Design of customized coronavirus receptors.

Although coronaviruses use diverse receptors, the characterization of coronaviruses with unknown receptors has been impeded by a lack of infection models1,2. Here we introduce a strategy to engineer functional customized viral receptors (CVRs). The modular design relies on building artificial receptor scaffolds comprising various modules and generating specific virus-binding domains. We identify key factors for CVRs to functionally mimic native receptors by facilitating spike proteolytic cleavage, membrane fusion, pseudovirus entry and propagation for various coronaviruses. We delineate functional SARS-CoV-2 spike receptor-binding sites for CVR design and reveal the mechanism of cell entry promoted by the N-terminal domain-targeting S2L20-CVR. We generated CVR-expressing cells for 12 representative coronaviruses from 6 subgenera, most of which lack known receptors, and show that a pan-sarbecovirus CVR supports propagation of a propagation-competent HKU3 pseudovirus and of authentic RsHuB2019A3. Using an HKU5-specific CVR, we successfully rescued wild-type and ZsGreen-HiBiT-incorporated HKU5-1 (LMH03f) and isolated a HKU5 strain from bat samples. Our study demonstrates the potential of the CVR strategy for establishing native receptor-independent infection models, providing a tool for studying viruses that lack known susceptible target cells.

A Phase II Study of Neoadjuvant PLD/Cyclophosphamide and Sequential nab-Paclitaxel Plus Dual HER2 Blockade in HER2-Positive Breast Cancer.

BACKGROUND: Neoadjuvant trastuzumab/pertuzumab (HP) plus chemotherapy for HER2-positive breast cancer (BC) achieved promising efficacy. The additional cardiotoxicity still existed. Brecan study evaluated the efficacy and safety of neoadjuvant pegylated liposomal doxorubicin (PLD)/cyclophosphamide and sequential nab-paclitaxel based on HP (PLD/C/HP-nabP/HP). PATIENTS AND METHODS: Brecan was a single-arm phase II study. Eligible patients with stages IIA-IIIC HER2-positive BC received 4 cycles of PLD, cyclophosphamide, and HP, followed by 4 cycles of nab-paclitaxel and HP. Definitive surgery was scheduled after 21 days for patients completing treatment or experiencing intolerable toxicity. The primary endpoint was the pathological complete response (pCR). RESULTS: Between January 2020 and December 2021, 96 patients were enrolled. Ninety-five (99.0%) patients received 8 cycles of neoadjuvant therapy and all underwent surgery with 45 (46.9%) breast-conserving surgery and 51 (53.1%) mastectomy. The pCR was 80.2% (95%CI, 71.2%-87.0%). Four (4.2%) experienced left ventricular insufficiency with an absolute decline in LVEF (43%-49%). No congestive heart failure and ≥grade 3 cardiac toxicity occurred. The objective response rate was 85.4% (95%CI, 77.0%-91.1%), including 57 (59.4%) complete responses and 25 (26.0%) partial responses. The disease control rate was 99.0% (95%CI, 94.3%-99.8%). For overall safety, ≥grade 3 AEs occurred in 30 (31.3%) and mainly included neutropenia (30.2%) and asthenia (8.3%). No treatment-related deaths occurred. Notably, age of >30 (P = .01; OR = 5.086; 95%CI, 1.44-17.965) and HER2 IHC 3+ (P = .02; OR = 4.398; 95%CI, 1.286-15.002) were independent predictors for superior pCR (ClinicalTrials.gov Identifier NCT05346107). CONCLUSION: Brecan study demonstrated the encouraging safety and efficacy of neoadjuvant PLD/C/HP-nabP/HP, suggesting a potential therapeutic option in HER2-positive BC.

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games.

(1) Background and Objective: Major League Baseball (MLB) is one of the most popular international sport events worldwide. Many people are very interest in the related activities, and they are also curious about the outcome of the next game. There are many factors that affect the outcome of a baseball game, and it is very difficult to predict the outcome of the game precisely. At present, relevant research predicts the accuracy of the next game falls between 55% and 62%. (2) Methods: This research collected MLB game data from 2015 to 2019 and organized a total of 30 datasets for each team to predict the outcome of the next game. The prediction method used includes one-dimensional convolutional neural network (1DCNN) and three machine-learning methods, namely an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). (3) Results: The prediction results show that, among the four prediction models, SVM obtains the highest prediction accuracies of 64.25% and 65.75% without feature selection and with feature selection, respectively; and the best AUCs are 0.6495 and 0.6501, respectively. (4) Conclusions: This study used feature selection and optimized parameter combination to increase the prediction performance to around 65%, which surpasses the prediction accuracies when compared to the state-of-the-art works in the literature.

[Psychological and behavioral problems in children and adolescents during the coronavirus disease 2019 epidemic: a Scoping review]. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žæµè¡ŒæœŸé—´å„¿ç«¥å’Œé’å°‘å¹´çš„å¿ƒç†è¡Œä¸ºé—®é¢˜ï¼šScoping综述.

OBJECTIVES: To investigate the psychological and behavioral problems and related influencing factors in children and adolescents during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: China National Knowledge Infrastructure, Wanfang Data, PubMed, and Web of Science were searched using the method of subject search for articles published up to March 31, 2022, and related data were extracted for Scoping review. RESULTS: A total of 3 951 articles were retrieved, and 35 articles from 12 countries were finally included. Most of the articles were from the journals related to pediatrics, psychiatry, psychology, and epidemiology, and cross-sectional survey was the most commonly used research method. Psychological and behavioral problems in children and adolescents mainly included depression/anxiety/stress, sleep disorder, internet behavior problems, traumatic stress disorder, and self-injury/suicide. Influencing factors were analyzed from the three aspects of socio-demographic characteristics, changes in living habits, and ways of coping with COVID-19. CONCLUSIONS: During the COVID-19 epidemic, the psychological and behavioral problems of children and adolescents in China and overseas are severe. In the future, further investigation and research can be carried out based on relevant influencing factors to improve the psychological and behavioral problems.

A Method for Confidence Intervals of High Quantiles.

The high quantile estimation of heavy tailed distributions has many important applications. There are theoretical difficulties in studying heavy tailed distributions since they often have infinite moments. There are also bias issues with the existing methods of confidence intervals (CIs) of high quantiles. This paper proposes a new estimator for high quantiles based on the geometric mean. The new estimator has good asymptotic properties as well as it provides a computational algorithm for estimating confidence intervals of high quantiles. The new estimator avoids difficulties, improves efficiency and reduces bias. Comparisons of efficiencies and biases of the new estimator relative to existing estimators are studied. The theoretical are confirmed through Monte Carlo simulations. Finally, the applications on two real-world examples are provided.

Regulation of cancerous progression and epithelial-mesenchymal transition by miR-34c-3p via modulation of MAP3K2 signaling in triple-negative breast cancer cells.

Emerging but limited data have evidenced an essential involvement of microRNAs (miRNAs) in the development and progression of triple negative breast cancer (TNBC), which empowers these small regulators as an innovative therapeutic approach, especially for this unique tumor subgroup still lacking an efficient and specific therapeutic target. Herein, we reported the down-regulation of miR-34c-3p level in TNBC tissues, and its expression was closely associated with estrogen receptor alpha (ERα), but not other receptors, in well-characterized breast cancer (BCa) cells. Functionally, ectopic expression of miR-34c-3p inhibited migration, invasion and epithelial-mesenchymal transition (EMT) in TNBC cells. From a mechanistic standpoint, bioinformatics coupled with luciferase and gain-of-function, loss-of-function assays showed that miR-34c-3p may regulate TNBC progression by directly targeting the 3'-untranslated region (UTR) of mitogen-activated protein kinase kinase kinase 2 (MAP3K2). Consistently, MAP3K2 overexpression could effectively rescue miR-34c-3p mimics-induced suppression of cell invasion and EMT. In light of these findings, miR-34c-3p may function as a tumor suppressor in regulating of TNBC invasiveness and EMT through negatively modulating MAP3K2 pathway. Future endeavor in this field may help to identify a novel biomarker to predict prognosis and response to therapy in TNBC.

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

[Expression of leukocyte-associated Ig-like receptor-1 in children with immune thrombocytopenia].

OBJECTIVE: To study the expression of leukocyte-associated Ig-like receptor-1(LAIR-1) in children with immune thrombocytopenia (ITP), in order to explore the possible role of LAIR-1 in the pathogenesis of childhood ITP. METHODS: Expression levels of LAIR-1 on CD4(+) T cells, CD8(+) T cells and CD19(+)CD20(+) B cells of peripheral blood were measured in 40 children with ITP by flow cytometry. Serum level of solubility LAIR-1 (sLAIR-1) was measured using ELISA. Real-time PCR was used to measure LAIR-1 mRNA expression. Thirty-two healthy children served as the control group. RESULTS: The percentages of CD19(+)CD20(+) B cells in the ITP group were significantly higher than in the control group (P<0.05). In contrast, the percentage of CD4(+) T cells in the ITP group was significantly lower than in the control group (P<0.05). The expression levels of LAIR-1 on CD4(+) T cells and CD8(+) T cells were significantly lower in the ITP group than in the control group (P<0.05). Serum sLAIR-1 level and LAIR-1 mRNA expression in the ITP group significantly increased compared with the control group (P<0.05). CONCLUSIONS: LAIR-1 expression on CD4(+) and CD8(+) T cells decreases and serum sLAIR-1 level increases in children with ITP, suggesting that LAIR-1 may play an important role in immune imbalance in these children.

Effectiveness of Walking Exercise in Improving Physical Fitness and Depression in Patients With Schizophrenia.

Patients with schizophrenia often lack physical activity, which, together with physical complications, can lower their expected lifespan. Exercise strengthens their physical and mental health. The primary aim of this study was to examine the effectiveness of a walking exercise intervention in improving physical fitness, body mass index, waist-to-hip ratio, and depressive symptoms in patients with schizophrenia. A quasi-experimental study design was used. Seventy-six participants were recruited from the psychiatric daycare center at a hospital in Northern Taiwan. They were divided into two groups. The intervention group received a walking exercise intervention, while the control completed their daily courses at the psychiatric daycare center. The changes in both groups' physical fitness, BMI, waist-to-hip ratio, and depressive symptoms were monitored. Cardiorespiratory endurance significantly improved in the intervention group, attesting to the effectiveness of the walking exercise intervention. Their depression level significantly decreased across all measurement stages. The group walking exercise reduced sedentary behaviors and increased the participants' autonomous motivation, hip circumference, and cardiorespiratory fitness. Structured exercise programs may increase the patients' hippocampal neuroplasticity and reduce their depressive symptoms. The walking exercise intervention positively affected physiological traits, physical fitness, and mental health of the participants.

An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification.

The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.

Sarbecovirus RBD indels and specific residues dictating multi-species ACE2 adaptiveness.

Our comprehensive understanding of the multi-species ACE2 adaptiveness of sarbecoviruses remains elusive, particularly for those with various receptor binding motif (RBM) insertions/deletions (indels). Here, we analyzed RBM sequences from 268 sarbecoviruses categorized into four RBM indel types. We examined the ability of 20 representative sarbecovirus Spike glycoproteins (S) and derivatives in utilizing ACE2 from various bats and several other mammalian species. We reveal that sarbecoviruses with long RBMs (type-I) can achieve broad ACE2 tropism, whereas viruses with single deletions in Region 1 (type-II) or Region 2 (type-III) exhibit narrower ACE2 tropism. Sarbecoviruses with double region deletions (type-IV) completely lost ACE2 usage, which is restricted by clade-specific residues within and outside RBM. Lastly, we propose the evolution of sarbecovirus RBM indels and illustrate how loop lengths, disulfide, and residue determinants shape multi-species ACE2 adaptiveness. This study provides profound insights into the mechanisms governing ACE2 usage and spillover risks of sarbecoviruses.

Citrus dataset for image classification.

Citrus fruits are a specialty of the subtropical region and are currently the most widely cultivated and produced fruits in Taiwan. They contain a wealth of vitamins and minerals, including vitamin C, potassium, magnesium, and more, offering anti-inflammatory and antioxidant benefits. Citrus plants are among the economically significant fruits in Taiwan, and there are several citrus species that share a similar appearance. We have constructed a database containing the four most commonly purchased citrus varieties in the market. This database comprises a total of 1379 original images, which have been expanded to 7584 images using six different data augmentation methods. We have chosen three Convolutional Neural Network (CNN) models that have achieved an accuracy rate exceeding 95 % in classifying the four varieties of citrus fruits.

GCS-YOLOV4-Tiny: A lightweight group convolution network for multi-stage fruit detection.

Fruits require different planting techniques at different growth stages. Traditionally, the maturity stage of fruit is judged visually, which is time-consuming and labor-intensive. Fruits differ in size and color, and sometimes leaves or branches occult some of fruits, limiting automatic detection of growth stages in a real environment. Based on YOLOV4-Tiny, this study proposes a GCS-YOLOV4-Tiny model by (1) adding squeeze and excitation (SE) and the spatial pyramid pooling (SPP) modules to improve the accuracy of the model and (2) using the group convolution to reduce the size of the model and finally achieve faster detection speed. The proposed GCS-YOLOV4-Tiny model was executed on three public fruit datasets. Results have shown that GCS-YOLOV4-Tiny has favorable performance on mAP, Recall, F1-Score and Average IoU on Mango YOLO and Rpi-Tomato datasets. In addition, with the smallest model size of 20.70 MB, the mAP, Recall, F1-score, Precision and Average IoU of GCS-YOLOV4-Tiny achieve 93.42 ± 0.44, 91.00 ± 1.87, 90.80 ± 2.59, 90.80 ± 2.77 and 76.94 ± 1.35%, respectively, on F. margarita dataset. The detection results outperform the state-of-the-art YOLOV4-Tiny model with a 17.45% increase in mAP and a 13.80% increase in F1-score. The proposed model provides an effective and efficient performance to detect different growth stages of fruits and can be extended for different fruits and crops for object or disease detections.

Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19.

RATIONALE AND OBJECTIVES: Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the effectiveness of COVID-19 diagnosis. This study proposes two classification models for multiple chest diseases including COVID-19. MATERIALS AND METHODS: The first is Stacking-ensemble model, which stacks six pretrained models including EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S and EfficientNetV2-M. The second model is self-designed model ECA-EfficientNetV2 based on ECA-Net and EfficientNetV2. Ten-fold cross validation was performed for each model on chest X-ray and CT images. One more dataset, COVID-CT dataset, was tested to verify the performance of the proposed Stacking-ensemble and ECA-EfficientNetV2 models. RESULTS: The best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.21%, Precision of 99.23%, Recall of 99.25%, F1-score of 99.20%, and (area under the curve) AUC of 99.51% on chest X-ray dataset; the best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.81%, Precision of 99.80%, Recall of 99.80%, F1-score of 99.81%, and AUC of 99.87% on chest CT dataset. The differences for five metrics between Stacking-ensemble and ECA-EfficientNetV2 models are not significant. CONCLUSION: Ensemble model achieves better performance than single pretrained models. Compared to the SOTA, Stacking-ensemble and ECA-EfficientNetV2 models proposed in this study demonstrate promising performance on classification of multiple chest diseases including COVID-19.

An Algorithm of Nonparametric Quantile Regression.

Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem. Regular linear quantile regression uses an L 1 loss function [Koenker in Quantile regression, Cambridge University Press, Cambridge, 2005], and the optimal solution of linear programming for estimating coefficients of regression. A problem with linear quantile regression is that the estimated curves for different quantiles can cross, a result that is logically inconsistent. To overcome the curves crossing problem, and to improve high quantile estimation in the nonlinear case, this paper proposes a nonparametric quantile regression method to estimate high conditional quantiles. A three-step computational algorithm is given, and the asymptotic properties of the proposed estimator are derived. Monte Carlo simulations show that the proposed method is more efficient than linear quantile regression method. Furthermore, this paper investigates COVID-19 and blood pressure real-world examples of extreme events by using the proposed method.

Traditional chinese god image dataset: A glimpse of chinese culture.

This data article describes a dataset of images of common Chinese deities. The dataset is divided into five categories according to the types of deities, and a total of 1314 original images were captured by smart phones from Chinese temples and through Google search engine. Each category were split into training, validation and test subsets in a ratio of 70:20:10. We rotated the pictures by 30°, 60°, 90°, 120°, 150°, and 180°; and zoomed in and out to augment the images for training and validation sets. After data enhancement, the total number of images reaches 10,786. Two models, EfficientNet-B0 and MobileNetV2, are used to identify five kinds of god images. After data augmentation, the accuracy, precision, recall, specificity and F1-score of EfficientNet-B0 were 96.15%, 96.44%, 96.18%, 96.16% and 97.60%, respectively; the accuracy, precision recall, specificity and F1-score of MobileNetV2 were 92.31%, 92.89%, 92.37%, 92.33% and 95.19%, respectively. This dataset can be used as a reference for traditional Chinese god statue images, and can also be used for object detection and image classification through machine learning and deep learning methods.

Factors Affecting the Effectiveness of Discharge Planning Implementation: A Case-Control Cohort Study.

BACKGROUND: In many hospitals, a discharge planning team works with the medical team to provide case management to ensure high-quality patient care and improve continuity of care from the hospital to the community. However, a large-scale database analysis of the effectiveness of overall discharge planning efforts is lacking. PURPOSE: This study was designed to investigate the clinical factors that impact the efficacy of discharge planning in terms of hospital length of stay, readmission rate, and survival status. METHODS: A retrospective study was conducted based on patient medical records and the discharge plans applied to patients hospitalized in a regional medical center between 2017 and 2018. The medical information system database and the care service management information system maintained by the Ministry of Health and Welfare were used to collect data and explore patients' medical care and follow-up status. RESULTS: Clinical factors such as activities of daily living ≤ 60, having indwelling catheters, having poor control of chronic diseases, and insufficient caregiver capacity were found to be associated with longer hospitalization stays. In addition, men and those with indwelling catheters were found to have a higher risk of readmission within 30 days of discharge. Moreover, significantly higher mortality was found after discharge in men, those ≥ 75 years old, those with activities of daily living ≤ 60, those with indwelling catheters, those with pressure ulcers or unclean wounds, those with financial problems, those with caregivers with insufficient capacity, and those readmitted 14-30 days after discharge. CONCLUSIONS: The findings of this study indicate that implementing case management for discharge planning does not substantially reduce the length of hospital stay nor does it affect patients' readmission status or prognosis after discharge. However, age, underlying comorbidities, and specific disease factors decrease the efficacy of discharge planning. Therefore, active discharge planning interventions should be provided to ensure transitional care for high-risk patients.

[Spectral reflectance response of plant leaf to simulated UVB stress].

In the present study, we evaluate the relative content of chlorophyll and spectral reflectance variations in the visible light under different intensity of UVB (L-UVB, CK and UVB) of three typical evergreen broadleaf plants in China subtropical area. In different simulated UVB condition, the experiment shows that different tree species have different UVB sensitivity, and chlorophyll content varies greatly with species, and the chlorophyll relative content with the filter UVB w as significantly higher than with enhanced UVB. In the spectral reflectance of the visible part, it is generally higher with enhanced UVB's treatment than with L-UVB treatment; and any treatments present adaptation, species under different stress. After roles of the different UVB intensity, for each tree species the visible part of the spectral reflectance shows difference between green and red mainly. The study results show that the subtropical evergreen broad-leaved species has a strong sensitivity to the UVB, and UVB response of different tree species varies greatly.

Emerging potential of ubiquitin-specific proteases and ubiquitin-specific proteases inhibitors in breast cancer treatment.

Breast cancer is the most frequently diagnosed cancer in women, accounting for 30% of new diagnosing female cancers. Emerging evidence suggests that ubiquitin and ubiquitination played a role in a number of breast cancer etiology and progression processes. As the primary deubiquitinases in the family, ubiquitin-specific peptidases (USPs) are thought to represent potential therapeutic targets. The role of ubiquitin and ubiquitination in breast cancer, as well as the classification and involvement of USPs are discussed in this review, such as USP1, USP4, USP7, USP9X, USP14, USP18, USP20, USP22, USP25, USP37, and USP39. The reported USPs inhibitors investigated in breast cancer were also summarized, along with the signaling pathways involved in the investigation and its study phase. Despite no USP inhibitor has yet been approved for clinical use, the biological efficacy indicated their potential in breast cancer treatment. With the improvements in phenotypic discovery, we will know more about USPs and USPs inhibitors, developing more potent and selective clinical candidates for breast cancer.