interleukin-6 (IL-6), and the sequential organ failure assessment (SOFA) score on the 28-day mortality risk in sepsis patients. Methods: Retrospective analysis of clinical data of 145 sepsis patients admitted to the Department of Critical Care Medicine at the Second Affiliated Hospital of Xinjiang Medical University. All patients were admitted to the hospital and laboratory tests such as blood gas analysis and routine blood tests were completed and SOFA scores were performed. All patients were followed up for 28 days. Based on survival outcome, the patients were divided into two groups: the survival group (46 cases) and the death group (99 cases). Comparative analysis was then carried out to assess the differences in blood lactate, IL-6, and SOFA scores between the two groups. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing sepsis patient mortality. Spearman correlation analysis was employed to examine the associations between blood lactate, IL-6, SOFA scores, and sepsis patient mortality. Furthermore, the predictive value of blood lactate, IL-6, and SOFA scores, as well as their combined tests, were evaluated using receiver operating characteristic (ROC) curves. The model underwent 500 resampling iterations using the Bootstrap method in R, followed by the generation of calibration curves and clinical decision curves.Results: Blood lactate levels, IL-6 and SOFA scores of patients in the sepsis death group were significantly higher than those in the survival group, and the differences were all statistically significant (P<0.05). The overall morbidity and mortality rate among patients with sepsis was 68% (99/145). Univariate logistic regression analysis shows diabetes, blood lactate, IL-6, and SOFA score as correlates of death in patients with sepsis. Multivariable logistic regression analysis revealed that high SOFA score, having diabetes mellitus, and high IL-6 were independent risk factors for death in sepsis patients. Furthermore, blood lactate, IL-6, and SOFA scores positively correlated with sepsis patient mortality, while the area under the ROC curve (AUC) for blood lactate, IL-6, SOFA scores, and the combination of the three tests were 0.863, 0.850, 0.897, and 0.949, respectively. The optimal threshold values for lactate, IL-6, and SOFA scores were identified as 2.950 mmol/L, 100.970 pg/mL, and 7.500 points, respectively. The diagnostic sensitivity and specificity for lactate were 77.800% and 89.100%, for IL-6 were 70.700% and 91.300%, and for SOFA score were 93.900% and 80.400%. The combined test exhibited a sensitivity of 90.900% and a specificity of 89.100%. Conclusion: Elevated levels of blood lactate, IL-6, and SOFA scores have been identified as independent risk factors for mortality in patients with sepsis. The clinical prediction model developed in this study demonstrates high accuracy and clinical applicability, while the simultaneous use of these three tests holds significant predictive value in prognostic analysis for sepsis patients.
Objective: To identify risk factors for surgical site infection (SSI) in spinal surgery and develop a nomogram prediction model, providing a basis for post-operative wound management. Methods: To identify risk factors for surgical site infection (SSI) in spinal surgery and develop a nomogram prediction model, providing a basis for post-operative wound management. Results: Key risk factors identified were intraoperative bleeding, time to drain removal, and postoperative albumin ≤30 g/L. The ROC AUCs for the training and validation groups were 0.775 and 0.754, respectively. Calibration curve analysis indicated close alignment of predicted and actual probabilities. DCA showed greater clinical utility in the training group. Conclusion: Intraoperative bleeding, drain removal time, and postoperative albumin ≤30 g/L are significant risk factors for SSI in spinal surgery. The nomogram model demonstrates good predictive performance, offering an effective tool for clinical evaluation and improving prediction accuracy in postoperative infection risk.
Objective: To explore the risk factors of subsyndromal delirium (SSD) after orthopedic surgery in adults and construct a risk model for verification. Methods: Using a convenient sampling method, selected 333 adult patients of Luoyang Orthopedics Hospital (Henan Orthopedic Hospital), from January 1, 2022, to June 30, collected clinical data and various indicators, discussed the risk factors of SSD in adults after orthopedic surgery through logistic regression analysis by univariate analysis and dichotomy surgery, constructed the risk model function and nomogram prediction model, The developed model was evaluated by both discrimination measured by the C-index and calibration evaluated by the Hosmer-Lemeshow test and calibration plot,which compares the prediction probability and the actual observation Probability.A nomogram was established based on the risk predictive model by using R for Windows with The rms package R. Results: 301 cases were included in the study, including 274 (non-SSD) and 27 (SSD), and the incidence of SSD was 8.97%. Univariate analysis found that the influencing factors of adult orthopedic postoperative SSD are blood sodium and hemoglobin on admission, FRAIL scale score, daily living ability at admission, APACHE Ⅱ score, intraoperative use of sedative, analgesics, intraoperative blood transfusion, hemoglobin in ICU, the two groups are significant difference (P<0.05). Dichotomous logistic regression analysis found that blood sodium at admission (OR=0.922), FRAIL scale score at admission (OR=1.418), and daily living ability score at admission (OR=0.973) were independent risk factors for subpatients (P<0.05). Y=11.484-0.081×cblood sodium at admission +0.349×FRAIL scale score at admission-0.027×daily living ability score at admission. An individualized nomogram prediction model was established with the above factors, and the C index is 0.788[95%CI(0.738, 0.833)], The model showed a good false acceptance rate is 8.97%, and the sensitivity and specificity of the model were 77.8% and 69.71%, and the correction curve was fit well (Hosmer-Lemeshow test,P=0.705).Conclusion: The prediction model of the column graph established in this study is helpful for clinical staff to carry out early assessment and screening of these factors, intuitively and easily identify the population with subsyndromal delirium, and provide reference for early screening and intervention, as well as for optimizing the management of postoperative delirium in orthopedic patients.
Objective: To establish the reference intervals for thyroid hormone measurements in healthy early pregnant women at different ages in Zigong, Sichuan Province, by using the indirect method, to provide evidence for clinical diagnosis and treatment. Methods: Data from healthy early pregnant women who had medical records in the Department of Obstetrics and Gynecology of the First People’s Hospital of Zigong from January 2019 to December 2021, 1 518 cases in total, were selected. The data were categorized into three groups according to the ages of pregnant women: group A (<26 years old), group B (26~34 years old), and group C (>34 years old), and the range from 2.5% to 97.5% was used as the reference interval. Conduct statistical analysis of the five thyroid hormones, including thyroid stimulating hormone (TSH), total triiodothyronine (TT3), total thyroxine (TT4), serum free triiodothyronine (FT3), and serum free thyroxine (FT4), in the data of three age groups. Results: There is no statistical difference among the three indicators of TSH, FT3 and FT4 in early pregnancy women of different ages, and the reference intervals for the three age groups were TSH(0.12~3.81 mIU/L), FT3(2.20~5.03 pmol/L) and FT4(4.79~16.13 pmol/L). There are statistical differences in TT3 and TT4 levels at different ages. The reference intervals of TT3 and TT4 in early pregnancy women under the age of 26 were 1.04~2.78 nmol/L and 91.80~204.88 nmol/L, while those in early pregnancy women over the age of 26 were 1.04~2.55 nmol/L and 82.70~190.00 nmol/L. The differences between group A, B, C and the non-pregnant group were statistically significant (P<0.05). Conclusion: The reference intervals for five thyroid hormone indicators in early pregnant women at different ages in Zigong, Sichuan Province have been established, providing a reference for clinical disease diagnosis, treatment, and prognosis judgment.
Objective: To develop an artificial intelligence (AI) algorithms tool for data collection in chronic hepatitis B, addressing the issue of low efficiency in traditional multicenter data collection. Methods: Based on internationally recognized data standards, this study applies mature AI techniques in the field of computer science, such as optical character recognition and natural language processing, to the data collection of real-world cohorts in chronic hepatitis B research. This tool enables the electronic collection of various data formats, including digitization of raw data in image format, structured processing, and automatic population of data into electronic case report forms (eCRFs) designed in the Research Electronic Data Capture (REDCap) system. Results: Experimental results demonstrate that the AI algorithmstool for data collection achieves the same average accuracy as manual data collection (P=0.23), with an accuracy rate of 98.66%, while reducing the time required by 75.49%(P<0.05) compared to manual collection. Conclusion: The AI algorithms tool for data collection developed in this study significantly improves the efficiency of research data collection and brings new developments to the real-world research data collection paradigm.
Artificial intelligence (AI), a branch of computer science has achieved significant breakthroughs in intelligent healthcare. AI holds tremendous potential in eye diseases such as retinopathy of prematurity (ROP), diabetic retinopathy (DR), and exudative age-related macular degeneration (AMD). Numerous AI studies have been conducted both domestically and internationally on the diagnosis and treatment of ROP, demonstrating high accuracy in diagnosis and prognosis. Through telemedicine technology, AI can enhance the screening efficiency for infants with ROP and reduce complications, so that alleviate the burden on society, healthcare workers and patients. Despite the potential demonstrated by AI in ROP screening systems, its widespread application in clinical practice has not yet been realized, with most researches still in the experimental phase and facing various challenges. Also, current researches on AI for intelligent treatment guidance on ROP is still insufficient. Furthermore, AI in ROP telemedicine technology continues to confront stringent requirements regarding to image quality and shooting orientation, as well as potential risks associated with data security. Therefore, future development could further establish and refine intelligent diagnostic algorithms and predictive models for ROP and introduce this technology to the bedside of infants to reduce the incidence of blindness caused by ROP. This article highlights the potential of AI in reducing the blindness rate from ROP and proposes directions for future research to lay the foundation for its widespread implementation in clinical practice.
Objective: To explore the rationality of thalidomide (TLD) in outpatient use by evidence-based medicine evaluation method. Methods: A retrospective method was used to select 360 prescriptions containing TLD issued in outpatient clinics of the Sixth Affiliated Hospital of Sun Yat-Sen University in 2022. The off-label use was statistically analyzed, evaluated in evidence-based medicine evaluation path. The causes of off-label use were also analyzed. Results: The clinical use of TLD was off-label. The main indications were Crohn’s disease, systemic lupus erythematosus and Behçet’s syndrome. Evidence-based medical evidence evaluation is acceptable for use or could be used when other drugs were in valid or ineffective. The main reason for off-label drug use is that doctors believe that off-label drug use of TLD conforms to guidelines. Conclusion: The off-label use of TLD in outpatient clinics is supported by evidence-based medical evidence, but the off-label use of TLD should be strictly recorded in accordance with the record process.
Objective: The present study sought to establish quality control standards for dried checkered Sipunculus nudus products, providing a value reference for quality assessment and scientific development and utilization. Methods: The characteristics, microscopic identification, and thin-layer chromatography (TLC) identification of 30 batches of dry Sipunculus nudus from three different sources were studied following the guidelines of the Chinese Pharmacopoeia (2020 edition). Additionally, the content of active substances, including amino acids and total sugars, was measured. The impurities, moisture content, total ash content, acid-insoluble ash content, heavy metals, and water-soluble extract content were determined using supplementary tests. The content of aflatoxin in dry Sipunculus nudus was determined using high-performance liquid chromatography (HPLC). Results: The body wall of dry Sipunculus nudus is reminiscent of an earthworm, with a lattice-like structure that crisscrosses. Microscopic examination reveals light brown, blunt, and rounded bristles at the apex. Furthermore, thin-layer chromatography (TLC) analysis exhibits characteristic chromatographic patterns for arginine, alanine, and glycine. The analysis of amino acid content revealed that glutamic acid, arginine, and aspartic acid were the most abundant. The total sugar content was 2%, while the impurities, moisture content, total ash content, and acid-insoluble ash content of dry Sipunculus nudus were 3%, 11%, 10%, and 2%, respectively. Moreover, the water-soluble extract content was 38%, while the aflatoxin content in dry Sipunculus nudus was not more than 10 μg/kg. Conclusion: The established quality testing methods for dry Sipunculus nudus possess great stability, providing a reference for the quality control of this medicinal material.
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