Adverse drug reactions （ADRs） refer to harmful or unintended reactions unrelated to the intended purpose of medication administration， which can lead to various issues such as accelerated disease progression and prolonged hospitalization. Traditional ADRs monitoring systems （such as spontaneous reporting systems） suffer from limitations such as low reporting rates and inconsistent data quality， which hinder the early prevention and control of ADRs. With the rapid development of information technology， machine learning has emerged as a powerful tool for management and decision-making of ADRs by leveraging its strengths in feature extraction and dynamic temporal pattern analysis. By reviewing relevant literature at home and abroad in recent years， this paper summarizes the progress in the application of machine learning for ADRs prediction. It is found that machine learning has gradually been applied to the early warning and risk prediction of ADRs in target organs such as the kidneys， liver， heart and bone marrow （such as acute kidney injury， drug-induced liver injury， and so on）. Although machine learning demonstrates significant application potential in the field of ADRs prediction， it still faces limitations such as inadequate quality control of clinical data， lack of standardized criteria for model performance evaluation， insufficient model interpretability and difficulties in clinical translation. In the future， the development trend of machine learning in the field of ADRs prediction should follow a “technology-validation-integration” pathway to systematically promote the practical implementation of models.

Polymyxin B, which is a last-line antibiotic for extensively drug-resistant Gram-negative bacterial infections, became available in China in Dec. 2017. As dose adjustments are based solely on clinical experience of risk toxicity, treatment failure, and emergence of resistance, there is an urgent clinical need to perform therapeutic drug monitoring (TDM) to optimize the use of polymyxin B. It is thus necessary to standardize operating procedures to ensure the accuracy of TDM and provide evidence for their rational use. We report a consensus on TDM guidelines for polymyxin B, as endorsed by the Infection and Chemotherapy Committee of the Shanghai Medical Association and the Therapeutic Drug Monitoring Committee of the Chinese Pharmacological Society. The consensus panel was composed of clinicians, pharmacists, and microbiologists from different provinces in China and Australia who made recommendations regarding target concentrations, sample collection, reporting, and explanation of TDM results. The guidelines provide the first-ever consensus on conducting TDM of polymyxin B, and are intended to guide optimal clinical use.
Humans ; Anti-Bacterial Agents/therapeutic use* ; China ; Drug Monitoring/methods* ; Polymyxin B ; Practice Guidelines as Topic

OBJECTIVEThe purpose of this study was to construct a eukaryotic expression vector for human amelogenin (AMG).

METHODSPCR was performed to amplify the AMG encoding region. Amplified fragments for human AMG were recovered and inserted into eukaryotic expression vectors PsecTaq2A. The recombinant plasmid PsecTaq2A-AMG was constructed and their positive clones were identified.

RESULTS1. Amplified products were checked by electrophoresis and the results were satisfactory. 2. The recombinant plasmid PsecTaq2A-AMG was analyzed by restriction endonuclease mapping and DNA sequencing. The results of sequencing were consistent with those from GenBank.

CONCLUSIONThe recombinant plasmid PsecTaq2A-AMG was successfully constructed with properly inserted DNA sequence encoding mature amelogenin.

Amelogenin ; Clone Cells ; metabolism ; Cloning, Molecular ; DNA, Recombinant ; biosynthesis ; genetics ; Dental Enamel Proteins ; biosynthesis ; genetics ; Escherichia coli ; genetics ; metabolism ; Eukaryotic Cells ; metabolism ; Genetic Vectors ; Humans ; Plasmids ; genetics ; Recombinant Proteins ; biosynthesis ; genetics

OBJECTIVETo prepare the polyclonal antibody to amelogenin.

METHODSThe fetal porcine dental enamel was collected. Enamel matrix protein was extracted in 4M guanidine HCl (pH 7.4) with protease inhibitors present. Polyacrylamide gel filtration was included to isolate amelogenin from the initial dissociated extraction. The purified amelogenin conjugated with or without complete/incomplete Freund's adjuvant was then used to immunize the rabbits subcutaneously or intravenously. The specific IgG antibody was further purified by DE-52 cellulose. The working concentration of IgG antibody was determined through ELISA test.

RESULTSThe Gel filtration showed that amelogenin components is at molecular weights of 15 kD and 13 kD apparently, which was consistent with those described before. The ELISA results showed that the working concentration for IgG was 1:1000.

CONCLUSIONThe antibody prepared in this study can be used for the detection of amelogenin.

Amelogenin ; Animals ; Animals, Newborn ; Antibodies, Monoclonal ; biosynthesis ; immunology ; Dental Enamel ; chemistry ; Dental Enamel Proteins ; immunology ; isolation & purification ; Enzyme-Linked Immunosorbent Assay ; Extracellular Matrix ; immunology ; Immunoglobulin G ; analysis ; biosynthesis ; Rabbits ; Swine ; Tooth Germ ; chemistry

Objective:To explore whether the recombinant plasmid PcDNA3.1/ sTNFRⅠcould be expressed properly in mice.Methods:The recombinant plasmid PcDNA3.1/sTNFRⅠand the empty vector PcDNA3.1 were directly injected into the right tibialis anterior muscle of each 6 mice respectively. The expression product, sTNFRⅠ, in muscle homogenates was measured by ELISA 7 and 10 days after injection respectively.Results:7 days after injection sTNFR Ⅰ(mg/g) in muscle homogenates in PcDNA3.1/sTNFRⅠ and PcDNA3.1 injected mice was 161.123?4.0 and 69.34?1.489(P