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Abnormal expression in long noncoding RNAs (lncRNAs) is closely associated with cancers. Herein, a novel CRISPR/Cas13a-enhanced photocurrent-polarity-switching photoelectrochemical (PEC) biosensor was engineered for the joint detection of dual lncRNAs, using deep learning (DL) to assist in cancer diagnosis. After target lncRNA-activated CRISPR/Cas13a cleaves to induce DNAzyme bidirectional walkers with the help of cofactor Mg2+, nitrogen-doped carbon–Cu/Cu2O octahedra are introduced into the biosensor, producing a photocurrent in the opposite direction of CdS quantum dots (QDs). The developed PEC biosensor shows high specificity and sensitivity with limits of detection down to 25.5 aM for lncRNA HOTAIR and 53.1 aM for lncRNA MALAT1. More importantly, this platform for the lncRNA joint assay in whole blood can successfully differentiate cancers from healthy people. Furthermore, the DL model is applied to explore the potential pattern hidden in data of the established technology, and the accuracy of DL cancer diagnosis can acquire 93.3%. Consequently, the developed platform offers a new avenue for lncRNA joint detection and early intelligent diagnosis of cancer.

Byline: Lei. Zhou, Lihua. Ding, Jie. Liu, Yanan. Zhang, Xiaoli. Luo, Lichun. Zhao, Jun. Ren Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. Aim: To [...]

In order to alleviate the current 'difficult employment' situation, this paper discusses the integrated development of innovation and entrepreneurship education curriculum structure optimisation and employment guidance. First, analyse the background of integration development and clarify its necessity, then elaborate the current innovation and entrepreneurship education curriculum structure and employment guidance mode, and analyse the five existing problems. Then it analyses the positive impact of integrated development, and puts forward a new concept of integrated development based on employment and driven by innovation and entrepreneurship, including: establishing a new way of integrated development, improving students' participation enthusiasm, improving and optimising the current curriculum structure, establishing a professional teacher resource team, etc. The results show that the application effect index of the integration strategy has reached more than 0.9, and the grade has reached excellent, which shows that the application of the integration strategy has achieved good results.

The narrow absorption and emission bands, long fluorescence lifetime, and excellent stability of rare earth nanoparticles (referred to as RE NPs) make them very attractive for multimodal imaging and therapy of cancer. Their narrow absorption requires the careful selection of laser wavelength to achieve the best performance, particularly for RE NPs simultaneously having photothermal and photoluminescent properties (e.g., Nd-based nanoparticles), which has not been investigated. Herein, we prepared a series of different-sized NaNdF4nanoparticles (referred to as NNF NPs) (i.e., 4.7, 5.9, 12.8, and 15.6 nm) from ultrasmall nanoclusters and investigated their in vitroand in vivosize-dependent photothermal conversion and photoluminescence under irradiation by a 793 nm laser and an 808 nm laser, respectively. We find that all nanoparticles exhibited the better photothermal conversion performance under the irradiation of the 808 nm laser than under the 793 nm laser, of which 12.8 nm NNF NPs showed the best performance, and the temperature of their solution can be quickly increased from 30 °C to around 60 °C within 10 min under the irradiation of the 808 nm laser with a power intensity of 0.75 W/cm2. When we used the 793 nm laser to excite these NNF NPs, we found that all nanoparticles exhibited the stronger photoluminescence in the second near-infrared window (NIR-II) than under the excitation by the 808 nm laser, of which 15.6 nm NNF NPs possessed the strongest NIR-II luminescence. We then modified 12.8 nm NNF NPs with phospholipid carboxyl PEG and functionalized with RGD for actively targeted imaging of cancer. The NaNdF4@PEG@RGD nanoparticles (referred to as NNF-P-R NPs) have good biocompatibility, stability, and excellent targeting capability. The in vivoresult show that 12.8 nm NNF NPs exhibited better photothermal conversion performance under the irradiation of the 808 nm laser, and stronger NIR-II fluorescence under irradiation of the 793 nm laser, which are consistent with the in vitroresult. This work demonstrates the significance of selection of the proper laser wavelength for maximally taking advantage of RE nanoparticles for the diagnosis and treatment of cancer.

In this study, we developed a novel magnetic enhanced chemiluminescence enzyme immunoassay (MCLEIA) to achieve the simple, economical and easily commercialized DNMT1 quantitative analysis in serum samples for early clinical diagnosis.

Exosome concentration and exosomal proteins are regarded as promising cancer biomarkers. Herein, a waxberry-like magnetic bead (magnetic-nanowaxberry) which has huge surface area and strong affinity was synthesized to couple with aptamer for exosome capture and recovery. Subsequently, we developed a fluorescent assay for the sensitive, accurate, and simultaneous quantification of exosome and cancer-related exosomal proteins [epidermal growth factor receptor (EGFR) and epithelial cell adhesion molecule (EpCAM)] by using triple-colored probes to recognize EGFR and EpCAM or spontaneously anchor to the lipid bilayer. In this design, the interference of soluble proteins can be avoided due to the dual recognition strategy. Moreover, the lipid-based quantification of exosome concentration can improve the accuracy. Besides, the simultaneous detection mode can save samples and simplify the operation steps. Consequently, the assay shows high sensitivity (the limits of detection are down to 0.96 pg/mL for EGFR, 0.19 pg/mL for EpCAM, and 2.4 × 104particles/μL for exosome), high specificity, and satisfactory accuracy. More importantly, this technique is successfully used to analyze exosomes in plasma to distinguish cancer patients from healthy individuals. To improve the diagnostic efficacy, the deep learning was used to exploit the potential pattern hidden in data obtained by the proposed method. Also, the accuracy for the intelligent diagnosis of cancer can achieve 96.0%. This study provides a new avenue for developing new biosensors for exosome analysis and intelligent disease diagnosis.

Implant level photolithography processes are becoming more and more challenging due to decreasing CD and photoresist edge placement requirements. In addition to the traditional proximity effects associated with the implant layer mask, the underlying active and gate layers can interact through a variety of mechanisms to influence the edge placement of the developed implant layer. For many implant layers, the impact of underlying topography cannot be subdued, because, because it is not desirable to employ bottom anti-reflection coating (BARC) for various reasons. Therefore, the implant OPC models calibrated on bare silicon wafers must include additional compensation components to counter the various substrate complexities from real device/design scenarios. In this paper, we show our systematic study on substrate effect (AA/STI) on implant level lithography CD printing including real wafer data analysis and simulation with wafer 3D OPC. Besides, we also show some study of the poly effect on CD. Based on the results, we generate an empirical model to match the substrate effect, and also proposed an approach with additional substrate-aware OPC rule to correct such effect.

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