GPCRs (G-protein-coupled receptors) are members of a family of proteins which are generally regarded as the largest group of therapeutic drug targets. Ligands of GPCRs do not usually activate all cellular signalling pathways linked to a particular seven-transmembrane receptor in a uniform manner. The fundamental idea behind this concept is that each ligand has its own ability, while interacting with the receptor, to activate different signalling pathways (or a particular set of signalling pathways) and it is this concept which is known as biased signalling. The importance of biased signalling is that it may selectively activate biological responses to favour therapeutically beneficial signalling pathways and to avoid adverse effects. There are two levels of biased signalling. First, bias can arise from the ability of GPCRs to couple to a subset of the available G-protein subtypes: Gα_s, Gα_q/11, Gα_i/o or Gα_12/13. These subtypes produce the diverse effects of GPCRs by targeting different effectors. Secondly, biased GPCRs may differentially activate G-proteins or β-arrestins. β-Arrestins are ubiquitously expressed and function to terminate or inhibit classic G-protein signalling and initiate distinct β-arrestin-mediated signalling processes. The interplay of G-protein and β-arrestin signalling largely determines the cellular consequences of the administration of GPCR-targeted drugs. In the present review, we highlight the particular functionalities of biased signalling and discuss its biological effects subsequent to GPCR activation. We consider that biased signalling is potentially allowing a choice between signalling through ‘beneficial’ pathways and the avoidance of ‘harmful’ ones.

The prevalence and detection rates of adrenal incidentalomas have been on the rise globally, with more than 90% of these lesions pathologically classified as adrenocortical adenomas. Among these, approximately 30% of patients present with hormone-secreting adenomas, leading to the deterioration of their health, with some requiring surgical resection. The available methods for adrenal function evaluation are invasive and costly. Moreover, their accuracy is influenced by numerous factors. Therefore, it is imperative to develop non-invasive and simplified preoperative diagnostic approach. A retrospective study was performed on 169 patients from two tertiary medical centers. Subsequently, radiomics features were extracted after tumor margins were delineated layer-by-layer using a semi-automatic contouring approach. Feature selection was achieved in two cycles, with the first round utilizing a support vector machine (SVM) and the second round using a LASSO-based recursive feature elimination algorithm. Finally, logistic regression models were constructed using the clinico-radiological, radiomics, and a combination of both. After a comprehensive evaluation of the predictive indicators, the logistic regression classifier model based on the combined clinico-radiological and radiomic features had an AUC of (0.945, 0.927, 0.856) for aldosterone-producing adenoma (APA), (0.963, 0.889, 0.887) for cortisol-producing adenoma (CPA), and (0.940, 0.765, 0.816) for non-functioning adrenal adenoma (NAA) in the training set, validation set, and external test set, respectively. This model exhibited superior predictive performance in differentiating between the three adrenal adenoma subtypes. A logistic regression model was constructed using radiomics and clinico-radiological features derived from multi-phase enhanced CT images and conducted external validation. The combined model showed good overall performance, highlighting the feasibility of applying the model for preoperative differentiation and prediction of various types of ACA.

Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs quickly and accurately holds great significance in preventing heart disease and reducing mortality. This research endeavors to outperform previous studies by developing a scientific neural network model capable of training and predicting ECG signals for 11 categories of arrhythmias, accounting for up to 5 co-existing labels. In this study, we initially address the issue of imbalanced datasets by employing Borderline SMOTE and Cluster Centroids techniques during preprocessing. Subsequently, we propose a novel SAR model that combines attention and resnet mechanisms. The dataset is subjected to a 10-fold validation process to train and evaluate the model. Finally, several metrics such as HammingLoss, RankingLoss, F1-score, AUC and Coverage are used to evaluate the model. By evaluating the results of the tests, the average Hamming Loss is 1.12 %, the average Ranking Loss is 1.17 %, the average Micro F1-score is 98.46 %, the average Micro AUC is 98.76 %, and the average Coverage is 3.2762. The results show that the SAR model outperforms previous related studies on the task of classifying arrhythmia signals with multiple categories and labels. The SAR model demonstrated excellent performance in accurately classifying multi-category and multi-label arrhythmia signals, affirming its scientific validity. Compared with previous studies, the model achieves a certain improvement in performance, which can help cardiologists to achieve scientific and accurate diagnosis of arrhythmia diseases.

Ganglioneuroblastoma (GNB), predominantly observed in children, is an uncommon malignant tumor in adults, with established treatment protocols notably lacking. The present study details the case of a 20‑year‑old woman who presented with a left adrenal gland mass, identified during a physical examination. Additionally, an unidentified mass was noted near the renal hilum in the preoperative evaluation. Following thorough preoperative preparation, both the primary adrenal gland mass and the renal hilar mass were surgically removed. The procedure concluded successfully. Pathological analysis confirmed that the left adrenal mass was a GNB and identified the renal hilar mass as a metastatic extension. Postoperative examination revealed a new formation at the original surgical site, later verified as a postoperative scar. Through the publication of a case report and extensive literature review, the present study aims to enhance our understanding of this condition, providing valuable diagnostic, therapeutic and post‑recovery references for this rare adult disease.