O-GlcNAcylation regulates the stability of transferrin receptor (TFRC) to control the ferroptosis in hepatocellular carcinoma cells.

Ferroptosis is an iron-dependent programmed cell death (PCD) enforced by lipid peroxidation accumulation. Transferrin receptor (TFRC), one of the signature proteins of ferroptosis, is abundantly expressed in hepatocellular carcinoma (HCC). However, post-translational modification (PTM) of TFRC and the underlying mechanisms for ferroptosis regulation remain less understood. In this study, we found that TFRC undergoes O-GlcNAcylation, influencing Erastin-induced ferroptosis sensitivity in hepatocytes. Further mechanistic studies found that Erastin can trigger de-O-GlcNAcylation of TFRC at serine 687 (Ser687), which diminishes the binding of ubiquitin E3 ligase membrane-associated RING-CH8 (MARCH8) and decreases polyubiquitination on lysine 665 (Lys665), thereby enhancing TFRC stability that favors labile iron accumulation. Therefore, our findings report O-GlcNAcylation on an important regulatory protein of ferroptosis and reveal an intriguing mechanism by which HCC ferroptosis is controlled by an iron metabolism pathway.

Role of epidural fat in the local milieu: what we know and what we don't.

PURPOSE: Traditionally, the epidural fat (EF) is known as a physical buffer for the dural sac against the force and a lubricant facilitating the relative motion of the latter on the osseous spine. Along with the development of the studies on EF, controversies still exist on vital questions, such as the underlying mechanism of the spinal epidural lipomatosis. Meanwhile, the scattered and fragmented researches hinder the global insight into the seemingly dispensable tissue. METHODS: Herein, we reviewed literature on the EF and its derivatives to elucidate the dynamic change and complex function of EF in the local milieu, especially at the pathophysiological conditions. We start with an introduction to EF and the current pathogenic landscape, emphasizing the interlink between the EF and adjacent structures. We generally categorize the major pathological changes of the EF into hypertrophy, atrophy, and inflammation. RESULTS AND CONCLUSIONS: It is acknowledged that not only the EF (or its cellular components) may be influenced by various endogenic/exogenic and focal/systematic stimuli, but the adjacent structures can also in turn be affected by the EF, which may be a hidden pathogenic clue for specific spinal disease. Meanwhile, the unrevealed sections, which are also the directions the future research, are proposed according to the objective result and rational inference. Further effort should be taken to reveal the underlying mechanism and develop novel therapeutic pathways for the relevant diseases.

Whole Genome Sequence-Based Analyses of Drug Resistance Characteristics, Genetic Diversity, and Transmission Dynamics of Drug-Resistant Mycobacterium tuberculosis in Urumqi City.

Objective: This study aims to analyze the drug resistance spectrum, genetic diversity, and transmission dynamics to provide a basis for the prevention and control of drug-resistant (DR) tuberculosis (TB) epidemics. Methods: This retrospective study is based on routine national drug resistance surveillance. The demographic, epidemiological, and clinical information on DR-TB patients from 2016 to 2021 was collected and used for phenotypic drug susceptibility testing and whole-genome sequencing. Results: It was indicated that L2.2.1 was the dominant lineage in Urumqi. The drug resistance spectrum in Urumqi was narrow, which means more drug combinations can be used for clinical treatment. Furthermore, mutations identification of drug-resistance gene katG, rpoB, embA/B, rrs, rpsL, eis, gyrA/B, folC and tryA are important for clinical drug use. However, mutations in cross-resistance genes rrs have limited guidance for clinical selection of KM, CPM and AK. Moreover, there is an increased risk of cluster transmission of DR-TB, and the difference in clustering rate among L2, L3, and L4 was not statistically significant (χ2 = 2.6410, p = 0.2670). Conclusion: In the Urumqi, DR-TB has a complex prevalence state, a narrow drug resistance spectrum, and a high clustering rate and burden of drug resistance. To reduce the burden of DR-TB, related research should be strengthened, and the development of prevention, control, and treatment strategies should be accelerated.

Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI.

OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. METHODS: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. ADVANCES IN KNOWLEDGE: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning.

BACKGROUND: Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images. PURPOSE: To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images. STUDY TYPE: Retrospective. POPULATION: Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system. ASSESSMENT: EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort. STATISTICAL TESTS: Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant. RESULTS: EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674). DATA CONCLUSION: The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Histogram Feature Analysis of Tumor Body on Diffusion-weighted MR Imaging in Differentiation between Granulosa Cell Tumors and Other Sex-cord Tumors in Ovary: Comparison with Histological Results.

OBJECTIVE: We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging. METHODS: We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice. RESULTS: The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs. CONCLUSION: In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE: Prospective. POPULATION/SUBJECTS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION: Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally found along the utero-placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue. PURPOSE: To develop a DL tool for antenatal diagnosis of PAS using T2-weighted MR images. STUDY TYPE: Retrospective. SUBJECTS: Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets. FIELD STRENGTH/SEQUENCE: Sagittal T2-weighted fast spin echo sequence at 1.5 T and 3 T. ASSESSMENT: An nnU-Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero-placental boundary region. The UPB image was then fed into DenseNet-PAS for PAS diagnosis. DenseNet-PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard. STATISTICAL TESTS: Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann-Whitney U-test or the chi-squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs. RESULTS: Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737-0.770). DATA CONCLUSION: By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Impact of quantitative safety targets on road fatality reduction: an empirical support toward governance plan.

Introduction: The role of quantitative target setting has become an important topic in debates on the improvement of road safety performance. Specifically, there are questions regarding the relationship between quantitative safety targets and their actual effects. Although previous studies have provided important insights into this subject, their empirical findings have largely been equivocal, and research on this topic remains inadequate. Methods: Based on panel data representing 20 years of observations from 34 OECD member states, we employed nonlinear and linear panel models to investigate whether and how the attributes of quantitative road safety targets (i.e., target ambition and duration) influence their success (i.e., target completion status and rate). Results: The results indicate that a quantitative target with a higher level of ambition is associated with a lower likelihood and rate of completion, whereas there is no support for a connection between target duration and final completion rate. This suggests that an excessively ambitious target does not necessarily result in better road safety performance and is detrimental to achieving expected fatality reductions. Conclusion: From an empirical perspective, this study revealed a potential interaction effect between quantitative road safety targets and practical fatality reduction performance, providing government officials and policymakers with essential references for future practices on target setting and governance planning in regard to public health.

Identification of glycogene-based prognostic signature and validation of B3GNT7 as a potential biomarker and therapeutic target in breast cancer.

BACKGROUND: Breast cancer is the most common cancer worldwide, with the fifth highest mortality rate among all cancers and high risk of metastasis. However, potential biomarkers and molecular mechanisms underlying the stratification of breast cancer in terms of clinical outcomes remain to be investigated. Therefore, we aimed to find a novel prognostic biomarker and therapeutic target for breast cancer patients. METHODS: Unsupervised hierarchical clustering was used to perform comprehensive transcriptomic study of total 185 glycogenes in public datasets of breast cancer with clinicopathological and survival information. A glycogene-based signature for subtype classification was discovered using Limma packages, and relevance to four known molecular features was identified by GSVA. Experimental verification was performed and biological functions of B3GNT7 were characterized by quantitative RT-PCR, western blot, transwell assays, and lectin immunofluorescence staining in breast cancer cells. RESULTS: A 23-glycogene signature was identified for the classification of breast cancer. Among the 23 glycogenes, B3GNTs showed significantly positive associations with ER-/Her2- subtype in breast cancer patients (n = 2655). Overexpressed B3GNT7 were correlated with poor prognosis in breast cancer patients based on public datasets. B3GNT7 depletion inhibited cell proliferation, migration, and invasion, and decreased global fucosylation in MDA-MB-231 and HCC1937 breast cancer cells. CONCLUSIONS: Herein, we discovered a unique 23-gene signature for breast cancer patient glycogene-type classification. Among these genes, B3GNT7 was shown to be a potential biomarker for unfavorable outcomes and therapeutic target of breast cancer.

Perception and memory retrieval states are reflected in distributed patterns of background functional connectivity.

The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in "background" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity (1) was fully independent from stimulus-related variance in the signal and, further, (2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Organic matter release characteristics of brown rice: A promising solid carbon source with unique dual-enzyme system for wastewater treatment.

Solid carbon source (SCS) has attracted increasing research interests considering its merits of sustainable organic matter release capacity, safe transportation, easy management, and no need for frequent addition. In this study, the organic matter release capacities of five selected natural (milled rice and brown rice) and synthetic (PLA, PHA, PCL) SCSs were systematically investigated. The results showed that brown rice was the preferable SCS with high COD release potential, release rate and maximum accumulation of 309.2 mg-COD/g-SCS, 581.3 mg-COD/L·d and 6183.3 mg-COD/L, respectively. The cost for COD supply of brown rice stood at $1.0/kg-COD with considerable economic viability. The organic matter release process of brown rice could be well depicted by Hixson-Crowell model with a rate constant of -1.10. The addition of activated sludge could enhance the organic matter release of brown rice, evidenced by the increased release of VFAs with a proportion up to 97.1 % in the total organic matter. Moreover, the mass flow of carbon showed that the addition of activated sludge could improve the carbon utilization rate, and the peak value could achieve 45.4 % in 12 days. The unique dual-enzyme system, consisting of exogenous hydrolase from microorganisms in activated sludge and the endogenous amylase from brown rice, was supposed to be the main reason for the superior carbon release capacity of brown rice over other SCSs. This study was expected to offer an economic and effective SCS for the biological treatment of low-carbon wastewater.

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

OBJECTIVE: To compare the capability of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) histogram analysis in epithelial ovarian tumor categorization. METHODS: We retrospectively recruited 52 patients with pathologically proven ovarian serous epithelial cancer from our institution. ADC histogram analysis was performed using FeAture Explorer software after outlining the whole lesion area on the ADC map. The ADC histogram parameter difference between subgroups was compared; the correlation between the quantitative parameters on MRI and Ki-67 expression was calculated for both groups. RESULTS: The repeatability of ADC measurements across the two methods was good; the area method (ADCarea) had better performance in repeatability than the ROI method (ADCroi). The ADCroi, ADCarea, Ktrans, and Kep values significantly differed between the groups (P < 0.05). The histogram parameters (percent10, entropy, minimum, range and variance) and DCE parameter (Ktrans) were strongly correlated with Ki-67 expression (P = 0.000), while the conventional ADC measurements were not significantly correlated with Ki-67 expression (P > 0.05). Overall, Ktrans had the best diagnostic performance for discriminating type I with type II ovarian cancers (AUC = 0.826). CONCLUSION: In the present study, both diffusion-weighted imaging (DWI) and DCE MRI could help classify ovarian cancer patients with high accuracy. ADC histogram analysis could accurately reflect the proliferative capability of tumor cells to some extent.

Integrated metabolomic and transcriptomic strategies to reveal alkali-resistance mechanisms in wild soybean during post-germination growth stage.

MAIN CONCLUSION: The keys to alkali-stress resistance of barren-tolerant wild soybean lay in enhanced reutilization of reserves in cotyledons as well as improved antioxidant protection and organic acid accumulation in young roots. Soil alkalization of farmlands is increasingly serious, adversely restricting crop growth and endangering food security. Here, based on integrated analysis of transcriptomics and metabolomics, we systematically investigated changes in cotyledon weight and young root growth in response to alkali stress in two ecotypes of wild soybean after germination to reveal alkali-resistance mechanisms in barren-tolerant wild soybean. Compared with barren-tolerant wild soybean, the dry weight of common wild soybean cotyledons under alkali stress decreased slowly and the length of young roots shortened. In barren-tolerant wild soybean, nitrogen-transport amino acids asparagine and glutamate decreased in cotyledons but increased in young roots, and nitrogen-compound transporter genes and genes involved in asparagine metabolism were significantly up-regulated in both cotyledons and young roots. Moreover, isocitric, succinic, and L-malic acids involved in the glyoxylate cycle significantly accumulated and the malate synthetase gene was up-regulated in barren-tolerant wild soybean cotyledons. In barren-tolerant wild soybean young roots, glutamate and glycine related to glutathione metabolism increased significantly and the glutathione reductase gene was up-regulated. Pyruvic acid and citric acid involved in pyruvate-citrate metabolism increased distinctly and genes encoding pyruvate decarboxylase and citrate synthetase were up-regulated. Integrated analysis showed that the keys to alkali-stress resistance of barren-tolerant wild soybean lay in enhanced protein decomposition, amino acid transport, and lipolysis in cotyledons as well as improved antioxidant protection and organic acid accumulation in young roots. This study provides new ideas for the exploitation and utilization of wild soybean resources.

Ordered Van der Waals Hetero-nanoribbon from Pressure-Induced Topochemical Polymerization of Azobenzene.

Pressure-induced topochemical polymerization of molecular crystals with various stackings is a promising way to synthesize materials with different co-existing sub-structures. Here, by compressing the azobenzene crystal containing two kinds of intermolecular stacking, we synthesized an ordered van der Waals carbon nanoribbon (CNR) heterostructure in one step. Azobenzene polymerizes via a [4 + 2] hetero-Diels-Alder (HDA) reaction of phenylazo-phenyl in layer A and a para-polymerization reaction of phenyl in layer B at 18 GPa, as evidenced by in situ Raman and IR spectroscopies, X-ray diffraction, as well as gas chromatography-mass spectrometry and the solid-state nuclear magnetic resonance of the recovered products. The theoretical calculation shows that the obtained CNR heterostructure has a type II (staggered) band gap alignment. Our work highlights a high-pressure strategy to synthesize bulk CNR heterostructures.

Transmission Characteristics and Inactivated Vaccine Effectiveness Against Transmission of SARS-CoV-2 Omicron BA.5 Variants in Urumqi, China.

Importance: In 2022, Omicron variants circulated globally, and Urumqi, China, experienced a COVID-19 outbreak seeded by Omicron BA.5 variants, resulting in the highest number of infections in the city's record before the exit of the zero COVID-19 strategy. Little was known about the characteristics of Omicron variants in mainland China. Objective: To evaluate transmission characteristics of Omicron BA.5 variants and the effectiveness of inactivated vaccine (mainly BBIBP-CorV) against their transmission. Design, Setting, and Participants: This cohort study was conducted using data from an Omicron-seeded COVID-19 outbreak in Urumqi from August 7 to September 7, 2022. Participants included all individuals with confirmed SARS-CoV-2 infections and their close contacts identified between August 7 and September 7, 2022 in Urumqi. Exposures: A booster dose was compared vs 2 doses (reference level) of inactivated vaccine and risk factors were evaluated. Main Outcomes and Measures: Demographic characteristics, timeline records from exposure to laboratory testing outcomes, contact tracing history, and contact setting were obtained. The mean and variance of the key time-to-event intervals of transmission were estimated for individuals with known information. Transmission risks and contact patterns were assessed under different disease-control measures and in different contact settings. The effectiveness of inactivated vaccine against the transmission of Omicron BA.5 was estimated using multivariate logistic regression models. Results: Among 1139 individuals diagnosed with COVID-19 (630 females [55.3%]; mean [SD] age, 37.4 [19.9] years) and 51 323 close contacts who tested negative for COVID-19 (26 299 females [51.2%]; mean [SD] age, 38.4 [16.0] years), the means of generation interval, viral shedding period, and incubation period were estimated at 2.8 days (95% credible interval [CrI], 2.4-3.5 days), 6.7 days (95% CrI, 6.4-7.1 days), and 5.7 days (95% CrI, 4.8-6.6 days), respectively. Despite contact tracing, intensive control measures, and high vaccine coverage (980 individuals with infections [86.0%] received ≥2 doses of vaccine), high transmission risks were found in household settings (secondary attack rate, 14.7%; 95% CrI, 13.0%-16.5%) and younger (aged 0-15 years; secondary attack rate, 2.5%; 95% CrI, 1.9%-3.1%) and older age (aged >65 years; secondary attack rate, 2.2%; 95% CrI, 1.5%-3.0%) groups. Vaccine effectiveness against BA.5 variant transmission for the booster-dose vs 2 doses was 28.9% (95% CrI, 7.7%-45.2%) and 48.5% (95% CrI, 23.9%-61.4%) for 15-90 days after booster dose. No protective outcome was detected beyond 90 days after the booster dose. Conclusions and Relevance: This cohort study revealed key transmission characteristics of SARS-CoV-2 as they evolved, as well as vaccine effectiveness against variants. These findings suggest the importance of continuously evaluating vaccine effectiveness against emerging SARS-CoV-2 variants.

Piezovoltaics from PdHx.

Metal hydrides have wide applications in energy science. A large pressure gradient propels the hydrogen atoms out. A piezovoltaic device, a pressure gradient-driven battery, can therefore be realized when the migrations of protons and electrons are separated by different conductors. Here we investigate the piezovoltaic performance of PdHx with various proton conductors as electrolytes and experimentally detect an output current of ≲40 nA and a voltage of ∼0.8 V for a 3 µg sample. We also demonstrate the escape of hydrogen atoms from a palladium lattice under an increasing pressure gradient using X-ray diffraction. The relationship between piezovoltaics (chemical process) and piezoelectricity (physical process) is like that between a chemical battery and a capacitor. Our work demonstrates the piezovoltaic application of metal hydrides and provides a new way to convert mechanical energy into electrical energy.

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 [Formula: see text] 0.25, 0.76 [Formula: see text] 0.18, and 0.60 [Formula: see text] 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists' assessment.

Thermal batteries based on inverse barocaloric effects.

To harvest and reuse low-temperature waste heat, we propose and realize an emergent concept-barocaloric thermal batteries based on the large inverse barocaloric effect of ammonium thiocyanate (NH4SCN). Thermal charging is initialized upon pressurization through an order-to-disorder phase transition, and the discharging of 43 J g-1 takes place at depressurization, which is 11 times more than the input mechanical energy. The thermodynamic equilibrium nature of the pressure-restrained heat-carrying phase guarantees stable long-duration storage. The barocaloric thermal batteries reinforced by their solid microscopic mechanism are expected to substantially advance the ability to take advantage of waste heat.