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Abstrak :
Skin cancer is a malignant growth on the skin caused by many factors. The most common skin cancers are Basal Cell Cancer (BCC) and Squamous Cell Cancer (SCC). This research uses a discriminant analysis to classify some tissues of skin cancer based on criterion number of independent variables. An independent variable is variation of excitation light sources (LED lamp), filters, and sensors to measure Autofluorescence Intensity (IAF) of visible light to near infrared (VIS/NIR) ratio of paraffin embedded tissue biopsy from BCC, SCC, and Lipoma. From the result of discriminant analysis, it is known that the discriminant function is determined by 4 (four) independent variables i.e., Blue LED-Red Filter, Blue LED-Yellow Filter, UV LED-Blue Filter, and UV LED-Yellow Filter. The accuracy of discriminant in classifying the analysis of three skin cancer tissues is 100 %.

Abstrak :
Latar belakang: Diagnosis memiliki peranan yang sangat penting dalam penatalaksanaan kanker usus halus. Namun, pemeriksaan sebelumnya memiliki kekurangan, yaitu; sensitivitas rendah, operator dependent, dan lama. Sehingga akan diobservasi spektrofotometri autofluorosensi menggunakan blok parafin yang memiliki sensitivitas, spesifisitas, akurasi, dan presisi dengan nilai yang baik. Metode: Studi ini mengukur perbedaan intensitas cahaya fluorosensi menggunakan spektrofotometri autofluorosensi cahaya UV pada blok parafin jaringan kanker usus halus mencit dalam panjang gelombang dari 420.2nm sampai 762.9nm. Hasil uji dianalisis menggunakan dua perangkat lunak, yaitu SPSS 26.0 serta Orange Data Mining. Dalam melakukan analisis Orange Data Mining (kualitatif), data akan dianalisis menggunakan PCA dan 7 jenis PC. Sedangkan machine learning (analisis kuantitatif) dengan cross validation kelipatan 5. Hasil: Dari 511 panjang gelombang yang menunjukkan adanya perbedaan signifikan intensitas cahaya pada ketiga kelompok sampel, perbedaan intensitas cahaya dapat dibedakan secara signifikan pada (panjang gelombang); 495 pada kelompok normal-prekanker, 495 pada kelompok normal-radang, 454 pada kelompok radang-prekanker. Selain itu, dalam hasil analis Machine Learning menunjukkan bahwa Neural Network memiliki performa terbaik dalam menganalisis klasifikasi derajat lesi kanker usus halus. Kesimpulan: Spektrofotometri autofluorosensi memiliki kemampuan mengklasifikasikan jaringan normal, radang, serta pre-kanker pada usus halus mencit dengan nilai sensitivitas dan spesifititas baik, namun masih terdapat kesulitan membedakan jaringan radang. ......Background: Diagnosis has a very important role in the management of small bowel cancer. The previous examination, on the other hand, had drawbacks, including low sensitivity, operator dependence, and a long time.So that autofluorescence spectrophotometry will be observed using a paraffin block that has good sensitivity, specificity, accuracy, and precision. Method: This study measured the difference in fluorescence intensity using UV light autofluorescence spectrophotometry on paraffin blocks of mouse small intestine cancer tissue at wavelengths from 420.2 nm to 762.9 nm. The test results were analyzed using two software programs, namely SPSS 26.0 and Orange Data Mining. Data will be analyzed using PCA and 7 different types of PCs in the orange data mining analysis (qualitative).while using machine learning (quantitative analysis) with a total of 5 cross-validations. Results: Of the 511 wavelengths that show a significant difference in light intensity in the third sample group, the difference in light intensity can be significantly different at 495 in the normal-precancer group, 495 in the normal-inflammation group, and 454 in the inflammatory-precancer group. In addition, the results of machine learning analysis show that the neural network has the best performance into analyze the classification of small intestine cancer lesion degrees. Conclusion: Autofluorescence spectrophotometry has the ability to classify normal, inflammatory, and precancerous tissues in the small intestine of mice with good sensitivity and specificity, but there are still difficulties in differentiating tissue inflammation

Abstrak :
Tujuan umum penelitian ini adalah mendapatkan suatu metode deteksi dini kanker berdasarkan analisis sifat optik antara jaringan normal dan adenokarsinoma menggunakan metode autofluoresensi multieksitasi. Pengamatan sifat autofluoresensi jaringan dilakukan pada sampel biopsi seksi jaringan adenokarsinoma, sampel mencit GR yang ditransplantasi adenokarsinoma, dan kultur sel SM 1. Eksitasi jaringan dilakukan menggunakan lampu Light Emiting Diode (LED) pada beberapa kisaran panjang gelombang cahaya tampak. Pada penelitian ini diperoleh data bahwa nilai intensitas autofluorsensi (IAF) pada kisaran panjang gelombang merah dari sel dan jaringan adenokarsinoma cenderung lebih rendah dibandingkan dengan jaringan normal jika dieksitasi oleh LED biru. Sebaliknya nilai IAF pada panjang gelombang infra merah dari sel dan jaringan karsinoma cenderung lebih tinggi dibandingkan dengan jaringan normal apabila dieksitasi oleh LED merah.

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Characterization of Adenocarcinoma?s Autofluorescence Properties Using Multiexcitation Analysis Method. General purpose of this research is to get an early cancer detection method based on the properties of optical analysis between normal and adenocarsinoma tissue using the multiexcitation autofluorescence method. Observation of autofluorescence properties was done on the biopsy sample of adenocarcinoma tissues, GR mice transplanted by adenocarsinoma, and cell culture SM 1. Excitation on tissue was done by using the lamp Light Emitting Diode (LED) at some visible light wavelength range. This research obtained that the value of Intensity Auto fluorescence (IAF) at range red wavelength of cells and adenocarsinoma tissues tend to lower compared to the cells normal tissues if its were excited by blue LED. On the contrary, the value of IAF at infra red wavelength from cells and carcinoma tissues tend to higher compared to the cells and normal tissues if its were excited by red LED.

Abstrak :
Latar belakang. Penelitian ini bertujuan menilai gambaran struktur dan fungsi retina serta menilai hubungan antara durasi terapi kelasi besi dan kadar feritin serum dengan abnormalitas struktur retina pada penyandang thalasemia-β mayor yang memperoleh terapi kelasi besi di RSCM. Metode. Penelitian potong lintang ini dilakukan pada penyandang thalasemia-β mayor berusia di atas 10 tahun yang memperoleh terapi kelasi besi dan menjalani kontrol di Pusat Thalasemia RSCM. Subjek dilakukan pemeriksaan oftalmologis, foto fundus, dan fundus autofluorescence. Selanjutnya dilakukan pengambilan subsampel dari subjek awal berdasarkan hasil fundus autofluorescence dan dilakukan pemeriksaan elektroretinografi multifokal dan elektrookulografi. Hasil. Abnormalitas struktur retina didapatkan pada 46,2% subjek sedangkan abnormalitas pemeriksaan fundus autofluorescence didapatkan pada 41,9% subjek. Sebagian besar subjek memiliki tajam penglihatan dan sensitivitas kontras yang normal. Nilai tengah seluruh parameter elektroretinografi multifokal dan rasio amplitudo light peak terhadap dark trough elektrookulografi kedua kelompok subjek berada dalam rentang normal. Didapatkan penurunan sensitivitas kontras yang signifikan pada subjek dengan abnormalitas struktur retina dan makula, namun tidak untuk tajam penglihatan. Kadar feritin serum yang lebih tinggi berhubungan dengan abnormalitas struktur retina. Kesimpulan. Rerata kadar feritin serum dalam periode satu tahun dengan titik potong ≥6.000 ng/ml dapat digunakan sebagai panduan untuk memulai pemeriksaan struktur dan fungsi retina. ......Introduction. This study aims to evaluate retinal structure and function and association between iron chelation treatment duration and serum ferritin level with retinal structure abnormality in β-thalassemia major patients treated with iron-chelating agent in Cipto Mangunkusumo Hospital. Methods. This cross-sectional study was performed on β-thalassemia major patients aged more than 10 years old in Thalassemia Center, Cipto Mangunkusumo Hospital, who received iron-chelating agent for at least one year. Patients underwent ophthalmologic examination, fundus photography, and fundus autofluorescence imaging. Afterwards subsample was chosen based on fundus autofluorescence imaging result, and underwent multifocal electroretinography and electrooculography examination. Results. Retinal structure abnormality was found in 46.2% patients and fundus autofluorescence abnormality in 41.9% patients. The majority of patients had normal visual acuity and contrast sensitivity. Each multifocal electroretinography parameters and light peak to dark trough amplitude ratio in electrooculography had normal median values. Significant contrast sensitivity reduction was found on patients with retinal and macular structure abnormality, but not for visual acuity. Significant association between higher ferritin serum level and retinal structure abnormality was found. Conclusion. Mean ferritin serum level within one year with cutoff point of ≥6.000 ng/ml can be used as a guide to start retinal structure and function evaluation.

Abstrak :
Latar belakang: Kanker payudara merupakan kanker paling umum yang terjadi pada wanita dan urutan kedua paling umum terjadi secara umum (2.089.000 kasus per tahun 2018), dengan salah satu mortalitas tertinggi (627.000 kematian per tahun 2018). Namun begitu, metode diagnosis histopatologi, standar baku emas penemuan kanker payudara, masih bersifat subjektif terhadap operator peneliti yang mengakibatkan rawannya terjadi diagnosis negatif palsu dan positif palsu. Beberapa studi kemudian meneliti aplikasi dari metode spektrofotometri autofluoresensi sebagai alat diagnosis tambahan dari beragam kanker dengan hasil yang memiliki sensitivitas tinggi dan periode akuisisi data yang singkat. Terlepas hasilnya yang menjanjikan, hingga saat ini belum ada studi aplikasi spektrofotometri autofluoresensi dalam klasifikasi derajat lesi kanker payudara. Penelitian ini dilakukan untuk mengetahui potensi spektrofotometer autofluoresensi sebagai metode klasifikasi jaringan payudara mencit normal, prekanker, dan kanker dalam sediaan blok parafin. Metode: Dalam penelitian ini diukur 640 panjang gelombang mulai dari 420.2–762.9nm terhadap 30 total sampel blok parafin jaringan payudara mencit normal, prekanker, dan kanker. Data autofluoresensi kemudian dianalisis melalui perangkat lunak SPSS untuk uji komparatif dan Orange Data Mining untuk analisis machine learning. Hasil: Terdapat 583 dari 640 panjang gelombang yang dapat menunjukan perbedaan intensitas cahaya antar derajat lesi, dengan 3 di antaranya dapat menunjukkan perbedaan yang bermakna. Logistic Regression merupakan machine learning dengan performa terbaik untuk mengklasifikasi derajat lesi jaringan kanker payudara berdasarkan skor AUC (91,2%), akurasi (83,3%), presisi (83,3%), recall (83,3%), F1 (82.9%), spesifisitas (77,8-100%), dan sensitivitas (87,5%-100%). Kesimpulan: Spektrofotometri autofluoresensi menunjukan performa yang cukup baik dalam aplikasinya mengklasifikasi jaringan payudara mencit normal, prekanker, dan kanker. ......Introduction: Being the most common cancer in women and the second most common in general (2,089,000 cases on 2018), Breast cancer also has one of the highest mortality rate (627,000 deaths on 2018). However, despite the histopathological diagnosis method being the gold standard for breast cancer detection, it is still very subjective to the operator, making it prone to false negative and false positive diagnoses. Several studies investigating the application of the autofluorescence spectrophotometric method as an additional diagnostic tool for various cancers shows high sensitivity results with short data acquisition period. Despite the promising results, until today, there has not been a study of the application of autofluorescence spectrophotometry in the classification of the breast cancer lesions. This study was conducted to determine the potential of the autofluorescence spectrophotometer as a method of classifying normal, precancerous, and cancerous mice breast tissue in paraffin block samples. Method: In this study, 640 wavelengths ranging from 420.2–762.9nm were measured against a total of 30 paraffin block samples of normal, precancerous, and cancer mice breast tissue. The autofluorescence data was then analyzed using SPSS software for comparative testing and Orange Data Mining for machine learning analysis. Result: There are 583 of 640 wavelengths that able to show differences in light intensity between the degrees of lesions, with 3 of them showing significant differences. Logistic Regression is a machine learning with the best performance to classify the degree of breast cancer tissue lesions based on the AUC score (91.2%), accuracy (83.3%), precision (83.3%), recall (83.3%), F1 (82.9%), specificity (77.8-100%), and sensitivity (87.5%- 100%). Conclusion: Autofluorescence spectrophotometry shows a fairly good performance in its application to classify normal, precancerous, and cancerous mice breast tissue.

Abstrak :
Cedera pada Anterior Cruciate Ligament (ACL) sering terjadi pada atlet yang berpartisipasi dalam olahraga intensitas tinggi. Namun, kerusakan ACL juga bisa terjadi pada masyarakat umum. Fatigue dijelaskan oleh perubahan protein struktural utama, heliks kolagen tipe 1. Hal ini merusak ligamen dan menyebabkan kelemahan pada jaringan. Delapan puluh persen kerusakan ACL dilaporkan sebagai kerusakan tanpa kontak langsung, yang bertentangan dengan gagasan bahwa kerusakan ACL terjadi dalam kasus kelebihan beban. Chen dkk. menunjukkan bahwa tanda-tanda fatigue seperti rongga struktural, kekuatan tarik yang lebih rendah dan perubahan komposisi kimia dari heliks kolagen normal menjadi untaian terdenaturasi (1740cm-1) dapat mengurangi integritas struktural ACL yang pada akhirnya menyebabkan kegagalan dini. Penelitian ini membutuhkan lebih banyak detail dalam tanda-tanda baru yang dapat menyebabkan kerusakan ACL akibat fatigue. Proyek ini menganalisis topografi, komposisi kimia, mekanika jaringan seperti kekakuan, dan perubahan sinyal autofluoresensi menggunakan Atomic Force Microscopy Infrared Spectroscopy (AFM-IR) dan endoskopi konfokal. Hasil penelitian menunjukkan bahwa hubungan antara kekakuan dan kerusakan akibat kelelahan adalah semakin tinggi proporsi kolagen gangguan, semakin rendah frekuensi PLL dan semakin rendah kekakuan material. Efek kerusakan mekanis juga ditunjukkan pada salah satu fitur topografi, D-spacing. Peningkatan D-spacing dapat disebabkan oleh deformasi plastik fibril kolagen. ......Injuries to the Anterior Cruciate Ligament (ACL) are prevalent in athletes who participate in high-intensity sports. However, ACL damage can also occur in the general public. Fatigue is explained by changes in the major structural protein, the type 1 collagen helix. This destroys the ligaments and causes weakness. Eighty percent of ACL damage is reported to be non-contact damage, which contradicts the notion that failure occurs in the case of a single overload. Chen et al. showed that fatigue signatures such as structural voids, lower tensile strength and change in chemical composition from normal collagen helices to denatured strands (1740cm-1) could reduce the structural integrity of the ACL ultimately leading to early failure. However, the study needs more detail in these novel signatures of fatigue damage. This project analyses topography, chemical composition, tissue mechanics such as stiffness, and changes in autofluorescence signal using Atomic Force Microscopy Infrared Spectroscopy (AFM-IR) and confocal endoscopy. Other techniques can be explored for the future but are not the focus of this project. The results show that the relationship between stiffness and fatigue damage is that the higher the proportion of disorder collagen, the lower the PLL frequency and the lower the material's stiffness. The effect of mechanical damage is also shown on one of the most critical topographical features, D-spacing. The increase in D-spacing may be due to the plastic deformation of collagen fibrils.