Cancer immunotherapy represents a paradigm-shifting achievement in oncology. Particularly, chimeric antigen receptor (CAR)-T cell therapy utilizing genetically engineered T cells has produced remarkable clinical responses in hematological malignancies.However, significant challenges still remain including limited efficacy in solid tumors and critical safety concerns. The functionality of CAR-T cells depends on their synthetic receptor, CAR, which redirects T cell specificity and enhances effector functions.Therefore, optimal CAR engineering is crucial for successful development of CAR-T cell therapy. In this review, we discuss the limitations of current CAR screening methods, which primarily assess antigen binding affinity in vitro and often fail to predict T cell function and in vivo therapeutic performance. Advanced cell-based screening platforms have been developed to overcome these limitations. We overview the principles of these CAR screening systems utilizing reporter cell lines. While most are based on the detection of antigen binding properties or CAR-T cell activation markers, we emphasize a FRET-based immunological synapse biosensor as a powerful system that directly assesses CAR activation upon antigen binding. This platform offers significant advantages in speed and scalability for predicting CAR-T cell functionality. We also discuss recent advances in CAR library screening directly in primary T cells, which provides more physiologically relevant data. Such advanced platforms are essential to accelerate the development of safe and effective CAR-T therapy for solid tumors, ultimately expanding the therapeutic potential of this transformative cancer treatment.

Background and Objectives: Accumulating evidence shows that sodium-glucose cotransporter 2 inhibitors (SGLT2is) reduce adverse cardiovascular outcomes. However, whether SGLT2i, compared with other antidiabetic drugs, reduce the new development of atrial fibrillation (AF) is unclear. In this study, we compared SGLT2i with dipeptidyl peptidase-4 inhibitors (DPP-4is) in terms of reduction in the risk of AF in individuals with type 2 diabetes. Methods: We included 42,786 propensity score-matched pairs of SGLT2i and DPP-4i users without previous AF diagnosis using the Korean National Health Insurance Service database between May 1, 2016, and December 31, 2018. Results: During a median follow-up of 1.3 years, SGLT2i users had a lower incidence of AF than DPP-4i users (1.95 vs. 2.65 per 1,000 person-years; hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.55–0.97; p=0.028]). In individuals without heart failure, SGLT2i users was associated with a decreased risk of AF incidence (HR, 0.70; 95% CI, 0.52–0.94; p=0.019) compared to DPP-4i users. However, individuals with heart failure, SGLT2i users was not significantly associated with a change in risk (HR, 1.04; 95% CI, 0.44–2.44; p=0.936). Conclusions In this nationwide cohort study of individuals with type 2 diabetes, treatment with SGLT2i was associated with a lower risk of AF compared with treatment with DPP-4i.

Purpose: Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [ 18 F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [ 18 F]FDG PET/CT. Methods: The whole-body [ 18 F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output. Results: The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. Conclusion The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [ 18 F]FDG PET/CT.

Since a novel beta-coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in December 2019, there has been a rapid global spread of the virus. Genomic surveillance was conducted on samples isolated from infected individuals to monitor the spread of genetic variants of SARS-CoV-2 in Korea. The Korea Disease Control and Prevention Agency performed whole genome sequencing of SARS-CoV-2 in Korea for 1 year (January 2020 to January 2021). A total of 2,488 SARSCoV-2 cases were sequenced (including 648 cases from abroad). Initially, the prevalent clades of SARSCoV-2 were the S and V clades, however, by March 2020, GH clade was the most dominant. Only international travelers were identified as having G or GR clades, and since the first variant 501Y.V1 was identified (from a traveler from the United Kingdom on December 22 nd , 2020), a total of 27 variants of 501Y.V1, 501Y.V2, and 484K.V2 have been classified (as of January 25 th , 2021). The results in this study indicated that quarantining of travelers entering Korea successfully prevented dissemination of the SARS-CoV-2 variants in Korea.

PURPOSE: This study tried to find the most significant factors predicting implant prognosis using machine learning methods. MATERIALS AND METHODS: The data used in this study was based on a systematic search of chart files at Seoul National University Bundang Hospital for one year. In this period, oral and maxillofacial surgeons inserted 667 implants in 198 patients after consultation with a prosthodontist. The traditional statistical methods were inappropriate in this study, which analyzed the data of a small sample size to find a factor affecting the prognosis. The machine learning methods were used in this study, since these methods have analyzing power for a small sample size and are able to find a new factor that has been unknown to have an effect on the result. A decision tree model and a support vector machine were used for the analysis. RESULTS: The results identified mesio-distal position of the inserted implant as the most significant factor determining its prognosis. Both of the machine learning methods, the decision tree model and support vector machine, yielded the similar results. CONCLUSION: Dental clinicians should be careful in locating implants in the patient's mouths, especially mesio-distally, to minimize the negative complications against implant survival.
Decision Trees ; Dental Implants ; Dentists ; Humans ; Machine Learning ; Methods ; Mouth ; Oral and Maxillofacial Surgeons ; Pilot Projects ; Prognosis ; Sample Size ; Seoul ; Support Vector Machine