CuO Nanozymes Catalyze Cysteine and Glutathione Depletion Induced Ferroptosis and Cuproptosis for Synergistic Tumor Therapy.

The latest research identifies that cysteine (Cys) is one of the key factors in tumor proliferation, metastasis, and recurrence. The direct depletion of intracellular Cys shows a profound antitumor effect. However, using nanozymes to efficiently deplete Cys for tumor therapy has not yet attracted widespread attention. Here, a (3-carboxypropyl) triphenylphosphonium bromide-derived hyaluronic acid-modified copper oxide nanorods (denoted as MitCuOHA) are designed with cysteine oxidase-like, glutathione oxidase-like and peroxidase-like activities to realize Cys depletion and further induce cellular ferroptosis and cuproptosis for synergistic tumor therapy. MitCuOHA nanozymes can efficiently catalyze the depletion of Cys and glutathione (GSH), accompanied by the generation of H2O2 and the subsequent conversion into highly active hydroxyl radicals, thereby successfully inducing ferroptosis in cancer cells. Meanwhile, copper ions released by MitCuOHA under tumor microenvironment stimulation directly bind to lipoylated proteins of the tricarboxylic acid cycle, leading to the abnormal aggregation of lipoylated proteins and subsequent loss of iron-sulfur cluster proteins, which ultimately triggers proteotoxic stress and cell cuproptosis. Both in vitro and in vivo results show the drastically enhanced anticancer efficacy of Cys oxidation catalyzed by the MitCuOHA nanozymes, demonstrating the high feasibility of such catalytic Cys depletion-induced synergistic ferroptosis and cuproptosis therapeutic concept.

Improvement of Z-Weighted Function Based on Fifth-Order Nonlinear Multi-Order Weighted Method for Shock Capturing of Hyperbolic Conservation Laws.

Based on a 5-point stencil and three 3-point stencils, a nonlinear multi-order weighted method adaptive to 5-3-3-3 stencils for shock capturing is presented in this paper. The form of the weighting function is the same as JS (Jiang-Shu) weighting; however, the smoothness indicator of the 5-point stencil adopts a special design with a higher-order leading term similar to the τ in Z weighting. The design maintains that the nonlinear weights satisfy sufficient conditions for the scheme to avoid degradation even near extreme points. By adjusting the linear weights to a specific value and using the τ in Z weighting, the method can be degraded to Z weighting. Analysis of linear weights shows that they do not affect the accuracy in the smooth region, and they can also adjust the resolution and discontinuity-capturing capability. Numerical tests of different hyperbolic conservation laws are conducted to test the performance of the newly designed nonlinear weights based on the weighted compact nonlinear scheme. The numerical results show that there are no obvious oscillations near the discontinuity, and the resolution of both the discontinuity and smooth regions is better than that of Z weights.

Superhydrophilic CoFe Dispersion of Hydrogel Electrocatalysts for Quasi-Solid-State Photoelectrochemical Water Splitting.

Photoelectrochemical (PEC) water splitting is an attractive strategy to convert solar energy to hydrogen. However, the lifetime of PEC devices is restricted by the photocorrosion of semiconductors and the instability of co-catalysts. Herein, we report a feasible in situ inherent cross-linking method for stabilizing semiconductors that uses a CoFe-dispersed polyacrylamide (PAM) hydrogel as a transparent protector. The CoFe-PAM hydrogel protected BiVO4 (BVO) photoanode reached a photocurrent density of 5.7 mA cm-2 at 1.23 VRHE under AM 1.5G illumination with good stability. The PAM hydrogel network improved the loading of Fe sites while enabling the retention of more CoFe co-catalysts and increasing the electron density of the reaction active sites, further improving the PEC performance and stability. More importantly, by tuning the polymerization network, we pioneer the use of quasi-solid-state electrolytes in photoelectrochemistry, where the high concentration of ionic solvent in the PAM hydrogel ensures effective charge transport and good water storage owing to the hydrophilic and porous structure of the hydrogel. This work expands the scope of PEC research by providing a class of three-dimensional hydrogel electrocatalysts and quasi-solid-state electrolytes with huge extension potential, and the versatility of these quasi-solid-state electrolytes can be employed for other semiconductors.

An optimized herbal combination for the treatment of liver fibrosis: Hub genes, bioactive ingredients, and molecular mechanisms.

ETHNOPHARMACOLOGICAL RELEVANCE: Liver fibrosis is a chronic liver disease that can lead to cirrhosis, liver failure, and hepatocellular carcinoma, and it is associated with long-term adverse outcomes and mortality. As a primary resource for complementary and alternative medicine, traditional Chinese medicine (TCM) has accumulated a large number of effective formulas for the treatment of liver fibrosis in clinical practice. However, studies on how to systematically optimize TCM formulas are still lacking. AIM OF THE REVIEW: To provide a methodological reference for the systematic optimization of TCM formulae against liver fibrosis and explored the underlying molecular mechanisms; To provide an efficient method for searching for lead compounds from natural sources and developing from herbal medicines; To enable clinicians and patients to make more reasonable choices and promote the effective treatment toward those patients with liver fibrosis. MATERIALS AND METHODS: TCM formulas related to treating liver fibrosis were collected from the Web of Science, PubMed, the China National Knowledge Infrastructure (CNKI), Wan Fang, and the Chinese Scientific Journals Database (VIP). Furthermore, the TCM compatibility patterns were mined using association analysis. The core TCM combinations were found by designing an optimized formulas algorithm. Finally, the hub target proteins, potential molecular mechanisms, and active compounds were explored through integrative pharmacology and docking-based inverse virtual screening (IVS) approaches. RESULTS: We found that the herbs for reinforcing deficiency, activating blood, removing blood stasis, and clearing heat were the basis of TCM formulae patterns. Furthermore, the combination of Salviae Miltiorrhizae (Salvia miltiorrhiza Bunge; Chinese salvia/Danshen), Astragali Radix (Astragalus membranaceus (Fisch.) Bunge; Astragalus/Huangqi), and Radix Bupleuri (Bupleurum chinense DC.; Bupleurum/Chaihu) was identified as core groups. A total of six targets (TNF, STAT3, EGFR, IL2, ICAM1, PTGS2) play a pivotal role in TCM-mediated liver fibrosis inhibition. (-)-Cryptotanshinone, Tanshinaldehyde, Ononin, Thymol, Daidzein, and Formononetin were identified as active compounds in TCM. And mechanistically, TCM could affect the development of liver fibrosis by regulating inflammation, immunity, angiogenesis, antioxidants, and involvement in TNF, MicroRNAs, Jak-STAT, NF-kappa B, and C-type lectin receptors (CLRs) signaling pathways. Molecular docking results showed that key components had good potential to bind to the target genes. CONCLUSION: In summary, this study provides a methodological reference for the systematic optimization of TCM formulae and exploration of underlying molecular mechanisms.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

The fourth scientific discovery paradigm for precision medicine and healthcare: Challenges ahead.

With the progression of modern information techniques, such as next generation sequencing (NGS), Internet of Everything (IoE) based smart sensors, and artificial intelligence algorithms, data-intensive research and applications are emerging as the fourth paradigm for scientific discovery. However, we face many challenges to practical application of this paradigm. In this article, 10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.

Data-driven microbiota biomarker discovery for personalized drug therapy of cardiovascular disease.

Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare.

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.

Physicochemical feature-based classification of amino acid mutations.

A huge quantity of gene and protein sequences have become available during the post-genomic era, and information about genetic variations, including amino acid substitutions and SNPs, is accumulating rapidly. To understand the effects of these changes, it is often essential to apply bioinformatics tools. Where there is a lack of homologous sequences or a three-dimensional structure, it becomes essential to predict the effects of mutations based solely on protein sequence information. Several computational methods utilizing machine learning techniques have been developed. These predictions generally use the 20-alphabet amino acid code to train the model. With limited available data, the 20-alphabet amino acid features may introduce so many parameters that the model becomes over-fitted. To decrease the number of parameters, we propose a physicochemical feature-based method to forecast the effects of amino acid substitutions on protein stability. Protein structure alterations caused by mutations can be classified as stabilizing or destabilizing. Based on experimental folding-unfolding free energy (DeltaDeltaG) values, we trained a support vector machine with a cleaned data set. The physicochemical properties of the mutated residues, the number of neighboring residues in the primary sequence and the temperature and pH were used as input attributes. Different kernel functions, attributes and window sizes were optimized. An average accuracy of 80% was obtained in cross-validation experiments.

[Study on the chemical reaction of cytosine with 3,3',5,5'-tetrabromo-m-cresojsulphonphthalein].

The system of reaction for t he cytosine (Cy) and 3,3',5,5'-tetrabrono-m-cresojsulphonphthalein (XJFL) was studied in acid solution using UV spectra. The factors that affect the reaction, such as temperature, time and concentration of ion were determined. The maximum absorption peak of the complex appeared at t he wavelength 620 nm, the molar absorptivity was epsilon = 1.32 x 10(3) L x cm(-1) x mol(-1), the linear range of standard was 0-32 micro x mL(-1), and the maximum combining number was 16. The reaction mechanism, and the interaction of inorganic substance, biological substance, and surface active agent were studied.

[Study on the reaction of Lys-ZCN-CTMAB complex].

The interaction of lysozyme (Lys), O-{2-[alpha-(2-Hydroxy-5-Sulphophenlylazo)-benzylidene] hydrazino} benzoic acid (ZCN) and cetyltrimethy ammonicumbromide (CTMAB) was investigated by UV spectrophotometric method. The effect of temperature, time, and ion intensity in the buffer solution (pH 4.46) on reaction system was determined. Linear range of the reaction was 0-10 microg x mL(-1). The molar absorptivity was epsilon = 4.6164 x 10(5) L x cm(-1) x mol(-1). The reaction mechanism and interference of inorganic substance, biological substance and surface active agent to the reaction was investigated. Study system was established.