Eun Ji

Iklan TEMPO.CO, Jakarta - Idol K-pop generasi 2 (gen 2), Jung Eun Ji, kini merambah jaringannya ke dalam dunia akting. Jung Eun Ji, lahir dengan nama Jeong Hye Rim pada 18 Agustus 1993 di Haeundae, Busan, Korea Selatan. Ia adalah seorang penyanyi, aktris, dan pengisi suara yang terkenal. Kariernya di dunia hiburan dimulai dari girl group Apink. Kariernya perlahan berkembang menjadi seorang artis multitalenta yang diakui dalam industri hiburan Korea Selatan.

Dr. Oregonin Inc., #802 Bodeum Hall, Kangwondaehakgil 1, Chuncheon 24341, Republic of Korea

Benign lung diseases are common and often do not require specific treatment, but they pose challenges in the distinguishing of them from lung cancer during low-dose computed tomography (LDCT). This study presents a comprehensive methylation analysis using real-time PCR for minimally invasive diagnoses of lung cancer via employing BALF exosome DNA. A panel of seven epigenetic biomarkers was identified, exhibiting specific methylation patterns in lung cancer BALF exosome DNA. This panel achieved an area under the curve (AUC) of 0.97, with sensitivity and specificity rates of 88.24% and 97.14%, respectively. Each biomarker showed significantly higher mean methylation levels (MMLs) in both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) compared to non-cancer groups, with fold changes from 1.7 to 13.36. The MMLs of the biomarkers were found to be moderately elevated with increasing patient age and smoking history, regardless of sex. A strong correlation was found between the MMLs and NSCLC stage progression, with detection sensitivities of 79% for early stages and 92% for advanced stages. In the validation cohort, the model demonstrated an AUC of 0.95, with 94% sensitivity and specificity. Sensitivity for early-stage NSCLC detection improved from 88.00% to 92.00% when smoking history was included as an additional risk factor.

Light Quality Influence on Growth Performance and Physiological Activity of Coleus Cultivars

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.

Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea

Background/Objectives: Our research introduces a novel screening method to identify antibodies that can suppress cell proliferation and induce apoptosis. Methods: By using an autocrine signaling system with lentivirus, we developed an antibody screening method based on FACS sorting assays and cell cycle analysis to inhibit tumor growth in vitro. This approach is particularly well suited for studying tumor suppressors. Inducing the G0 phase in tumor cells with specific antibodies may arrest their growth permanently or trigger apoptosis. The cell cycle is composed of tightly regulated phases for cell growth and division, with tumorigenesis or apoptosis occurring when these regulatory mechanisms fail. Results: In our study, we identified RACK1 as a key regulator of cancer cell growth. The H9 antibody against RACK1 selected from a human antibody library effectively suppressed cell proliferation by inhibiting RACK1 function. Conclusions: These findings suggest that RACK1 plays a crucial role in tumor cell cycling and could represent a novel therapeutic target for cancer treatment. Although RACK1 is recognized as a significant target protein in various tumors, no commercial therapeutic agents currently exist. Our results suggest that the H9 antibody could be a promising candidate for the development of novel cancer therapies.

Research Center for Microbiome-Brain Disorders, Chungnam University, Daejeon 34134, Republic of Korea

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Department of Biomedical Science and Research Institute of Bioscience & Biotechnology, Hallym University, Chuncheon 24252, Republic of Korea