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Glucagon-like peptide-1 (GLP-1) is a 30-amino acid peptide hormone that is mainly expressed in the intestine and hypothalamus. In recent years, basic and clinical studies have shown that GLP-1 is closely related to lipid metabolism, and it can participate in lipid metabolism by inhibiting fat synthesis, promoting fat differentiation, enhancing cholesterol metabolism, and promoting adipose browning. GLP-1 plays a key role in the occurrence and development of metabolic diseases such as obesity, nonalcoholic fatty liver disease, and atherosclerosis by regulating lipid metabolism. It is expected to become a new target for the treatment of metabolic disorders. The effects of GLP-1 and dual agonists on lipid metabolism also provide a more complete treatment plan for metabolic diseases. This article reviews the recent research progress of GLP-1 in lipid metabolism.

Objectives To investigate the diagnostic value of intravoxel incoherent motion (IVIM) DWI for placenta accreta by comparing diffusion and perfusion characteristics of placentas with accreta lesions (APs) with those of normal placentas (NPs). Methods Twenty-five pregnant women with AP and 24 with NP underwent 3-T magnetic resonance examinations with IVIM-DWI. The perfusion percentage ( f ), pseudo-diffusion coefficient ( D *), and diffusion coefficient ( D ) values were calculated from different ROIs: the entire-plane of the AP (AP-ROI) and NP (NP-ROI) and the implanted (IR-ROI) and non-implanted region (NIR-ROI) of the AP. The AP-ROIs and NP-ROIs were compared using covariance analysis; the IR-ROIs and NIR-ROIs were compared using the Wilcoxon signed-rank test. ROC curves were produced to evaluate the parameters for predicting placenta accreta. Results The f and D * values for the AP-ROIs ([45.0 ± 7.63]%, [11.64 ± 2.15]mm 2 /s) were significantly higher than those for the NP-ROIs ([31.85 ± 5.96]%, [9.04 ± 3.13]mm 2 /s) (both p < 0.05); the IR-ROIs (54.8%, 14.03 mm 2 /s) were also significantly higher than the NIR-ROIs (37.4%, 11.4 mm 2 /s) (both p < 0.05). No significant differences were found between the D values of the AP-ROIs and NP-ROIs ( p > 0.05) or of the IR-ROIs and NIR-ROIs ( p > 0.05). The areas under the curve for f and D * of the ROC curves were 0.93 and 0.79, respectively. Conclusions These results suggest that the IVIM parameters f and D * can be used to quantitatively evaluate the higher perfusion of AP when compared with NP. Furthermore, IVIM may be a useful functional diagnostic technique to predict placenta accreta. Key Points • Intravoxel incoherent motion (IVIM) may be a useful diagnostic technique to quantitatively estimate the perfusion of the placenta. • The perfusion percentage (f) and pseudo-diffusion coefficient (D*) values differed significantly between placentas with accreta lesions and normal placentas. • ROC curves showed that perfusion percentage (f) and pseudo-diffusion coefficient (D*) values could accurately predict placenta accreta.

Weeds present a pervasive challenge in agricultural fields. The integration of herbicide-resistant crops with chemical weed management offers an effective solution for sustainable weed control while reducing labor inputs, particularly in large-scale intensive farming systems. Consequently, the development of herbicide-resistant cultivars has emerged as an urgent priority. In this study, we found that the G311E mutant gene of Arabidopsis MATE (multidrug and toxic compound extrusion) family transporter DTX6, designated DTX6m, confers robust resistance to bipyridyl herbicides paraquat and diquat in rice. DTX6m-overexpression lines exhibited marked resistance to these two herbicides, tolerating diquat concentrations up to 5 g/L, which is five-fold higher than the recommended field application dosage. Agronomic assessments demonstrated that grain yields of DTX6m-overexpressing plants were statistically equivalent to those of wild-type plants. Moreover, the plants displayed beneficial phenotypic changes, such as accelerated flowering and a slight reduction in height. Seed morphometric analysis indicated that in comparison with the wild-type control, DTX6m-transgenic lines exhibited altered grain dimensions while maintaining consistent 1000-grain weight. Nutritional assays further demonstrated that DTX6m increased the levels of free amino acids in seeds, while normal protein and starch contents were retained. Collectively, these results establish that DTX6m effectively boosts rice resistance to paraquat and diquat, validating DTX6m as a candidate gene for engineering plant herbicide resistance and also implying a potential role for DTX6m in amino acid homeostasis in plants.

Introduction To investigate the clinical significance of obesity measurement indicators in patients with type 2 diabetes mellitus(T2DM) complicated with carotid plaque. Methods A total of 1009 subjects with T2DM were recruited in the cross-sectional study, and body measurements were collected. According to the results of carotid artery ultrasound, the study subjects were divided into T2DM without carotid plaque group (NCP: n  = 617) and with carotid plaque group (WCP: n  = 392). Results Compared with the NCP group, the waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), Chinese visceral fat index (CVAI), body roundness index (BRI), body fat index (BAI), body shape index (ABSI), and abdominal volume index (AVI) were significantly increased in the WCP group ( P < 0.05). The results of multivariate stepwise logistic regression analysis showed that BAI, CVAI and ABSI had the greatest effect on carotid plaque ( P < 0.05). After adjusting for multiple confounding factors, CVAI and ABSI remained independently associated with carotid plaques, and the combination of the three indicators exhibited superior predictive value for carotid plaques. Conclusion CVAI and ABSI are closely related to the occurrence and development of carotid plaque in subjects with T2DM, and the combined application has a good effect on predicting carotid plaque.

Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological activities effectively. However, molecular feature optimization and model integration remain challenges. To address these challenges, we propose MoleculeFormer, a multi-scale feature integration model based on Graph Convolutional Network-Transformer architecture. It uses independent Graph Convolutional Network and Transformer modules to extract features from atom and bond graphs while incorporating rotational equivariance constraints and prior molecular fingerprints. The model captures both local and global features and introduces 3D structural information with invariance to rotation and translation. Experiments on 28 datasets show robust performance across various drug discovery tasks, including efficacy/toxicity prediction, phenotype screening, and ADME evaluation. The integration of attention mechanisms enhances interpretability, and the model demonstrates strong noise resistance, establishing MoleculeFormer as an effective, generalizable solution for molecular prediction tasks. MoleculeFormer is a GCN-Transformer architecture that integrates atomic and bond-level graphs with 3D features and molecular fingerprints, enabling comprehensive, interpretable, and accurate molecular property prediction.

To retrospectively analyze whether traction bronchiectasis was reversible in coronavirus disease 2019 (COVID-19) survivors with acute respiratory distress syndrome (ARDS), and whether computed tomography (CT) findings were associated with the reversibility, 41 COVID-19 survivors with ARDS were followed-up for more than 4 months. Demographics, clinical data, and all chest CT images were collected. The follow-up CT images were compared with the previous CT scans. There were 28 (68%) patients with traction bronchiectasis (Group I) and 13 (32%) patients without traction bronchiectasis (Group II) on CT images. Traction bronchiectasis disappeared completely in 21 of the 28 (75%) patients (Group IA), but did not completely disappear in seven of the 28 (25%) patients (Group IB). In the second week after onset, the evaluation score on CT images in Group I was significantly higher than that in Group II ( p = 0.001). The proportion of reticulation on the last CT images in Group IB was found higher than that in Group IA (p < 0.05). COVID-19 survivors with ARDS might develop traction bronchiectasis, which can be absorbed completely in most patients. Traction bronchiectasis in a few patients did not disappear completely, but bronchiectasis was significantly relieved. The long-term follow-up is necessary to further assess whether traction bronchiectasis represents irreversible fibrosis.

African swine fever (ASF) has widely spread around the world in the last 100 years since its discovery. The African swine fever virus (ASFV) particles are made of more than 150 proteins, with the p17 protein encoded by the D117L gene serving as one of the major capsid proteins and playing a crucial role in the virus’s morphogenesis and immune evasion. Thus, monoclonal antibody (mAb) targeting p17 is important for the research and detection of ASFV infection. Here, we produced two specific mAbs against p17, designated as 1G2 and 6G3, respectively, and both have been successfully used in enzyme-linked immunosorbent assay (ELISA), Western blotting, and immunofluorescence assay. Moreover, we found that both 1G2 and 6G3 mAbs recognize a novel epitope of 72–78 amino acids of p17 protein, highly conserved across all genotype I and II strains. Based on this epitope, an indirect ELISA has been established to effectively detect antibodies during ASFV infection, and it exhibits high consistency with commercial ASFV ELISA kits. In summary, the production of the specific p17 mAbs and the identification of the recognized epitope will significantly promote the serological diagnosis of ASFV.

BackgroundTesticular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements.PurposeBased on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV.Materials and methodsThe study cohort consisted of retrospectively collected patient data ( N  = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model.ResultsThe deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV ( R 2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV.ConclusionThe MRI-based deep learning model is an accurate and reliable tool for measuring TV.

SummaryThe clinical data analysis found that, compared with the traditional obesity index, the waist-weight ratio (WWR) has more advantages in predicting abnormal bone mineral density in subjects with type 2 diabetes. WWR may serve as a new predictive indicator for osteoporosis in T2DM patients.PurposeThis study was designed to explore the correlation between obesity-related indices and bone mineral density (BMD) and its influencing factors in type 2 diabetes mellitus (T2DM) patients.MethodsA total of 528 patients with type 2 diabetes were recruited. Glucose tolerance, insulin stimulation, and blood biochemical tests were conducted on all participants. All subjects underwent dual-energy X-ray bone density testing and were grouped based on the bone density results.ResultsCompared with those in the normal BMD group, the waist-to-body weight ratio (WWR) and weight-adjusted-waist index (WWI) in the osteopenia and osteoporosis groups were significantly greater, while body mass index (BMI) was significantly lower (P < 0.05). The logistic regression results showed that the WWR, WWI, and BMI were independently correlated with abnormal BMD in T2DM patients (P < 0.05). WWR and the WWI were negatively correlated with the T-value of bone density in various parts of the body, while BMI was positively correlated with the T-value of bone density (P < 0.05). The area under the working characteristic curve (AUC) for T2DM patients with abnormal bone mass predicted by the WWR [0.806, 95% CI = (0.770–0.843), P < 0.001] was greater than that for patients with other obesity indicators, such as the WWI and BMI.ConclusionWe found a positive correlation between the WWR and bone density in T2DM patients. Compared with other obesity indicators, such as BMI and WWI, the WWR has a stronger discriminative ability for T2DM patients with abnormal bone density. Therefore, more attention should be given to the WWR in T2DM patients.

A survey of hospitals on three continents was performed to assess their infection control preparedness and measures, and their infection rate in hospital health care workers during the COVID-19 pandemic. All surveyed hospitals used similar PPE but differences in preparedness, PPE shortages, and infection rates were reported.