Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly.

OBJECTIVES: To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs. METHODS: Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA. CONCLUSIONS: Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs. CLINICAL RELEVANCE STATEMENT: It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations. KEY POINTS: Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Interfragmentary compression force and fixation stability of lateral tibial plateau fractures in normal and osteoporotic bones.

Lateral platform collapse in fixations of lateral tibial plateau fractures (TPFs) using either double-lag screws fixation (DSF) or locking-plate fixation (LPF) is not rare. This study aimed to explore the effect of enhancing the interfragmentary compression force (IFCF) on fixation stability in lateral TPFs in normal and osteoporotic bones using finite element analysis. Finite element models of DSF in normal bone and LPF in normal and osteoporotic bones were established to simulate the fixations of lateral TPF. After model validation, axial compressive forces of 500, 1000, 1500, and 2500 N to the tibial plateau along with an IFCF of 0, 100, 200, and 300 N were applied. The maximum axial micromotion of the lateral fragment (MAM-LF), maximal translational micromotion of the lateral fragment (MTM-LF), peak von Mises stress (VMS), and peak equivalent elastic strain of the lateral fragment (EES-LF) were evaluated. The MAM-LF showed a decreasing trend as the IFCF increased in all models. For DSF models, the peak VMS of implants increased as the IFCF increased when the axial loads were 500 and 1000 N. The peak EES-LF decreased as the IFCF increased under axial loads of 1000, 1500, and 2500 N. For the normal and osteoporotic LPF models, the peak VMS of the implants decreased as the IFCF increased. Peak EES-LF decreased as IFCF increased. In conclusion, enhancing IFCF was beneficial in improving the fixation stability of lateral TPF. The optimal IFCF for DSF and LPF should be as high as reasonably feasible.

Application of a combined cancellous lag screw enhances the stability of locking plate fixation of osteoporotic lateral tibial plateau fracture by providing interfragmentary compression force.

BACKGROUND: Insufficient interfragmentary compression force (IFCF) frequently leads to unstable fixation of osteoporotic lateral tibial plateau fractures (OLTPFs). A combined cancellous lag screw (CCLS) enhances IFCF; however, its effect on OLTPF fixation stability remains unclear. Therefore, we investigated the effect of CCLS on OLTPF stability using locking plate fixation (LPF). MATERIALS AND METHODS: Twelve synthetic osteoporotic tibial bones were used to simulate OLTPFs, which were fixed using LPF, LPF-AO cancellous lag screws (LPF-AOCLS), and LPF-CCLS. Subsequently, 10,000 cyclic loadings from 30 to 400 N were performed. The initial axial stiffness (IAS), maximal axial micromotion of the lateral fragment (MAM-LF) measured every 1000 cycles, and failure load after 10,000 cycles were tested. The same three fixations for OLTPF were simulated using finite element analysis (FEA). IFCFs of 0, 225, and 300 N were applied to the LPF, LPF-AOCLS, and LPF-CCLS, respectively, with a 1000-N axial compressive force. The MAM-LF, peak von Mises stress (VMS), peak equivalent elastic strain of the lateral fragment (EES-LF), and nodes of EES-LF > 2% (considered bone destruction) were calculated. RESULTS: Biomechanical tests revealed the LPF-AOCLS and LPF-CCLS groups to be superior to the LPF group in terms of the IAS, MAM-LF, and failure load (all p < 0.05). FEA revealed that the MAM-LF, peak VMS, peak EES-LF, and nodes with EES-LF > 2% in the LPF were higher than those in the LPF-AOCLS and LPF-CCLS. CONCLUSION: IFCF was shown to enhance the stability of OLTPFs using LPF. Considering overscrewing, CCLS is preferably recommended, although there were no significant differences between CCLS and AOCLS.

Correlation of glymphatic system abnormalities with Parkinson's disease progression: a clinical study based on non-invasive fMRI.

BACKGROUND: The glymphatic system is reportedly involved in Parkinson's disease (PD). Based on previous studies, we aimed to confirm the correlation between the glymphatic system and PD progression by combining two imaging parameters, diffusion tensor image analysis along the perivascular space (DTI-ALPS), and enlarged perivascular spaces (EPVS). METHODS: Fifty-one PD patients and fifty healthy control (HC) were included. Based on the Hoehn-Yahr scale, the PD group was divided into early-stage and medium-to late-stage. All PD patients were scored using the Unified PD Rating Scale (UPDRS). We assessed the DTI-ALPS indices in the bilateral hemispheres and EPVS numbers in bilateral centrum semiovale (CSO), basal ganglia (BG), and midbrain. RESULTS: The DTI-ALPS indices were significantly lower bilaterally in PD patients than in the HC group, and EPVS numbers in any of the bilateral CSO, BG, and midbrain were significantly higher, especially for the medium- to late-stage group and the BG region. In PD patients, the DTI-ALPS index was significantly negatively correlated with age, while the BG-EPVS numbers were significantly positively correlated with age. Furthermore, the DTI-ALPS index was negatively correlated with UPDRS II and III scores, while the BG-EPVS numbers were positively correlated with UPDRS II and III scores. Similarly, the correlation was more pronounced in the medium- to late-stage group. CONCLUSION: The DTI-ALPS index and EPVS numbers (especially in the BG region) are closely related to age and PD progression and can serve as non-invasive assessments for glymphatic dysfunction and its interventions in clinical studies.

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

Assessing the potential and kinetics of coupled nutrients uptake in mesotrophic streams in Chaohu Lake Basin, China.

Interactions among multiple nutrients uptake certainly have a great effect on their retention in headwater streams, yet little research has been made to explore the quantitative characteristics of their interactions, especially in mesotrophic streams. In response, we conducted an identical series of instantaneous nutrient addition experiments, using ammonium nitrogen (NH4-N) and phosphate phosphorus (PO4-P) alone or together, in two mesotrophic agricultural headwater streams in Chaohu Lake Basin, China, to quantify the relationships between nutrient concentrations and uptake rates, and examine how NH4-N and PO4-P interact to affect their individual uptake. Both the Michaelis-Menten (M-M) equation and response surface model were utilized to analyze coupled NH4-N and PO4-P uptake patterns across a range of nutrient concentrations, by fitting the kinetic processes of NH4-N and PO4-P uptake in single- and dual-nutrient additions. The capacity of both NH4-N and PO4-P uptake was increased in different degrees in dual-nutrient additions. Response surface models could quantitatively characterize the three-dimensional dynamic evolution trend of NH4-N or PO4-P uptake rates at different concentrations. The influence of PO4-P additions on NH4-N uptake was generally greater than that of NH4-N on PO4-P uptake in the five tracer tests. In addition, results of correlation analysis indicated that water temperature might be the main factor affecting the coupling of N and P uptake in mesotrophic streams and followed by hydrological factors (e.g., discharge) and channel geomorphology.

Effects of channel morphology on nitrate retention in a headwater agricultural stream in Lake Chaohu Basin, China.

Five field tracer experiments and relevant detailed investigations of physical characterizations were conducted to investigate the effects of channel geomorphic settings on nitrate uptake efficiency on a 310-m long geomorphically distinct stream reach in a headwater agricultural stream in Hefei District, Lake Chaohu Basin. The model-fitted parameters from the one-dimensional transport with inflow and storage model were used to estimate the transient storage metric ([Formula: see text]) and determine the total nitrate uptake coefficient (k) for the study reach. And then, a nutrient spiraling approach was applied to reach-scale nitrate uptake estimates (Sw, Vf, and U). The results showed that the main channel was the major contributor to nitrate uptake retention, and the higher geomorphic complexity might result in better nitrate uptake efficiency. The partial least squares regression (PLSR) analysis showed strong correlations between the independent variables as geomorphic settings, Reynolds number and transient storage, and the dependent variables as nitrate uptake metrics, which further underscored the importance of stream physical characteristics on measurement of stream nitrate uptake.

Interfragmentary compression and pull-out properties of 6.5-mm AO cancellous lag screws in a uniform synthetic material during tightening procedures.

AO lag screws are widely used in surgical intra-articular fracture treatment for anatomical reduction and rigid fixation. Interfragmentary compressive force (IFCF) and pull-out strength (POS) are two critical parameters generated by AO lag screws during tightening, and both of these parameters could be used to estimate screw insert conditions to prevent screw stripping. The aim of this study is to evaluate the IFCF and POS of AO cancellous screws inserted into uniform synthetic cancellous bone during tightening procedures. Seven synthetic cancellous bone blocks were used for this research. Each test contained two continuous portions as follows: the rotation test portion and the pull-out test portion. IFCF and POS were captured by the pressure transducer and the sensor of the test machine. The properties of IFCF and POS based on tightening degrees were obtained in this study. The ideal balance between POS and IFCF during screw tightening exists, and the peak values of these parameters cannot be simultaneously achieved. Moreover, rotation angles of 100-150° appear to serve as the optimum balance between IFCF and POS in the present study.