Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

Objective To enhance the data quality of the front page of inpatient medical records.Methods Ten coders were engaged to perform manual quality control,and system verification rules were integrated for the basic information,diagnosis,treatment information,and hospitalization process information on the front page of inpatient medical records before and after the implementation of grid management.Results A total of 808 defects were found on the front pages of 728 inpatient medical re-cords.Among these deficiencies,basic information,diagnosis and treatment information,and process information accounted for 40.84％,3.96％,and 55.20％respectively.Totally,282 defects on the front pages of the 796 inpatient medical records man-aged under grid management.Among them,basic information,diagnosis and treatment information,and process information ac-counted for 39.00％,7.80％,and 53.19％,respectively.The overall defect rate was significantly decreased under grid manage-ment compared to the rate without it,(x2=6.553 5,P＜0.05).After the implementation of grid management,the numbers of the defects in admission condition,ID number incompletion,incision healing status,and coding were significantly decreased(P＜0.05).After the normalized coding of the ID number,the defect in ID number incompletion on the pages disappeared con-sequently.Conclusion Grid management reduces communication and feedback time,improves the data quality of the front page of inpatient medical records,and enhances problem-solving efficiency.It is important to coordinate the control of the data on the front pages of inpatient medical records to enhance standardization,consistency,and integrity.Moreover,it can also be applied to other areas of hospital management,offering scientific methods to optimize overall hospital operations.

Humans ; Urinary Bladder Neoplasms/pathology* ; Carcinoma, Transitional Cell/pathology* ; Genetic Markers ; Biomarkers, Tumor/genetics* ; Urothelium/pathology*

Objective To formulate strategies for the construction of clinical specialty capabilities in hospitals,providing relevant experiences for peers to enhance clinical specialty capability development.Methods Taking a public hospital in Shenz-hen as an example,this study employs SWOT-PEST analysis to explore the external environment(opportunities and challenges)and internal environment(strengths and weaknesses)facing clinical specialty capability construction from four aspects:policy en-vironment,economic environment,social environment,and technological environment.Results The hospital's strengths include opportunities such as the construction of a national science city in the region,an aging population,and increased governmental fo-cus on the healthcare industry.Additional strengths include the integration of the hospital with community health services,strong support for key disciplines,and a deeply ingrained commitment to public welfare services.However,weaknesses include intense regional competition,the development of external internet hospitals,and increasingly strained doctor-patient relationships.The hospital also faces challenges due to inadequate research capabilities and levels,insufficient information technology infrastructure,and an incomplete internal incentive mechanism.Conclusion Based on the results of the SWOT-PEST analysis,the following strategies are proposed:SO Strategies(Strengths+Opportunities):Collaborate between the hospital and community health cen-ters;attract and cultivate talent to develop specialty clusters and key disciplines;and implement a comprehensive health manage-ment plan for the entire population and lifecycle in the region.ST Strategies(Strengths+Threats):Integrate management across various campuses and community health services,and pursue differentiated development.WO Strategies(Weaknesses+Opportu-nities):Engage in school-local collaborations for cross-disciplinary research innovation and translation;and deepen performance distribution reforms.WT Strategies(Weaknesses+Threats):Strengthen clinical research;enhance the hospital's information technology capabilities;and improve patient experience.Ultimately,these strategies aim to support the construction of clinical specialty capabilities within the hospital.

Objective To evaluate the prognostic value of urine paraquat (PQ) concentrations combined with poisoning time and creatinine clearance rate (CCr) on prognosis of patients with acute paraquat poisoning (APP). Methods A retrospective case control study was conducted. Clinical data of 96 patients with APP admitted to Department of Emergency of Shengjing Hospital of China Medical University from March 2014 to May 2016 were analyzed. The gender, age, body weight, urine PQ concentrations (determined by semi-quantitative colorimetric method), poisoning time (time from oral poison to urine detection) and CCr of patients were collected, and poisoning index (poisoning index = urine PQ concentrations × poisoning time/CCr) and simplified poisoning index (simplified poisoning index = urine PQ concentrations × poisoning time) were calculated. The patients were divided into death group and survival group according to 2-month outcome after poisoned with clinical data and telephone follow-up. The urine PQ concentrations, poisoning index, and simplified poisoning index between the two groups were compared. Binary classification logistic regression was used to analyze the risk factors affecting prognosis. Receiver-operating characteristic curve (ROC) and diagnostic test were used to analyze the prognostic value of the parameters. Results Compared with survival group, the urine PQ concentrations [mg/L: 30.00 (10.00, 100.00) vs. 10.00 (3.00, 10.00)], poisoning index [mg·h-1·μmol-1: 12.72 (1.86, 33.75) vs. 0.56 (0.18, 1.12)], and simplified poisoning index [mg·h-1·L-1: 600.00 (150.00, 1 000.00) vs. 60.00 (18.00, 120.00)] in death group were significantly increased (all P < 0.01). It was shown by logistic regression analysis that both urine PQ concentrations [odds ratio (OR) = 1.046, 95% confidence interval (95%CI) = 1.006-1.087, P = 0.022] and poisoning index (OR = 1.353, 95%CI = 0.029-1.815, P = 0.031) were independent risk factors affecting the prognosis of patients with APP. It was shown by ROC curve and diagnostic test that the poisoning index had greater area under ROC curve (AUC was 0.902) for evaluating the prognosis of patients with APP. When the best cut-off value was greater than 1.23 mg·h-1·μmol-1, the sensitivity was 90.91%, and the specificity was 73.08%. The AUC of urine PQ concentrations for evaluating the prognosis was 0.759. When the best cut-off value was greater than 20.00 mg/L, the sensitivity was 63.64%, and the specificity was 76.92%. The AUC of simplified poisoning index for evaluating the prognosis was 0.846. When the best cut-off value was greater than 135.00 mg·h-1·L-1, the sensitivity was 81.82%, and the specificity was 76.92%. Conclusion The poisoning index calculated with urine PQ concentrations combined with poisoning time and CCr has prognostic value for prognosis of APP patients, and the prognostic value of poisoning index is greater than that of the urine PQ concentrations alone.