Fabricating functional circuits on 3D freeform surfaces via intense pulsed light-induced zinc mass transfer.

Deployment of functional circuits on a 3D freeform surface is of significant interest to wearable devices on curvilinear skin/tissue surfaces or smart Internet-of-Things with sensors on 3D objects. Here we present a new fabrication strategy that can directly print functional circuits either transient or long-lasting onto freeform surfaces by intense pulsed light-induced mass transfer of zinc nanoparticles (Zn NPs). The intense pulsed light can locally raise the temperature of Zn NPs to cause evaporation. Lamination of a kirigami-patterned soft semi-transparent polymer film with Zn NPs conforming to a 3D surface results in condensation of Zn NPs to form conductive yet degradable Zn patterns onto a 3D freeform surface for constructing transient electronics. Immersing the Zn patterns into a copper sulfate or silver nitrate solution can further convert the transient device to a long-lasting device with copper or silver. Functional circuits with integrated sensors and a wireless communication component on 3D glass beakers and seashells with complex surface geometries demonstrate the viability of this manufacturing strategy.

Artificial intelligence in theranostics of gastric cancer, a review.

Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

Confining Prepared Ultrasmall Nanozymes Loading ATO for Lung Cancer Catalytic Therapy/Immunotherapy.

Nanozymes with inherent enzyme-mimicking catalytic properties combat malignant tumor progression via catalytic therapy, while the therapeutic efficacy still needs to be improved. In this work, ultrasmall platinum nanozymes (nPt) in a confined domain of a wormlike pore channel in gold nanobipyramidal-mesoporous silica dioxide nanocomposites, producing nanozyme carriers AP-mSi with photoenhanced peroxidase ability, are innovatively synthesized. Afterward, based on the prepared AP-mSi, a lung-cancer nanozymes probe (AP-HAI) is ingeniously produced by removing the SiO2 template, modifying human serum albumin, and loading atovaquone molecules (ATO) as well as IR780. Under NIR light irradiation, inner AuP and IR780 collaborate for photothermal process, thus facilitating the peroxidase-like catalytic process of H2 O2 . Additionally, loaded ATO, a cell respiration inhibitor, can impair tumor respiration metabolism and cause oxygen retention, hence enhancing IR780's photodynamic therapy (PDT) effectiveness. As a result, IR780's PDT and nPt nanozymes' photoenhanced peroxidase-like ability endow probes a high ROS productivity, eliciting antitumor immune responses to destroy tumor tissue. Systematic studies reveal that the obvious reactive oxygen species (ROS) generation is obtained by the strategy of using nPt nanozymes and reducing oxygen consumption by ATO, which in turn enables lung-cancer synergetic catalytic therapy/immunogenic-cell-death-based immunotherapy. The results of this work would provide theoretical justification for the practical use of photoenhanced nanozyme probes.

Metal-organic frameworks based surface-enhanced Raman spectroscopy technique for ultra-sensitive biomedical trace detection.

Metal-organic frameworks (MOFs) have attracted widespread interest due to their unique and unprecedented advantages in microstructures and properties. Besides, surface-enhanced Raman scattering (SERS) technology has also rapidly developed into a powerful fingerprint spectroscopic technique that can provide rapid, non-invasive, non-destructive, and ultra-sensitive detection, even down to single molecular level. Consequently, a considerable amount of researchers combined MOFs with the SERS technique to further improve the sensing performance and broaden the applications of SERS substrates. Herein, representative synthesis strategies of MOFs to fabricate SERS-active substrates are summarized and their applications in ultra-sensitive biomedical trace detection are also reviewed. Besides, relative barriers, advantages, disadvantages, future trends, and prospects are particularly discussed to give guidance to relevant researchers.

Metal-organic framework engineered corn-like SERS active Ag@Carbon with controllable spacing distance for tracking trace amount of organic compounds.

Probing water-soluble organic compounds via Surface-enhanced Raman scattering (SERS) technique could be helpful to prevent harmful impacts of polluted water. A key limitation of restraining SERS technique in probing these pollutants is the difficulty to control the spacing distance of plasmonic nanoparticles within 10 nm so that SERS effect can be efficiently induced. Herein, a strategy of mass-producing Ag-based SERS active material with tunable spacing distance is reported. In brevity, metal-organic framework (MOF) engineered corn-like Ag@Carbon is synthesized by simply thermal treating Ag-MOF. The thermal treatment in-situ turns Ag+ into Ag nanoparticles (NPs), resulting in Ag NPs well-dispersed on the surface of the carbonized MOF and forming ordered SERS hotspots. Due to the spatial distance of Ag+ directly depends on the molecular diameter of MOF organic ligands, spacing distance of Ag NP is fixed at around 7 nm. Theoretical analysis and experimental study confirm that the uniformly distributed Ag NPs lead to desirable SERS activity. Further study evidences the presented corn-like Ag@Carbon could be a good candidate for tacking organic compounds with satisfactory sensitivity, specificity and low detection limit (10-8 M). Conclusively, these impressive results indicate a bright future of adopting the proposed strategy to design future SERS active materials.

Engineered mesenchymal stem cell-derived exosomes with high CXCR4 levels for targeted siRNA gene therapy against cancer.

Gene therapy has been used in a variety of diseases and shows brilliant anticancer or cancer suppression effects. Gene therapy is gradually evolving as the most compelling frontier hotspot in the field of cancer therapy. The current vehicles used in gene therapy have poor safety and low delivery efficiency, and thus, it is urgent to develop novel delivery vehicles for gene therapy. Due to the excellent stability and biosafety of exosomes, their use as drug carriers for novel nucleic acid therapy is in full swing, revealing huge prospects for clinical application. Mesenchymal stem cells (MSCs) have a natural homing property and can spontaneously accumulate at injury sites, inflammation sites, and even tumour sites. This feature is attributed to a variety of tropism factors expressed on their surface; for example, CXC chemokine receptor type 4 (CXCR4) can specifically bind to the highly expressed stromal cell derived factor-1 (SDF-1) on the tumour surface, which is essential for accumulation of MSCs at the tumour site. The mesenchymal stem cells used in this study were genetically engineered to obtain exosomes with high CXCR4 expression as carriers for targeted gene-drug delivery, and then, the Survivin gene was loaded via electrotransformation to construct a brand-new gene-drug delivery system (CXCR4high Exo/si-Survivin). Finally, related in vivo and in vitro experiments were conducted. We observed that the new delivery system can efficiently aggregate at the tumour site and release siRNA into tumour cells, knocking down the Survivin gene in tumour cells in vivo and thereby inhibiting tumour growth. This new gene-drug delivery system has tremendous clinical transformation value and provides a new strategy for clinical treatment of tumours.

Intelligent COVID-19 screening platform based on breath analysis.

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p< 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and anF1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network,k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.

Smartphone Case-Based Gas Sensing Platform for On-site Acetone Tracking.

Gas sensor-embedded smartphones would offer the opportunity of on-site tracking of gas molecules for various applications, for example, harmful air pollutant alarms or noninvasive assessment of health status. Nevertheless, high power consumption and difficulty in replacing malfunctioned sensors as well as limited space in the smartphone to host the sensor restrain the relevant advancements. In this article, we create a smartphone case-based sensing platform by integrating the functional units into a smartphone case, which performs a low detection limit of 117 ppb to acetone and high specificity. Particularly, dimming glass-regulated light fidelity (Li-Fi) communication is successfully developed, allowing the sensing platform to operate with relatively low power consumption (around 217 mW). Experimental proof on harmful gas sensing and potential clinic application is implemented with the sensing platform, demonstrating satisfactory sensing performance and acceptable health risk pre-warning accuracy (87%). Thus, the developed smartphone case-based sensing platform would be a good candidate for realizing harmful gas alarms and noninvasive assessment of health status.

Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network.

Deep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

Manganese/iron-based nanoprobes for photodynamic/chemotherapy combination therapy of tumor guided by multimodal imaging.

Early diagnosis of tumors is crucial in selecting appropriate treatment options to achieve the desired therapeutic effect, but it is difficult to accurately diagnose cancer by a single imaging modality due to technical constraints. Therefore, we synthesized a type of Fe3O4 nanoparticle with manganese dioxide grown on the surface and then prepared it by loading photosensitive drugs and traditional Chinese medicine monomers to create an integrated diagnosis/treatment multifunctional nanoplatform: Fe3O4@MnO2-celastrol (CSL)/Ce6. This nanoplatform can have full advantage of the tumor microenvironment (TME) characteristics of hypoxia (hypoxia), acidic pH (acidosis), and increased levels of reactive oxygen species (e.g., H2O2), even outside the TME. Specific imaging and drug release can also enhance tumor therapy by adjusting the hypoxic state of the TME to achieve the combined effect of chemotherapy (CT) and photodynamic therapy (PDT). Moreover, the obtained Fe3O4@MnO2-CSL/Ce6 has H2O2- and pH-sensitive biodegradation and can release the anticancer drug celastrol (CSL) and photosensitizer Ce6 in TME and simultaneously generate O2 and Mn2+. Therefore, the "dual response" synergistic strategy also confers specific drug release on nanomaterials, relieves tumor hypoxia and antioxidant capacity, and achieves significant optimization of CT and PDT. Furthermore, the resulting Mn2+ ions and Fe3O4 nanoparticles can be used for T1/T2 magnetic resonance imaging on tumor-bearing mice, and the released Ce6 can simultaneously provide fluorescence imaging functions. Therefore, Fe3O4@MnO2-CSL/Ce6 realized the synergistic treatment of PDT and CT under multimodal near-infrared fluorescence/photoacoustic (photoacoustic) imaging monitoring, showing its great potential in the accurate medical treatment of tumors.

Preparation and characterization of a fully human monoclonal antibody specific for human tumor necrosis factor alpha.

Tumor necrosis factor alpha (TNFα) is an important inflammatory factor. It plays a cardinal role in inflammatory synovitis and articular matrix degradation, and is, therefore, a prime target for directed immunotherapy in autoimmune diseases. In this study, we screened and isolated the B cells secreting anti-TNFα antibody from patients with rheumatoid arthritis. The heavy-chain and light-chain sequences of the antibody were cloned and used to generate a stable Chinese hamster ovary (CHO) cell line producing the antibody, which was named Haidalimumab. Haidalimumab showed a TNFα binding affinity comparable to that of the antibody Humira, which is the best TNF inhibitor on the market. Furthermore, Haidalimumab could effectively neutralize recombinant human tumor necrosis factor alpha (rhTNFα) toxicity in a C57BL/6 mouse model and showed significant therapeutic effect in a tumor necrosis factor transgenic (TNF-Tg) mouse arthritis model. In conclusion, we developed a high-affinity, fully human anti-TNFα antibody with low immunogenicity that could potentially have significant therapeutic applications in rheumatoid arthritis or other autoimmune diseases.Abbreviations: ELISAenzyme linked immunosorbent assayRArheumatoid arthritisSDS-PAGEsodium dodecyl sulfate polyacrylamide gel electrophoresisrhTNFαrecombinant human tumor necrosis factor-alphaEC50concentration for 50% of maximal effectTNF-Tg micetumor necrosis factor transgenic miceAMDactinomycin DMTTmethylthiazolyldiphenyl-tetrazolium bromidePBSphosphate-buffered saline.

A novel anticoagulant affinity membrane for enhanced hemocompatibility and bilirubin removal.

Affinity membrane is widely employed to promote specific adsorption of toxins and reduce the blood purification therapeutic time. However, it suffers from insufficient toxin binding and low hemocompatibility. Herein, a novel anticoagulant affinity membrane (AAM) was developed to clear bilirubin from human blood in a pore-flow-through way. Firstly, a nylon net membrane with a regularly arranged pore as the matrix was coated with poly(pyrrole-3-carboxylic acid) via chemical vapor deposition (CVD) method. Then, poly(L-arginine) (PLA) as a highly specific ligand of bilirubin, was immobilized onto the surface of the composited membrane after the modification of heparin. Owing to the 3-dimensional molecular architecture of PLA, up to 86.1 % of bilirubin was efficiently cleared. Besides, the AAM exhibited effective anticoagulant activity in the measurement of clotting time, with suppressed thrombus formation, low hemolysis ratio, minimized platelet and leukocyte adhesion, and excellent biosafety. Therefore, the AAM has enormous potential in blood purification therapy for enhancing hemocompatibility and bilirubin removal.

ARV-825 Demonstrates Antitumor Activity in Gastric Cancer via MYC-Targets and G2M-Checkpoint Signaling Pathways.

OBJECTIVE: Suppression of bromodomain and extra terminal (BET) proteins has a bright prospect to treat MYC-driven tumors. Bromodomain containing 4 (BRD4) is one of the BET proteins. ARV-825, consisting of a BRD4 inhibitor conjugated with a cereblon ligand using proteolysis-targeting chimera (PROTAC) technology, was proven to decrease the tumor growth effectively and continuously. Nevertheless, the efficacy and mechanisms of ARV-825 in gastric cancer are still poorly understood. METHODS: Cell counting kit 8 assay, lentivirus infection, Western blotting analysis, Annexin V/propidium iodide (PI) staining, RNA sequencing, a xenograft model, and immunohistochemistry were used to assess the efficacy of ARV-825 in cell level and animal model. RESULTS: The messenger RNA (mRNA) expression of BRD4 in gastric cancer raised significantly than those in normal tissues, which suggested poor outcome of patients with gastric cancer. ARV-825 displayed higher anticancer efficiency in gastric cancer cells than OTX015 and JQ1. ARV-825 could inhibit cell growth, inducing cell cycle block and apoptosis in vitro. ARV-825 induced degradation of BRD4, BRD2, BRD3, c-MYC, and polo-like kinase 1 (PLK1) proteins in four gastric cancer cell lines. In addition, cleavage of caspase 3 and poly-ADP-ribose polymerase (PARP) was elevated. Knockdown or overexpression CRBN could increase or decrease, respectively, the ARV-825 IC50 of gastric cancer cells. ARV-825 reduced MYC and PLK1 expression in gastric cancer cells. ARV-825 treatment significantly reduced tumor growth without toxic side effects and downregulated the expression of BRD4 in vivo. CONCLUSIONS: High mRNA expression of BRD4 in gastric cancer indicated poor prognosis. ARV-825, a BRD4 inhibitor, could effectively suppress the growth and elevate the apoptosis of gastric cancer cells via transcription downregulation of c-MYC and PLK1. These results implied that ARV-825 could be a good therapeutic strategy to treat gastric cancer.

Fully Water-Soluble, High-Performance Transient Sensors on a Versatile Galactomannan Substrate Derived from the Endosperm.

Green electronics on biodegradable substrates from natural sources have gained broad interest because of the advantages of being biodegradable, recyclable, sustainable, and cost-efficient. This study presents a low-cost, yet simple extraction and purification method that explores aqueous extraction and precipitation with ethanol for the synthesis of galactomannan films. In salient contrast to the other materials of natural origin, the process to obtain galactomannan films is energy efficient and environmentally friendly. As an alternative biodegradable material, galactomannan has direct relevance to the recent emerging biodegradable or transient electronics. The galactomannan substrate with temperature sensors and electrodes fabricated from zinc, a biodegradable material noted for its essential biological function, demonstrates a high-precision measurement of temperature and high-fidelity monitoring of electrophysiological signals (electromyogram or electrocardiogram). The resulting disposable sensors disappear without a trace in water and produce environmentally benign end products that could even be used for alkaline soil amendments. The set of materials explored in this study is also stable in organic solutions, enabling solvent-based fabrication that may be combined with recent advances in additive manufacturing techniques for a novel manufacturing method.