Can board of directors' faultlines improve corporate innovation performance? Evidence from China.

Innovation, which is a pivotal force behind the sustained growth of enterprises, has garnered significant attention across various industries. As the epicenter of corporate decision-making, the board of directors plays a pivotal role in allocating resources and overseeing the execution of strategy. Consequently, it inevitably impacts the innovation capabilities of a business. Previous research has focused primarily on single board member characteristics, such as age, gender, tenure, and educational qualifications, overlooking the cumulative influence of multiple attributes. Therefore, this study aims to investigate the impact of faultlines arising from the intersection of these characteristics within a board on innovation performance. Using panel data from China's A-share listed companies from 2010 to 2021, this research constructs a framework to analyze board faultlines based on gender, age, education, director tenure, independence status, and occupational background. A comprehensive examination reveals a positive association between board faultlines and innovation performance, indicating that diversity within a board can foster enhanced innovative outputs. These findings persist even after rigorous robustness tests, including two-stage instrumental variable regression using lagged innovation performance as well as when substituting explanatory variables and sample intervals. Further analysis reveals that the influence of board faultlines on innovation performance is significant only under certain contextual conditions: when equity concentration is low, industry competition is intense, and risk aversion is greater. This study offers valuable insights for optimizing board membership configurations and thus, ultimately, enhancing the innovation capabilities of enterprises.

Self-assembled gel microneedle formed by MS deep eutectic solvent as a transdermal delivery system for hyperpigmentation treatment.

Hyperpigmentation (HP) is an unfavorable skin disease that typically caused by injury, inflammation, or photoaging and leads to numerous physical and psychological issues in patients. Recently, development and application of natural whitening substances, particularly compound curcumin (CUR), is one of the most prevalent treatments for HP. However, it is still a formidable challenge to improve the percutaneous delivery of CUR due to its inadequate solubility in water and excellent barrier function of skin. To overcome the limitations of conventional delivery and increase the percutaneous absorption of CUR, the efficient delivery of CUR is urgently required. Herein, we developed a new malic acid-sorbitol deep eutectic solvent (MS/DES) gel microneedle loaded with CUR as a transdermal delivery system for HP treatment. The MS/DES gel produces three-dimensional (3D) network structure by self-assembly of hydrogen bond interactions, which conferred the CUR-MS/DES-GMN with sufficient mechanical properties to successfully penetrate skin tissue while also helping to enhance the drug's release rate. The CUR-MS/DES-GMN exhibit high biocompatibility and mechanical property in vivo of mice. The zebrafish experiments also show that CUR-MS/DES gel has significant effect of anti-pigmentation. Therefore, the designed CUR-MS/DES-GMN system provides a novel strategy for HP treatment based on self-assembly of naturally molecules.

Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.

Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice.