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Financial reporting is a snapshot of the business activities and financial status of companies and is widely used for various purposes by different stakeholders, including insiders and outsiders of the company. The quality and relevance of the financial reporting made by the company’s managers with mixed incentives largely influenced investment decision-making, resulting in information asymmetry in the capital market. The financial statement analysis (FSA) plays an important part to mitigate the information gap and assists investors in making unbiased investment decisions, which consists of four parts: business strategy analysis, accounting analysis, financial analysis and prospective analysis. This paper analyses inside information from Pfizer Inc. using FSA based on the financial reporting from the financial year of 2019 to 2022 (FY2019 to FY2022) in conjunction with other supplement materials. This paper finds Pfizer primarily takes the product differentiation strategy as its key profit driver, and the related accounting policies and estimations generally reflect the economic reality. The short-term liquidity of Pfizer should be enhanced, and the firm’s market value is overvalued compared to its intrinsic value on December 20, 2022.

The tumor microenvironment (TME), which is regulated by intrinsic oncogenic mechanisms and epigenetic modifications, has become a research hotspot in recent years. Characteristic features of TME include hypoxia, metabolic dysregulation, and immunosuppression. One of the most common RNA modifications, N6-methyladenosine (m 6 A) methylation, is widely involved in the regulation of physiological and pathological processes, including tumor development. Compelling evidence indicates that m 6 A methylation regulates transcription and protein expression through shearing, export, translation, and processing, thereby participating in the dynamic evolution of TME. Specifically, m 6 A methylation-mediated adaptation to hypoxia, metabolic dysregulation, and phenotypic shift of immune cells synergistically promote the formation of an immunosuppressive TME that supports tumor proliferation and metastasis. In this review, we have focused on the involvement of m 6 A methylation in the dynamic evolution of tumor-adaptive TME and described the detailed mechanisms linking m 6 A methylation to change in tumor cell biological functions. In view of the collective data, we advocate treating TME as a complete ecosystem in which components crosstalk with each other to synergistically achieve tumor adaptive changes. Finally, we describe the potential utility of m 6 A methylation-targeted therapies and tumor immunotherapy in clinical applications and the challenges faced, with the aim of advancing m 6 A methylation research.

Background Hypercholesterolemia increases the risk of tendon pain and tendon rupture. Tendon-derived stem cells (TDSCs) play a vital role in the development of tendinopathy. Our previous research found that high cholesterol inhibits tendon-related gene expression in TDSCs. Whether high cholesterol has other biological effects on TDSCs remains unknown. Methods TDSCs isolated from female SD rats were exposed to 10 mg/dL cholesterol for 24 h. Then, cell apoptosis was assessed using flow cytometry and fluorescence microscope. RFP-GFP-LC3 adenovirus transfection was used for measuring autophagy. Signaling transduction was measured by immunofluorescence and immunoblotting. In addition, Achilles tendons from ApoE −/− mice fed with a high-fat diet were histologically assessed using HE staining and immunohistochemistry. Results In this work, we verified that 10 mg/dL cholesterol suppressed cell proliferation and migration and induced G0/G1 phase arrest. Additionally, cholesterol induced apoptosis and autophagy simultaneously in TDSCs. Apoptosis induction was related to increased expression of cleaved caspase-3 and BAX and decreased expression of Bcl-xL. The occurrence of autophagic flux and accumulation of LC3-II demonstrated the induction of autophagy by cholesterol. Compared with the effects of cholesterol treatment alone, the autophagy inhibitor 3-methyladenine (3-MA) enhanced apoptosis, while the apoptosis inhibitor Z-VAD-FMK diminished cholesterol-induced autophagy. Moreover, cholesterol triggered reactive oxygen species (ROS) generation and activated the AKT/FOXO1 pathway, while the ROS scavenger NAC blocked cholesterol-induced activation of the AKT/FOXO1 pathway. NAC and the FOXO1 inhibitor AS1842856 rescued the apoptosis and autophagy induced by cholesterol. Finally, high cholesterol elevated the expression of cleaved caspase-3, Bax, LC3-II, and FOXO1 in vivo. Conclusion The present study indicated that high cholesterol induced apoptosis and autophagy through ROS-activated AKT/FOXO1 signaling in TDSCs, providing new insights into the mechanism of hypercholesterolemia-induced tendinopathy. Graphical abstract High cholesterol induces apoptosis and autophagy through the ROS-activated AKT/FOXO1 pathway in tendon-derived stem cells.

Accurate prediction of boundary layer transition and turbulence phenomena is fundamental for assessing drag, heat flux and flow noise in underwater vehicles. While transition-turbulence prediction models for air have made encouraging progress over the past few decades, advancements for water-based boundary layers have been comparatively sluggish, with existing models for air often being directly applied to water. However, these models become invalid in the presence of a temperature gradient in the water medium. This is due to the fact that, in contrast to air, the trend of the temperature effect on the flow stability characteristics of water boundary layer is completely reversed. To address this critical gap, this study proposes a local-variable-based transition-turbulence prediction model specifically tailored for underwater boundary layers. This model is developed upon the flow stability analysis and is compatible with modern computational fluid dynamics (CFD) techniques, including unstructured grids and massively parallel computing. Validation of the model is achieved by comparing its predictions with experimental data for zero-pressure-gradient flat plates, an adiabatic axisymmetric body of Power [(1977). Drag, flow transition, and laminar separation on nine bodies of revolution having different forebody shapes (Tech. Rep.). David W. Taylor Naval Ship Research and Development Center], and an axisymmetric body of Lauchle & Gurney [(1984). Laminar boundary-layer transition on a heated underwater body. Journal of Fluid Mechanics, 144, 79–101. https://doi.org/10.1017/S0022112084001518] with a heated wall. The results demonstrate that the proposed model provides accurate predictions of transition locations in underwater flows with or without temperature gradients. This work establishes a reliable and scientifically sound foundation for advancing drag reduction, noise mitigation, and flow control research of underwater vehicles.

Cartilage defects are common joint disorders that, if left untreated, may progress to severe degenerative joint conditions. Inflammatory response plays a critical role in the pathogenesis of cartilage damage. Hydrogels incorporating diacerein, an anti‐inflammatory drug used in clinical settings, can mitigate inflammation that impairs cartilage repair. It is hypothesized that the direct injection of a hydrogel scaffold combining diacerein and polydopamine into cartilage defect sites can enhance localized treatment, reduce surgical risks, and expedite recovery. Therefore, in this study, a hydrogel infused with diacerein is developed to investigate its efficacy for cartilage restoration. By crosslinking poly(ethylene glycol) diacrylate, four‐arm polyethylene glycol‐functionalized diacerein, hyaluronic acid, and polydopamine, an injectable hydrogel with superior properties is achieved. In vitro evaluations confirm the mechanical strength and biocompatibility of the hydrogel, and in vivo studies demonstrate its effectiveness in cartilage repair and anti‐inflammatory activity in a rat model. These findings indicate that hydrogels are promising materials for addressing cartilage defects and advancing tissue engineering and biological implantation strategies. In this paper, an injectable hydrogel is prepared that can be utilized as a tissue engineering scaffold for cartilage repair. This scaffold is capable of promoting cartilage regeneration and reducing inflammation caused by cartilage defects, thus holding broad application prospects in the repair of cartilage defects.

In this study, we designed and developed a DOX nanodrug delivery system (PEG-GA@ZIF-8@DOX) using ZIF-8 as the carrier and glycyrrhetinic acid (GA) as the targeting ligand. We confirmed that DOX was loaded and PEG-GA was successfully modified on the surface of the nanoparticles. The in vitro release profile of the system was investigated at pH 5.0 and 7.4. The cellular uptake, in vitro cytotoxicity, and lysosomal escape characteristics were examined using HepG2 cells. We established an H22 tumor-bearing mouse model and evaluated the in vivo antitumor activity. The results showed that the system had a uniform nanomorphology. The drug loading capacity was 11.22 ± 0.87%. In acidic conditions (pH 5.0), the final release rate of DOX was 57.73%, while at pH 7.4, it was 25.12%. GA-mediated targeting facilitated the uptake of DOX by the HepG2 cells. PEG-GA@ZIF-8@DOX could escape from the lysosomes and release the drug in the cytoplasm, thus exerting its antitumor effect. When the in vivo efficacy was analyzed, we found that the tumor inhibition rate of PEG-GA@ZIF-8@DOX was 67.64%; it also alleviated the loss of the body weight of the treated mice. This drug delivery system significantly enhanced the antitumor effect of doxorubicin in vitro and in vivo, while mitigating its toxic side effects.

This study proposes a few-shot learning (FSL) approach based on prototypical networks for anomaly detection in gas-fired power plants with limited labeled data. A dataset containing 70 labeled operational samples from five types of abnormal conditions was used. The model was trained and evaluated under a 5-way 5-shot experimental setup, with classical machine learning methods such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR) employed as comparative baselines. The proposed FSL model achieved 92.9% accuracy, 91.7% precision, 93.5% recall, and an F1-score of 92.4%, outperforming all baseline models. Experimental results demonstrate that the prototypical network can effectively learn discriminative feature representations under small-sample constraints, offering a lightweight and efficient solution for real-time anomaly detection in industrial systems.

As an important component of the immunosuppressive tumor microenvironment (TME), it has been established that mesenchymal stem cells (MSCs) promote the progression of tumor cells. MSCs can directly promote the proliferation, migration, and invasion of tumor cells via cytokines and chemokines, as well as promote tumor progression by regulating the functions of anti-tumor immune and immunosuppressive cells. MSCs-derived extracellular vesicles (MSCs-EVs) contain part of the plasma membrane and signaling factors from MSCs; therefore, they display similar effects on tumors in the immunosuppressive TME. The tumor-promoting role of macrophage migration inhibitory factor (MIF) in the immunosuppressive TME has also been revealed. Interestingly, MIF exerts similar effects to those of MSCs in the immunosuppressive TME. In this review, we summarized the main effects and related mechanisms of tumor-associated MSCs (TA-MSCs), TA-MSCs-EVs, and MIF on tumors, and described their relationships. On this basis, we hypothesized that TA-MSCs-EVs, the MIF axis, and TA-MSCs form a positive feedback loop with tumor cells, influencing the occurrence and development of tumors. The functions of these three factors in the TME may undergo dynamic changes with tumor growth and continuously affect tumor development. This provides a new idea for the targeted treatment of tumors with EVs carrying MIF inhibitors.

Large-sized seven-degrees-of-freedom (7-DoF) hybrid spray-painting robots combine ample working space and high flexibility, making them lucrative for the spray painting of aircraft and rocket surfaces. However, their kinematic calibration is hindered by gravitational deformation, which problem is addressed in this study by introducing a rigid-flexible coupling error modeling method. The latter combines the finite element method (FEM) and stiffness matrix method to assess the spatial gravitational deformation of a hybrid robot, which is then introduced into a geometric error model to establish the rigid-flexible coupling error identification model. Given many redundant parameters in the identification model for 7-DoF robots, these parameters are classified and simplified using the nonlinear least-square regularization method for parameter identification. Combining the inverse solution of 7-DoF spray-painting robots with dynamic characteristics considered, an error compensation method for 7-DoF robots is proposed. The kinematic calibration test results strongly indicate that position errors are significantly reduced with gravity compensation taken into consideration, and error convergence speed increases, demonstrating that the kinematic calibration method is feasible and can effectively improve the accuracy of spray-painting robots. The mean errors in the X- and Y-directions are reduced by 20 and 17%, respectively, compared to the conventional method. The proposed method is instrumental in the accurate kinematic calibration of large-sized 7-DoF hybrid robots.

In this paper, we study a class of optimization problems with separable constraint structures, characterized by a combination of convex and nonconvex constraints. To handle these two distinct types of constraints, we introduce a partial augmented Lagrangian function by retaining nonconvex constraints while relaxing convex constraints into the objective function. Specifically, we employ the Moreau envelope for the convex term and apply second-order variational geometry to analyze the nonconvex term. For this partial augmented Lagrangian function, we study its saddle points and establish their relationship with KKT conditions. Furthermore, second-order optimality conditions are developed by employing tools such as second-order subdifferentials, asymptotic second-order tangent cones, and second-order tangent sets.