Investigation of phonons and magnons in [Ni80Fe20/Au/Co/Au]Nmultilayers.

The interaction between phonons and magnons is a rapidly developing area of research, particularly in the field of acoustic spintronics. To discuss this interaction, it is necessary to observe two different waves (acoustic and spin waves) with the same frequency and wavelength. In the Ni80Fe20/Au/Co/Au system deposited on a silicon substrate, we observe the interaction between spin waves and surface acoustic waves using Brillouin light scattering spectroscopy. As a result, we can selectively control (activate or deactivate) the magnetoelastic interaction between the fundamental spin wave mode and surface acoustic waves. This is achieved by adjusting the magnetostrictive layer thickness in the multilayer. We demonstrate that by adjusting the number of layers in a multilayer structure, it is possible to precisely control the dispersion of surface acoustic waves while having minimal impact on the fundamental spin wave mode.

Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations.

Cervical cancer is a common cancer in women globally, with treatment usually involving radiation therapy (RT). Accurate segmentation for the tumour site and organ-at-risks (OARs) could assist in the reduction of treatment side effects and improve treatment planning efficiency. Cervical cancer Magnetic Resonance Imaging (MRI) segmentation is challenging due to a limited amount of training data available and large inter- and intra- patient shape variation for OARs. The proposed Masked-Net consists of a masked encoder within the 3D U-Net to account for the large shape variation within the dataset, with additional dilated layers added to improve segmentation performance. A new loss function was introduced to consider the bounding box loss during training with the proposed Masked-Net. Transfer learning from a male pelvis MRI data with a similar field of view was included. The approaches were compared to the 3D U-Net which was widely used in MRI image segmentation. The data used consisted of 52 volumes obtained from 23 patients with stage IB to IVB cervical cancer across a maximum of 7 weeks of RT with manually contoured labels including the bladder, cervix, gross tumour volume, uterus and rectum. The model was trained and tested with a 5-fold cross validation. Outcomes were evaluated based on the Dice Similarity Coefficients (DSC), the Hausdorff Distance (HD) and the Mean Surface Distance (MSD). The proposed method accounted for the small dataset, large variations in OAR shape and tumour sizes with an average DSC, HD and MSD for all anatomical structures of 0.790, 30.19mm and 3.15mm respectively.

A Case Report Describing Hemophilus parainfluenza Recovered From Pleural Effusion.

Hemophilus parainfluenza, a less common pathogen typically found in the oropharyngeal flora, has been associated with various clinical conditions. However, its role in pleural effusions remains scarcely documented. We present a unique case of a 42-year-old female with a history of asthma, hypertension, and obesity who presented with epigastric pain and a moderate right-sided pleural effusion. Hemophilus parainfluenza was isolated from the pleural fluid despite an atypical, asymptomatic presentation without pneumonia. Antibiotic treatment led to a positive response, highlighting the importance of recognizing Hemophilus parainfluenza as a potential causative agent in pleural effusion cases.

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative.

In medical image analysis, automated segmentation of multi-component anatomical entities, with the possible presence of variable anomalies or pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based models to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images in individuals with varying grades of knee osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies. For anomaly detection, U-Net-based models were developed to reconstruct bone volume profiles of the femur and tibia in images via inpainting so anomalous bone regions could be replaced with close to normal appearances. The reconstruction error was used to detect bone anomalies. An anomaly-aware segmentation network, which was compared to anomaly-naïve segmentation networks, was used to provide a final automated segmentation of the individual femoral, tibial and patellar bone and cartilage volumes from the knee MR images which contain a spectrum of bone anomalies. The anomaly-aware segmentation approach provided up to 58% reduction in Hausdorff distances for bone segmentations compared to the results from anomaly-naïve segmentation networks. In addition, the anomaly-aware networks were able to detect bone anomalies in the MR images with greater sensitivity and specificity (area under the receiver operating characteristic curve [AUC] up to 0.896) compared to anomaly-naïve segmentation networks (AUC up to 0.874).

Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex-specific differences.

Cam femoroacetabular impingement (FAI) syndrome is associated with hip osteoarthritis (OA) development. Hip shape features, derived from statistical shape modeling (SSM), are predictive for OA incidence, progression, and arthroplasty. Currently, no three-dimensional (3D) SSM studies have investigated whether there are cam shape differences between male and female patients, which may be of potential clinical relevance for FAI syndrome assessments. This study analyzed sex-specific cam location and shape in FAI syndrome patients from clinical magnetic resonance examinations (M:F 56:41, age: 16-63 years) using 3D focused shape modeling-based segmentation (CamMorph) and partial least squares regression to obtain shape features (latent variables [LVs]) of cam morphology. Two-way analysis of variance tests were used to assess cam LV data for sex and cam volume severity differences. There was no significant interaction between sex and cam volume severity for the LV data. A sex main effect was significant for LV 1 (cam size) and LV 2 (cam location) with medium to large effect sizes (p < 0.001, d > 0.75). Mean results revealed males presented with a superior-focused cam, whereas females presented with an anterior-focused cam. When stratified by cam volume, cam morphologies were located superiorly in male and anteriorly in female FAI syndrome patients with negligible, mild, or moderate cam volumes. Both male and female FAI syndrome patients with major cam volumes had a global cam distribution. In conclusion, sex-specific cam location differences are present in FAI syndrome patients with negligible, mild, and moderate cam volumes, whereas major cam volumes were globally distributed in both male and female patients.

AliasNet: Alias artefact suppression network for accelerated phase-encode MRI.

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application.

Computed Tomographic Analysis of Position of Mandibular Canal and Mandibular Foramen in Patients with Mandibular Asymmetry.

Introduction: Mandibular osteotomies in facial asymmetry are complicated by the abnormal position and course of inferior alveolar nerve. This manuscript aims to evaluate the possible variations in the preoperative positions of mandibular canal and mandibular foramen in patients with mandibular asymmetry due to condylar hyperplasia or condylar hypoplasia. Materials & Methods: This study included 15 patients with mandibular asymmetry due to condylar hyperplasia or condylar hypoplasia for which bilateral sagittal split osteotomy (BSSO) was performed as a corrective procedure. The presence/absence and extent of postoperative neurosensory deficiency was recorded subjectively and objectively. The measurements were done using multiplanar reconstruction (MPR) of three-dimensional radiographic imaging and were compared to normal subjects. Discussion: The results revealed that the mandibular canal was closer to the buccal cortex on the affected side and the inferior border on both sides in the region of second molar in condylar hyperplasia. In condylar hypoplasia, the canal was nearer to the inferior border and the alveolar crest in relation to second and third molars respectively on the affected and contralateral sides.The mandibular foramen was also more superior to the occlusal plane on both sides in both condylar hyperplasia and hypoplasia. Conclusion: Based on the study outcomes, the authors propose that assessment of the positions of mandibular canal and mandibular foramen is crucial to avoid postoperative neurosensory deficiencies.

Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data.

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer's Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilisation. Adding to the fact that labelled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformers architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization in missing data scenarios. The model is trained and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with the state-of-the-art performance and further evaluated with The Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) dataset with missing data.

Strategies for ABO Incompatible Liver Transplantation.

Liver transplantation (LT) is a definitive treatment for the decompensated liver cirrhosis and fulminant liver failure. With limited availability of cadaveric liver allograft, ABO incompatible (ABOi) living donor liver transplantation (LDLT) plays an important part in further expansion of donor pool. Over the years, with the introduction of Rituximab and improving desensitisation protocol, outcomes of ABOi LDLT are on par with ABO compatible LT. However, ABOi LDLT protocol varies markedly from centre to centre. Intravenous Rituximab followed by plasmapheresis or immunoadsorption effectively reduce ABO isoagglutinins titre before transplant, thereby reducing the risk of antibody mediated rejection in the post-transplant period. Local infusion therapy and splenectomy are not used routinely at most of the centres in Rituximab era. Post-transplant immunosuppression usually consists of standard triple drug regime, and tacrolimus trough levels are targeted at higher level compared to ABO compatible LT. Introduction of newer therapies like Belatacept and Obinutuzumab hold promise to further improve outcomes and reduce the risk of antibody mediated rejection related complications. ABOi LT in emergency situations like acute liver failure and deceased donor LT is challenging due to limited time period for desensitisation protocol before transplant, and available evidence are still limited but encouraging.

Influence of CoFeB layer thickness on elastic parameters in CoFeB/MgO heterostructures.

The surface acoustic waves, i.e., surface phonons may have huge potential for future spintronic devices, if coupled to other waves (e.g., spin waves) or quasiparticles. In order to understand the coupling of acoustic phonons with the spin degree of freedom, especially in magnetic thin film-based heterostructures, one needs to investigate the properties of phonons in those heterostructures. This also allows us to determine the elastic properties of individual magnetic layers and the effective elastic parameters of the whole stacks. Here, we study frequency versus wavevector dispersion of thermally excited SAWs in CoFeB/MgO heterostructures with varying CoFeB thickness by employing Brillouin light spectroscopy. The experimental results are corroborated by finite element method-based simulations. From the best agreement of simulation results with the experiments, we find out the elastic tensor parameters for CoFeB layer. Additionally, we estimate the effective elastic parameters (elastic tensors, Young's modulus, Poisson's ratio) of the whole stacks for varying CoFeB thickness. Interestingly, the simulation results, either considering elastic parameters of individual layers or considering effective elastic parameters of whole stacks, show good agreement with the experimental results. These extracted elastic parameters will be very useful to understand the interaction of phonons with other quasiparticles.

Incidence of Japanese Encephalitis and Acute Encephalitis Syndrome Hospitalizations in the Medium-Endemic Region in Central India.

BACKGROUND: We estimated the incidence of Japanese encephalitis (JE) and acute encephalitis syndrome (AES) following routine immunization with the live-attenuated SA 14-14-2 JE vaccine. METHODS: We implemented enhanced surveillance of AES and JE hospitalizations in endemic districts in Maharashtra and Telangana States during 2015-2016 and 2018-2020. We estimated incidence and compared differences in the incidence of JE and AES between two states, and vaccinated and unvaccinated districts during two study periods. We also considered secondary data from public health services to understand long-term trends from 2007 to 2020. RESULTS: The annual AES incidence rate of 2.25 cases per 100,000 children in Maharashtra during 2018-2020 was significantly lower than 3.36 cases per 100,000 children during 2015-2016. The six JE-vaccinated districts in Maharashtra had significantly lower incidence rates during 2018-2020 (2.03, 95% CI 1.73-2.37) than in 2015-16 (3.26, 2.86-3.70). In addition, the incidence of both JE and AES in two unvaccinated districts was higher than in the vaccinated districts in Maharashtra. Telangana had a lower incidence of both JE and AES than Maharashtra. The AES incidence rate of 0.95 (0.77-1.17) during 2018-2020 in Telangana was significantly lower than 1.67 (1.41-1.97) during 2015-2016. CONCLUSIONS: The annual incidence rate of Japanese encephalitis was < 1 case per 100,000 children. It indicated accelerated control of Japanese encephalitis after routine immunization. However, the annual incidence of acute encephalitis syndrome was still > 1 case per 100,000 children. It highlights the need for improving surveillance and evaluating the impacts of vaccination.

PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI.

INTRODUCTION: Traumatic brain injury (TBI) is a heterogeneous condition with a broad spectrum of injury severity, pathophysiological processes and variable outcomes. For moderate-to-severe TBI survivors, recovery is often protracted and outcomes can range from total dependence to full recovery. Despite advances in medical treatment options, prognosis remains largely unchanged. The objective of this study is to develop a machine learning predictive model for neurological outcomes at 6 months in patients with a moderate-to-severe TBI, incorporating longitudinal clinical, multimodal neuroimaging and blood biomarker predictor variables. METHODS AND ANALYSIS: A prospective, observational, cohort study will enrol 300 patients with moderate-to-severe TBI from seven Australian hospitals over 3 years. Candidate predictors including demographic and general health variables, and longitudinal clinical, neuroimaging (CT and MRI), blood biomarker and patient-reported outcome measures will be collected at multiple time points within the acute phase of injury. The predictor variables will populate novel machine learning models to predict the Glasgow Outcome Scale Extended 6 months after injury. The study will also expand on current prognostic models by including novel blood biomarkers (circulating cell-free DNA), and the results of quantitative neuroimaging such as Quantitative Susceptibility Mapping and Dynamic Contrast Enhanced MRI as predictor variables. ETHICS AND DISSEMINATION: Ethical approval has been obtained by the Royal Brisbane and Women's Hospital Human Research Ethics Committee, Queensland. Participants or their substitute decision-maker/s will receive oral and written information about the study before providing written informed consent. Study findings will be disseminated by peer-review publications and presented at national and international conferences and clinical networks. TRIAL REGISTRATION NUMBER: ACTRN12620001360909.

Effectiveness of Japanese encephalitis vaccination among children in central India.

Japanese encephalitis (JE) disease among children continues in central India despite vaccination implemented in the routine immunization program. Therefore, we planned to estimate the JE vaccination effectiveness among children by undertaking a 1:2 individually-matched population-based case-control study from August 2018 to October 2020. The laboratory-confirmed JE cases aged 1-15 years were enrolled along with neighborhood controls without fever and encephalitis matched on the residence area, age and sex. The JE vaccination history was enquired from parents and verified independently from the vaccination cards available at home and records at health facilities. We enrolled 35 JE cases and 70 matched controls. The vaccination effectiveness of 86.7% (95% confidence interval [CI]: 30.8-94.7) was estimated on the per-protocol analysis of 31 case-control sets. The screening method provided an effectiveness of 89.5% (CI: 78.9-94.7) on using the population vaccination coverage of 90% reported earlier in the same area. In conclusion, JE vaccination offered a moderate level of protection among children in JE medium-endemic central India, similar to reports from high-endemic areas in India. The operational aspects of vaccination program implementation need to be evaluated to assess the impact of vaccination on the disease burden of JE in medium-endemic regions of India.

An assessment of admission pattern and treatment outcomes of neonates admitted in centers under facility-based newborn care program in Maharashtra, India.

Background: Newborn period is the most vulnerable period of life. As the rate of decline in neonatal mortality was slow compared to infant mortality rate, improved neonatal health-care services were started in existing facilities under facility-based newborn care program. Materials and Methods: A total of six special newborn care units at the district hospitals and 12 newborn stabilization units at the subdistrict/rural hospitals were included in the study. The outcome parameters were analyzed in the admitted babies using statistical tests. RESULTS: The mortality pattern was higher among low-birth-weight and preterm babies. The lower health-care centers had less neonatal deaths, as the neonates were referred. The common causes of admission were neonatal jaundice, respiratory distress syndrome, birth asphyxia, and so on. Conclusion: The focus on the high-risk babies needs to be strengthened, as these babies are at a high risk for neonatal mortality.

Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images.

Background: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients. Methods: The novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate 'healthy' femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients. The CamMorph pipeline, which generates patient-specific data within 5 min, was used to analyse multi-site clinical MR images of the hip to measure and assess cam morphology in male (n=56) and female (n=41) FAI patients. Results: There was excellent agreement between manual and CamMorph segmentations of the proximal femur as demonstrated by the mean Dice similarity index (DSI; 0.964±0.006), 95% Hausdorff distance (HD; 2.123±0.876 mm) and average surface distance (ASD; 0.539±0.189 mm) values. Compared to female FAI patients, male patients had a significantly larger median cam volume (969.22 vs. 272.97 mm3, U=240.0, P<0.001), mean surface area [657.36 vs. 306.93 mm2, t(95)=8.79, P<0.001], median maximum-height (3.66 vs. 2.15 mm, U=407.0, P<0.001) and median average-height (1.70 vs. 0.86 mm, U=380.0, P<0.001). Conclusions: The fully automated 3D CamMorph pipeline developed in the present study successfully segmented and measured cam morphology from clinical MR images of the hip in male and female patients with differing FAI severity and pathoanatomical characteristics.