Background Human adenoviruses are a common group of viruses that cause acute infectious diseases. Human adenovirus (HAdV) 3 and HAdV 7 cause major outbreaks of severe pneumonia. A reliable and practical method for HAdV typing in clinical laboratories is lacking. A simple, rapid and accurate molecular typing method for HAdV may facilitate clinical diagnosis and epidemiological control. Methods We developed and evaluated duplex real-time recombinase-aided amplification (RAA) assays incorporating competitive internal controls for detection of HAdV 3 and HAdV 7, respectively. The assays were performed in a one-step in a single tube reaction at 39° for 20 min. Results The analytical sensitivities of the duplex RAA assays for HAdV 3 and HAdV 7 were 5.0 and 14.8 copies per reaction, respectively (at 95% probability by probit regression analysis). No cross-reaction was observed with other types of HAdV or other common respiratory viruses. The duplex RAA assays were used to detect 152 previously-defined HAdV-positive samples. These results agreed with those obtained using a published triplex quantitative real-time PCR protocol. Conclusions We provide the first report of internally-controlled duplex RAA assays for the detection of HAdV 3 and HAdV 7. These assays effectively reduce the rate of false negative results and may be valuable for detection of HAdV 3 and HAdV 7 in clinical laboratories, especially in resource-poor settings.

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BackgroundLiver transplantation surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients.ObjectiveTo develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms.MethodsA total of 1,239 patients who underwent liver transplantation surgery in three large grade lll-A general hospitals of China from March 2014 to November 2021 were included and analyzed. A total of 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was an intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and the traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models.ResultsFifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, thrombin time, direct bilirubin, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of the CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms.ConclusionA prediction model for massive blood transfusion in liver transplantation surgery was successfully established based on the CatBoost algorithm, and a certain degree of generalization verification is carried out in the validation set. The model may be superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.

Perceived trustworthiness based on facial appearance plays an important role in interpersonal trust and cooperative behavior. Interpersonal trust behaviors involve both trustors and trustees. However, there is no clear conclusion on how the age of the two individuals affects interpersonal trust behaviors. Therefore, this study used the trust game task to explore the differences in trust behaviors between two different age groups in response to faces of different ages and analyzed whether such differences were apparent in the face processing stage. The behavioral results showed that only younger adults invested more money with older partners than younger ones; that is, younger adults trusted older faces more. The event-related potential (ERP) analyses showed that in the early stage of face processing, younger faces elicited more negative N170 than older faces; at the same time, older faces elicited more positive VPP than younger faces, and younger adults had more positive VPP than older adults. In the middle and late stages of face processing, younger faces elicited more negative FRN than older faces in younger adults but not in older adults. In addition, older faces elicited more positive LPP than younger faces in older adults but not in younger adults. The neural analyses suggested that age-related differences in facial trustworthiness judgments might occur in the later stages of face processing. Combining the behavioral and neural results, we found a dissociation between trustworthiness perceptions and trust behaviors in both younger and older adults, which may provide insight into how to prevent older adults from being deceived.