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.

ObjectiveTo screen the differential markers by analyzing volatile components in Dalbergia odorifera and its counterfeits， in order to provide reference for authentication of D. odorifera. MethodThe volatile components in D. odorifera and its counterfeits were detected by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the GC conditions were heated by procedure（the initial temperature of the column was 50 ℃， the retention time was 1 min， and then the temperature was raised to 300 ℃ at 10 ℃ for 10 min）， the carrier gas was helium， and the flow rate was 1.0 mL·min^-1， the split ratio was 10∶1， and the injection volume was 1 mL. The MS conditions used electron bombardment ionization（EI） with the scanning range of m/z 35-550. The compound species were identified by database matching， the relative content of each component was calculated by the peak area normalization method， and principal component analysis（PCA）， orthogonal partial least squares-discrimination analysis（OPLS-DA） and cluster analysis were performed on the detection results by SIMCA 14.1 software， and the differential components of D. odorifera and its counterfeits were screened out according to the variable importance in the projection（VIP） value>2 and P<0.05. ResultA total of 26， 17， 8， 22， 24 and 7 volatile components were identified from D. odorifera， D. bariensis， D. latifolia， D. benthamii， D. pinnata and D. cochinchinensis， respectively. Among them， there were 11 unique volatile components of D. odorifera， 6 unique volatile components of D. bariensis， 3 unique volatile components of D. latifolia， 6 unique volatile components of D. benthamii， 8 unique volatile components of D. pinnata， 4 unique volatile components of D. cochinchinensis. The PCA results showed that， except for D. latifolia and D. cochinchinensis， which could not be clearly distinguished， D. odorifera and other counterfeits could be distributed in a certain area， respectively. The OPLS-DA results showed that D. odorifera and its five counterfeits were clustered into one group each， indicating significant differences in volatile components between D. odorifera and its counterfeits. Finally， a total of 31 differential markers of volatile components between D. odoriferae and its counterfeits were screened. ConclusionHS-GC-MS combined with SIMCA 14.1 software can systematically elucidate the volatile differential components between D. odorifera and its counterfeits， which is suitable for rapid identification of them.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Masquelet technique has become a safe and effective treatment for chronic osteomyelitis of the long limb shaft. The vast majority of osteomyelitis can be ultimately controlled, segmental bone defects repaired and limb functions restored. Accumulation of clinical applications and development of imaging technology have led to rapid progress in determining the infection scope of chronic limb osteomyelitis, precise preoperative design for repair of soft tissue defects, evaluation of bone structure stability, and use of bone grafting materials. This article reviews the progress of Masquelet technique in the treatment of chronic limb osteomyelitis from the aspects of its theoretical foundation, key operations, and selection of fixation methods, hoping to deepen the understanding of current Masquelet technique.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Heart failure(HF)is a highly morbid syndrome that seriously affects the physical and mental health of patients and generates an enormous socio-economic burden.In addition to cardiac myocyte oxidative stress and apoptosis,which are considered mechanisms for the development of HF,alterations in cardiac energy metabolism and pathological autophagy also contribute to cardiac abnormalities and ultimately HF.Silent information regulator 1(Sirt1)and adenosine monophosphate-activated protein kinase(AMPK)are nicotinamide adenine dinucleotide(NAD+)-dependent deacetylases and phosphorylated kinases,respectively.They play similar roles in regulating some pathological processes of the heart through regulating targets such as peroxisome proliferator-activated receptor γ coactivator 1α(PGC-1α),protein 38 mitogen-activated protein kinase(p38 MAPK),peroxisome proliferator-activated receptors(PPARs),and mammalian target of rapamycin(mTOR).We summarized the synergistic effects of Sirt1 and AMPK in the heart,and listed the traditional Chinese medicine(TCM)that exhibit cardioprotective properties by modulating the Sirt1/AMPK pathway,to provide a basis for the development of Sirt1/AMPK activators or inhibitors for the treatment of HF and other cardiovascular diseases(CVDs).

Objective:Based on the health ecological model, this paper systematically explores the influencing factors of mild cognitive impairment among the elderly herders in Nanshan pastoral area of Xinjiang, and provides the basis for local medical institutions to formulate prevention and control strategies for mild cognitive impairment among the elderly herders.Methods:A total of 1 145 valid questionnaires were collected, all of them were permanent herdsmen aged over 60 years in Nanshan pastoral area of Xinjiang were selected from June 2022 to February 2023 by stratified cluster random sampling method in a cross-sectional survey. Under the guidance of health ecological model, the research variables were included from five dimensions: physiology, psychology, behavioral lifestyle, social network and medical and health environment, and questionnaires were conducted. SPSS 23.0 was used for chi-square test and binary Logistic regression to analyze the influencing factors of mild cognitive impairment in elderly herders.Results:There were 564 males and 581 females with age of (70.84 ± 5.69) years old in the study. The prevalence rate of mild cognitive impairment among elderly herdsmen in Nanshan pastoral area of Xinjiang was 36.1%(413/1 145). Binary Logistic regression analysis showed that: personal monthly income (1 000-2 999 yuan)( OR = 0.583, 95% CI 0.366 - 0.926, P<0.05), education level (junior high school and above)( OR = 0.479, 95% CI 0.315 - 0.728, P<0.01) were the protective factors for mild cognitive impairment among the elderly herdsmen in Nanshan pastoral area. Hypertension ( OR = 1.842, 95% CI 1.256 - 2.702, P<0.01), dyslipidemia ( OR = 1.449, 95% CI 1.069 - 1.964, P<0.05) and chronic pain ( OR = 1.549, 95% CI 1.082 - 2.216, P<0.05) were the risk factors of mild cognitive impairment in elderly herders in Nanshan pastoral area. Conclusions:The prevalence rate of mild cognitive impairment among elderly herders in Nanshan pastoral area of Xinjiang is high, so it is necessary to carry out mild cognitive impairment screening as soon as possible, especially focusing on people suffering from hypertension, dyslipidemia and chronic pain, and making intervention plans to delay the occurrence and development of mild cognitive impairment and improve the quality of life of elderly herders.

Objective:To investigate the risk factors of pleural effusion after spinal separation surgery for patients with spinal metastatic tumors.Methods:A total of 427 patients with spinal metastatic tumors from January 2014 to January 2022 in the Second Affiliated Hospital of Zhejiang University School of Medicine were retrospectively analyzed. There were 252 males and 175 females, with an average age of 59±12 years (range, 15-87 years). All patients underwent separation surgery. Based on the chest CT within 1 month after surgery, the volume of pleural effusion was measured individually by reconstruction software. Pleural effusion was defined as small volume (0-500 ml), moderate volume (500-1 000 ml), and large volume (above 1 000 ml). Baseline data and perioperative clinical outcomes were compared between the groups, and indicators with statistically significant differences were included in a binary logistic regression analysis to determine the independent risk factors for the development of pleural effusion after isolation of spinal metastatic cancer. Receiver operating characteristic (ROC) curves were conducted to calculate the area under the curve (AUC) for each independent risk factor.Results:All patients successfully completed the operation. Among the 427 patients, there were 35 cases of large pleural effusion, 42 cases of moderate pleural effusion, and 350 cases of small pleural effusion. There were significant differences in tumor size (χ 2=9.485, P=0.013), intraoperative blood loss ( Z=-2.503, P=0.011), blood transfusion ( Z=-2.983, P=0.003), preoperative total protein ( Z=2.681, P=0.007), preoperative albumin ( Z=1.720, P= 0.085), postoperative hemoglobin ( t=2.950, P=0.008), postoperative total protein ( Z=4.192, P<0.001), and postoperative albumin ( t=2.268, P=0.032) in the large pleural effusion group versus the small and moderate pleural effusion group. Logistic regression analysis showed that decreased preoperative albumin ( OR=0.89, P=0.045) and metastases located in the thoracic spine ( OR=4.01, P=0.039) were independent risk factors for the occurrence of large pleural effusion after separation surgery. The ROC curve showed that the AUC and 95% CI for preoperative albumin, lesion location, and the combined model were 0.637 (0.54, 0.74), 0.421 (0.36, 0.48), and 0.883 (0.81, 0.92). The combined predictive model showed good predictive value. Conclusion:The volume of pleural effusion can be measured individually and quantitatively based on chest CT. Decreased preoperative albumin and metastases located in the thoracic spine are independent risk factors for the occurrence of large pleural effusion after separation surgery. The combined prediction of the two factors has better predictive efficacy.