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Abstract Background Frailty becomes more pronounced with advancing age, tightly intertwined with adverse clinical outcomes. Across diverse medical disciplines, frailty is now universally recognized as not only a risk factor but also a predictive indicator for unfavorable clinical prognosis. Methods This study was a retrospective cohort study that included clinical data from patients (aged ≥ 65 years) with esophageal cancer treated surgically at the First Affiliated Hospital of Anhui Medical University in 2021. For each patient, we calculated their 11-index modified frailty index(mFI-11) scores and categorized the patients into a frailty group (mFI-11hign) and a non-frailty group (mFI-11low) based on the optimal grouping cutoff value of 0.27 from a previous study. The primary study index was the incidence of postoperative pulmonary infection, arrhythmia, anastomotic fistula, chylothorax, and electrolyte disturbance complications. Secondary study indicators included postoperative ICU stay, total hospitalization time, readmission rate within 30 days of discharge, and mortality within 30 days after surgery. We performed univariate and multivariate analyses to assess the association between mFI-11 and adverse outcomes as well as postoperative complications. Results Five hundred and fifteen patients were included, including 64.9% (334/515) in the non-frailty group and 35.1% (181/515) in the frailty group. Comparing postoperative complication rates between the two groups revealed lower incidences of postoperative anastomotic fistula (21.5% vs. 4.5%), chylothorax (16.0% vs. 2.1%), cardiac arrhythmia (61.9% vs. 9.9%), pulmonary infections (85.1% vs. 26.6%), and electrolyte disturbance (84.5% vs. 15.0%) in patients of the non-frailty group was lower than that in the frailty group (p

Summary: The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model “Modified-DeepSurv” for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model “CPH.” The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946–0.974) and 0.836 (95% CI: 0.774–0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908–0.964) and 0.777 (95%CI: 0.633–0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.

Alcohol-related liver fibrosis (ALF) is a form of alcohol-related liver disease (ALD) that generally occurs in response to heavy long-term drinking. Ecto-5′-nucleotidase (NT5E), also known as CD73, is a cytomembrane protein linked to the cell membrane via a GPI anchor that regulates the conversion of extracellular ATP to adenosine. Adenosine and its receptors are important regulators of the cellular response. Previous studies showed that CD73 and adenosine A1 receptor (A1R) were important in alcohol-related liver disease, however the exact mechanism is unclear. The aim of this study was to elucidate the role and mechanism of the CD73-A1R axis in both a murine model of alcohol and carbon tetrachloride (CCl4) induced ALF and in an in vitro model of fibrosis induced by acetaldehyde. The degree of liver injury was determined by measuring serum AST and ALT levels, H & E staining, and Masson’s trichrome staining. The expression levels of fibrosis indicators and PLC-IP3-Ca2+/DAG-PKC signaling pathway were detected by quantitative real-time PCR, western blotting, ELISA, and calcium assay. Hepatic stellate cell (HSC) apoptosis was detected using the Annexin V-FITC/PI cell apoptosis detection kit. Knockdown of CD73 significantly attenuated the accumulation of α-SMA and COL1a1 damaged the histological architecture of the mouse liver induced by alcohol and CCl4. In vitro, CD73 inhibition attenuated acetaldehyde-induced fibrosis and downregulated A1R expression in HSC-T6 cells. Inhibition of CD73/A1R downregulated the expression of the PLC-IP3-Ca2+/DAG-PKC signaling pathway. In addition, silencing of CD73/A1R promoted apoptosis in HSC-T6 cells. In conclusion, the CD73-A1R axis can regulate the activation and apoptosis of HSCs through the PLC-IP3-Ca2+/DAG-PKC signaling pathway.

The detection of Lingwu long jujubes in a natural environment is of great significance for robotic picking. Therefore, a lightweight network of target detection based on the SSD (single shot multi-box detector) is presented to meet the requirements of a low computational complexity and enhanced precision. Traditional object detection methods need to load pre-trained weights, cannot change the network structure, and are limited by equipment resource conditions. This study proposes a lightweight SSD object detection method that can achieve a high detection accuracy without loading pre-trained weights and replace the Peleenet network with VGG16 as the trunk, which can acquire additional inputs from all of the previous layers and provide itself characteristic maps to all of the following layers. The coordinate attention module and global attention mechanism are added in the dense block, which boost models to more accurately locate and identify objects of interest. The Inceptionv2 module has been replaced in the first three additional layers of the SSD structure, so the multi-scale structure can enhance the capacity of the model to retrieve the characteristic messages. The output of each additional level is appended to the export of the sub-level through convolution and pooling operations in order to realize the integration of the image feature messages between the various levels. A dataset containing images of the Lingwu long jujubes was generated and augmented using pre-processing techniques such as noise reinforcement, light variation, and image spinning. To compare the performance of the modified SSD model to the original model, a number of experiments were conducted. The results indicate that the mAP (mean average precision) of the modified SSD algorithm for object inspection is 97.32%, the speed of detection is 41.15 fps, and the parameters are compressed to 30.37% of the original networks for the same Lingwu long jujubes datasets without loading pre-trained weights. The improved SSD target detection algorithm realizes a reduction in complexity, which is available for the lightweight adoption to a mobile platform and it provides references for the visual detection of robotic picking.