Fibroblast growth factor 2 promotes osteo/odontogenic differentiation in stem cells from the apical papilla by inhibiting PI3K/AKT pathway.

Fibroblast growth factor 2 (FGF2) is a crucial factor in odontoblast differentiation and dentin matrix deposition, which facilitates pulpodentin repair and regeneration. Nevertheless, the specific biological function of FGF2 in odontoblastic differentiation remains unclear because it is controlled by complex signalling pathways. This study aimed to investigate the mechanism underlying the effect of FGF2 on osteo/odontogenic differentiation of stem cells from the apical papilla (SCAP). SCAP were pretreated with conditioned media containing FGF2 for 1 week, followed by culturing in induced differentiation medium for another week. RNA sequencing (RNA-seq) combined with quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to evaluate the pathways affected by FGF2 in SCAP. Osteo/odontogenic differentiation of SCAP was determined using Alizarin red S staining, alkaline phosphatase staining, RT-qPCR, and western blotting. Pretreatment with FGF2 for 1 week increased the osteo/odontogenic differentiation ability of SCAP. RNA-seq and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that phosphatidylinositol 3-kinase (PI3K)/AKT signalling is involved in the osteogenic function of FGF2. RT-qPCR results indicated that SCAP expressed FGF receptors, and western blotting showed that p-AKT was reduced in FGF2-pretreated SCAP. The activation of the PI3K/AKT pathway partially reversed the stimulatory effect of FGF2 on osteo/odontogenic differentiation of SCAP. Our findings suggest that pretreatment with FGF2 enhances the osteo/odontogenic differentiation ability of SCAP by inhibiting the PI3K/AKT pathway.

Determination and prediction of the net energy content of wheat bran for pregnant sow.

Two experiments were conducted to determine net energy (NE) values of wheat bran ingredients and develop a prediction equation for NE of wheat bran. In each experiment, 12 multiparous pregnant sows were allocated to two 3 × 6 Youden squares with three consecutive periods and six diets in each square. The study consisted of six diets, including a corn-soybean meal basal diet and five diets formulated with 29.2% wheat bran. Each period lasted for 10 d, with 5 d allocated for adaptation and followed by 5 d for heat production measurement. Sows were provided feed at 604 kJ/kg BW0.75·d-1. On day 10, sows underwent fasting to measure fasting heat production. Results indicated that the inclusion of wheat bran in the diets significantly reduced digestibility of energy and nutrients in (p < 0.05). The average net energy (NE) content of wheat bran was determined to be 8.8 MJ/kg DM. A regress equation NE = 7.968 + 0.28 × CP + 0.607 × EE - 0.782 × ash - 0.05 × hemicellulose (R2 = 0.98, p < 0.01) was found to accurately predit the NE value when feeding pregnant sows with wheat bran-based diets. In conclusion, the net energy content of wheat bran fed to pregnant sows ranged from 7.24 to 10.67 MJ/kg DM and can be effectively estimated using proximate analysis methods.

Long non-coding RNA SNHG12 regulates leptomeningeal collateral remodeling via RGMa after ischemic stroke.

Leptomeningeal anastomoses or pial collateral arteries are crucial for restoring cerebral blood flow (CBF) after an ischemic stroke. Vascular smooth muscle cells (VSMCs) are hypothesized to regulate the extent of this adaptive response, while the specific molecular mechanisms underlying this process are still being investigated. SNHG12, a long non-coding RNA, has been shown to influence several diseases related angiogenesis, including osteosarcoma and gastric cancer. However, the role of SNHG12 in contractile VSMC dedifferentiation during collateral arteriogenesis-related strokes remains unclear. Here we demonstrated that SNHG12 is a positive regulator of MMP9 and VSMC dedifferentiation, which enhances pial collateral arteriogenesis following cerebrovascular occlusion. Pial collateral remodeling is limited by the crosstalk between SNHG12-MMP9 signaling in VSMCs, which is mediated through repulsive guidance molecule a (RGMa) regulation. Thus, targeting SNHG12 may represent a therapeutic strategy for improving collateral function, neural tissue health, and functional recovery following ischemic stroke.

Exceptional point enhanced nanoparticle detection in deformed Reuleaux-triangle microcavity.

In this paper, we propose a deformed Reuleaux-triangle resonator (RTR) to form exceptional point (EP) which results in the detection sensitivity enhancement of nanoparticle. After introducing single nanoparticle to the deformed RTR at EP, frequency splitting obtains an enhancement of more than 6 times compared with non-deformed RTR. In addition, EP induced a result that the far field pattern of chiral mode responses significantly to external perturbation, corresponding to the change in internal chirality. Therefore, single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far field directional emission. Compared to traditional frequency splitting, the far field pattern produced in deformed RTR provides a cost-effective and convenient path to detect single nanoparticle at a long distance, without using tunable laser and external coupler. Our structure indicates great potential in high sensitivity sensor and label-free detector.

Genetically proxied appendicular lean mass and stroke risk: A two-step mendelian randomization study.

BACKGROUND AND PURPOSE: Prior observational studies have suggested a strong correlation between sarcopenia and stroke, but the causal link between them remains uncertain. This study aims to investigate the associations between genetically predicted sarcopenia-related traits and stroke using a two-step Mendelian randomization (MR) approach. METHODS: Genome-wide association study (GWAS) summary data for sarcopenia-related traits were acquired from the UK Biobank. Genetic associations for ischemic stroke (IS) and its subtypes were selected from the MEGASTROKE consortium comprising European ancestry participants. GWAS summary data for cerebral hemorrhage were obtained from the FinnGen consortium, including intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH). MR estimates were calculated using the inverse-variance weighted (IVW) method. The robustness of results was assessed for heterogeneity and pleiotropy of individual single nucleotide polymorphisms (SNPs). RESULTS: Higher appendicular lean mass (ALM) exhibited a potential causal association with a reduced incidence of large artery atherosclerosis (LAA) (odds ratio [OR] = 0.81, 95% confidence interval [CI]:0.71-0.93; P = 0.003) and small vessel disease (SVD) (OR = 0.83, 95% CI:0.74-0.94; P = 0.002). The associations of ALM with IS and ICH were compromised after adjusting for body fat and physical activity with multivariable MR. Two-step MR mediation analysis explored 33 candidate mediators, among which hypertension and SBP accounted for more than 10% of the mediation proportion in the relationship between ALM and stroke and its subtypes. CONCLUSION: Our research findings indicate that lower ALM is associated with a increased risk of stroke . It is necessary to explore the specific protective mechanisms of higher ALM for preventing stroke occurrence.

Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL). METHODS: We retrospectively reviewed the clinical data of 337 patients who received PCNL between May 2020 and June 2022. In our study, 80% of the data were used as the training set, and the remaining data were used as the testing set. Separate prediction models based on the six machine learning algorithms were created using the training set. The predictive performance of each machine learning model was determined by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity using the testing set. We used coefficients to interpret the contribution of each variable to the predictive performance. RESULTS: Among the six machine learning algorithms, the support vector machine (SVM) delivered the best performance with accuracy of 0.868, AUC of 0.942 (95% CI 0.890-0.994) in the testing set. Further analysis using the SVM model showed that prealbumin contributed the most to the prediction of the outcome, followed by preoperative urine culture, systemic immune-inflammation (SII), neutrophil to lymphocyte ratio (NLR), staghorn stones, fibrinogen, operation time, preoperative urine white blood cell (WBC), preoperative urea nitrogen, hydronephrosis, stone burden, sex and preoperative lymphocyte count. CONCLUSION: Machine learning-based prediction models can accurately predict the possibility of SIRS after PCNL in advance by learning patient clinical data, and should be used to guide surgeons in clinical decision-making.

Advancements in Scaling Up Perovskite Solar Cells: From Small-Area Devices to Large-Scale Modules.

The power conversion efficiency (PCE) of perovskite solar cells (PSCs) has exceeded those of conventional thin-film solar cell technologies, and the speed at which this increase has been achieved is unprecedented in the history of photovoltaics. Despite the significant progress achieved by PSCs at the laboratory level, their commercial prospects still face two significant challenges: scaling up in size and ensuring long-term stability. Small-area devices (~1 cm2) are typically fabricated using spin-coating. However, this approach may not be suitable for preparing the large-area (>100 cm2) substrates required for commercialization. Thus, new materials and methods must be developed to facilitate the coating of large-area PSCs. This review will discuss the development of scaling up organic-inorganic hybrid PSCs and the challenges of increasing the device area. Furthermore, it will provide an overview of the methodologies for achieving high-efficiency perovskite solar modules.

The chloroplast genome of a subtropical tree Sassafras randaiense (Hayata) Rehder, 1920 (Lauraceae).

The complete chloroplast genome sequence of Sassafras randaiense (Hayata) Rehder, 1920, a subtropical tree in the family Lauraceae, was determined. For a better understanding of the differences between S. randaiense and S. tzumu, the complete chloroplast genome of S. randaiense was sequenced and analyzed. The complete chloroplast genome is 151,781 bp in length, consisting of a pair of inverted repeat (IR) regions of 20,114 bp, one large single-copy (LSC) region of 92,740 bp, and one small single-copy (SSC) region of 18,813 bp. The overall GC content of the complete chloroplast genome is 39.2%. Further, maximum-likelihood phylogenetic analysis was conducted using 31 complete plastome sequences, which support that S. randaiense and S. tzumu are nested among the members of Cinnamomum, suggesting that Sassafras belongs to Cinnamomum.

Bi-parental graph strategy to represent and analyze hybrid plant genomes.

Hybrid plants are found extensively in the wild, and they often demonstrate superior performance of complex traits over their parents and other selfing plants. This phenomenon, known as heterosis, has been extensively applied in plant breeding for decades. However, the process of decoding hybrid plant genomes has seriously lagged due to the challenges associated with genome assembly and the lack of appropriate methodologies for their subsequent representation and analysis. Here, we present the assembly and analysis of two hybrids, an intraspecific hybrid between two maize (Zea may ssp. mays) inbred lines and an interspecific hybrid between maize and its wild relative teosinte (Zea may ssp. parviglumis), utilizing a combination of PacBio High Fidelity (HiFi) sequencing and chromatin conformation capture sequencing data. The haplotypic assemblies are well-phased at chromosomal scale, successfully resolving the complex loci with extensive parental structural variations (SVs). By integrating into a bi-parental genome graph, the haplotypic assemblies can facilitate downstream short-reads-based SV calling and allele-specific gene expression analysis, demonstrating outstanding advantages over a single linear genome. Our work offers a comprehensive workflow that aims to facilitate the decoding of numerous hybrid plant genomes, particularly those with unknown or inaccessible parentage, thereby enhancing our understanding of genome evolution and heterosis.

Smart Response Biomaterials for Pain Management.

The intricate nature of pain classification and mechanism constantly affects the recovery of diseases and the well-being of patients. Key medical challenges persist in devising effective pain management strategies. Therefore, a comprehensive review of relevant methods and research advancements in pain management is conducted. This overview covers the main categorization of pain and its developmental mechanism, followed by a review of pertinent research and techniques for managing pain. These techniques include commonly prescribed medications, invasive procedures, and noninvasive physical therapy methods used in rehabilitation medicine. Additionally, for the first time, a systematic summary of the utilization of responsive biomaterials in pain management is provided, encompassing their response to physical stimuli such as ultrasound, magnetic fields, electric fields, light, and temperature, as well as changes in the physiological environment like reactive oxygen species (ROS) and pH. Even though the application of responsive biomaterials in pain management remains limited and at a fundamental level, recent years have seen the examination and debate of relevant research findings. These profound discussions aim to provide trends and directions for future research in pain management.

A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements.

The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O3 concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 µg/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distribution through the established CNN model.

The Effect of Different Thiamethoxam Concentrations on Riptortus pedestris Development and Fecundity.

The stink bug, Riptortus pedestris (Fabricius) (Hemiptera: Alydidae), is a highly destructive pest that significantly damages legume crops in East and South Asia. Neonicotinoid insecticides containing thiamethoxam are widely used to control R. pedestris in soybean fields. However, the current knowledge on the impact of different thiamethoxam concentrations on R. pedestris growth and reproduction is lacking and insufficient. The present study investigated the effects of thiamethoxam on the biological traits of R. pedestris after treatment with LC10 (19.8 mg/L), LC20 (31.6 mg/L), LC30 (44.2 mg/L), LC40 (58.9 mg/L), and LC50 (77.0 mg/L) concentrations. These five thiamethoxam concentrations (LC10~LC50) reduced adult longevity and fecundity in the F1 generation females. Thiamethoxam treatment also significantly decreased the population trend index, intrinsic rate of increase, net reproductive rate, gross reproductive rate, and finite rate of increase and increased the mean generation time. These results show that thiamethoxam hinders and suppresses the development and growth of the F1 population of R. pedestris. Thiamethoxam is recommended for spray control during peak adult emergence, as it not only has a controlling effect on the parental generation but also a negative impact on the F1 generations.

Src inhibition modulates AMBRA1-mediated mitophagy to counteract endothelial-to-mesenchymal transition in renal allograft fibrosis.

Chronic allograft dysfunction (CAD) poses a significant challenge in kidney transplantation, with renal vascular endothelial-to-mesenchymal transition (EndMT) playing a vital role. While renal vascular EndMT has been verified as an important contributing factor to renal allograft interstitial fibrosis/tubular atrophy in CAD patients, its underlying mechanisms remain obscure. Currently, Src activation is closely linked to organ fibrosis development. Single-cell transcriptomic analysis in clinical patients revealed that Src is a potential pivotal mediator in CAD progression. Our findings revealed a significant upregulation of Src which closely associated with EndMT in CAD patients, allogeneic kidney transplanted rats and endothelial cells lines. In vivo, Src inhibition remarkably alleviate EndMT and renal allograft interstitial fibrosis in allogeneic kidney transplanted rats. It also had a similar antifibrotic effect in two endothelial cell lines. Mechanistically, the knockout of Src resulted in an augmented AMBRA1-mediated mitophagy in endothelial cells. We demonstrate that Src knockdown upregulates AMBRA1 level and activates mitophagy by stabilizing Parkin's ubiquitination levels and mitochondrial translocation. Subsequent experiments demonstrated that the knockdown of the Parkin gene inhibited mitophagy in endothelial cells, leading to increased production of Interleukin-6, thereby inducing EndMT. Consequently, our study underscores Src as a critical mediator of renal vascular EndMT and allograft interstitial fibrosis, exerting its impact through the regulation of AMBRA1/Parkin-mediated mitophagy.

Lever-enabled milli-Newton mechanical force detection via a microbottle resonator.

We demonstrate a milli-Newton mechanical force sensor based on a whispering gallery mode microbottle resonator (MBR). A lever model is established by coupling the MBR with a tapered fiber, whose ratio of load arm to effort arm (RLE) is flexibly adjusted to enlarge the detection range. The mechanical force is induced by attaching a capillary on the MBR stem and applying the downward displacement, which deforms the MBR's radius and thus shifts the resonance wavelength. The dependence of the capillary displacement on the mechanical force is theoretically deduced and verified. Experimentally, the sensors with different RLEs are built, and the maximum sensitivity of -10.48 pm/mN with a resolution of 40 µN is obtained. The achieved detection range is 0-4 mN, which depends on the capillary displacement and RLE of the lever. With the merits of easy fabrication and flexible structure, the proposed sensor shows great potential in biomedical and structural health monitoring.

Iguratimod ameliorates antibody-mediated rejection after renal transplant by modulating the Th17/Treg paradigm.

BACKGROUND: Iguratimod (IGU) is widely used in clinical practice due to its stable anti-inflammatory effects. Our previous studies have confirmed that the proportion of Th17/Treg balance in patients taking IGU altered significantly. This study aims to explore the role of IGU in antibody-mediated rejection (ABMR) and its potential mechanisms. METHODS: We conducted bioinformatics analysis of sequencing data from the GEO database to analyze the abundance of immune cell infiltration in transplanted kidney tissues. In vivo, IGU was intervened in a mice secondary skin transplantation model and a mice kidney transplantation ABMR model, and histological morphology of the grafts were examined by pathological staining, while relevant indicators were determined through qRT-PCR, immunohistochemistry, and enzyme-linked immunosorbent assay, observed T cell differentiation by flow cytometry, and preliminarily assessed the immunosuppressive effect of IGU. In vitro, we established Th17 and Treg cell induction and stimulation differentiation culture systems and added IGU for intervention to explore its effects on their differentiation. RESULTS: Through bioinformatics analysis, we found that Th17 and Treg may play important roles in the occurrence and development of ABMR. In vivo, we found that IGU could effectively reduce the damage caused by ABMR to the grafts, alleviate the infiltration of inflammatory cells in the graft tissues, and reduce the deposition of C4d in the grafts. Moreover, it is also found that IGU regulated the differentiation of Th17 and Treg cells in the spleen and peripheral blood and reduced the expression of IL-17A in the grafts and serum. In addition, same changes were observed in the induction and differentiation culture system of Th17 and Treg cells in vitro after the addition of IGU. CONCLUSION: IGU can inhibit the progression of ABMR by regulating the differentiation of Th17 and Treg cells, providing novel insights for optimizing clinical immunosuppressive treatment regimens.

Rictor/mTORC2 signalling contributes to renal vascular endothelial-to-mesenchymal transition and renal allograft interstitial fibrosis by regulating BNIP3-mediated mitophagy.

BACKGROUND: Renal allograft interstitial fibrosis/tubular atrophy (IF/TA) constitutes the principal histopathological characteristic of chronic allograft dysfunction (CAD) in kidney-transplanted patients. While renal vascular endothelial-mesenchymal transition (EndMT) has been verified as an important contributing factor to IF/TA in CAD patients, its underlying mechanisms remain obscure. Through single-cell transcriptomic analysis, we identified Rictor as a potential pivotal mediator for EndMT. This investigation sought to elucidate the role of Rictor/mTORC2 signalling in the pathogenesis of renal allograft interstitial fibrosis and the associated mechanisms. METHODS: The influence of the Rictor/mTOR2 pathway on renal vascular EndMT and renal allograft fibrosis was investigated by cell experiments and Rictor depletion in renal allogeneic transplantation mice models. Subsequently, a series of assays were conducted to explore the underlying mechanisms of the enhanced mitophagy and the ameliorated EndMT resulting from Rictor knockout. RESULTS: Our findings revealed a significant activation of the Rictor/mTORC2 signalling in CAD patients and allogeneic kidney transplanted mice. The suppression of Rictor/mTORC2 signalling alleviated TNFα-induced EndMT in HUVECs. Moreover, Rictor knockout in endothelial cells remarkably ameliorated renal vascular EndMT and allograft interstitial fibrosis in allogeneic kidney transplanted mice. Mechanistically, Rictor knockout resulted in an augmented BNIP3-mediated mitophagy in endothelial cells. Furthermore, Rictor/mTORC2 facilitated the MARCH5-mediated degradation of BNIP3 at the K130 site through K48-linked ubiquitination, thereby regulating mitophagy activity. Subsequent experiments also demonstrated that BNIP3 knockdown nearly reversed the enhanced mitophagy and mitigated EndMT and allograft interstitial fibrosis induced by Rictor knockout. CONCLUSIONS: Consequently, our study underscores Rictor/mTORC2 signalling as a critical mediator of renal vascular EndMT and allograft interstitial fibrosis progression, exerting its impact through regulating BNIP3-mediated mitophagy. This insight unveils a potential therapeutic target for mitigating renal allograft interstitial fibrosis.

Effect of surface functional groups of polystyrene micro/nano plastics on the release of NOM from flocs during the aging process.

Currently, the hidden risk of microplastics in the coagulation process has attracted much attention. However, previous studies aimed at improving the removal efficiency of microplastics and ignored the importance of interactions between microplastics and natural organic matter (NOM). This study investigated how polystyrene micro/nano particles impact the release of NOM during the aging of flocs formed by aluminum-based coagulants Al13 and AlCl3. The results elucidated that nano-particles with small particle sizes and agglomerative states are more likely to interact with coagulants. After 7 years of floc aging, the DOC content of the nano system decreased by more than 40%, while the micron system did not change significantly. During coagulation, the benzene rings in polystyrene particles form complexes with electrophilic aluminum ions through π-bonding, creating new Al-O bonds. NOM tends to adsorb at micro/nano plastic interfaces due to hydrophobic interactions and conformational entropy. In the aging process, the structure of PS-Al13 or PS-AlCl3 flocs and the functional groups on the surface of micro/nano plastics control the absorption and release of organic matter through hydrophobic, van der Waals forces, hydration, and polymer bridging. In the system with the addition of nano plastics, several DBPs such as TCAA, DCAA, TBM, DBCM and nitrosamines were reduced by more than 50%. The reaction order of different morphological structures and surface functional groups of microplastics to Al13 and AlCl3 systems is aromatic C-H > C-OH > C-O > NH2 > aromatic CC > aliphatic C-H and C-O>H-CO> NH2 >C-OH> aliphatic C-H. The results provided a new sight to explore the effect of micro/nano plastics on the release of NOM during flocs aging.

Stress and Deformation Analysis of Prestressed Wound Composite Components with an Arch-Shaped Metal Liner.

The stress distribution in prestressed filament wound components plays a crucial role in determining the quality of these components during their operational lifespan. This article proposes a physical model to analyze the stress and deformation of prestressed wound composite components with arch-shaped sections. Drawing upon the principles of beam theory, we delve into the analysis of prestressed wound components with metal liners featuring arch-shaped sections. Our investigation revealed a noteworthy phenomenon termed the "additional bending moment effect" within prestressed wound components with arch-shaped sections. Furthermore, this study establishes a relationship between this additional bending moment and the external pressure. In addition, a 3D finite element (FE) model for prestressed wound components with arch-shaped sections incorporating metal liners was developed. The model's accuracy was validated through a comparison with prestressed wound experiments, showcasing an error margin of less than 2%. In comparison with prestressed wound components with circular cross-sections under identical load and dimensional parameters, it was observed that prestressed wound components with arch-shaped sections exhibit stress distributions in the arc segments akin to their circular counterparts, with differences not exceeding 5%. Notably, when the ratio of the straight segment length to the inner diameter of the arc segment inner is less than 4, the deformation on the symmetric plane of the arc segment in an arch-shaped component can be effectively considered as the summation of deformations in equivalent-sized arc and straight segments under identical loading conditions. This yields an equivalent physical model and a streamlined analysis and design methodology for describing the deformation characteristics of prestressed wound components with arch-shaped sections.

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.

Objective: This study aims to review the application of deep learning techniques in the imaging diagnosis and treatment of aortic aneurysm (AA), focusing on screening, diagnosis, lesion segmentation, surgical assistance, and prognosis prediction. Methods: A comprehensive literature review was conducted, analyzing studies that utilized deep learning models such as Convolutional Neural Networks (CNNs) in various aspects of AA management. The review covered applications in screening, segmentation, surgical planning, and prognosis prediction, with a focus on how these models improve diagnosis and treatment outcomes. Results: Deep learning models demonstrated significant advancements in AA management. For screening and diagnosis, models like ResNet achieved high accuracy in identifying AA in non-contrast CT scans. In segmentation, techniques like U-Net provided precise measurements of aneurysm size and volume, crucial for surgical planning. Deep learning also assisted in surgical procedures by accurately predicting stent placement and postoperative complications. Furthermore, models were able to predict AA progression and patient prognosis with high accuracy. Conclusion: Deep learning technologies show remarkable potential in enhancing the diagnosis, treatment, and management of AA. These advancements could lead to more accurate and personalized patient care, improving outcomes in AA management.