Reactive astrocytes, which exhibit a correlation with the degeneration of dopaminergic neurons, are present in a considerable number during the progression of Parkinson's disease (PD). However, the underlying factors shaping astrocyte reactivity and neuroinflammation in PD remain inadequately elucidated. Here, we demonstrate that fibroblast growth factor 7 (FGF7)/FGF receptor 2 (FGFR2) autocrine signaling intensifies astrocyte reactivity and inflammation. Genetic deletion of Arrb2, β-Arrestin2 encoding gene, led to escalated astrocyte reactivity in MPTP-treated mice, which was further substantiated in astrocyte-specific Arrb2 knockdown mice. RNA sequencing profiling of Arrb2 knockout astrocytes identified Fgf7 as a critical effector of astrocyte reactivity. Subsequently, conditional knockdown of Fgf7 and its receptor Fgfr2 in astrocytes elicited advantageous effects for MPTP-treated mice by restraining the inflammatory phenotypic transition of reactive astrocytes. Furthermore, deletion of astrocytic Fgf7 mitigated MPTP-induced pathology in Arrb2 knockout mice. Mechanistically, STAT1 was distinguished as the transcription factor suppressing Fgf7 expression, while β-Arrestin2 counteracted the proteasomal degradation of STAT1 by binding to RNF220, an E3 ubiquitin ligase for STAT1. More importantly, selectively engaging dopamine D2 receptor (Drd2)/β-Arrestin2-biased signaling using the agonist UNC9995 exhibited therapeutic potential in MPTP-treated mice via moderation of astrocytic FGF7 production, thereby restoring balance in astrocyte reactivity. Collectively, our study bridges a crucial knowledge gap by elucidating the novel functions of FGF family members within the central nervous system, particularly within the context of PD. The autocrine signaling of FGF7/FGFR2 represents a novel mechanism and a potential druggable target for modulating astrocyte-derived inflammation.

OBJECTIVE The regression equation of cognitive impairment in patients with obstructive sleep apnea hypopnea syndrome(OSAHS)was constructed based on Logistic regression analysis,to lay a foundation for the prevention of cognitive impairment in OSAHS patients.METHODS A total of 302 OSAHS patients admitted to Huai'an First People's Hospital from October 2022 to October 2024 were selected for the study,and the cognitive impairment group(n=83)and the non-cognitive impairment group(n=219)were included according to the occurrence of cognitive impairment.The status quo of OSAHS patients complicated with cognitive impairment was analyzed,univariate and multivariate Logistic regression was used to analyze the early warning factors of OSAHS combined with cognitive impairment,regression equation was established,and receiver operating characteristic(ROC)curve was drawn to analyze the predictive value of regression equation on OSAHS combined with cognitive impairment.RESULTS Among 302 OSAHS patients,83 cases were complicated with cognitive impairment(27.48%).Multivariate Logistic analysis showed that age increase,physical exercise<3 times/week,apnea hypopnea index increase,longest apnea duration prolongation,serum levels of neuropeptide Y(NPY)increase were early warning factors of OSAHS combined with cognitive impairment.The increase of minimum blood oxygen saturation and serum gamma-aminobutyric acid(GABA)were protective factors of OSAHS combined with cognitive impairment(P<0.05).The above factors were included in the regression equation:logit(P)=-1.875+age×0.059+physical exercise<3 times/week×0.041+apnea hypopnea index×0.031+longest apnea duration×0.073-minimum blood oxygen saturation×0.081+NPY×0.043-GABA×0.049.The regression equation was evaluated,and the likelihood ratio of Chi-square=7.632,DF=7,P<0.001,the regression equation was statistically significant.Patients with OSAHS combined with cognitive impairment were included as positive,while those OSAHS without cognitive impairment were included as negative.The ROC curve for predicting OSAHS combined with cognitive impairment was drawn according to the regression equation.The results showed that when logit(P)>2.02,the area under the curve(AUC)was 0.896.The diagnostic sensitivity and specificity were 85.54%and 83.56%respectively.CONCLUSION OSAHS patients with cognitive impairment were not optimistic,early warning factors included increasing age,less than 3 times/week of physical exercise,increased apnea hypopnea index,prolonged longest apnea time,elevated serum NPY,protective factors included elevated minimum blood oxygen saturation and elevated serum GABA,the regression equation constructed on this basis had good predictive value.

BACKGROUND:In recent years,some scholars in the field of tendon bone injury have attached stromal cell-derived factor 1 to tissue engineering scaffolds to promote tendon bone healing,and achieved good results.However,whether stromal cell-derived factor 1 promotes tendon bone healing mechanisms and participates in the repair of natural healing has not yet been defined. OBJECTIVE:To study the expression of stroma-cell derived factor 1 during tendon bone healing after rupture of the whole supraspinatus muscle of the rabbit rotator cuff and its migration effect and optimal in vitro migration promoting concentration on stem cells during tendon bone injury. METHODS:Totally 18 adult New Zealand rabbits were randomly selected to establish rotator cuff injury models,and an additional 3 rabbits were selected as blank controls.At 3,5,7,14,21,and 28 days after modeling,three rabbits were executed separately and the rabbits in the blank group were sacrificed.The tissues of tendon bone junction were taken and stored in a-80℃refrigerator.The expression of stromal cell-derived factor 1 was detected by ELISA at each time point after injury.Mesenchymal stem cells were isolated from the bone marrow of young rabbit femur,cultured,and identified.Transwell assay was performed to verify the migration-promoting effect of stromal cell-derived factor 1 on stem cells and the optimal migration-promoting concentration in vitro.The stem cells cultured to P3 were co-cultured with BrdU and injected into the rabbit ear marginal vein,and immunohistochemical staining was used to verify whether the stem cells migrated to the injury site. RESULTS AND CONCLUSION:(1)Stromal cell-derived factor 1 gene expression was bimodal during rotator cuff tendon bone healing.Stromal cell-derived factor 1 gene expression increased significantly at 3 days post-injury(P<0.01)and then decreased,reaching a minimum at 5 days post-injury.It increased again and reached a peak 14 days after injury(P<0.01)and then decreased.(2)Cell immunohistochemical staining displayed that stem cells labeled with BrdU did migrate to the injury site.(3)The results of the transwell experiment exhibited that 60-80 ng/mL stromal cell-derived factor 1 had the best effect on promoting migration of stem cells,while a concentration of 200 ng/mL inhibited migration.(4)Stromal cell-derived factor 1 is involved in the healing of rotator cuff tendon bone during the inflammatory response phase and the proliferation phase.The mechanism of action may be to promote the migration of stem cells to the injury and their differentiation into various types of cells to promote repair.In addition,the pro-migration effect of stromal cell-derived factor 1 exists at a range of concentrations,beyond which it may act as an inhibitor.

To systematically construct a guideline to provide a methodological guide for researchers to develop patient decision aids.

To systematically construct a guideline to provide a methodological guide for researchers to develop patient decision aids.

Since the 1970s, the laboratory animal industry in Suzhou has gone through five stages: its inception, emergence, growth, transformation, and scaling up. It began with the manufacturing of caging equipment for laboratory animals, initially by imitation and later through independent innovation. The industry evolved from sporadic factories to clustered enterprises, gradually growing and opening up the export market for caging equipment. In the 21st century, with industrial upgrading and transformation, purification systems and related products began to develop, and industry organizations emerged. As China has modernized, the rise of automation and intelligent production has led to technological innovation in enterprises and the emergence of various outsourcing services in the laboratory animal industry, driving the large-scale development of the industrial chain. After nearly half a century of growth, the laboratory animal industry in Suzhou has formed a complete industrial chain, including the production of laboratory animals, caging equipment, feed and bedding materials, design and construction of laboratory animal facilities, quality testing of laboratory animals and environments, and animal experimentation services. Laboratory animal breeding equipment, the core of the industry, has reached the level of developed countries, and the industry's scale and influence are unmatched in China. Since the 21st century, biopharmaceuticals have become the "No.1 industry" in the development of Suzhou. With government support, the guidance of the local economy, and the assistance from universities and research institutes, the animal experiment outsourcing industry has begun to cluster in Suzhou. The continuous influx of CROs has driven the construction of large-scale laboratory animal facilities, and key research projects have been initiated, significantly enhancing the industry's R&D capabilities. The Suzhou laboratory animal industry has quickly expanded alongside the "No. 1 industry," creating a unique "Suzhou Path" for laboratory animals. Over nearly fifty years, the laboratory animal industry in Suzhou has been essential to the rapid development of the biopharmaceutical industry in Suzhou and China.

Objective To construct and validate a risk prediction model for enteral nutrition feeding intolerance (FI) in patients with severe cerebral hemorrhage based on machine learning algorithms. Methods The clinical data of 485 patients with cerebral hemorrhage admitted to the neurological intensive care unit of Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2020 to December 2022 were retrospectively analyzed. The patients were randomly divided into training set (n=339) and validation set (n=146) in a 7 to 3 ratio. Five machine learning algorithms were used to construct FI risk prediction models. The receiver operating characteristic (ROC) curve was plotted, and the model with the best predictive performance was selected based on the area under the curve (AUC). A nomogram model was constructed based on the optimal model. The calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical net benefit of the nomogram model. Results The incidence of enteral nutrition FI in patients with severe cerebral hemorrhage was 38.4%(186/485). Among the five machine learning algorithm models, the Logistic regression model had the best predictive performance(AUC=0.88). The analysis results of the Logistic regression model showed that the use of diuretics, mechanical ventilation, Glasgow Coma Scale score ≤5, vasoactive drugs, and albumin level<35 g/L were risk factors for enteral nutrition FI in patients with severe cerebral hemorrhage. A nomogram model was further constructed based on these five risk factors. The calibration curve analysis showed that the calibration curve fitted well with the ideal curve, indicating a high calibration degree of the nomogram model. The DCA results showed that when the threshold probability was 5% to 73%, the application of the nomogram model for screening could clinically benefit patients. Conclusion The construction of a nomogram model for predicting the risk of enteral nutrition FI in patients with severe cerebral hemorrhage based on machine learning methods can help to early screen high-risk patients for enteral nutrition FI and timely formulate preventive measures, thereby reducing the incidence of enteral nutrition FI in patients with severe cerebral hemorrhage.

AIM: To analyze the clinical characteristics of anticoagulant rat poisoning and vitamin K

Objective:To evaluate the performance of Autoderm, an artificial intelligence application, in the diagnosis of skin diseases in Chinese patients.Methods:Totally, 920 patients with confirmed skin diseases were prospectively recruited in the Department of Dermatology, the First Affiliated Hospital of Zhengzhou University. Every patient provided 1 clinical image, which was uploaded onto the Autoderm application for the diagnosis of skin diseases. The diagnostic sensitivity, specificity and accuracy of the Autoderm application were estimated, and the kappa values for the diagnostic agreement between the Autoderm application and dermatologists were calculated.Results:Among the 920 patients, 871 (94.7%) could be diagnosed with an Autoderm′s in-distribution skin disease, whereas 49 (5.3%) had out-of-distribution skin diseases. According to the top 1 and 3 diagnoses given by the Autoderm application for the 920 patients separately, its mean diagnostic sensitivities were 41.8% and 65.8%, mean specificities 96.8% and 91.5%, and mean accuracies 92.9% and 89.9%, respectively, and there was moderate overall agreement between the Autoderm application and dermatologists (κ = 0.420, 0.464, respectively). However, for an out-of-distribution skin disease, the Autoderm application could output 5 definitely false diagnoses.Conclusion:Autoderm may be used as as clinical decision support tool for the diagnosis of common skin diseases in most Chinese patients, with moderate diagnostic sensitivity, high specificity, and high accuracy, but misdiagnosis may occur.

Objective:To investigate the hepatitis B surface antigen (HBsAg) clearance condition and its predictive factors after treatment with nucleos(t)ide analogues to pegylated interferon-α add-on therapy in patients with chronic hepatitis B.Methods:Patients with chronic hepatitis B who visited the First Affiliated Hospital of Zhengzhou University from 2018~2019 were prospectively enrolled. HBsAg≤ 1500 IU/mL, hepatitis B e antigen-negative, HBV DNA undetectable, received antiviral treatment with nucleos(t)ide analogues for at least one year, and pegylated interferon-α add-on therapy for 48 weeks were included. The primary endpoint of study was to determine the proportion of HBsAg clearance at 72 weeks. Concurrently, the predictive factors for HBsAg clearance were analyzed. Quantitative and qualitative data were analyzed using a t-test or non-parametric test and a Fisher's exact test.Results:A total of 38 cases were included in this study, of which 13 cases obtained HBsAg clearance at 48 weeks of therapy and another six cases obtained HBsAg clearance throughout the extended treatment period of 72 weeks, accounting for 50.00% of all enrolled patients. There was a significant difference in HBsAg dynamics between the HBsAg clearance group and the non-clearance group (P < 0.05). Univariate logistic regression analysis showed that patients' age, baseline, 12-and 24-week HBsAg levels, and early HBsAg reduction were predictive factors for HBsAg clearance at 72 weeks of treatment. Multivariate logistic regression analysis showed that age (OR = 1.311; P = 0.016; 95% confidence interval: 1.051~1.635) and HBsAg levels at 24 weeks of treatment (OR = 4.481; P = 0.004; 95% confidence interval: 1.634~12.290) were independent predictors for HBsAg clearance.Conclusion:Hepatitis B e antigen-negative, nucleos(t)ide analogue treated, HBsAg ≤ 1500 IU/mL, and HBV DNA undetectable, peg-IFNα add-on treatment for 48 weeks could promote HBsAg clearance in patients with chronic hepatitis B. Six of the sixteen cases (37.50%) who did not obtain HBsAg clearance at week 48 did so with the course of therapy extended to week 72. Hence, the optimal individualized treatment strategy should be customized according to the predictors rather than the fixed 48-week course. Age (≤ 38), baseline HBsAg level (≤2.86 log 10IU/ml), HBsAg level at 24 weeks (≤ 0.92 log 10IU/ml), and 12-week HBsAg decrease from baseline (≥ 0.67 log 10IU/ml) indicate that patients are highly likely to obtain HBsAg clearance at the 72 weeks of combination therapy, in which the combined indicator based on HBsAg level ≤0.92 log 10IU/ml at 24 weeks will identify 85.0% to 100.0% of patients with HBsAg clearance.