Abstract: Systems and methods are provided for a battery management system (BMS) having a protection circuit. In one example, a vehicle battery system may include the BMS, the BMS including a cutoff circuit electrically coupled to the protection circuit, and a battery pack, a positive supply line of the battery pack being electrically coupled to the cutoff circuit, wherein the protection circuit may include each of an input electrically coupled to a control input of the cutoff circuit, an output electrically coupled to an output of the cutoff circuit, and a control input of the protection circuit electrically coupled to the output of the cutoff circuit. In some examples, the protection circuit may further include a low-current leakage transistor configured to maintain the cutoff circuit in an OFF state upon detection of a reverse bias voltage. In this way, the protection circuit may mitigate unexpected switching ON of the cutoff circuit.

Abstract: The present disclosure relates to a construction method for a prognostic model of hepatocellular carcinoma based on DNA damage repair (DDR) and immunogenic cell death (ICD) gene expression, including the following steps of: Step 1, acquiring transcription profile expression data of multiple hepatocellular carcinoma patients; step 2, screening candidate genes based on the transcription profile expression data of multiple hepatocellular carcinoma patients; step 3, determining prognostic genes related to lifetime through single-factor Cox regression analysis based on the candidate genes; step 4, screening the genes related to the lifetime through LASSO Cox regression analysis; and step 5, assessing the prediction performance of the risk score model based on the above training dataset.

Abstract: The present disclosure provides a construction method for a treatment reactivity predicating model of hepatocellular carcinoma based on a gene expression quantity. According to the method of the present disclosure, samples are selectively distinguished through the expression quantity of the genes, and the reaction of patients to TACE treatment can be more accurately predicted.

Abstract: The present disclosure specifically provides a construction method of a survival prediction model for hepatocellular carcinoma patients based on cell death-related genes, including the following steps of: S1, constructing a preliminary survival risk score prediction model for hepatocellular carcinoma patients; S2, using a public database TCGA as a training set, and expressing genes based on the basis of differential expression related to three novel programmed cell death pathways including cell autophagy, cell ferroptosis and cell pyroptosis; S3, determining genes related to the lifetime through single-factor Cox regression analysis; S4, screening the genes related to the lifetime through multi-factor Cox regression analysis, and training to obtain a final survival risk score prediction model for hepatocellular carcinoma patients; and S5, calculating a risk index based on the gene related expression quantity and the risk related coefficient, and analyzing and performing external verification.

Abstract: The present disclosure relates to a newly discovered tumor marker for hepatocellular carcinoma (HCC) and related applications thereof in clinical detection. The tumor marker for HCC in the present disclosure is OIT3, which is abnormally expressed at low levels in HCC tissues and can be used in the diagnosis and treatment of HCC. The present application further relates to primer sequences of OIT3 as an HCC marker, which can be used as a reagent for the detection and diagnosis of HCC. The present disclosure can improve the accuracy, efficiency, and diagnosis of early HCC and is suitable for popularization in the clinical diagnosis of HCC.

Abstract: The present disclosure describes drug-loaded nanoparticles for hepatic artery chemoembolization, wherein the drug-loaded nanoparticles are novel doxorubicin-loaded metal organic framework (MOF) nanoparticles and UiO-66/Bi2S3 nanocomposites. The preparation method of the drug-loaded nanoparticles includes the following steps: mixing UiO-66/Bi2S3 with DOX solution, stirring the resultant mixture overnight at 25° C.±5° C. in a dark environment, centrifuging the mixture, and washing with deionized water to obtain UiO-66/UiO-66/Bi2S3@DOX composite nanomaterials. The present disclosure adopts a “one-pot reaction” with a simple preparation process. The pH-reactive release performance of the MOF material indicates that the tumor tissue of hepatocellular carcinoma (HCC) has a lower pH than normal tissue, and the acidic tumor environment can induce the release of doxorubicin from the nanomaterials.