Highly sensitive antibiotic sensing based on optical weak value amplification: A case study of chloramphenicol.

The public health concern of antibiotic residues in animal-origin food has been a long-standing issue. In this work, we present a novel method for antibiotic detection, leveraging optical weak value amplification and harnessing an indirect competitive inhibition assay, which significantly boosts the system's sensitivity in identifying small molecule antibiotics. We chose chloramphenicol as a model compound and mixed it with chloramphenicol-bovine serum albumin conjugates to bind to the chloramphenicol antibody competitively. We achieved a broad linear detection range of up to 3.24 ng/mL and a high concentration resolution of 33.20 pg/mL. To further validate the universality of our proposed detection methodology, we successfully applied it to testing gibberellin and tetracycline. Moreover, we conducted regeneration experiments and real-sample correlation studies. This study introduces a novel strategy for the label-free optical sensing of small molecule antibiotics, greatly expanding the range of applications for sensors utilizing optical weak value amplification.

Realization of high-performance biosensor through sandwich analysis utilizing weak value amplification.

A label-free optical sandwich immunoassay sensor, utilizing weak value amplification and total internal reflection, was devised for real-time, high-sensitivity analysis and detection of low-concentration targets. 3D printed channels and sodium chloride solution were employed to ensure reproducibility, reliability, and stability of the measurements for calibration. The sandwich structure demonstrated enhanced responsiveness in the proposed optical biosensor through a comparative analysis of the direct assay and sandwich assay for detecting alpha-fetoprotein (AFP) at the same concentration. By optimizing the binding sequences of the coating antibody, target, and detection antibody in the sandwich method, a more suitable sandwich sensing approach based on weak value amplification was achieved. With this approach, the limit of detection (LOD) of 6.29 ng/mL (pM level) for AFP in PBS solution was achieved. AFP testing and regeneration experiments in human serum have proved the feasibility of our methods in detecting complex samples and the reusability of sensing chips. Additionally, the method demonstrated excellent selectivity for unpaired antigens. The efficacy of this methodology was evaluated by simultaneously detecting AFP, carcinoembryonic antigen (CEA), and CA15-3 on a singular sensor chip. In conclusion, the label-free sandwich immunoassay sensing scheme holds promise for advancing the proposed optical sensors based on weak value amplification in early diagnosis and prevention applications. Compared to other biomarker detection methods, it will be easier to promote in practical applications.

Optical Label-Free Aptasensor Based on Weak Value Amplification for Real-Time and Ultrasensitive Detection of IgE.

Allergy is a prevalent disease, and the potential allergic population is expanding with industrialization and changes in people's living standards. Serum immunoglobulin E (IgE) level is one of the critical indicators for determining allergy. Here, we proposed a simple, real-time monitoring, low chip cost, label-free aptamer biosensing strategy based on weak value amplification (WVA) for the quantitative detection of IgE in serum samples, enabling early and accurate diagnosis of allergic or hypersensitive patients. The aptasensor combined an imaging weak measurement system with the high specificity of the aptamer for the marker IgE. By modifying the amino group at the 3-terminal end, the anti-IgE aptamers can attach to a dopamine-modified prism's surface and selectively recognize IgE in human serum. In the presence of IgE, a specific binding reaction occurred, resulting in a change in the refractive index of the reactive region's surface, manifested as a change in the light intensity of the camera acquired experimental images. As the concentration of IgE increased, the relative light intensity advanced sequentially. The WVA-aptasensing strategy achieved a wide detection range of 0.01 ng/mL to 2 µg/mL in phosphate buffered saline buffer, with the resolution as low as 4.3 pg/mL. IgE testing experiments in human serum have proved the feasibility of our methods in detecting complex samples. In addition, the method specifically recognized IgE without interference from other proteins. We believe that our proposed sensing strategy opens up new possibilities for ultrahigh sensitivity screening of IgE and can be expanded to detecting other biomolecules.

DeepTree: Pathological Image Classification Through Imitating Tree-Like Strategies of Pathologists.

Digitization of pathological slides has promoted the research of computer-aided diagnosis, in which artificial intelligence analysis of pathological images deserves attention. Appropriate deep learning techniques in natural images have been extended to computational pathology. Still, they seldom take into account prior knowledge in pathology, especially the analysis process of lesion morphology by pathologists. Inspired by the diagnosis decision of pathologists, we design a novel deep learning architecture based on tree-like strategies called DeepTree. It imitates pathological diagnosis methods, designed as a binary tree structure, to conditionally learn the correlation between tissue morphology, and optimizes branches to finetune the performance further. To validate and benchmark DeepTree, we build a dataset of frozen lung cancer tissues and design experiments on a public dataset of breast tumor subtypes and our dataset. Results show that the deep learning architecture based on tree-like strategies makes the pathological image classification more accurate, transparent, and convincing. Simultaneously, prior knowledge based on diagnostic strategies yields superior representation ability compared to alternative methods. Our proposed methodology helps improve the trust of pathologists in artificial intelligence analysis and promotes the practical clinical application of pathology-assisted diagnosis.

Ultra-precise weak measurement-based interfacial biosensors.

In this study, an interfacial biosensing scheme with ultra-precision is proposed. The scheme uses weak measurement techniques to ensure ultra-high sensitivity of the sensing system while improving the stability of the system through self-referencing and pixel point averaging, thus achieving ultra-high detection accuracy of biological samples. In specific experiments, we have used the biosensor in this study to perform specific binding reaction experiments for protein A and Mouse IgG with a detection line of 2.71 ng/mL for IgG. In addition, the sensor is non-coated, simple in structure, easy to operate, and low in cost of use.

Predictive and prognostic molecular markers for cholangiocarcinoma in Han Chinese population.

Cholangiocarcinoma (CCA) is the most common malignant heterogeneous polygenetic carcinoma with a high incidence in Asia. Most patients would die within 1 year after diagnosis and the 5 year survival rate is less than 10-20% worldwide. Single nucleotide polymorphisms (SNPs) in genes regulate telomere maintenance, mitosis, and inflammation, and may help predict individual susceptibility to certain drugs, environmental factor, and risks to particular diseases. The gene-gene interaction and the regulation of SNPs have not been assessed extensively in CCA. According to our previous study, the GRB2-associated-binding protein (Gab1) gene rs3805246 (X(2) =5.015, P=0.025, OR=0.531, 95% CI 0.304-0.928) and epidermal growth factor receptor (EGFR) gene rs2007000 (X(2) =7.934, P=0.005, OR=2.148, 95% CI 1.255-3.675) presented significant difference between CCA patients and controls. This study conducted a population-based analysis using 225 CCA cases (153 biliary tract cancer patients and 72 gall bladder cancer patients) to assess the association between SNPs and progression of CCA patients, including the overall survival and the prognosis analysis. Results showed that an increased susceptibility of BTC was significantly associated with SNP loci distribution frequency in EGFR rs2107000 (X(2) =7.934, P=0.005, OR=2.148, 95% CI 1.255-3.675). Furthermore, multivariate factor regression analysis represented cholelithiasis medical history of BTC patients can be an effective evaluation criteria of BTC susceptibility in early stage. This study also assessed the relationship between these genotypic polymorphisms and clinicopathologic data, including tumor differentiation stage and overall survival. This is the first study identifying that EGFR polymorphisms are associated with BTC and EGFR rs2017000 polymorphisms may be an important survival predictor in BTC patients.

Essential role of polymorphism of Gab1, EGFR, and EGF for the susceptibility of biliary tract cancer.

Cholangiocarcinoma is a malignant neoplasm arising from the epithelial cells lining the biliary ducts and its occurrence can be anatomically classified as within the liver (intrahepatic) or outside the liver (extrahepatic). Extrahepatic cholangiocarcinoma, which can be called as biliary tract cancer (BTC), is the most common form of this malignancy, and its etiology is still unclear. In this study, we tried to elucidate the complicated association between receptor tyrosine kinase (RTK) gene polymorphisms and susceptibility of BTC by analyzing frequency distribution of genotypes and alleles of GRB2-associated-binding protein 1 (Gab1), endothelial growth factor receptor (EGFR), and endothelial growth factor (EGF) and identified potential risk of BTC for people carrying specific genotype of Gab1 and EGFR. Two hundred twenty-five and 300 patients with BTC and cholelithiasis (gallstone (GS)), respectively, and 300 controls matched by age, sex, and ethnicity with patients were recruited from Shengjing Hospital of China Medical University from January 2008 to July 2011 with informed consents. Genomic DNA of BTC group was extracted and purified from formalin-fixed, paraffin-embedded tumor tissue sections using QiAamp DNA FFPE Tissue kit. For GS group and controls, DNA was extracted from peripheral blood leukocytes using genomic DNA extraction kit from Aid Lab. Target genes of RTK family were identified from National Center of Biotechnology Information (NCBI) SNP database and Japanese Single Nucleotide Polymorphisms (JSNP) database. Frequency distribution of genotypes and alleles was analyzed using HapMap Project database. All of the statistical analysis was conducted with SPSS 13.0 software. Eight loci were identified for Gab1 (4), EGFR (3), and EGF (1) as the target single-nucleotide polymorphisms (SNPs) for the association of gene polymorphisms and BTC. A/A genotype and A allele of rs3805246 in Gab1 and G/G genotype and G allele of rs2017000 in EGFR were significantly higher in BTC group than in GS group or controls. After controlling for BMI, age, gender, and smoking habit, patients with "A/A + G/A" had 2.154 times odds to have BTC; as for patients with "A/A" only, they still had 1.976 times odds to have BTC. In the rs2017000 of EGFR, patients with "G/G + G/A" had 1.772 times odds to have BTC, and patients with "G/G" only had 1.530 times odds to have BTC. Furthermore, patients with A/A in rs3805246 and G/G in rs2017000 simultaneously had 1.620 times chance to have BTC than people with other genotypes. This study explored the independent potential effect of EGFR signaling transduction pathway and its downstream element Gab1 and the gene-gene interaction on the disease mechanism of BTC in the perspective of genetics and molecular epidemiology.