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Objective: To evaluate subclinical LV systolic dysfunction in obese patients by global myocardial work (MW)., Methods: A total of 589 obese patients and 100 normal controls were enrolled in the study. The global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE) were generated by a noninvasive pressure-strain loop (PSL) in apical 3-, 4- and 2-chamber views acquired by two-dimensional echocardiography. All obese patients were divided into three groups: class I obesity (mild) 30-35 kg/m 2 , class II obesity (moderate) 35-40 kg/m 2 and class III obesity (severe) > 40 kg/m 2 . These values were compared among the three groups. The independent influencing factors of subclinical LV systolic dysfunction in obese patients were explored by constructing a multiple regression model. ROC analysis was performed to determine the performance of MW to detect subclinical LV systolic dysfunction in obese patients., Results: The absolute value of GLS in obese patients was significantly lower than that in normal controls (P < 0.001). The values of GWI, GCW, GWE and GCW/GWW in obese patients were significantly lower than those in normal controls (P < 0.05), while GWW was significantly larger than that in normal controls (P < 0.001). Subgroup analysis and trend analysis showed that the values of GWI, GCW, GWE and GCW/GWW in severe obese patients were lower than those in moderate obese patients and lower than those in mild obese patients (P < 0.01), while GWW in severe obese patients was larger than that in moderate obese patients and larger than that in mild obese patients (P < 0.05). Female sex, BMI and SBP were independent influencing factors of impaired GWI (β = 0.15, P < 0.001) (β=-0.18, P < 0.001) (β = 0.50, P < 0.001) and GCW (β = 0.17, P < 0.001) (β=-0.19, P < 0.001) (β = 0.57, P < 0.001). ROC analysis showed that the AUC of the combined global MW was significantly higher than the AUCs of the individual indices (P < 0.05)., Conclusion: In this study, we conclude that subclinical LV systolic dysfunction was detected by the novel global MW technique in obese patients. Elevated BMI in obese patients results in an increased risk of subclinical LV systolic dysfunction, although the LVEF is normal. Controlling BMI in obese patients may reduce the impairment to the LV myocardial systolic function. Global MW is a novel and reproducible technique that can be well applied in the clinical evaluation of subclinical LV systolic dysfunction., (© 2023. The Author(s).)

In this study, we characterized a WRKY family member gene, SsWRKY1, which is located in the nucleus and contains multiple stress-related cis-acting elements. In addition, constructed SsWRKY1-overexpressing Arabidopsis thaliana had higher antioxidant enzyme activity and proline content under drought stress conditions, with lower malondialdehyde content and reactive oxygen species (ROS) accumulation, and the expression levels of six stress-related genes were significantly upregulated. This indicates that the overexpression of SsWRKY1 in Arabidopsis thaliana improves resistance to drought stress. SsWRKY1 does not have transcriptional autoactivation activity in yeast cells. The yeast two-hybrid (Y2H) system and the S. spontaneum cDNA library were used to screen 21 potential proteins that interact with SsWRKY1, and the interaction between SsWRKY1 and ATAF2 was verified by GST pull-down assay. In summary, our results indicate that SsWRKY1 plays an important role in the response to drought stress and provide initial insights into the molecular mechanism of SsWRKY1 in response to drought stress., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Objective: To investigate the main factors affecting the surgical level of major amputations in patients with severe diabetic foot., Methods: A case-control study was conducted to analyze the clinical data of severe diabetic foot patients who had major amputations and were admitted to the Intensive Care Unit (ICU), Air Force Hospital of PLA Eastern Theater Command between July 2020 and July 2022. According to their surgical level of amputation, patients were divided into transtibial amputation (TT) group and transfemoral amputation (TF) group. Correlation analysis was performed with the clinical data of the patients, and multivariate logistic regression was performed to screen for relevant factors affecting the surgical level of major amputation., Results: The data of 48 patients with major amputations were collected, including 15 patients in the TT group and 33 patients in the TF group. The proportion of patients who had cardiovascular and cerebrovascular complications in the TT group was lower than that in the TF group (26.67% [4/15] vs. 57.58% [19/33], P <0.05), the proportion of patients who had lower extremity arterial intervention history was higher in the TT group than that in the TF group (40% [6/15] vs. 9.09% [3/33], P <0.05), and the proportion of patients who had elevated creatinine level was lower in the TT group than that in the TF group (70.31±22.98 vs. 127.98±108.38, P <0.05). Moreover, the history of lower extremity arterial intervention may be an independent protective factor for determining the surgical level of major amputations (odds ratio [ OR ]=0.15, 95% confidence interval [ CI ]: 0.03-0.72, P =0.018)., Conclusion: History of cardiovascular and cerebrovascular diseases, serum creatinine level and history of lower extremity arterial intervention are the main factors affecting the surgical level of major amputations in patients with severe diabetic foot, and the history of lower extremity arterial intervention may be an independent protective factor., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To investigate the potential independent risk factors of body mass rebound following laparoscopic sleeve gastrectomy (LSG) and construct a nomogram prediction model based on these factors. Methods: In this retrospective observational study, patients with obesity who had undergone LSG at the Department of Gastrointestinal Surgery of the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University between January 2015 and July 2017 were retrospectively enrolled. These patients were divided according to their status of postoperative body mass rebound. The inclusion criteria were patients aged between 16 and 65 years who had undergone LSG bariatric surgery with surgical indications according to the 2014 Chinese Guidelines for the Surgical Management of Obesity and Type 2 Diabetes Mellitus. The exclusion criteria were patients who had undergone other bariatric surgeries, who were taking weight-loss drugs or drugs that affected their body weight, who had severe gastroesophageal reflux and hiatal hernia, who were pregnant, who had incomplete clinical data, and who were lost to follow-up or were followed up for <3 years. In total, 241 patients with obesity (69 males and 172 females) who had undergone LSG surgery were enrolled. The mean age and body mass index (BMI) were (29.9±5.8) years and (40.8±4.8) kg/m 2 , respectively. The patients were followed up till July 2022, with a focus on their body weight. Postoperative body mass rebound was defined as a percentage increase of ≥10% from the nadir body mass, which was the lowest body mass during the 3-year follow-up period. The body weight rebound following LSG and its influencing factors were observed, based on which a nomogram model was constructed and evaluated. The relationships between the patients' basic data, clinical indicators, preoperative hematological indicators, postoperative indicators, and body weight rebound following LSG were analyzed via univariate analysis. Independent risk factors were further screened by multivariate logistic regression analysis. Factors with a statistically significant difference were included into the nomogram prediction model. Moreover, the model was internally (modeling set) and externally (validation set, 80 baseline data-matched patients with obesity from our center) validated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) via R software. ROC curve analysis was used to analyze the predictive and cutoff values of the measurement data for body mass rebound. Results: Overall, 90 patients (37.3%) exhibited postoperative body weight rebound, with the lowest BMI of (29.5±2.6) kg/m 2 and time to reach the lowest BMI of (15.4±2.3) months; 151 patients (62.7%) did not exhibit body weight rebound, with the lowest BMI of (29.8±2.3) kg/m 2 and time to reach the lowest BMI of (14.7±2.1) months. The results of univariate analysis showed that BMI, depression, anxiety, C-reactive protein (CRP) levels, systemic immune inflammatory index (SII), prognostic nutritional index (PNI), and albumin/fibrinogen ratio (AFR) were associated with body weight rebound following LSG with statistically significant differences (all P <0.05). The results of multivariate regression analyses suggested that depression [odds ration (OR) = 1.31, 95% confidence interval (CI): 1.08-1.62, P =0.010], preoperative CRP levels of ≥8 mg/L (OR = 1.34, 95% CI: 1.09-1.69, P =0.007), SII (OR = 0.58, 95% CI: 0.41-0.86, P =0.013), PNI (OR = 2.06, 95% CI: 1.03-4.21, P =0.007), and AFR (OR: 0.49, 95% CI: 0.33-0.69, P =0.011) were five independent risk factors for body mass rebound. A nomogram prediction model was constructed based on the multivariate analysis results. The scores of PNI, SII, AFR, CRP, and depression were 92.5, 100, 72.5, 25, and 27.5, respectively. The total score was calculated by adding the individual scores of each risk factor, which was used to calculate the probability of body mass rebound following LSG. The evaluation results of the nomogram model showed a C-index of 0.713 and 0.762, sensitivity of 0.656 and 0.594, and specificity of 0.715 and 0.909 in the modeling and validation sets, respectively. The calibration curve analysis and DCA indicated that the nomogram model has a good predictive value for body mass rebound after LSG. Conclusion: Preoperative depression, CRP of ≥8 mg/L, SII, PNI, and AFR were independent risk factors for body mass rebound following LSG. Hence, the nomogram prediction model based on these factors can effectively predict body mass rebound in patients undergoing LSG.