A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

HTLV-1 activates YAP via NF-κB/p65 to promote oncogenesis.

Adult T-cell leukemia/lymphoma (ATL) is an aggressive malignancy caused by human T-cell leukemia virus type 1 (HTLV-1) infection. HTLV-1 exerts its oncogenic functions by interacting with signaling pathways involved in cell proliferation and transformation. Dysregulation of the Hippo/YAP pathway is associated with multiple cancers, including virus-induced malignancies. In the present study, we observe that expression of YAP, which is the key effector of Hippo signaling, is elevated in ATL cells by the action of the HTLV-1 Tax protein. YAP transcriptional activity is remarkably enhanced in HTLV-1-infected cells and ATL patients. In addition, Tax activates the YAP protein via a mechanism involving the NF-κB/p65 pathway. As a mechanism for this cross talk between the Hippo and NF-κB pathways, we found that p65 abrogates the interaction between YAP and LATS1, leading to suppression of YAP phosphorylation, inhibition of ubiquitination-dependent degradation of YAP, and YAP nuclear accumulation. Finally, knockdown of YAP suppresses the proliferation of ATL cells in vitro and tumor formation in ATL-engrafted mice. Taken together, our results suggest that p65-induced YAP activation is essential for ATL pathogenesis and implicate YAP as a potential therapeutic target for ATL treatment.

CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass.

The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (mE ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected mE is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n-type and p-type semiconductors with low mE . Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the mE of any structure, with the area under the curve of 0.904 for n-type semiconductors and 0.896 for p-type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest mE database is built that contains 126 335 entries and screens out 466 semiconductors with low mE for transparent conductive materials, photovoltaic materials, and water-splitting materials. Moreover, a user-friendly and interactive CEEM web is provided that supports query, prediction, and explanation of mE . CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high-performance semiconductors.

Anode-Free Aqueous Aluminum Ion Batteries.

Aqueous aluminum ion batteries (AAIBs) possess the advantages of high safety, cost-effectiveness, eco-friendliness and high theoretical capacity. However, the Al2O3 film on the Al anode surface, a natural physical barrier to the plating of hydrated aluminum ions, is a key factor in the decomposition of the aqueous electrolyte and the severe hydrogen precipitation reaction. To circumvent the obnoxious Al anode, a proof-of-concept of an anode-free AAIB is first proposed, in which Al2TiO5, as a cathode pre-aluminum additive (Al source), can replenish Al loss by over cycling. The Al-Cu alloy layer, formed by plating Al on the Cu foil surface during the charge process, possesses a reversible electrochemical property and is paired with a polyaniline (cathode) to stimulate the battery to exhibit high initial discharge capacity (175 mAh g-1), high power density (≈410 Wh L-1) and ultra-long cycle life (4000 cycles) with the capacity retention of ≈60% after 1000 cycles. This work will act as a primer to ignite the enormous prospective researches on the anode-free aqueous Al ion batteries.

Clustered tree regression to learn protein energy change with mutated amino acid.

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Surfactants Influencing the Biocatalytic Performance of Natural Alkane-Degrading Bacteria via Interfacial Biocatalysis in Pickering Emulsions.

Setting superhydrophobic Mycobacterium sp. as an example, the hydrophobic bacteria acting as demulsifying agents of surfactant-stabilized conventional emulsions, vice versa, the synergistic/antagonistic influence of nonionic surfactants (Tween 80 or Span 80) on the stability of the bacteria-stabilized Pickering emulsions was investigated. At the same time, the activated/suppression effect of nonionic surfactants on microbial degradation of tetradecane, which exhibited a dose-response relationship, was also found. The hydrophobic bacteria acting as demulsifying agents and the suppression influence of nonionic surfactants on the biocatalytic performance (indexing as biomass) of natural alkane-degrading bacteria, believed to be totally separated concepts previously, are for the first time found to be closely related to in situ surface modification of bacteria with nonionic surfactants. During the degradation of tetradecane by Mycobacterium sp. in the presence of nonionic surfactants, demulsification due to the bacteria acting as demulsifying agents and interfacial biocatalysis in the bacteria-stabilized Pickering emulsions are involved, which provides useful information to select optimal dispersants for marine oil spills.

Potential inhibitors for the novel coronavirus (SARS-CoV-2).

The lack of a vaccine or any effective treatment for the aggressive novel coronavirus disease (COVID-19) has created a sense of urgency for the discovery of effective drugs. Several repurposing pharmaceutical candidates have been reported or envisaged to inhibit the emerging infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but their binding sites, binding affinities and inhibitory mechanisms are still unavailable. In this study, we use the ligand-protein docking program and molecular dynamic simulation to ab initio investigate the binding mechanism and inhibitory ability of seven clinically approved drugs (Chloroquine, Hydroxychloroquine, Remdesivir, Ritonavir, Beclabuvir, Indinavir and Favipiravir) and a recently designed α-ketoamide inhibitor (13b) at the molecular level. The results suggest that Chloroquine has the strongest binding affinity with 3CL hydrolase (Mpro) among clinically approved drugs, indicating its effective inhibitory ability for SARS-CoV-2. However, the newly designed inhibitor 13b shows potentially improved inhibition efficiency with larger binding energy compared with Chloroquine. We further calculate the important binding site residues at the active site and demonstrate that the MET 165 and HIE 163 contribute the most for 13b, while the MET 165 and GLN 189 for Chloroquine, based on residual energy decomposition analysis. The proposed work offers a higher research priority for 13b to treat the infection of SARS-CoV-2 and provides theoretical basis for further design of effective drug molecules with stronger inhibition.

Machine learning builds full-QM precision protein force fields in seconds.

Full-quantum mechanics (QM) calculations are extraordinarily precise but difficult to apply to large systems, such as biomolecules. Motivated by the massive demand for efficient calculations for large systems at the full-QM level and by the significant advances in machine learning, we have designed a neural network-based two-body molecular fractionation with conjugate caps (NN-TMFCC) approach to accelerate the energy and atomic force calculations of proteins. The results show very high precision for the proposed NN potential energy surface models of residue-based fragments, with energy root-mean-squared errors (RMSEs) less than 1.0 kcal/mol and force RMSEs less than 1.3 kcal/mol/Å for both training and testing sets. The proposed NN-TMFCC method calculates the energies and atomic forces of 15 representative proteins with full-QM precision in 10-100 s, which is thousands of times faster than the full-QM calculations. The computational complexity of the NN-TMFCC method is independent of the protein size and only depends on the number of residue species, which makes this method particularly suitable for rapid prediction of large systems with tens of thousands or even hundreds of thousands of times acceleration. This highly precise and efficient NN-TMFCC approach exhibits considerable potential for performing energy and force calculations, structure predictions and molecular dynamics simulations of proteins with full-QM precision.

Single atom modified two-dimensional bismuthenes for toxic gas detection.

Accurate detection of toxic gases at low concentrations is often difficult because they are colorless, odorless, flammable and denser than air. Therefore, it is urgent to develop highly stable and sensitive toxic gas detectors. However, most gas sensors operate at high temperatures, making the detection of toxic gases more challenging. Two-dimensional materials with high specific surface area and abundant modulation methods of properties provide new inspirations for the development of new toxic gas sensing materials. Here, bismuthene, a single element two-dimensional material with high carrier mobility and excellent stability, was used as a substrate material to investigate the effects of anchoring and doping on its gas detection performance by density functional theory (DFT) calculations. It is revealed that the surface structure altered by single metal atoms (Ba, Be, Ca, K, Li, Mg, Na, and Sr) can promote the improvement of gas detection sensitivity. Buckled honeycomb bismuthene (bBi) with the Be atom anchored (A-Be-Bi) show superior sensitivity to H2S, while D-Ca-Bi, D-Li-Bi, D-Mg-Bi and D-Sr-Bi also have relatively high toxic gas detection sensitivity. We further discussed the recovery times of these modified bBis at various temperatures to determine the potential for applications. The ultra-fast recovery time of less than 0.5 seconds demonstrates the potential of these systems at room temperature and can be applied to the manufacture of toxic gas sensors used under practical sensing conditions.

Adding nanoparticles to improve emulsion efficiency and enhance microbial degradation in Pickering emulsions.

Interfacial microbial degradation of alkane in Pickering emulsions stabilized by hydrophobic bacterial cells is a new mechanism for microbial degradation of water-insoluble chemicals, where both water-insoluble chemicals in the oil phase and water-soluble nutrients (such as nitrogen and phosphorus) in the water phase are bio-accessible to living microorganisms anchoring onto the oil-water interfaces. In the present work, super-hydrophobic Mycobacterium sp. (contact angle 168.6°) degradation of tetradecane was set up as a model. Addition of fumed SiO2 particles (Aerosil® R974) as a new strategy was developed to enhance tetradecane degradation where the biodegradation rate (based on the accumulated biomass) increased by approximately 80%. The enhanced effect of SiO2 particles on the tetradecane degradation attributed to the synergistic effect of SiO2 particles on the emulsion efficiency of Pickering emulsions stabilized by bacterial cells and then on the enhancement of interfacial microbial degradation in Pickering emulsions. KEY POINTS: • Interfacial microbial degradation in bacterial cells stabilized Pickering emulsions. • Adding fumed SiO2 particles to enhance microbial degradation of tetradecane. • Correlation relationship between emulsion efficiency and interfacial microbial degradation.

Epidemic trends of dyslipidemia in young adults: a real-world study including more than 20,000 samples.

BACKGROUND: There is an urgent need to learn more about the epidemiological features of dyslipidemia in youth to address the high burden of cardiovascular disease. METHODS: This experiment was an observational, cross-sectional study. The samples were collected from 22,379 college students at Xinjiang Medical University. RESULT: The overall prevalence of dyslipidemia was 13.17%, which was significantly higher in men (23%) than in women (7.2%), p < 0.01. Similarly, the prevalence rate of obesity in men (11.4%) was significantly higher than that in women (3.4%). The composition of blood lipids, such as triglyceride (TG), total cholesterol (TC), and low density lipoprotein cholesterol (LDL-C), began to increase gradually from the age of 22 and showed a sharp increase after the age of 30; however, a reverse trend was present in high density lipoprotein cholesterol (HDL-C). In terms of the proportion of dyslipidemia in both men and women, low HDL-C accounted for the largest proportion (74%), followed by elevated TGs (14.5%). The overall distribution of rates of dyslipidemia and excess weight showed a U-shaped trend with increasing age, with the lowest rates seen in the 20-24 age group. CONCLUSION: Our study sheds light on the epidemiological features of dyslipidemia in young adults and enriches the limited data available on dyslipidemia, providing a reference for the close monitoring and control of risk factors to reduce the occurrence and progression of atherosclerotic cardiovascular disease events.

Downregulated INHBB in endometrial tissue of recurrent implantation failure patients impeded decidualization through the ADCY1/cAMP signalling pathway.

PURPOSE: This study aims to identify the mechanism of Inhibin Subunit Beta B (INHBB), a member of the transforming growth factor-ß (TGF-ß) family involved in the regulation of human endometrial stromal cells (HESCs) decidualization in recurrent implantation failure (RIF). METHODS: RNA-seq was conducted to identify the differentially expressed genes in the endometria from control and RIF patients. RT-qPCR, WB, and immunohistochemistry were performed to analyse the expression levels of INHBB in endometrium and decidualised HESCs. RT-qPCR and immunofluorescence were used to detect changes in the decidual marker genes and cytoskeleton after knockdown INHBB. Then, RNA-seq was used to dig out the mechanism of INHBB regulating decidualization. The cAMP analogue (forskolin) and si-INHBB were used to investigate the involvement of INHBB in the cAMP signalling pathway. The correlation of INHBB and ADCY expression was analysed by Pearson's correlation analysis. RESULTS: Our results showed significantly reduced expression of INHBB in endometrial stromal cells of women with RIF. In addition, INHBB was increased in the endometrium of the secretory phase and significantly induced in in-vitro decidualization of HESCs. Notably, with RNA-seq and siRNA-mediated knockdown approaches, we demonstrated that the INHBB-ADCY1-mediated cAMP signalling pathway regulates the reduction of decidualization. We found a positive association between the expression of INHBB and ADCY1 in endometria with RIF (R2 = 0.3785, P = 0.0005). CONCLUSIONS: The decline of INHBB in HESCs suppressed ADCY1-induced cAMP production and cAMP-mediated signalling, which attenuated decidualization in RIF patients, indicating that INHBB is an essential component in the decidualization process.

Click Chemistry-Mediated Particle Counting Sensing via Cu(II)-Polyglutamic Acid Coordination Chemistry and Enzymatic Reaction.

An electrical resistance-based particle counter (ERPC) with simple operation and high resolution has proved to be a promising biosensing toolkit, whereas amplification-free ERPC biosensors are incapable of analyzing trace small molecules due to their relatively low sensitivity. In this work, click chemistry-mediated particle counting sensing of small-molecule hazards in food samples with high sensitivity was developed. In this strategy, unbound alkyne-functionalized polystyrene microspheres were collected by magnetic separation from the copper-ion-mediated click reaction between alkyne-functionalized polystyrene microspheres and azido-functionalized magnetic beads, which could be used as signal probes for the readout. This click chemistry-mediated ERPC biosensor converts the detection of targets to the quantification of copper ions or ascorbic acid by performing competitive immunoassay-based coordination chemistry and enzymatic reaction, respectively. The sensitivity of the ERPC biosensor has been improved by an order of magnitude due to the signal amplification effects of click chemistry, coordination adsorption, and enzyme catalysis. Furthermore, because of the efficient separation and enrichment of immunomagnetic beads and the robustness of click chemistry, the interference from food matrixes and immunoassay is effectively reduced, and thus, our strategy is exceedingly suitable for detecting trace targets in complex samples.

Azaphilone alkaloids: prospective source of natural food pigments.

Azaphilone, biosynthesized by polyketide synthase, is a class of fungal metabolites. In this review, after brief introduction of the natural azaphilone diversity, we in detail discussed azaphilic addition reaction involving conversion of natural azaphilone into the corresponding azaphilone alkaloid. Then, setting red Monascus pigments (a traditional food colorant in China) as example, we presented a new strategy, i.e., interfacing azaphilic addition reaction with living microbial metabolism in a one-pot process, to produce azaphilone alkaloid with a specified amine residue (red Monascus pigments) during submerged culture. Benefit from the red Monascus pigments with a specified amine residue, the influence of primary amine on characteristics of the food colorant was highlighted. Finally, the progress for screening of alternative azaphilone alkaloids (production from interfacing azaphilic addition reaction with submerged culture of Talaromyces sp. or Penicillium sp.) as natural food colorant was reviewed. KEY POINTS: • Azaphilic addition reaction of natural azaphilone is biocompatible • Red Monascus pigment is a classic example of azaphilone alkaloids • Azaphilone alkaloids are alterative natural food colorant.

Crystal substrate inhibition during microbial transformation of phytosterols in Pickering emulsions.

Water-oil interface of bacterial cell-stabilized Pickering emulsions is an exceptional habitat for microbial assimilation of both hydrophobic nutrients solubilized in oil phase and hydrophilic ones solubilized in water phase. Crystal substrate inhibition, i.e., decreasing phytosterol degradation with the increase loading of crystal phytosterols, is always observed during microbial transformation of phytosterols into steroid synthons in Mycolicibacterium sp (China Center of Industrial Culture Collection, CICC 21,097) cell-stabilized Pickering emulsions. In the present work, we confirmed that crystal substrate inhibition was attributed to the interaction between M. neoaurum and phytosterol crystals that led to the detachment of bacterial cells from the oil-water interfaces in bacterial cell-stabilized Pickering emulsions. Under the selected operation condition (25 ml BEHP per 40 ml water, 60 g/L glucose, 25 g/L phytosterols), the product androst-4-ene-3, 17-dione (AD) and androsta-1, 4-dien-3, 17-dione (ADD) concentration increased linearly with the progress of microbial transformation and reached almost 6 g/L at the 11th day. This is a paradigm for microbial transformation of crystal substrates as well as in the presence of other surface active additives (such as chitosan and nonionic surfactants) in bacterial cell-stabilized Pickering emulsions. KEY POINTS: • Microbial transformation of crystal phytosterols in Pickering emulsions • Crystal substrate inhibition occurring during microbial transformation • Interaction between phytosterol crystals and bacterial cells leading to demulsification.

Endobronchial ultrasound-guided transbronchial needle aspiration in patients with previously treated malignancies: diagnostic performance and predictive value.

BACKGROUND: Endobronchial ultrasound-guided transbronchial needle aspiration is a minimally invasive and effective sampling approach for patients with mediastinal or hilar lymphadenopathy. Increased recognition of the ultrasonographic features revealed the value of its images in predicting mediastinal lymph node malignancy. However, its diagnostic validity and the predictive value of its ultrasonographic features have not been demonstrated well in patients after systemic anti-tumor therapy. This study aimed to evaluate the efficiency of endobronchial ultrasound-guided transbronchial needle aspiration in patients with suspicious lymph nodes after anti-tumor therapy. METHODS: We retrospectively reviewed cases of endobronchial ultrasound-guided transbronchial needle aspiration performed between January 2019 and August 2021 at a single tertiary hospital center. Patients with suspected mediastinal or hilar lymph nodes within 5 years of systemic anti-tumor therapy were enrolled. Final diagnoses were determined by pathologic diagnoses of samples from transbronchial needle aspiration, surgery, or follow-up for at least 6 months. Ultrasonographic features were analyzed to assess the predictive value of malignant lymph nodes after treatment. RESULTS: Overall, 168 lymph nodes of 138 patients were analyzed. Among 110 (65.5%) malignant lymph nodes, 75 originated from lung cancers; the other 35 were from other malignancies. No complications related to endobronchial ultrasound-guided transbronchial needle aspiration were observed. Of 58 negative results of transbronchial needle aspiration, 51 were proven to be true negatives; 7 were false. The overall sensitivity and the negative predictive value were 94.02% and 87.93%, respectively. Univariate and multivariate analysis revealed the absence of central hilar structure and short axis > 10 mm as independent predictive factors for malignancy. CONCLUSIONS: Endobronchial ultrasound-guided transbronchial needle aspiration performs satisfactorily in diagnosing mediastinal and hilar lymphadenopathy even after anti-tumor treatment.

Correlation between imaging features on computed tomography and combined positive score of PD-L1 expression in patients with gastric cancer.

Objective: To explore the correlation between computed tomography (CT) features and combined positive score (CPS) of programmed cell death ligand 1 (PD-L1) expression in patients with gastric cancer (GC). Methods: This study reviewed an institutional database of patients who underwent GC operation without neoadjuvant chemotherapy between December 2019 and September 2020. The CPS results of PD-L1 expression of postoperative histological examination were recorded by pathology. Baseline CT features were measured, and their correlation with CPS 5 or 10 score groups of PD-L1 expression was analyzed. Results: Data for 153 patients with GC were collected. Among them, 124 were advanced GC patients, and 29 were early GC patients. None of the CT features significantly differed between CPS groups with a cutoff score of 5 and a score of 10 in patients with early GC. In advanced GC, the presence of lymph nodes with short diameters >10 mm was significantly different (P=0.024) between the CPS<5 and CPS≥5 groups. CT features such as tumor attenuation in the arterial phase, long and short diameter of the largest lymph node, the sum of long diameter of the two largest lymph nodes, the sum of short diameter of the two largest lymph nodes, and the presence of lymph nodes with short diameters >10 mm significantly differed between the CPS<10 and CPS≥10 groups in advanced GC. The sensitivity, specificity and area under receiver operating characteristic (ROC) curve of logistic regression model for predicting CPS≥10 was 71.7%, 50.0% and 0.671, respectively. Microsatellite instability (MSI) status was significantly different in CPS groups with cutoff score of 5 and 10 in advanced GC patients. Conclusions: CT findings of advanced GC patients with CPS≥10 showed greater arterial phase enhancement and larger lymph nodes. CT has the potential to help screen patients suitable for immunotherapy.

Direct Transverse Relaxation Time Biosensing Strategy for Detecting Foodborne Pathogens through Enzyme-Mediated Sol-Gel Transition of Hydrogels.

In this work, we develop a direct transverse relaxation time (T2) biosensing strategy and employ it for assaying foodborne pathogens relying on the alkaline phosphatase (ALP)-mediated sol-gel transition of hydrogels. ALP can catalyze the reaction to generate an acidic environment to transform the sol-state alginate solution to hydrogel, and this hydrogelation process can directly regulate the diffusion rate of water protons that results in a T2 change of water molecules. By means of enzyme-modulated sol-gel transition and antigen-antibody interactions, this T2 biosensor displays high sensitivity for detecting 50 CFU/mL S. enteritidis within 2 h. This biosensing strategy directly modulates the water molecules rather than magnetic probes in traditional methods, offering a straightforward, novel, and sensitive platform for pathogen detection.

Versatile Biosensing Toolkit Using an Electronic Particle Counter.

Development of a versatile biosensing toolkit is in urgent need for rapid and multiplexed detection applications. In this work, an electronic particle counter-implemented versatile biosensing toolkit has been developed for detecting a range of targets with high sensitivity, broad detection range, multiplexibility, simple operation, and low cost. The electrical resistance-based particle counter conventionally measuring the number of microspheres (1-100 µm) can quantify analytes. The versatility of this approach is verified by assaying small molecules, protein biomarkers, pathogen bacteria, and tumor cells using three strategies: (1) antigen-antibody interaction, (2) DNA hybridization, and (3) polypeptide recognition. More importantly, this biosensing toolkit allows the simultaneous detection of multiple targets with a broad detection range from pg mL-1 to µg mL-1, showing great potential as a powerful technique for food safety testing and biomedical diagnosis.