An intelligent process analysis method for rapidly evaluating the quality of Chinese medicine with near-infrared non-contact hyperspectral imaging: A case study of Weifuchun concentrate.

INTRODUCTION: The quality of Chinese medicine preparations can be greatly influenced by the quality of the intermediates such as extracts or concentrates. However, it is highly challenging to evaluate the quality in a rapid and non-contact manner during manufacturing. Here, we introduce an intelligent hyperspectral analysis method integrating a self-built abnormal region removal algorithm with machine learning and demonstrate its utility using the concentrate of Weifuchun (WFC), a traditional Chinese medicine preparation made from Ginseng Radix et Rhizoma Rubra, Rabdosia Amethystoides, and Aurantii Fructus. OBJECTIVE: To rapidly and non-destructively detect quality attributes of the intermediates in the manufacturing processes of Chinese medicine, an intelligent hyperspectral analysis method was developed for simultaneously quantifying the contents of naringin, neohesperidin, rosmarinic acid, and relative density of WFC concentrates. METHODOLOGY: Samples were evenly spread on solid white flat bottom containers, which were batch placed on a horizontal sample stage. Subsequent to the acquisition of near-infrared (NIR) hyperspectral images, abnormal pixels such as large/small bubbles and fine solids were first removed according to the differential pixel values in the binary grayscale map and the Mahalanobis distance metric. Then, partial least squares (PLS) and support vector machine (SVM) algorithms were used to construct hyperspectral quantitative calibration models for quality attributes. The hyperspectral images were reconstructed based on these models to visually evaluate the quality of the concentrates during manufacturing. RESULTS: As a case study, quality attributes of the WFC concentrates including contents of naringin, neohesperidin, rosmarinic acid, and relative density were determined simultaneously, and coefficients of determination of these quantitative correction models were 0.900, 0.891, 0.851, and 0.920, respectively. CONCLUSION: The method proposed in this study favors real-time determination of multiple attributes in viscous samples with industrial application prospects.

Dual-binding nanoparticles improve the killing effect of T cells on solid tumor.

Adoptive cell therapy (ACT) was one of the most promising anti-tumor modalities that has been confirmed to be especially effective in treating hematological malignancies. However, the clinical efficacy of ACT on solid tumor was greatly hindered by the insufficient tumor-infiltration of cytotoxic CD8 + T cells. Herein, we constructed a nanoplatform termed dual-binding magnetic nanoparticles (DBMN) that comprised PEG-maleimide (Mal), hyaluronic acid (HA) and Fe3O4 for adoptive T cell-modification and ACT-sensitization. After a simple co-incubation, DBMN was anchored onto the cell membrane (Primary linking) via Michael addition reaction between the Mal and the sulfhydryl groups on the surface of T cells, generating magnetized T cells (DBMN-T). Directed by external magnetic field and in-structure Fe3O4, DBMN-T was recruited to solid tumor where HA bond with the highly expressed CD44 on tumor cells (Secondary Linking), facilitating the recognition and effector-killing of tumor cells. Bridging adoptive T cells with host tumor cells, our DBMN effectively boosted the anti-solid tumor efficacy of ACT in a mouse model and simultaneously reduced toxic side effects.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements.

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the "three-in-one" SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm-1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL-1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL-1 to 2.15 × 105 CFU mL-1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

A multi-component, multi-target, and multi-pathway prediction method for Chinese medicine based on the combination of mass spectrometry analysis and network analysis: An example using Weifuchun.

Weifuchun, a Chinese medicinal prescription made from herbs of natural origin including Hongshen (Ginseng Radix et Rhizoma Rubra), Xiangchacai (Rabdosia Amethystoides), and Zhiqiao (Aurantii Fructus), has attracted increasing attention for clinically treating chronic atrophic gastritis, which is characterized by the chronic inflammation of the gastric mucosa leading to progressive loss of gastric glandular cells. To investigate the active ingredients and potential mechanisms of WFC, it was analyzed using a novel multi-component, multi-target, and multi-pathway prediction method. High/ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC/UPLC-Q-TOF-MS) was employed to separate and profile the chemical constituents of WFC with high precision and efficiency. Network analysis and molecular docking were used to predict bioactive compounds and their interactions with biological targets. The results highlight 42 significant compounds potentially contributing to the therapeutic effects of WFC by effecting on several key pathways, including proved PI3K/Akt, NF-κB, and JAK/STAT signaling pathways. This study showcases the efficacy of combining advanced chromatographic techniques with computational methods to elucidate the pharmacological mechanisms of complex botanical drugs.

A next-generation STING agonist MSA-2: From mechanism to application.

The stimulator of interferon genes (STING) connects the innate and adaptive immune system and plays a significant role in antitumor immunity. Over the past decades, endogenous and CDN-derived STING agonists have been a hot topic in the research of cancer immunotherapies. However, these STING agonists are either in infancy with limited biological effects or have failed in clinical trials. In 2020, a non-nucleotide STING agonist MSA-2 was identified, which exhibited satisfactory antitumor effects in animal studies and is amenable to oral administration. Due to its distinctive binding mode and enhanced bioavailability, there have been accumulating interests and an array of studies on MSA-2 and its derivatives, spanning its structure-activity relationship, delivery systems, applications in combination therapies, etc. Here, we provide a comprehensive review of MSA-2 and interventional strategies based on this family of STING agonists to help more researchers extend the investigation on MSA-2 in the future.

Remodeling the hepatic immune microenvironment and demolishing T cell traps to enhance immunotherapy efficacy in liver metastasis.

Immune checkpoint inhibitors (ICIs) exhibit compromised therapeutic efficacy in many patients with advanced cancers, particularly those with liver metastases. Much of this incapability can be ascribed as an irresponsiveness resulting from the "cold" hepatic tumor microenvironment that acts as T cell "traps" for which there currently lack countermeasures. We report a novel nanomedicine that converts the hepatic immune microenvironment to a "hot" phenotype by targeting hepatic macrophage-centric T cell elimination. Using the nanomedicine, composed of KIRA6 (an endothelium reticulum stress inhibitor), α-Tocopherol nanoemulsions, and anti-PD1 antibodies, we found its potency in murine models of orthotopic colorectal tumors and hepatic metastases, restoring immune responses and enhancing anti-tumor effects. A post-treatment scrutiny of the immune microenvironment landscape in the liver reveals repolarization of immunosuppressive hepatic macrophages, upregulation of Th1-like effector CD4+ T cells, and rejuvenation of dendritic cells along with CD8+ T cells. These findings suggest adaptations of liver-centric immune milieu modulation strategies to improve the efficacy of ICIs for a variety of "cold" tumors and their liver metastases.

Discovering the digital biomarker of hepatocellular carcinoma in serum with SERS-based biosensors and intelligence vision.

By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.

Endoplasmic reticulum-targeted inhibition of CYP2E1 with vitamin E nanoemulsions alleviates hepatocyte oxidative stress and reverses alcoholic liver disease.

Alcoholic liver disease (ALD) is a global healthcare problem and socioeconomic issue that is primarily driven by chronic and/or excessive alcohol consumption. Upon alcohol exposure, parenchymal hepatocytes (HCs) up-regulate endoplasmic reticulum (ER)-localized monooxygenase Cytochrome P450 family 2 subfamily E member 1 (CYP2E1) to accelerate the metabolism of ethanol (EtOH), which concurrently exacerbates the production and accumulation of toxic metabolic intermediates, especially reactive oxygen species (ROS), playing a decisive role in the initiation and perpetuation of alcohol-induced liver injury. ALD patients without timely intervention may develop a spectrum of metabolic and functional disorders in the liver, including hepatic steatosis, hepatitis, fibrosis, and even cirrhosis. However, up to now, there have been no FDA-approved pharmacological or nutritional therapeutics for treating patients with ALD, and an effective amelioration of alcohol-induced hepatotoxicity with satisfactory biosafety is still demanding. In this study, antioxidant Vitamin E-incorporating nanoemulsions modified with ER-targetable small molecule p-dodecylbenzene sulfonamide (p-DBSN) was constructed to load and deliver CYP2E1 inhibitor Clomethiazole (CMZ) to the ER of HCs for site-specific inhibition, which displayed remarkable hepatoprotective effects against chronic alcohol exposure without off-target toxicity, both intravenously injected and orally administrated. Generally, our work may provide a promising nanoplatform for reversing ALD.

Nanoporous silver nanorods as surface-enhanced Raman scattering substrates.

Structures with dense nanopores are desirable as surface-enhanced Raman scattering (SERS) sensing substrates because the nanopores can behave as both analyte containers and SERS-active sites (known as hot spots). Inspired by the dealloying process to prepare nanoporous structures through selectively removing active metals from their alloy, we developed a method to prepare nanoporous Ag nanorods through chemical reduction of the electrodeposited Ag7O8NO3 nanorods using a strong reducing agent (e.g., NaBH4). The length and the thickness of the Ag7O8NO3 nanorods could be controlled by the electrodeposition voltage and time. Nitrogen and oxygen elements were immediately removed from Ag7O8NO3 nanorods by the reducing agent, leaving behind a tremendous number of nanopores with a mean size of 20 nm, which can efficiently trap and enrich analytes. Meanwhile, the densely packed nanopores can behave as SERS hot spots to provide strong SERS enhancement. The nanoporous Ag nanorods as SERS substrates were used to sensitively detect adenine, spike glycoprotein, and polychlorinated biphenyls pollutants, as well as identify different types of bacteria. The simple fabrication process and the outstanding SERS performance of the nanoporous Ag nanorods make them promising candidates for SERS applications towards trace detection of pollutants, narcotics, food additives, and biomolecules.

An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning.

The clinical needs of rapidly screening liver cancer in large populations have asked for a facile and low-cost point-of-care testing (POCT) method. We present a nanoplasmonics biosensing chip (NBC) that would empower antibody-free detection with simplified analysis procedures for POCT. The cheaply fabricable NBC consists of multiple silver nanoparticle-decorated ZnO nanorods on cellulose filter paper and would enable one-drop blood tests through surface-enhanced Raman spectroscopy (SERS) detection. In this work, utilizing such an NBC and deep neural network (DNN) modeling, a direct serological detection platform was constructed for automatically identifying liver cancer within minutes. This chip could enhance Raman signals enough to be applied to POCT. A classification DNN model was established by spectrum-based deep learning with 1140 serum SERS spectra in equal proportions from hepatocellular carcinoma (HCC) patients and healthy individuals, achieving an identification accuracy of 91% on an external validation set of 100 spectra (50 HCC versus 50 healthy). The intelligent platform, based on the biosensing chip and DNN, has the potential for clinical applications and generalizable use in quickly screening or detecting other types of cancer.

A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier.

Direct serological detection, due to its clinical facility and testing economy, affords prominent clinical values to the early detection of cancer. Surface-enhanced Raman spectroscopy (SERS)-based sensors have shown great promise in realizing this form of detection. Detecting liver cancer early with such a form, especially in terms of monitoring the pathogenic progression from hepatic inflammations to cancer, is the most effective clinical path to reducing the mortality rate. However, the methodology investigation for this purpose remains a formidable challenge. We fabricated a SERS-based sensor, consisting of Au-Ag nanocomplex-decorated ZnO nanopillars on paper. The sensor has an analytic enhancement factor of 1.02 × 107, which is enough to sense the biomolecular information of liver diseases through direct serum SERS analysis. A convolutional neural network (CNN) classifier for recognizing serum SERS spectra was constructed by deep learning. Integrating this sensor with the CNN, we established an intelligent biosensing method and realized direct serological detection of liver diseases within 1 min. As a proof-of-concept, the method achieved a prediction accuracy of 97.78% on an independent test dataset randomly sampled from 30 normal controls, 30 hepatocellular carcinoma (HCC) cases, and 30 hepatitis B (HB) patients. The results suggest this method can be developed for detecting liver diseases clinically and is worthy of exploration as a means of liver cancer surveillance. The presented sensor holds potential for clinical translation to the direct serological detection of diseases.

Determination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability.

Shifting from population-wide to personalized cancer prognosis with microarrays.

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment.