Regression analysis of doubly censored failure time data with ancillary information.

Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.

Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies.

BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

Biomimetic porous silk fibroin/biphasic calcium phosphate scaffold for bone tissue regeneration.

The purpose of our study is to prepare a biomimetic porous silk fibroin (SF)/biphasic calcium phosphate (BCP) scaffold, and evaluate its performance in bone tissue regeneration. The differences in pore size, porosity, mechanical strength and biocompatibility of four different fibroin-containing scaffolds (0, 20, 40, and 60% SF) were studied in vitro. After inoculation with MC3T3-E1 cells, the ectopic bone formation ability of the SF/BCP bionic scaffold was evaluated in a rat model. The SEM and CT demonstrated that compared with pure BCP group (0% SF), the pore size and porosity of SF/BCP scaffolds were proportional to SF content, of which 40% of SF and 60% of SF groups were more suitable for cell growth. The compressive strength of SF/BCP scaffold was greater than that of the pure BCP scaffold, and showed a trend of first increasing and then decreasing with the increase of SF content, among which 40% of SF group had the maximum compressive strength (40.80 + 0.68) MPa. The SF/BCP scaffold had good biocompatibility, under the electron microscope, the cells can be smoothly attached to and propagated on the scaffold. After loading the osteoblasts, it showed excellent osteogenic capacity in the rat model. The SF/BCP scaffold can highly simulate the micro-environment of natural bone formation and can meet the requirements of tissue engineering. The SF/BCP biomimetic porous scaffold has excellent physical properties and biocompatibility. It can highly simulate the natural bone matrix composition and microenvironment, and can promote the adhesion and proliferation of osteoblasts. The SF/BCP scaffold has good ectopic osteogenesis after loading with osteoblasts, which can meet the requirements of scaffold materials in tissue engineering, and has broad application prospects in clinical application.

Comparison of Structural and Physicochemical Characteristics of Skin Collagen from Chum Salmon (Cold-Water Fish) and Nile Tilapia (Warm-Water Fish).

This study compared collagens from cold-water and warm-water fish for their structural, rheological, and functional properties, and explored their potential applications, aiming to realize the high-value utilization of marine biological resources. To this end, chum salmon skin collagen (CSSC) and Nile tilapia skin collagen (NTSC) were both successfully extracted. Collagens from the two species had different primary and secondary structures, with NTSC having a higher molecular weight, imino acid content, and α-helices and ß-turns content. The denaturation temperatures were 12.01 °C for CSSC and 31.31 °C for NTSC. CSSC was dominated by viscous behavior and its structure varied with temperature, while NTSC was dominated by elastic behavior and its structure remained stable with temperature. Both collagens had good oil holding capacity, foaming capacity, and emulsifying activity, but NTSC had better water holding capacity and foaming and emulsifying stability. Their different properties make CSSC more suitable for the preservation of frozen and chilled foods and the production of sparkling beverages, and give NTSC greater potential in biofunctional materials and solid food processing.

Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images.

Very high-resolution (VHR) remote sensing (RS) image classification is the fundamental task for RS image analysis and understanding. Recently, Transformer-based models demonstrated outstanding potential for learning high-order contextual relationships from natural images with general resolution ( ≈ 224 × 224 pixels) and achieved remarkable results on general image classification tasks. However, the complexity of the naive Transformer grows quadratically with the increase in image size, which prevents Transformer-based models from VHR RS image ( ≥ 500 × 500 pixels) classification and other computationally expensive downstream tasks. To this end, we propose to decompose the expensive self-attention (SA) into real and imaginary parts via discrete Fourier transform (DFT) and, therefore, propose an efficient complex SA (CSA) mechanism. Benefiting from the conjugated symmetric property of DFT, CSA is capable to model the high-order contextual information with less than half computations of naive SA. To overcome the gradient explosion in Fourier complex field, we replace the Softmax function with the carefully designed Logmax function to normalize the attention map of CSA and stabilize the gradient propagation. By stacking various layers of CSA blocks, we propose the Fourier complex Transformer (FCT) model to learn global contextual information from VHR aerial images following the hierarchical manners. Universal experiments conducted on commonly used RS classification datasets demonstrate the effectiveness and efficiency of FCT, especially on VHR RS images. The source code of FCT will be available at https://github.com/Gao-xiyuan/FCT.

Development of A Blockchain Framework for Virtual Clinical Trials.

Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. "Blockchain," a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable "Smart Contract" feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.