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The Relationship of Anxiety Level with Osce Graduation on Students of The Faculty of Medicine YARSI University Class 2019 and 2020, and The Review According to Islamic Perspective Muhammad Kholik Sanaba; Nunung Ainur Rahmah; Firman Arifandi
Junior Medical Journal Vol 1, No 1 (2022)
Publisher : Junior Medical Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (312.958 KB) | DOI: 10.33476/jmj.v1i1.2684

Abstract

Background: Anxiety is a condition that causes changes in a person's emotional atmosphere that can affect students' cognitive function when facing the Objective Structured Clinical Examination and affect student performance. According to the Qur'an, anxiety is explained by the phrase kha?f, which is a condition of unease about the future. This is caused by doubts in the heart or daiq so that hal?'a or anxiety occurs and results in feelings of suffering or hazn. The purpose of this study was to determine the relationship between anxiety levels with online and offline OSCE graduation, age, sex and identify the level of anxiety in YARSI University Faculty of Medicine Students Class of 2019 and 2020.Methods: This type of research is descriptive analytic with a cross sectional research design. The questionnaire was distributed via google form. This research was conducted on students of the Faculty of Medicine, YARSI University batch 2019 and 2020 with a total sample of 209 respondents. Data analysis using Kruskal Wallis test and Kolmogorov-Smirnov test.Results: Based on the results of statistical tests, there is no relationship between anxiety levels with sex, age, and online and offline OSCE graduation in YARSI University Faculty of Medicine Students Class of 2019 and 2020. The level of anxiety experienced by most students is a mild level of anxiety.Conclusion: There are no significant results between the level of anxiety with sex, age, and online and offline OSCE graduation.
Optimizing Image Preprocessing for AI-Driven Cervical Cancer Diagnosis Chandra Prasetyo Utomo; Neng Suhaeni; Nashuha Insani; Elan Suherlan; Nunung Ainur Rahmah; Ahmad Rusdan Utomo; Indra Kusuma; Muhamad Fathurachman; Dewa Nyoman Murti Adyaksa
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1128

Abstract

Cervical cancer ranks among the top causes of cancer-related deaths in women globally. Early detection is vital for improving patient survival rates. The multiclass classification of cervical cell images presents challenges primarily due to the notable variations in cell sizes across different classes. Conventional AI methods for diagnosing cervical cancer often rely on image-resizing techniques that overlook crucial features like relative cell dimensions, which impairs the models' ability to distinguish between classes effectively. This paper presents a novel AI-driven approach that employs constant padding to maintain the natural size differences among cells. Our method utilizes deep learning for both feature extraction and multiclass classification. We assessed the method using the publicly accessible SIPaKMeD dataset. Experimental findings indicate that our approach surpasses traditional image-resizing methods, especially in classes that are more challenging to predict. This strategy highlights AI's potential to improve cervical cancer diagnosis, offering a more precise and dependable tool for early detection. A reliable and precise AI model for diagnosing cervical cancer is crucial for promoting widespread screening and ensuring timely and effective treatment, which can ultimately lower mortality rates. By aiding early and accurate diagnosis, this approach aligns with global health efforts to alleviate the burden of cancer and other diseases, especially in areas with limited access to advanced healthcare services facilities.
Pengaruh Efek Ekstrak Kayu Bajakah Merah (Sphatolobus littoralis) Terhadap Kadar Protein mTOR pada Sel Kanker Payudara MCF-7 Ratu Salia Siskowati; Harliansyah Harliansyah; Nunung Ainur Rahmah; Ahmad Randy; Pendrianto Pendrianto
Jurnal Ners Vol. 10 No. 1 (2026): JANUARI 2026
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v10i1.54258

Abstract

Abstrak Salah satu penyakit yang paling umum terjadi pada wanita adalah kanker payudara, yang terkait dengan pembelahan sel yang tidak terkendali melalui jalur sinyal mTORC. Studi ini bertujuan untuk menentukan bagaimana ekstrak Kayu Bajakah Merah (Spatholobus littoralis) mempengaruhi ekspresi protein mTORC secara in vitro pada sel kanker payudara MCF-7. Penelitian menggunakan desain true experimental dengan post-test only control group, melibatkan enam kelompok perlakuan, termasuk kontrol negatif, kontrol positif (Tamoxifen), dan perlakuan ekstrak Bajakah Merah pada konsentrasi IC₅ 72 jam. Sel dikultur, diolah, dan viabilitas sel diukur menggunakan CCK-8 untuk menentukan IC₅, sedangkan kadar protein mTORC dianalisis menggunakan ELISA. Hasil penelitian menunjukkan bahwa ekstrak Bajakah Merah menurunkan viabilitas sel secara dosis- dan waktu-dependen, dengan IC₅ terendah pada inkubasi 72 jam. Kadar mTORC meningkat signifikan pada kelompok perlakuan ekstrak dibanding kontrol negatif, menunjukkan pengaruh ekstrak terhadap jalur pensinyalan mTORC. Meskipun efek sitotoksik masih lebih rendah dibanding Tamoxifen, ekstrak kayu Bajakah Merah memiliki potensi sebagai agen kemopreventif berbasis bahan alam.. Kata Kunci: kanker payudara, MCF-7, ekstrak kayu Bajakah Merah, mTORC, sitotoksik, kemoprevensi. Abstract One of the most prevalent diseases in women is breast cancer, which is linked to unchecked cell division via the mTORC signaling pathway. This study sought to determine how Red Bajakah Wood (Spatholobus littoralis) extract affected the in vitro expression of the mTORC protein in MCF-7 breast cancer cells. A true experimental design with post-test only control group was employed, including six treatment groups: negative control, positive control (Tamoxifen), and Red Bajakah Wood extract at IC₅₀ 72-hour concentration. Cells were cultured, treated, and viability assessed using CCK-8 to determine IC₅₀, while mTORC protein levels were measured via ELISA. Results showed that Red Bajakah Wood extract reduced cell viability in a dose- and time-dependent manner, with the lowest IC₅₀ at 72 hours incubation. mTORC levels increased significantly in extract-treated cells compared to negative control, indicating the extract’s effect on the mTORC signaling pathway. Although its cytotoxicity was lower than Tamoxifen, Red Bajakah Wood extract demonstrated potential as a natural-based chemopreventive agent.. Keywords: breast cancer, MCF-7, Red Bajakah Wood extract, mTORC, cytotoxicity, chemoprevention.
Pengaruh Ekstrak Kayu Bajakah Merah (Sphatolobus Littoralis) terhadap Kadar Protein Pro-Apoptosis P53 pada Sel MCF-7 Raka Auriza Fathaya; Harliansyah Harliansyah; Ahmad Randy; Pendrianto Pendrianto; Nunung Ainur Rahmah
Jurnal Ners Vol. 10 No. 1 (2026): JANUARI 2026
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v10i1.54282

Abstract

Abstrak Kanker payudara merupakan jenis kanker dengan angka kejadian dan kematian tertinggi pada wanita di dunia. Salah satu model sel kanker payudara yang banyak digunakan dalam penelitian adalah sel MCF-7 yang bersifat sensitif terhadap estrogen. Protein p53 berperan penting sebagai supresor tumor melalui pengaturan siklus sel dan induksi apoptosis. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian ekstrak kayu bajakah merah (Spatholobus littoralis) terhadap kadar protein pro-apoptosis p53 pada sel kanker payudara MCF-7. Penelitian ini merupakan penelitian eksperimental dengan rancangan true-experimental pretest–posttest control group design. Sel MCF-7 dibagi menjadi empat kelompok, yaitu blanko, kontrol negatif, kontrol positif (tamoxifen 4,81 ppm), dan kelompok perlakuan ekstrak kayu bajakah merah dengan konsentrasi IC₅₀ sebesar 116,4 ppm. Sel diinkubasi selama 72 jam pada suhu 37°C dengan 5% CO₂. Kadar protein p53 diukur menggunakan metode ELISA. Hasil penelitian menunjukkan bahwa pemberian ekstrak kayu bajakah merah meningkatkan kadar protein p53 secara signifikan dibandingkan kelompok kontrol, meskipun masih lebih rendah dibandingkan tamoxifen. Temuan ini menunjukkan bahwa ekstrak kayu bajakah merah berpotensi sebagai agen antikanker melalui aktivasi jalur p53 dan sejalan dengan prinsip pengobatan herbal dalam Islam. Kata Kunci: kanker payudara, sel MCF-7, kayu bajakah merah, p53, apoptosis Abstrak Breast cancer is the most common cancer and a leading cause of cancer-related mortality among women worldwide. The MCF-7 cell line is widely used as a model of estrogen receptor–positive breast cancer. The tumor suppressor protein p53 plays a crucial role in cell cycle regulation and apoptosis induction. This study aimed to evaluate the effect of red Bajakah wood (Spatholobus littoralis) extract on pro-apoptotic p53 protein levels in MCF-7 breast cancer cells. This experimental study employed a true-experimental pretest–posttest control group design. MCF-7 cells were divided into four groups: blank, negative control, positive control (tamoxifen 4.81 ppm), and treatment group receiving red Bajakah extract at the IC₅₀ concentration of 116.4 ppm. Cells were incubated for 72 hours at 37°C with 5% CO₂. p53 protein levels were measured using an ELISA method. The results demonstrated that red Bajakah extract significantly increased p53 protein levels compared to the control group, although the levels remained lower than those induced by tamoxifen. These findings suggest that red Bajakah wood extract has potential anticancer activity through p53 pathway activation and supports its use as a herbal therapeutic agent consistent with Islamic medical principles. Keywords: breast cancer, MCF-7 cells, red Bajakah wood, p53, apoptosis.