Cellular senescence, stable cell cycle arrest that can be triggered in normal cells in response to various intrinsic and extrinsic stressors, has been highlighted as one of the most important mechanisms involved in kidney diseases. It not only serves as a fundamental biological process promoting normal organogenesis and successful wound repair but also contributes to organ dysfunction, tissue fibrosis, and the generalized aging phenotype. Moreover, senescent cells exhibit reduced regenerative capacity, which impairs renal function recovery from injuries. Importantly, senescent cells are involved in immune regulation via secreting a diverse array of proinflammatory and profibrotic factors known as senescence-associated secretory phenotype (SASP) with autocrine, paracrine, and endocrine activities. Thus, eliminating detrimental senescent cells or inhibiting SASP production holds great promise for developing innovative therapeutic strategies for kidney diseases. In this review, we summarize the current knowledge of the intricate mechanisms and hallmarks of cellular senescence in kidney diseases and emphasize novel therapeutic targets, including epigenetic regulators, G protein-coupled receptors, and lysosome-related proteins. Particularly, we highlight the recently identified senotherapeutics, which provide new therapeutic strategies for treating kidney diseases.
Humans ; Cellular Senescence/genetics* ; Kidney Diseases/pathology* ; Senescence-Associated Secretory Phenotype/physiology* ; Animals ; Epigenesis, Genetic/physiology*

Objective:To investigate whether neoadjuvant therapy can improve the prognosis of patients with pancreatic cancer.Methods:A retrospective case-control study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database on 12, 103 patients who underwent surgical treatment between January 1, 2010, and December 31, 2021. Patients were divided into the neoadjuvant therapy group ( n=3 276) and the upfront surgery group ( n=8 827) based on whether they received neoadjuvant treatment. The neoadjuvant therapy group included 2 342 patients receiving neoadjuvant chemotherapy and 934 patients receiving neoadjuvant chemoradiotherapy. The upfront surgery group consisted of 4 335 patients receiving adjuvant chemotherapy, 1 987 patients receiving adjuvant chemoradiotherapy, 63 patients receiving adjuvant radiotherapy, and 2 442 patients undergoing surgery alone. Propensity score matching was used to eliminate group differences and create a cohort with no statistical differences in other clinicopathological features except for the grouping variable. Variables such as age, gender, tumor location, race, population of residence, tumor diameter, household income, TNM stage, and information on radiotherapy and chemotherapy were used for 1∶1 case matching. T stage, N stage, and the use of radiotherapy or chemotherapy were matched exactly. After matching, 1 182 patients were included in each group: the neoadjuvant therapy group contained 1 155 patients receiving neoadjuvant chemoradiotherapy and 27 receiving neoadjuvant chemotherapy, while the upfront surgery group comprised 848 patients receiving adjuvant chemotherapy and 334 receiving adjuvant chemoradiotherapy. TNM staging was reported according to the 7th edition of the AJCC guidelines. The primary outcome was overall survival. Measurement data with skewed distributions were expressed as M( Q1, Q3), and intergroup comparisons were conducted using the Wilcoxon rank-sum test. Categorical data were compared using the chi-square test or the Fisher′s exact test. The Log-rank test and subgroup analyses to assess interactions between neoadjuvant therapy and subgroup in COX regression models were used to compare survival benefits across variables. Landmark analysis was performed to create segmented survival curves, studying the impact of neoadjuvant therapy on prognosis during different follow-up periods. Results:The neoadjuvant therapy group had a higher proportion of T 4 tumor involving celiac axis, superior mesenteric artery, and/or common hepatic artery compared to the upfront surgery group (14.7% vs 2.8%, P<0.001). Additionally, significant differences were observed between groups in terms of race, location, population of residence, age, tumor diameter, tumor stage, and adjuvant therapy regimen ( P<0.05). The median overall survival time in the neoadjuvant therapy group was 30 months, compared to 22 months in the upfront surgery group ( P<0.001). In the neoadjuvant therapy group, the median survival was 30 months for both neoadjuvant chemotherapy and chemoradiotherapy patients; in the upfront surgery group, it was 26 months for both adjuvant chemotherapy and chemoradiotherapy patients, 17 months for adjuvant radiotherapy patients, and 12 months for surgery-only patients. After propensity score matching, there were no differences in the distribution of clinical characteristics between groups ( P>0.05), and all patients in the matched cohort had received chemotherapy. The matched neoadjuvant therapy group had a longer median overall survival compared to the upfront surgery group (30 months vs 27 months, P<0.001). Subgroup interaction analysis revealed that T stage had a significant interaction with neoadjuvant therapy, both before (T 4 stage: HR=0.382, 95% CI: 0.319-0.458; T 2-T 3 stages: HR=0.696, 95% CI: 0.656-0.738; T 1 stage: HR=1.199, 95% CI: 0.867-1.657; interaction P<0.001) and after matching (T 4 stage: HR=0.581, 95% CI: 0.414-0.814; T 2-T 3 stages: HR=0.827, 95% CI: 0.734-0.931; T 1 stage: HR=1.320, 95% CI: 0.716-2.433; interaction P=0.043). Subgroup interaction analysis indicated that T 1 patients did not benefit from neoadjuvant therapy; survival curves plotted for matched T 1 patients showed no difference in survival between the neoadjuvant therapy group and the upfront surgery group ( P=0.323). Conversely, non-T 1 (T 2-T 4) stage patients showed significant survival benefits in both unmatched and matched cohorts ( P<0.001). Landmark analysis showing that the survival benefits occurred mainly in the early postoperative period of up to 3 years ( P<0.001), but there was no difference in overall survival between the neoadjuvant therapy group and the upfront surgery group of >3 years ( P>0.05). Patients with Arterial invasion (T 4 stage compared to T 1-T 3 stages) showed a similarly significant interaction with the benefit of neoadjuvant therapy in both the pre-matching cohort (interaction P<0.001) and the post-matching cohort (interaction P=0.037). Patients with T 4 stage disease in the neoadjuvant therapy group had longer overall survival compared to the upfront surgery group (median overall survival in pre-matching cohort: 30 months vs 13 months, P<0.001; median overall survival in post-matching cohort: 28 months vs 18 months, P=0.001). Among T 4 stage patients in the post-matching cohort, neoadjuvant therapy provided significant survival benefits during the early postoperative period of up to 3 years ( P=0.001). However, there was no difference in overall survival between the neoadjuvant therapy group and the direct surgery group beyond 3 years( P=0.729). Conclusions:The prognosis in the neoadjuvant therapy group was better than in the upfront surgery group. Propensity score matching and subgroup interaction analysis showed that non-T 1 and T 4 stage patients benefited more from neoadjuvant therapy, with benefits mainly seen in the early postoperative period (≤3 years).

BACKGROUND:De novo adipose regeneration induced by small extracellular vesicles has become a promising method for repairing soft tissue defects.However,due to different animal models and small extracellular vesicle application dosages,it is difficult to quantitatively compare the therapeutic effect of small extracellular vesicles from various sources on adipose regeneration. OBJECTIVE:To compare the regenerative effects of small extracellular vesicles derived from stem cells and small extracellular vesicles from tissue. METHODS:Small extracellular vesicles derived from adipose-derived stem cells and from adipose tissue were isolated by ultracentrifugation.The particle number,particle size,morphology,and protein expression of small extracellular vesicles were identified by nanoparticle tracking analysis,transmission electron microscopy and western blot assay.A quantitative and evaluative subcutaneous model for adipose regeneration in C57 mice was established using a customized silicone tube.The regenerative effects of induced de novo adipose were compared by cell counting and hematoxylin-eosin staining. RESULTS AND CONCLUSION:(1)Small extracellular vesicles derived from adipose-derived stem cells and from adipose tissue were isolated by ultracentrifugation.Both small extracellular vesicles were round-shape in transmission electron microscopy with particle size between 50-200 nm,and abundant with the small extracellular vesicles marker protein CD81,CD63 and TSG101.(2)An equal number of small extracellular vesicles were mixed with matrigel in customized silicone tubes,implanted subcutaneously in the back of mice to establish a cell-free and quantifiable adipose regeneration model.(3)On days 3 and 7 after implantation,the results of cell counting and hematoxylin-eosin staining showed that both small extracellular vesicle groups recruited more host cells than the blank group,and the small extracellular vesicles derived from adipose tissue group were superior to the small extracellular vesicles derived from adipose-derived stem cell group.(4)4 weeks after implantation,hematoxylin-eosin staining of the contents in silicone tubes showed that small extracellular vesicles induced de novo adipose regeneration in vivo,and the small extracellular vesicles derived from adipose tissue group were superior to the small extracellular vesicles derived from adipose-derived stem cell group.The above results indicated that small extracellular vesicles derived from tissues have a superior effect on inducing de novo adipose regeneration compared to small extracellular vesicles derived from stem cells.

Positron emission tomography (PET) now plays an important role in the research and development (R&D) of central nervous system (CNS) drugs. PET could characterize the biodistribution, pharmacokinetics, and receptor binding of CNS drugs quantitatively. The present review summarized the quantitative methods of PET used in the pharmacokinetics and receptor occupancy analysis of CNS drugs. Moreover, the present review listed various applications of PET supporting R&D of CNS drugs, which could provide a new direction for the R&D of CNS drugs.

OBJECTIVE To study the inhibitory effect and mechanism of curcumin and its derivatives A and B on TGF-β induced LX-2 cell fibrosis. METHODS Established the liver fibrosis model of LX-2 cells induced by TGF-β(10 ng·mL−1).The effects on cell proliferation were detected by CCK-8. The effects on cell apoptosis was detected by flow cytometry. The effects on fibrosis related factors(Collagen I, Collagen Ⅳ, Fibronectin, Vimentin, α-SMA, PDGFRβ, TGFβR1, TGFβR2, MMP2, MMP9, TIMP1 and TIMP2) protein expression and gene transcription levels were detected by Western blotting and q-PCR. RESULTS The curcumin and its derivative A and B had the inhibition effects on normal LX-2 cells, and the IC25 values were 15.7, 2.6, 10.2 μmol·L−1, respectively. Compared to the model group, the curcumin(15.7 μmol·L−1) and its derivative A(2.6 μmol·L−1) and B(10.2 μmol·L−1) had the significant inhibition effects on cell proliferation of the TGF-β induced LX-2 cells(P<0.05). The cell apoptosis rate of curcumin derivative B group was higher than the model group(P<0.05). Collagen I, Fibronectin, Vimentin, α-SMA, TGFβR1 and TIMP-1 protein expression levels in curcumin group were lower, while the protein expression level of MMP-9 was higher(P<0.05). The protein expression levels of Collagen I, Collagen IV, Fibronectin, Vimentin, α-SMA, TIMP-1 and TIMP-2 in curcumin derivative A group were lower, while the protein expression level of MMP-2 was higher(P<0.05). The protein expression levels of Collagen I, Collagen IV, Fibronectin, Vimentin, α-SMA, PDGFRβ, TGFβR1, TGFβR2, TIMP-1 and TIMP-2 in curcumin derivative B group were lower, while the protein expression level of MMP-2 was higher(P<0.05). The gene transcription levels of Collagen I, Fibronectin, α-SMA and TIMP-1 in curcumin group were lower(P<0.05). The gene transcription levels of Collagen I, Fibronectin and α-SMA in curcumin derivative A and B groups were lower(P<0.05). CONCLUSION Curcumin and its derivatives A and B inhibit the abnormal activation and proliferation of TGF-β-induced LX-2 cells, inhibit the excessive secretion and accumulation of its extracellular matrix components, and promote its degradation, thus playing an anti-fibrotic effect in vitro, especially the curcumin derivative B.

Objective:To explore the relationship among suicide risk,distress tolerance,and resilience in pa-tients with depression.Methods:A total of 130 patients with depression who met the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)diagnostic criteria were included.The Chinese version of the Mini International Neuropsychiatric Interview(MINI)suicide module was used to assess suicide risk.According to the interview results,the patients with depression were divided into suicide risk group(98 cases)and non-suicide risk group(32 cases).The Hamilton Depression Rating Scale(HAMD),Distress Tolerance Scale(DTS),10-item Connor-Davidson Resilience Scale(CD-RISC-10)were used to assess severity of depressive symptoms,level of distress tolerance and resilience,respectively.Results:The DTS total scores were positively correlated with the CD-RISC-10 total scores(r=0.50,P＜0.01).The total scores of MINI suicide module were negatively correlated with the total scores of DTS and CD-RISC-10(r=-0.34,-0.34,Ps＜0.01).Distress tolerance had a direct effect on suicide risk(β=-0.26,P＜0.05),and resilience played a mediating effect on the relationship between distress tolerance and suicide risk(β=-0.13,P＜0.05),and the mediating effect accounted for 33.5％of the total effect.Conclusion:Patients with depression with lower levels of distress tolerance may have higher suicide risk,and resilience may play a partially mediating role in the relationship between distress tolerance and suicide risk.

Objective:To establish a determination method for sodium hyaluronate injection.Methods:Sodium hyaluronate was specifically hydrolyzed by hyaluronidase,and the optimal enzymolysis conditions were enzymolysis at 37 ℃ for 4 h at 100 IU·mg-1 of enzyme reaction concentration.The analysis was performed on a of Shodex sug-ar SH1011 column(300 mm ×8 mm,6 μm)with a mobile phase of 1％H3PO4 at a flow rate of 0.6 mL·min-1.The eluent was detected at 230 nm.Results:Linear ranges were 101.38-1 013.76 μg·mL-1(r=0.999 5),RSDs of precision,stability and repeatability tests were lower than 1％,and average recovery was 100.4％,RSD=2.0％(n=9).The RAD of determination results of this method and colorimetric was 0.1％-1.2％.Conclusions:The method is accurate and reproducible,and can be used for the determination of sodium hyalur-onate injection.

Purpose/Significance To explore the application and predictive accuracy of various models in predicting the risk of ather-osclerosis in diabetic patients.Method/Process Based on the biochemical data table from the"Diabetes Complications Warning Dataset"provided by the National Population Health Science Data Center,MATLAB software is used to construct risk prediction models for diabe-tes-induced atherosclerosis.The models are built by using k-nearest neighbors(KNN),decision trees,backpropagation(BP)neural networks,and Naive Bayes algorithms,and which are subjected to comparative analysis.Result/Conclusion In terms of effectiveness,the predictive accuracy of Naive Bayes algorithm is the highest(61.6％),followed by the decision tree model(58.2％),the KNN mod-el(57.7％),and the BP neural network model(55.9％).The results of the confusion matrix and the receiver operating characteristic(ROC)curve indicate that the Naive Bayes model performs best.When comparing the models in terms of effectiveness,performance and stability,the Naive Bayes model is superior.

Objective:To preliminarily investigate the risk factors for distant metastasis and prognosis, and construct a prognostic nomogram in T 4 stage pancreatic cancer. Methods:A retrospective case-control study was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database for pancreatic patients from January 1, 2010, to December 31, 2021. Based on whether the tumor invaded the celiac axis, superior mesenteric artery, and/or common hepatic artery, 38 759 patients were divided into an arterial invasion group (T 4 stage, n=7 471) and a non-arterial invasion group (non-T 4 stage, n=31 288). Clinical and pathological data, including demographic characteristics, treatment information, and tumor data were collected. The primary outcome was overall survival. Categorical data were expressed as numbers (percentages), and intergroup comparisons were made using the chi-square test. Survival benefits were measured using the Log-Rank test. A multivariate logistic model was used to identify high-risk factors for metastasis in T 4 stage pancreatic cancer. Patients were randomly divided into training ( n=5 232) and validation ( n=2 239) sets at a 7∶3 ratio. A nomogram model was created based on independent prognostic factors from the multivariate Cox regression analysis, and the model′s predictive ability was evaluated using the C-index and calibration curves. Results:The overall metastasis rate in the arterial invasion group was higher than that in the non-arterial invasion group (32.8% vs 29.0%, P<0.001), with fewer patients showing no metastasis or single-organ metastasis (86.3% vs 89.7%, P<0.001) and higher rates of lung metastasis ( P<0.001), distant lymph node metastasis ( P<0.001), and other metastases excluding liver, lung, brain, bone, and distant lymph node metastases ( P<0.001). However, no significant difference was found between groups for liver, brain, or bone metastasis rates ( P>0.05). Surgical rates for T 4 stage patients were significantly lower than for non-T 4 stage patients (all patients: 10.7% vs 38.4%, P<0.001; M 0 stage patients: 15.0% vs 52.4%, P<0.001; M 1 stage patients: 2.1% vs 4.1%, P<0.001). Additionally, significant differences were observed in age, race, radiotherapy, chemotherapy, tumor location, tumor size, and tumor stage ( P<0.05). The median survival for patients with arterial invasion was 8 months, significantly lower than the 10-month median survival for non-arterial invasion patients ( P<0.001). The median survival for surgical patients with arterial invasion was 22 months, lower than the 24-month median for non-T 4 stage patients underwent surgery ( P<0.001) but significantly higher than for patients without surgery (T 4 stage patients without surgery: 8 months, P<0.001; non-T 4 stage patients without surgery: 6 months, P<0.001). For lymph node metastasis, patients with or without positive local lymph node metastasis had similar overall survival ( P>0.05). However, Patients with distant lymph node metastasis had significantly lower overall survival than that in patients without distant lymph node metastasis ( P<0.001). The multivariate logistic model indicated that tumor location in the body and tail ( OR=2.591, 95% CI: 2.343-2.867), positive regional lymph nodes ( OR=2.033, 95% CI: 1.836-2.252), and age <70 years old ( OR=1.183, 95% CI: 1.067-1.312) were risk factors for distant metastasis in arterial invasion patients. The multivariate Cox model showed that surgery ( HR=0.451, 95% CI: 0.405-0.503), radiotherapy ( HR=0.729, 95% CI: 0.677-0.784), chemotherapy ( HR=0.277, 95% CI: 0.258-0.297), tumor location in the body and tail ( HR=0.928, 95% CI: 0.874-0.985), and household income ≥$80, 000 ( HR=0.908, 95% CI: 0.853-0.968) were independent protective factors for prognosis in arterial invasion patients. Living in areas with a population ≤1 million ( HR=1.109, 95% CI: 1.044-1.178), age ≥70 years old ( HR=1.220, 95% CI: 1.150-1.296), larger tumor size (>2 cm but ≤4 cm: HR=1.124, 95% CI: 0.954-1.323; >4 cm: HR=1.310, 95% CI: 1.114-1.541), and having a metastatic burden (lung metastasis: HR=1.049, 95% CI: 0.869-1.267; distant lymph node metastasis: HR=1.179, 95% CI: 0.910-1.527; bone metastasis: HR=1.419, 95% CI: 0.854-2.359; brain or other metastasis: HR=1.519, 95% CI: 1.350-1.709; liver metastasis: HR=1.737, 95% CI: 1.600-1.886; two types of metastasis: HR=1.913, 95% CI: 1.689-2.168; three or more types: HR=2.436, 95% CI: 1.947-3.048) were independent risk factors for prognosis. The nomogram based on these prognostic factors had a C-index of 0.749 in the training set and 0.745 in the validation set; calibration curves in both sets were near the 45° line. Conclusions:High metastasis rates and low surgery rates are characteristic of pancreatic cancer with arterial invasion. Investigating the risk factors for distant metastasis and developing a prognostic nomogram incorporating metastatic burden hold significant clinical value for T 4 stage pancreatic cancer.

Public hospitals stand in need of strengthening cost control and improving operational efficiency to balance public welfare and economic benefits.Taking practice and exploration of ultrasound department as an exam-ple,it integrates the advantages of TDABC with cost management.Take the merits of time driver and activity driver,realizes direct cost accounting under the clinical pathway,strengthen capacity management capabilities un-der the integration of industry and finance,with a view to providing a powerful cost control tool for public hospitals to sensibly respond to pricing reform and reasonably make operational decisions.