Model test study on bearing effect prestressing anchors in shallow buried tunnels

The use of prestressing anchor active support technology in tunnel engineering is becoming morecommon.However, the support characteristics and mechanism of action have not been fully understood for shallow, large-span rocky tunnels.In order to investigate the bearing characteristics of the surrounding rock under the prestressed anchor support system, a concealed excavation station of Qingdao Metro Line 6 was used as the engineering background, and based on the similarity principle of formulating experimental materials for stratum and support structure modelling, the bearing characteristics of the anchors under the prestressed anchor and ordinary anchor support were investigated by hydraulic loading tests.The results indicate that: ① The interaction between prestressed anchors and the surrounding rock creates a load-bearing anchor solid that can effectively support most of the overlying loads.The application of prestressed anchors during the overburden loading process increased the warning load value of tunnel instability damage by 42.8% and the ultimate load value by 41.2%.② The overburden loading process involved the prestressing anchors going through the tight anchorage load holding stage and the de-anchorage unloading stage.Simultaneously, the lining underwent the strain accumulation stage, strain surge stage, and strain release stage during the overlay loading process.③ The prestressed anchor under active support has better force synergy with the rock body than an ordinary anchor, without the axial force mutation phenomenon.This allows the support performance of the anchor to be fully utilized.Additionally, the prestressed active support effectively inhibits the development of fissures and significantly improves the overall stability of the tunnel

The bidirectional relationship between sarcopenia and disability in China: a longitudinal study from CHARLS

ObjectivesSarcopenia and disability represent significant concerns impacting the health of older people. This study aimed to explore the bidirectional relationship between sarcopenia and disability in Chinese older people.MethodsThis study recruited older people ≥60 years old from the China Health and Retirement Longitudinal Study. In phase I, the study analyzed the relation between disability and subsequent sarcopenia using multinomial logistic regression models. Conversely, in phase II, the study assessed whether sarcopenia was associated with future disability using binary logistic regression models.ResultsIn phase I, 65 (16.80%) new cases of possible sarcopenia, 18 (4.65%) cases of sarcopenia, and 9 (2.33%) cases of severe sarcopenia were observed in the disabled older people and 282 (10.96%) new cases of possible sarcopenia, 97 (3.77%) cases of sarcopenia, 35 (1.36%) cases of severe sarcopenia were observed in the older people without disability. The OR (95% CI) for sarcopenia in older disabled individuals compared to those without disability was 1.61 (1.25–2.07). Adjusting for all covariates in 2011, the OR (95% CI) value for disabled individuals vs. those without disability was 1.35 (1.02–1.79). Subgroup analyses showed that disabled participants aged < 80 years were more likely to have sarcopenia (OR = 1.42, 95% CI: 1.07–1.89), and the risk of sarcopenia did not differ significantly between sex subgroups. In phase II, 114 cases (33.83%) in the possible sarcopenia patients, 85 cases (28.91%) in the sarcopenia patients, 23 cases (35.94%) in the severe sarcopenia patients, and 501 cases (16.10%) in the individuals without sarcopenia showed symptoms of disability. The OR (95% CI) for disability was 2.66 (2.08–3.40) in the possible sarcopenia patients, 2.12 (1.62–2.77) in the sarcopenia patients, and 2.92 (1.74–4.91) in the severe sarcopenia patients compared with the no sarcopenia patients. After adjusting for all covariates in 2011, the OR (95% CI) values were 2.21 (1.70–2.85) in the possible sarcopenia patients, 1.58 (1.14–2.19) in the sarcopenia patients, and 1.99 (1.14–3.49) in the severe sarcopenia patients, as compared to the older people without sarcopenia. Subgroup analyses showed that compared with men, women with possible sarcopenia had a higher risk of disability (OR = 2.80, 95% CI: 1.98–3.97). In addition, participants aged < 80 years with sarcopenia or severe sarcopenia s were more likely to have disability (OR = 2.13, 95% CI: 1.52–2.98; OR = 2.98, 95% CI: 1.60–5.54).ConclusionThe occurrence of disability increase the risk of sarcopenia in the older people, and baseline sarcopenia predicts the future disability in older people

Synthesis, biological evaluation and mechanism studies of C-23 modified 23-hydroxybetulinic acid derivatives as anticancer agents

A series of C-23 modified 23-hydroxybetulinic acid (HBA) derivatives were synthesized and evaluated for their antiproliferative activity against a panel of cancer cell lines (A2780, A375, B16, MCF-7 and HepG2). The biological screening results showed that most of the derivatives exhibited more potent antiproliferative activity than HBA, and compound 6e exhibited the most potent activity with IC50 values of 2.14 μM, 2.89 μM, and 3.97 μM against A2780, B16, and MCF-7 cells, respectively. Further anticancer mechanism studies revealed that compound 6e induced the generation of intracellular reactive oxygen species (ROS) and reduction of mitochondrial membrane potential (MMP) of B16 cells in a dose-dependent manner. Moreover, western blot analysis indicated that compound 6e downregulated the expression of anti-apoptotic protein Bcl-2 and upregulated the expression of proapoptotic protein Bax, activation of caspase 3 to induce cell apoptosis. Meanwhile, compound 6e significantly inhibited the phosphorylation of MEK, ERK, and Akt without affecting the expression of MEK, ERK, and Akt. Furthermore, the in vivo anti-tumor activity of 6e was validated (tumor inhibitory ratio of 68.4% at the dose of 30 mg/kg) in mice with B16 melanoma

Assessment of left ventricular function in patients with type 2 diabetes mellitus by non-invasive myocardial work

BackgroundDiabetes mellitus (DM) is a chronic disease that poses a serious risk of cardiovascular diseases. Therefore, early detection of impaired cardiac function with non-invasive myocardial imaging is critical for improving the prognosis of patients with DM.PurposeThis study aimed to assess the left ventricular (LV) function in patients with type 2 diabetes mellitus (T2DM) by non-invasive myocardial work technique.Materials and methodsIn all, 67 patients with T2DM and 28 healthy controls were included and divided into a DM group and a control group. Two-dimensional dynamic images of apical three-chamber view, apical two-chamber view, and apical four-chamber view were collected from all subjects, consisting of at least three cardiac cycles. LV myocardial strain parameters, including global longitudinal strain (GLS) and peak strain dispersion (PSD), as well as myocardial work parameters, including global constructive work (GCW), global wasted work (GWW), global work index (GWI), and global work efficiency (GWE), were obtained and analyzed.ResultsA total of 15 subjects were randomly selected to assess intra-observer and inter-observer consistency of myocardial work parameters and strain parameters, which showed excellent results (intra-class correlation coefficients: 0.856 - 0.983, P<0.001). Compared with the control group, the DM group showed significantly higher PSD (37.59 ± 17.18 ms vs. 27.72 ± 13.52 ms, P<0.05) and GWW (63.98 ± 43.63 mmHg% vs. 39.28 ± 25.67 mmHg%, P<0.05), and lower GWE (96.38 ± 2.02% vs. 97.72 ± 0.98%, P<0.001). Furthermore, the PSD was positively correlated with GWW (r = 0.565, P<0.001) and negatively correlated with GWE (r = -0.569, P<0.001).ConclusionUncoordinated LV myocardial strain, higher GWW, and lower GWE in patients with T2DM may serve as indicators for the early assessment of cardiac impairment in T2DM

A map of human cancer signaling

We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancer-signaling architectural and functional organization. From the network, we extracted an oncogene-signaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few ‘oncogene-signaling-dependent blocks' in which frequently used oncogene-signaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancer-associated genes and biomarkers

Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS)

Background: Sarcopenia is an aging syndrome that increases the risks of various adverse outcomes, including falls, fractures, physical disability, and death. Sarcopenia can be diagnosed through medical images-based body part analysis, which requires laborious and time-consuming outlining of irregular contours of abdominal body parts. Therefore, it is critical to develop an efficient computational method for automatically segmenting body parts and predicting diseases.Methods: In this study, we designed an Artificial Intelligence Body Part Measure System (AIBMS) based on deep learning to automate body parts segmentation from abdominal CT scans and quantification of body part areas and volumes. The system was developed using three network models, including SEG-NET, U-NET, and Attention U-NET, and trained on abdominal CT plain scan data.Results: This segmentation model was evaluated using multi-device developmental and independent test datasets and demonstrated a high level of accuracy with over 0.9 DSC score in segment body parts. Based on the characteristics of the three network models, we gave recommendations for the appropriate model selection in various clinical scenarios. We constructed a sarcopenia classification model based on cutoff values (Auto SMI model), which demonstrated high accuracy in predicting sarcopenia with an AUC of 0.874. We used Youden index to optimize the Auto SMI model and found a better threshold of 40.69.Conclusion: We developed an AI system to segment body parts in abdominal CT images and constructed a model based on cutoff value to achieve the prediction of sarcopenia with high accuracy