18 results about "Potential toxicity" patented technology

The invention discloses application of lanthanum carbonate in treatment of inflammatory bowel diseases, lanthanum carbonate achieves the effect of treating inflammatory diseases by depriving cfDNA, eATP and other proinflammatory factors generated at an inflammatory part, and symptoms induced by IBD are effectively relieved. The lanthanum carbonate disclosed by the invention has a strong cfDNA and eATP removal function, has good biocompatibility, has no direct or indirect toxic action on a human body, has no potential toxicity, and can be used for treating inflammatory bowel diseases.

The invention belongs to the technical field of drug toxicity prediction, and particularly relates to a drug toxicity prediction method based on metabolism guidance. The invention aims to guide a prodrug or a non-toxic substance through a metabolism method and consider the toxicity of the prodrug or the non-toxic substance in the metabolic process (for example, the prodrug is converted into an effective component in the metabolic process), so as to predict the potential toxicity of the prodrug or the non-toxic substance. By means of the guidance of metabolism, it is desirable to accurately predict whether or not a prodrug will generate toxicity in the body. In the technical scheme provided by the invention, molecules are learned by using different characterization modes for the molecular map, so that different features under different levels and different segmentation methods can be captured. Through the guidance of the metabolic diagram, the reactants can obtain product-related attributes for explaining the toxicity change generated in the metabolic process of the reactants.

The invention discloses a health assessment method system based on toxicity heterogeneity of fine particulate matters in different microenvironments, and belongs to the technical field of environmental health and exposure science. The method comprises the following steps: defining and dividing a plurality of microenvironment types with different potential toxicity characteristics according to a social occupational structure and a typical activity mode; acquiring time-activity mode data of the target crowd in various microenvironments; collecting fine particulate matter samples in the microenvironments, and calculating the comprehensive toxicity intensity of each microenvironment; carrying out weighted average calculation on the comprehensive toxicity intensity and the staying duration of the target population to obtain toxicity daily average exposure intensity; and comparing and analyzing the daily average toxicity exposure intensity, and identifying high-risk crowds and corresponding key exposure microenvironments thereof. According to the method, refined health risk assessment based on toxicity heterogeneity is realized, and the transformation of a pollution control strategy from mass concentration orientation to health risk orientation is promoted.

The invention belongs to the technical field, and particularly relates to a plant extract composition with a whitening effect and a preparation method thereof. The composition provided by the invention takes natural plants and bioactive components as cores, avoids the use of chemical whitening agents with potential toxicity, such as hydroquinone, mercury salt and the like, and is high in safety and low in irritation. All the components cooperate with one another in a reasonable proportion, so that melanin generation can be inhibited from the source, and metabolism of generated melanin can be promoted; according to the present invention, the whitening effect is improved, the multiple skin care functions such as moisturizing, anti-aging, repairing and the like are further considered, and the mask is suitable for various skin types, and is especially suitable for people with dark skin color, hyperpigmentation, dryness, water shortage and mild sensitive skin.

The invention discloses a non-targeted extract screening and toxicity driving factor identification method based on IDA-UPLC-QTOF-MS / MS. Real extracts are obtained by simulating a real use scene and are subjected to chemical analysis, qualitative chemical characteristics are subjected to three-stage screening, then potential toxic action targets and mechanisms of the real extracts are revealed through calculation and simulation, and the toxicity driving factors of the real extracts are identified. A few toxicity driving factors which contribute to health risks are positioned, and a clear direction is provided for subsequent experimental verification, so that traditional high-flux toxicity screening is greatly replaced, and a large amount of research and development and detection cost is saved.

The invention relates to the technical field of biology, and provides a cell culture meat food safety evaluation method based on a nematode model, and the nematode model is a caenorhabditis elegans model, comprises individuals at the L1 stage and the L4 stage development stage, and is used for establishing a chronic exposure model and an acute exposure model. Biological parameters are obtained by measuring the model of the cell culture meat protein after ingestion of the marker, and the parameters are compared and analyzed with parameters of a control group, so that the food safety of the cell culture meat protein is evaluated. According to the method, caenorhabditis elegans is taken as a model organism, the provided cell culture meat food safety evaluation method is simple and rapid, low in cost, short in test period and good in repeatability, early warning is provided for potential toxic components in food, and the safety evaluation process is accelerated.

The present invention discloses a method and system for evaluating the safety risk level of feed for raising cattle and sheep, specifically relating to the technical field of raising cattle and sheep; through sample detection and data analysis, potential toxic substances in the feed are accurately identified, and combined with the biological enrichment anomaly index and the metabolic pathway deviation index, a model for predicting the accumulation level of chronic toxic substances in cattle and sheep is constructed. According to this model, the system can evaluate the health risks of different toxic substances, divide the safety risks of the feed into three levels: high, medium, and low, and formulate corresponding management measures. When the risk level is medium, the system will further dynamically adjust the risk level to respond in real time to possible high-risk evolutions, thereby realizing precise, dynamic monitoring and effective management of the safety of cattle and sheep feed, and avoiding the potential safety hazards brought by the inability of the static risk assessment system to cope with the long-term cumulative effects.

The invention discloses a compound toxicity prediction method based on artificial intelligence and an HSD11b2 inhibitor, which comprises the following steps: constructing a training sample set, each training sample in the training sample set being a compound sample in which the HSD11b2 inhibitor is positive or negative; respectively extracting molecular descriptor features for each training sample to form feature vectors of the training samples; taking the feature vector of each training sample as input data, and respectively training a plurality of models of different types to obtain a plurality of toxicity prediction models; taking a compound of which the toxicity needs to be predicted as a test sample, and extracting molecular descriptor characteristics of the test sample to form a characteristic vector of the test sample; respectively inputting the feature vectors of the test sample into each toxicity prediction model, and judging whether the test sample has potential toxicity or not according to the output result of each toxicity prediction model. The method can rapidly and accurately predict the potential toxicity of the compound, and has the advantages of simple operation and high intelligent degree.

The invention discloses a method for analyzing a toxic target of evodiamine based on a network toxicology system. The method comprises the following steps: SA, locking the toxic target; wherein the step SA comprises the following steps: step SA1, obtaining a potential toxic target spot of evodiamine of each toxic object in at least two toxic objects; sA2, locking a core toxic target spot of each toxic object; step SA3, locking a comprehensive core toxicity target spot; the comprehensive core toxicity target spot is a core toxicity target spot simultaneously existing in all toxic objects; wherein the sequence of the step SA1 and the step SA2 is not different. Based on the steps, the method not only locks the core toxicity target spot of each toxicity redemption, but also further locks the core toxicity target spots existing in all toxic objects at the same time, and can help to guide subsequent research to preferentially and sequentially determine research key points, improve research efficiency and save manpower and material resources.

The present invention relates to a pharmaceutical dosage form comprising a drug with rapid absorption in the intestinal tract, which drug is absorbed in the distal intestinal tract, including the colon, and for which a pulsed release is necessary to avoid the potential toxicity of sustained absorption of the drug, and a method of preparing the pharmaceutical composition. In particular, the present invention relates to a dosage form comprising a formulation in the form of small coated units to deliver an active ingredient after oral administration for a predetermined period of time. In a preferred embodiment, the drug is an iron chelating agent deferiprone.

The invention discloses a compound toxicity prediction method and device based on artificial intelligence and an androgen receptor (AR) inhibitor, and the method comprises the following steps: firstly, constructing a training sample set, training samples being compound samples with known AR inhibitor properties (positive or negative); aiming at each training sample, respectively extracting molecular fingerprint features to form feature vectors; taking the feature vector of each training sample as input data, and respectively training a plurality of models of different types to obtain a plurality of toxicity prediction models; during testing, molecular fingerprint features extracted from a test sample serve as feature vectors to be input into each toxicity prediction model, whether a consensus is achieved or not is determined based on output results of each toxicity prediction model, and whether a compound corresponding to the test sample has potential toxicity or not is further judged based on a consensus result. The method can rapidly and accurately predict the potential toxicity of the compound.

The invention discloses a nano-composite for treating ulcerative colitis and a preparation method of the nano-composite, and belongs to the technical field of medicines. The nano-composite is a zeolite imidazate skeleton-8 nano-particle loaded by alpha-terpilenol, namely, an alpha TL (at) ZIF-8 nano-composite. The compound is obtained by encapsulating alpha-terpilenol in ZIF-8 nano particles formed by coordination of zinc ions and 2-methylimidazole. The preparation method comprises the following steps: mixing and reacting a zinc salt solution, a 2-methylimidazole solution and an alpha-terpilenol solution, and centrifugally washing to obtain a product. According to the nanocomposite, the water solubility and the stability of alpha-terpilenol are remarkably improved, and targeted release of a medicine at a colitis disease part is realized by utilizing the characteristic that ZIF-8 degrades in an acid environment, so that ulcerative colitis is efficiently treated, and meanwhile, the potential toxicity risk caused by whole body exposure is reduced.

The application discloses a kind of pharmaceutical composition for treating pulmonary fibrosis and application thereof, belong to biological medicine technical field.The ginkgolide K (GinK) raw material source of the present application is easy, preparation cost is low, with lower toxicity, as natural industrialization small molecule.Small molecule STING inhibitor C176 has the ability to inhibit STING signal and have positive effect on fibrosis endpoint, can clearly target inhibition STING signal.Ginkgolide K (GinK) and small molecule STING inhibitor C176 are combined to use, can improve histological lesions and down-regulate the expression of several fibrosis-related proteins, have obvious synergistic effect on histological improvement, fibrosis-related protein expression inhibition and other endpoints, so as to achieve the synergistic effect that single drug is difficult to achieve, and reduce the potential toxicity and cost problem brought by single drug high dose.

This application relates to the biomedical field and discloses a method for constructing and using a co-culture system for colorectal and liver organoids. The method involves optimizing the culture conditions for human colorectal and liver organoids separately, and then seeding both types of organoids into a co-culture environment to form a stable co-culture system. This system can simulate the physiological environment of the colorectum and liver in vivo and can be used to screen drugs that affect the colorectal-liver axis, assess the potential toxicity of drugs during metabolism, and also provides an important experimental tool for exploring disease mechanisms and developing novel treatment strategies.

The application relates to the technical field of biomedical materials, in particular to a hydroxyapatite-based facial filling material and a preparation process thereof. The preparation process of the hydroxyapatite-based facial filling material comprises the following steps: preparing strontium-doped hydroxyapatite; preparing composite hydroxyapatite; preparing hyaluronic acid-coated hydroxyapatite; preparing aloe extract and moringa seed extract; and preparing the facial filling material. The application prepares sulfhydrylated hyaluronic acid and acrylated hyaluronic acid respectively, mixes the two with the composite hydroxyapatite, crosslinks the hyaluronic acid in the absence of a crosslinking agent through click chemistry, and prepares the hyaluronic acid-coated hydroxyapatite through an organic phase solidification ball method, so that the crosslinked hyaluronic acid is wrapped on the surface of the composite hydroxyapatite to form a core-shell structure, the potential toxicity risk caused by the residual crosslinking agent is eliminated from the source, and the wrapping rate of the hyaluronic acid to the hydroxyapatite is effectively improved.

The invention discloses a light aging simulation and toxicity detection method for plastic microbeads from wash care products, and belongs to the technical field of cosmetic detection, and the method comprises the following steps: (1) extraction and purification; (2) aging treatment; (3) physical and chemical characterization and free radical detection; and (4) biotoxicity detection. The method provided by the invention provides a set of complete methodological process of extracting the microbeads from the wash care products, simulating light aging, detecting free radicals and evaluating biological toxicity; the system reveals the toxicity risk of the light aging plastic microbeads in the environment, and provides a scientific basis for environmental health risk assessment and product safety supervision; the method is an operable and repeatable standard method, can simulate the aging process of the microbeads in the environment at the same time, and comprehensively evaluates the potential toxicity effect of the microbeads.

This invention relates to a machine learning-based method and system for predicting interspecific toxicity. The method acquires toxicity data of the same chemical to both alternative and predicted species, calculates its molecular descriptors, and preprocesses them to select descriptors suitable for modeling. Combining the toxicity data of the alternative species with the selected descriptors as independent variables and the toxicity data of the predicted species as the dependent variable, a machine learning algorithm is used to construct an interspecific toxicity relationship prediction model. The model's performance is then evaluated and validated through cross-validation. For a chemical to be predicted, only its toxicity data to an alternative species and related molecular descriptors need to be acquired. The validated final interspecific relationship prediction model is then used to predict the toxicity of the chemical to the predicted species. Compared to existing technologies, this invention utilizes a reliable model to efficiently and accurately predict the potential toxicity of a chemical to a target predicted species, effectively reducing the cost and time of traditional cross-species toxicity testing.

The present invention relates to the field of biochemical technology, and more specifically to a hydrogel-state human recombinant collagen self-assembly polymer, a preparation method, and an application thereof. The human recombinant collagen of the present invention is composed of a stacked short sequence and a human collagen short sequence as a repeating unit repeated 8 times to form a specific amino acid sequence structure. The stacked short sequence and calcium ions (Ca 2+ ) form chelates, thereby enabling self-assembly between human recombinant collagen molecules to form stable hydrogel-like human recombinant collagen self-assembly aggregates. The preparation method described herein does not use chemical crosslinkers, avoiding potential toxicity and side effects. The resulting hydrogel-like human recombinant collagen self-assembly aggregates can mimic the body's own collagen to the greatest extent possible, reducing the risk of immune response, increasing biosafety, ensuring biocompatibility, and expanding its application prospects in the field of medical aesthetics.