Search

Background: The tendons within the soleus muscle are commonly utilized to delineate location of injury for soleus muscle strains. Strains within the soleus frequently involve the myotendinous (MTJ) or myofascial (MFJ) junctions, although spatial relationship between these tendinous structures are not well understood. This study aimed to structurally identify the MTJs and MFJs within the soleus as a starting point to understanding the distribution of connective tissue for further anatomical classification. Methods: Forty (n = 40) soleus muscles, representing left and right sides from twenty (n = 20) formalin-embalmed cadavers (average age 78 years old; 10 males, 10 females) were dissected in-situ to analyze the distribution and orientation of the MTJs and MFJs within classified morphological variants. Muscles were cut in cross-section at three measured locations, proximal, middle, and distal, which allowed for analyzation of tendons through the course of the muscle. Additionally, anterior surfaces of morphological variants were visualized and reconstructed in three dimensions using a handheld blue light 3D scanner. Results: The study revealed five morphological variants. Bipennate-Midline (n = 25), Bipennate-Medial Deviation (n = 6), Bipennate-Lateral Deviation (n = 3), Unipennate (n = 3), and Hypopennate (n = 3). Muscles included an anterior aponeurosis that was split into medial and lateral components, with each side made up of interconnections between the MTJ and MJF. The average width of the medial aponeurosis was greatest in the middle location, while the average lateral aponeurotic width decreased from proximal to distal. Regression analysis at the middle location revealed that 65 % of the change in width of the medial aponeuroses is due to the width of the medial MFJ. Conclusions: Proximal-to-distal interconnections between the lateral and medial anterior aponeuroses and their corresponding MTJs and MFJs likely play a role in soleus injury patterns, especially in morphological variants. Awareness of anatomical variations in the location and orientation of these tendinous relationships is crucial for understanding lesions on diagnostic imaging.

Background: To optimize patients' functional external rotation outcomes, reverse total shoulder arthroplasties (rTSAs) including a latissimus dorsi tendon transfer were undertaken with promising early results and no significant increase in complications in comparison to traditional rTSAs. This was especially utilized for patients with a pronounced combined loss of elevation and external rotation. The purpose of this study is to evaluate and synthesize the findings of all relevant publications assessing the outcomes of rTSAs with associated latissimus dorsi transfer. Methods: We thoroughly searched the literature within the PubMed database using a standardized methodology. For our inclusion criteria, we included any study regarding rTSAs that contained functional outcome scores for postoperative range of motion (such as elevation, external rotation, etc.) or postoperative outcomes such as complications (reoperation, infection, etc.) and patient satisfaction. For the extraction of data, we used pilot-tested Google Forms to record extracted data. These data were then converted to spreadsheets (Microsoft Excel [Microsoft, Redmond, WA, USA]). This was done on 2 separate scenarios by 2 authors to ensure accuracy. We used the modified Coleman Methodology Score to assess the methodological quality of the studies in our samples. Meta-analysis mathematics and statistical analysis were performed using Stata software 17 (StataCorp, College Station, TX, USA). Results: Our search returned a total of 12 studies containing data of 213 shoulders receiving RTSAs with a latissimus dorsi transfer. Functional outcomes were available for 160 shoulders. The mean preoperative elevation of the affected shoulder was 73.57 degrees, and the mean postoperative elevation was 141.80 degrees. For external rotation, the mean preoperative average was −6.71 degrees, and the mean postoperative average was 22.73 degrees. The absolute Constant score average was 31.56 preoperatively, while the postoperative value was 68.93. In our sample, 25 patients (11.73%) required a revision of the RTSA implant due to complications. Discussion: Combined loss of elevation and external rotation can be a severely debilitating condition for those with a glenohumeral pathology. Latissimus dorsi transfer for this condition has been proven to be an effective modality. The reoperation and complication rate appears to be sizable, and as such surgeons should consider this when considering this modality for their patients.

AimsThe mid-term results of kinematic alignment (KA) for total knee arthroplasty (TKA) using image derived instrumentation (IDI) have not been reported in detail, and questions remain regarding ligamentous stability and revisions. This paper aims to address the following: 1) what is the distribution of alignment of KA TKAs using IDI; 2) is a TKA alignment category associated with increased risk of failure or poor patient outcomes; 3) does extending limb alignment lead to changes in soft-tissue laxity; and 4) what is the five-year survivorship and outcomes of KA TKA using IDI?MethodsA prospective, multicentre, trial enrolled 100 patients undergoing KA TKA using IDI, with follow-up to five years. Alignment measures were conducted pre- and postoperatively to assess constitutional alignment and final implant position. Patient-reported outcome measures (PROMs) of pain and function were also included. The Australian Orthopaedic Association National Joint Arthroplasty Registry was used to assess survivorship.ResultsThe postoperative HKA distribution varied from 9° varus to 11° valgus. All PROMs showed statistical improvements at one year (p < 0.001), with further improvements at five years for Knee Osteoarthritis Outcome Score symptoms (p = 0.041) and Forgotten Joint Score (p = 0.011). Correlation analysis showed no difference (p = 0.610) between the hip-knee-ankle and joint line congruence angle at one and five years. Sub-group analysis showed no difference in PROMs for patients placed within 3° of neutral compared to those placed > 3°. There were no revisions for tibial loosening; however, there were reports of a higher incidence of poor patella tracking and patellofemoral stiffness.ConclusionPROMs were not impacted by postoperative alignment category. Ligamentous stability was maintained at five years with joint line obliquity. There were no revisions for tibial loosening despite a significant portion of tibiae placed in varus; however, KA executed with IDI resulted in a higher than anticipated rate of patella complications.Cite this article: Bone Jt Open 2022;3(8):656–665.

BackgroundWhile message-based therapy has been shown to be effective in treating a range of mood disorders, it is critical to ensure that providers are meeting a consistently high standard of care over this medium. One recently developed measure of messaging quality–The Facilitative Interpersonal Skills Task for Text (FIS-T)–provides estimates of therapists’ demonstrated ability to convey psychotherapy's common factors (e.g., hopefulness, warmth, persuasiveness) over text. However, the FIS-T's scoring procedure relies on trained human coders to manually code responses, thereby rendering the FIS-T an unscalable quality control tool for large messaging therapy platforms.ObjectiveIn the present study, researchers developed two algorithms to automatically score therapist performance on the FIS-T task.MethodsThe FIS-T was administered to 978 messaging therapists, whose responses were then manually scored by a trained team of raters. Two machine learning algorithms were then trained on task-taker messages and coder scores: a support vector regressor (SVR) and a transformer-based neural network (DistilBERT).ResultsThe DistilBERT model had superior performance on the prediction task while providing a distribution of ratings that was more closely aligned with those of human raters, versus SVR. Specifically, the DistilBERT model was able to explain 58.8% of the variance (R2 = 0.588) in human-derived ratings and realized a prediction mean absolute error of 0.134 on a 1–5 scale.ConclusionsAlgorithms can be effectively used to ensure that digital providers meet a consistently high standard of interactions in the course of messaging therapy. Natural language processing can be applied to develop new quality assurance systems in message-based digital psychotherapy.

ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection induces inflammatory response, cytokine storm, venous thromboembolism, coagulopathy, and multiple organ damage. Resting endothelial cells prevent coagulation, control blood flow, and inhibit inflammation. However, it remains unknown how SARS-CoV-2 induces strong molecular signals in distant cells for immunopathogenesis. In this study, we examined the consequence of human endothelial cells, microvascular endothelial cells (HMEC-1), and liver endothelial cells (TMNK-1) to exosomes isolated from plasma of mild or severe COVID-19 patients. We observed a significant induction of NLRP3, caspase-1, and interleukin-1β (IL-1β) mRNA expression in endothelial cells following exposure to exosomes from severe COVID-19 patients compared with that from patients with mild disease or healthy donors. Activation of caspase-1 was noted in the endothelial cell culture medium following exposure to the COVID-19 exosomes. Furthermore, COVID-19 exosomes significantly induced mature IL-1β secretion in both HMEC-1 and TMNK-1 endothelial cell culture medium. Thus, our results demonstrated for the first time that exosomes from COVID-19 plasma trigger NLRP3 inflammasome in endothelial cells of distant organs resulting in IL-1β secretion and inflammatory response. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global health problem. Although the vaccine controls infection, understanding the molecular mechanism of pathogenesis will help in developing future therapies. Furthermore, several investigators predicted the involvement of endothelial cell-related inflammation in SARS-CoV-2 infection and using extracellular vesicles as a cargo to carry a drug or vaccine for combating SARS-CoV-2 infection. However, the mechanism by which endothelial cells are inflamed remains unknown. Our present study highlights that exosomes from severe COVID-19 patients can enhance inflammasome activity in distant endothelial cells for augmentation of immunopathogenesis and opens an avenue for developing therapies.

Summary: A recent report on screening in the UK proposed that the responsibility for recommendations on population and targeted screening programmes should be held by one new integrated advisory body. There is no wide international consensus on the definition of targeted screening. Our review identified and compared the defining components of screening terms: targeted, population, selective, and cascade screening, and case finding. Definitions of targeted screening and population screening were clearly demarcated by the eligible population; targeted and selective screening were found to be conceptually interchangeable; cascade screening, whilst conceptually similar to targeted screening across several components, was only used within the context of genetic diseases. There was little consensus between different definitions of case finding. These comparisons contributed to an updated definition of targeted screening. Considerable overlap between definition components across terms implies that a broad range of disease areas may fall into the remit of the new advisory body.

While there have been a very large number of academic studies of proposed machine learning-based health predictive models, it is widely recognized that machine learning-based models in all domains typically degrade in performance over time, post training. This known characteristic of machine learning-based models could present significant risks in the healthcare setting to patient quality of care or safety. Nevertheless, there has been little study of the performance degradation of such models on real-world data. In this article, we empirically measure performance degradation of predictive models that predict at time of admission, emergency patient mortality, drawing upon a large dataset of over 1.83 million patient discharge records. We demonstrate important empirical results including both relatively slow performance degradation over two and a half years, but also significant differences in the rate and extent of performance degradation between different machine learning model types and time period of the training set.