With the continuous development of China's aging society and the prevalence of unhealthy lifestyles, the incidence of cardiovascular disease in China has been increasing in recent years. Among them, atrial fibrillation (AF) is the most common arrhythmia disease. In recent years, pulsed field ablation (PFA) has been continuously applied to AF treatment as a novel treatment. This paper first introduces the principle of PFA applied to AF treatment, and introduces the research progress of PFA in different directions, such as the comparison of different ablation methods, the study of physical parameters, the study of ablation area, the study of tissue specificity and clinical research. Then, the clinical prior research of PFA is discussed, including the use of simulation software to obtain the simulation effect of different parameters, the evaluation of ablation effect during animal research, and finally the current AF treatment. Various prior studies and clinical studies are summarized, and suggestions are made for the shortcomings found in the study of AF treatment and the future research direction is prospected.

Breast cancer (BC) is one of the most prevalent malignant tumors affecting women worldwide, with its incidence rate continuously increasing. As a result, treatment strategies for this disease have received considerable attention. Research has highlighted the crucial role of the Hedgehog (Hh) signaling pathway in the initiation and progression of BC, particularly in promoting tumor growth and metastasis. Therefore, molecular targets within this pathway represent promising opportunities for the development of novel BC therapies. This study aims to elucidate the therapeutic mechanisms by which natural compounds modulate the Hh signaling pathway in BC. By conducting a comprehensive review of various natural compounds, including polyphenols, terpenes, and alkaloids, we reveal both common and unique regulatory mechanisms that influence this pathway. This investigation represents the first comprehensive analysis of five distinct mechanisms through which natural compounds modulate key molecules within the Hh pathway and their impact on the aggressive behaviors of BC. Furthermore, by exploring the structure-activity relationships between these compounds and their molecular targets, we shed light on the specific structural features that enable natural compounds to interact with various components of the Hh pathway. These novel insights contribute to advancing the development and clinical application of natural compound-based therapeutics. Our thorough review not only lays the groundwork for exploring innovative BC treatments but also opens new avenues for leveraging natural compounds in cancer therapy.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Breast cancer(BC)is one of the most prevalent malignant tumors affecting women worldwide,with its incidence rate continuously increasing.As a result,treatment strategies for this disease have received considerable attention.Research has highlighted the crucial role of the Hedgehog(Hh)signaling pathway in the initiation and progression of BC,particularly in promoting tumor growth and metastasis.There-fore,molecular targets within this pathway represent promising opportunities for the development of novel BC therapies.This study aims to elucidate the therapeutic mechanisms by which natural com-pounds modulate the Hh signaling pathway in BC.By conducting a comprehensive review of various natural compounds,including polyphenols,terpenes,and alkaloids,we reveal both common and unique regulatory mechanisms that influence this pathway.This investigation represents the first comprehen-sive analysis of five distinct mechanisms through which natural compounds modulate key molecules within the Hh pathway and their impact on the aggressive behaviors of BC.Furthermore,by exploring the structure-activity relationships between these compounds and their molecular targets,we shed light on the specific structural features that enable natural compounds to interact with various components of the Hh pathway.These novel insights contribute to advancing the development and clinical application of natural compound-based therapeutics.Our thorough review not only lays the groundwork for exploring innovative BC treatments but also opens new avenues for leveraging natural compounds in cancer therapy.

Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Introduction::Observational studies have revealed an association between waist circumference (WC) and atrial fibrillation (AF). However, it is difficult to infer a causal relationship from observational studies because the observed associations could be confounded by unknown risk factors. Therefore, the causal role of WC in AF is unclear. This study was designed to investigate the causal association between WC and AF using a two-sample Mendelian randomization (MR) analysis.Methods::In our two-sample MR analysis, the genetic variation used as an instrumental variable for MR was acquired from a genome-wide association study (GWAS) of WC (42 single nucleotide polymorphisms with a genetic significance of P <5 × 10 –8). The data of WC (from the Genetic Investigation of ANthropometric Traits consortium, containing 232,101 participants) and the data of AF (from the European Bioinformatics Institute database, containing 55,114 AF cases and 482,295 controls) were used to assess the causal role of WC on AF. Three different approaches (inverse variance weighted [IVW], MR–Egger, and weighted median regression) were used to ensure that our results more reliable. Results::All three MR analyses provided evidence of a positive causal association between high WC and AF. High WC was suggested to increase the risk of AF based on the IVW method (odds ratio [OR] = 1.43, 95% confidence interval [CI], 1.30–1.58, P = 2.51 × 10 -13). The results of MR–Egger and weighted median regression exhibited similar trends (MR–Egger OR = 1.40 [95% CI, 1.08–1.81], P = 1.61 × 10 -2; weighted median OR = 1.39 [95% CI, 1.21–1.61], P = 1.62 × 10 -6). MR–Egger intercepts and funnel plots showed no directional pleiotropic effects between high WC and AF. Conclusions::Our findings suggest that greater WC is associated with an increased risk of AF. Taking measures to reduce WC may help prevent the occurrence of AF.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.

Objective:To explore the high-risk factors for parenteral nutrition associated cholestasis（PNAC）in extremely/ultra-low birth weight infants，and establish a risk Alignment Diagram prediction model.Methods:We retrospectivly analyzed the clinical data of hospitalized extremely/ultra-low birth weight infants admitted to Neonatology Department at Quanzhou Children's Hospital from January 2019 to December 2020，using multivariate Logistic regression analysis to screen for independent risk factors for the occurrence of PNAC.An Alignment Diagram model prediction model for PNAC was constructed by using R software，and the performance of the model was evaluated through receiver operating characteristic curves.Results:A total of 203 extremely/ultra-low birth weight infants were included，with a median gestational age of 29.14（28.00，30.86）weeks and a median birth weight of 1　170（1　000,1　300）g.Among them，26（12.81%）cases developed PNAC.Multivariate Logistic regression analysis showed that the duration of parenteral nutrition（ OR=1.015 ，95% CI 1.003-1.034），the cumulative amount of glucose（ OR=1.014 ，95% CI 1.001-1.028），small for gestational age（ OR=3.455 ，95% CI 1.127-10.589），and neonatal sepsis（ OR=3.142 ，95% CI 1.039-9.503）were independent risk factors for PNAC（ P＜0.05）；The four independent risk factors mentioned above were introduced into R software to construct an Alignment Diagram model，the area under the receiver operating characteristic curve was 0.835（95% CI 0.842-0.731），and the results of the Hosmer Limeshow goodness of fit test show that：χ 2=5.34，degree of freedom=8， P=0.72.A calibration curve indicated good consistency between the predicted probability of the model and the actual occurrence rate，with good accuracy. Conclusion:The Alignment Diagram model constructed based on four independent risk factors of the duration of parenteral nutrition，glucose accumulation，small for gestational age infants，and neonatal sepsis exhibits high predictive ability，and is expected to provide an intuitive and convenient visualization tool for preventing or reducing the occurrence of PNAC in extremely/ultra-low birth weight infants

Objective To investigate the involvement of calmodulin(CaM)in the pathogenesis of Alzheimer disease(AD)and the mechanism by which CaM binds to amyloid-β(Aβ).Methods The hub genes expressed in AD and predicted to be the target proteins for AD prevention and treatment were obtained using bioinformatics methods.The GST-Aβ1-42 recombinant plasmid was constructed through genetic recombination and was then sequenced.The recombinant plasmids were identified using agarose gel electrophoresis,while the extracted and purified GST-Aβ1-42 fusion protein was confirmed using SDS-PAGE gel electrophoresis.GST pull-down assay was used to detect the interaction between GST-Aβ1-42 protein and CaM,expressed in the plasmid.Results The top 20 hub genes in degree ranking were obtained.The DNA sequencing results of the plasmid proved that the recombinant plasmid was successfully constructed.The agarose gel electrophoresis results indicated that the fragment digested by the enzyme was similar to the molecular weight of the Aβ1-42 gene seg-ments,further proving the successful construction of the recombinant plasmid.Binding of GST-Aβ1-42 protein to CaM in a concentration dependent manner was revealed through the GST pull down experiment.Conclusion The GST-Aβ1-42 recombinant plasmid is success-fully constructed and is shown to bind to CaM.

BACKGROUND: Low-density computed tomography (LDCT) improved early lung cancer diagnosis but introduces an excess of false-positive pulmonary nodules data. Hence, accurate diagnosis of early-stage lung cancer remains challenging. The purpose of the study was to assess the feasibility of using circulating tumour cells (CTCs) to differentiate malignant from benign pulmonary nodules. METHODS: 122 patients with suspected malignant pulmonary nodules detected on chest CT in preparation for surgery were prospectively recruited. Peripheral blood samples were collected before surgery, and CTCs were identified upon isolation by size of epithelial tumour cells and morphological analysis. Laser capture microdissection, MALBAC amplification, and whole-exome sequencing were performed on 8 samples. The diagnostic efficacy of CTCs counting, and the genomic variation profile of benign and malignant CTCs samples were analysed. RESULTS: Using 2.5 cells/5 mL as the cut-off value, the area under the receiver operating characteristic curve was of 0.651 (95% confidence interval: 0.538-0.764), with a sensitivity and specificity of 0.526 and 0.800, respectively, and positive and negative predictive values of 91.1% and 30.3%, respectively. Distinct sequence variations differences in DNA damage repair-related and driver genes were observed in benign and malignant samples. TP53 mutations were identified in CTCs of four malignant cases; in particular, g.7578115T>C, g.7578645C>T, and g.7579472G>C were exclusively detected in all four malignant samples. CONCLUSIONS CTCs play an ancillary role in the diagnosis of pulmonary nodules. TP53 mutations in CTCs might be used to identify benign and malignant pulmonary nodules.
Humans ; Lung Neoplasms ; Exome Sequencing ; Multiple Pulmonary Nodules ; Carcinoma ; DNA Repair