Influence of barrier effect on barrier sheet pile wharf

This study aims to investigate the barrier effect of front wall-soil-barrier interactions in barrier sheet pile wharf structures. Berth 32 of the Jingtang Port was taken as the prototype structure, and the prototype observation experiment, centrifugal model test, and numerical calculation analysis were performed to study the influence of the length of the barrier pile, the spacing D between wall piles, and the net spacing L of the barrier pile on the barrier effect. The results show that, to maximize the barrier effect, the ratio N of the pile length of the full barrier pile to the depth of the front wall should be between 1.0 and 1.1. To maximize the barrier effect, the top elevation of the semi-barrier pile should not be excessively low. When the bottom elevation is fixed, the ratio of the length of the semi-barrier pile to the depth of the front wall is approximately N= 0.7. The change in the wall pile spacing D has a considerable impact on the barrier effect. Moreover, D has a logarithmic relationship to the horizontal displacement of the front wall. When D exceeds 3 m, the change in the barrier effect can be ignored. The part of the earth pressure shared by the sea and land sides of the barrier pile to the soil between the barrier pile and the barrier pile has a logarithmic relationship to the net spacing L of the barrier pile. The smaller the L, the better the barrier effect. When L exceeds 2 m, the earth pressure shared by the two parts tends to be average, and the barrier effect can be ignored

A Pilot Study: Changes of Gut Microbiota in Post-surgery Colorectal Cancer Patients

Colorectal cancer (CRC) is a growing health problem throughout the world. Strong evidences have supported that gut microbiota can influence tumorigenesis; however, little is known about what happens to gut microbiota following surgical resection. Here, we examined the changes of gut microbiota in CRC patients after the surgical resection. Using the PCoA analysis and dissimilarity tests, the microbial taxonomic compositions and diversities of gut microbiota in post-surgery CRC patients (A1) were significantly different from those in pre-surgery CRC patients (A0) and healthy individuals (H). Compared with A0 and H, the Shannon diversity and Simpson diversity were significantly decreased in A1 (P < 0.05). Based on the LEfSe analysis, the relative abundance of phylum Proteobacteria in A1 was significantly increased than that in A0 and H. The genus Klebsiella in A1 had higher proportions than that in A0 (P < 0.05). Individual variation was distinct; however, 90% of CRC patients in A1 had more abundances of Klebsiella than A0. The Klebsiella in A1 was significantly associated with infectious diseases (P < 0.05), revealed by the correlation analysis between differentiated genera and metabolic pathway. The Klebsiella (Proteobacteria, Gammaproteobacteria, Enterobacteriales, Enterobacteriaceae) in A1 was significantly linked with lymphatic invasion (P < 0.05). Furthermore, the PCA of KEGG pathways indicated that gut microbiota with a more scattered distribution in A1 was noticeably different from that in A0 and H. The nodes, the links, and the kinds of phylum in each module in A1 were less than those in A0 and H, indicating that gut microbiota in A1 had a relatively looser ecologcial interaction network. To sum up, this pilot study identified the changes of gut microbiota in post-surgery CRC patients, and highlights future avenues in which the gut microbiota is likely to be of increasing importance in the care of surgical patients

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression

Abstract Background HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. Methods This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. Results Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. Conclusion To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases

The age-dependent changes in risk weights of the prognostic factors for multiple myeloma

ABSTRACTObjective Multiple myeloma is a highly heterogenous plasma cell malignancy, commonly seen in older patients. Age is one of the important prognostic factors. However, nearly all the prognostic staging systems are based on clinical trials, where patients were relatively fit and young. It is unknown how the presence of biochemical or cytogenetic prognostic factors and their risk weights changes with older age. To further investigate this question, we retrospectively analyzed the data from a consecutive cohort of patients treated with either bortezomib or thalidomide-based therapy.Methods This retrospective study was carried out on a cohort of 1125 newly diagnosed multiple myeloma patients, from January 2008 to December 2019. Patients received bortezomib or thalidomide-based induction and maintenance therapy. Patients accepted hematopoietic stem cell transplantation if eligible. Statistical analysis was conducted by Stata/MP 16.0 and SPSS 26.0.Results With age increasing, the proportion of patients with ISS 3, performance status score ≥2, and the incidence rate of gain(1q) significantly increased. We also found that ISS became less important in older patients. However, cytogenetic abnormalities exerted a consistently adverse impact on survival, both in young and old patients. Older patients had an inferior outcome than their young counterparts. All patients in our cohort benefitted more from bortezomib than thalidomide-based induction therapy, except for patients ≥71 years old.Conclusions ISS may lose prognostic value in patients ≥71 years old. Older patients had an inferior outcome and needed more effective and less toxic treatment.Plain Language SummaryMultiple myeloma is a type of blood cancer commonly seen in older people. To treat this disease, genetic abnormality, the poor physical status of patients and the abundance of tumor cells are the main difficulties. We often draw these conclusions from clinical trials. However, clinical trials always enrolled relatively younger patients, so the presence and significance of these factors may vary from clinical trials to the real world. We conducted the study to find out the real risk in both young and old patients. We found that older patients were more likely to have anemia, poor nutritional status and renal function. We also found older patients had more risk of relapse, progression or death than young patients. Frail physical status is the key obstacle to treating older patients, and tumor burden no longer impacts the outcome of these people. Bortezomib is a powerful drug to treat this disease, but patients ≥71 years old had less benefit than younger ones. More studies should focus on older or frail patients as these patients need more effective and less toxic treatment

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

Abstract Objective This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. Methods In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. Results The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. Conclusion PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty

Abstract Background In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. Methods The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. Results The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. Conclusion In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk

A two-stage association study suggests BRAP as a susceptibility gene for schizophrenia.

Schizophrenia (SZ) is a neurodevelopmental disorder in which altered immune function typically plays an important role in mediating the effect of environmental insults and regulation of inflammation. The breast cancer suppressor protein associated protein (BRAP) is suggested to exert vital effects in neurodevelopment by modulating the mitogen-activated protein kinase cascade and inflammation signaling. To explore the possible role of BRAP in SZ, we conducted a two-stage study to examine the association of BRAP polymorphisms with SZ in the Han Chinese population. In stage one, we screened SNPs in BRAP from our GWAS data, which detected three associated SNPs, with rs3782886 being the most significant one (P  =  2.31E-6, OR  =  0.67). In stage two, we validated these three SNPs in an independently collected population including 1957 patients and 1509 controls, supporting the association of rs3782886 with SZ (P  =  1.43E-6, OR  =  0.73). Furthermore, cis-eQTL analysis indicates that rs3782886 genotypes are associated with mRNA levels of aldehyde dehydrogenase 2 family (ALDH2) (P  =  0.0039) and myosin regulatory light chain 2 (MYL2) (P < 1.0E-4). Our data suggest that the BRAP gene may confer vulnerability for SZ in Han Chinese population, adding further evidence for the involvement of developmental and/or neuroinflammatory cascades in the illness