Chemical Biology Approaches toward Precise Structure Control of IgG-Based Antibody-Oligonucleotide Conjugates.

Antibody-oligonucleotide conjugates (AOCs) are important tools for drug development and biochemical analysis. However, the structural heterogeneity of AOCs synthesized through conventional coupling methods raises reproducibility and safety concerns in clinical trials. To address these issues, different covalent coupling approaches have been developed to synthesize AOCs with precise site-specificity and degree of conjugation. This Concept article categorizes these approaches as linker-free or linker-mediated and provides details on their chemistry and potential applications. Several factors, including site-specificity, conjugation control, accessibility, stability, and efficiency, are highlighted when evaluating the pros and cons of these approaches. The article also discusses the future of AOCs, including the development of better conjugation approaches to ensure stimuli-responsive release and the application of high-throughput methods to facilitate their development.

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator.

BACKGROUND: Changes to human body composition reflect changes in health status to some extent. It has been recognized that these changes occur earlier than diseases. This means that a reasonable prediction of body composition helps to improve model users' health. To overcome the low accuracy and poor adaptability of existing human body composition prediction models and obtain higher efficiency, we proposed a novel method for predicting human body composition which uses a modified adaptive genetic algorithm (MAGA). METHODS: Firstly, since there are many parameters for a human body composition model, and these parameters are associated, we designed a new parameter selection approach by combining the improved RReliefF method with the mRMR method. Following this, selected parameters were used to establish a model that fits body composition. Secondly, in order to accurately calculate the weight of parameters in this model, we proposed a solution which used a modified adaptive genetic algorithm, taking advantage of both roulette and optimum reservation strategies. This solution also has an improved selection operator. Thirdly, taking the percentage of body fat (PBF) as an example of body composition, we conducted experiments to compare performance between our algorithm and other algorithms. RESULTS: Through our simulations, we demonstrated that the adaptability of the proposed model is 0.9921, the mean relative error is 0.05%, the mean square error is 1.3 and the correlation coefficient is 0.982. When compared with the indexes of other models, our model has the highest adaptability and the smallest error. Additionally, the suggested model, which has a training time of 28.58s and a running time of 2.84s, is faster than some models. CONCLUSION: The PBF prediction model established by MAGA has high accuracy, stronger generalization ability and higher efficiency, which could provide a new method for human composition prediction.

The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine.

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

The anti-fibrotic effect of betulinic acid is mediated through the inhibition of NF-κB nuclear protein translocation.

The purpose of the study was to investigate the anti-fibrotic effect and the potential mechanisms of action of betulinic acid (BA) against hepatic fibrosis in vivo and in vitro. BA is an active compound isolated from the bark of the birch tree Betula spp. (Betulaceae). Liver fibrosis was induced by intraperitoneal injections of thioacetamide (TAA, 200mg/kg) twice weekly for 6weeks in Wistar rats. The administration of BA (20 or 50mg/kg) was started following TAA injections and was continued for 6 or 8weeks to evaluate both the preventive and the protective effects. BA demonstrated great efficacy in preventing and curing hepatic fibrosis via attenuating the TAA-mediated increases in liver tissue hydroxyproline and α-smooth muscle actin (α-SMA). In vitro, BA effectively decreased the HSC-T6 cell viability induced by TNF-α and showed low toxicity in normal human chang liver cells. Moreover, BA significantly attenuated the expression of α-SMA and tissue inhibitor of metalloproteinase-1 (TIMP-1) and increased the levels of matrix metalloprotease (MMP)-13. BA also inhibited the expression of Toll-like receptor 4 (TLR4), myeloid differentiation factor 88 (MyD88) and the activation of nuclear factor-κB (NF-κB) in a time-dependent manner. This study provides evidence that BA exerts a significant anti-fibrosis effect by modulating the TLR4/MyD88/NF-κB signaling pathway.

Cryptotanshinone inhibits LPS-induced proinflammatory mediators via TLR4 and TAK1 signaling pathway.

Cryptotanshinone (CTN), one of the major constituents of tanshinones, was investigated for anti-inflammatory activity in the murine macrophage cell line RAW 264.7. CTN inhibited the production of nitric oxide (NO) production, as well as expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2) in lipopolysaccharide (LPS)-stimulated macrophages. Since CTN was considered as inhibiting LPS-triggered phosphorylation of mitogen-activated protein kinase (MAPK) and nuclear factor (NF)-κB activation, we consequently evaluated the expression of toll-like receptor 4 (TLR4) and CD14, as well as phosphorylation of TGF-ß-activated kinase 1 (TAK1). CTN reduced the expression of CD14 and TLR4, and suppressed LPS-induced phosphorylation of TAK1. Furthermore, CTN significantly increased the survival rate against LPS challenge in D-galactosamine-sensitized mice, which was in line with in vitro results. These results suggested that CD14/TLR4 and TAK1 might be the potential molecular targets for addressing the protective effects of CTN on LPS-induced inflammatory effects in macrophages.

Gentiana manshurica Kitagawa reverses acute alcohol-induced liver steatosis through blocking sterol regulatory element-binding protein-1 maturation.

This study was undertaken to investigate the protective effects of Gentiana manshurica Kitagawa (GM) on acute alcohol-induced fatty liver. Mice were treated with ethanol (5 g/kg of body weight) by gavage every 12 h for a total of three doses to induce acute fatty liver. Methanol extract of GM (50, 100, or 200 mg/kg) or silymarin (100 mg/kg) was gavaged simultaneously with ethanol for three doses. GM administration significantly reduced the increases in serum ALT and AST levels, the serum and hepatic triglyceride levels, at 4 h after the last ethanol administration. GM was also found to prevent ethanol-induced hepatic steatosis and necrosis, as indicated by liver histopathological studies. Additionally, GM suppressed the elevation of malondialdehyde (MDA) levels, restored the glutathione (GSH) levels, and enhanced the superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX) activities. The concurrent administration of GM efficaciously abrogated cytochrome P450 2E1 (CYP2E1) induction. Moreover, GM significantly reduced the nuclear translocation of sterol regulatory element-binding protein-1 (nSREBP-1) in ethanol-treated mice. These data indicated that GM possessed the ability to prevent ethanol-induced acute liver steatosis, possibly through blocking CYP2E1-mediated free radical scavenging effects and SREBP-1-regulated fatty acid synthesis. Especially, GM may be developed as a potential therapeutic candidate for ethanol-induced oxidative damage in liver.

Anti-apoptotic activity of gentiopicroside in D-galactosamine/lipopolysaccharide-induced murine fulminant hepatic failure.

This study investigated the hepatoprotective effects of gentiopicroside on d-galactosamine (d-GalN) and lipopolysaccharide (LPS)-induced fulminant hepatic failure. Mice were administrated orally with gentiopicroside (40 or 80 mg/kg body weight) at 12h and 1h before d-GalN (700 mg/kg)/LPS (10 microg/kg) injection. Gentiopicroside markedly reduced the increases in serum aminotransferase activities and lipid peroxidation. The glutathione content decreased in d-GalN/LPS alone group, and this decrease was attenuated by gentiopicroside. Increases in serum tumor necrosis factor-alpha (TNF-alpha), which were observed in d-GalN/LPS alone group, were significantly reduced by gentiopicroside. Importantly, gentiopicroside attenuated d-GalN/LPS-induced apoptosis of hepatocytes, as estimated by the caspase-3 cleavage, poly(ADP-ribose) polymerase (PARP) cleavage, and DNA fragmentation. d-GalN/LPS-induced caspase-8 and -9 activation was significantly suppressed by gentiopicroside. Moreover, increased cytosolic cytochrome c protein was reduced by gentiopicroside. Also, the increased ratio of Bax and Bcl-2 protein was significantly attenuated by gentiopicroside. After 6h of d-GalN/LPS injection, phosphorylated c-jun N-terminal kinase (JNK) and extracellular signal regulated kinase (ERK) was significantly increased, whereas phosphorylation JNK and ERK were attenuated by gentiopicroside. Our results suggest that gentiopicroside offers remarkable hepatoprotection against damage induced by d-GalN/LPS related with its anti-apoptotic activities.