Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor.

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

A pH-/Enzyme-Responsive Nanoparticle Selectively Targets Endosomal Toll-like Receptors to Potentiate Robust Cancer Vaccination.

Toll-like receptor (TLR) agonists are potent immune-stimulators that hold great potential in vaccine adjuvants as well as cancer immunotherapy. However, TLR agonists in free form are prone to be eliminated quickly by the circulatory system and cause systemic inflammation side effects. It remains a challenge to achieve precise release of TLR7/8 agonist in the native form at the receptor site in the endosomal compartments while keeping stable encapsulation and inactive in nontarget environment. Here, we report a pH-/enzyme-responsive TLR7/8 agonist-conjugated nanovaccine (TNV), which responds intelligently to the acidic environment and cathepsin B in the endosome, precisely releases TLR7/8 agonist to activate its receptor signaling at the endosomal membrane, stimulates DCs maturation, and provokes specific cellular immunity. In vivo experiments demonstrate outstanding prophylactic and therapeutic efficacy of TNV in mouse melanoma and colon cancer. The endosome-targeted responsive nanoparticle strategy provides a potential delivery toolbox of adjuvants to advance the development of tumor nanovaccines.

Design, synthesis and biological activity evaluation of a series of bardoxolone methyl prodrugs.

Bardoxolone methyl (CDDO-Me) has exhibited positive therapeutic effects in clinical trials for diabetic nephropathy (DN), but serious safety risks in the heart exist because of the potential off-target response resulting from the highly active part of CDDO-Me. Herein, we reported a novel strategy to prepare Cathepsin B (CTSB) cleavable and improved water-soluble prodrugs of CDDO-Me. CTSB linkers connection to the highly active α-cyano-α, ß-unsaturated ketone (CUK) part of CDDO-Me with the incorporation of polyethylene glycol (PEG) moieties, provided a series of prodrugs of CDDO-Me without CUK part exposure. Theoretically, these prodrugs shielding CUK part can be stably circulated and finally cleaved by CTSB in lysosomes to release CDDO-Me. In this paper, preliminary curative effects and safety of all prodrugs were determined. Wherein, prodrug 20 displayed relatively better activities than other prodrugs in inhibiting the release of NO from RAW264.7 cells, activating Keap1-Nrf2-ARE signaling pathway and inhibiting NF-κB signaling pathway, which were comparable to CDDO-Me. More importantly, prodrug 20 showed relatively lower human ether-a-go-go-related gene (hERG) inhibitory activity compared with CDDO-Me, which demonstrated prodrug 20 might be safer than CDDO-Me. In conclusion, the novel strategy of shielding CUK part with CTSB linkers provided a new idea for solving the limitations of CDDO-Me in clinical application.

Design, Synthesis, and Renal Targeting of Methylprednisolone-Lysozyme.

Methylprednisolone (MP) is often used in the treatment of various kidney diseases, but overcoming the systemic side effects caused by its nonspecific distribution in the body is a challenge. This article reports the design, synthesis, and renal targeting of methylprednisolone-lysozyme (MPS-LZM). This conjugate was obtained by covalently linking MP with the renal targeting carrier LZM through a linker containing an ester bond, which could utilize the renal targeting of LZM to deliver MP to renal proximal tubular epithelial cells and effectively release MP. The reaction conditions for the preparation of the conjugate were mild, and the quality was controllable. The number of drug payloads per LZM was 1.1. Cell-level studies have demonstrated the safety and endocytosis of the conjugate. Further pharmacokinetic experiments confirmed that, compared with that of free MP, the conjugate increased the renal exposure (AUC0-t) of active MP from 17.59 to 242.18 h*ng/mL, and the targeting efficiency improved by approximately 14 times. Tissue and organ imaging further revealed that the conjugate could reach the kidneys quickly, and fluorescence could be detected in the kidneys for up to 12 h. This study preliminarily validates the feasibility of a renal targeting design strategy for MPS-LZM, which is expected to provide a new option for improving kidney-specific distribution of glucocorticoids.