SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8.

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

High-Throughput µPAD with Cascade Signal Amplification through Dual Enzymes for arsM in Paddy Soil.

The arsM gene is a critical biomarker for the potential risk of arsenic exposure in paddy soil. However, on-site screening of arsM is limited by the lack of high-throughput point-of-use (POU) methods. Here, a multiplex CRISPR/Cas12a microfluidic paper-based analytical device (µPAD) was constructed for the high-throughput POU analysis of arsM, with cascade amplification driven by coupling crRNA-enhanced Cas12a and horseradish peroxidase (HRP)-modified probes. First, seven crRNAs were designed to recognize arsM, and their LODs and background signal intensities were evaluated. Next, a step-by-step iterative approach was utilized to develop and optimize coupling systems, which improved the sensitivity 32 times and eliminated background signal interference. Then, ssDNA reporters modified with HRP were introduced to further lower the LOD to 16 fM, and the assay results were visible to the naked eye. A multiplex channel microfluidic paper-based chip was developed for the reaction integration and simultaneous detection of 32 samples and generated a recovery rate between 87.70 and 114.05%, simplifying the pretreatment procedures and achieving high-throughput POU analysis. Finally, arsM in Wanshan paddy soil was screened on site, and the arsM abundance ranged from 1.05 × 106 to 6.49 × 107 copies/g; this result was not affected by the environmental indicators detected in the study. Thus, a coupling crRNA-based cascade amplification method for analyzing arsM was constructed, and a microfluidic device was developed that contains many more channels than previous paper chips, greatly improving the analytical performance in paddy soil samples and providing a promising tool for the on-site screening of arsM at large scales.

PlantIntronDB: a database for plant introns that host functional elements.

Although more and more attention has been focused on introns and the important role of plant introns in plant growth and development has been discovered, there is still a lack of an open and comprehensive database on plant introns with functional elements in current research. In order to make full use of large-scale sequencing data and help researchers in related fields to achieve high-throughput functional verification of identified plant introns with functional elements, we designed a database containing five plant species, PlantIntronDB and systematically analyzed 358, 59, 185, 210 and 141 RNA-seq samples from Arabidopsis thaliana (Arabidopsis), Gossypium raimondii (cotton), Zea mays (maize), Brassica napus (oilseed rape) and Oryza sativa Japonica Group (rice). In total, we found 100 126 introns that host functional elements in these five species. Specifically, we found that among all species, the number of introns with functional elements on the positive and negative strands is similar, with a length mostly smaller than 1500 bp, and the Adenine/Thymine (A/T) content is much higher than that of Guanine/Cytosine (G/C). In addition, the distribution of functional elements in introns varies among different species. All the above data can be downloaded for free in this database. This database provides a concise, comprehensive and user-friendly web interface, allowing users to easily retrieve target data based on their needs, using relevant organizational options. The database operation is simple and convenient, aiming to provide strong data support for researchers in related fields to study plant introns that host functional elements, including circular RNAs, lncRNAs, etc. Database URL:  http://deepbiology.cn/PlantIntronDB/.

Double Immobilized Superhydrophobic and Lubricated Slippery Surface with Antibacterial and Antifouling Properties.

A "double immobilized" superhydrophobic and lubricated slippery surface was prepared by simultaneously immobilizing lubricating oil and bactericide molecules. The coordination function of metal organic frameworks (MOFs) was utilized to immobilize trimesic acid, a fungicide, as a ligand of the MOF by the cathodic electrodeposition technique. Aminated silicone oil was used as a lubricating oil and was immobilized to the superhydrophobic MOF film by the curing reaction with isocyanates. This technique is a facile strategy to conductive substrates for fabricating superhydrophobic and lubricated slippery surfaces with satisfactory antibacterial and antifouling properties.

Intron-capture RNA-seq reveals the landscape of intronic RNAs in Arabidopsis.

Intronic RNAs have been overlooked for a long time: They are functional, but treated as "junk." In this work, we designed a new sequencing strategy to investigate intronic RNAs. By using intron-capture RNA-seq, we systematically analyzed the intronic RNAs in Arabidopsis by zooming into the intronic regions an order of magnitude deeper than in previous work. Our key findings include: (1) Intron-capture RNA-seq is a much more efficient approach to analyze intronic RNAs than total RNA-seq and mRNA-seq. (2) We identified three types of intronic RNAs, and found that the GC pattern differs significantly between the introns with and without intronic RNAs. (3) We detected many hidden elements in introns, including circular RNAs, splice junctions, and transcripts that have previously been overlooked. (4) The expression of these intronic RNAs varies during the time course of pathogen infection, which indicates that an unknown mechanism may exist for these RNAs. (5) We also demonstrated that most of intronic RNAs are detectable in both Arabidopsis and rice, suggesting that these non-coding molecules are conserved. Taken together, this work proposes an efficient strategy to analyze intronic RNAs, and provides an unprecedented view of this essential component in biological pathways.

Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a common nervous system disease to affect mostly elderly people over the age of 65 years. However, the diagnosis of AD is mainly depend on the imaging examination, clinical assessments and neuropsychological tests, which may get error diagnosis results and are not able to detect early AD. Here, a rapid, non-invasive, and high accuracy diagnostic method for AD especially early AD is provided based on the laser tweezers Raman spectroscopy (LTRS) combined with machine learning algorithms. AD platelets from different 3xTg-AD transgenic rats at different stages of disease are captured to collect high signal-to-noise ratio Raman signals without contact by LTRS, which is then combined with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and principal component analysis (PCA)-canonical discriminate function (CDA) for classification. The results show that the normal and diseased platelets at 3-, 6- and 12-month AD are successfully distinguished and the accuracy is 91%, 68% and 97% respectively, which demonstrates the suggested method can provide a precise detection for AD diagnosis at early, middle and advanced stages.

Raman inks based on triple-bond-containing polymeric nanoparticles for security.

Developing security inks with spectral outputs/multiple colors, which have unique identification characteristics, is of great importance in enhancing the anti-counterfeiting strength of ink anti-counterfeiting technology. Herein, a print-driven triple-bond coding mode is proposed for the first time. Two kinds of triple-bond-containing polymeric nanoparticles (NPs) with Raman shifts at 2227 and 2241 cm-1 have been designed into printable ink, and the decimal coding output can be easily obtained by reasonably adjusting the proportions of the two polymeric NPs. Single Raman scattering inks can be used as invisible inks to print monochromatic patterns and words that the decoder can read out. According to the two-dimensional pixels of the graphics decoder, invisible colorful graphics can be printed with mixed inks under different polymer proportions. More interestingly, three-dimensional invisible patterns with stronger anti-counterfeiting strength can also be obtained in the double-layer anti-counterfeiting patterns with different proportions of ink by the spatial complementary coding mode. It is predicted that more security inks associated with triple-bond Raman signals will spur the application of the anti-counterfeiting field.

Photoreduced Ag+ surrounding single poly(4-cyanostyrene) nanoparticles for undifferentiated SERS sensing and killing of bacteria.

Developing a rapid, low cost and sensitive sensing strategy for undifferentiated detection and fast killing of bacterial pathogens are critical to alleviating bacteria infections. Here, we propose a direct photoreduction method to synthesize the SERS tag by integrating poly(4-cyanostyrene) nanoparticles (NPs) and silver ions, which are applied as bio-sensing system for bacteria sensing and fast killing. Under a focused laser spot, silver ions on the surface of the poly(4-cyanostyrene) NPs could be photoreduced into Ag NPs, thereby causing the Raman signal amplification of poly(4-cyanostyrene) NPs up to 40 times, and there is a good linear correlation between the Raman intensity of poly(4-cyanostyrene) NPs and different concentrations of Ag+. Moreover, 4-mercaptophenylboronic acid, performing the same recognition function for both the Gram-positive and Gram-negative bacteria, is used as bridge between the bacteria and Ag+ by the inherent chemical bonding. Based on further constructed bio-sensing system, we achieved the quick count and killing of both Gram-positive bacteria, e.g., Staphylococcus aureus (S. aureus), and Gram-negative bacteria, e.g., Escherichia coli (E. coli). Notably, the sensing strategy can detect at least ∼100 cells of E. coli, ∼10 cells of S. aureus and ∼10 cells of their mixture in less than 40 min. The detection accuracy for actual samples can also reach over 80% and the bacteria were entirely killed by Ag+ after the detection, avoiding bacterial contamination in the environment. This novel method is anticipated to perform as a simple yet effective tool for fast and sensitive bacteria counting and killing.

Global Research Trends in Tyrosine Kinase Inhibitors: Coword and Visualization Study.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) have achieved revolutionary results in the treatment of a wide range of tumors, and many studies on this topic continue to be published every year. Some of the published reviews provide great value for us to understand TKIs. However, there is a lack of studies on the knowledge structure, bibliometric analysis, and visualization results in TKIs research. OBJECTIVE: This paper aims to investigate the knowledge structure, hotspots, and trends of evolution of the TKIs research by co-word analysis and literature visualization and help researchers in this field to gain a comprehensive understanding of the current status and trends. METHODS: We retrieved all academic papers about TKIs published between 2016 and 2020 from the Web of Science. By counting keywords from those papers, we generated the co-word networks by extracting the co-occurrence relationships between keywords, and then segmented communities to identify the subdirections of TKIs research by calculating the network metrics of the overall and local networks. We also mapped the association network topology, including the network within and between TKIs subdirections, to reveal the association and structure among varied subdirections. Furthermore, we detected keyword bursts by combining their burst weights and durations to reveal changes in the focus of TKIs research. Finally, evolution venation and strategic diagram were generated to reveal the trends of TKIs research. RESULTS: We obtained 6782 unique words (total frequency 26,175) from 5584 paper titles. Finally, 296 high-frequency words were selected with a threshold of 10 after discussion, the total frequency of which accounted for 65.41% (17,120/26,175). The analysis of burst disciplines revealed a variable number of burst words of TKIs research every year, especially in 2019 and 2020, such as HER2, pyrotinib, next-generation sequencing, immunotherapy, ALK-TKI, ALK rearrangement. By network calculation, the TKIs co-word network was divided into 6 communities: C1 (non-small-cell lung cancer), C2 (targeted therapy), C3 (chronic myeloid leukemia), C4 (HER2), C5 (pharmacokinetics), and C6 (ALK). The venation diagram revealed several clear and continuous evolution trends, such as non-small-cell lung cancer venation, chronic myeloid leukemia venation, renal cell carcinoma venation, chronic lymphocytic leukemia venation. In the strategic diagram, C1 (non-small-cell lung cancer) was the core direction located in the first quadrant, C2 (targeted therapy) was exactly at the junction of the first and fourth quadrants, which meant that C2 was developing; and C3 (chronic myeloid leukemia), C4 (HER2), and C5 (pharmacokinetics) were all immature and located in the third quadrant. CONCLUSIONS: Using co-word analysis and literature visualization, we revealed the hotspots, knowledge structure, and trends of evolution of TKIs research between 2016 and 2020. TKIs research mainly focused on targeted therapies against varied tumors, particularly against non-small-cell lung cancer. The attention on chronic myeloid leukemia and pharmacokinetics was gradually decreasing, but the focus on HER2 and ALK was rapidly increasing. TKIs research had shown a clear development path: TKIs research was disease focused and revolved around "gene targets/targeted drugs/resistance mechanisms." Our outcomes will provide sound and effective support to researchers, funders, policymakers, and clinicians.

Rational synthesis of Three-Layered plasmonic nanocomposites of copper Sulfide/Gold/Zinc-Doped Prussian blue analogues for improved photothermal disinfection and wound healing.

Bacteria-infected wounds have imposed serious challenges in human health whereas the abuse of antibiotics makes bacteria drug-resistant and becoming more and more difficult to deal with. Herein, we developed a drug-free three-layered photothermal bactericide from inside to outside consisting of copper sulfide (CuS), gold (Au) and zinc-doped Prussian blue analogues (ZnPBA) (named as CuS@Au@ZnPBA). The CuS@Au@ZnPBA was demonstrated to possess remarkably-improved photothermal property and excellent biosafety. Local heat generated by CuS@Au@ZnPBA under the irradiation of 808 nm laser enables efficient bacteria ablation in vitro and in a mouse model of cutaneous wound infection. Meanwhile, the released zinc ions (Zn2+) could upregulate the genes involved in collagen deposition to accelerate wound healing. Overall, the finely-designed nanocomposites can serve as a promising kind of antibacterial alternative to current antibiotic therapies against bacterial wound infections.

A functional peptide-mediated colorimetric assay for mercury ion based on dual-modified gold nanoparticles.

In the work, a rapid and accurate biosensor for mercury ions (Hg2+) was constructed, with which aggregation of dual-modified (DGPFHR- and CALNN-) gold nanoparticles (D/C-AuNPs) could be triggered by the high specificity of peptides to Hg2+. The given peptide DGPFHR possesses great capability of capturing Hg2+, accompanied by the conformational folding. Under the circumstances, D/C-AuNPs were employed as the detection probes to accomplish the quantitative analysis of Hg2+. This is primarily because the specific Hg2+-induced folding of peptides reduces the electrostatic repulsion and steric hindrance, thus accelerating the AuNPs aggregation. The principle and application potential of this proposal was proved by evidence. And the results demonstrated that Hg2+ ions could be selectively detected as low as 28 nM with a linear range of 100-800 nM. In consideration of superior simplicity, selectivity, accuracy and stability, the protocol was advantageous over other projects in practical measurement of various water samples.

Precise Encoding of Triple-Bond Raman Scattering of Single Polymer Nanoparticles for Multiplexed Imaging Application.

Stimulated Raman scattering (SRS) microscopy in combination with innovative tagging strategies offers great potential as a universal high-throughput biomedical imaging tool. Here, we report rationally tailored small molecular monomers containing triple-bond units with large Raman scattering cross-sections, which can be polymerized at the nanoscale for enhancement of SRS contrast with smaller but brighter optical nanotags with artificial fingerprint output. From this, a class of triple-bond rich polymer nanoparticles (NPs) was engineered by regulating the relative dosages of three chemically different triple-bond monomers in co-polymerization. The bonding strategy allowed for 15 spectrally distinguishable triple-bond combinations. These accurately structured nano molecular aggregates, rather than long-chain macromolecules, could establish a universal method for generating small-sized biological SRS imaging tags with high sensitivity for high-throughput multi-color biomedical imaging.

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection.

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm-1 were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.

Factors influencing risk perception and nosocomial infection prevention practices of frontline nurses during the COVID-19 pandemic.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, exploring factors influencing nosocomial infection among frontline nurses may provide evidence to optimize prevention strategies in hospitals. METHOD: A large-scale online questionnaire survey of nurses' state-trait anxiety, job burnout, risk perception, workplace safety perception, knowledge about nosocomial infection, and preventive practices was conducted with 2795 frontline nurses working in the COVID-19 wards of six hospitals in Hubei Province, China, from February 1 to April 1, 2020. The questionnaire data were analyzed using the structural equation modeling (SEM) method to reveal the mechanisms influencing nurses' risk perception and preventive practices related to nosocomial COVID-19 infection. RESULTS: A model of the factors that influence nurses' risk perception and preventive practices regarding nosocomial COVID-19 infection was established. The model verified hypotheses regarding the impact of nurses' risk perception and preventive practices. Notably, the hypothesis that risk perception has an impact on nurses' preventive practices regarding nosocomial infection is not valid. Moreover, different marital and educational conditions are associated with significant differences in the impact of state anxiety on the execution of preventive practices, the impact of workplace safety perceptions on risk perception, and the impact of workplace safety perceptions on the execution of preventive practices. The effect of state anxiety on preventive practices differed significantly with different durations of work experience. CONCLUSIONS: According to the results of the influencing factor model, promoting the quality of training on nosocomial infection, meliorating workplace safety, and conducting timely and effective psychological interventions would aid in improving nurses' preventive practices. Meliorating workplace safety and easing state anxiety would be beneficial to reduce nurses' risk perception. These strategies are conducive to the optimization of policies for preventing nosocomial COVID-19 infections and similar infectious diseases.

A straightforward and reliable evaluation of Ag(I) binding affinity mediated by a peptide ligand for constructing an efficient sensing platform.

The reliable determination of the Ag(I) affinity for biomolecules is an essential issue in the fields of structural analysis and sensor design. However, the urgent problem confronting researchers is lack of a direct and accurate Ag(I) affinity evaluation as a reference standard for ligand analysis. We communicated here a straightforward and high-efficiency method of measuring Ag(I) affinity exactly on the basis of the unique calculation algorithm and the design of a special peptide RFPRDD (P) as Ag(I) binding motif. According to UV-vis competition between the corresponding complexes (AgP) and biomolecules (peptides, amino acids and ssDNA), the decrease of the signature at 300 nm characteristic of AgP was obtained for quantitative analysis. The primary advantages of this strategy were the widespread application, high accuracy and reference significance, which were corroborated by theoretical calculations. To identify its potential in biosensing, two kinds of testing models for Ag(I) were proposed by AgBP2-decorated and Ag4-decorated gold nanoparticles, the detection limits of which were 2 nM and 75 nM respectively. By contrast of the sensing property of the functional peptides (AgBP2, Ag4), we afforded evidence that this conception could be regarded as an evaluation criterion for the selection and performance optimization of sensitive elements, thereby holding a dominant position in the biosensors.

Promoted "Click" SERS Detection for Precise Intracellular Imaging of Caspase-3.

Although homogeneous detection of some biomolecules has been of great significance in clinical assay, it faces great challenges in achieving precise in situ imaging of biomolecules. In addition, nonspecific adsorption between probes and biomolecules and low sensitivity are still unfathomed problems. Herein, we developed a promoted "Click" surface enhanced Raman scattering (SERS) strategy for realizing highly selective homogeneous detection of biomolecules by simultaneous dual enhanced SERS emissions, obtaining mutually confirmed logical judgment. Taking caspase-3 as one of the biotargets, we have realized highly selective homogeneous detection of caspase-3 using this strategy, and precise intracellular imaging of caspase-3 can be in situ monitored in living cells or during cell apoptosis. In detail, polyA-DNA and the Asp-Glu-Val-Asp (DEVD)-containing peptide sequence were modified into alkyne and nitrile-coded Au nanoparticles (NPs). During the cell apoptosis process, the generated caspase-3 would lead to the cleavage of the tetra-peptide sequence DEVD, thereby removing the negative protection part from the peptide on Au NPs. Interestingly, two different triple bond-labeled Au NPs can be connected together through DNA hybridization to form SERS "hotspot", resulting in simultaneously enlarged triple bond Raman signals. Moreover, we found that the SERS intensity was positively related with caspase-3 concentration, which has a wide linear range (0.1 ng/mL to 10 µg/mL) and low detection limit (7.18 × 10-2 ng/mL). Remarkably, these simultaneously enlarged signals by "Click" SERS could be used for more precise imaging of caspase-3, providing mutually confirmed logical judgment based on two spliced SERS emissions, especially for their relative intensity.

On-site and quantitative SERS detection of trace 1, 2, 3-benzotriazole in transformer oil with colloidal lignin particles-based green pretreatment reagents.

Since 1, 2, 3-Benzotriazole (BTA) is one of the most commonly used metal passivators in transformer oil, on-site and quantitative detection of BTA plays a significant role in fast evaluation of the performance of the insulating oil. Herein, we proposed a cycle-growth synthetic protocol for yielding two-dimensional (2D) plane-based surface-enhanced Raman scattering (SERS) substrates with tunable optical property and controllable interparticle distance, and an extraction material, so called colloidal lignin particles (CLPs), for the fast separation of BTA from oil matrix. After BTA from transformer oil were adsorbed by hydrophilic CLPs, highly reproducible SERS signal of BTA can be obtained by dropping on the substrate. The characteristic Raman shift at 1386 cm-1 of BTA has been selected to establish a good linearity between its relative intensity and concentration in the range of 1-300 mg/L, and the detection limit for BTA was down to 0.12 mg/L. Moreover, the time consumption for the whole detection process of real sample including sample pretreatment and SERS measurements was less than 30 min. It is highly expected that the combination of CLPs with SERS can accomplish the on-site detection of trace BTA in transformer oil.

The small silver nanoparticle-assisted homogeneous sensing of thiocyanate ions with an ultra-wide window based on surface-enhanced Raman-extinction spectroscopy.

For the first time, we present an original sensing strategy with an ultra-wide detection window from 17 nM to 20 mM to detect SCN- ions. Initially, we investigated the clustering and optical properties of noble metal sol nanoparticles (NPs) due to the competitive interaction of thiocyanate ions (SCN-) and cetyltrimethylammonium bromide (CTAB) under weak acidic conditions, and found that different dimensions and scales of nanoclusters containing the alkyne-embedded Au@Ag NPs and relatively small Ag NPs could be achieved by the mediation of CTAB through electrostatic forces and hydrophobic interaction, in which SCN- could be covalently bonded with the silver surface of NPs to form a compact molecular layer (-Ag-S-C[triple bond, length as m-dash]N), and CTAB could only occupy remaining sites. In this process, we found that SCN- always runs counter to CTAB and tends to dissolve nanoclusters, so that they occupy the exposed surface of NPs in nanoclusters rather than the binding sites of one another. Remarkably, when the concentration of SCN- initially increased, two highly recognizable SERS emissions, which were assigned to alkyne reporter molecules (2208 cm-1) and C[triple bond, length as m-dash]N of SCN- (2110 cm-1), respectively, were rapidly detected, and their ratios (I2110/I2208) increased linearly proportional to the concentration of SCN- over a range of 17 nM to 172 µM, with a limit of detection (LOD) of 10 nM. With the further increase of SCN-, small Ag NPs started to desorb from the surface of individual Au@Ag NPs and dissociated in the solution but did not contribute to SERS signals. Instead, the surface plasmon resonance (SPR) peak of pure silver NPs at 385 nm increased gradually in the range from 0.5 to 20 mM with an LOD of 0.2 mM. Of particular significance, this simple sensor in conjunction with surface-enhanced Raman-extinction spectroscopy can be used for the rapid detection of extensive samples with an ultra-wide detection window, such as body fluids (saliva, urine, and serum) and food (milk powder and brassica vegetables), which is far superior to that of ion chromatography (IC).

Fine synthesis of Prussian-blue analogue coated gold nanoparticles (Au@PBA NPs) for sorting specific cancer cell subtypes.

Multiplex surface-enhanced Raman scattering (SERS) detection of markers without background in tumor biosystems has its superiority over other optical methods. Herein, we reported a strategy of quantitative discrimination of two breast cancer cell subtypes. Based on our previous studies, two kinds of Prussian blue analogue coated gold nanoparticles (Au@PBA NPs) were designed and synthesized by the replacement of Fe2+ with Pb2+ or Cu2+. Therefore, two distinct SERS emissions of C≡N bonds at 2122 cm-1 and 2176 cm-1 have been acquired. When modified with aptamers of epithelial cell adhesion molecule (EpCAM) and epidermal growth factor receptor (EGFR), which are both expressed in MCF-7 and MDA-MB-231 cell lines but in different levels, the SERS nanoprobes simultaneously identified the relative expression of these biomarkers on the cell surface, providing a good example for ratiometric detection in biosystems without any interference. Each surface marker of tumor cells corresponds to a single SERS emission. Thus, each subtype could be described in a molecular profiling way through duplex C≡N bonds-based SERS emission, which is more advanced than traditional flow cytometry method.

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization.

BACKGROUND: Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear. OBJECTIVE: The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. METHODS: A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. RESULTS: The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications. CONCLUSIONS: This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine.