Biomechanical Comparison of Four- versus Six-Strand Transosseous Suture Repair for Patellar Tendon Rupture.

BACKGROUND: Surgical repair is indicated for patellar tendon ruptures that result in loss of knee extensor mechanism function. However, biomechanical studies report conflicting results when comparing transosseous suture versus suture anchor repair techniques. This discrepancy may be due to inconsistencies in experimental design as these studies use various numbers of suture strands. Therefore, the main objective of this study is to compare the ultimate load of four- versus six-strand transosseous suture repair. Secondary objectives are to compare gap formation after cyclical loading and mode of failure. METHODS: Six pairs of fresh-frozen cadaveric specimen were randomly allocated to either four- or six-strand transosseous suture repair. Specimen underwent preconditioning cyclical loading and then load to failure. RESULTS: The six-strand repair had a significantly higher maximum load to failure compared with the four-strand repair (mean difference = 319.3 N [57.9%], p = 0.03). There was no significant difference in gap length after cyclical loading or at max load. There were no significant differences in mode of failure. CONCLUSION: Utilizing a six-stand transosseous patella tendon repair construct with one additional suture increases overall construct strength by over 50% compared with a four-strand construct.

MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding.

There is a growing body of evidence suggesting that microRNAs (miRNAs), small biological molecules, play a crucial role in the diagnosis, treatment, and prognostic assessment of diseases. However, it is often inefficient to verify the association between miRNAs and diseases (MDA) through traditional experimental methods. Based on this situation, researchers have proposed various computational-based methods, but the existing methods often have many drawbacks in terms of predictive effectiveness and accuracy. Therefore, in order to improve the prediction performance of computational methods, we propose a transformer-based prediction model (MDformer) for multi-source feature information. Specifically, first, we consider multiple features of miRNAs and diseases from the molecular biology perspective and utilize them in a fusion. Then high-quality node feature embeddings were generated using a feature encoder based on the transformer architecture and meta-path instances. Finally, a deep neural network was built for MDA prediction. To evaluate the performance of our model, we performed multiple 5-fold cross-validations as well as comparison experiments on HMDD v3.2 and HMDD v2.0 databases, and the experimental results of the average ROC area under the curve (AUC) were higher than the comparative methods for both databases at 0.9506 and 0.9369. We conducted case studies on five highly lethal cancers (breast, lung, colorectal, gastric, and hepatocellular cancers), and the first 30 predictions for these five diseases achieved 97.3% accuracy. In conclusion, MDformer is a reliable and scientifically sound tool that can be used to accurately predict MDA. In addition, the source code is available at https://github.com/Linda908/MDformer.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome.

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

Open reduction internal fixation of rib fractures: a biomechanical comparison between the RibLoc U Plus® system and anterior plate in rib implants.

OBJECTIVES: In this study, we assessed the bending strength of two surgical repairs of rib fracture using RibLoc® U Plus system made by Acute Innovations and the anterior plate by Synthes. METHODS: After a rib fracture was created in seven pairs of cadaveric rib specimens, one side was repaired with the anterior plate and the other side repaired with the RibLoc U Plus® plate. Each of the rib is loaded using a custom device over 360,000 bending cycles to simulate in vivo fatiguing related to respiration. Upon completion of the cyclic loading, the specimens were compressively loaded to failure and the failure bending moment was determined. RESULTS: The ribs repaired with the RibLoc U Plus® system showed 79% higher failure bending moment than that of the anterior plate, with a p value of 0.033. The ribs repaired with RibLoc U Plus® showed a trend of less stiffness reduction over the 360,000 loading cycles. CONCLUSION: The biomechanical study showed that the RibLoc U Plus® system is stronger in the bending moment loading of repaired ribs, possibly due to the U-shape structure supporting both the inner and outer cortices of a repaired rib.

Identification of adaptor proteins by incorporating deep learning and PSSM profiles.

Adaptor proteins, also known as signal transduction adaptor proteins, are important proteins in signal transduction pathways, and play a role in connecting signal proteins for signal transduction between cells. Studies have shown that adaptor proteins are closely related to some diseases, such as tumors and diabetes. Therefore, it is very meaningful to construct a relevant model to accurately identify adaptor proteins. In recent years, many studies have used a position-specific scoring matrix (PSSM) and neural network methods to identify adaptor proteins. However, ordinary neural network models cannot correlate the contextual information in PSSM profiles well, so these studies usually process 20×N (N > 20) PSSM into 20×20 dimensions, which results in the loss of a large amount of protein information; This research proposes an efficient method that combines one-dimensional convolution (1-D CNN) and a bidirectional long short-term memory network (biLSTM) to identify adaptor proteins. The complete PSSM profiles are the input of the model, and the complete information of the protein is retained during the training process. We perform cross-validation during model training and test the performance of the model on an independent test set; in the data set with 1224 adaptor proteins and 11,078 non-adaptor proteins, five indicators including specificity, sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) metric and Matthews correlation coefficient (MCC), were employed to evaluate model performance. On the independent test set, the specificity, sensitivity, accuracy and MCC were 0.817, 0.865, 0.823 and 0.465, respectively. Those results show that our method is better than the state-of-the art methods. This study is committed to improve the accuracy of adaptor protein identification, and laid a foundation for further research on diseases related to adaptor protein. This research provided a new idea for the application of deep learning related models in bioinformatics and computational biology.

Association of mental distress with chronic diseases in 1.9 million individuals: A population-based cross-sectional study.

OBJECTIVE: Mental distress has a high global prevalence and is associated with poor health outcomes. This study aimed to estimate the relationship between mental distress and the risk of 10 chronic diseases using data from the Behavioral Risk Factor Surveillance System (BRFSS). METHODS: Cross-sectional data from the 2013, 2014, 2015, 2016 and 2017 BRFSS were analyzed. The association between mental distress based on the number of days of poor mental health and the risk of 10 chronic diseases, namely obesity, diabetes, asthma, chronic obstructive pulmonary disease (COPD), arthritis, kidney disease, coronary heart disease (CHD), stroke, skin cancer, and other cancers, were assessed by logistic regression models to calculate odds ratios and 95% confidence intervals. Subgroup analyses stratified by age and sex were also conducted. RESULTS: Positive associations between mental distress and chronic diseases were observed. We also found a dose-response gradient between mental distress levels and the risk of all chronic diseases except skin cancer. In respondents aged 18-44 years reporting ≥23 days/month of mental distress, there has the largest odds ratio between mental distress levels and each chronic disease. Moreover, mental distress was associated with higher risks of obesity and arthritis in women relative to men. CONCLUSIONS: Mental distress was positively associated with chronic diseases. Age and sex are crucial in this relationship. Further studies with longitudinal data are needed to clarify the direction of this association.

Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling.

Bladder cancer is a highly complex and heterogeneous malignancy. Tumor heterogeneity is a barrier to effective diagnosis and treatment of bladder cancer. Human carcinogenesis is closely related to abnormal gene expression, and DNA methylation is an important regulatory factor of gene expression. Therefore, it is of great significance for bladder cancer research to characterize tumor heterogeneity by integrating genetic and epigenetic characteristics. This study explored specific molecular subtypes based on DNA methylation status and identified subtype-specific characteristics using patient samples from the TCGA database with DNA methylation and gene expression were measured simultaneously. The results were validated using an independent cohort from GEO database. Four DNA methylation molecular subtypes of bladder cancer were obtained with different prognostic states. In addition, subtype-specific DNA methylation markers were identified using an information entropy-based algorithm to represent the unique molecular characteristics of the subtype and verified in the test set. The results of this study can provide an important reference for clinicians to make treatment decisions.

Psychological Distress and All-Cause, Cardiovascular Disease, Cancer Mortality Among Adults with and without Diabetes.

AIM: To examine the association of psychological distress with all-cause, cardiovascular disease (CVD) and cancer mortality in US adults, and verified whether the associations differed between participants with and without diabetes. METHODS: A total of 485,864 adults (446,288 without diabetes and 39,576 with diabetes) who participated in the National Health Interview Survey from 1997 to 2013 were linked to the National Death Index through December 31, 2015. Psychological distress was measured by the Kessler 6 distress scale (K6). Multivariable Cox proportional hazards regression models were performed to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) for the association between psychological distress and mortality. RESULTS: We ascertained 11,746 deaths (mean follow-up, 7. 7 years) among people with diabetes and 51,636 deaths (9.9 years) among those without diabetes. Psychological distress was associated with higher all-cause, CVD, and cancer mortality. Compared to non-diabetic adults without psychological distress, HRs (95% CI) were 1.07 (1.04 to 1.09) for mild, 1.26 (1.22 to 1.30) for moderate and 1.46 (1.38 to 1.55) for severe psychological distress. Compared to the same reference group, in diabetic participants the HRs were 1.39 (1.33 to 1.44) for no psychological distress, 1.59 (1.53 to 1.66) for mild, 1.90 (1.80 to 2.00) for moderate and 1.98 (1.82 to 2.17) for severe psychological distress. Similar associations were also observed for CVD and cancer mortality but with non-statistically significant interaction. CONCLUSION: Psychological distress was associated with higher mortality, particularly in participants with diabetes. Strategies to ameliorate psychological distress may be important to reduce mortality in this population.

Lower risk of liver cancer in patients with schizophrenia: a systematic review and meta-analysis of cohort studies.

Previous studies regarding the association between schizophrenia and the subsequent risk of liver cancer have shown inconsistent results. We aimed to perform a systematic review and meta-analysis to evaluate the association between schizophrenia and liver cancer incidence. We systematically searched the PubMed and Embase electronic databases for cohort studies reporting the standardized incidence ratio (SIR) for the risk of liver cancer in patents with schizophrenia as compared with the general population. A random-effects model was used to analyze the data. Stratified analyses were performed according to the gender of the patients. Seven studies comprising 312,834 patients with schizophrenia were included. During follow-up, 581 liver cancer cases were confirmed. The meta-analysis results showed that schizophrenia was associated with a trend of a lower liver cancer incidence (SIR: 0.83, 95% confidence interval [CI]: 0.66-1.04, p = 0.10) with significant heterogeneity (I2 = 81%). Sensitivity analysis of five cohorts of patients with cancer events before the diagnosis of schizophrenia indicated that schizophrenia was associated with a significantly lower incidence of liver cancer (SIR: 0.76, 95% CI: 0.61-0.96, p = 0.02; I2 = 84%). The reduction of a subsequent incidence of liver cancer was significant in male patients with schizophrenia (SIR: 0.71, p = 0.005), and a trend of a reduced risk of liver cancer was also detected in female patients (SIR: 0.83, p = 0.12). Significant publication bias was detected. However, "trim and fill" analyses by including the imputed unpublished studies showed similar results. In summary, schizophrenia may be protective against the incidence of liver cancer.

Different roles of the medial and lateral hamstrings in unloading the anterior cruciate ligament.

INTRODUCTION: Anterior cruciate ligament injuries are closely associated with excessive loading and motion about the off axes of the knee, i.e. tibial rotation and knee varus/valgus. However, it is not clear about the 3-D mechanical actions of the lateral and medial hamstring muscles and their differences in loading the ACL. The purpose of this study was to investigate the change in anterior cruciate ligament strain induced by loading the lateral and medial hamstrings individually. METHODS: Seven cadaveric knees were investigated using a custom testing apparatus allowing for six degree-of-freedom tibiofemoral motion induced by individual muscle loading. With major muscles crossing the knee loaded moderately, the medial and lateral hamstrings were loaded independently to 200N along their lines of actions at 0°, 30°, 60° and 90° of knee flexion. The induced strain of the anterior cruciate ligament was measured using a differential variable reluctance transducer. Tibiofemoral kinematics was monitored using a six degrees-of-freedom knee goniometer. RESULTS: Loading the lateral hamstrings induced significantly more anterior cruciate ligament strain reduction (mean 0.764 [SD 0.63] %) than loading the medial hamstrings (mean 0.007 [0.2] %), (P=0.001 and effect size=0.837) across the knee flexion angles. CONCLUSION: The lateral and medial hamstrings have significantly different effects on anterior cruciate ligament loadings. More effective rehabilitation and training strategies may be developed to strengthen the lateral and medial hamstrings selectively and differentially to reduce anterior cruciate ligament injury and improve post-injury rehabilitation. CLINICAL RELEVANCE: The lateral and medial hamstrings can potentially be strengthened selectively and differentially as a more focused rehabilitation approach to reduce ACL injury and improve post-injury rehabilitation. Different ACL reconstruction procedures with some of them involving the medial hamstrings can be compared to each other for their effect on ACL loading.