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Objective To investigate the key genes associated with ferroptosis in peri-implantitis and explore the potential mechanisms regulating peri-implantitis. Methods Several datasets were obtained from the GEO database. Differential expressed genes were screened, and GO and KEGG analyses were performed. A PPI network was constructed using the STRING website. Key genes were validated using a test set, and the diagnostic value of key genes was determined. The content and proportion of 22 immune cells in peri-implantitis tissues were obtained through immune infiltration analysis. Key genes were validated by qRT-PCR and Western Blot(WB). Results There were 1 138 differential genes between peri-implantitis tissues and normal gingival tissues, of which 29 were related to ferroptosis. The gene expression in peri-implantitis tissues mainly involved processes such as immune response activation. Five key genes in the ferroptosis-related differential genes, namely SOX2, GJA1, IL1B, GPX2 and CHAC1, were differentially expressed in peri-implantitis tissues and had high diagnostic value. Immune infiltration analysis showed significant changes in immune cells such as memory B cells and plasma cells in peri-implantitis tissues. qRT-PCR and WB confirmed significant differential expression of mRNA and the protein transcribed by key genes. Conclusion Differential genes between peri-implantitis and ferroptosis are screened using bioinformatics analysis and biological validation, providing new insights into the study on peri-implantitis.

Based on the problem that considerable ballistic error and inaccurate target coverage of terminal-sensitive projectiles while fighting without weather conditions causing the reduction of combat effectiveness and/or even can’t complete combat tasks, this work elaborated two commonly used solutions and methods. According to the real working experience of the test range, a new proposal of using the meteorological forecast data to calculate the ballistic elements of the terminal-sensitive projectile was studied. Furthermore, the deviation between the ballistic landing point and the test landing point under various environmental factors such as standard meteorological conditions, meteorological forecast data and actual meteorological data was analyzed using numerous testing data and ballistic simulation results, which indicates the accuracy and reliability of meteorological forecast data in the terminal sensitive projectiles range and side deflection prediction. In summary, this study proves a theoretical reference for improving the combat effectiveness of terminal-sensitive projectiles under real combat environments, and also provides convenience for the development of terminal-sensitive projectile tests.

Aiming at the aerodynamic and trajectory design problem of gliding guided projectiles, this paper stu-died a comprehensive optimization method of aerodynamic and trajectory so as to improve the gliding performance of projectile. This optimization method used engineering algorithms for aerodynamic analysis and adaptive Radau pseudo-spectral method for trajectory analysis, and the static parameter optimization of aerodynamic shape and the dynamic optimization of angle of attack control law during the gliding process can be considered comprehensively. The mapping relationship between shape parameters and performance indicators (such as range and flight time) was established by using Kriging model, which realized the tight coupling of shape optimization and trajectory optimization. At the same time, the Kriging model was constantly updated by using the combined infill sampling criterion until convergence to obtain the optimal shape and optimal trajectory. This paper took the maximum range and shortest flight time/minimum consumption of control energy as the objective functions to comprehensively optimize the shape parameters of canard and tail of the gliding guided projectile. The simulation results show that the comprehensive optimization scheme has better trajectory performance when compared with the reference scheme. The Opt-1 scheme increases the range by 37.8%, the Opt-2 scheme can effectively reduce the flight time and reduce control energy consumption by 46% when attacking fixed targets, which verifies the feasibility and effectiveness of this comprehensive optimization method of aero-dynamic and trajectory.

Background: Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). Objective: This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. Methods: The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Results: Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Conclusion: Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability. [ABSTRACT FROM AUTHOR]

DC Beads and CalliSpheres are commonly used microspheres in clinical transcatheter arterial chemoembolization, but these microspheres cannot be visualized by themselves. Therefore, in our previous study, we developed multimodal imaging nano-assembled microspheres (NAMs), which are visualized under CT/MR and the location of embolic microspheres can be determined during postoperative review, facilitating the evaluation of embolic areas and guiding subsequent treatment. Moreover, the NAMs can be carried with positively and negatively charged drugs, increasing the choice of drugs. Systematic comparative analysis of the pharmacokinetics of NAMs with commercially available DC Bead and CalliSpheres microspheres is important for evaluating the clinical application of NAMs. In our study, we compared the similarities and differences between NAMs and two drug-eluting beads (DEBs) in respect to drug loading capacity, drug release profiles, diameter variation and morphological characteristics. The results indicate that NAMs had good drug delivery and release characteristics as well as DC Bead and CalliSpheres in vitro experimental stage. Therefore, NAMs have a good application prospect in transcatheter arterial chemoembolization treatment of hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Can blastocyst aneuploidy be predicted for patients with previous aneuploid pregnancy loss (PAPL) and receiving preimplantation genetic testing for aneuploidy (PGT-A)? SUMMARY ANSWER Multivariable logistic regression models were established to predict high risk of blastocyst aneuploidy using four identified factors, presenting good predictive performance. WHAT IS KNOWN ALREADY Aneuploidy is the most common embryonic chromosomal abnormality leading to pregnancy loss. Several studies have demonstrated a higher embryo aneuploidy rate in patients with PAPL, which has suggested that PGT-A should have benefits in PAPL patients intending to improve their pregnancy outcomes. However, recent studies have failed to demonstrate the efficacy of PGT-A for PAPL patients. One possible way to improve the efficacy is to predict the risk of blastocyst aneuploidy risk in order to identify the specific PAPL population who may benefit from PGT-A. STUDY DESIGN, SIZE, DURATION We conducted a multicenter retrospective cohort study based on data analysis of 1119 patients receiving PGT-A in three reproductive medical centers of university affiliated teaching hospitals during January 2014 to June 2020. A cohort of 550 patients who had one to three PAPL(s) were included in the PAPL group. In addition, 569 patients with monogenic diseases without pregnancy loss were taken as the non-PAPL group. PARTICIPANTS/MATERIALS, SETTING, METHODS PGT-A was conducted using single nucleotide polymorphism microarrays and next-generation sequencing. Aneuploidy rates in Day 5 blastocysts of each patient were calculated and high-risk aneuploidy was defined as a rate of ≥50%. Candidate risk factors for high-risk aneuploidy were selected using the Akaike information criterion and were subsequently included in multivariable logistic regression models. Overall predictive accuracy was assessed using the confusion matrix, discrimination by area under the receiver operating characteristic curve (AUC), and calibration by plotting the predicted probabilities versus the observed probabilities. Statistical significance was set at P  < 0.05. MAIN RESULTS AND THE ROLE OF CHANCE Blastocyst aneuploidy rates were 30 ± 25% and 21 ± 19% for PAPL and non-PAPL groups, respectively. Maternal age (odds ratio (OR) = 1.31, 95% CI 1.24–1.39, P  <   0.001), number of PAPLs (OR = 1.40, 95% CI 1.05–1.86, P  =   0.02), estradiol level on the ovulation trigger day (OR = 0.47, 95% CI 0.30–0.73, P  <   0.001), and blastocyst formation rate (OR = 0.13, 95% CI 0.03–0.50, P  =   0.003) were associated with high-risk of blastocyst aneuploidy. The predictive model based on the above four variables yielded AUCs of 0.80 using the training dataset and 0.83 using the test dataset, with average and maximal discrepancies of 2.89% and 12.76% for the training dataset, and 0.98% and 5.49% for the test dataset, respectively. LIMITATIONS, REASONS FOR CAUTION Our conclusions might not be compatible with those having fewer than four biopsied blastocysts and diminished ovarian reserves, since all of the included patients had four or more biopsied blastocysts and had exhibited good ovarian reserves. WIDER IMPLICATIONS OF THE FINDINGS The developed predictive model is critical for counseling PAPL patients before PGT-A by considering maternal age, number of PAPLs, estradiol levels on the ovulation trigger day, and the blastocyst formation rate. This prediction model achieves good risk stratification and so may be useful for identifying PAPL patients who may have higher risk of blastocyst aneuploidy and can therefore acquire better pregnancy outcomes by PGT-A. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Natural Science Foundation of China under Grant (81871159). No competing interest existed in the study. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]