Ventricular fibrillation causes more than 300,000 sudden deaths each year in the USA alone. In approximately 5-12% of these cases, there are no demonstrable cardiac or non-cardiac causes to account for the episode, which is therefore classified as idiopathic ventricular fibrillation (IVF). A distinct group of IVF patients has been found to present with a characteristic electrocardiographic pattern. Because of the small size of most pedigrees and the high incidence of sudden death, however, molecular genetic studies of IVF have not yet been done. Because IVF causes cardiac rhythm disturbance, we investigated whether malfunction of ion channels could cause the disorder by studying mutations in the cardiac sodium channel gene SCN5A. We have now identified a missense mutation, a splice-donor mutation, and a frameshift mutation in the coding region of SCN5A in three IVF families. We show that sodium channels with the missense mutation recover from inactivation more rapidly than normal and that the frameshift mutation causes the sodium channel to be non-functional. Our results indicate that mutations in cardiac ion-channel genes contribute to the risk of developing IVF.

Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second most deadly cancer by 2040, owing to the high incidence of metastatic disease and limited responses to treatment1,2. Less than half of all patients respond to the primary treatment for PDAC, chemotherapy3,4, and genetic alterations alone cannot explain this5. Diet is an environmental factor that can influence the response to therapies, but its role in PDAC is unclear. Here, using shotgun metagenomic sequencing and metabolomic screening, we show that the microbiota-derived tryptophan metabolite indole-3-acetic acid (3-IAA) is enriched in patients who respond to treatment. Faecal microbiota transplantation, short-term dietary manipulation of tryptophan and oral 3-IAA administration increase the efficacy of chemotherapy in humanized gnotobiotic mouse models of PDAC. Using a combination of loss- and gain-of-function experiments, we show that the efficacy of 3-IAA and chemotherapy is licensed by neutrophil-derived myeloperoxidase. Myeloperoxidase oxidizes 3-IAA, which in combination with chemotherapy induces a downregulation of the reactive oxygen species (ROS)-degrading enzymes glutathione peroxidase 3 and glutathione peroxidase 7. All of this results in the accumulation of ROS and the downregulation of autophagy in cancer cells, which compromises their metabolic fitness and, ultimately, their proliferation. In humans, we observed a significant correlation between the levels of 3-IAA and the efficacy of therapy in two independent PDAC cohorts. In summary, we identify a microbiota-derived metabolite that has clinical implications in the treatment of PDAC, and provide a motivation for considering nutritional interventions during the treatment of patients with cancer.

Spin angular momentum enables fundamental insights for topological matters, and practical implications for information devices. Exploiting the spin of carriers and waves is critical to achieving more controllable degrees of freedom and robust transport processes. Yet, due to the curl-free nature of longitudinal waves distinct from transverse electromagnetic waves, spin angular momenta of acoustic waves in solids and fluids have never been unveiled only until recently. Here, we demonstrate a metasurface waveguide for sound carrying non-zero acoustic spin with tight spin-momentum coupling, which can assist the suppression of backscattering when scatters fail to flip the acoustic spin. This is achieved by imposing a soft boundary of the π reflection phase, realized by comb-like metasurfaces. With the special-boundary-defined spin texture, the acoustic spin transports are experimentally manifested, such as the suppression of acoustic corner-scattering, the spin-selected acoustic router with spin-Hall-like effect, and the phase modulator with rotated acoustic spin.

Understanding unidirectional and topological wave phenomena requires the unveiling of intrinsic geometry and symmetry for wave dynamics. This is essential yet challenging for the flexible control of near-field evanescent waves, highly desirable in broad practical scenarios ranging from information communication to energy radiation. However, exploitations of near-field waves are limited by a lack of fundamental understanding about inherent near-field symmetry and directional coupling at sub-wavelengths, especially for longitudinal waves. Here, based on the acoustic wave platform, we show the efficient selective couplings enabled by near-field symmetry properties. Based on the inherent symmetry properties of three geometrically orthogonal vectors in near-field acoustics, we successfully realize acoustic Janus, Huygens, spin sources and quadrupole hybrid sources, respectively. Moreover, we experimentally demonstrate fertile symmetry selective directionality of those evanescent modes, supported by two opposite meta-surfaces. The symmetry properties of the near-field acoustic spin angular momenta are revealed by directly measuring local vectorial fields. Our findings advance the understanding of symmetries in near-field physics, supply feasible approaches for directional couplings, and pave the way for promising acoustic devices in the future.

BackgroundGraph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer’s disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning.MethodsWith the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling.ResultsWe found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular.ConclusionsThese results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.