Image-based molecular representation learning for drug development: a survey.

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development.

ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images.

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models.

Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images.

Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively. Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity.

Template Based Synthesis of Porous Graphdiyne Nanosheet for Reversible and Fast NO2 Detection by UV Irradiation.

Two-dimensional graphdiyne (GDY) formed by sp and sp2 hybridized carbon has been found to be an efficient toxic gas sensing material by density functional theory (DFT). However, little experimental research concerning its gas sensing capability has been reported owing to the complex preparation process and harsh experimental conditions. Herein, porous GDY nanosheets are successfully synthesized through a facile solvothermal synthesis technique by using CuO microspheres (MSs) as both template and source of catalyst. The porous GDY nanosheets exhibit a broadband optical absorption, rendering it suitable for the light-driven optoelectronic gas sensing applications. The GDY-based gas sensor was demonstrated to have excellent reversible to NO2 behaviors at 25 °C for the first time. More importantly, higher response value and faster response-recovery time once exposed to NO2 gas molecules are achieved by the illumination of UV light. In this way, our work paves the way for the exploration of GDY-based gas detection experimentally.

Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-Cohort Study.

BACKGROUND: This pilot study aims at exploiting machine learning techniques to extract color Doppler ultrasound (CDUS) features and to build an artificial neural network (ANN) model based on these CDUS features for improving the diagnostic performance of thyroid cancer classification. METHODS: A total of 674 patients with 712 thyroid nodules (TNs) (512 from internal dataset and 200 from external dataset) were randomly selected in this retrospective study. We used ANN to build a model (TDUS-Net) for classifying malignant and benign TNs using both the automatically extracted quantitative CDUS features (whole ratio, intranodular ratio, peripheral ratio, and number of vessels) and gray-scale ultrasound (US) features defined by the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Then, we compared the diagnostic performance of the model, the performance of another ANN model based on the gray-scale US features alone (TUS-Net), and that of radiologists. RESULTS: The TDUS-Net (0.898, 95% CI: 0.868-0.922) achieved a higher area under the curve (AUC) than that of TUS-Net (0.881, 95% CI: 0.850-0.908) in the internal tests. Compared with radiologists, TDUS-Net (AUC: 0.925, 95% CI: 0.880-0.958) performed better than radiologists (AUC: 0.810, 95% CI: 0.749-0.862) in the external tests. CONCLUSIONS: Applying a machine learning model by combining both gray-scale US features and CDUS features can achieve comparable or even higher performance than radiologists in classifying TNs.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

The hydration properties of carboxybetaine zwitterion brushes.

Combined quantum mechanical calculations and classical molecular dynamics simulations were conducted to investigate the hydration properties of carboxybetaine zwitterion brushes with varying separation distances between the quaternary ammonium cation and carboxylic anion. The brushes consist of zwitterion trimers and are investigated to mimic interacting zwitterion chains grafted on a substrate as well as polymers with interacting zwitterion side chains. Our results show that the values of both positive and negative charges, their separation distances as well as chain interactions appear to play a critical role in the hydration properties of the zwitterions. The overall hydration property of these zwitterions is dictated by the competition between the strong hydration of the charged groups and the dehydration of the hydrocarbon chains. The strongest hydration occurs when the -CH2- unit in the hydrocarbon chain reaches 6-8 for these trimers. Further increase in the hydrocarbon chain length to 10-14 leads to significant and sudden dehydration of the trimers. The water structure and the water residence time surrounding the zwitterions also demonstrate substantial alteration at this length scale. This hydrophilic-to-hydrophobic transition is induced by the hydrophobic interactions of the trimer chains. Our hydration results could explain the observed trend of the superiority of the methylated carbohydrates and poly(ethylene glycol) as antifouling materials compared to corresponding hydroxyl-terminated compounds.

Membrane surface engineering for protein separations: experiments and simulations.

A bisphosphonate derived ligand was successfully synthesized and grafted from the surface of regenerated cellulose membrane using atom transfer radical polymerization (ATRP) for protein separations. This ligand has a remarkable affinity for arginine (Arg) residues on protein surface. Hydrophilic residues N-(2-hydroxypropyl) methacrylamide (HPMA) was copolymerized to enhance the flexibility of the copolymer ligand and further improve specific protein adsorption. The polymerization of bisphosphonate derivatives was successful for the first time using ATRP. Static and dynamic binding capacities were determined for binding and elution of Arg rich lysozyme. The interaction mechanism between the copolymer ligand and lysozyme was elucidated using classical molecular dynamics (MD) simulations.

Electronic and vibrational properties of stable isomers of (SiO)n((0,±)) (n = 2-7) clusters.

First-principles calculations based on density functional theory have been performed to explore the stable configurations, electronic structures, and vibrational spectra of neutral and charged silicon monoxide clusters (SiO)n((0,±)) (n = 2-7), which could be used as precursors in the synthesis of silicon nanowires. Our theoretical calculations provide new results on characteristic electron affinity, ionization potential, and vibrational spectroscopy, guiding future experiments in the synthesis of high-quality silicon nanowires. Specifically, as the number of SiO units n increases, IR spectra of (SiO)n(±) and Raman spectra of (SiO)n(-) show an evident blue shift, and Raman spectra of (SiO)n demonstrate a red shift. Moreover, most of the neutral silicon monoxide clusters have strong IR intensities and weak Raman activities, while most of the anionic counterparts have relatively weak IR intensities and strong Raman activities. Some other energetically competitive isomers of some (SiO)n((0,±)) species were also studied for comparison.

[Efficacy and safety of integrative medical program based on blood cooling and detoxification recipe in treating patients with hepatitis B virus related acute-on-chronic liver failure: a randomized controlled clinical study].

OBJECTIVE: To evaluate the clinical efficacy and safety of integrative medical program based on blood cooling and detoxification recipe (BCDR) in treating patients with hepatitis B virus related acute-on-chronic liver failure (HBV-ACLF) of heat-toxicity accumulation syndrome (HTAS). METHODS: Adopting randomized controlled clinical design, a total of 105 HBV-ACLF patients of HTAS were randomly assigned to the trial group (64 cases) and the control group (41 cases). Patients in the control group were treated with comprehensive Western therapy, while those in the trial group were treated with comprehensive Western therapy plus BCDR. All were treated for 8 weeks and followed up for 40 weeks. Effect and safety of the treatment were assessed, including fatality, liver functions [total bilirubin (TBIL), albumin (ALB), alanine aminotransferase (ALT), and aspartate transaminase (AST)], and prothrombin activity (PTA) after treatment and at week 48 of follow-ups. RESULTS: After 8-week treatment, there was statistical difference in the overall fatality rate (15.63% vs 34.15%), the fatality rate in the mid-term (25.0% vs 64.7%), TBIL at week 8 (64.54 +/- 79.75), AST [at week 2: (178.97 +/- 44.24) U/L vs (288.48 +/- 58.49) U/L; at week 4: (61.65 +/- 27.36) U/L vs (171.12 +/- 89.11) U/L] and PTA [at week 4: (58.30 +/- 15.29) vs (42.56 +/- 15.27); at week 6: (60.77 +/- 20.40) vs (43.08 +/- 12.79)] (all P < 0.05). At week 48 of the followup, the fatality rate of the trial group (21.88%) decreased by 17. 14% when compared with that of the control group (39.02%; P < 0.05). No obvious adverse event occurred in the two groups during the 8-week treatment period. CONCLUSION: BCDR could significantly reduce the mortality of HBV-ACLF patients.

Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics.

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions' decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types.

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design.

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions.

The effect of STAT1, miR-99b, and MAP2K1 in alcoholic liver disease (ALD) mouse model and hepatocyte.

Alcoholic liver disease (ALD) serves as the leading cause of chronic liver diseases-related morbidity and mortality, which threatens the life of millions of patients in the world. However, the molecular mechanisms underlying ALD progression remain unclear. Here, we applied microarray analysis and experimental approaches to identify miRNAs and related regulatory signaling that associated with ALD. Microarray analysis identified that the expression of miR-99b was elevated in the ALD mouse model. The AML-12 cells were treated with EtOH and the expression of miR-99b was enhanced in the cells. The expression of miR-99b was positively correlated with ALT levels in the ALD mice. The microarray analysis identified the abnormally expressed mRNAs in ALD mice and the overlap analysis was performed with based on the differently expressed mRNAs and the transcriptional factors of miR-99b, in which STAT1 was identified. The elevated expression of STAT1 was validated in ALD mice. Meanwhile, the treatment of EtOH induced the expression of STAT1 in the AML-12 cells. The expression of STAT1 was positively correlated with ALT levels in the ALD mice. The positive correlation of STAT1 and miR-99b expression was identified in bioinformatics analysis and ALD mice. The expression of miR-99b and pri-miR-99b was promoted by the overexpression of STAT1 in AML-12 cells. ChIP analysis confirmed the enrichment of STAT1 on miR-99b promoter in AML-12 cells. Next, we found that the expression of mitogen-activated protein kinase kinase 1 (MAP2K1) was negatively associated with miR-99b. The expression of MAP2K1 was downregulated in ALD mice. Consistently, the expression of MAP2K1 was reduced by the treatment of EtOH in AML-12 cells. The expression of MAP2K1 was negative correlated with ALT levels in the ALD mice. We identified the binding site of MAP2K1 and miR-99b. Meanwhile, the treatment of miR-99b mimic repressed the luciferase activity of MAP2K1 in AML-12 cells. The expression of MAP2K1 was suppressed by miR-99b in the cells. We observed that the expression of MAP2K1 was inhibited by the overexpression of STAT1 in AML-12 cells. Meanwhile, the apoptosis of AML-12 cells was induced by the treatment of EtOH, while miR-99b mimic promoted but the overexpression of MAP2K1 attenuated the effect of EtOH in the cells. In conclusion, we identified the correlation and effect of STAT1, miR-99b, and MAP2K1 in ALD mouse model and hepatocyte. STAT1, miR-99b, and MAP2K1 may serve as potential therapeutic target of ALD.

Regression-based Chinese norms of number connection test A and digit symbol test for diagnosing minimal hepatic encephalopathy.

Number connection test A (NCT-A) and digit symbol test (DST), the preferential neuropsychological tests to detect minimal hepatic encephalopathy (MHE) in China, haven't been standardized in Chinese population. We aimed to establish the norms based on a multi-center cross-sectional study and to detect MHE in cirrhotic patients. NCT-A and DST were administered to 648 healthy controls and 1665 cirrhotic patients. The regression-based procedure was applied to develop demographically adjusted norms for NCT-A and DST based on healthy controls. Age, gender, education, and age by education interaction were all predictors of DST, while age, gender, and education by gender interaction were predictors of log10 NCT-A. The predictive equations for expected scores of NCT-A and DST were established, and Z-scores were calculated. The norm for NCT-A was set as Z ≤ 1.64, while the norm for DST was set as Z ≥ - 1.64. Cirrhotic patients with concurrent abnormal NCT-A and DST results were diagnosed with MHE. The prevalence of MHE was 8.89% in cirrhotic patients, and only worse Child-Pugh classification (P = 0.002, OR = 2.389) was demonstrated to be the risk factor for MHE. The regression-based normative data of NCT-A and DST have been developed to detect MHE in China. A significant proportion of Chinese cirrhotic patients suffered from MHE, especially those with worse Child-Pugh classification.

Introgression and selection shaping the genome and adaptive loci of weedy rice in northern China.

As a weed of rice paddy fields, weedy rice has spread worldwide. In northern China, the expansion of weedy rice has been rapid over the past two decades. Its evolutionary history and adaptive mechanisms are poorly understood. Evolutionary relationships between northern weedy rice and rice cultivars were analyzed using presumed neutral markers sampled across the rice genome. Genes involved in rice domestication were evaluated for their potential roles in weedy rice adaptation. Seed longevity, a critical trait of weedy rice, was examined in an F(2) population derived from a cross between weedy rice and a rice cultivar to evaluate weedy rice adaptation and the potential effect of candidate genes. Weedy rice in northern China was not derived directly from closely related wild Oryza species or from the introgression of indica subspecies. Introgression with local cultivars, coupled with selection that maintained weedy identity, shaped the evolution of weedy rice in northern China. Weedy rice is a unique system with which to investigate how weedy plants adapt to an agricultural environment. Our finding that extensive introgression from local cultivars, combined with the continuing ability to maintain weedy genes, is characteristic of weedy rice in northern China provides a clue for the field control of weedy rice.

Study on the mechanism of peanut resistance to Fusarium oxysporum infection induced by Bacillus thuringiensis TG5.

Peanut root rot, commonly referred to as rat tail or root rot, is caused by a range of Fusarium species. A strain of bacteria (named TG5) was isolated from crop rhizosphere soil in Mount Taishan, Shandong Province, China, through whole genome sequencing that TG5 was identified as Bacillus thuringiensis, which can specifically produce chloramphenicol, bacitracin, clarithromycin, lichen VK21A1 and bacitracin, with good biological control potential. Based on liquid chromatography tandem mass spectrometry metabonomics analysis and transcriptome conjoint analysis, the mechanism of TG5 and carbendazim inducing peanut plants to resist F. oxysporum stress was studied. In general, for peanut root rot caused by F. oxysporum, B. thuringiensis TG5 has greater advantages than carbendazim and is environmentally friendly. These findings provide new insights for peanut crop genetics and breeding, and for microbial pesticides to replace traditional highly toxic and highly polluting chemical pesticides. Based on the current background of agricultural green cycle and sustainable development, it has significant practical significance and broad application prospects.

A Sequential Membrane Process of Ultrafiltration Forward Osmosis and Reverse Osmosis for Poultry Slaughterhouse Wastewater Treatment and Reuse.

To address some challenges of food security and sustainability of the poultry processing industry, a sequential membrane process of ultrafiltration (UF), forward osmosis (FO), and reverse osmosis (RO) is proposed to treat semi-processed poultry slaughterhouse wastewater (PSWW) and water recovery. The pretreatment of PSWW with UF removed 36.7% of chemical oxygen demand (COD), 38.9% of total phosphorous (TP), 24.7% of total solids (TS), 14.5% of total volatile solids (TVS), 27.3% of total fixed solids (TFS), and 12.1% of total nitrogen (TN). Then, the PSWW was treated with FO membrane in FO mode, pressure retarded osmosis (PRO) mode, and L-DOPA coated membrane in the PRO mode. The FO mode was optimal for PSWW treatment by achieving the highest average flux of 10.4 ± 0.2 L/m2-h and the highest pollutant removal efficiency; 100% of COD, 100% of TP, 90.5% of TS, 85.3% of TVS, 92.1% of TFS, and 37.2% of TN. The performance of the FO membrane was entirely restored by flushing the membrane with 0.1% sodium dodecyl sulfate solution. RO significantly removed COD, TS, TVS, TFS, and TP. However, TN was reduced by only 62% because of the high ammonia concentration present in the draw solution. Overall, the sequential membrane process (UF-FO-RO) showed excellent performance by providing high rejection efficiency for pollutant removal and water recovery.

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma.

Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.

Association between leptin and NAFLD: a two-sample Mendelian randomization study.

BACKGROUND: The etiology of nonalcoholic fatty liver disease (NAFLD) involves a complex interaction of genetic and environmental factors. Previous observational studies have revealed that higher leptin levels are related to a lower risk of developing NAFLD, but the causative association remains unknown. We intended to study the causal effect between leptin and NAFLD using the Mendelian randomization (MR) study. METHODS: We performed a two-sample Mendelian randomization (TSMR) analysis using summary GWAS data from leptin (up to 50,321 individuals) and NAFLD (8,434 cases and 770,180 controls) in a European population. Instrumental variables (IVs) that satisfied the three core assumptions of Mendelian randomization were selected. The TSMR analysis was conducted using the inverse variance weighted (IVW) method, MR-Egger regression method, and weighted median (WM) method. To ensure the accuracy and stability of the study results, heterogeneity tests, multiple validity tests, and sensitivity analyses were conducted. RESULTS: The findings of the TSMR correlation analysis between NAFLD and leptin were as follows: IVW method (odds ratio (OR) 0.6729; 95% confidence interval (95% CI) 0.4907-0.9235; P = 0.0142), WM method (OR 0.6549; 95% CI 0.4373-0.9806; P = 0.0399), and MR-Egger regression method (P = 0.6920). Additionally, the findings of the TSMR correlation analysis between NAFLD and circulating leptin levels adjusted for body mass index (BMI) were as follows: IVW method (OR 0.5876; 95% CI 0.3781-0.9134; P = 0.0181), WM method (OR 0.6074; 95% CI 0.4231-0.8721; P = 0.0069), and MR-Egger regression method (P = 0.8870). It has also been shown that higher levels of leptin are causally linked to a lower risk of developing NAFLD, suggesting that leptin may serve as a protective factor for NAFLD. CONCLUSIONS: Using TSMR analysis and the GWAS database, we investigated the genetic relationship between elevated leptin levels and lowered risk of NAFLD in this study. However, further research is required to understand the underlying mechanisms.

Is it possible to intervene early cirrhosis by targeting toll-like receptors to rebalance the intestinal microbiome?

Cirrhosis is a progressive chronic liver disease caused by one or more causes and characterized by diffuse fibrosis, pseudolobules, and regenerated nodules. Once progression to hepatic decompensation, the function of the liver and other organs is impaired and almost impossible to reverse and recover, which often results in hospitalization, impaired quality of life, and high mortality. However, in the early stage of cirrhosis, there seems to be a possibility of cirrhosis reversal. The development of cirrhosis is related to the intestinal microbiota and activation of toll-like receptors (TLRs) pathways, which could regulate cell proliferation, apoptosis, expression of the hepatomitogen epiregulin, and liver inflammation. Targeting regulation of intestinal microbiota and TLRs pathways could affect the occurrence and development of cirrhosis and its complications. In this paper, we first reviewed the dynamic change of intestinal microbiota and TLRs during cirrhosis progression. And further discussed the interaction between them and potential therapeutic targets to reverse early staged cirrhosis.