MnO2/Ce6 microbubble-mediated hypoxia modulation for enhancing sono-photodynamic therapy against triple negative breast cancer.

Sono-photodynamic therapy (SPDT) has emerged as a promising treatment modality for triple negative breast cancer (TNBC). However, the hypoxic tumor microenvironment hinders the application of SPDT. Herein, in this study, a multifunctional platform (MnO2/Ce6@MBs) was designed to address this issue. A sono-photosensitizer (Ce6) and a hypoxia modulator (MnO2) were loaded into microbubbles and precisely released within tumor tissues under ultrasound irradiation. MnO2in situ reacted with the excess H2O2 and H+ and produced O2 within the TNBC tumor, which alleviated hypoxia and augmented SPDT by increasing ROS generation. Meanwhile, the reaction product Mn2+ was able to achieve T1-weighted MRI for enhanced tumor imaging. Additionally, Ce6 and microbubbles served as a fluorescence imaging contrast agent and a contrast-enhanced ultrasound imaging agent, respectively. In in vivo anti-tumor studies, under the FL/US/MR imaging guidance, MnO2/Ce6@MBs combined with SPDT significantly reversed tumor hypoxia and inhibited tumor growth in 4T1-tumor bearing mice. This work presents a theragnostic system for reversing tumor hypoxia and enhancing TNBC treatment.

Correction: MnO2/Ce6 microbubble-mediated hypoxia modulation for enhancing sono-photodynamic therapy against triple negative breast cancer.

Correction for 'MnO2/Ce6 microbubble-mediated hypoxia modulation for enhancing sono-photodynamic therapy against triple negative breast cancer' by Ping Li et al., Biomater. Sci., 2024, https://doi.org/10.1039/d3bm00931a.

Efficient Defect Detection of Rotating Goods under the Background of Intelligent Retail.

Dynamic visual vending machines are rapidly growing in popularity, offering convenience and speed to customers. However, there is a prevalent issue with consumers damaging goods and then returning them to the machine, severely affecting business interests. This paper addresses the issue from the standpoint of defect detection. Although existing industrial defect detection algorithms, such as PatchCore, perform well, they face challenges, including handling goods in various orientations, detection speeds that do not meet real-time monitoring requirements, and complex backgrounds that hinder detection accuracy. These challenges hinder their application in dynamic vending environments. It is crucial to note that efficient visual features play a vital role in memory banks, yet current memory repositories for industrial inspection algorithms do not adequately address the problem of location-specific feature redundancy. To tackle these issues, this paper introduces a novel defect detection algorithm for goods using adaptive subsampling and partitioned memory banks. Firstly, Grad-CAM is utilized to extract deep features, which, in combination with shallow features, mitigate the impact of complex backgrounds on detection accuracy. Next, graph convolutional networks extract rotationally invariant features. The adaptive subsampling partitioned memory bank is then employed to store features of non-defective goods, which reduces memory consumption and enhances training speed. Experimental results on the MVTec AD dataset demonstrate that the proposed algorithm achieves a marked improvement in detection speed while maintaining accuracy that is comparable to state-of-the-art models.

Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.

BACKGROUND: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. METHODS: This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. FINDINGS: The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865-0.968) in the internal validation cohort, and 0.900 (95% CI 0.819-0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719-0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704-0.878], p = 0.013) in the external test cohort. INTERPRETATION: The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. FUNDING: Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).

Standing and Lying Ni(OH)2 Nanosheets on Multilayer Graphene for High-Performance Supercapacitors.

For conventional synthesis of Ni(OH)2/graphene hybrids, oxygen-containing functional groups should be firstly introduced on graphene to serve as active sites for the anchoring of Ni(OH)2. In this work, a method for growing Ni(OH)2 nanosheets on multilayer graphene (MLG) with molecular connection is developed which does not need any pre-activation treatments. Moreover, Ni(OH)2 nanosheets can be controlled to stand or lie on the surface of MLG. The prepared hybrids were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The growth processes are suggested according to their morphologies at different growth stages. The enhanced electrochemical performances as supercapacitor electrode materials were confirmed by cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) techniques. Ni(OH)2 nanosheets standing and lying on MLG show specific capacities of 204.4 mAh g-1 and 131.7 mAh g-1, respectively, at 1 A g-1 based on the total mass of the hybrids and 81.5% and 92.8% capacity retention at a high current density of 10 A g-1, respectively. Hybrid supercapacitors with as-prepared hybrids as cathodes and activated carbon as anode were fabricated and tested.

Identification of microRNA expression in sentinel lymph nodes from patients with breast cancer via RNA sequencing for diagnostic accuracy.

BACKGROUND: Sentinel lymph node (SLN) property assessment (with or without metastasis) is important when deciding the surgery for breast cancer; however, the current diagnosis of SLN metastasis remains to be studied. microRNAs (miRNAs) have been confirmed previously as a molecular marker for the diagnosis, development and prognosis of tumors. However, the detailed role of miRNAs in the diagnosis of SLN metastasis has not been reported. METHODS: The present study aimed to explore the potential use of miRNAs in the diagnosis of SLN using RNA sequencing (RNA-seq) and a quantitative real-time polymerase chain reaction (qRT-PCR) to compare the expression profiles of miRNAs in patients with breast cancer with or without SLN metastasis. RESULTS: The RNA-seq results revealed that 1993 miRNAs were differentially expressed in patients with breast cancer with SLN metastasis. Among these miRNAs, 1960 were up-regulated and 33 were down-regulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that these differentially expressed miRNAs were associated with tumor growth and metastasis and were also predicted to regulate a series of tumorigenesis and metastasis genes. In particular, the most differentially expressed miRNAs were validated by qRT-PCR, such that miR-200a-3p and miR-96-5p were up-regulated and miR-1-3p and miR-486-3p were down-regulated in patients with breast cancer with SLN metastasis. CONCLUSIONS: The findings of the present study suggest that there is an association of miRNAs with SLN metastasis and also that miRNAs function as biomarkers with respect to the choice of therapy and disease prognosis.

How Many Cores Does Systematic Prostate Biopsy Need?: A Large-Sample Retrospective Analysis.

OBJECTIVES: To explore the best individualized systematic prostate biopsy method. METHODS: We retrospectively analyzed the clinical data of 1211 patients who underwent 12-core systematic prostate biopsy guided by transrectal ultrasound from January 2011 to March 2018. Other biopsy core methods (6-, 8-, and 10-core) were estimated from the 12-core biopsy that was performed. Differences in the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) were compared. RESULTS: A total of 498 cases of PCa (41.1%) were detected, and 423 cases (34.9%) were csPCa. There was no significant difference between the 12- and 10-core prostate biopsy strategies in the total detection rates of PCa and csPCa (P > .05). In the subgroup of patients with a maximal prostate cross-sectional area of less than 15 cm2 , there was a significant difference between the 12-core method and the standard 6-core method (P = .03) but no significant differences between the other methods in the detection rate of PCa (P > .05), but in the detection rate of csPCa, the 12-core method differed significantly from the other methods (P = .02-.04) except for the 10-core method (P > .05). In patients with a prostate-specific antigen concentration of 20 ng/mL or higher, there were no significant differences between the 12-core method and all of the other methods (P > 0.05). In patients younger than 70 years and 70 years or older, the 12-core method differed significantly from the other methods (P < .01-.03) except for the 10-core method (P > .05). CONCLUSIONS: Ten- or 12-core biopsy showed a higher detection rate than the other schemes. However, for patients with a prostate-specific antigen concentration of 20 ng/mL or higher, the 6-core systematic biopsy is preferred.

A simple lattice-based PKE scheme.

In this paper, we first present a new lattice-based PKE scheme on SIS, proving that it achieves CPA-security under DBi-ISIS assumption. Compared to some lattice-based schemes, ours has some advantages and is quite efficient as well as great simplicity. Similarly, we give a lattice-based PKE with multiple bits which is CPA secure under DBi-ISIS assumption. We hope that our contributions help to pave the way for the development of lattice-based PKEs in the future work.