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Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.

Realizing an age-adjusted correction in adult spine deformities (ASD) is demanding. We focus on rigid ASD patients requiring three-column osteotomies (TCO) and analyze the capability to obtain a normative alignment. Additionally, we introduce a new age-adjusted alignment parameter: the C7-SA norm. We retrospectively included 26 patients (68.2 ± 1.6 years) with rigid ASD who underwent lumbar TCO for deformity correction. All patients were positioned on a ProAxis table (Mizuho OSI, USA) with the shoulders, hips and ankles aligned for passive correction without bending the surgical table. The mean pelvic incidence—lumbar lordosis, sagittal vertical axis, T1 pelvic angle, and C7-SA norm before and after surgery were as follows: 25.7 ± 2.9° versus 10.1 ± 1.4° ( p  < 0.001), 113.6 ± 9.8 mm versus 36.2 ± 4.9 mm ( p  < 0.001), 30.1 ± 2.3° versus 12.0 ± 1.4° ( p  < 0.001), and 85.7 ± 9.5 mm versus 30.6 ± 4.4 mm ( p  < 0.001), respectively. The mean correction of lumbar lordosis was 27.3 ± 3.2°. A C7-SA norm of ≥ 66.1 mm required a correction of lumbar lordosis of > 20.8° which confirmed the necessity to conduct a TCO. Realigning the C7-SA norm in rigid ASD by TCOs and optimal patient positioning is a simple method for age-adjusted deformity correction without the need for preoperative complex calculations, intraoperative measurements, and repositioning of the patient during surgery.

Long distances to facilities, topographical constraints, inadequate service capacity of institutions and insufficient/ rudimentary road & transportation network culminate into unprecedented barriers to access. These barriers gets exacerbated in presence of external factors like conflict and political disruptions. Thus, this study was conducted in rural, remote and fragile region in India measuring geographical accessibility and modelling spatial coverage of public healthcare network. Vector and raster based approaches were used to discern accessibility for various packages of service delivery. Alternative scenarios derived from local experiences were modelled using health facility, population and ancillary data. Based on that, a raster surface of travel time between facilities and population was developed by incorporating terrain, physical barriers, topography and travelling modes and speeds through various land-cover classes. Concomitantly, spatial coverage was modelled to delineate catchment areas. Further, underserved population and zonal statistics were assessed in an interactive modelling approach to ascertain spatial relationship between population, travel time and zonal boundaries. Finally, raster surface of travel time was re-modelled for the conflict situation in villages vulnerable to obstruction of access due to disturbed security scenario. Euclidean buffers revealed 11% villages without ambulatory & immunization care within 2 km radius. Similarly, for 5 km radius, 11% and 12% villages were bereft of delivery and inpatient care. Travel time accessibility analysis divulged walking scenario exhibiting lowest level of accessibility. Enabling motorized travel improved accessibility measures, with highest degree of accessibility for privately owned vehicle (motorcycle and cars). Differential results were found between packages of services where ambulatory & immunization care was relatively accessible by walking; whereas, delivery and inpatient care had a staggering average of three hours walking time. Even with best scenario, around 2/3rd population remained unserved for all package of services. Moreover, 90% villages in conflict zone grapples with inaccessibility when the scenario of heightened border tensions was considered. Our study demonstrated the application of GIS technique to facilitate evidence backed planning at granular level. Regardless of the scenario, the analysis divulged inaccessibility to delivery and inpatient care to be most pronounced and majority of population to be unserved. It was suggested to have concerted efforts to bolster already existing facilities and adapt systems approach to exploit synergies of inter-sectoral development.

Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain–spinal cord interfaces combining brain–computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain–spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.

Background Financing for NCDs is encumbered by out-of-pocket expenditure (OOPE) assuming catastrophic proportions. Therefore, it is imperative to investigate the extent of catastrophic health expenditure (CHE) on NCDs, which are burgeoning in India. Thus, our paper aims to examine the extent of CHE and impoverishment in India, in conjunction with socio-economic determinants impacting the CHE. Methods We used cross-sectional data from nationwide healthcare surveys conducted in 2014 and 2017–18. OOPE on both outpatient and inpatient treatment was coalesced to estimate CHE on NCDs. Incidence of CHE was defined as proportion of households with OOPE exceeding 10% of household expenditure. Intensity of catastrophe was ascertained by the measure of Overshoot and Mean Positive Overshoot Indices . Further, impoverishing effects of OOPE were assessed by computing Poverty Headcount Ratio and Poverty Gap Index using India’s official poverty line. Concomitantly, we estimated the inequality in incidence and intensity of catastrophic payments using Concentration Indices . Additionally, we delineated the factors associated with catastrophic expenditure using Multinomial Logistic Regression. Results Results indicated enormous incidence of CHE with around two-third households with NCDs facing CHE. Incidence of CHE was concentrated amongst poor that further extended from 2014(CI = − 0.027) to 2017–18(CI = − 0.065). Intensity of CHE was colossal as households spent 42.8 and 34.9% beyond threshold in 2014 and 2017-18 respectively with poor enduring greater overshoot vis-à-vis rich (CI = − 0.18 in 2014 and CI = − 0.23 in 2017–18). Significant immiserating impact of NCDs was unraveled as one-twelfth in 2014 and one-eighth households in 2017–18 with NCD burden were pushed to poverty with poverty deepening effect to the magnitude of 27.7 and 30.1% among those already below poverty on account of NCDs in 2014 and 2017–18 respectively. Further, large inter-state heterogeneities in extent of CHE and impoverishment were found and multivariate analysis indicated absence of insurance cover, visiting private providers, residing in rural areas and belonging to poorest expenditure quintile were associated with increased likelihood of incurring CHE. Conclusion Substantial proportion of households face CHE and subsequent impoverishment due to NCD related expenses. Concerted efforts are required to augment the financial risk protection to the households, especially in regions with higher burden of NCDs.

AbstractSuper-hydrophobic (SH) surfaces mostly motivated by the primordial surface designs considerably increase the life of the substrate. In the present work, effort has been made towards rational development of the SH surface on ASTM A479 steels substrates coated with WC–10Co–4Cr using high-velocity oxy-fuel. Distinct coating configurations have been investigated to analyse the effect of circular texturing patterns using nanosecond laser fibre texturing machine for the development of SH surfaces. Here, a freshly prepared solution of Hexafor 644-D/PFOA/Al2O3 nanoparticles was utilized for simple HVLP spraying and dip coating methods to formulate SH coatings on untextured and textured surfaces. The developed SH surfaces were characterized by field emission scanning electron microscope, X-ray diffraction, energy-dispersive spectroscopy and surface roughness tester. Water repellency of fabricated SH surfaces and its durability were studied by measuring the water contact angle (WCA), sliding angle, sandpaper abrasion test, tape peeling tests and water impact tests. All the configurations of the developed SH surfaces were found to be sustainable against the different wear tests performed during the study and exhibited excellent repellence to water and ink droplets. The highest WCA and sliding angle for textured and dip-coated SH surface (configuration—C 2.1) were found to be 162.6° and 10°, respectively. Further, it was found that during the sandpaper abrasion test, and the configuration corresponding to textured surfaces after thermal spray coating sustained super-hydrophobicity up to 30 cycles. Because of simple steps and the desired properties obtained by proposed methods, the coatings may be used for producing SH surfaces in apt industrial applications.Graphic Abstract

The EOS™2D/3D system is a low-dose, 3D imaging system that utilizes two perpendicular X-ray beams to create simultaneous frontal and lateral images of the body. This is a useful modality to assess spinal pathologies. However, due to the slow imaging acquisition time up to 25 s, motion artifacts (MA) frequently occur. These artifacts may not be distinguishable from pathological findings, such as scoliosis, and may impair the diagnostic process. The aim of this study was to design a method to detect MA in EOS X-ray. We retrospectively analyzed EOS imaging from 40 patients wearing a radiopaque reference device during imaging. We drew a straight vertical line along the reference device. We measured deviations from it to quantify MA, presenting these findings through descriptive statistics. For a subset of patients with high MA, acquisitions were repeated after giving specific instructions to stand still. For these patients, we compared MA between the two acquisitions. In our study, a substantial proportion of patients exhibited MA ≥ 1 mm, with 80% in frontal projections and 87.9% in lateral projections. In the subjects who received a second acquisition, MA was significantly lower in the second images. Our method allows for a precise detection of MA on EOS images through a simple, yet reliable solution. Our method may improve the reliability of spine measurements, and reduce the risk of wrong diagnosis due to low imaging quality.

After spinal cord injury (SCI), reorganization processes and changes in brain connectivity occur. Besides the sensorimotor cortex, the subcortical areas are strongly involved in motion and executive control. This exploratory study focusses on the cerebellum and vermis. Resting-state functional magnetic resonance imaging (fMRI) was performed. Between-group differences were computed using analysis of covariance and post-hoc tests for the seed-based connectivity measure with vermis and cerebellum as regions of interest. Twenty participants with complete SCI (five subacute SCI, 15 with chronic SCI) and 14 healthy controls (HC) were included. Functional connectivity (FC) was lower in all subjects with SCI compared with HC in vermis IX, right superior frontal gyrus ( p FDR  = 0.008) and right lateral occipital cortex ( p FDR  = 0.036). In addition, functional connectivity was lower in participants with chronic SCI compared with subacute SCI in bilateral cerebellar crus I, left precentral- and middle frontal gyrus ( p FDR  = 0.001). Furthermore, higher amplitude of low-frequency fluctuations (ALFF) was found in the left thalamus in individuals with subacute SCI ( p FDR  = 0.002). Reduced FC in SCI indicates adaptation with associated deficit in sensory and motor function. The increased ALFF in subacute SCI might reflect reorganization processes in the subacute phase.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can quantify indices related to brain structure and their change in pathology. However, only few studies have applied these techniques to spinal cord injury (SCI), and subtle microstructural changes in the brain of SCI individuals are not well understood. Our goal was to investigate structural changes in the brain using DTI (fractional anisotropy, FA; mean diffusivity, MD) and DKI parameters (kurtosis anisotropy, KA; mean kurtosis, MK) in subacute SCI and to study whether these changes were associated with clinical outcomes. Twenty-eight individuals with SCI underwent brain MRI 3 months post-injury, alongside 20 healthy controls. Imaging included a multi-shell diffusion protocol, from which DTI and DKI metrics (FA, MD, KA and MK) were derived. Group comparisons were conducted for each metric across 17 brain regions selected based on their relevance to SCI from previous studies. Multiple comparison corrections were applied per metric to account for the number of examined regions. Effect sizes were calculated using Cohen's . For regions showing significant group differences, Spearman correlations were performed to assess associations between imaging metrics and clinical outcomes, including neurological status (ISNCSCI) and functional independence (SCIM III), with correction for multiple comparisons. MD was significantly higher in the right genu of the corpus callosum in the SCI group (adjusted  = 0.021). In this region, MD negatively correlated with SCIM scores (  = -0.51,  = 0.022), whereas MK showed a positive correlation (  = 0.482,  = 0.038). Structural changes in the corpus callosum may reflect impaired interhemispheric communication, linked to reduced functional independence after SCI. DTI and DKI could serve as complementary tools for identifying brain-based biomarkers, potentially informing recovery trajectories.