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  1. Song Z, Zhang W, Jiang Q, Deng L, Du L, Mou W, et al.
    Int J Surg, 2023 Dec 01;109(12):3848-3860.
    PMID: 37988414 DOI: 10.1097/JS9.0000000000000862
    BACKGROUND: The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa.

    PATIENTS AND METHODS: A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses.

    RESULTS: Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively.

    CONCLUSIONS: The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.

  2. Nyon MP, Du L, Tseng CK, Seid CA, Pollet J, Naceanceno KS, et al.
    Vaccine, 2018 03 27;36(14):1853-1862.
    PMID: 29496347 DOI: 10.1016/j.vaccine.2018.02.065
    Middle East respiratory syndrome coronavirus (MERS-CoV) has infected at least 2040 patients and caused 712 deaths since its first appearance in 2012, yet neither pathogen-specific therapeutics nor approved vaccines are available. To address this need, we are developing a subunit recombinant protein vaccine comprising residues 377-588 of the MERS-CoV spike protein receptor-binding domain (RBD), which, when formulated with the AddaVax adjuvant, it induces a significant neutralizing antibody response and protection against MERS-CoV challenge in vaccinated animals. To prepare for the manufacture and first-in-human testing of the vaccine, we have developed a process to stably produce the recombinant MERS S377-588 protein in Chinese hamster ovary (CHO) cells. To accomplish this, we transfected an adherent dihydrofolate reductase-deficient CHO cell line (adCHO) with a plasmid encoding S377-588 fused with the human IgG Fc fragment (S377-588-Fc). We then demonstrated the interleukin-2 signal peptide-directed secretion of the recombinant protein into extracellular milieu. Using a gradually increasing methotrexate (MTX) concentration to 5 μM, we increased protein yield by a factor of 40. The adCHO-expressed S377-588-Fc recombinant protein demonstrated functionality and binding specificity identical to those of the protein from transiently transfected HEK293T cells. In addition, hCD26/dipeptidyl peptidase-4 (DPP4) transgenic mice vaccinated with AddaVax-adjuvanted S377-588-Fc could produce neutralizing antibodies against MERS-CoV and survived for at least 21 days after challenge with live MERS-CoV with no evidence of immunological toxicity or eosinophilic immune enhancement. To prepare for large scale-manufacture of the vaccine antigen, we have further developed a high-yield monoclonal suspension CHO cell line.
  3. Koh CP, Bahirvani AG, Wang CQ, Yokomizo T, Ng CEL, Du L, et al.
    Gene, 2023 Jan 30;851:147049.
    PMID: 36384171 DOI: 10.1016/j.gene.2022.147049
    A cis-regulatory genetic element which targets gene expression to stem cells, termed stem cell enhancer, serves as a molecular handle for stem cell-specific genetic engineering. Here we show the generation and characterization of a tamoxifen-inducible CreERT2 transgenic (Tg) mouse employing previously identified hematopoietic stem cell (HSC) enhancer for Runx1, eR1 (+24 m). Kinetic analysis of labeled cells after tamoxifen injection and transplantation assays revealed that eR1-driven CreERT2 activity marks dormant adult HSCs which slowly but steadily contribute to unperturbed hematopoiesis. Fetal and child HSCs that are uniformly or intermediately active were also efficiently targeted. Notably, a gene ablation at distinct developmental stages, enabled by this system, resulted in different phenotypes. Similarly, an oncogenic Kras induction at distinct ages caused different spectrums of malignant diseases. These results demonstrate that the eR1-CreERT2 Tg mouse serves as a powerful resource for the analyses of both normal and malignant HSCs at all developmental stages.
  4. Du L, Pang Y
    Sci Rep, 2021 06 24;11(1):13275.
    PMID: 34168200 DOI: 10.1038/s41598-021-92484-6
    Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient's lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor's diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.
  5. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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