Modified all-arthroscopic reconstruction of medial patellofemoral ligament for the treatment of recurrent patellar dislocation / 中华创伤杂志

Objective:To evaluate the clinical efficacy of modified all-arthroscopic reconstruction of medial patella femoral ligament (MPFL) for the treatment of recurrent patellar dislocation.Methods:A retrospective case series study was conducted to analyze the clinical data of 38 patients (46 knees) with recurrent patellar dislocation, who were treated at First Affiliated Hospital of Shenzhen University from January 2017 to January 2020. The patients included 12 males (12 knees) and 26 females (34 knees), aged 14-40 years [(24.6±5.4)years]. All patients underwent the modified all-arthroscopic MPFL reconstruction procedure. The femoral tunnel locations were assessed by 3D-CT immediately after surgery. The MRI was performed at 6 and 12 months after operation to assess the healing morphology of the reconstructed MPFL. The Lysholm score and Kujala score were used to assess the knee function before operation, at 6 months after operation, at 12 months after operation and at the last follow-up. The time to return to sports as well as complications were observed.Results:All patients were followed up for 26-48 months [(32.4±8.6)months]. Postoperative 3D-CT examination showed that the femoral tunnels were located in the groove area of the medial epicondyle of the femur and the adductor tubercle. At 6 and 12 months after operation, MRI T2 images showed that the reconstructed MPFL had a low signal and well tensioned ligament tissue, indicating that the MPFL was healed well. The Lysholm scores at 6 and 12 months postoperatively and at the last follow-up were (81.1±12.0)points, (91.2±3.8)points, and (92.2±9.8)points, respectively, being significantly higher than the preoperative (52.4±10.6)points (all P<0.01). The Kujala scores at 6 and 12 months postoperatively and at the last follow-up were (85.4±3.9)points, (91.4±3.6)points, and (93.1±8.5)points, respectively, being significantly higher than the preoperative (55.2±6.8)points (all P<0.01). Compared with 6 months postoperatively, the Lysholm score and Kujala score were significantly improved at 12 months postoperatively and at the last follow-up (all P<0.05). All patients returned to sports, with the time to return to sports for 3-12 months [(8.7±2.3)months] after operation. One patient had poor wound healing but was healed after dressing changes. No wound infection, nerve injury, joint stiffness, patella re-dislocation or other complications occurred. Conclusion:For recurrent patellar dislocation, the modified all-arthroscopic MPFL reconstruction has advantages of accurate bone tunnel positioning, good ligament healing, good function recovery, early return to sports, and less postoperative complications.

Mechanisms of PiT2-loop7 Missense Mutations Induced Pi Dyshomeostasis / 神经科学通报·英文版

PiT2 is an inorganic phosphate (Pi) transporter whose mutations are linked to primary familial brain calcification (PFBC). PiT2 mainly consists of two ProDom (PD) domains and a large intracellular loop region (loop7). The PD domains are crucial for the Pi transport, but the role of PiT2-loop7 remains unclear. In PFBC patients, mutations in PiT2-loop7 are mainly nonsense or frameshift mutations that probably cause PFBC due to C-PD1131 deletion. To date, six missense mutations have been identified in PiT2-loop7; however, the mechanisms by which these mutations cause PFBC are poorly understood. Here, we found that the p.T390A and p.S434W mutations in PiT2-loop7 decreased the Pi transport activity and cell surface levels of PiT2. Furthermore, we showed that these two mutations attenuated its membrane localization by affecting adenosine monophosphate-activated protein kinase (AMPK)- or protein kinase B (AKT)-mediated PiT2 phosphorylation. In contrast, the p.S121C and p.S601W mutations in the PD domains did not affect PiT2 phosphorylation but rather impaired its substrate-binding abilities. These results suggested that missense mutations in PiT2-loop7 can cause Pi dyshomeostasis by affecting the phosphorylation-regulated cell-surface localization of PiT2. This study helps understand the pathogenesis of PFBC caused by PiT2-loop7 missense mutations and indicates that increasing the phosphorylation levels of PiT2-loop7 could be a promising strategy for developing PFBC therapies.

Information Theoretic Model Selection for Accurately Estimating Unreported COVID-19 Infections

Estimating the true magnitude of infections was one of the significant challenges in combating the COVID-19 outbreak early on. Our inability in doing so allowed unreported infections to drive up disease spread in numerous regions in the US and worldwide. Even today, identifying the true magnitude (the number of total infections) is still challenging, despite the use of surveillance-based methods such as serological studies, due to their costs and biases. This paper proposes an information theoretic approach to estimate total infections accurately. Our approach is built on top of ordinary differential equations based epidemiological models, which have been used extensively in understanding the dynamics of COVID-19, and aims to estimate the true total infections and a parameterization that "best describes" the observed reported infections. Our experiments show that the parameterization learned by our framework leads to a better estimation of total infections and forecasts of the reported infections compared to a "baseline" parameterization, which is learned via usual model calibration. We also demonstrate that our framework can be leveraged to simulate what-if scenarios with non-pharmaceutical interventions. Our results also support earlier findings that most COVID-19 infections were unreported and non-pharmaceutical interventions indeed helped mitigate the COVID-19 outbreak. Our approach gives a general method to use information theoretic techniques to improve epidemic modeling, which can also be applied to other diseases.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DO_SCPLOWEEPC_SCPLOWCO_SCPLOWOVIDC_SCPLOW, an operational deep learning frame-work designed for real-time COVID-19 forecasting. DO_SCPLOWEEPC_SCPLOW-CO_SCPLOWOVIDC_SCPLOW works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.