Risk Factors of the 2-Year Mortality after Bipolar Hemiarthroplasty for Displaced Femoral Neck Fracture.

Purpose: This study investigates the relationship between preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-C-reactive protein ratio (LCR), albumin, and 2-year mortality in elderly patients having hemiarthroplasty for displaced femoral neck fracture (FNF). Materials and Methods: We retrospectively reviewed 284 elderly patients who underwent hemiarthroplasty for Garden type IV FNF from September 2014 to September 2020. Using the receiver operating characteristic curve, optimal cutoff values for LCR, NLR, and albumin were established, and patients were categorized as low or high. Associations with 2-year mortality were evaluated through univariate and multivariate Cox regression analyses. Results: Of the 284 patients, 124 patients (45.9%) died within 2 years post-surgery. The optimal cutoff values were: LCR at 7.758 (specificity 58.5%, sensitivity 25.0%), NLR at 3.854 (specificity 39.2%, sensitivity 40.0%), and albumin at 3.750 (specificity 65.9%, sensitivity 21.9%). Patients with low LCR (<7.758), high NLR (≥3.854), and low albumin (<3.750) had a statistically significant reduced survival time compared to their counterparts. Conclusion: Lower preoperative LCR and albumin levels, along with higher NLR, effectively predict 2-year mortality and 30-day post-surgery complications in elderly patients with Garden type IV FNF undergoing hemiarthroplasty.

Estimation of Bone Mineral Density in the Femoral Neck and Lumbar Spine using Texture Analysis of Chest and Pelvis Computed Tomography Hounsfield Unit.

OBJECTIVE: This study aimed to establish an academic basis for using a computed tomography (CT) model for predicting osteoporosis in the clinical setting by illustrating the effectiveness of morphometric texture analysis. We introduce texture analysis and quantitative approaches using CT Hounsfield units (HU) to screen osteoporosis. METHODS: From March 6th, 2013, to August 11th, 2020, a total of 4,333 cases (1,766 patients) were included in the study. After applying exclusion criteria concerning the patient status and scan interval between CT and DXA, we selected only 1,647 samples (736 patients) and analyzed both their CT and DXA bone mineral density (BMD) results. BMD was measured in the femoral neck and L1 spine body. A region of interest (ROI) was extracted from each patient's CT as the maximum trabecular area of the L1 spine body and femoral neck. A total of 45 texture features were extracted from every ROI using gray-level co-occurrence matrices. Machine-learning techniques, including linear regression (LR) and artificial neural network (ANN), were applied to predict BMD. RESULTS: We assigned samples to (1) Set 1 (857 lumbar spine samples in chest model, L1 spine DXA BMD), (2) Set 2 (392 lumbar spine samples in lumbar spine CT model, L1 spine DXA BMD), (3) Set 3 (1,249 lumbar spine samples in both chest and lumbar spine CT model, L1 spine DXA BMD), (4) Set 4 (398 femoral neck samples in hip and pelvis CT model, femoral neck DXA BMD), and (5) Set 5 (a total of 1,647 samples). When we applied LR, the correlation coefficients between estimated and reference values for Sets 1, 2, 3, and 4 were 0.783, 0.784, 0.757, and 0.652, respectively. For total samples (Set 5), LR and ANN provided correlation coefficients of 0.707 and 0.782, respectively. CONCLUSION: The modality using morphometric texture analysis with CT HU can be an additional diagnostic tool for osteoporosis and an alternative for DXA.

Prediction of bone mineral density in CT using deep learning with explainability.

Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score = - 1.0 ), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.

Application of RAPD and SCAR markers for purity testing of F1 hybrid seed in chili pepper (Capsicum annuum).

A simpler and better method for purity testing of hybrid pepper seed was developed. The simplest method for extracting genomic DNA, the NaOH method, was chosen. Two RAPD markers identifying male and female parents were also developed, and the PCR products of male- and female-specific RAPD markers were cloned and sequenced. From these sequences, new longer primers were constructed for conversion into SCAR markers. In blind tests the RAPD and SCAR markers were able to reliably detect contaminating exotic seeds. These PCR-based markers are therefore directly applicable for purity testing by seed companies. In addition, the PCR products of the SCAR markers could be identified by direct staining methods such as ethidium bromide and pellet painting without electrophoresis.