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Background Preoperative evaluation of femoral anteversion to predict postoperative stem anteversion aids the selection of an appropriate prosthesis and optimizes the combined anteversion in total hip arthroplasty (THA) for developmental dysplasia of the hip (DDH). The conventional prediction methods are based on the femoral anteversion measurement at the location of the femoral head and/or neck. However, varied differences between femoral anteversion and postoperative stem anteversion were demonstrated. This study investigated the predictive role of a new method based on the principle of sagittal three‐point fixation. Methods From January 2017 to December 2018, a total of 133 DDH hips that underwent THA were retrospectively analyzed. There were 76 Crowe type I, 27 type II, and 30 type III hips. The single‐wedge stem was used in 49 hips, and the double‐wedge stem was used in 84 hips. Preoperative native femoral anteversion at the femoral head–neck junction, anterior cortex anteversion at 2 levels of the lesser trochanter, posterior cortex anteversion at 5 levels of the femoral neck, and postoperative stem anteversion were measured using two‐dimensional computed tomography. Predictive anteversion by the new method was calculated as the average anteversion formed by the anterior cortex at the lesser trochanter and the posterior cortex at the femoral neck. Results For hips with different neck heights, different Crowe types, different stem types, or different femoral anteversions, native femoral anteversion showed widely varied differences and correlations with stem anteversion, with differences ranging from −1.27 ± 8.33° to −13.67 ± 9.47° and correlations ranging from 0.122 (p = 0.705, no correlation) to 0.813. Predictive anteversion formed by the anterior cortex at the lesser trochanter proximal base and posterior cortex 10 mm above the lesser trochanter proximal base showed no significant difference with stem anteversion, with less varied differences (0.92 ± 7.52°) and good to excellent correlations (r = 0.826). Conclusion Adopting our new method, predictive anteversion, measured as the average anteversion of the anterior cortex at the lesser trochanter proximal base and posterior cortex 10 mm above the lesser trochanter proximal base, predicted postoperative stem anteversion more reliably than native femoral anteversion.

Unveiling of the mechanism involved in the occurrence and development of trauma-induced heterotopic ossification (tHO) is highly demanding due to current ineffective clinical treatment for it. Previous studies proposed that hydrogen sulfide (H2S) was vital for fate determination of stem cells, suggesting a potential role in the regulation of tHO development. In the current study, We found that expression of metabolic enzyme within sulfur conversion pathway was enhanced after tendon injury, leading to H2S accumulation within the tHO region. Increased production of endogenous H2S was shown to promote aberrant osteogenic activity of tendon-derived stem cells (TDSCs), which accelerated tHO formation. The inhibition of metabolic enzyme of H2S production or directly absorption of H2S could abolished osteogenic induction of TDSCs and the formation of tHO. Mechanistically, through RNA sequencing combined with rescue experiments, we demonstrated that activation of Ca2+/ERK pathway was the downstream molecular event of H2S-induced osteogenic commitment of TDSCs and tHO. For treatment strategy exploration, zine oxide nanoparticles (ZnO) as an effective H2S elimination material was validated to ideally halt the tHO formation in this study. Furthermore, in terms of chirality of nanoparticles, D-ZnO or L-ZnO nanoparticles showed superiority over R–ZnO nanoparticles in both clearing of H2S and inhibition of tHO. Our study not only revealed the mechanism of tHO through the endogenous gas signaling event from a new perspective, but also presented a applicable platform for elimination of the inordinate gas production, thus aiding the development of clinical treatment for tHO.

Background: There are few methods for accurately assessing the risk of total hip arthroplasty (THA) in patients with osteoarthritis. A novel and reliable method that could play a substantial role in research and clinical routine should be investigated. The purpose of the present study was to develop a deep-learning model that can reliably predict the risk of THA with use of radiographic images and clinical symptom data. Methods: This retrospective, multicenter, case-control study assessed hip joints on weighted-bearing anteroposterior pelvic radiographs obtained from Osteoarthritis Initiative (OAI) participants. Participants who underwent THA were matched to controls according to age, sex, body mass index, and ethnicity. Cases and controls were uniformly split into training, validation, and testing data sets at proportions of 72% (n = 528), 14% (n = 104), and 14%(n = 104), respectively. Images and clinical symptom data were passed through a detectionmodel and a deep convolutional neural network (DCNN)model to predict the probability of THA within 9 years as well as the most likely time period for THA (0 to 2 years, 3 to 5 years, 6 to 9 years). Model performance was assessed with use of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing set. Results: A total of 736 participants were evaluated, including 184 cases and 552 controls. The prediction model achieved an overall accuracy, sensitivity, and specificity of 91.35%, 92.59% and 86.96%, respectively, with an AUC of 0.944, for THA within 9 years. The AUC of the DCNN model for assessing the most likely time period was 0.907 for 0 to 2 years, 0.916 for 3 to 5 years, and 0.841 for 6 to 9 years. Gradient-weighted class activation mapping closely corresponded to regions affecting the prediction of the DCNN model. Conclusions: The proposed DCNN model is a reliable and valid method to predict the probability of THA--within limitations. It could assist clinicians in patient counseling and decision-making regarding the timing of the intervention. In the future, by increasing the size of the data set, enhancing the ethnic and socioeconomic diversity of the participants, and improving the follow-up rate, the quality of the conclusions can be further improved. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

As a generalization of intuitionistic fuzzy sets, single-valued neutrosophic sets have certain advantages in solving indeterminate and inconsistent information. In this paper, we study the fuzzy inference full implication method based on single-valued neutrosophic t-representable t-norm. Firstly, single-valued neutrosophic fuzzy inference triple I principles for fuzzy modus ponens and fuzzy modus tollens are given. Then, single-valued neutrosophic R-type triple I solutions for FMP and FMT are given. Finally, the robustness of the full implication triple I method based on the leftcontinuous single-valued neutrosophic t-representable t-norm is investigated. As a special case of the main results, the sensitivity of full implication triple I solutions based on three special single-valued neutrosophic t-representable t-norms are given. [ABSTRACT FROM AUTHOR]