The maintenance of skeletal muscle mass contributes substantially to health and to issues associated with the quality of life. It has been well recognized that skeletal muscle mass is regulated by mechanically induced changes in protein synthesis, and that signaling by mTOR is necessary for an increase in protein synthesis and the hypertrophy that occurs in response to increased mechanical loading. However, the role of mTOR signaling in the regulation of protein synthesis and muscle mass during decreased mechanical loading remains largely undefined. In order to define the role of mTOR signaling, we employed a mouse model of hindlimb immobilization along with pharmacological, mechanical and genetic means to modulate mTOR signaling. The results first showed that immobilization induced a decrease in the global rates of protein synthesis and muscle mass. Interestingly, immobilization also induced an increase in mTOR signaling, eIF4F complex formation and cap-dependent translation. Blocking mTOR signaling during immobilization with rapamycin not only impaired the increase in eIF4F complex formation, but also augmented the decreases in global protein synthesis and muscle mass. On the other hand, stimulating immobilized muscles with isometric contractions enhanced mTOR signaling and rescued the immobilization-induced decrease in global protein synthesis through a rapamycin-sensitive mechanism that was independent of ribosome biogenesis. Unexpectedly, the effects of isometric contractions were also independent of eIF4F complex formation. Similar to isometric contractions, overexpression of Rheb in immobilized muscles enhanced mTOR signaling, cap-dependent translation and global protein synthesis, and prevented the reduction in fiber size. Therefore, we conclude that the activation of mTOR signaling is both necessary and sufficient to alleviate the decreases in protein synthesis and muscle mass that occur during immobilization. Furthermore, these results indicate that the activation of mTOR signaling is a viable target for therapies that are aimed at preventing muscle atrophy during periods of mechanical unloading.

Skeletal muscle rapidly remodels in response to various stresses, and the resulting changes in muscle mass profoundly influence our health and quality of life. We identified a diacylglycerol kinase ζ (DGKζ)–mediated pathway that regulated muscle mass during remodeling. During mechanical overload, DGKζ abundance was increased and required for effective hypertrophy. DGKζ not only augmented anabolic responses but also suppressed ubiquitin-proteasome system (UPS)–dependent proteolysis. We found that DGKζ inhibited the transcription factor FoxO that promotes the induction of the UPS. This function was mediated through a mechanism that was independent of kinase activity but dependent on the nuclear localization of DGKζ. During denervation, DGKζ abundance was also increased and was required for mitigating the activation of FoxO-UPS and the induction of atrophy. Conversely, overexpression of DGKζ prevented fasting-induced atrophy. Therefore, DGKζ is an inhibitor of the FoxO-UPS pathway, and interventions that increase its abundance could prevent muscle wasting.

Purpose (I) To determine the incidence of periprosthetic tibial fractures in cemented and cementless unicompartmental knee arthroplasty (UKA) and (II) to summarize the existing evidence on characteristics and risk factors of periprosthetic fractures in UKA. Methods Pubmed, Cochrane and Embase databases were comprehensively searched. Any clinical, laboratory or case report study describing information on proportion, characteristics or risk factors of periprosthetic tibial fractures in UKA was included. Proportion meta-analysis was performed to estimate the incidence of fractures only using data from clinical studies. Information on characteristics and risk factors was evaluated and summarized. Results A total of 81 studies were considered to be eligible for inclusion. Based on 41 clinical studies, incidences of fractures were 1.24% (95%CI 0.64–2.41) for cementless and 1.58% (95%CI 1.06–2.36) for cemented UKAs (9451 UKAs). The majority of fractures in the current literature occurred during surgery or presented within 3 months postoperatively (91 of 127; 72%) and were non-traumatic (95 of 113; 84%). Six different fracture types were observed in 21 available radiographs. Laboratory studies revealed that an excessive interference fit (press fit), excessive tibial bone resection, a sagittal cut too deep posteriorly and low bone mineral density (BMD) reduce the force required for a periprosthetic tibial fracture to occur. Clinical studies showed that periprosthetic tibial fractures were associated with increased body mass index and postoperative alignment angles, advanced age, decreased BMD, female gender, and a very overhanging medial tibial condyle. Conclusion Comparable low incidences of periprosthetic tibial fractures in cementless and cemented UKA can be achieved. However, surgeons should be aware that an excessive interference fit in cementless UKAs in combination with an impaction technique may introduce an additional risk, and could therefore be less forgiving to surgical errors and patients who are at higher risk of periprosthetic tibial fractures. Level of evidence V.

Background: There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies.Methods: PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality.Results: Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). Conclusions:The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.T he application of artificial intelligence (AI) within orthopaedic surgery research has undergone a substantial increase in recent years [1][2][3][4][5][6][7][8][9][10][11][12] . Machine learning (ML) represents a subset within AI that includes algorithms that autonomously learn from data and make predictions without explicit programmed instructions. The adoption of ML in orthopaedic research has been a function of increasing data availability and several purported benefits including (1) optimization of model performance through "learning," (2) avoidance of limitations inherent to traditional regression analyses, and (3) ability to integrate individualized patient elements to provide individu-alized explanations of anticipated outcomes 6,[13][14][15] . Despite the proliferation of ML literature and the theoretical benefits of ML, it is important to assess whether these analyses have resulted in meaningful results that may influence clinical practice.Current investigations using ML within the total j...