BACKGROUND:In recent years,improving the health of skeletal muscles through exercise has become an important research concern for scholars.Appropriate exercise has a positive effect on skeletal muscles.Among them,how to activate the sphingosine kinase1(SphK1)/sphingosine-1-phase(S1P)/sphingosine-1-phase receptor2(S1PR2)signaling pathway during exercise so as to improve the health of skeletal muscles is receiving attention from researchers. OBJECTIVE:To investigate how exercise improves the health of skeletal muscles through the SphK1/S1P/S1PR2 signaling pathway,and to explore new methods for treating related muscle diseases in order to improve human skeletal muscle health. METHODS:The first author searched for relevant literature from the establishment of the database to the present in the Web of Science,PubMed,CNKI,WanFang,and VIP databases.The search terms were"signaling pathway,SphK1,S1P,S1PR2,skeletal muscle,satellite cell,myogenesis,exercise"in Chinese and English.Finally,69 articles were included for review and analysis. RESULTS AND CONCLUSION:The SphK1/S1P/S1PR2 signaling pathway is a complex regulatory network that triggers downstream signal transduction processes by SphK1 to catalyze the interaction between S1P and receptors such as S1PR2,thereby regulating multiple biological functions of cells,tissues,organs,and systems.The SphK1/S1P/S1PR2 signaling pathway can regulate satellite cell proliferation and myoblast differentiation,improving myogenesis.The physiological basis of the SphK1/S1P/S1PR2 signaling pathway and the potential impact of exercise on it were analyzed through literature research.Acute aerobic exercise can increase the expression of SphK1 in skeletal muscle.Both human and animal studies have confirmed that acute and long-term exercise can increase the expression of S1P in skeletal muscle.In addition,studies have shown that long-term resistance exercise can increase the expression of S1PR2 in skeletal muscle.Some experimental results indicate that acute and long-term exercise have no significant effect on muscle or blood S1P levels,and the reason for different results may be due to different research subjects,methods,intensities,and frequencies selected,while the specific mechanism is not yet clear.Research suggests that exercise can promote the expression of the SphK1/S1P/S1PR2 signaling pathway in skeletal muscle and regulate downstream related signaling pathways.Research on this signaling pathway may provide new strategies and methods for the treatment of skeletal muscle diseases,thereby improving skeletal muscle health.In the future,we should deepen the research on the association between SphK1/S1P/S1PR2 signaling pathway and skeletal muscle health,further reveal its regulatory relationship with satellite cells and myoblasts as well as its interactions with the upstream and downstream pathways,explore its clinical application value,take into account the changes of this pathway when formulating the rehabilitation program,explore the specific mechanisms by which different types of exercise affect the SphK1/S1P/S1PR2 signaling pathway in skeletal muscles,and use the SphK1/S1P/S1PR2 signaling pathway as a potential therapeutic target for diseases.Further development and application of human muscle models should be developed to improve research depth and accuracy.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective To investigate the effect of pneumoperitoneum pressure during robotic-assisted kidney transplantation (RAKT) on the function of the transplant kidney. Methods The data of 243 kidney transplant recipients were retrospectively analyzed and divided into open kidney transplantation (OKT) group (n=105) and RAKT group (n=138). The RAKT group was further divided into 13 mmHg group (n=67) and 7 mmHg group (n=71) based on pneumoperitoneum pressure. The donor information, recipient's preoperative general data, intraoperative data, and postoperative recovery of the three groups were compared. In the RAKT group, the renal artery, segmental artery, interlobar artery, and venous flow velocity of the transplant kidney were measured using laparoscopic ultrasound. Results There was a statistically significant difference in donor types among the groups (P<0.05), while other donor information and recipient's preoperative general data showed no statistically significant differences (all P>0.05). There were no statistically significant differences in serum creatinine and complications at 30 days and 1 year postoperatively among the groups (all P>0.05). The OKT group and 7 mmHg group had more intraoperative urine output than the 13 mmHg group. Both RAKT groups had less intraoperative blood loss and shorter hospital stays than the OKT group, and longer operation times than the OKT group (all P<0.05). There were no statistically significant differences in operation time, intraoperative blood loss, and hospital stay between the two RAKT groups (all P>0.05). The vascular flow velocity of the transplant kidney decreased at 13 mmHg compared to 7 mmHg pneumoperitoneum pressure, but the differences were not statistically significant (all P>0.05). Conclusions Controllable pneumoperitoneum pressure has a limited impact on the vascular flow velocity of the transplanted kidney. RAKT is a safe and effective surgical method under appropriate pneumoperitoneum pressure, and choosing a lower pneumoperitoneum pressure is more conducive to the early recovery of renal function postoperatively.

Objective: To establish a method for determining the molecular weight and molecular weight distribution of Poly(Lactide-co-Glycolide Acid) (PLGA) using Size Exclusion Chromatography-Refractive Index-Multiangle Laser Light Scattering (SEC-RI-MALLS) and Size Exclusion Chromatography-Refractive Index (SEC-RID), and to compare the results obtained from these two methods.
Methods: For SEC-RI-MALLS, tetrahydrofuran was used as the mobile phase, Shodex GPC KF-803L was employed as the chromatographic column with a flow rate of 1 mL·min^-1, column temperature at 30 ℃, and an injection volume of 100 μL. For SEC-RID, tetrahydrofuran was also used as the mobile phase, Agilent PLgel 5 μm MIXD-D was used as the chromatographic column with a flow rate of 1 mL·min^-1, column temperature at 30 ℃, differential detector temperature at 35 ℃, and an injection volume of 20 μL. The molecular weight and molecular weight distribution were calculated using Agilent’s GPC software. The newly established methods were validated methodologically, and the molecular weight and molecular weight distribution of 13 batches of samples were determined.
Results: The precision, accuracy, stability, and repeatability tests for SEC-RI-MALLS showed RSD values of 1.35%, 1.58%, 1.53%, and 1.26%, respectively. The SEC-RID method exhibited good linearity (r=0.999 9), with RSD values for precision, accuracy, stability, and repeatability tests (n=6) of 2.05%, 1.62%, 1.30%, and 2.97%, respectively. The results obtained from SEC-RI-MALLS were lower than those from SEC-RID, and the molecular weight distribution coefficient was smaller, but the results from the paired T-test performed with the value measured by SEC-RID method and the value measured by SEC-RI-MALLS method multiplied a conversion coefficient of 1.5 showed no significant difference between the two methods.
Conclusion: Both methods are stable and reliable, and can be used for the determination of PLGA molecular weight and molecular weight distribution based on the specific situations.

We propose a strategy to reduce unnecessary prostate biopsies in Chinese patients with total prostate-specific antigen (tPSA) >10 ng ml -1 and Prostate Imaging Reporting and Data System (PI-RADS) scores between 1 and 3. Clinical data derived from 517 patients of The First Affiliated Hospital of USTC (Hefei, China) from January 2020 to December 2023 who met the screening criteria for the study were retrospectively collected. Independent predictors were identified via univariate and multivariate logistic regression analysis. The diagnostic capacity of clinical variables was evaluated using the receiver operating characteristic (ROC) curves and area under the curve (AUC). A prostate biopsy strategy was developed via risk stratification. Of the 517 patients, 17/348 (4.9%) with PI-RADS 1-2 were diagnosed with clinically significant prostate cancer (csPCa), and 27/169 (16.0%) patients with PI-RADS 3 were diagnosed with csPCa. The appropriate prostate-specific antigen density (PSAD) cut-off values were 0.45 ng ml -2 for PI-RADS 1-2 patients and 0.3 ng ml -2 for PI-RADS 3 patients. The appropriate prostate volume (PV) cut-off values were 40 ml for PI-RADS 1-2 patients and 50 ml for PI-RADS 3 patients. The prostate biopsy strategy based on PSAD and PV developed in this study can reduce unnecessary prostate biopsies in patients with tPSA >10 ng ml -1 and PI-RADS 1-3. In the study, 66.5% (344/517) patients did not need to undergo prostate biopsy, at the expense of missing only 1.7% (6/344) patients with csPCa.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Prostate-Specific Antigen/blood* ; Aged ; Middle Aged ; Retrospective Studies ; Prostate/diagnostic imaging* ; Unnecessary Procedures/statistics & numerical data* ; Biopsy/statistics & numerical data* ; China ; ROC Curve

ObjectiveTo investigate the clinical features and outcomes of recompensation in patients with autoimmune hepatitis （AIH）-related decompensated cirrhosis， to identify independent predictive factors， and to construct a nomogram prediction model for the probability of recompensation. MethodsA retrospective cohort study was conducted among the adult patients with AIH-related decompensated cirrhosis who were admitted to The Fifth Medical Center of PLA General Hospital from January 2015 to August 2023 （n=211）. The primary endpoint was achievement of recompensation， and the secondary endpoint was liver-related death or liver transplantation. According to the outcome of the patients at the end of the follow-up， the patients were divided into the recompensation group （n=16） and the persistent decompensation group（n=150）.The independent-samples t test was used for comparison of normally distributed continuous data with homogeneity of variance， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data with heterogeneity of variance； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups； the Kaplan-Meier method was used for survival analysis； the Cox proportional-hazards regression model was used to identify independent predictive factors， and a nomogram model was constructed and validated. ResultsA total of 211 patients were enrolled， with a median age of 55.0 years and a median follow-up time of 44.0 months， and female patients accounted for 87.2%. Among the 211 patients， 61 （with a cumulative proportion of 35.5%） achieved recompensation. Compared with the persistent decompensation group， the recompensation group had significantly higher white blood cell count， platelet count （PLT）， total bilirubin （TBil）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， total bile acid， prothrombin time， international normalized ratio （INR）， SMA positive rate， Model for End-Stage Liver Disease （MELD） score， Child-Pugh score， and rate of use of glucocorticoids （all P0.05）， as well as significantly lower age at baseline， number of complications， and death/liver transplantation rate （all P0.05）. At 3 and 12 months after treatment， the recompensation group had continuous improvements in AST， TBil， INR， IgG， MELD score， and Child-Pugh score， which were significantly lower than the values in the persistent decompensation group （all P0.05）， alongside with continuous increases in PLT and albumin， which were significantly higher than the values in the persistent decompensation group （P0.05）. The multivariate Cox regression analysis showed that baseline ALT （hazard ratio ［HR］=1.067， 95% confidence interval ［CI］： 1.010 — 1.127， P=0.021）， IgG （HR=0.463，95%CI：0.258 — 0.833， P=0.010）， SMA positivity （HR=3.122，95%CI：1.768 — 5.515， P0.001）， and glucocorticoid therapy （HR=20.651，95%CI：8.744 — 48.770， P0.001） were independent predictive factors for recompensation， and the nomogram model based on these predictive factors showed excellent predictive performance （C-index=0.87，95%CI：0.84 — 0.90）. ConclusionAchieving recompensation significantly improves clinical outcomes in patients with AIH-related decompensated cirrhosis. Baseline SMA positivity， a high level of ALT， a low level of IgG， and corticosteroid therapy are independent predictive factors for recompensation. The predictive model constructed based on these factors can provide a basis for decision-making in individualized clinical management.