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Vaccination is the most cost-effective means of preventing and even eliminating infectious diseases. However, adverse reactions after vaccination are inevitable. In addition to common vaccine-related adverse reactions, some rare but serious adverse reactions have been reported, including secondary immune thrombocytopenia (ITP). The measles-mumps-rubella (MMR) vaccine is currently the only vaccine for which a cause-effect relationship with immune thrombocytopenia has been demonstrated with an incidence of approximately 0.087-4 per 100,000 doses, and the complication is mostly observed in children. In addition, thrombocytopenia can be induced by coronavirus disease 2019 (COVID-19) vaccines following COVID-19 vaccination primarily occurs within a few weeks post-vaccination. The condition mostly occurs in elderly individuals with no sex differences. Its incidence is approximately 0.80 to 11.3 per million doses. Some patients have previously suffered from chronic ITP likely to develop exacerbation of ITP after COVID-19 vaccines, especially those who have undergone splenectomy or are being treated with5 medications. Based on clinical practice, first-line treatments for vaccine-associated thrombocytopenia are essentially limited to those used for primary ITP, including glucocorticoids and intravenous immunoglobulin (IVIg).

Prussian blue (PB) nanoparticles possess excellent physicochemical properties, including imaging features, robust photothermal conversion ability, catalytic activity, surface modifiability, effective drug loading, good stability, biocompatibility and biodegradability. With the advancement of nanotechnology, diverse PB-based nanoplatforms have been developed for biomedical applications. This review systematically summarized recent studies on PB-based nanoplatforms in the treatment of tumor (photothermal therapy, photodynamic therapy, chemotherapy, immunotherapy, theranostics, etc.), cardiovascular and cerebrovascular diseases (restenosis, atherosclerosis, thrombosis, ischemic stroke, etc.), bacterial infections (photothermal sterilization, biofilms disruption, etc.), and other inflammation-related diseases (liver injury, acute pancreatitis, inflammatory bowel disease, osteoarthritis, etc.). Finally, the existing challenges associated with current studies are discussed, and the future possible research and application directions for PB-based nanoplatforms are proposed, providing paradigms for subsequent development.

Finite element modeling is a popular method for predicting kinematics and kinetics in spine biomechanics. With the advancement of powerful computational equipment, more detailed finite element models have been developed for the various spine segments. In this study, five detailed finite element models of the cervical spine are developed and validated. The geometric boundaries of the vertebrae are determined from computed tomography (CT) scans of five female subjects. The models include the C2–C7 vertebrae, intervertebral discs, nuclei, endplates, and five major ligaments (anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), ligamentum flavum (LF), interspinous ligament (ISL), and capsular ligament (CL)). The ligaments follow nonlinear stress–strain curves whereas all other parts adopt linear material properties. All the material properties are taken from existing literature. The mesh convergence test is performed under flexion/extension. For flexion/extension motion, a pure moment is applied at the top surface of the odontoid process of the C2 vertebra while nodes at the bottom surface of the C7 vertebra are fixed in all directions. The models are extensively validated in flexion/extension, lateral bending, and axial rotation against experimental and finite element studies in the literature. Intervertebral rotation and range of motion are studied under different loading conditions found in the literature. This research also investigates intersubject variability for the cervical spine among five finite element models from five different subjects. Predicted angular displacements and ranges of motion of the current models are consistent with the literature. The validated models are expected to be applicable to simulate neck-related trauma like whiplash and high-g acceleration, among other scenarios.

Rechargeable Li metal batteries have attracted widespread attention because of their high energy density and long cycling life. However, its further development is limited by dendritic Li growth and safety...

Neural reflex is hypothesized as a regulating step in spine stabilizing system. However, neural reflex control is still in its infancy to consider in the previous finite element analysis of head-neck system for various applications. The purpose of this study is to investigate the influences of neural reflex control on neck biomechanical responses, then provide a new way to achieve an accurate biomechanical analysis for head-neck system with a finite element model.A new FE head-neck model with detailed active muscles and spinal cord modeling was established and globally validated at multi-levels. Then, it was coupled with our previously developed neuromuscular head-neck model to analyze the effects of vestibular and proprioceptive reflexes on biomechanical responses of head-neck system in a typical spinal injury loading condition (whiplash). The obtained effects were further analyzed by comparing a review of epidemiologic data on cervical spine injury situations.The results showed that the active model (AM) with neural reflex control obviously presented both rational head-neck kinematics and tissue injury risk referring to the previous experimental and epidemiologic studies, when compared with the passive model (PM) without it. Tissue load concentration locations as well as stress/strain levels were both changed due to the muscle activation forces caused by neural reflex control during the whole loading process. For the bony structures, the AM showed a peak stress level accounting for only about 25% of the PM. For the discs, the stress concentrated location was transferred from C2-C6 in the PM to C4-C6 in the AM. For the spinal cord, the strain concentrated locations were transferred from C1 segment to around C4 segment when the effects of neural reflex control were implemented, while the gray matter and white matter peak strains were reduced to 1/3 and 1/2 of the PM, respectively. All these were well correlated with epidemiological studies on clinical cervical spine injuries.In summary, the present work demonstrated necessity of considering neural reflex in FE analysis of a head-neck system as well as our model biofidelity. Overall results also verified the previous hypothesis and further quantitatively indicated that the muscle activation caused by neural reflex is providing a protection for the neck in impact loading by decreasing the strain level and changing the possible injury to lower spinal cord level to reduce injury severity.

Phagocytosis is required for the optimal efficacy of many approved and promising therapeutic antibodies for various malignancies. However, the factors that determine the response to therapies that rely on phagocytosis remain largely elusive. Here, we demonstrate that mitochondrial fission in macrophages induced by multiple antibodies is essential for phagocytosis of live tumor cells. Tumor cells resistant to phagocytosis inhibit mitochondrial fission of macrophages by overexpressing glutamine-fructose-6-phosphate transaminase 2 (GFPT2), which can be targeted to improve antibody efficacy. Mechanistically, increased cytosolic calcium by mitochondrial fission abrogates the phase transition of the Wiskott-Aldrich syndrome protein (WASP)-Wiskott-Aldrich syndrome interacting protein (WIP) complex and enables protein kinase C-θ (PKC-θ) to phosphorylate WIP during phagocytosis. GFPT2-mediated excessive use of glutamine by tumor cells impairs mitochondrial fission and prevents access of PKC-θ to compartmentalized WIP in macrophages. Our data suggest that mitochondrial dynamics dictate the phase transition of the phagocytic machinery and identify GFPT2 as a potential target to improve antibody therapy.

Electricity theft causes great economic losses and potential safety hazards to society. With the development of smart meters and artificial intelligence, data-driven methods have gradually been applied to electricity theft detection, which improves efficiency and reduces costs. At present, data-driven electricity theft detection methods are dedicated to improving accuracy. To achieve higher accuracy, this paper proposes a hybrid method combining adaptive boosting algorithm (AdaBoost) and convolutional neural networks (CNN) for electricity theft detection. In the proposed method, multiple CNN-based classifiers are trained to extract different features from the electricity consumption data. Then, AdaBoost combines these classifiers into a strong one according to their performance. The experimental results based on the smart energy data from the Irish Smart Energy Trial show the hybrid classifier has better performance than other conventional data-driven methods in electricity theft detection.

Electricity theft decreases electricity revenues and brings risks to power usage’s safety, which has been increasingly challenging nowadays. As the mainstream in the relevant studies, the state-of-the-art data-driven approaches mainly detect electricity theft events from the perspective of the correlations between different daily or weekly loads, which is relatively inadequate to extract features from hours or more of fine-grained temporal data. In view of the above deficiencies, we propose a novel electricity theft detection scheme based on text convolutional neural networks (TextCNN). Specifically, we convert electricity consumption measurements over a horizon of interest into a two-dimensional time-series containing the intraday electricity features. Based on the data structure, the proposed method can accurately capture various periodical features of electricity consumption. Moreover, a data augmentation method is proposed to cope with the imbalance of electricity theft data. Extensive experimental results based on realistic Chinese and Irish datasets indicate that the proposed model achieves a better performance compared with other existing methods.

The ever-increasing proportion of HVDC and wind power has a great impact on the reliability of the receiving-end power system. It is essential to consider the HVDC transmission system power flow when assessing the reliability of the receiving-end power system integrated with wind power. Considering the symmetry characteristic of HVDC system, a novel HVDC system reliability model, which uses the number of available converter units for equivalent failure, is used to assess the reliability to reduce the complexity of the calculation. The uncertainty of wind power will be modelled based on the wind speed which follows to Weibull distribution. Then, the AC-DC coupling equation considering converter units and the optimal power flow model of AC-DC coupling system are introduced. Moreover, based on the optimal power flow model of AC-DC coupling system, the EENS is calculated by Monte-Carlo method. Furthermore, the reliability of receiving-end power system and nodal reliability is assessed with five different scenarios in a modified IEEE-RTS-24 system.

Background: Adjacent segment disease (ASD) is a well-known complication after interbody fusion. Pedicle screw-rod revision possessed sufficient strength and rigidity. However, is a surgical segment with rigid fixation necessary for ASD reoperation? This study aimed to investigate the biomechanical effect on LLIF with different instrumentation for ASD treatment.Methods: A validated L2~5 finite element (FE) model was modified to simulate. ASD was considered the level cranial to the upper-instrumented segment(L3/4). Bonegraft fusion in LLIF with bilateral pedicle screw fixation (BPS) has occurred at the L4/5. The ASD segment for each group was underwent a) LLIF + posterior extension of BPS, b) PLIF + posterior extension of BPS, c) LLIF + lateral screw, d) Stand-alone LLIF. L3/4 Range of motion (ROM), interbody cage stress and strain, screw-boneinterface stress, cage-endplate interface stress, and L2/3 nucleus pulposus of intradiscal pressure (NP-IDP) analysis were calculated for the comparisons among fourmodels.Results: All reconstructive models displayed decreased motion at L3/4. In each loading condition, difference was not significant between model a and b, which providedthe maximum ROM reduction (73.8% to 97.7%, 68.3% to 98.4%, respectively). Model c also provided a significant ROM reduction (64.9% to 77.5%). Model d provided a minimal restriction of ROM (18.3% to 90.1%), which exceeded that of model a by 13.1 times in flexion-extension, 10.3 times in lateral bending and 4.8 times in rotation. Model b generated greater cage stress than other models, particularly in flexion. The maximum displacement of the cage and the peak stress of cage-endplate interface were found to be the highest in the model d in all loading conditions. For the screw bone interface, the stress was significantly greater in lateral instrumentation than that of posterior instrumentation.Conclusions: Stand-alone LLIF is likely to have limited stability, particularly in lateral bending and axial rotation. Posterior extension of BPS can provide the reliablystability and excellently protective effect on instrumentation and endplate. However, LLIF with in situ screw may be an alternative for ASD reoperation.

T The fovea (centre of the retinal macula region) is responsible for central vision. Conditions which can affect the macula region in particular include AMD and macular oedema, which lead to a direct loss in visual acuity due to the effect on central vision. The detection of the fovea is an important step in the assessment of retinal images for pathologies relating to these visual conditions. In this paper, we propose a method to automatically detect the fovea in retinal fundus images. The method makes use of optic disc detection as a starting point. Using information from an extensive database, typical locations of the fovea are used to define search regions for subsequent analysis. We make use of a combination of the image characteristics of the macular region with noise and vessel removal for fovea detection. The proposed method is tested on a large database of 750 retinal fundus images, achieving an accuracy of 86.53%.

The sensitivity characteristics of the sensors based on high-birefringence Sagnac interferometer are theoretically analyzed and experimentally verified. We analyze the sensitivity of the sensor in terms of the frequency instead of the wavelength of the light and the sensitivity can be improved by reducing the length of high birefringence fiber. The strain sensing experiments are carried out and the experimental results agree well with the theoretical analysis. Published Version

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. The disease is highly associated with age, and becoming increasingly prevalent in our aging societies. Drusen is a pathological feature that is well-associated with AMD. In this paper, we present a method of detecting drusen in retinal fundus images. The method first determines the location of the macula, which is used as a landmark for a clinical drusen grading overlay. Subsequently, regions of drusen are identified though a maximal region-based pixel intensity approach via RGB and HSV channels. Methods of reducing the effect of retinal and choroidal vessels are also described. The system is tested on a sample set of 16 fundus images from a clinical study, with half having drusen. Experiments on the results show a sensitivity and specificity of 0.75 on the test image set.