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Basketball players are a high-risk group for anterior cruciate ligament (ACL) injuries. This study aimed to identify the critical factors contributing to ACL injuries in male basketball players and evaluate the performance of machine learning (ML) algorithms in injury prediction. This study protocol was registered with International Standard Registered Clinical/soCial sTudy Number (ISRCTN) (Registration number: 18009799). A total of 104 male collegiate basketball players volunteered to participate in this study. Data on the athletes' profile, physical functions, basketball-specific skills, biomechanics, and electromyography (EMG) of seven lower limb muscles during unanticipated side-cutting maneuvers were collected. A 12-month follow-up was conducted to compare these variables between the injured (  = 11) and non-injured (  = 93) groups. Only the variables with significant differences between the groups were included in the predictive modeling. The performance of machine learning models in predicting ACL injury risk was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUC-ROC values ranged from 0.66 to 0.80, with the random forest algorithm achieving the highest performance (AUC-ROC = 0.80). The most influential predicting feature observed during the emergency stop phase, included a greater knee flexion moment, reduced knee flexion angle, increased backward ground reaction force, and increased activation of the vastus lateralis muscle. The random forest model demonstrated superior predictive performance, providing valuable insights into the key risk factors associated with ACL injury among male basketball players. This study highlighted the importance of biomechanical testing based on sport-specific movements to accurately predict the ACL injury risk.

Severe acute respiratory syndrome virus 2 (SARS-CoV-2) belongs to the single-stranded positive-sense RNA family. The virus contains a large genome that encodes four structural proteins, small envelope (E), matrix (M), nucleocapsid phosphoprotein (N), spike (S), and 16 nonstructural proteins (nsp1-16) that together, ensure replication of the virus in the host cell. Among these proteins, the interactions of N and Nsp3 are essential that links the viral genome for processing. The N proteins reside at CoV RNA synthesis sites known as the replication–transcription complexes (RTCs). The N-terminal of N has RNA-binding domain (N-NTD), capturing the RNA genome while the C-terminal domain (N-CTD) anchors the viral Nsp3, a component of RTCs. Although the structural information has been recently released, the residues involved in contacts between N-CTD with Nsp3 are still unknown. To find the residues involved in interactions between two proteins, three-dimensional structures of both proteins were retrieved and docked using HADDOCK. Residues at N-CTD were detected in interaction with L499, R500, K501, V502, P503, T504, D505, N506, Y507, I508, T509, K529, K530K532, S533 of Nsp3 and N-NTD to synthesize SARS-CoV-2 RNA. The interaction between Nsp3 and CTD of N protein may be a potential drug target. The current study provides information for better understanding the interaction between N protein and Nsp3 that could be a possible target for future inhibitors.Graphic abstract

With the development of artificial intelligence, there are numerous analysis methods for human action recognition. In basketball, its technical action features are obvious, so the feasibility of recognizing and classifying its technical actions is high. However, due to the existing action recognition methods are difficult to effectively utilize continuous frames, resulting in poor accuracy of basketball technical action recognition. Thus, the study suggests a continuous frame action identification approach based on the single-shot multi-edge detection algorithm and 3D convolutional neural network in order to enhance the performance of technical action recognition. The experimental results revealed that single shot multibox detector algorithm accurately recognizes the human body in the image and labels its confidence level. In addition, in basketball action recognition, the loss value of original frame was 6.0 and 6.8 on the training set and validation set, respectively, and the loss value of crop frame was 5.1 and 5.9 on the training set and validation set, respectively. 3D convolutional neural network achieved the highest classification accuracy of about 88.3% for the stop-and-go jump shot action in the original frame and its crop frame with an average recognition rate of about 90.3%. The recognition accuracies of original frame and crop frame increased with the increase of epoch, and reached a stable state when the epoch was 30, due to the presence of variable features in the European step, change of direction, and Sam Gould's action, which led to misjudgment of both original frame and crop frame. The accuracies of the original frame training set and test set were 0.91 and 0.81, respectively, and the accuracies of the crop frame training set and test set were about 0.92 and 0.81, respectively. After the fusion of the original frame features and the crop frame features, the average recognition rate was about 94.6%, which was significantly higher than that of the single-resolution recognition. Recognition. In addition, with the increase of frame input, the F1-measure gradually increased, while the false positive rate gradually decreased. When the frame input was 7, the F1-measure and the misjudgment rate were 0.79 and 0.19, respectively. When the frame input was 16, the F1-measure and the misjudgment rate were 0.92 and 0.05, respectively. The above results show that the continuous frame action recognition method based on single-shot multi-frame detection algorithm and three-dimensional convolutional neural network can realize the accurate recognition of the technical action in basketball video.

The development of deep learning and artificial intelligence has made the large amount of data generated by various types of human actions of great analytical value. The continuous updating of recognition algorithms based on text and picture frames has also made the movement recognition in video of some research value. Currently, there are few studies on technical movement recognition in basketball. Based on this, this study tests the performance of the constructed target detection algorithm and movement recognition algorithm. The experimental results found that the maximum detection accuracy of Fast R-CNN, YOLO, and SSD algorithms on basketball dataset were 85.9, 84.9, and 93.8, respectively. in addition, the recognition accuracy of ( 3D Convolutional Neural Network, 3D-CNN ) 3D-CNN and dual-resolution 3D-CNN were compared under different video frames. When the quantity of video frames is 20, the two algorithm models have the highest recognition accuracy of basketball basic movements, 89.6 and 95.8, respectively.

The development of deep learning and artificial intelligence has made the large amount of data generated by various types of human actions of great analytical value. The continuous updating of recognition algorithms based on text and picture frames has also made the movement recognition in video of some research value. Currently, there are few studies on technical movement recognition in basketball. Based on this, this study tested the performance of the constructed target detection algorithm and movement recognition algorithm. Experiments were conducted using a self-compiled basketball movement recognition dataset containing 10,000 video clips from different competition and training scenarios, each lasting 10 seconds and with a resolution of 720p. The dataset was divided into training and test sets in an 8:2 ratio. The experimental setup included using PyTorch as the deep learning framework, leveraging an NVIDIA Tesla V100 GPU for computation. Key results demonstrated that the Single Shot Detector (SSD) algorithm achieved a maximum detection accuracy of 93.8%, outperforming Fast R-CNN and YOLO, which achieved 85.9% and 84.9%, respectively. Furthermore, the dual-resolution Three-Dimensional Convolutional Neural Network (3D-CNN) model achieved a recognition accuracy of 95.8% for basic basketball movements, significantly higher than the single-resolution 3D-CNN's 89.6%. These results highlight the effectiveness of combining SSD and 3D-CNN for basketball movement recognition, offering a robust and efficient solution for real-time applications.

Background Spatial chromatin structure is intricately linked with somatic aberrations, and somatic mutations of various cancer-related genes, termed co-mutations (CoMuts), occur in certain patterns during cancer initiation and progression. The functional mechanisms underlying these genetic events remain largely unclear in thyroid cancer (TC). With discrepant differentiation, papillary thyroid cancer (PTC) and anaplastic thyroid cancer (ATC) differ greatly in characteristics and prognosis. We aimed to reveal the spatial gene alterations and regulations between the two TC subtypes. Methods We systematically investigated and compared the spatial co-mutations between ATC (8305C), PTC (BCPAP and TPC-1), and normal thyroid cells (Nthy-ori-3–1). We constructed a framework integrating whole-genome sequencing (WGS), high-throughput chromosome conformation capture (Hi-C), and transcriptome sequencing, to systematically detect the associations between the somatic co-mutations of cancer-related genes, structural variations (SVs), copy number variations (CNVs), and high-order chromatin conformation. Results Spatial co-mutation hotspots were enriched around topologically associating domains (TADs) in TC. A common set of 227 boundaries were identified in both ATC and PTC, with significant overlaps between them. The spatial proximities of the co-mutated gene pairs in the two TC types were significantly greater than in the gene-level and overall backgrounds, and ATC cells had higher TAD contact frequency with CoMuts > 10 compared with PTC cells. Compared with normal thyroid cells, in ATC the number of the created novel three-dimensional chromatin structural domains increased by 10%, and the number of shifted TADs decreased by 7%. We found five TAD blocks with CoMut genes/events specific to ATC with certain mutations in genes including MAST-NSUN4 , AM129B / TRUB2 , COL5A1 / PPP1R26 , PPP1R26 / GPSM1 / CCDC183 , and PRAC2 / DLX4 . For the majority of ATC and PTC cells, the HOXA10 and HIF2α signals close to the transcription start sites of CoMut genes within TADs were significantly stronger than those at the background. CNV breakpoints significantly overlapped with TAD boundaries in both TC subtypes. ATCs had more CNV losses overlapping with TAD boundaries, and noncoding SVs involved in intrachromosomal SVs, amplified inversions, and tandem duplication differed between ATC and PTC. TADs with short range were more abundant in ATC than PTC. More switches of A/B compartment types existed in ATC cells compared with PTC. Gene expression was significantly synchronized, and orchestrated by complex epigenetics and regulatory elements. Conclusion Chromatin interactions and gene alterations and regulations are largely heterogeneous in TC. CNVs and complex SVs may function in the TC genome by interplaying with TADs, and are largely different between ATC and PTC. Complexity of TC genomes, which are highly organized by 3D genome-wide interactions mediating mutational and structural variations and gene activation, may have been largely underappreciated. Our comprehensive analysis may provide key evidence and targets for more customized diagnosis and treatment of TC.

131 I therapy is clinically unfeasible for anaplastic thyroid carcinoma (ATC), due to lack of active targets and ATC’s resistance to radiation. Novel radionuclide-labeled targeted nano-drug delivery systems have exhibited the potential of prominent tumor imaging and remedy. Capitalizing on recent research achievements in nanotechnology and nuclear medicine, we sought to develop a radiolabeled nano-drug, which could specifically accumulate in ATCs via tumor-selective targeted delivery system and which could treat the tumors with both targeted and radionuclide therapeutics. Epidermal growth factor receptor (EGFR) and mutant P53 expressions were positive in 80% and 60% of patients with ATC, respectively. Herein, core–shell nanoparticles-based poly (ethyleneglycol)-crosslinker (PEG-CL) was fabricated, by encapsulating bovine serum albumin (BSA) inside the core and an enzyme with various tyrosine residues for 131 I radiolabeling, and by loading anlotinib, a multi-kinase inhibitor which can site-selectively target overexpressed EGFR in ATC cells and which also suppresses angiogenesis, onto the PEG-CL shell surface. The Anlotinib-BSA nano-capsule (nBSA) showed a mostly uniform size distribution centering at 21–23 nm, and the nano-drug had a characteristic absorption peak at the wavelength of 325 nm. The Anlotinib-nBSA had a high labeling efficiency with the radiochemical purity being approximately 100%. The cellular uptake efficiency of Anlotinib-nBSA- 131 I was much higher than that of free 131 I in both 8305C (3.6% vs 0.0%) and C643 (7.0% vs 0.1%; with a higher EGFR expression level) ATC cell lines. Anlotinib-nBSA- 131 I showed the strongest cytotoxicity against ATC cells with different concentrations of anlotinib, and induced the highest rate of apoptosis (C643 cells, 81.7%). The nanoparticles could actively target tumor surface with anlotinib exhibiting enhanced radio-sensitization effects by functionally upregulating P53 and Bax. In vivo SPECT/CT imaging showed that the concentration of Anlotinib-nBSA- 125 I in tumors peaked at 24 h, and the intense signal persisted for at least one week. Anlotinib-nBSA- 131 I showed the strongest tumor inhibition effects in tumor-bearing mice, with no evident pathological changes observed. Together, the optimal nanoparticles co-loading anlotinib and 131 I satisfactorily demonstrated efficient drug delivery and prominent antitumor effects both in vitro and in vivo, without obvious in vivo bio-toxicity. Our innovation could offer novel effective strategies for targeted management of ATC, a highly-aggressive disease with dismal prognosis. Graphical Abstract

A cluster of differentiation 47 (CD47) and immune‐modulatory protein for myeloid cells has been implicated in cisplatin (CDDP) resistance. Exosome delivery of drugs has shown great potential for targeted drug delivery in the treatment of various diseases. In the current study, we explored the approach of co‐delivering CDDP and CD47 antibody with MDA‐MB‐231 cell‐derived exosome 231‐exo (CaCE) and assessed the phagocytosis activity of bone marrow flow cytometry derived macrophages (BMDM) against co‐cultured A549 cells. CD8+ T‐cell proliferation was examined with flow cytometry analysis. In vivo, we used the Lewis lung carcinoma (LLC) tumor‐bearing mouse model and assessed survival rate, tumor weight, phagocytosis, and T‐cell proliferation, as well as cytokine levels in tumors analyzed by enzyme‐linked immunoassay (ELISA). Although co‐administration of CDDP with anti‐CD47 (CDDP and aCD47) showed a significant antitumor effect, CaCE had an even more dramatic anticancer effect in survival rate and tumor weight. We observed increased phagocytosis activity selectively against lung tumor cells in vivo and in vitro with exosome CaCE treatment. CaCE treatment also increased T‐cell proliferation compared to the vehicle treatment and co‐administration groups. Furthermore, immunostimulatory interleukin (IL)‐12p and interferon (IFN)‐γ were increased, whereas transforming growth factor β (TGF‐β) were decreased, indicating the improved CDDP anticancer effect is related to a tumor microenvironmental change. Our study demonstrates a dramatically improved anticancer effect of CDDP when administered by exosome co‐delivery with anti‐CD47. An approach of co‐delivering cisplatin (CDDP) and cluster of differentiation 47 (CD47) antibody with MDA‐MB‐231 cell‐derived exosome 231‐exo (CaCE) was developed. It was demonstrated that a dramatically improved anticancer effect of CDDP when administered by exosome co‐delivery with anti‐CD47.

Background: This study examines the microcirculation of patients with sepsis and septic shock using Laser Speckle Contrast Imaging (LSCI) technology, to enhance monitoring and predict outcomes of sepsis and septic shock. Methods: From 01 July 2021, to 31 January 2022, 44 patients diagnosed with septic shock and sepsis were included in the study, their clinical data were collected, and LSCI was used to monitor the mean peripheral blood flow perfusion index (PI). Results: The average peripheral blood flow PI of septic shock patients was significantly lower than that of septic patients, with a cutoff value of 26.25. The average peripheral blood flow PI negatively correlated with acute physiology and chronic health evaluation (APACHE) Ⅱ score ( p = .01 < .05), sequential organ failure assessment (SOFA) score ( p < .01), and lactic acid levels ( p = .01 < .05). We report average peripheral blood flow no correlation with age, mean arterial pressure, body temperature, oxygen saturation, heart rate, and body mass index. There was no correlation with procalcitonin, C-reactive protein (CRP), red blood cell distribution width, or platelet distribution width ( p > .05). PI significantly correlated with the group sepsis and septic shock ( p < .001, r = −.865). And PI significantly correlated with the outcome or mortality ( p = .007 < .05, r = −.398). The ROC curve was calculated for PI and the sensitivity was 81.3%, and the specificity was 75% when PI cutoff value chooses 20.88. Conclusion: LSCI technology successfully detected the fingertip microcirculation of patients with septic shock. LSCI can reliably differentiate patients with sepsis vs patients with septic shock. Additionally, the average peripheral blood PI negatively correlated with APACHE Ⅱ, SOFA score, and lactate acid levels, providing useful and supplementary information for the diagnosis and monitoring of septic shock. Trial registration: Chictr2100046761. Registered on May 28, 2021. Clinical Trial Registration: clinicaltrials.gov , identifier Chictr2100046761

In a trial in China involving participants with symptomatic mild-to-moderate Covid-19, VV116 (an oral analogue of remdesivir) was noninferior to nirmatrelvir–ritonavir with respect to the time to sustained clinical recovery.