Biomechanical Gait Analysis Using a Smartphone-Based Motion Capture System (OpenCap) in Patients with Neurological Disorders.

This study evaluates the utility of OpenCap (v0.3), a smartphone-based motion capture system, for performing gait analysis in patients with neurological disorders. We compared kinematic and kinetic gait parameters between 10 healthy controls and 10 patients with neurological conditions, including stroke, Parkinson's disease, and cerebral palsy. OpenCap captured 3D movement dynamics using two smartphones, with data processed through musculoskeletal modeling. The key findings indicate that the patient group exhibited significantly slower gait speeds (0.67 m/s vs. 1.10 m/s, p = 0.002), shorter stride lengths (0.81 m vs. 1.29 m, p = 0.001), and greater step length asymmetry (107.43% vs. 91.23%, p = 0.023) compared to the controls. Joint kinematic analysis revealed increased variability in pelvic tilt, hip flexion, knee extension, and ankle dorsiflexion throughout the gait cycle in patients, indicating impaired motor control and compensatory strategies. These results indicate that OpenCap can effectively identify significant gait differences, which may serve as valuable biomarkers for neurological disorders, thereby enhancing its utility in clinical settings where traditional motion capture systems are impractical. OpenCap has the potential to improve access to biomechanical assessments, thereby enabling better monitoring of gait abnormalities and informing therapeutic interventions for individuals with neurological disorders.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.

PURPOSE: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

T1 Map-Based Radiomics for Prediction of Left Ventricular Reverse Remodeling in Patients With Nonischemic Dilated Cardiomyopathy.

OBJECTIVE: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). MATERIALS AND METHODS: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. RESULTS: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). CONCLUSION: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

Stability selection for LASSO with weights based on AUC.

Stability selection is a variable selection algorithm based on resampling a dataset. Based on stability selection, we propose weighted stability selection to select variables by weighing them using the area under the receiver operating characteristic curve (AUC) from additional modelling. Through an extensive simulation study, we evaluated the performance of the proposed method in terms of the true positive rate (TPR), positive predictive value (PPV), and stability of variable selection. We also assessed the predictive ability of the method using a validation set. The proposed method performed similarly to stability selection in terms of the TPR, PPV, and stability. The AUC of the model fitted on the validation set with the selected variables of the proposed method was consistently higher in specific scenarios. Moreover, when applied to radiomics and speech signal datasets, the proposed method had a higher AUC with fewer variables selected. A major advantage of the proposed method is that it enables researchers to select variables intuitively using relatively simple parameter settings.

Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.

We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 consecutive patients with 285 cardiac CT scans that were reconstructed with DLR, IR, and FBP, were retrospectively enrolled. Radiomic features were extracted from the left ventricular (LV) myocardium, and from the periprosthetic mass if patients had cardiac valve replacement. Radiomic features of LV myocardium from each reconstruction were compared using a fitting linear mixed model. Radiomics models were developed to diagnose periprosthetic abnormality, and the performance was evaluated using the area under the receiver characteristics curve (AUC). Most radiomic features of LV myocardium (73 of 88) were significantly different in pairwise comparisons between all three reconstruction methods (P < 0.05). The radiomics model on IR exhibited the best diagnostic performance (AUC 0.948, 95% CI 0.880-1), relative to DLR (AUC 0.873, 95% CI 0.735-1) and FBP (AUC 0.875, 95% CI 0.731-1), but these differences did not reach significance (P > 0.05). In conclusion, applying DLR to cardiac CT scans yields radiomic features distinct from those obtained with IR and FBP, implying that feature robustness is not guaranteed when applying DLR.

A Prospective Randomized Study to Compare Postoperative Drainage After Mastectomy Using Electrosurgical Bipolar Systems and Conventional Electro-Cautery.

PURPOSE: Advanced energy devices, including electrosurgical bipolar systems or ultrasonic shears, are widely used in various surgeries. An electrosurgical bipolar device allows surgeons to grasp and dissect tissues, as well as simultaneously ligate and cut vessels and lymphatics during surgery. This study aimed to evaluate the effects of advanced bipolar energy devices on the reduction in seroma formation during mastectomy, axillary staging, and/or reconstruction. METHODS: This prospective randomized clinical trial with a 1:1 ratio compared the use of an electrosurgical bipolar device, LigaSureTM (LGS), against conventional cut-and-ligate techniques in mastectomy with axillary procedures for patients with breast cancer. A total of 82 patients with breast cancer who underwent definitive surgery were enrolled in this study. The primary endpoint was the total drainage volume after surgery. RESULTS: The clinicopathological characteristics of the two groups were not significantly different. The total postoperative drainage volume was significantly lower in the LGS group than in the control group (756.26 mL vs. 1,167.74 mL, p = 0.009). The actual postoperative drainage volume and duration also decreased significantly in the LGS group compared with those in the control group (all p < 0.05). The rate of postoperative complications was lower in the LGS group than in the control group (9.8% vs. 27.5%, p = 0.05). CONCLUSION: Electrosurgical bipolar devices showed better performance in terms of decreasing postoperative drainage during mastectomy and axillary staging and/or reconstruction.

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform.

Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.

Early Correction of N-Methyl-D-Aspartate Receptor Function Improves Autistic-like Social Behaviors in Adult Shank2-/- Mice.

BACKGROUND: Autism spectrum disorder involves neurodevelopmental dysregulations that lead to visible symptoms at early stages of life. Many autism spectrum disorder-related mechanisms suggested by animal studies are supported by demonstrated improvement in autistic-like phenotypes in adult animals following experimental reversal of dysregulated mechanisms. However, whether such mechanisms also act at earlier stages to cause autistic-like phenotypes is unclear. METHODS: We used Shank2-/- mice carrying a mutation identified in human autism spectrum disorder (exons 6 and 7 deletion) and combined electrophysiological and behavioral analyses to see whether early pathophysiology at pup stages is different from late pathophysiology at juvenile and adult stages and whether correcting early pathophysiology can normalize late pathophysiology and abnormal behaviors in juvenile and adult mice. RESULTS: Early correction of a dysregulated mechanism in young mice prevents manifestation of autistic-like social behaviors in adult mice. Shank2-/- mice, known to display N-methyl-D-aspartate receptor (NMDAR) hypofunction and autistic-like behaviors at postweaning stages after postnatal day 21 (P21), show the opposite synaptic phenotype-NMDAR hyperfunction-at an earlier preweaning stage (∼P14). Moreover, this NMDAR hyperfunction at P14 rapidly shifts to NMDAR hypofunction after weaning (∼P24). Chronic suppression of the early NMDAR hyperfunction by the NMDAR antagonist memantine (P7-P21) prevents NMDAR hypofunction and autistic-like social behaviors from manifesting at later stages (∼P28 and P56). CONCLUSIONS: Early NMDAR hyperfunction leads to late NMDAR hypofunction and autistic-like social behaviors in Shank2-/- mice, and early correction of NMDAR dysfunction has the long-lasting effect of preventing autistic-like social behaviors from developing at later stages.

Sexually dimorphic behavior, neuronal activity, and gene expression in Chd8-mutant mice.

Autism spectrum disorders (ASDs) are four times more common in males than in females, but the underlying mechanisms are poorly understood. We characterized sexually dimorphic changes in mice carrying a heterozygous mutation in Chd8 (Chd8+/N2373K) that was first identified in human CHD8 (Asn2373LysfsX2), a strong ASD-risk gene that encodes a chromatin remodeler. Notably, although male mutant mice displayed a range of abnormal behaviors during pup, juvenile, and adult stages, including enhanced mother-seeking ultrasonic vocalization, enhanced attachment to reunited mothers, and isolation-induced self-grooming, their female counterparts do not. This behavioral divergence was associated with sexually dimorphic changes in neuronal activity, synaptic transmission, and transcriptomic profiles. Specifically, female mice displayed suppressed baseline neuronal excitation, enhanced inhibitory synaptic transmission and neuronal firing, and increased expression of genes associated with extracellular vesicles and the extracellular matrix. Our results suggest that a human CHD8 mutation leads to sexually dimorphic changes ranging from transcription to behavior in mice.