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<abstract><p><italic>Human Papillomavirus</italic> (HPV), which is the main causal factor of cervical cancer, infects normal cervical cells on the specific cell's age interval, i.e., between the $ G_1 $ to $ S $ phase of cell cycle. Hence, the spread of the viruses in cervical tissue not only depends on the time, but also the cell age. By this fact, we introduce a new model that shows the spread of HPV infections on the cervical tissue by considering the age of cells and the time. The model is a four dimensional system of the first order partial differential equations with time and age independent variables, where the cells population is divided into four sub-populations, i.e., susceptible cells, infected cells by HPV, precancerous cells, and cancer cells. There are two types of the steady state solution of the system, i.e., disease-free and cancerous steady state solutions, where the stability is determined by using Fatou's lemma and solving some integral equations. In this case, we use a non-standard method to calculate the basic reproduction number of the system. Lastly, we use numerical simulations to show the dynamics of the age-structured system.</p></abstract>

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Table of contentsA1 Hope and despair in the current treatment of nasopharyngeal cancerIB TanI1 NPC international incidence and risk factorsEllen T ChangI2 Familial nasopharyngeal carcinoma and the use of biomarkersChien-Jen Chen, Wan-Lun Hsu, Yin-Chu ChienI3 Genetic susceptibility risk factors for sporadic and familial NPC: recent findingsAllan HildesheimI5 Genetic and environmental risk factors for nasopharyngeal cancer in Southeast AsiaJames D McKay, Valerie Gaborieau, Mohamed Arifin Bin Kaderi, Dewajani Purnomosari, Catherine Voegele, Florence LeCalvez-Kelm, Graham Byrnes, Paul Brennan, Beena DeviI6 Characterization of the NPC methylome identifies aberrant epigenetic disruption of key signaling pathways and EBV-induced gene methylationLi L, Zhang Y, Fan Y, Sun K, Du Z, Sun H, Chan AT, Tsao SW, Zeng YX, Tao QI7 Tumor exosomes and translational research in NPCPierre Busson, Claire Lhuillier, Olivier Morales, Dhafer Mrizak, Aurore Gelin, Nikiforos Kapetanakis, Nadira DelhemI8 Host manipulations of the Epstein-Barr virus EBNA1 proteinSheila Mansouri, Jennifer Cao, Anup Vaidya, and Lori FrappierI9 Somatic genetic changes in EBV-associated nasopharyngeal carcinomaLo Kwok WaiI10 Preliminary screening results for nasopharyngeal carcinoma with ELISA-based EBV antibodies in Southern ChinaSui-Hong Chen, Jin-lin Du, Ming-Fang Ji, Qi-Hong Huang, Qing Liu, Su-Mei CaoI11 EBV array platform to screen for EBV antibodies associated with NPC and other EBV-associated disordersDenise L. Doolan, Anna Coghill, Jason Mulvenna, Carla Proietti, Lea Lekieffre, Jeffrey Bethony, and Allan HildesheimI12 The nasopharyngeal carcinoma awareness program in IndonesiaRenske Fles, Sagung Rai Indrasari, Camelia Herdini, Santi Martini, Atoillah Isfandiari, Achmad Rhomdoni, Marlinda Adham, Ika Mayangsari, Erik van Werkhoven, Maarten Wildeman, Bambang Hariwiyanto, Bambang Hermani, Widodo Ario Kentjono, Sofia Mubarika Haryana, Marjanka Schmidt, IB TanI13 Current advances and future direction in nasopharyngeal cancer managementBrian O’SullivanI14 Management of juvenile nasopharyngeal cancerEnis OzyarI15 Global pattern of nasopharyngeal cancer: correlation of outcome with access to radiotherapyAnne WM LeeI16 The predictive/prognostic biomarker for nasopharyngeal carcinomaMu-Sheng ZengI17 Effect of HLA and KIR polymorphism on NPC riskXiaojiang Gao, Minzhong Tang, Pat Martin, Yi Zeng, Mary CarringtonI18 Exploring the Association between Potentially Neutralizing Antibodies against EBV Infection and Nasopharyngeal CarcinomaAnna E Coghill, Wei Bu, Hanh Nguyen, Wan-Lun Hsu, Kelly J Yu, Pei-Jen Lou, Cheng-Ping Wang, Chien-Jen Chen, Allan Hildesheim, Jeffrey I CohenI19 Advances in MR imaging in NPCAnn D KingO1 Epstein-Barr virus seromarkers and risk of nasopharyngeal carcinoma: the gene-environment interaction study on nasopharyngeal carcinoma in TaiwanYin-Chu Chien, Wan-Lun Hsu, Kelly J Yu, Tseng-Cheng Chen, Ching-Yuan Lin, Yung-An Tsou, Yi-Shing Leu, Li-Jen Laio, Yen-Liang Chang, Cheng-Ping Wang, Chun-Hun Hua, Ming-Shiang Wu, Chu-Hsing Kate Hsiao, Jehn-Chuan ...

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<abstract><p>In this paper, a mathematical model describing the dynamical of the spread of hepatitis C virus (HCV) at a cellular level with a stochastic noise in the transmission rate is developed from the deterministic model. The unique time-global solution for any positive initial value is served. The Ito's Formula, the suitable Lyapunov function, and other stochastic analysis techniques are used to analyze the model dynamics. The numerical simulations are carried out to describe the analytical results. These results highlight the impact of the noise intensity accelerating the extinction of the disease.</p></abstract>

Pandemi COVID-19 yang muncul pertama kali pada akhir tahun 2019 saat ini telah menyebar ke seluruh dunia dan mempengaruhi segala sendi kehidupan manusia. Di Indonesia, kasus ini mulai berkembang sejak akhir bulan Februari 2020 dan hingga saat ini masih terus terjadi peningkatan infeksi baru. Beberapa model dan prediksi kasus COVID-19 di Indonesia telah dilakukan oleh para peneliti, namun hasilnya belum sepenuhnya akurat. Hal ini kemungkinan disebabkan adanya pola yang berbeda-beda di setiap daerah, sehingga prediksi yang dilakukan di tingkat nasional perlu mengakomodir perbedaan pola tersebut. Pada artikel ini, akan diperkenalkan model matematika untuk melakukan prediksi awal kasus COVID-19 di wilayah Daerah Istimewa Yogyakarta. Pemodelan dilakukan berbasis model SIR yang parameter-parameternya diestimasi berdasarkan data. Dengan menggunakan model tersebut, akan dikaji dua skenario yang bersifat optimistik dan pesimistik.

The highest prevalence of breast cancer in Indonesia is in the Province of Yogyakarta. dr. Sardjito General Hospital has quite complete clinical data on breast cancer patients. Characteristics of the population in various regions in Indonesia are different from one another. This problem is the basis for doing this research. Statistical data analysis needs to be done in each area for better diagnosis and treatment of cancer. Data recording is carried out continuously during outpatient treatment at dr. Sardjito General Hospital. Data for breast cancer patients was taken from July 2018 to June 2020. The data obtained were grouped into four categories: laboratory investigation, socio-demographic, clinical examination, and pathology. Descriptive and correlation analysis aims to determine the characteristics of breast cancer patients seeking treatment at dr. Sardjito General Hospital and anticipate their possibility of developing neutropenia after chemotherapy. The results of the descriptive analysis are significant to determine patient characteristics and treatment steps that can be taken. Correlation analysis variables closely related to neutrophils included leucocyte count, lymphocyte, monocyte, albumin, age at first diagnosis, and height. These variables can be a severe concern of medical personnel before undergoing chemotherapy, especially lymphocytes, which have the largest (negative) correlation and can be an early sign of neutropenia.