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Despite the numerous bacteria detection and elimination techniques available nowadays, sensitive diagnosis and treatment of sepsis (caused by the presence of bacteria in the bloodstream), especially at the early stage, remain big challenges. Here we report a nanosystem for early sepsis diagnosis and complete extracorporeal blood disinfection, based on iron oxide magnetic nanoparticles functionalized with chlorin e6 molecules and bacterial species-identifiable aptamers (FeO-Ce6-Apt). We demonstrate that the FeO-Ce6-Apt nanosystem can achieve simultaneous blood bacterial species identification and enrichment in a single step, and the enriched bacteria can be easily detected with the assistance of fluorescence microscopic determination. Based on this FeO-Ce6-Apt nanosystem, successful diagnosis of sepsis caused by a single (Staphylococcus aureus) or multiple species (Staphylococcus aureus and Escherichia coli) of bacteria in mice has been realized. Compared to the gold standard blood culture method, this FeO-Ce6-Apt nanosystem-based strategy has a comparable detection sensitivity (around 10 colony-forming units) but a significantly shortened diagnosis turnaround time (within 1.5 h), revealing its great potential for early sepsis diagnosis in clinical settings. Moreover, benefitting from the strong photodynamic effect of the FeO-Ce6-Apt nanosystem, complete extracorporeal blood disinfection has been achieved. Remarkably, we also demonstrate that the disinfected blood can be reused for mice transfusion application without inducing adverse reactions, indicating the fruitful potential of the FeO-Ce6-Apt nanosystem for sepsis treatment. Apart from the sepsis-associated applications, we believe that the FeO-Ce6-Apt nanosystem could find wide applications in the fields of health and environmental sciences that require bacteria monitoring and sterilization.

A flexible graphene field-effect transistor (Gr-FET) biosensor for ultrasensitive and specific detection of miRNA without labeling and functionalization is reported. The flexible biosensor presents robust performance even after multiple cycles of bending to a cylinder with an 8 mm radius. A DNA probe is designed with partial segment complementary to target miRNA, and immobilized on the graphene surface though π−π stacking interaction. After capture of target miRNA, a Dirac point shift in Gr-FET is induced, which shows a linear relationship with the target miRNA concentration on a semi-log scale. The Gr-FET-based biosensor finishes miRNA detection in 20 min, and is able to achieve a miRNA detection limit as low as 10 fM without any functionalization and labeling. The interaction processes of DNA-graphene and DNA-miRNA are confirmed through surface-enhanced Raman scattering technology. The proposed biosensor will have prospective applications in wearable electronics for health monitoring and disease diagnosis.

The recent outbreak of coronavirus disease 2019 (COVID-19) is highly infectious, which threatens human health and has received increasing attention. So far, there is no specific drug or vaccine for COVID-19. Therefore, it is urgent to establish a rapid and sensitive early diagnosis platform, which is of great significance for physical separation of infected persons after rapid diagnosis. Here, we propose a colorimetric/SERS/fluorescence triple-mode biosensor based on AuNPs for the fast selective detection of viral RNA in 40 minutes. AuNPs with average size of 17 nm were synthesized, and colorimetric, surface enhanced Raman scattering (SERS), and fluorescence signals of sensors are simultaneously detected based on their basic aggregation property and affinity energy to different bio-molecules. The sensor achieves a limit detection of femtomole level in all triple modes, which is 160 fM in absorbance mode, 259 fM in fluorescence mode, and 395 fM in SERS mode. The triple-mode signals of the sensor are verified with each other to make the experimental results more accurate, and the capacity to recognize single-base mismatch in each working mode minimizes the false negative/positive reading of SARS-CoV-2. The proposed sensing platform provides a new way for the fast, sensitive, and selective detection of COVID-19 and other diseases.

The rapid development of two-dimensional (2D) transition-metal dichalcogenides has been possible owing to their special structures and remarkable properties. In particular, palladium diselenide (PdSe2) with a novel pentagonal structure and unique physical characteristics have recently attracted extensive research interest. Consequently, tremendous research progress has been achieved regarding the physics, chemistry, and electronics of PdSe2. Accordingly, in this review, we recapitulate and summarize the most recent research on PdSe2, including its structure, properties, synthesis, and applications. First, a mechanical exfoliation method to obtain PdSe2 nanosheets is introduced, and large-area synthesis strategies are explained with respect to chemical vapor deposition and metal selenization. Next, the electronic and optoelectronic properties of PdSe2 and related heterostructures, such as field-effect transistors, photodetectors, sensors, and thermoelectric devices, are discussed. Subsequently, the integration of systems into infrared image sensors on the basis of PdSe2 van der Waals heterostructures is explored. Finally, future opportunities are highlighted to serve as a general guide for physicists, chemists, materials scientists, and engineers. Therefore, this comprehensive review may shed light on the research conducted by the 2D material community.

(Mi)RNAs are important biomarkers for cancers diagnosis and pandemic diseases, which require fast, ultrasensitive, and economical detection strategies to quantitatively detect exact (mi)RNAs expression levels. The novel coronavirus disease (SARS-CoV-2) has been breaking out globally, and RNA detection is the most effective way to identify the SARS-CoV-2 virus. Here, we developed an ultrasensitive poly- l -lysine (PLL)-functionalized graphene field-effect transistor (PGFET) biosensor for breast cancer miRNAs and viral RNA detection. PLL is functionalized on the channel surface of GFET to immobilize DNA probes by the electrostatic force. The results show that PGFET biosensors can achieve a (mi)RNA detection range of five orders with a detection limit of 1 fM and an entire detection time within 20 min using 2 μL of human serum and throat swab samples, which exhibits more than 113% enhancement in terms of sensitivity compared to that of GFET biosensors. The performance enhancement mechanisms of PGFET biosensors were comprehensively studied based on an electrical biosensor theoretical model and experimental results. In addition, the PGFET biosensor was applied for the breast cancer miRNA detection in actual serum samples and SARS-CoV-2 RNA detection in throat swab samples, providing a promising approach for rapid cancer diagnosis and virus screening.

Autophagy, a metabolic pathway that plays an important role in maintaining the dynamic balance of cells, has two types, i.e. non-selective autophagy and selective autophagy. The role of non-selective autophagy is primarily to allow cells to circulate nutrients in an energy-limited environment, while selective autophagy primarily cleans up the organelles inside the cells to maintain the cell structure. The NLRP3 inflammasome is an innate immune response produced by the organism that can promote the secretion of interleukin-1β and interleukin-18 through caspase-1 activation and resist the damage of some pathogens. However, when the NLRP3 inflammasome is overactivated, it can cause various inflammatory diseases, such as inflammatory liver disease and inflammatory bowel disease. Many previous studies have shown that autophagy can inhibit the NLRP3 inflammasome, while in recent years, new studies have found that autophagy can also promote the NLRP3 inflammasome in some cases, and the NLRP3 inflammasome can, in turn, affect autophagy. In this review, the interaction between autophagy and the NLRP3 inflammasome is explored, and then the application of this interaction in disease treatment is discussed.

Coverage-based fuzzing has been actively studied and widely adopted for finding vulnerabilities in real-world software applications. With coverage information, such as statement coverage and transition coverage, as the guidance of input mutation, coverage-based fuzzing can generate inputs that cover more code and thus find more vulnerabilities without prerequisite information such as input format. Current coveragebased fuzzing tools treat covered code equally. All inputs that contribute to new statements or transitions are kept for future mutation no matter what the statements or transitions are and how much they impact security. Although this design is reasonable from the perspective of software testing that aims at full code coverage, it is inefficient for vulnerability discovery since that 1) current techniques are still inadequate to reach full coverage within a reasonable amount of time, and that 2) we always want to discover vulnerabilities early so that it can be fixed promptly. Even worse, due to the non-discriminative code coverage treatment, current fuzzing tools suffer from recent anti-fuzzing techniques and become much less effective in finding vulnerabilities from programs enabled with anti-fuzzing schemes. To address the limitation caused by equal coverage, we propose coverage accounting, a novel approach that evaluates coverage by security impacts. Coverage accounting attributes edges by three metrics based on three different levels: function, loop and basic block. Based on the proposed metrics, we design a new scheme to prioritize fuzzing inputs and develop TortoiseFuzz, a greybox fuzzer for finding memory corruption vulnerabilities. We evaluated TortoiseFuzz on 30 real-world applications and compared it with 6 state-of-the-art greybox and hybrid fuzzers: AFL, AFLFast, FairFuzz, MOPT, QSYM, and Angora. Statistically, TortoiseFuzz found more vulnerabilities than 5 out of 6 fuzzers (AFL, AFLFast, FairFuzz, MOPT, and Angora), and it had a comparable result to QSYM yet only consumed around 2% of QSYM's memory usage on average. We also compared coverage accounting metrics with two other metrics, AFL-Sensitive and LEOPARD, and TortoiseFuzz performed significantly better than both metrics in finding vulnerabilities. Furthermore, we applied the coverage accounting metrics to QSYM and noticed that coverage accounting helps increase the number of discovered vulnerabilities by 28.6% on average. TortoiseFuzz found 20 zero-day vulnerabilities with 15 confirmed with CVE identifications.