The assessment of the knee alignment using standing weight-bearing full-leg radiographs (FLR) is a standardized method. Determining the load-bearing axis of the leg requires time-consuming manual measurements. The aim of this study is to develop and validate a novel algorithm based on artificial intelligence (AI) for the automated assessment of lower limb alignment. In the first stage, a customized mask-RCNN model was trained to automatically detect and segment anatomical structures and implants in FLR. In the second stage, four region-specific neural network models (adaptations of UNet) were trained to automatically place anatomical landmarks. In the final stage, this information was used to automatically determine five key lower limb alignment angles. For the validation dataset, weight-bearing, antero-posterior FLR were captured preoperatively and 3 months postoperatively. Preoperative images were measured by the operating orthopedic surgeon and an independent physician. Postoperative images were measured by the second rater only. The final validation dataset consisted of 95 preoperative and 105 postoperative FLR. The detection rate for the different angles ranged between 92.4% and 98.9%. Human vs. human inter-(ICCs: 0.85–0.99) and intra-rater (ICCs: 0.95–1.0) reliability analysis achieved significant agreement. The ICC-values of human vs. AI inter-rater reliability analysis ranged between 0.8 and 1.0 preoperatively and between 0.83 and 0.99 postoperatively (all p < 0.001). An independent and external validation of the proposed algorithm on pre- and postoperative FLR, with excellent reliability for human measurements, could be demonstrated. Hence, the algorithm might allow for the objective and time saving analysis of large datasets and support physicians in daily routine.

Pancreatic cancer is known for its tumor microenvironment (TME), which is rich in stromal and immune cells supporting cancer growth and therapy resistance. In particular, tumor-associated macrophages (TAMs) are known for their angiogenesis- and metastasis-promoting properties, which lead to the failure of conventional therapies for pancreatic cancer. Hence, treatment options targeting TAMs are needed. The C-C chemokine receptor type 4 (CCR4) is critical for immune cell recruitment into the TME, and in this paper we explore the effects of its genetic or immunotherapeutic blockade in pancreatic-cancer-bearing mice. Murine PDA6606 pancreatic cancer cells and murine peritoneal macrophages were used for in vitro migration assays. In vivo, a syngeneic, orthotropic pancreatic cancer model was established. Tumor growth and survival were monitored under prophylactic and therapeutic application of a CCR4 antagonist (AF-399/420/18025) in wildtype (CCR4^wt) and CCR4-knockout (CCR4^−/−) mice. Immune infiltration was monitored in tumor tissue sections and via flow cytometry of lysed tumors. PDA6606 cells induced less migration in CCR4^−/− than in CCR4^wt macrophages in vitro. Pancreatic TAM infiltration was higher, and survival was reduced in CCR4^wt mice compared to CCR4^−/− mice. Antagonizing CCR4 in wildtype mice revealed similar results as in CCR4^−/− mice without antagonization. Prophylactic CCR4 antagonist application in wildtype mice was more efficient than therapeutic antagonization. CCR4 seems to be critically involved in TAM generation and tumor progression in pancreatic cancer. CCR4 blockade may help prolong the relapse-free period after curative surgery in pancreatic cancer and improve prognosis.

Purpose: Periprosthetic joint infection (PJI) is a rare but serious complication of total joint arthroplasty (TJA). An accurate diagnosis of PJI preoperatively does not exist. Alpha-defensin (AD) is a proven and common indicator. The diagnostic marker of leukocyte esterase (LE) promises some advantages: feasibility, availability, and fast result reporting. The aim of this study was the evaluation of the predictive quality and correlation between both diagnostic tools in the diagnosis of PJI. Methods: A prospective study was conducted between April 2018 and August 2022. All patients with suspicion of PJI on hip and knee joint were included and underwent a routine and standardized joint punction. For laboratory diagnostics of AD, the synovial liquid was analyzed by ELISA. The sample was additionally applied to a LE test strip (Combur 10 Test, Roche Diagnostics, Mannheim, Germany). Results: A total of 249 patients were examined (mean age 67.12 ± 11.89; gender distribution man/woman 139 (55.8%)/110(44.2%), hip/knee 71(28.5%)/178 (71.5%). According to EBJIS criteria, PJI was diagnosed in 54 (21.7%) patients. AD showed excellent results with an AUC of 0.930 (sensitivity/specificity 0.870/0.990). LE yielded very good results with an AUC of 0.820 (sensitivity/specificity 0.722/0.918). Both parameters showed a strong positive correlation. Conclusion: LE is a rapidly available alternative in PJI diagnostics. The simultaneous determination of both markers may enhance diagnostic reliability. A routine usage may shorten the time from diagnosis to treatment of PJI.

As non-unions are still common, a predictive assessment of healing complications could enable immediate intervention before negative impacts for the patient occur. The aim of this pilot study was to predict consolidation with the help of a numerical simulation model. A total of 32 simulations of patients with closed diaphyseal femoral shaft fractures treated by intramedullary nailing (PFNA long, FRN, LFN, and DePuy Synthes) were performed by creating 3D volume models based on biplanar postoperative radiographs. An established fracture healing model, which describes the changes in tissue distribution at the fracture site, was used to predict the individual healing process based on the surgical treatment performed and full weight bearing. The assumed consolidation as well as the bridging dates were retrospectively correlated with the clinical and radiological healing processes. The simulation correctly predicted 23 uncomplicated healing fractures. Three patients showed healing potential according to the simulation, but clinically turned out to be non-unions. Four out of six non-unions were correctly detected as non-unions by the simulation, and two simulations were wrongfully diagnosed as non-unions. Further adjustments of the simulation algorithm for human fracture healing and a larger cohort are necessary. However, these first results show a promising approach towards an individualized prognosis of fracture healing based on biomechanical factors.