Tendon adaptations to eccentric exercise and the implications for older adults

© 2019 by the authors. The purpose of this short review is to discuss the effects of eccentric exercise in modifying the properties of tendon tissue in healthy individuals. The tendon provides a mechanical link between muscle and bone, allowing force transmission to the skeleton, and thus, its properties have significant functional implications. Chronic resistance training has long been shown to increase the stiffness and Young’s modulus of the tendon and even tendon cross-sectional area. However, as the tendon responds to the amount and/or frequency of strain, it has been previously suggested that eccentric training may result in greater adaptations due to the potential for greater training loads. Thus, this review discusses the effects of eccentric training upon healthy tendon tissue and compares these to other training modalities. Furthermore, it has been reported that the tendon may undergo adverse age-related changes. Thus, this review also discusses the potential application of eccentric resistance training as a preferential modality for counteracting these age-related changes. We conclude that while there may be no difference between contraction types for overall tendon adaptation, the lower demands of eccentric contractions may make it more appealing for the elderly population

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Asymmetric recurrent laryngeal nerve conduction velocities and dorsal cricoarytenoid muscle electromyographic characteristics in clinically normal horses

The dorsal cricoarytenoid (DCA) muscles, are a fundamental component of the athletic horse’s respiratory system: as the sole abductors of the airways, they maintain the size of the rima glottis which is essential for enabling maximal air intake during intense exercise. Dysfunction of the DCA muscle leads to arytenoid collapse during exercise, resulting in poor performance. An electrodiagnostic study including electromyography of the dorsal cricoarytenoid muscles and conduction velocity testing of the innervating recurrent laryngeal nerves (RLn) was conducted in horses with normal laryngeal function. We detected reduced nerve conduction velocity of the left RLn, compared to the right, and pathologic spontaneous activity (PSA) of myoelectrical activity within the left DCA muscle in half of this horse population and the horses with the slowest nerve conduction velocities. The findings in this group of horses are consistent with left sided demyelination and axonal loss, consistent with Recurrent Laryngeal Neuropathy (RLN), a highly prevalent degenerative disorder of the RLn in horses that predominantly affects the left side. The detection of electromyographic changes compatible with RLN in clinically unaffected horses is consistent with previous studies that identified “subclinical” subjects, presenting normal laryngeal function despite neuropathologic changes within nerve and muscle confirmed histologically

Plant-RRBS, a bisulfite and next-generation sequencing-based methylome profiling method enriching for coverage of cytosine positions

Background: Cytosine methylation in plant genomes is important for the regulation of gene transcription and transposon activity. Genome-wide methylomes are studied upon mutation of the DNA methyltransferases, adaptation to environmental stresses or during development. However, from basic biology to breeding programs, there is a need to monitor multiple samples to determine transgenerational methylation inheritance or differential cytosine methylation. Methylome data obtained by sodium hydrogen sulfite (bisulfite)-conversion and next-generation sequencing (NGS) provide genome- wide information on cytosine methylation. However, a profiling method that detects cytosine methylation state dispersed over the genome would allow high-throughput analysis of multiple plant samples with distinct epigenetic signatures. We use specific restriction endonucleases to enrich for cytosine coverage in a bisulfite and NGS-based profiling method, which was compared to whole-genome bisulfite sequencing of the same plant material. Methods: We established an effective methylome profiling method in plants, termed plant-reduced representation bisulfite sequencing (plant-RRBS), using optimized double restriction endonuclease digestion, fragment end repair, adapter ligation, followed by bisulfite conversion, PCR amplification and NGS. We report a performant laboratory protocol and a straightforward bioinformatics data analysis pipeline for plant-RRBS, applicable for any reference-sequenced plant species. Results: As a proof of concept, methylome profiling was performed using an Oryza sativa ssp. indica pure breeding line and a derived epigenetically altered line (epiline). Plant-RRBS detects methylation levels at tens of millions of cytosine positions deduced from bisulfite conversion in multiple samples. To evaluate the method, the coverage of cytosine positions, the intra-line similarity and the differential cytosine methylation levels between the pure breeding line and the epiline were determined. Plant-RRBS reproducibly covers commonly up to one fourth of the cytosine positions in the rice genome when using MspI-DpnII within a group of five biological replicates of a line. The method predominantly detects cytosine methylation in putative promoter regions and not-annotated regions in rice. Conclusions: Plant-RRBS offers high-throughput and broad, genome- dispersed methylation detection by effective read number generation obtained from reproducibly covered genome fractions using optimized endonuclease combinations, facilitating comparative analyses of multi-sample studies for cytosine methylation and transgenerational stability in experimental material and plant breeding populations

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence

MicroRNA-181a modulates gene expression of zinc finger family members by directly targeting their coding regions

MicroRNAs (miRNAs) are small endogenous, non-coding RNAs that specifically bind to the 3′ untranslated region (3′UTR) of target genes in animals. However, some recent studies have demonstrated that miRNAs also target the coding regions of mammalian genes. Here, we show that miRNA-181a downregulates the expression of a large number of zinc finger genes (ZNFs). Bioinformatics analysis revealed that these ZNFs contain many miR-181a seed-matched sites within their coding sequences (CDS). In particular, miR-181a 8-mer-matched sequences were mostly localized to the regions coding for the ZNF C2H2 domain. A series of reporter assays confirmed that miR-181a inhibits the expression of ZNFs by directly targeting their CDS. These inhibitory effects might be due to the multiple target sites located within the ZNF genes. In conclusion, our findings indicate that some miRNA species may regulate gene family by targeting their coding regions, thus providing an important and novel perspective for decoding the complex mechanism of miRNA/mRNA interplay

Muscle and tendon adaptations to moderate load eccentric vs. concentric resistance exercise in young and older males.

Resistance exercise training (RET) is well-known to counteract negative age-related changes in both muscle and tendon tissue. Traditional RET consists of both concentric (CON) and eccentric (ECC) contractions; nevertheless, isolated ECC contractions are metabolically less demanding and, thus, may be more suitable for older populations. However, whether submaximal (60% 1RM) CON or ECC contractions differ in their effectiveness is relatively unknown. Further, whether the time course of muscle and tendon adaptations differs to the above is also unknown. Therefore, this study aimed to establish the time course of muscle and tendon adaptations to submaximal CON and ECC RET. Twenty healthy young (24.5 ± 5.1 years) and 17 older males (68.1 ± 2.4 years) were randomly allocated to either isolated CON or ECC RET which took place 3/week for 8 weeks. Tendon biomechanical properties, muscle architecture and maximal voluntary contraction were assessed every 2 weeks and quadriceps muscle volume every 4 weeks. Positive changes in tendon Young's modulus were observed after 4 weeks in all groups after which adaptations in young males plateaued but continued to increase in older males, suggesting a dampened rate of adaptation with age. However, both CON and ECC resulted in similar overall changes in tendon Young's modulus, in all groups. Muscle hypertrophy and strength increases were similar between CON and ECC in all groups. However, pennation angle increases were greater in CON, and fascicle length changes were greater in ECC. Notably, muscle and tendon adaptations appeared to occur in synergy, presumably to maintain the efficacy of the muscle-tendon unit

Transplantation of Muscle-Derived Stem Cells into the Corpus Cavernosum Restores Erectile Function in a Rat Model of Cavernous Nerve Injury

21

A data mining approach in home healthcare: outcomes and service use

BACKGROUND: The purpose of this research is to understand the performance of home healthcare practice in the US. The relationships between home healthcare patient factors and agency characteristics are not well understood. In particular, discharge destination and length of stay have not been studied using a data mining approach which may provide insights not obtained through traditional statistical analyses. METHODS: The data were obtained from the 2000 National Home and Hospice Care Survey data for three specific conditions (chronic obstructive pulmonary disease, heart failure and hip replacement), representing nearly 580 patients from across the US. The data mining approach used was CART (Classification and Regression Trees). Our aim was twofold: 1) determining the drivers of home healthcare service outcomes (discharge destination and length of stay) and 2) examining the applicability of induction through data mining to home healthcare data. RESULTS: Patient age (85 and older) was a driving force in discharge destination and length of stay for all three conditions. There were also impacts from the type of agency, type of payment, and ethnicity. CONCLUSION: Patients over 85 years of age experience differential outcomes depending on the condition. There are also differential effects related to agency type by condition although length of stay was generally lower for hospital-based agencies. The CART procedure was sufficiently accurate in correctly classifying patients in all three conditions which suggests continuing utility in home health care