Construction of a mortality risk prediction model for elderly people at risk of lobectomy for NSCLC

BackgroundAn increasing number of lung cancer patients are opting for lobectomy for oncological treatment. However, due to the unique organismal condition of elderly patients, their short-term postoperative mortality is significantly higher than that of non-elderly patients. Therefore, there is a need to develop a personalised predictive tool to assess the risk of postoperative mortality in elderly patients.MethodsInformation on the diagnosis and survival of 35,411 older patients with confirmed lobectomy NSCLC from 2009 to 2019 was screened from the SEER database. The surgical group was divided into a high-risk mortality population group (≤90 days) and a non-high-risk mortality population group using a 90-day criterion. Survival curves were plotted using the Kaplan-Meier method to compare the differences in overall survival (OS) and lung cancer-specific survival (LCSS) between the two groups. The data set was split into modelling and validation groups in a ratio of 7.5:2.5, and model risk predictors of postoperative death in elderly patients with NSCLC were screened using univariate and multifactorial logistic regression. Columnar plots were constructed for model visualisation, and the area under the subject operating characteristic curve (AUC), DCA decision curve and clinical impact curve were used to assess model predictiveness and clinical utility.ResultsMulti-factor logistic regression results showed that sex, age, race, histology and grade were independent predictors of the risk of postoperative death in elderly patients with NSCLC. The above factors were imported into R software to construct a line graph model for predicting the risk of postoperative death in elderly patients with NSCLC. The AUCs of the modelling and validation groups were 0.711 and 0.713 respectively, indicating that the model performed well in terms of predictive performance. The DCA decision curve and clinical impact curve showed that the model had a high net clinical benefit and was of clinical application.ConclusionThe construction and validation of a predictive model for death within 90 days of lobectomy in elderly patients with lung cancer will help the clinic to identify high-risk groups and give timely intervention or adjust treatment decisions

Seismic hazard analysis for bridge design in the Hong Kong region

published_or_final_versionCivil EngineeringMasterMaster of Philosoph

RESEARCH ON STRESS SPECTRUM GROUPING METHOD OF HIGH SPEED EMU BOGIE

The determination of structural dynamic stress spectrum is of great significance in the structural fatigue strength evaluation as well as reliability design.To evaluate structure of fatigue strength accurately,stress history on fatigue key parts of motor bogie framework was obtained through dynamic stress test of high-speed EMU on actual operation line.Fatigue damage values per kilometer of stress spectra with different grouping numbers were calculated using equidistant grouping method and Palmgren-Miner linear cumulative damage theory.Then,the effect of different group number on fatigue damage was analyzed.The results show that when the grouping number is small,it can lead to deviation of fatigue damage.As the grouping number increasing,damage decreasing,and tends to the theoretical value.This phenomenon can be interpreted by the EMU framework dynamic stress characteristic of small stress cycles high concentration.Meanwhile,an unequal distance grouping method was presented which is applicable to framework structure stress spectrum.The study can provide the basis for framework structure design

Non-weight-bearing exercise attenuates papain-induced knee osteoarthritis in rats via the TLR4/MyD88/NF-κB signaling pathway

Abstract Background and Aim Knee osteoarthritis (KOA) is characterized by joint wear and degeneration. Unfortunately, the medical community currently lacks effective treatment options for this disease. Suspension exercise therapy is considered an effective form of non-weight-bearing exercise for treating KOA. However, its mechanism of intervention in KOA is unclear. Therefore, this study aimed to evaluate the protective effects of non-weight-bearing exercise on rats with KOA and attempted to explore the underlying mechanisms. Methods In this study, a papain-induced KOA model was constructed, and the pathological changes in cartilage tissue were observed by hematoxylin and eosin (H&E) staining and scored according to the Mankin scoring principle. The serum levels of interleukin (IL)-1β, IL-6, and tumor necrosis factor-α (TNF-α) were detected by enzyme-linked immunosorbent assay. Reverse transcription–quantitative polymerase chain reaction and Western blotting were used to detect the expression of mRNA and proteins in the TLR4/MyD88/NF-κB signaling pathway. Results H&E staining and Mankin score data confirmed that non-weight-bearing exercise significantly improved articular cartilage degradation compared with that in the model group. Further, we observed that non-weight-bearing exercise differentially reduced serum levels of IL-1β, IL-6, and TNF-α. Mechanistically, non-weight-bearing exercise downregulated gene and protein expression of TLR4, MyD88, and NF-κB in cartilage tissue. Conclusion Non-weight-bearing exercise resulted in the progression of KOA by modulating the TLR4/MyD88/NF-κB signaling pathway and decreasing the levels of the inflammatory cytokines IL-1β, IL-6, and TNF-α to slow down the degeneration of articular cartilage

Failure Of Hearing Acquisition in Mice With Reduced Expression of Connexin 26 Correlates With the Abnormal Phasing of Apoptosis Relative to Autophagy and Defective ATP-Dependent Ca2+ Signaling in Kölliker’s Organ

Mutations in the GJB2 gene that encodes connexin 26 (Cx26) are the predominant cause of prelingual hereditary deafness, and the most frequently encountered variants cause complete loss of protein function. To investigate how Cx26 deficiency induces deafness, we examined the levels of apoptosis and autophagy in Gjb2loxP/loxP; ROSA26CreER mice injected with tamoxifen on the day of birth. After weaning, these mice exhibited severe hearing impairment and reduced Cx26 expression in the cochlear duct. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) positive cells were observed in apical, middle, and basal turns of Kölliker’s organ at postnatal (P) day 1 (P1), associated with increased expression levels of cleaved caspase 3, but decreased levels of autophagy-related proteins LC3-II, P62, and Beclin1. In Kölliker’s organ cells with decreased Cx26 expression, we also found significantly reduced levels of intracellular ATP and hampered Ca2+ responses evoked by extracellular ATP application. These results offer novel insight into the mechanisms that prevent hearing acquisition in mouse models of non-syndromic hearing impairment due to Cx26 loss of function

Multibiometric cryptosystem: Model structure and performance analysis

Abstract-Single biometric cryptosystems were developed to obtain win-win scenarios for security and privacy. They are seriously threatened by spoof attacks, in which a forged biometric copy or artificially recreated biometric data of a legitimate user may be used to spoof a system. Meanwhile, feature alignment and quantization greatly degrade the accuracy of single biometric cryptosystems. In this paper, by trying to bind multiple biometrics to cryptography, a cryptosystem named multibiometric cryptosystem (MBC), is demonstrated from the theoretical point of view. First, an MBC with two fusion levels: fusion at the biometric level, and fusion at the cryptographic level, is formally defined. Then four models, namely biometric fusion model, MN-split model, nonsplit model, and package model, adopted at those two levels for fusion are presented. Shannon entropy analysis shows that even if the biometric ciphertexts and some biometric traits are disclosed, the new constructions still can achieve consistently data security and biometric privacy. In addition, the achievable accuracy is analyzed in terms of false acceptance rate/false rejection rate at each model. Finally, a comparison on the relative advantages and disadvantages of the proposed models is discussed

Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models

Separation and Extraction of Mixed Grinding Chips of Artificial Joints with Different Densities by Multiple Centrifugal Separations

Aseptic loosening caused by the wear and tear of the artificial joint prosthesis after implantation is one of the main causes of artificial joint failure. Therefore, it is important to investigate the wear debris generated due to wear when developing new artificial joint materials. Aseptic loosening is related to the size, number, and morphology of wear debris, and this study proposed the separation and extraction of mixed wear debris with different density ratios of artificial joints by centrifugation to study the characteristics of different artificial joint wear and wear debris extraction rates. The results showed that multiple centrifugations to separate the mixed wear debris were able to reintroduce the wear debris on the wall of the centrifuge tube into the solution and that the wear debris extraction rate was increased. Suspensions with different density ratios of artificially jointed mixed wear debris were effectively separated by this method. The total extraction rate of the three repeated extractions compared to the first extraction rate, the extraction rate of CoCrMo wear debris increased by 6.7%, ultra-high molecular weight polyethylene (UHMWPE) wear debris increased by 15.1–23.44%, ZrO2 wear debris increased by 10.91%, and that of polyether ether ketone (PEEK) wear debris increased by 9.95%. This method for separating and extracting wear debris from artificial joints can realize the separation of mixed wear debris from artificial joints and obtain a high extraction rate and high-quality wear debris images, investigate the wear mechanism of artificial joint implants, and provide valuable information on the wear performance of new artificial joint implants under investigation

Genome-Wide Identification of GASA Gene Family in Ten Cucurbitaceae Species and Expression Analysis in Cucumber

Gibberellic acid-stimulated in Arabidopsis (GASA), a unique small molecular protein of plants, plays an essential role in plant growth and development. The GASA family genes have been identified and studied in many plants. However, the identification of GASA gene family in Cucurbitaceae species has not been reported yet. Therefore, in this study, based on the available genome information on the Cucurbitaceae species, the GASA family genes in 10 Cucurbitaceae species including cucumber (Cucumis sativus), watermelon (Citrullus lanatus), melon (Cucumis melo), pumpkin (Cucurbita moschata), wax gourd (Benincasa hispida), sponge gourd (Luffa cylindrica), bottle gourd (Lagenaria siceraria), bitter gourd (Momordica charantia), chayote (Sechium edule), and snake gourd (Trichosanthes anguina) were identified with bioinformatics methods. To understand the molecular functions of GASA genes, the expression pattern analysis of cucumber GASA family genes in different tissues and stress responses were also analyzed. The results showed that a total of 114 GASA genes were identified in the 10 Cucurbitaceae species, which were divided into three subfamilies. Synteny analysis of GASA genes among cucumber, Arabidopsis and rice showed that nine cucumber GASA genes were colinear with 12 Arabidopsis GASA genes, and six cucumber GASA genes were colinear with six rice GASA genes. The cis-acting elements analysis implied that the cucumber GASA genes contained many cis-elements associated with stress and hormone response. Tissue-specific expression analysis of cucumber GASA family genes revealed that only the CsaV3_2G029490 gene was lowly or not expressed in all tissues, the CsaV3_3G041480 gene was highly expressed in all tissues, and the other seven GASA genes showed tissue-specific expression patterns. Furthermore, nine cucumber GASA family genes exhibited different degrees of regulatory response under GA, abiotic and biotic stresses. Two cucumber GASA genes, CsaV3_3G042060 and CsaV3_3G041480, were differentially expressed under multiple biotic and abiotic stresses, which indicated that these two GASA genes play important roles in the growth and development of cucumber