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Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration

Impact of regenerative procedure on the healing process following surgical root canal treatment: A systematic review and meta-analysis

Introduction Different Guided Tissue Regeneration (GTR) procedures, such as membranes, bone substitute materials, and Autologous Platelet Concentrates (APCs), have been applied after surgical root canal treatment (SRCT), which produce different outcomes. This study aimed to evaluate the impact of regenerative procedures on the healing process following SRCT. Methods A comprehensive search of PubMed, Embase, Scopus, Cochrane, and the Web of Science found Randomized Controlled Trials (RCTs) published until February 25, 2024. Manual searches were also conducted. Our main outcome was SRCT success or failure after GTR procedures. The Risk Ratio (RR) and failure rate meta-analysis used a fixed effects model with a 95% confidence interval (CI). Subgroup analyses were conducted based on the use of different GTR procedures for varying lesion types in SRCT. Results Out of 1,605 records, 16 studies with 690 lesions were included. Overall, GTR procedures significantly improved healing after SRCT in both 2D (RR: 0.50; 95% CI, 0.34–0.73; P < 0.001) and 3D evaluation methods (RR: 0.36; 95% CI, 0.15–0.90; P < 0.001) with no significant difference between the two methods. Conclusion GTR significantly improved SRCT healing regardless of the evaluation method used. Combining collagen membranes with bovine bone-derived hydroxyapatite significantly enhanced the healing process. Additionally, GTR procedures significantly improve healing in through-and-through lesions.

Collaboration among governments, agribusinesses, and rural households for improving the effectiveness of conservation tillage technology adoption

Relationship between proximal to distal phalangeal articular angle and Hallux Pronation in Hallux Valgus deformity

Graph Geometric Algebra networks for graph representation learning

Structural insights into the mechanism of phosphate recognition and transport by XPR1

Liver-specific gene PGRMC1 blocks c-Myc-induced hepatocarcinogenesis through ER stress-independent PERK activation

Inpatient treatment for severe obesity: A retrospective cohort study in Brazil, comparing exposure variables in a secondary data analysis

Introduction: Very low-calorie diets with hospitalization have demonstrated promise as a viable therapeutic option for severe obesity and its associated comorbidities. However, large studies providing a comprehensive longitudinal observation of patients undergoing this therapy are lacking. We evaluated the effectiveness of treating severe obesity in hospitalized patients, using very low-calorie diets and clinical support to develop lifestyle changes. Methods: This study was a retrospective cohort comparing exposure variables in a secondary data analysis with a pre-post treatment design. Data were obtained from medical records of patients with severe obesity (grade II or III) treated in a Brazilian obesity specialist hospital from 2016 to 2022. The patients underwent a very low-calorie diet (500–800 kCal/day) and immersive changes in lifestyle habits, monitored by a multidisciplinary team. At 3 months, 777 patients presented complete data and 402 presented complete data at 6 months. The study compared changes in bioimpedance and laboratory tests, between men and women and age groups. Results: Three months of hospitalization yielded significant reductions in weight, body mass index (BMI), body fat, skeletal muscle mass, glucose, inflammatory, and lipid parameters. These reductions were more pronounced after 6 months, nearly doubling those observed at 3 months. In women, BMI and fat mass reduced by 10.4% and 15.2% at 3 months and 20.4% and 31.3% at 6 months, respectively. In men, BMI and fat mass decreased by 12.9% and 25.3 at 3 months and 23.6% and 45.3% at 6 months, respectively. Elderly individuals (aged ≥ 60 years) had smaller reductions in BMI and fat mass than non-elderly individuals (aged < 60 years) but still presented significant improvements. Conclusion: This study suggests the viability of treating severe obesity by hospitalization with low-calorie diets and immersive lifestyle changes. This treatment modality significantly improves anthropometric measurements, glucose, lipids, and inflammatory markers, thereby reducing cardiovascular risk.

l-Aspartic acid-functionalized magnetic nanoparticles: as a new magnetically reusable bifunctional acid–base catalysts for the synthesis of benzo[b]pyran and pyrano[3,2–c] chromene derivatives

Constitutive activation of the Src-family kinases Fgr and Hck enhances the tumor burden of acute myeloid leukemia cells in immunocompromised mice

The influence of active metabolites from the decomposition of camelina and barley straw on the development of phytoplankton from eutrophic freshwater ecosystem

The molecular reach of antibodies crucially underpins their viral neutralisation capacity

Abstract Key functions of antibodies, such as viral neutralisation, depend on high-affinity binding. However, viral neutralisation poorly correlates with antigen affinity for reasons that have been unclear. Here, we use a new mechanistic model of bivalent binding to study  >45 patient-isolated IgG1 antibodies interacting with SARS-CoV-2 RBD surfaces. The model provides the standard monovalent affinity/kinetics and new bivalent parameters, including the molecular reach: the maximum antigen separation enabling bivalent binding. We find large variations in these parameters across antibodies, including reach variations (22–46 nm) that exceed the physical antibody size (~15 nm). By using antigens of different physical sizes, we show that these large molecular reaches are the result of both the antibody and antigen sizes. Although viral neutralisation correlates poorly with affinity, a striking correlation is observed with molecular reach. Indeed, the molecular reach explains differences in neutralisation for antibodies binding with the same affinity to the same RBD-epitope. Thus, antibodies within an isotype class binding the same antigen can display differences in molecular reach, substantially modulating their binding and functional properties.

G-quadruplex stabilization provokes DNA breaks in human PKD1, revealing a second hit mechanism for ADPKD

AbstractThe “secondhit” pathway is responsible for biallelic inactivation of many tumor suppressors, where a pathogenic germline allele is joined by somatic mutation of the remaining functional allele. The mechanisms are unresolved, but the human PKD1 tumor suppressor is a good experimental model for identifying the molecular determinants. Inactivation of PKD1 results in autosomal dominant polycystic kidney disease, a very common disorder characterized by the accumulation of fluid-filled cysts and end-stage renal disease. Since human PKD1 follows second hit and mouse Pkd1 heterozygotes do not, we reasoned that there is likely a molecular difference that explains the elevated mutagenesis of the human gene. Here we demonstrate that guanine quadruplex DNA structures are abundant throughout human, but not mouse, PKD1 where they activate the DNA damage response. Our results suggest that guanine quadruplex DNAs provoke DNA breaks in PKD1, providing a potential mechanism for cystogenesis in autosomal dominant polycystic kidney disease specifically and for the inactivation of guanine quadruplex-rich tumor suppressors generally.

Contribution of paranasal sinus, chest, and abdomen/pelvis computed tomography in patients with febrile neutropenia

Objective Febrile neutropenia (FN) is a serious clinical event, associated with significant morbidity and mortality. Imaging has a central role in the identification of the fever cause. The study objectives were to assess abnormalities of potential infectious origin on paranasal sinus, chest and abdomen/pelvis CT scans performed during an episode of FN, in patients with or without specific clinical signs, and to evaluate their impact on the frequency of changes in treatment. Materials and methods This retrospective study included 306 patients with FN from a single academic center between October 2018 and December 2019. Clinical and biological data, final clinical diagnosis for the FN episode, and change in treatment following CT scans were collected from medical records. CT images were reviewed for positive findings in each anatomical region. Results Overall, 188 patients (61.4%) had at least one positive CT finding on paranasal sinus, chest and/or abdomen/pelvis regions, and 153 patients (50.0%) received a final clinical diagnosis of infection, based on microbiological and/or radiological findings. There were more chest and abdomen/pelvis CT positive findings in patients with specific clinical signs than in patients without (65.7% vs. 25.8%, and 59.0% vs. 22.7% respectively, p <0.001), with a higher frequency of therapeutic modifications in positive CTs. There were significantly more paranasal sinus CT abnormalities in symptomatic patients (33.3% vs. 8.5%, p = 0.03), but without any impact on treatment. Conclusion These results support recommendations to perform systematic chest CT in patients with FN and may encourage the widespread use of abdomen/pelvis CT in this indication, even without symptoms. However, the actual impact of their use on the prognosis of the patients remains to be evaluated.

Study on physical clogging process and practical application of horizontal subsurface flow constructed wetland

A novel method for intelligent operation and maintenance of transformers using deep visual large model DETR + X and digital twin

Abstract To achieve real-time monitoring and intelligent maintenance of transformers, a framework based on deep vision and digital twin has been developed. An enhanced visual detection model, DETR + X, is proposed, implementing multidimensional sample data augmentation through Swin2SR and GAN networks. This model converts one-dimensional DGA data into three-dimensional feature images based on Gram angle fields, facilitating the transformation and fusion of heterogeneous modal information. The Pyramid Vision Transformer (PVT) is innovatively adopted as the backbone for image feature extraction, replacing the traditional ResNet structure. A Deformable Attention mechanism is employed to handle the complex spatial structure of multi-scale features. Testing results indicate that the improved DETR + X model performs well in transformer state recognition tasks, achieving a classification accuracy of 100% for DGA feature maps. In object detection tasks, it surpasses advanced models such as Faster R-CNN, RetinaNet, YOLOv8, and Deformable DETR in terms of overall mAP50 scores, particularly demonstrating significant enhancements in small object detection. Furthermore, the Llava-7b model, fine-tuned based on domain expertise, serves as an expert decision-making tool for transformer maintenance, providing accurate operational recommendations based on visual detection results. Finally, based on digital twin and inference models, a comprehensive platform has been developed to achieve real-time monitoring and intelligent maintenance of transformers.

Study on the simulation of bridge deformation in a mining subsidence area

The transcriptional repressor HEY2 regulates mitochondrial oxidative respiration to maintain cardiac homeostasis

Limitation of switching sensory information flow in flexible perceptual decision making

Exploring happiness factors with explainable ensemble learning in a global pandemic

Happiness is a state of contentment, joy, and fulfillment, arising from relationships, accomplishments, and inner peace, leading to well-being and positivity. The greatest happiness principle posits that morality is determined by pleasure, aiming for a society where individuals are content and free from suffering. While happiness factors vary, some are universally recognized. The World Happiness Report (WHR), published annually, includes data on ‘GDP per capita’, ‘social support’, ‘life expectancy’, ‘freedom to make life choices’, ‘generosity’, and ‘perceptions of corruption’. This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. We also show the impact of COVID-19 pandemic on the happiness features. We design two ensemble ML and DL models using blending and stacking ensemble techniques, namely, Blending RGMLL, which combines Ridge Regression (RR), Gradient Boosting (GB), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Linear Regression (LR), and Stacking LRGR, which combines LR, Random Forest (RF), GB, and RR. Among the trained models, Blending RGMLL demonstrates the highest predictive accuracy with R2 of 85%, MSE of 0.15, and RMSE of 0.38. We employ Explainable Artificial Intelligence (XAI) techniques to uncover changes in happiness indices, variable importance, and the impact of the COVID-19 pandemic on happiness. The study utilizes an open dataset from the WHR, covering 156 countries from 2018 to 2023. Our findings indicate that ‘GDP per capita’ is the most critical indicator of happiness score (HS), while ‘social support’ and ‘healthy life expectancy’ are also important features before and after the pandemic. However, during the pandemic, ‘social support’ emerged as the most important indicator, followed by ‘healthy life expectancy’ and ‘GDP per capita’, because social support is the prime necessity in the pandemic situation. The outcome of this research helps people understand the impact of these features on increasing the HS and provides guidelines on how happiness can be maintain during unwanted situations. Future research will explore advanced methods and include other related features with real-time monitoring for more comprehensive insights.