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CRISPR/Cas12a-based biosensing is advancing rapidly; however, achieving sensitive and cost-effective reporting of Cas12a activation remains a challenge. In response, we have developed a label-free system capable of postamplifying Cas12a activation by integrating hybridization chain reaction (HCR) and DNA-copper nanoclusters (DNA-CuNCs). The trans-cleavage of Cas12a triggers a silenced HCR, leading to the in situ assembly of fluorescent DNA-CuNCs, allowing for the turn-on reporting of Cas12a activation. Without preamplification, this assay can detect DNA with a detection limit of 5 fM. Furthermore, when coupled with preamplification, the system achieves exceptional sensitivity, detecting the monkeypox virus (MPXV) plasmid at 1 copy in human serum. In a MPXV pseudovirus-based validation test, the obtained results are in agreement with those obtained by qPCR, reinforcing the robustness of this method. Our study represents the first effort to manipulate DNA-CuNC formation on HCR for highly sensitive and cost-effective reporting of Cas12a, resulting in an efficient synthetic biology-enabled sensing platform for biosafety applications.

The detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for preventing and controlling infectious diseases and disease treatment. In this work, a Au@Ti3C2@PEI-Ru(dcbpy)32+nanocomposite-based electrochemiluminescence (ECL) biosensor was rationally designed, which realized sensitive detection of the RNA-dependent RNA polymerase (RdRp) gene of SARS-CoV-2. In addition, a DNA walker was also used to excise the hairpin DNAs under the action of Nb.BbvCI endonuclease. Furthermore, model DNA–Ag nanoclusters (model DNA–AgNCs) were used to quench the initial ECL signal. As a result, the ECL biosensor was used to sensitively detect the SARS-CoV-2 RdRp gene with a detection range of 1 fM to 100 pM and a limit of detection of 0.21 fM. It was indicated that the ECL biosensor had a great application potential for clinical medical detection. Furthermore, the DNA walker amplification also played a reliable candidate strategy for other detection methods.

Background: Glucagon-like peptide 1 receptor (GLP-1R) is an important biomarker for diagnosis and therapy of the endocrine cancers due to overexpression. Recently, in human prostate cancer cell lines the receptor was also observed, therefore it may be a potential target for the disease. 18F-Al-NOTA-MAL-Cys39- exendin-4 holds great promise for GLP-1R. Therefore, the feasibility of the 18F-labeled exendin-4 analog for prostate cancer imaging was investigated. Methods: New probe 18F-Al-NOTA-MAL-Cys39-exendin-4 was made through one-step fluorination. Prostate cancer PC3 cell xenograft model mice were established to primarily evaluate the imaging properties of the tracer via small animal PET studies in vivo. Pathological studies and Western Blots were also performed. Results: PC-3 prostate xenografts were clearly imaged under baseline conditions. At 30 and 60 min postinjection, the tumor uptakes were 2.90±0.41%ID/g and 2.26±0.32 %ID/g respectively. The presence of cys39-exendin-4 significantly reduced the tumor uptake to 0.82±0.10 %ID/g at 60 min p.i. Findings of ex vivo biodistribution studies were similar to those of in vivo PET imaging. The tumors to blood and muscles were significantly improved with the increase of time due to rapid clearance of the tracer from normal organs. Low levels of radioactivity were also detected in the GLP-1R positive tumor and normal organs after coinjection with excessive unlabeled peptides. Immunohistochemistry and Western Blots results confirmed that GLP-1R was widely expressed in PC-3 prostate cancers. Conclusion: 18F-Al labeled exendin-4 analog might be a promising tracer for in vivo detecting GLP-1R positive prostate cancer with the advantage of facile synthesis and favorable pharmacokinetics. It may be useful in differential diagnosis, molecularly targeted therapy and prognosis of the cancers.

Ferroptosis and autophagy are the two forms of the regulation of cell death that play important roles in cancer therapy. However, little is known about the combination of the therapeutic effects of ferroptosis and autophagy in cancer therapy. Here, in this study, we constructed a novel carrier-free nanodrug called nanoparticle ferritin-bound erastin and rapamycin (NFER). The NFER nanodrug was prepared by the emulsification technique; it exhibited an average size of 78.8 nm and zeta potential of −25.9 ± 3.3 mV. Controllable drug encapsulation efficiency and loading ratios in NFER could be obtained. This nanodrug showed high stability in both water and PBS for several days. The release studies demonstrated that rapamycin and erastin could reach equilibrium after 24 h and 36 h, respectively; the maximum values of the released percentages of both reached beyond 30%. An in vitrostudy revealed that NFER showed robust ferroptosis-inducing capability by the downregulation of glutathione peroxidase-4 (GPX4) and lipid peroxidation accumulation. The autophagy process induced by rapamycin in NFER also played an important role in strengthening ferroptosis. The selective cancer cell killing ability of NFER was verified in cancer cells and normal cells. The ferroptosis-induced cytotoxicity was confirmed through several ferroptosis and autophagy inhibitors. Furthermore, the NFER nanodrug showed an improved control of tumor recurrence in the 4T1 tumor resection model. In summary, these results demonstrated that NFER exhibited excellent properties as a nanodrug, and the cell death induced by NFER was through an autophagy-associated ferroptosis pathway. This study based on protein nanodrug-induced autophagy-associated ferroptosis would provide a new insight into cancer therapy.

Background:Intracranial aneurysm (IA) is one of the most common causes of subarachnoid hemorrhage which is associated with high mortality and morbidity. Predicting aneurysms’ rupture risk has been an ongoing challenge clinically. The objective of this study is to utilize machine learning algorithms to predict IA’s rupture status based on simple parameters that can be measured in the daily clinical setting.Methods:A total of 448 IA cases with known rupture status (228 unruptured and 220 ruptured) were collected across three tertiary hospitals between November 2017 and November 2020. Twelve demographic and morphological parameters were obtained from all cases. Six machine learning models, namely Naive Bayes, Support Vector Machine, K-Nearest Neighbours, Gradient Boosting, Decision tree and Random Forest algorithms were applied to the dataset. Sensitivity, specificity and accuracy of each model was calculated along with Area Under the Curve (AUC) and F1 score. Furthermore, the relevant importance of all 12 variables was assessed.Results:Gradient Boosting and Random Forest algorithms were the ML algorithms with the highest AUC of 0.69, followed by Naive Bayes (AUC=0.66). Overall, Random Forest algorithm was the best-formed model with 67% sensitivity and 63% specificity. Support Vector Machine model gave the lowest favorable AUC of 0.45 with 45% sensitivity and 48% specificity. The most significant variable was the aneurysm neck width, followed by the parent artery diameter.Conclusions:Machine learning has the potential to assist with the IA’s rupture prediction by using simple variables that can be easily obtained in clinical practice. Further research, especially a larger sample size is required to improve diagnostic accuracy before its clinical translation.

Abstract: This paper focuses on how to determine the instantaneous damping of the semi-active tuned mass damper (SATMD) with continuously variable damping. An off-and-towards-equilibrium (OTE) algorithm is employed to examine the control performance of the structure/SATMD system by considering the damping as an assumptive control action. The damping modification of the SATMD is carried out according to the proposed OTE algorithm, which is formulated based on analysis of the structural movement under external excitations, and the measured responses of the structure at every time instant. As examples two numerical simulations of a five-storey and a ten-storey shear structures with a SATMD on the roof are conducted. The effectiveness on vibration reduction of MDOF systems subjected to seismic excitations is discussed. Analysis results show that the behavior of the structure with a SATMD is significantly improved and the feasibility of applying the OTE algorithm to the structural control design of SATMD is also verified.

The particle morphological properties, such as sphericity, concavity and convexity, of a granular assembly significantly affect its macroscopic and microscopic compressive behaviors under isotropic loading condition. However, limited studies on investigating the microscopic behavior of the granular assembly with real particle shapes under isotropic compression were reported. In this study, X-ray computed tomography (μCT) and discrete element modeling (DEM) were utilized to investigate isotropic compression behavior of the granular assembly with regard to the particle morphological properties, such as particle sphericity, concavity and interparticle frictions. The μCT was first used to extract the particle morphological parameters and then the DEM was utilized to numerically investigate the influences of the particle morphological properties on the isotropic compression behavior. The image reconstruction from μCT images indicated that the presented particle quantification algorithm was robust, and the presented microscopic analysis via the DEM simulation demonstrated that the particle surface concavity significantly affected the isotropic compression behavior. The observations of the particle connectivity and local void ratio distribution also provided insights into the granular assembly under isotropic compression. Results found that the particle concavity and interparticle friction influenced the most of the isotropic compression behavior of the granular assemblies.

Self‐assembled supramolecular nanoparticles have remarkable benefits in bioimaging and drug delivery. Here it is first reported that polyphenol and poloxamer self‐assemble supramolecular nanoparticles (PPNPs). PPNPs are fabricated by multivalent hydrogen bonding between tannic acid and Pluronic F‐127 together with hydrophobic interactions of poly(propylene oxide) chains, to be applied in tumor near‐infrared fluorescence (NIRF) imaging and positron emission tomography (PET) imaging. With near‐infrared fluorescent dyes such as IR780 encapsulated via hydrophobic interactions, PPNPs are used in NIRF imaging. PPNPs with excess phenolic hydroxyl groups chelating positron emitting radionuclide 89Zr function as a PET contrast agent. The in vivo results show surprisingly higher fluorescence intensity in tumors than in other tissues. In addition, PPNPs exhibit good biocompatibility in various cell lines and do not induce hemolysis in vitro. In this study, it is demonstrated that biodegradable and biocompatible PPNPs are an excellent bimodal contrast agent for in vivo tumor imaging. Polyphenol–poloxamer supramolecular nanoparticlesare fabricated with a facile self‐assembly process. Near‐infrared fluorescence (NIRF) dye IR780 and positron emitting radionuclide 89Zr can be loaded by taking advantage of the hydrophobic center and excess phenolic hydroxyl in the nanoparticle. This supramolecular nanoparticle obtains excellent tumor NIRF and positron emission tomography imaging abilities.