Comparison of three implant systems under preload loss: A finite element analysis validated by digital image correlation methods.

PURPOSE: This study evaluated the effects of screw preload loss on three implant systems, both in silico and in vitro. METHODS: Three finite element analysis (FEA) models of implant restorations were created using bone-level (BL, 4.8×12 mm; BLX, 4.5×12 mm) and tissue-level (TL, 4.8×12 mm) implant systems. The screws in each group were subjected to preloads of 100 N and 200 N, with an additional 130 N load applied to the crown tops. An in vitro study of the principal strain was conducted using digital image correlation (DIC) under the same conditions as for the FEA models. The results were evaluated for von Mises stress, principal strain, and sensitivity index. RESULTS: During loading, the highest stress levels were observed in the implants and screws. In the BL group, the screws experienced the highest von Mises stress at 466.04 MPa and 795.26 MPa in the 100 N and 200 N groups, respectively. The BLX group showed the highest von Mises stress at 439.33 MPa and 780.88 MPa in the implants in the 100 N and 200 N groups. Sensitivity analysis revealed that the screws and abutments in the TL group were significantly more affected by the preload changes. CONCLUSIONS: The abutment in the TL group was particularly sensitive to preload changes compared with those in the BL and BLX groups. Variations in the preload significantly affect the stress distribution in implants and screws. Maintaining screw preload stability under loading is crucial in clinical practice to prevent mechanical failure.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

iDPPIV-SI: identifying dipeptidyl peptidase IV inhibitory peptides by using multiple sequence information.

Currently, diabetes has become a great threaten for people's health in the world. Recent study shows that dipeptidyl peptidase IV (DPP-IV) inhibitory peptides may be a potential pharmaceutical agent to treat diabetes. Thus, there is a need to discriminate DPP-IV inhibitory peptides from non-DPP-IV inhibitory peptides. To address this issue, a novel computational model called iDPPIV-SI was developed in this study. In the first, 50 different types of physicochemical (PC) properties were employed to denote the peptide sequences. Three different feature descriptors including the 1-order, 2-order correlation methods and discrete wavelet transform were applied to collect useful information from the PC matrix. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was employed to select these most discriminative features. All of these chosen features were fed into support vector machine (SVM) for identifying DPP-IV inhibitory peptides. The iDPPIV-SI achieved 91.26% and 98.12% classification accuracies on the training and independent dataset, respectively. There is a significantly improvement in the classification performance by the proposed method, as compared with the state-of-the-art predictors. The datasets and MATLAB codes (based on MATLAB2015b) used in current study are available at https://figshare.com/articles/online_resource/iDPPIV-SI/20085878.Communicated by Ramaswamy H. Sarma.

Down-regulation and clinical significance of Sorbin and SH3 domain-containing protein 1 in bladder cancer tissues.

Bladder cancer (BC) is a common cancer worldwide with a high prevalence. This study was conducted to elucidate the expression and clinical significance of Sorbin and SH3 domain-containing protein 1 (SORBS1) in BC as well as to explore its molecular mechanism in BC tumourigenesis. RNA-sequencing data, microarray, and Immunohistochemistry (IHC) were applied to elucidated the SORBS1 expression at multiple levels. After that, the relationship between tumour-immune infiltration and SORBS1 was also explored. Finally, SORBS1-related genes in BC were identified to perform functional enrichment analyses. The expression integration revealed that the comprehensive expression of SORBS1 at the mRNA level was -1.02 and that at the protein level was -3.73, based on 12 platforms, including 1221 BC and 187 non-BC samples. SORBS1 was negatively correlated with tumour purity (correlation = -0.342, p < 0.001) and positively correlated with macrophage (correlation = 0.358, p < 0.001). The results of enrichment analyses revealed that the most significant biological pathways of SORBS1-related genes were epithelial-mesenchymal transition. SORBS1 was significantly down-regulated in BC and may play a role as tumour suppressor. This study provides new directions and biomarkers for future BC diagnosis.

The unsolved issue with out-of-plane bending frequencies for CC multiply bonded systems.

More than 30 years ago two groups independently identified a problem in the calculation of the out-of-plane bending (OPB) vibrational frequencies for the ethylene molecule using correlated electronic structure methods. Several studies have been done in the meantime to try and understand and resolve this issue. In so doing this problem has been found to be far more insidious than previously realized for acetylene-like and benzene-like molecules, which can become non-linear and non-planar, respectively. The one common feature that all molecules with this problem have is that they contain CC multiple bonds, and so this has been called the "CC multiple bond OPB frequency issue" or "the CC OPB problem." Various explanations for this problem have been advanced such as basis set superposition error, basis set incompleteness error, linear dependences in the basis set, proper balancing of the basis set between saturation and inclusion of higher angular momentum functions, etc. and possible solutions have arisen from these suggestions. All of these proposed solutions, however, amount to one main point connecting them all: modifying the one-particle basis set in some way. None of the explanations that have been advanced, however, really fit all of the data for all of the molecules where this problem has been identified, and importantly, none of these diagnostic tests have been applied to similar molecules where this issue does not appear. In this review, the studies over the last 30 plus years are discussed and relevant data from each of these is compared and contrasted. It is hoped that by collecting and analyzing the data from these studies a path forward to understanding and resolving this issue will become evident.

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals.

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

Analysis of physiological signals using state space correlation entropy.

In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.

Attenuating Away the Errors in Inter- and Intramolecular Interactions from Second-Order Møller-Plesset Calculations in the Small Aug-cc-pVDZ Basis Set.

Second-order Møller-Plesset perturbation theory (MP2) treats electron correlation at low computational cost, but suffers from basis set superposition error (BSSE) and fundamental inaccuracies in long-range contributions. The cost differential between complete basis set (CBS) and small basis MP2 restricts system sizes where BSSE can be removed. Range-separation of MP2 could yield more tractable and/or accurate forms for short- and long-range correlation. Retaining only short-range contributions proves to be effective for MP2 in the small aug-cc-pVDZ (aDZ) basis. Using one range-separation parameter, superior behavior is obtained versus both MP2/aDZ and MP2/CBS for inter- and intramolecular test sets. Attenuation of the long-range helps to cancel both BSSE and intrinsic MP2 errors. Direct scaling of the MP2 correlation energy proves useful as well. The resulting SMP2/aDZ, MP2(erfc, aDZ), and MP2(terfc, aDZ) methods perform far better than MP2/aDZ across systems with hydrogen-bonding, dispersion, and mixed interactions at a fraction of MP2/CBS computational cost.

Fully Automated Incremental Evaluation of MP2 and CCSD(T) Energies: Application to Water Clusters.

A fully automated implementation of the incremental scheme for CCSD energies has been extended to treat MP2 and CCSD(T) energies. It is shown in applications on water clusters that the error of the incremental expansion for the total energy is below 1 kcal/mol already at second or third order. It is demonstrated that the approach saves CPU time, RAM, and disk space. Finally it is shown that the calculations can be run in parallel on up to 50 CPUs, without significant loss of computer time.