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A sensitive ic-ELISA method for detecting AP in herbal tea was established for the first time and can be used for rapid detection.

B-cell lymphoma 6 (BCL6) is a transcriptional repressor that regulates the differentiation of B lymphocytes and mediates the formation of germinal centers (GCs) by recruiting corepressors through the BTB domain of BCL6. Physiological processes regulated by BCL6 involve cell activation, differentiation, DNA damage, and apoptosis. BCL6 is highly expressed when the gene is mutated, leading to the malignant proliferation of cells and drives tumorigenesis. BCL6 overexpression is closely correlated with tumorigenesis in diffuse large B-cell lymphoma (DLBCL) and other lymphomas, and BCL6 inhibitors can effectively inhibit some lymphomas and overcome resistance. Therefore, targeting BCL6 might be a promising therapeutic strategy for treating lymphomas. Herein, we comprehensively review the latest development of BCL6 inhibitors in diffuse large B-cell lymphoma and discuss the overview of the pharmacophores of BCL6 inhibitors and their efficacies

Aim: The first inhibitor targeting myoferlin (MYOF), WJ460, bears poor metabolic stability and water solubility. Therefore, this study aimed to improve the drug-like properties of WJ460. Materials & methods: The authors synthesized an array of 1,5-diaryl-1,2,4-triazole analogs and appraised the binding activities with MYOF and their antiproliferative and antimigratory activities against pancreatic cancer cells. Results: Molecular docking and surface plasmon resonance results showed that E4 was directly bound to the MYOF-C2D domain. E4 effectively inhibited the proliferation and migration of pancreatic cancer cells in vitro. An in silico study suggested that the water solubility of E4 was improved by about 22-times than that of WJ460. Conclusion: The findings suggested that the druglike ability of E4 was significantly improved.

Developing an easily-prepared, sensitive, and accurate point-of-need immunochromatographic assay (ICA) is significant in food safety screening, clinical diagnosis, and environmental monitoring. However, the current single-modal ICAs are limited in certain instinct drawbacks that restrict analytical performances. Herein, we introduce an ultrasensitive dual-modal colorimetric/reversed ratiometric fluorescence ICA based on facilely prepared immunoprobes with a high loading capacity of red quantum dots and AuNPs. By smartly integrating these red-colored/fluorescent signal probes with an immobilized green quantum dot antigen on the test lines, discrete "turn-on"visual inspection and reversed ratiometric quantification via a portable smartphone-based analyzer were accomplished. As an application, this method was employed to detect 11 phosphodiesterase-5 inhibitors in health foods with ultralow detection limits (0.0028-0.045 ng/mL), high repeatability (coefficient of variations of 0.3-1.91%), and reasonable accuracy (recoveries of 86.6-107%). The proposed method was further validated by the authorized liquid chromatography with tandem mass spectrometry method in actual sample detection. This new assay format can be extended to ultrasensitive flexible detection of other food contaminants, environmental pollutants, or tumor biomarkers within minutes, and it just requires simply prepared signal reporters, easy-to-operate procedures, and a low-cost miniaturized analyzer.

Risk category judged by clinicians

Supplementary file

Purpose:Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making.Experimental Design:A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort.Results:The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854–0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859–0.916), higher than those of the Brock model [0.806 (0.771–0.838)], PKU model [0.780 (0.743–0.817)], Mayo model [0.739 (0.697–0.776)], and VA model [0.682 (0.640–0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815–0.915)] was higher than that of surgeons [0.790 (0.711–0.852), 0.741 (0.662–0.804), and 0.727 (0.650–0.788)], radiologist [0.748 (0.671–0.814)], and the CADx system [0.757 (0.682–0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity.Conclusions:After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.

ObjectivePrecise risk stratification is increasingly essential in the management of multiple myeloma (MM) as some standard-risk (SR) patients still exhibit similar poor outcomes as genetically high-risk (GHR) patients in the era of novel agents. It has recently been demonstrated that functional high-risk (FHR) patients, those with suboptimal response to first-line induction therapy or early relapse within 12 months, have identifiable molecular characteristics from the SR group in the CoMMpass dataset. However, these findings lack practical validation in the real world.MethodsMM cells purified by CD138 microbeads from newly diagnosed MM (NDMM) patients received fluorescence in situ hybridization and sequencing with a 92-gene Panel. Cytogenetic abnormalities defined GHR patients with t(4;14) or t(14;16) or complete loss of functional P53 or 1q21 gain and International Staging System (ISS) stage 3. SR group was patients who did not fulfill any criteria for GHR or FHR.ResultsThere were 145 patients with NDMM, 78 in the SR group, 56 in the GHR group, and 11 in the FHR group. In the FHR group, eight patients were suboptimal responses to induction therapy, and three relapsed within 12 months. We found that male patients, patients with extra-medullary plasmacytoma (EMD), circulating clonal plasma cells (CPC) ≥0.05%, and P53 mono-allelic inactivation were significantly higher in the FHR group compared to the SR group. After a median follow-up of 21.0 months, the median progression-free survival (PFS) and overall survival (OS) were 5.0 months, 19.1 months and 36.6 months in the FHR, GHR, and SR groups, respectively. Compared to the SR group, FHR patients had a higher frequency of mutations in MKI67, ERN1, and EML4. GO analysis showed that mutations in FHR were enriched for oxidative stress, chromosomal segregation, and hypoxia tolerance.ConclusionThe FHR found in the SR NDMM patient group has unique clinical features, including being male, with EMD and CPC, and genetic characteristics of mutations affecting oxidative stress, chromosome segregation, and hypoxia tolerance. In contrast to previous reports, our data suggested that patients with P53 mono-allelic inactivation should be classified in the GHR group rather than the FHR group.

Flow battery electrodes are vital for performing redox reactions, and an in-depth understanding of reaction kinetics and spatial distribution differences in electrodes is very important for improving the efficiency of electrochemical reactions. In this study, a reflection-type phase-sensitive weak measurement imaging system was developed for the detection of flow batteries. The phase difference between two polarization components in total internal reflection caused by electrode redox processes was measured by weak value amplification. The resulting refractive index resolution of the imaging system was estimated to be 2.8-4.2 × 10-6 RIU. The real-time monitoring ability of the system was demonstrated by linear sweep voltammetry tests of vanadium redox batteries. Compared to traditional optical methods, the proposed weak measurement imaging sensor did not require coating, as it can be used in acid electrolytes of vanadium flow batteries. Meanwhile, the weak value amplification effect led to a higher resolution than the total internal reflection system shown in our previous work, thereby resulting in more accurate detection of electrochemical reactions. In sum, the proposed sensor looks very promising for the detection of electrochemical reactions in flow batteries, water splitting, electrochemical corrosion, and electrocatalysis.

Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy.We designed a regional multiple-instance learning algorithm to predict the OBMC based on hematoxylin-eosin (HE) staining slides alone. We collected 1041 cases from eight different hospitals and labeled 26,431 regions of interest to train the model. The performance of the model was assessed by ten-fold cross validation and external validation. Under the guidance of top3 predictions, we conducted an IHC test on 175 cases of unknown origins to compare the consistency of the results predicted by the model and indicated by the IHC markers. We also applied the model to identify whether there was tumor or not in a region, as well as distinguishing squamous cell carcinoma, adenocarcinoma, and neuroendocrine tumor.In the within-cohort, our model achieved a top1-accuracy of 91.35% and a top3-accuracy of 97.75%. In the external cohort, our model displayed a good generalizability with a top3-accuracy of 97.44%. The top1 consistency between the results of the model and the immunohistochemistry markers was 83.90% and the top3 consistency was 94.33%. The model obtained an accuracy of 98.98% to identify whether there was tumor or not and an accuracy of 93.85% to differentiate three types of cancers.Our model demonstrated good performance to predict the OBMC from routine histology and had great potential for assisting pathologists with determining the OBMC accurately.National Science Foundation of China (61875102 and 61975089), Natural Science Foundation of Guangdong province (2021A15-15012379 and 2022A1515 012550), Science and Technology Research Program of Shenzhen City (JCYJ20200109110606054 and WDZC20200821141349001), and Tsinghua University Spring Breeze Fund (2020Z99CFZ023).

Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (HE) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to HE staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from HE-stained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67-stained image generation from corresponding HE-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.