MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

A novel PCDscore based on programmed cell death-related genes can effectively predict prognosis and therapy responses of colon adenocarcinoma

Emerging evidence suggests a correlation between oncogenesis and programmed cell death (PCD). However, comprehensive studies that incorporate all identified PCD-related genes to guide colon adenocarcinoma (COAD) prognosis and precision treatment strategies are lacking. In this study, a series of bioinformatics analyses were comprehensively conducted using data from the TCGA-COAD, GSE17538, and GSE39582 cohorts. A total of 21 PCD-associated prognostic genes were identified through univariate Cox analysis. LASSO and multivariate Cox methods were employed to establish a prognostic gene signature (ALOX12, HSPA1A, IL13, MID2, RFFL, and SLC39A8) and the corresponding scoring system, termed PCDscore, which exhibited robust predictive ability. The ssGSEA and ESTIMATE algorithms were utilized to evaluate the tumor microenvironment of COAD. The high PCDscore group demonstrated a poorer prognosis, characterized by lower CD4+ T cell infiltration and a higher stromal score. In contrast, the low PCDscore group exhibited sensitivity to common chemotherapy drugs such as Cisplatin and 5-Fluorouracil. Single-cell sequencing analysis further revealed that the high-PCDscore group displayed a lower proportion of CD4+ T cells. Colorectal cancer samples from the years 2013-2017 were employed to validate the PCDscore, while those from 2018 to 2019 served as a temporal external validation set for the PCDscore. In vitro experimental results indicated that the overexpression of SLC39A8 inhibited the proliferation and invasion of colorectal cancer cells. The study developed a novel PCDscore system based on the analysis of genes related to all identified PCD types, providing valuable insights into clinical prognosis and drug sensitivity for patients with COAD.

Robust optical edge detection enabled by a twisted reflective q-plate

Optical edge detection significantly reduces the image information load and is highly sought after in instant image processing. Robustness to the wavelength and polarization of light as well as mechanical vibration is a key requirement for practical applications. Here, a robust optical edge detector is proposed and demonstrated based on a reflective twisted liquid crystal q-plate. The device is composed of a mirror and a 1.46-μm-thick liquid crystal layer with a twist angle of 69.2°. The backtracking of the light inside the twisted medium forms a mirror symmetric twisted design and thus leads to a broadband self-compensated spiral phase modulation. By this means, an optical edge detector with excellent wavelength and polarization independence is presented for both coherent and partially coherent light sources. Additionally, the reflective design makes the system more compact and stable. This work supplies a practical design for robust optical edge detection, which may upgrade existing image processing techniques.

Correlating the Microstructure and Current Density of the Li/Garnet Interface

Solid-state lithium batteries hold great promise for next-generation energy storage systems. However, the formation of lithium filaments within the solid electrolyte remains a critical challenge. In this study, we investigate the crucial role of morphology in determining the resistance of garnet-type electrolytes to lithium filaments. By proposing a new test method, namely, cyclic linear sweep voltammetry, we can effectively evaluate the electrolyte resistance against lithium filaments. Our findings reveal a strong correlation between the microscopic morphology of the solid electrolyte and its resistance to lithium filaments. Samples with reduced pores and multiple grain boundaries demonstrate remarkable performance, achieving a critical current density of up to 3.2 mA cm-2 and excellent long-term cycling stability. Kelvin probe force microscopy and finite element method simulation results shed light on the impact of grain boundaries and electrolyte pores on lithium-ion transport and filament propagation. To inhibit lithium penetration, minimizing pores and achieving a uniform morphology with small grains and plenty of grain boundaries are essential.

Context Label Learning: Improving Background Class Representations in Semantic Segmentation

Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over background logit activations may shift across the decision boundary, leading to systematic over-segmentation across different datasets and tasks. In this study, we propose context label learning (CoLab) to improve the context representations by decomposing the background class into several subclasses. Specifically, we train an auxiliary network as a task generator, along with the primary segmentation model, to automatically generate context labels that positively affect the ROI segmentation accuracy. Extensive experiments are conducted on several challenging segmentation tasks and datasets. The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy. Code is available at https://github.com/ZerojumpLine/CoLab.

Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation

Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. In this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation scenarios: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-site prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).

Self-Supervised Learning for Few-Shot Medical Image Segmentation

Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments.

Chiral RuII -PtII Complexes Inducing Telomere Dysfunction against Cisplatin-Resistant Cancer Cells

The application of G-quadruplex stabilizers presents a promising anticancer strategy. However, the molecular crowding conditions within cells diminish the potency of current G-quadruplex stabilizers. Herein, chiral Ru^II -Pt^II dinuclear complexes were developed as highly potent G-quadruplex stabilizers even under challenging molecular crowding conditions. The compounds were encapsulated with biotin-functionalized DNA cages to enhance sub-cellular localization and provide cancer selectivity. The nanoparticles were able to efficiently inhibit the endogenous activities of telomerase in cisplatin-resistant cancer cells and cause cell death by apoptosis. The nanomaterials demonstrated high antitumor activity towards cisplatin-resistant tumor cells as well as tumor-bearing mice. To the best of our knowledge, this study presents the first example of a Ru^II -Pt^II dinuclear complex as a G-quadruplex stabilizer with an anti-cancer effect towards drug-resistant tumors inside an animal model.

Supramolecular Assembly of An Organoplatinum(II) Complex with Ratiometric Dual Emission for Two-Photon Bioimaging

The organoplatinum(II) complex [Pt(C^N^N)(Cl)] (C^N^N=5,6-diphenyl-2,2'-bipyridine, Pt1) can assemble into nanoaggregates via π-π stacking and complementary hydrogen bonds, rather than Pt-Pt interactions. Pt1 exhibits ratiometric dual emission, including rare blue emission (λ_em =445 nm) and assembly-induced yellow emission (λ_em =573 nm), under one- and two-photon excitation. Pt1 displays blue emission in cells with an intact membrane due to its low cellular uptake. In cells where the membrane is disrupted, uptake of the complex is increased and at higher concentrations yellow emission is observed. The ratio of yellow to blue emission shows a linear relationship to the loss of cell membrane integrity. Pt1 is, to our knowledge, the first example of an assembly-induced two-photon ratiometric dual emission organoplatinum complex. The excellent and unique characteristics of the complex enabled its use for the tracking of cell apoptosis, necrosis, and the inflammation process in zebrafish.

Synthesis, characterization and anticancer mechanism studies of fluorinated cyclometalated ruthenium(ii) complexes

The drug-resistance of cancer cells has become a major obstacle to the development of clinical drugs for chemotherapy. In order to overcome cisplatin-resistance, seven cyclometalated ruthenium(ii) complexes were synthesized with a varying degree of fluorine substitution, for use as anticancer agents. A cytotoxicity assay testified that the complexes possessed a more cytotoxic effect than cisplatin towards the cisplatin-resistant cell line A549R. The number of fluorine atoms regulated the lipophilicity of the complexes, but the relationship was not linear. Ru1 containing one fluorine atom had the highest lipophilicity and the best therapeutic effect. The complexes enter cells through an energy-dependent pathway and then localize in the nuclei and mitochondria. The complexes induced nuclear dysfunction by the inhibition of DNA replication as well as mitochondrial dysfunction by the loss of membrane potential. The damage to these vital organelles leads to cell apoptosis via the caspase 3/7 pathway. Our results indicated that the modulation of the number of fluorine atoms in therapeutic agents can have a profound effect and Ru1 is a complex with a high potential as a drug for the treatment of cisplatin-resistant cancer.

Correction: Synthesis, characterization and anticancer mechanism studies of fluorinated cyclometalated ruthenium(ii) complexes

Correction for ‘Synthesis, characterization and anticancer mechanism studies of fluorinated cyclometalated ruthenium(ii) complexes’ by Ya Wen et al., Dalton Trans., 2020, DOI: 10.1039/d0dt01412e.

Mitochondrial DNA targeting and impairment by a dinuclear Ir–Pt complex that overcomes cisplatin resistance

A dinuclear complex [(ppy)Ir(tpy)PtCl]²⁺ (Ir–Pt) can exhibit strong antitumor activity towards cisplatin-resistant cancer cells and induce cell necrosis via mtDNA damage and mitochondrial dysfunction.

The stepwise photodamage of organelles by two-photon luminescent ruthenium(ii) photosensitizers

Ru(ii) polypyridyl complexes, containing a morpholine moiety, and possessing two-photon absorption properties and pH dependent singlet oxygen production were used for stepwise lysosomes-to-mitochondria photodamage of cancer cells.

A mitochondria-targeting dinuclear Ir-Ru complex as a synergistic photoactivated chemotherapy and photodynamic therapy agent against cisplatin-resistant tumour cells

A mitochondria-targeting hetero-binuclear Ir(iii)-Ru(ii) complex was developed as a photoactivated chemotherapy (PACT) and photodynamic therapy (PDT) bifunctional agent to achieve a synergistic effective therapeutic outcome for the therapy of cisplatin-resistant tumour cells.

Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer

BACKGROUND

Microsatellite instability (MSI) is a biomarker for response to immune checkpoint inhibitors (ICPIs). PD-1 inhibitors in metastatic colorectal carcinoma (mCRC) with MSI-high (MSI-H) have demonstrated a high disease control rate and favorable progression-free survival (PFS); however, reported response rates to pembrolizumab and nivolumab are variable and often <50%, suggesting that additional predictive biomarkers are needed.

METHODS

Clinicopathologic data were collected from patients with MSI-H mCRC confirmed by hybrid capture-based next-generation sequencing (NGS) treated with PD-1/L1 inhibitors at five institutes. Tumor mutational burden (TMB) was determined on 0.8-1.1 Mb of sequenced DNA and reported as mutations/Mb. Potential biomarkers of response and time to progression were analyzed by univariate and multivariate analyses. Once TMB was confirmed as a predictive biomarker, a larger dataset of 18 140 unique CRC patients was analyzed to define the relevance of the identified TMB cut-point.

RESULTS

A total of 22 patients were treated with PD-1/L1 inhibitors including 19 with pembrolizumab monotherapy. Among tested variables, TMB showed the strongest association with objective response (OR; P < 0.001) and PFS, by univariate (P < 0.001) and multivariate analysis (P < 0.01). Using log-rank statistics, the optimal predictive cut-point for TMB was estimated between 37 and 41 mutations/Mb. All 13 TMBhigh cases responded, while 6/9 TMBlow cases had progressive disease. The median PFS for TMBhigh has not been reached (median follow-up >18 months) while the median PFS for TMBlow was 2 months. A TMB of 37.4 mutations/Mb in a large MSI-H mCRC population (821/18, 140 cases; 4.5%) evaluated by NGS corresponded to the 35th percentile cut-point.

CONCLUSIONS

TMB appears to be an important independent biomarker within MSI-H mCRC to stratify patients for likelihood of response to ICPIs. If validated in prospective studies, TMB may play an important role in guiding the sequencing and/or combinations of ICPIs in MSI-H mCRC.

Parallel beamlet dose calculation via beamlet contexts in a distributed multi-GPU framework

PURPOSE

Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model-based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under-utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi-GPU computational nodes.

METHODS

The proposed context-based transformation separates beamlet-local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed-cone convolution superposition procedure, producing the set of beamlet-specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager-worker architecture is constructed around the context-based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context-based approach compared to Monte Carlo and a reference CPU-CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet-sequential GPU-CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.

RESULTS

The dosimetric accuracy of the context-based method displays <1.35% and 2.35% average error from the existing serialized CPU-CCCS algorithm and Monte Carlo simulation for beamlet-specific PDDs in water and slab phantoms, respectively. The context-based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet-sequential GPU-CCCS method in the tested configurations. The context-based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization.

CONCLUSIONS

The context-based approach demonstrates a new expectation of performance for beamlet-based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large-scale treatment planning problems - such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets - in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large-scale treatment planning tasks and clinical workflows.

Biofriendly, Stretchable, and Reusable Hydrogel Electronics as Wearable Force Sensors

The ever-growing overlap between stretchable electronic devices and wearable healthcare applications is igniting the discovery of novel biocompatible and skin-like materials for human-friendly stretchable electronics fabrication. Amongst all potential candidates, hydrogels with excellent biocompatibility and mechanical features close to human tissues are constituting a promising troop for realizing healthcare-oriented electronic functionalities. In this work, based on biocompatible and stretchable hydrogels, a simple paradigm to prototype stretchable electronics with an embedded three-dimensional (3D) helical conductive layout is proposed. Thanks to the 3D helical structure, the hydrogel electronics present satisfactory mechanical and electrical robustness under stretch. In addition, reusability of stretchable electronics is realized with the proposed scenario benefiting from the swelling property of hydrogel. Although losing water would induce structure shrinkage of the hydrogel network and further undermine the function of hydrogel in various applications, the worn-out hydrogel electronics can be reused by simply casting it in water. Through such a rehydration procedure, the dehydrated hydrogel can absorb water from the surrounding and then the hydrogel electronics can achieve resilience in mechanical stretchability and electronic functionality. Also, the ability to reflect pressure and strain changes has revealed the hydrogel electronics to be promising for advanced wearable sensing applications.

A mitochondria-targeting hetero-binuclear Ir(iii)–Pt(ii) complex induces necrosis in cisplatin-resistant tumor cells

A hetero-binuclear Ir(iii)–Pt(ii) complex can selectively accumulate in the mitochondria to induce mitochondrial DNA (mtDNA) damage and evoke cellular events consistent with necrosis in A549R cells.

[Effect of a new ROCK inhibitor thiazovivin on the morphology and function of human corneal endothelial cells]

OBJECTIVE

To evaluate the effect of thiazovivin, a novel ROCK inhibitor, on the morphology and function of human corneal endothelial cells(HCECs).

METHODS

The primary HCECs were identified by light microscopy and immunofluorescence staining of neuron-specific enolase. To screen the optimal concentration and action time of thiazovivin for maintaining the morphology and function of primary HCECs, Na (+)/K (+)-ATPase and N-cadherin were chosen as indicators, and the morphology and function of HCECs in various concentrations(0 μmol/L, 2 μmol/L, 4 μmol/L, and 6 μmol/L)for different durations(24 h and 48 h)were examined by immunofluorescence experiments. The effect of thiazovivin on the expression of ROCK was investigated by immunofluorescence and Western blot.

RESULTS

The primary HCECs cultured were hexagonal, closely packed, homogeneously and obviously stained by neuron-specific enolase. The immunofluorescence staining of Na(+)/K(+)-ATPase showed that when the primary HCECs cultured with various concentrations of thiazovivin(0, 2, 4, 6 μmol/L)for 24 h, the fluorescence were obvious, and the average absorbance values(A)were 1.27±0.08, 3.72±0.17, 21.07±4.67, 3.69±0.34, respectively. And the immunofluorescence staining of N-cadherin revealed that when the primary HCECs treated with 4 μmol/L thiazovivin for 24 h, the cell boundary was clear and the structure of the cells was intact. While the treating time of thiazovivin(4 μmol/L)on HCECs extended to 48 h, the immunofluorescence staining of Na(+)/K(+)-ATPase and N-cadherin showed that compared to HCECs treated with thiazovivin(4 μmol/L)for 24 h, the fluorescence intensity did not change significantly, but the cells arranged slightly untidy. In addition, the immunofluorescence staining of ROCK was weakened and the expression of ROCK was reduced by thiazovivin. Thiazovivin was effective for protecting the morphology and function of HCECs. An optimal improvement in the morphology, connection and function of HCECs was found when the primary HCECs were cultured with 4 μmol/L thiazovivin for 24 h. Moreover, the expression of ROCK protein could be significantly inhibited by thiazovivin. (Chin J Ophthalmol, 2016, 52: 686-692).

Cyclometalated Iridium(III) Complexes as AIE Phosphorescent Probes for Real-Time Monitoring of Mitophagy in Living Cells

Mitophagy, which is a special autophagy that removes damaging mitochondria to maintain sufficient healthy mitochondria, provides an alternative path for addressing dysfunctional mitochondria and avoiding cellular death. In the present study, by coupling the triphenylamine group with 2-phenylimidazo[4,5-f][1,10]phenanthroline derivatives, we synthesized five Ir(III) complexes with an AIE property that are expected to fulfill requirements for real-time monitoring of mitophagy. Ir1-Ir5 were exploited to image mitochondria with a short incubation time by confocal microscopy and inductive coupled plasma-mass spectrometry (ICP-MS). Due to aggregation-induced emission (AIE), Ir1-Ir5 exhibited excellent photostability compared to MitoTracker Green (MTG). Moreover, Ir1-Ir5 manifested satisfactory photostability in the mitochondrial physiological pH range. In addition, the uptake mechanism of Ir1 was investigated using confocal microscopy and flow cytometry analysis. Finally, using both Ir1 and LysoTracker Green, we were able to achieve real-time monitoring of mitophagy.

Extreme Sensitivity of Room-Temperature Photoelectric Effect for Terahertz Detection

Extreme sensitivity of room-temperature photoelectric effect for terahertz (THz) detection is demonstrated by generating extra carriers in an electromagnetic induced well located at the semiconductor, using a wrapped metal-semiconductor-metal configuration. The excellent performance achieved with THz detectors shows great potential to open avenues for THz detection.

Liquid on Paper: Rapid Prototyping of Soft Functional Components for Paper Electronics

This paper describes a novel approach to fabricate paper-based electric circuits consisting of a paper matrix embedded with three-dimensional (3D) microchannels and liquid metal. Leveraging the high electric conductivity and good flowability of liquid metal, and metallophobic property of paper, it is possible to keep electric and mechanical functionality of the electric circuit even after a thousand cycles of deformation. Embedding liquid metal into paper matrix is a promising method to rapidly fabricate low-cost, disposable, and soft electric circuits for electronics. As a demonstration, we designed a programmable displacement transducer and applied it as variable resistors and pressure sensors. The unique metallophobic property, combined with softness, low cost and light weight, makes paper an attractive alternative to other materials in which liquid metal are currently embedded.

Z-Spectrum analysis provides proton environment data (ZAPPED): a new two-pool technique for human gray and white matter

A new technique - Z-spectrum Analysis Provides Proton Environment Data (ZAPPED) - was used to map cross-relaxing free and restricted protons in nine healthy subjects plus two brain tumor patients at 3T. First, MT data were acquired over a wide symmetric range of frequency offsets, and then a trio of quantitative biomarkers, i.e., the apparent spin-spin relaxation times (T2,f, T2,r) in both free and restricted proton pools as well as the restricted pool fraction Fr, were mapped by fitting the measured Z-spectra to a simple two-Lorentzian compartment model on a voxel-by-voxel basis. The mean restricted exchangeable proton fraction, Fr, was found to be 0.17 in gray matter (GM) and 0.28 in white matter (WM) in healthy subjects. Corresponding mean values for apparent spin-spin relaxation times were 785 µs (T2,f) and 17.7 µs (T2,r) in GM, 672 µs (T2,f) and 23.4 µs (T2,r) in WM. The percentages of Ff and Fr in GM are similar for all ages, whereas Fr shows a tendency to decrease with age in WM among healthy subjects. The patient ZAPPED images show higher contrast between tumor and normal tissues than traditional T2-weighted and T1-weighted images. The ZAPPED method provides a simple phenomenological approach to estimating fractions and apparent T2 values of free and restricted MT-active protons, and it may offer clinical useful information.

Plasma FGF23 levels and heart rate variability in patients with stage 5 CKD

Fibroblast growth factor 23(FGF23) is a bone-derived hormone which regulates mineral homeostasis but may also have a role in cardiovascular disease. Here, we found that higher plasma FGF23 was independently associated with decreased heart rate variability in stage 5 CKD patients and parathyroidectomy may reverse these abnormal indicators.

INTRODUCTION

Lower heart rate variability (HRV) in patients with chronic kidney disease (CKD) compared with healthy controls is associated with increased risk of cardiovascular disease (CVD). Higher levels of plasma FGF23 also predict higher risk of CVD. Here, we aimed to evaluate the relationship between plasma FGF23 levels and HRV in patients with stage 5 CKD and to investigate longitudinal changes of them together with the correlation between their changes in two severe secondary hyperparathyroidism (SHPT) subgroups with successful parathyroidectomy (PTX) and persistent SHPT.

METHODS

This cross-sectional study included 100 stage 5 CKD patients, 78 controls, and a prospective study in two PTX subgroups classified as successful PTX (n = 24) and persistent SHPT (n = 4) follow-up. Blood examination and 24-h Holter monitoring for HRV were measured.

RESULTS

Most HRV indices were lower in stage 5 CKD patients than in healthy controls, and plasma FGF23 levels were higher. In multivariate stepwise regression models, levels of plasma FGF23 and serum parathyroid hormone (PTH) were correlated with HRV. The successful PTX subgroup had significant improvements over baseline in HRV indices. Persistent SHPT subgroup had numerically similar changes in HRV indices. However, plasma FGF23 levels decreased in both subgroups.

CONCLUSIONS

Plasma FGF23 levels were higher in CKD patients than in controls, much higher in patients with severe SHPT. FGF23 was independently associated with decreased HRV in stage 5 CKD. Successful PTX may reverse these abnormal indicators and contribute to decreases in the risk of cardiovascular disease.