An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels

Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2%  ±  2.8% measured by zero-one loss for the first evaluation session and 79.6%  ±  3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic

Dietary Heme-Containing Proteins: Structures, Applications, and Challenges

Heme-containing proteins, commonly abundant in red meat and blood, are considered promising dietary sources for iron supplementation and fortification with higher bioavailability and less side effects. As the precise structures and accurate bioactivity mechanism of various heme-containing proteins (hemoglobin, myoglobin, cytochrome, etc.) are determined, many methods have been explored for iron fortification. Based on their physicochemical and biological functions, heme-containing proteins and the hydrolyzed peptides have been also widely utilized as food ingredients and antibacterial agents in recent years. In this review, we summarized the structural characterization of hemoglobin, myoglobin, and other heme proteins in detail, and highlighted recent advances in applications of naturally occurring heme-containing proteins as dietary iron sources in the field of food science and nutrition. The regulation of absorption rate, auto-oxidation process, and dietary consumption of heme-containing proteins are then discussed. Future outlooks are also highlighted with the aim to suggest a research line to follow for further studies

Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis

Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent

The state-of-art polyurethane nanoparticles for drug delivery applications

Nowadays, polyurethanes (PUs) stand out as a promising option for drug delivery owing to their versatile properties. PUs have garnered significant attention in the biomedical sector and are extensively employed in diverse forms, including bulk devices, coatings, particles, and micelles. PUs are crucial in delivering various therapeutic agents such as antibiotics, anti-cancer medications, dermal treatments, and intravaginal rings. Effective drug release management is essential to ensure the intended therapeutic impact of PUs. Commercially available PU-based drug delivery products exemplify the adaptability of PUs in drug delivery, enabling researchers to tailor the polymer properties for specific drug release patterns. This review primarily focuses on the preparation of PU nanoparticles and their physiochemical properties for drug delivery applications, emphasizing how the formation of PUs affects the efficiency of drug delivery systems. Additionally, cutting-edge applications in drug delivery using PU nanoparticle systems, micelles, targeted, activatable, and fluorescence imaging-guided drug delivery applications are explored. Finally, the role of artificial intelligence and machine learning in drug design and delivery is discussed. The review concludes by addressing the challenges and providing perspectives on the future of PUs in drug delivery, aiming to inspire the design of more innovative solutions in this field

The Fate and Distribution of Autologous Bone Marrow Mesenchymal Stem Cells with Intra-Arterial Infusion in Osteonecrosis of the Femoral Head in Dogs

This study aimed to investigate if autologous bone marrow mesenchymal stem cells (MSCs) could treat osteonecrosis of the femoral head (ONFH) and what the fate and distribution of the cells are in dogs. Twelve Beagle dogs were randomly divided into two groups: MSCs group and SHAM operated group. After three weeks, dogs in MSCs group and SHAM operated group were intra-arterially injected with autologous MSCs and 0.9% normal saline, respectively. Eight weeks after treatment, the necrotic volume of the femoral heads was significantly reduced in MSCs group. Moreover, the trabecular bone volume was increased and the empty lacunae rate was decreased in MSCs group. In addition, the BrdU-positive MSCs were unevenly distributed in femoral heads and various vital organs. But no obvious abnormalities were observed. Furthermore, most of BrdU-positive MSCs in necrotic region expressed osteocalcin in MSCs group and a few expressed peroxisome proliferator-activated receptor-γ (PPAR-γ). Taken together, these data indicated that intra-arterially infused MSCs could migrate into the necrotic field of femoral heads and differentiate into osteoblasts, thus improving the necrosis of femoral heads. It suggests that intra-arterial infusion of autologous MSCs might be a feasible and relatively safe method for the treatment of femoral head necrosis