Stabilized, ROS-sensitive ß-cyclodextrin-grafted hyaluronic supramolecular nanocontainers for CD44-targeted anticancer drug delivery.

Hyaluronic acid (HA)-based tumor microenvironment-responsive nanocontainers are attractive candidates for anticancer drug delivery due to HA's excellent biocompatibility, biodegradability, and CD44-targeting properties. Nevertheless, the consecutive synthesis of stabilized, stealthy, responsive HA-based multicomponent nanomedicines generally requires multi-step preparation and purification procedures, leading to batch-to-batch variation and scale-up difficulties. To develop a facile yet robust strategy for promoted translations, a silica monomer containing a cross-linkable diethoxysilyl unit was prepared to enable in situ crosslinking without any additives. Further combined with the host-guest inclusion complexation between ß-cyclodextrin-grafted HA (HA-CD) and ferrocene-functionalized polymers, ferrocene-terminated poly(oligo(ethylene glycol) methyl ether methacrylate (Fc-POEGMA) and Fc-terminated poly(ε-caprolactone)-b-poly(3-(diethoxymethylsilyl)propyl(2-(methacryloyloxy)ethyl) carbamate) (Fc-PCL-b-PDESPMA), a reactive oxygen species (ROS)-sensitive supramolecular polymer construct, Fc-POEGMA/Fc-PCL-b-PDESPMA@HA-CD was readily fabricated to integrate stealthy POEGMA, tumor active targeting HA, and an in situ cross-linkable PDESPMA sequence. Supramolecular amphiphilic copolymers with two different POEGMA contents of 25 wt% (P1) and 20 wt% (P2) were prepared via a simple physical mixing process, affording two core-crosslinked (CCL) micelles via an in situ sol-gel process of ethoxysilyl groups. The P1-based CCL micelles show not only desired colloidal stability against high dilution, but also an intracellular ROS-mimicking environment-induced particulate aggregation that is beneficial for promoted intracellular release of the loaded cargoes. Most importantly, P1-based nanomedicines exhibited greater cytotoxicity in CD44 receptor-positive HeLa cells than that in CD44 receptor-negative MCF-7 cells. Overall, this work developed HA-based nanomedicines with sufficient extracellular colloidal stability and efficient intracellular destabilization properties for enhanced anticancer drug delivery via smart integration of in situ crosslinking and supramolecular complexation.

Acidification alleviates the inhibition of hyposaline stress on physiological performance of tropical seagrass Thalassia hemprichii.

Since the Industrial Revolution, increasing atmospheric CO2 concentrations have had a substantial negative impact influence on coastal ecosystems because of direct effects including ocean acidification and indirect effects such as extreme rainfall events. Using a two-factor crossover indoor simulation experiment, this study examined the combined effects of acidification and hyposaline stress on Thalassia hemprichii. Seawater acidification increased the photosynthetic pigment content of T. hemprichii leaves and promoted seagrass growth rate. Hyposaline stress slowed down seagrass growth and had an impact on the osmotic potential and osmoregulatory substance content of seagrass leaves. Acidification and salinity reduction had significant interaction effects on the photosynthesis rate, photosynthetic pigment content, chlorophyll fluorescence parameters, and osmotic potential of T. hemprichii, but not on the growth rate. Overall, these findings have shown that the hyposaline stress inhibitory effect on the T. hemprichii physiological performance and growth may be reduced by acidification.

Full-length transcriptome sequencing of Arabidopsis plants provided new insights into the autophagic regulation of photosynthesis.

Autophagy is a highly conserved eukaryotic pathway and plays a crucial role in cell survival under stress conditions. Here, we applied a full-length transcriptome approach to study an Arabidopsis autophagy mutant (atg5-1) subjected to nitrogen-starvation, using Oxford Nanopore Technologies. A total of 39,033 transcripts were identified, including 11,356 new transcripts. In addition, alternative splicing (AS) events and lncRNAs were also detected between Col-0 (WT) and atg5-1. Differentially expressed transcript enrichment showed that autophagy upregulates the expression of many stress-responsive genes and inhibits the transcription of photosynthesis-associated genes. The qRT-PCR results showed that the expression patterns of photosynthesis-related genes in the atg5-1 differed under the conditions of nitrogen starvation and carbon starvation. Under nitrogen starvation treatment, many genes related to photosynthesis also exhibited AS. Chlorophyll fluorescence images revealed that the Fv/Fm and ΦPSII of old atg5-1 leaves were significantly reduced after nitrogen starvation treatment, but the Y(NPQ) indices were significantly increased compared to those of the WT plants. The results of qRT-PCR suggest that autophagy appears to be involved in the degradation of genes related to photodamage repair in PSII. Taken together, the full-length transcriptiome sequencing provide new insights into how new transcripts, lncRNAs and alternative splicing (AS) are involved in plant autophagy through full-length transcriptome sequencing and suggest a new potential link between autophagy and photosynthesis.

Diagnostic and Prognostic Values of KLF5 and RUNX1 in Acute Kidney Injury in Septic Patients.

Diagnostic and prognostic values of Kruppel-like factor 5 (KLF5) and Runt-related transcription factor 1 (RUNX1) were determined in sepsis-induced acute kidney injury (SI-AKI). The study included 120 septic patients and set two groups: SI-AKI group (n = 60) or non-AKI group (n = 60). Fasting venous blood was drawn, and KLF5 and RUNX1 levels were measured. The receiver operating characteristic curve was plotted for diagnostic evaluation of KLF5 and RUNX1 in SI-AKI. The correlation between KLF5 and RUNX1 and serum creatinine (Scr), cystatin C (Cys-C), and kidney injury molecule 1 (KIM-1) were assessed by Pearson method. Predictive values of KLF5 and RUNX1 in 28-day survival of SI-AKI patients were considered by Kaplan-Meier survival curves and multivariate Cox regression analysis. Serum KLF5 and RUNX1 in SI-AKI patients were upregulated. Serum KLF5 and RUNX1 were of high diagnostic value in distinguishing SI-AKI patients from non-AKI patients. KLF5 and RUNX1 were in a positive correlation with Scr, Cys-C, and KIM-1, respectively. The 28-day survival of SI-AKI patients with high serum KLF5 or RUNX1 expression was poor, and serum KLF5 and RUNX1 expression were independently correlated with SI-AKI patients' survival. KLF5 and RUNX1 have diagnostic and prognostic values in SI-AKI patients.

Comparison of the Measurement Properties of EQ-5D-5L and SF-6Dv2 in COVID-19 Patients in China.

BACKGROUND AND OBJECTIVES: There are limited studies comparing the health utility values of EQ-5D-5L and SF-6Dv2 within the same patient cohorts. The widespread transmission and recurring infections associated with Omicron variants amid the COVID-19 pandemic have resulted in substantial health detriments and increased utilisation of health care resources. This highlights the crucial need to assess the loss in quality-adjusted life years (QALYs). Therefore, this study aims to compare the ceiling and floor effects, agreement, correlation and responsiveness between EQ-5D-5L and SF-6Dv2 based on COVID-19 patients during the Omicron outbreak in China. METHODS: We recruited 694 COVID-19 patients across mainland China to participant in an online questionnaire survey from January to February 2023. The questionnaire encompassed queries concerning the sociodemographic and health details of the participants, who were requested to recollect their health status during and after experiencing COVID-19 using the EQ-5D-5L and SF-6Dv2 questionnaires. Epanechnikov kernel density plots were used to visualise the ceiling and floor effects for both instruments. Agreement was assessed by Bland-Altman graph and intraclass correlation coefficient (ICC). Correlation was evaluated using linear regression, Pearson's correlation and Spearman's correlation. The standardised response mean (SRM) and relative efficiency (RE) were used to examine the responsiveness of EQ-5D-5L and SF-6Dv2 at detecting the health improvement after COVID-19 infection and the difference in dichotomous health indicators. RESULTS: In total, 648 valid responses from patients aged 35.6 ± 15.0 years were involved in analysis. The EQ-5D-5L utility indices were 0.58 ± 0.33 and 0.92 ± 0.14 during and after COVID-19 infection, respectively, which were significantly higher than indices of the SF-6Dv2 utility (0.43 ± 0.31 and 0.81 ± 0.19, p < 0.001). A ceiling effect of EQ-5D-5L larger than that of SF-6Dv2 was observed during COVID-19 infection (49.5% vs 21.6%). Intraclass correlation coefficients between EQ-5D-5L and SF-6Dv2 during and after COVID-19 infection were 0.69 and 0.55, respectively. The utility indices of EQ-5D-5L and SF-6Dv2 were highly correlated, with Pearson's correlation coefficients of 0.76 and 0.70 (p < 0.001) during and after COVID-19 infection, respectively. The spearman's correlations were moderate to high between dimensions of EQ-5D-5L and SF-6Dv2 (p < 0.01). Both EQ-5D-5L and SF-6Dv2 were responsive to detect health improvement after COVID-19 and the differences in dichotomous health indicators. CONCLUSIONS: The utility indices generated by EQ-5D-5L and SF-6Dv2 in COVID-19 patients demonstrate strong correlation and responsiveness. However, the agreement between these two instruments does not reach a satisfactory level. Consequently, these two measures cannot be used interchangeably. In situations where apprehensions about ceiling effects affecting outcome measurement arise, it is advisable to consider SF-6Dv2 as a preferable outcome measure for studies on patients with COVID-19.

Association between a body shape index and prostate cancer: a cross-sectional study of NHANES 2001-2018.

OBJECTIVE: Abdominal obesity, especially visceral fat, may have negative effects on the development and progression of prostate cancer (PCa). A body shape index (ABSI) can more accurately measure visceral fat accumulation. This study aimed to investigate the association between ABSI and PCa in US adults. METHODS: 11,013 participants were enrolled in the National Health and Nutrition Examination Survey from 2001 to 2018. Weighted multivariate logistic regression analyses were employed to explore the independent relationship between ABSI and PCa. Moreover, restricted cubic spline (RCS) analysis, subgroup analysis, and interaction tests were performed. RESULTS: ABSI was positively associated with the presence of PCa. When comparing the second, third, and fourth ABSI quartile to the lowest quartile, the adjusted odds ratios (95% confidence intervals) for PCa risk were 1.34 (0.77, 2.31), 1.75 (1.03, 3.00), and 1.91 (1.12, 3.27), respectively (p for trend = 0.011). The restricted cubic spline regression analysis did not reveal a non-linear correlation between ABSI and PCa (p for non-linearity = 0.076). Subgroup analysis showed a significant interaction effect in subgroups of different BMI (p for interaction = 0.01). CONCLUSIONS: Elevated ABSI is significantly associated with an increased risk of PCa, particularly among individuals who are under/normal weighted or obese.

Novel Circular RNA CircUBAP2 Drives Tumor Progression by Regulating the miR-143/TFAP2B Axis in Prostate Cancer.

BACKGROUND: More and more investigations reveal that circular RNAs (circRNAs) are involved in cancer progression. CircRNA UBAP2 was closely related to prostate cancer. However, the biological function and specifical mechanism of circUBAP2 are still poorly discovered in prostate cancer (PCa). OBJECTIVES: This study aims to explore the biological function and mechanism of circUBAP2 in PCa. METHODS: The levels of mRNA and proteins were assessed by qRT-PCR assay and Western blot, respectively. Cell growth, migration, and invasion ability were measured using CCK-8 assay and Transwell assay. Apoptosis was assessed using flow cytometry. The interactions between circUBAP2, miR-143, and TFAP2B were determined by luciferase report assay. The tumor growth was determined by in vivo tumor formation assay. The tumor morphology was assessed using H&E staining assay, and immunohistochemistry assay was conducted to assess the level of KI67. RESULTS: We found circUBAP2 and TFAP2B were notably elevated, while miR-143 was largely attenuated in prostate cancer cells and tissues. CircUBAP2 was found to affect cell viability, metastasis and EMT, while attenuating the apoptosis rate of prostate cancer cells. CircUBAP2 directly targeted miR-143, and miR-143 inhibitor could reverse the effects that circUBAP2 interference-induced in prostate cancer cells. TFAP2B is directly bound to miR-143, and overexpression of TFAP2B could attenuate the influences that miR-143-induced in prostate cancer cells. CONCLUSION: CircUBAP2 promoted prostate cancer progression via miR-143/TFAP2B axis.

First-principles molten salt phase diagrams through thermodynamic integration.

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. We show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

Immunogenicity and Safety of One versus Two Doses of Quadrivalent Inactivated Influenza Vaccine (IIV4) in Vaccine-Unprimed Children and One Dose of IIV4 in Vaccine-Primed Children Aged 3-8 Years.

Two doses of the inactivated influenza vaccine (IIV) are generally recommended for children under 9 years old. This study assessed the necessity for a second dose of quadrivalent IIV (IIV4) in children aged 3-8 years. In this randomized, open-label, paralleled-controlled study, 400 children aged 3-8 years who were vaccine-unprimed were randomly assigned at a 1:1 ratio to receive a two-dose (Group 1) or one-dose (Group 2) regimen of IIV4, and 200 who were vaccine-primed received one dose of IIV4 (Group 3). A serum sample was collected before and 28 days after the last dose to determine the hemagglutination inhibition (HI) antibody level. Adverse events were collected within 28 days after each dose. One-dose or two-doses of IIV4 were well tolerated and safe in children aged 3-8 years, and no serious adverse events related to the vaccine were reported. The seroconversion rates (SCRs) of HI antibody ranged from 61.86% to 95.86%, and the post-vaccination seroprotection rates (SPRs) were all >70% in three groups against the four virus strains. The two-dose regimen in vaccine-unprimed participants (Group 1) achieved similar SPRs in comparison with the one-dose in the vaccine-primed group (Group 3), and the SPRs in Group 1 and Group 3 were higher in vaccine-unprimed participants of the one-dose regimen (Group 2). The present study supports the recommendations of a two-dose regimen for IIV4 use in children aged 3-8 years.

The Change in Whole-Genome Methylation and Transcriptome Profile under Autophagy Defect and Nitrogen Starvation.

Through whole-genome bisulfite sequencing and RNA-seq, we determined the potential impact of autophagy in regulating DNA methylation in Arabidopsis, providing a solid foundation for further understanding the molecular mechanism of autophagy and how plants cope with nitrogen deficiency. A total of 335 notable differentially expressed genes (DEGs) were discovered in wild-type Arabidopsis (Col-0-N) and an autophagic mutant cultivated under nitrogen starvation (atg5-1-N). Among these, 142 DEGs were associated with hypomethylated regions (hypo-DMRs) and were upregulated. This suggests a correlation between DNA demethylation and the ability of Arabidopsis to cope with nitrogen deficiency. Examination of the hypo-DMR-linked upregulated DEGs indicated that the expression of MYB101, an ABA pathway regulator, may be regulated by DNA demethylation and the recruitment of transcription factors (TFs; ERF57, ERF105, ERF48, and ERF111), which may contribute to the growth arrest induced by abscisic acid (ABA). Additionally, we found that DNA methylation might impact the biosynthesis of salicylic acid (SA). The promoter region of ATGH3.12 (PBS3), a key enzyme in SA synthesis, was hypomethylated, combined with overexpression of PBS3 and its potential TF AT3G46070, suggesting that autophagy defects may lead to SA-activated senescence, depending on DNA demethylation. These findings suggest that DNA hypomethylation may impact the mechanism by which Arabidopsis autophagy mutants (atg5-1) respond to nitrogen deficiency, specifically in relation to ABA and SA regulation. Our evaluation of hormone levels verified that these two hormones are significantly enriched under nitrogen deficiency in atg5-1-N compared to Col-0-N.

Metal-Insulator Transition of Single-Crystal V2O3 through van der Waals Interface Engineering.

Strongly correlated electron materials harbor interesting materials physics, such as high-Tc superconductivity, colossal magnetoresistance, and metal-insulator transition. These physical properties can be greatly influenced by the dimensionality and geometry of the hosting materials and their interaction strengths with underlying substrates. In a classic strongly correlated oxide vanadium sesquioxide (V2O3), the coexistence of a metal-insulator and paramagnetic-antiferromagnetic transitions at ∼150 K makes this material an excellent platform for exploring basic physics and developing future devices. So far, most studies have been focused on epitaxial thin films in which the strongly coupled substrate has a pronounced effect on V2O3, leading to the observations of intriguing phenomena and physics. In this work, we unveil the kinetics of a metal-insulator transition of V2O3 single-crystal sheets at nano and micro scales. We show the presence of triangle-like alternating metal/insulator phase patterns during phase transition, which is drastically different from the epitaxial film. The observation of single-stage metal-insulator transition in V2O3/graphene compared to the multistage in V2O3/SiO2 evidence the importance of sheet-substrate coupling. Harnessing the freestanding form of the V2O3 sheet, we show that the phase transition of V2O3 sheet can generate a large dynamic strain to monolayer MoS2 and tune its optical property based on the MoS2/V2O3 hybrid structure. The demonstration of the capability in tuning phase transition kinetics and phase patterns using designed hybrid structure of varied sheet-substrate coupling strengths suggests an effective knob in the design and operation of emerging Mott devices.

Succession of Bacteria Attached to Microplastics After Transferring from a Mariculture Area to a Seagrass Meadow.

Microplastics have been recognized as a novel niche for bacteria. However, studies have characterized the plastisphere microbial community in situ without exploring the microbial changes after transferring to other ecosystems. Here we focus on bacterial succession on typical microplastics (polypropylene and expanded polystyrene) and natural substrates (wood) after transferring from mariculture area to seagrass meadows system. Using high-throughput sequencing of 16 S rRNA, we found that alpha diversity significantly reduced after transferring and microplastics especially PP had significant separations on PCoA plots at different succession stages. The abundance and metabolic pathways of potential pathogen-associated microorganisms are significantly decreased. The relative abundance of xenobiotics biodegradation pathways was significantly lower and of energy metabolism pathways was significantly higher by comparing before and after transferring. Main environmental factors affecting microbial communities changed from nutrient characteristics to basic physicochemical properties after transferring. The succession times of the microbial communities of the three materials were different.

High-throughput manufacturing of epitaxial membranes from a single wafer by 2D materials-based layer transfer process.

Layer transfer techniques have been extensively explored for semiconductor device fabrication as a path to reduce costs and to form heterogeneously integrated devices. These techniques entail isolating epitaxial layers from an expensive donor wafer to form freestanding membranes. However, current layer transfer processes are still low-throughput and too expensive to be commercially suitable. Here we report a high-throughput layer transfer technique that can produce multiple compound semiconductor membranes from a single wafer. We directly grow two-dimensional (2D) materials on III-N and III-V substrates using epitaxy tools, which enables a scheme comprised of multiple alternating layers of 2D materials and epilayers that can be formed by a single growth run. Each epilayer in the multistack structure is then harvested by layer-by-layer mechanical exfoliation, producing multiple freestanding membranes from a single wafer without involving time-consuming processes such as sacrificial layer etching or wafer polishing. Moreover, atomic-precision exfoliation at the 2D interface allows for the recycling of the wafers for subsequent membrane production, with the potential for greatly reducing the manufacturing cost.

Temporal contexts for motion tracking in ultrasound sequences with information bottleneck.

BACKGROUND: Recently, deep convolutional neural networks (CNNs) have been widely adopted for ultrasound sequence tracking and shown to perform satisfactorily. However, existing trackers ignore the rich temporal contexts that exists between consecutive frames, making it difficult for these trackers to perceive information about the motion of the target. PURPOSE: In this paper, we propose a sophisticated method to fully utilize temporal contexts for ultrasound sequences tracking with information bottleneck. This method determines the temporal contexts between consecutive frames to perform both feature extraction and similarity graph refinement, and information bottleneck is integrated into the feature refinement process. METHODS: The proposed tracker combined three models. First, online temporal adaptive convolutional neural network (TAdaCNN) is proposed to focus on feature extraction and enhance spatial features using temporal information. Second, information bottleneck (IB) is incorporated to achieve more accurate target tracking by maximally limiting the amount of information in the network and discarding irrelevant information. Finally, we propose temporal adaptive transformer (TA-Trans) that efficiently encodes temporal knowledge by decoding it for similarity graph refinement. The tracker was trained on 2015 MICCAI Challenge on Liver Ultrasound Tracking (CLUST) dataset to evaluate the performance of the proposed method by calculating the tracking error (TE) between the predicted landmarks and the ground truth landmarks for each frame. The experimental results are compared with 13 state-of-the-art methods, and ablation studies are conducted. RESULTS: On CLUST 2015 dataset, our proposed model achieves a mean TE of 0.81 ± 0.74 mm and a maximum TE of 1.93 mm for 85 point-landmarks across 39 ultrasound sequences in the 2D sequences. Tracking speed ranged from 41 to 63 frames per second (fps). CONCLUSIONS: This study demonstrates a new integrated workflow for ultrasound sequences motion tracking. The results show that the model has excellent accuracy and robustness. Reliable and accurate motion estimation is provided for applications requiring real-time motion estimation in the context of ultrasound-guided radiation therapy.

Advection-Dispersion Behavior for Simulation of H-3 and Pu-238 Transport in Undisturbed Argillaceous Shale of a Near-Surface Repository.

In this study, a column experiment was employed to evaluate the nuclide migration behavior in the surrounding rock medium of a near-surface disposal site in China and to investigate the advection-dispersion behavior of tritium (H-3) and plutonium-238 (Pu-238) in highly weathered argillaceous shale. A reasonable numerical model was selected to fit the experimental breakthrough curves (BTCs) and to obtain the relevant migration parameters. The results show the following: (1) the internal structure of the highly weathered argillaceous shale exhibited heterogeneity, and the nuclide migration BTC showed characteristics of a "curve peak moving forward" and a "tail curve trailing"; (2) compared with other models, the stream tube mode could better fit the BTCs and obtain the average dispersion coefficient , average distribution coefficient , and other parameters; (3) compared to the results of the batch experiment, the distribution coefficient Kd obtained from the column experiment was smaller than that obtained from the batch experiment, which is speculated to be due to the influence of contact time and the contact area between the nuclide and the medium.

A Soft Label Method for Medical Image Segmentation with Multirater Annotations.

In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels' learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentation with multirater annotations. In this work, we aim to improve soft label methods by leveraging interpretable information from multiraters. Considering that mis-segmentation occurs in areas with weak supervision of annotations and high difficulty of images, we propose to reduce the reliance on local uncertain soft labels and increase the focus on image features. Therefore, we introduce local self-ensembling learning with consistency regularization, forcing the model to concentrate more on features rather than annotations, especially in regions with high uncertainty measured by the pixelwise interclass variance. Furthermore, we utilize a label smoothing technique to flatten each rater's annotation, alleviating overconfidence of structural edges in annotations. Without introducing additional parameters, our method improves the accuracy of the soft label baseline by 4.2% and 2.7% on a synthetic dataset and a fundus dataset, respectively. In addition, quantitative comparisons show that our method consistently outperforms existing multirater strategies as well as state-of-the-art methods. This work provides a simple yet effective solution for the widespread multirater label segmentation problems in clinical diagnosis.

Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer.

Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel Semantic Pyramid Network with a Transformer Self-attention, named SPN-TS, for tumor detection. Specifically, the paper first designs a new Feature Pyramid Network in the feature extraction stage. It changes the traditional cross-layer connection scheme and focuses on enriching the features of small-sized tumor regions. Then, we introduce the transformer attention mechanism into the framework to learn the local feature of tumor boundaries. Extensive experimental evaluations were performed on the publicly available CBIS-DDSM dataset, which is a Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The proposed method achieved better performance in these models, achieving 93.26% sensitivity, 95.26% specificity, 96.78% accuracy, and 87.27% Matthews Correlation Coefficient (MCC) value, respectively. The method can achieve the best detection performance by effectively solving the difficulties of small objects and boundaries ambiguity. The algorithm can further promote the detection of other diseases in the future, and also provide algorithmic references for the general object detection field.

Dual-Modal Information Bottleneck Network for Seizure Detection.

In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https://github.com/LLLL1021/Dual-modal-IB.

Permeability-Engineered Compartmentalization Enables In Vitro Reconstitution of Sustained Synthetic Biology Systems.

In nature, biological compartments such as cells rely on dynamically controlled permeability for matter exchange and complex cellular activities. Likewise, the ability to engineer compartment permeability is crucial for in vitro systems to gain sustainability, robustness, and complexity. However, rendering in vitro compartments such a capability is challenging. Here, a facile strategy is presented to build permeability-configurable compartments, and marked advantages of such compartmentalization are shown in reconstituting sustained synthetic biology systems in vitro. Through microfluidics, the strategy produces micrometer-sized layered microgels whose shell layer serves as a sieving structure for biomolecules and particles. In this configuration, the transport of DNAs, proteins, and bacteriophages across the compartments can be controlled an guided by a physical model. Through permeability engineering, a compartmentalized cell-free protein synthesis system sustains multicycle protein production; ≈100 000 compartments are repeatedly used in a five-cycle synthesis, featuring a yield of 2.2 mg mL-1 . Further, the engineered bacteria-enclosing compartments possess near-perfect phage resistance and enhanced environmental fitness. In a complex river silt environment, compartmentalized whole-cell biosensors show maintained activity throughout the 32 h pollutant monitoring. It is anticipated that permeability-engineered compartmentalization should pave the way for practical synthetic biology applications such as green bioproduction, environmental sensing, and bacteria-based therapeutics.

Batch-to-batch consistency trial of an adenovirus type-5 vector-based COVID-19 vaccine in adults aged 18 years and above.

BACKGROUND: The demonstration of batch-to-batch consistency is indispensable for quality control of vaccines. METHODS: We conducted a randomized, double-blind, parallel-controlled trial to evaluate the immunogenicity consistency of a single shot of Ad5-nCoV in healthy adults who had not previously received any COVID-19 vaccine. All eligible participants were randomly assigned equally to receive one of the three consecutive batches of Ad5-nCoV (5 × 1010 viral particles/vial, 0.5 mL). The primary endpoint was geometric mean titers (GMTs) of serum SARS-CoV-2 receptor-binding domain (RBD)-specific IgG on day 28 post-vaccination. RESULTS: One thousand fifty participants were enrolled, with 350 (33%) participants per group. On day 28 post-vaccination, GMTs in three groups were 78.3 binding antibody units (BAU)/mL (95% CI 70.3-87.3), 82.9 BAU/mL (73.9-92.9), and 78.8 BAU/mL (70.2-88.4), respectively. The two-sided 95% CIs for the GMT ratios between each pair of batches were all between 0.67 and 1.5. The highest incidence of solicited adverse reactions within 7 days post-vaccination was reported by batch 3 recipients (23.1% versus 15.1% in batch 1 recipients and 14.6% in bath 2 recipients; p = 0.0039). None of the serious adverse events were related to vaccination. CONCLUSIONS: Immunogenicity consistency between consecutive batches of Ad5-nCoV was well established in adults. CLINICAL TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (NCT05313646).