Clinical Characteristics, Mechanism, and Outcome of Humeral Shaft Fractures Sustained during Arm Wrestling in Young Men: A Retrospective Study

OBJECTIVE

Humeral fractures are common in arm wrestling and other sports and military activities requiring similar movements; however, the precise mechanism is poorly understood. Here, we present an overview of the characteristics, possible mechanisms, and treatment of humeral shaft fractures sustained during arm wrestling.

METHODS

We reviewed 8 years (January 2013 to January 2021) of medical records and retrospectively analyzed data from 27 patients with humeral shaft fractures sustained during arm wrestling. The clinical data included sex, age, affected arm, alcohol consumption, muscle warm-up, history of competitive participation, opponents' characteristics, wrist position, and post-fracture radial nerve injuries. The fracture configurations were radiographically assessed and analyzed. Surgical management included single or dual plating. Scores on the Disability of the Arm, Shoulder, and Hand questionnaire (DASH) were evaluated preoperatively and postoperatively at the last follow-up visit.

RESULTS

All fractures sustained during arm wrestling were spiral fractures of the distal third of the humerus. Of these, 11 were 12-A1 type and 16 were 12-B2 type with a wedge fragment. The two subtypes differed in the total fracture line length (12-A1: 0.18 ± 0.04; 12-B2: 0.23 ± 0.04; P < 0.001). The radial nerve injury rate was 0/11 (0%) in patients with 12-A1 type fractures and 7/16 (43.8%) in patients with 12-B2 type fractures (P = 0.011). Most patients were young men (mean age, ~25 years) with a history of competitively participating in arm wrestling for >2 years. Cold seasonal temperatures and a lack of warm-ups increased the risk of injury. All patients showed improved DASH scores at the last follow-up (12-A1:77.82 ± 5.14 to 10.25 [5.38]; 12-B2:78.91 ± 7.46 to 8.95 [3.17]; P < 0.001). No significant differences were observed among the different surgical treatments.

CONCLUSIONS

Individuals who participated in arm wrestling were at risk of humeral shaft fractures (type 12-A1 or 12-B2). The 12-B2 type occurs with a wedge fragment and is frequently accompanied by radial nerve injuries. The characteristics of arm-wrestling fractures and the mechanism(s) underlying these fractures can help orthopedic surgeons understand the causes of these fractures and similar fractures sustained in traditional sports. This understanding will help surgeons choose more effective surgical treatments that will result in more desirable functional outcomes and a faster return to work.

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

iDMer: an integrative and mechanism-driven response system for identifying compound interventions for sudden virus outbreak

Emerging viral infections seriously threaten human health globally. Several challenges exist in identifying effective compounds against viral infections: (1) at the initial stage of a new virus outbreak, little information, except for its genome information, may be available; (2) although the identified compounds may be effective, they may be toxic in vivo and (3) cytokine release syndrome (CRS) triggered by viral infections is the primary cause of mortality. Currently, an integrative tool that takes all those aspects into consideration for identifying effective compounds to prevent viral infections is absent. In this study, we developed iDMer, as an integrative and mechanism-driven response system for addressing these challenges during the sudden virus outbreaks. iDMer comprises three mechanism-driven compound identification modules, that is, a virus-host interaction-oriented module, an autophagy-oriented module and a CRS-oriented module. As a one-stop integrative platform, iDMer incorporates compound toxicity evaluation and compound combination identification for virus treatment with clear mechanisms. iDMer was successfully tested on five viruses, including the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our results indicated that, for all five tested viruses, compounds that were reported in the literature or experimentally validated for virus treatment were enriched at the top, demonstrating the generalized effectiveness of iDMer. Finally, we demonstrated that combinations of the individual modules successfully identified combinations of compounds effective for virus intervention with clear mechanisms.

Learning for single-cell assignment

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.