Femoral head translation in borderline and definite dysplastic hips during weight-bearing: 2D/3D image registration analysis.

PURPOSE: The aims of this study were to 1) assess femoral head translation during weight-bearing in symptomatic developmental dysplasia of the hip (DDH) and 2) compare it between borderline DDH and definite DDH. METHODS: The study included four individuals with borderline DDH and nine with definite DDH, scheduled for periacetabular osteotomy. Anteroposterior X-ray images of the hip joint were obtained in the standing position, and computed tomography images of the pelvis were obtained in the supine position. Femoral head translation from the supine to a standing position was measured using 2D/3D X-ray image registration. RESULTS: From a supine to a standing position, the femoral head translated 0.3 mm laterally, 0.5 mm anteriorly, and 0.5 mm superiorly on average. The mean femoral head translation in 3D between the supine and standing positions was 1.5 mm. The 3D femoral head translation in the borderline DDH group was significantly greater than that in the definite DDH group. In the definite DDH group, there was a significant correlation between the center edge (CE) angle and 3D femoral head translation (ρ = -0.78, P = 0.012). CONCLUSIONS: Symptomatic DDH showed femoral head translation in the anterior, lateral, and superior directions during weight-bearing. In definite DDH, the amount of femoral head translation was negatively correlated with the CE angle. The amount of 3D translation in patients with borderline DDH was larger than that in definite DDH. Dynamic joint instability during weight-bearing was observed in borderline DDH as well as definite DDH. Treatment to enhance joint stability during weight-bearing is important in both cases.

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network.

OBJECTIVES: We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners. METHODS: We enrolled 402 patients who underwent noncontrast CT examinations, including L1-L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMDDL and TBSDL were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMDDL and BMD, and TBSDL and TBS. The diagnostic performance of BMDDL for osteopenia/osteoporosis and that of TBSDL for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis. RESULTS: BMDDL and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBSDL and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMDDL for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBSDL for identifying patients with bone microarchitecture impairment were 73% for all values. CONCLUSIONS: The BMDDL and TBSDL derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.

Study protocol and baseline characteristics of newly induced dialysis patients: a prospective multi-center cohort study with a biological sample bank, the Ibaraki Dialysis Initiation Cohort (iDIC) study.

BACKGROUND: Patients with end-stage kidney disease (ESKD) face higher risks of life-threatening events including cardiovascular disease. Various risk factors are identified as agents influencing the life prognosis of ESKD patients. Herein, we evaluated the risk factors related to the outcomes of Japanese patients with dialysis induction. We present the study protocol, the patients' baseline characteristics, and their outcomes. METHODS: The Ibaraki Dialysis Initiation Cohort (iDIC) Study is a prospective multi-center cohort study in collaboration with 60 tertiary-care facilities in Ibaraki Prefecture, Japan. We collected baseline data from clinical records and analyzed blood and urine samples of these facilities' patients with diabetic nephropathy, hypertensive nephrosclerosis, and chronic glomerulonephritis (CGN). The study's primary outcome was the survival rate at 24 months after dialysis induction. We performed a Kaplan-Meier analysis for cumulative survival and a Cox proportional hazards analysis for all-cause mortality and hospitalization. RESULTS: We analyzed 636 patients' cases (424 males, 212 females, age 67.4 ± 13.1 yrs. [mean ± SD]). We compared the patients' baseline data with those of similar cohort studies. As the primary kidney disease, 327 cases (51.4%) were diagnosed as diabetic nephropathy, 101 (15.9%) as hypertensive nephrosclerosis, and 114 (17.9%) as CGN. The mean serum creatinine value was 9.1 ± 2.9 mg/dL. The mean estimated glomerular filtration rate was 5.6 ± 1.8 mL/min/1.73m2. The cumulative survival rates at 6 months and 24 months after dialysis induction were 95.2 and 87.7%, respectively. The cumulative survival rate was significantly lower with increasing age. A Cox proportional hazards regression analysis demonstrated that high age was significantly associated with all-cause mortality. CONCLUSIONS: Regarding the clinical characteristics of these newly induced dialysis patients, the same trend as in other cohort studies was observed. Another study is underway to explore prognostic factors based on the iDIC Study's findings.

A new likelihood approach to autonomous multiple model estimation.

This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules. The numerical results show that the proposed algorithm, so called Competing AMM (CAMM), guarantees both steady-state estimation accuracy and quick parameter tracking.

Effect of mitragynine, derived from Thai folk medicine, on gastric acid secretion through opioid receptor in anesthetized rats.

Mitragynine, an indole alkaloid from Thai folk medicine Mitragyna speciosa, exerts agonistic effects on opioid receptors. Gastric acid secretion is proposed to be regulated by opioid receptors in the central nervous system (CNS). Previously, we reported the dual roles (inhibition via micro-opioid receptors and stimulation via kappa-opioid receptors) of the opioid system in the central control of gastric acid secretion. We investigated whether mitragynine affects gastric acid secretion via opioid receptors in the CNS. Injection of mitragynine (30 microg) alone into the lateral cerebroventricle did not have a significant effect on basal gastric acid secretion in the perfused stomach of anesthetized rats. Injection of mitragynine (3-30 microg) into the fourth cerebroventricle, like morphine, inhibited 2-deoxy-D-glucose-stimulated gastric acid secretion. The inhibitory effect of mitragynine (30 microg) was reversed by naloxone (100 microg). These results suggest that mitragynine has a morphine-like action on gastric acid secretion in the CNS.