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"Typical clinical symptoms and chest X-ray is a marker of Tuberculosis (TB) sufferers. However, the diagnosis of TB in adults should be supported by microscopic examination. Currently, Bacilli microscopic examination of acid-fast bacilli (AFB) in sputum by Ziehl-Neelsen (ZN) coloring is the most widely used. However, for reasons of convenience,especially for laboratories with a considerable amount of smear samples, and due to higher sensitivity compared with ZN staining, the World Health Organization (WHO) has recommended the use of auramine-O-staining (fluorochrome staining), which is visualized by light emitting diode (LED) fluorescence microscopy. The aim of this study was to evaluate the performance of modified light microscope with homemade LED additional attachment for examination ofAFB in sputum using auramine-O-staining method. We compared the sensitivity and specificity of 2 kinds of AFB in sputum methods: ZN and fluorochrome, using culture on Lowenstein-Jensen media as the gold standard. The results showed auramine-O-staining gives more proportion of positive findings (81%) compared to the ZN method (70%). These results demonstrated that the sensitivity of auramine-O-staining was higher than ZN, however it gives more potential false positive results than ZN. The sensitivity of auramine-O-staining in detecting AFB in sputum was 100% while the specificity was 88%."

"Skin cancer is a malignant growth on the skin caused by many factors. The most common skin cancers are Basal CellCancer (BCC) and Squamous Cell Cancer (SCC). This research uses a discriminant analysis to classify some tissues ofskin cancer based on criterion number of independent variables. An independent variable is variation of excitation lightsources (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 discriminantanalysis, it is known that the discriminant function is determined by 4 (four) independent variables i.e., Blue LED-RedFilter, Blue LED-Yellow Filter, UV LED-Blue Filter, and UV LED-Yellow Filter. The accuracy of discriminant inclassifying the analysis of three skin cancer tissues is 100 %."

"Measurement of non-invasive blood glucose is one way to increase the frequency of self-monitoring of blood glucose(SMBG). For NIR reflectance spectroscopy, its application in non-invasive constrained by high value of standard errorof prediction. The mean standard error of prediction was 25 mg/dL. Theoretically, NIR reflectance spectroscopy stillcan be used to predict blood glucose levels in certain conditions such as hypoglycemia (<55 mg/dL), controlled fastingblood glucose (FBG) (70-115 mg/dL), and hyperglycemia (>225 mg/dL), which the difference between the threeconditions is more than 25 mg/dL. The results showed that there were significant differences in standards values ofphotometer measurement between controlled FBG and hyperglycemic conditions (p = 0.002). The results also showedthat the photometer can be used to assist the monitoring of blood glucose in FBG under control and hyperglycemicconditions. It can be seen from the average percentage of the daily controlled FBG conditionsin patients conductingSMBG in photometer-assisted compared to in patientsonly use SMBG once a day (28% versus 18%, p = 0.344).Fotometer Sederhana sebagai Alat Bantu Pengukuran Glukosa Darah. Pengukuran glukosa darah secara noninvasifmerupakan salah satu cara untuk meningkatkan frekuensi pemantauan glukosa darah mandiri (PGDM). Untukyang berbasis spektoskopi reflektansi NIR, penerapannya secara non-invasif terkendala nilai standar error of predictionyang tinggi. Namun demikian metode ini secara teori masih dapat dipakai untuk memprediksi kadar glukosa darah padakondisi tertentu seperti keadaan hipoglikemia (<55 mg/dL), gula darah puasa (GDP) terkendali (70-115 mg/dL), danhiperglikemia (>225 mg/dL). Hasil penelitian menunjukkan bahwa terdapat perbedaan bermakna standar nilaipengukuran fotometer antara kondisi GDP terkendali dan hiperglikemia (p = 0,002). Fotometer yang digunakan dapatmembantu pemantauan glukosa darah (PGDM pada kondisi GDP terkendali dan hiperglikemia). Hal ini dapat dilihatdari rata-rata persentase jumlah hari dengan kondisi GDP harian terkendali yang lebih besar pada PGDM yang dibantudengan fotometer dibandingkan PDGM yang dilakukan hanya satu kali sehari (28% berbanding 18%, p = 0,344)."

"Di Indonesia, kanker serviks termasuk penyakit kanker dengan jumlah penderita terbesar kedua setelah kanker payudara. Tata laksana kanker serviks masih bersifat nonselektif dan menimbulkan efek samping berat, sehingga perkembangan pengobatan dan pencegahan kanker terus berlanjut, termasuk pemanfaatan tanaman obat seperti miana (Plectranthus scutellarioides). Miana telah dimanfaatkan sebagai jamu tradisional di Indonesia karena memiliki banyak manfaat kesehatan dan mudah dijangkau. Daun miana juga memiliki kandungan senyawa yang bersifat antikanker dan dapat menurunkan risiko infeksi HPV. Dengan demikian, penulis bermaksud meneliti efek sitotoksik ekstrak etil asetat daun miana terhadap sel kanker serviks HeLa. Ekstrak etil asetat daun miana atau doksorubisin (sebagai kontrol positif) diberikan dalam konsentrasi 0 ppm, 1,625 ppm, 6,25 ppm, 12,5 ppm, 25 ppm, 50 ppm, 100 ppm, dan 200 ppm pada sel HeLa. uji MTT dilakukan untuk memperoleh nilai absorbansi dan nilai IC50-nya. Perbandingan antarkelompok perlakuan dilakukan untuk mengetahui perbedaan antara kelompok ekstrak dengan doksorubisin. Ditemukan bahwa nilai IC50 kelompok ekstrak adalah 26,16 ppm, dan nilai p = 0,047 pada uji kemaknaan antara kelompok ekstrak dengan doksorubisin. Oleh karena itu, disimpulkan bahwa ekstrak etil asetat daun miana memiliki IC50 yang tergolong memiliki sitotoksisitas moderat dan memiliki perbedaan inhibisi sel HeLa yang signifikan dibandingkan dengan kontrol positif doksorubisin.

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In Indonesia, cervical cancer is the second most common cancer. Cervical cancer treatment options have limitations including their nonselective properties and serious side effects. Scientific developments regarding cancer treatment and prevention continue to be carried out, such as the use of medicinal plants like miana (Plectranthus scutellarioides). Miana has been used as Indonesian traditional medicine for its health benefits and abundance in tropical areas. Miana leaves has anticancer properties and can reduce HPV infection risk. This research aims to study the cytotoxic effect of ethyl acetate extract of miana leaves on cervical cancer HeLa cells. Ethyl acetate extract of miana leaves or doxorubicin (as positive control) was given in 0 ppm, 1.625 ppm, 6.25 ppm, 12.5 ppm, 25 ppm, 50 ppm, 100 ppm, and 200 ppm into HeLa cells. MTT test was done to obtain the IC50 value. Comparison between treatment groups was done to determine the difference between the extract and doxorubicin group. IC50 value of the extract group was 26,16 ppm and the significance test between the extract and doxorubicin group showed the p value of 0,047. In conclusion, the extract had moderate cytotoxicity and had a significant difference in its inhibition against HeLa cells compared to doxorubicin."

"Latar belakang: Kanker kolorektal merupakan kanker dengan insidensi tertinggi ketiga di dunia dengan angka mortalitas 880.792 jiwa (Globocan 2018). Saat ini tata laksana kanker kolorektal terbatas pada kemoterapi dan operasi dengan hasil klinis yang buruk. Adapun terapi target yang baru-baru ini dikembangkan ternyata memiliki efek samping yang cukup parah dan indikasinya terbatas. Di sisi lain, protein iNOS ditemukan meningkat pada jaringan yang mengalami inflamasi, termasuk pada kanker kolorektal. Peningkatan ekspresi iNOS dikorelasikan dengan prognosis kanker yang buruk sehingga berpotensi dijadikan sebagai target terapi dalam penanganan kanker kolorektal. Moringa oleifera merupakan tanaman obat yang diketahui khasiatnya sebagai agen antioksidan, antiinflamasi, dan antikanker. Penelitian ini ditujukan untuk menilai pengaruh ekstrak etanol daun Moringa oleifera terhadap ekspresi iNOS sel HT-29.Metode: Ekstrak etanol daun Moringa oleifera diuji secara in vitro pengaruhnya terhadap ekspresi iNOS sel HT-29. Pengujian dilakukan secara imunositokimia dengan tiga serial konsentrasi ekstrak, yaitu 50, 100, dan 200 ppm, tanpa disertai kontrol. Ekspresi iNOS diukur dengan H-score melalui bantuan aplikasi ImageJ.Hasil: Ekstrak etanol daun Moringa oleifera menurunkan ekspresi iNOS sel HT-29 pada konsentrasi 200 ppm dengan rerata H-score sebesar 118,67 ± 1,68.Kesimpulan: Pemberian ekstrak etanol daun Moringa oleifera pada konsentrasi 200 ppm secara signifikan (p < 0,05) dapat menurunkan ekspresi iNOS sel kanker kolorektal HT-29.

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Introduction: Colorectal cancer is cancer with the third-highest incidence globally with 880.792 mortality (Globocan 2018). Currently, the management of colorectal cancer is limited to chemotherapy and surgery with poor clinical outcomes. The recently developed targeted therapy has quite severe side effects and has limited indication. On the other hand, the iNOS protein was found to be increased in number in inflamed tissues, including colorectal cancer. Increased iNOS expression is correlated with a poor cancer prognosis so that it has the potential to be used as a therapeutic target in the treatment of colorectal cancer. Moringa oleifera is a medicinal plant known for its properties as an antioxidant, anti-inflammatory, and anticancer agent. This study aimed to assess the effect of Moringa oleifera leaf extract on iNOS expression in HT-29 cells.

Method: Ethanol extract from Moringa oleifera leaf tested in vitro for its effect on iNOS expression in HT-29 cells. The test was carried out through an immunocytochemical procedure with three serial concentrations of the extract, 50, 100, and 200 ppm. iNOS expression was measured by H-score using ImageJ application.

Result: Moringa oleifera leaf extract decreased the iNOS expression of HT-29 cells when given the extract with a concentration of 200 ppm with an average H-score of 118.67 ± 1.68.

Conclusion: The administration of Moringa oleifera leaf extract at a concentration of 200 ppm significantly (p < 0.05) can decrease iNOS expression in HT-29 colorectal cancer cells."

"Latar belakang: Menurut Global Cancer Statistics 2020 (GLOBOCAN), kanker kolorektal masih menduduki posisi ke-3 pada penyebab kanker tersering di dunia, dan posisi ke-2 pada penyebab kematian tersering akibat kanker (9.4%). Evaluasi histopatologi dari hasil biopsi jaringan kolorektal yang merupakan baku emas dalam diagnosis saat ini pun masih memiliki berbagai keterbatasan. Penentuan derajat keparahan dari kanker kolorektal, dilakukan secara subjektif oleh ahli patologi anatomik melalui observasi mikroskop, sehingga data yang dimiliki bersifat kualitatif. Studi menggunakan prinsip spektrofotometri sudah pernah dilakukan dalam upaya diagnostik kanker sebelumnya. Namun, hingga saat ini masih belum ada studi yang menggunakan spektrofotometer reflektansi VIS-NIR sebagai metode diagnostik kuantitatif dan objektif untuk kanker kolorektal.Tujuan: Penelitian ini adalah studi pendahuluan untuk mengetahui potensi dan kemampuan dari spektrofotometer reflektansi VIS-NIR dalam membedakan jaringan normal, prekanker, dan radang pada blok parafin jaringan kolon mencit.Metode: Penelitian ini memiliki desain eksperimental yang menggunakan sampel blok parafin jaringan kolorektal mencit Mus musculus. Sampel diklasifikasikan oleh ahli patologi anatomi menjadi tiga kategori berdasarkan derajat lesinya, yaitu normal, radang, dan prekanker. Sebanyak 30 sampel tersebut diukur intensitas cahaya reflektansinya pada 454 panjang gelombang berbeda yang termasuk dalam spektrum VIS-NIR. Hasil pengukuran dianalisis dengan perangakat lunak SPSS 26.0 untuk uji komparatif dan perangkat lunak Orange Data Mining untuk pengujian machine learning dalam pegelompokan sampel berdasarkan derajat lesinya.Hasil dan Pembahasan: Hasil uji komparatif membuktikan bahwa 429 dari 454 panjang gelombang cahaya VIS-NIR memiliki perbedaan intensitas cahaya reflektansi yang bermakna antarkelompok derajat lesi (p<0.05). Machine learning yang terbaik dalam pengelompokan sampel menurut derajat lesi berdasarkan data intensitas cahaya reflektansi adalah model SVM dengan nilai Area under the Curve (AUC) 98.3%, Classification Accuracy (CA) 86.7%, Skor F1 0.862, Precision 86.9%, Recall 86.7%, sensitivitas 70-100%, dan spesifisitas 90-95%.Kesimpulan: Spektrofotometri Reflektansi VIS-NIR dapat membedakan jaringan normal, radang dan prekanker kolorektal pada mencit Mus musculus dengan sensitivitas dan spesifisitas yang baik

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Background: According to the Global Cancer Statistics 2020 (GLOBOCAN), colorectal cancer is still the 3rd most common cause of cancer in the world and the 2nd most common cause of cancer death (9.4%). Histopathological evaluation of colorectal tissue biopsy results, which is currently still the gold standard in colorectal cancer diagnosis, has its limitations. Determining the severity of colorectal cancer is done subjectively by anatomical pathologists through microscopic observation. Results from this evaluation are qualitative data which can contribute to the high level of false positive and negatives of the diagnosis. Studies using spectrophotometric principles have been carried out in previous diagnostic efforts. However, to date, there are still no studies using the VIS-NIR reflectance spectrophotometer as a quantitative and objective diagnostic tool for colorectal cancer.

Objective: This is a pilot study to determine the potential and ability of the VIS-NIR reflectance spectrophotometer in differentiating normal, precancerous, and inflammatory parrafin-block of mouse colorectal tissues.

Method: This experimental study uses paraffin-block samples of colorectal tissue from Mus musculus mice. Samples were classified by anatomical pathologists into three categories based on the degree of lesion, namely normal, inflammatory, and precancerous. A total of 30 samples were measured by their light intensity reflectance at 454 different wavelengths included in the VIS-NIR spectrum. Results are evaluated using SPSS 26.0 for comparative testing and Orange Data Mining for machine learning to evaluate their competence in differentiating samples based on the degree of lesion.

Results and Discussion: Comparative test results proved that 429 of the 454 wavelengths in the VIS-NIR light spectrum had a significant difference in light intensity reflectance between the three degree groups of lesion (p<0.05). The best machine learning in differentiating samples according to the degree of lesions based on light reflectance intensity is the SVM model with the value of Area Under the Curve (AUC) 98.3%, Classification Accuracy (CA) 86.7%, F1 score 0.862, Precision 86.9%, Recall 86.7%, sensitivity 70-100%, and specificity 90-95%.

Conclusion: VIS-NIR Reflectance spectrophotometry can distinguish normal, inflammatory, and precancerous colorectal tissue in Mus musculus mice with good sensitivity and specificity."

"Kanker kolorektal terus menyumbang jumlah kasus kanker dan kematian yang tinggi setiap tahunnya. Salah satu metode diagnosis progresi kanker ini adalah dengan interpretasi biopsi dari ahli patologi anatomi. Akan tetapi, seringkali terjadi misinterpretasi antar patolog karena lesinya yang kurang spesifik. Maka dari itu, perlunya ada alat bantu yang dapat menunjang pekerjaan ahli patologi anatomi dalam menginterpretasi progresi kanker kolorektal. Penelitian ini bertujuan untuk melihat kemampuan spektrofotometer untuk mengklasifikasikan jaringan kolorektal mencit. Data jaringan mencit yang sudah diklasifikasikan menurut ahli PA diuji menggunakan cahaya tampak dan akan dibaca oleh spektrofotometer reflektansi. Hasil dari spektrofotometer kemudian akan dibaca oleh Theremino Spectrophotometer. Semua data kemudian diuji normalitas menggunakan uji Saphiro Wilk, dilanjutkan dengan uji ANOVA atau Kruskal-Wallis, kemudian uji Post Hoc atau Mann-Whitney. Data juga dianalisis menggunakan supervised dan unsupervised machine learning. Dari uji hipotesis hanya didapatkan 2 panjang gelombang yang dapat membedakan jaringan normal dan prekanker secara signifikan (696,7 dan 699.8 nm). Sedangkan yang lainnya kurang dapat membedakan jaringan normal, radang, dan prekanker. Hasil dari machine learning menunjukkan sensitivitas, spesifisitas, AUC, akurasi, dan presisi yang rendah. Maka dari itu, dapat disimpulkan dari penelitian ini bahwa metode spektrofotometri reflektans cahaya tampak kurang cocok digunakan untuk membedakan jaringan kolon normal, radang, dan prekanker pada sediaan preparat mencit.

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Colorectal cancer continues to account for a high number of cancer cases and deaths every year. The gold standard of diagnosing this cancer progression is by interpretation of a biopsy from an anatomical pathologist. However, there is often misinterpretation among pathologists due to their unspesific lesions. Therefore, it is required to have a tool that can support the work of anatomical pathologists in interpreting the progression of colorectal cancer. This study aims to see the ability of the spectrophotometer to classify the colorectal tissue of mice. Mice tissue data that has been classified according to PA experts was tested using visible light and would be read by a reflectance spectrophotometer. The results of the spectrophotometer will then be read by the Theremino Spectrophotometer. All data were then tested for normality using the Saphiro Wilk test, followed by the ANOVA or Kruskal-Wallis test, then the Post Hoc or Mann-Whitney test. Data were also analyzed using supervised and unsupervised machine learning. From the hypothesis test, only 2 wavelengths were found that could significantly differentiate normal and precancerous tissue (696.7 and 699.8 nm). While others are less able to distinguish normal, inflammatory, and precancerous tissue. The results from machine learning show low sensitivity, specificity, AUC, accuracy, and precision to distinguish between the three categories. Therefore, it can be concluded from this research that the visible light reflectance spectrophotometric method is not suitable for distinguishing normal, inflammatory, and precancerous colonic tissue in mice preparations."

"Latar belakang: Kanker lambung bertanggung jawab atas lebih dari 1.000.000 kasus kanker baru pada tahun 2020 dan diperkirakan 769.000 kematian atau sama dengan satu dari setiap 13 kematian secara global. Deteksi dini menjadi kunci penurunan angka kematian dan perbaikan prognosis, dengan baku emas berupa avaluasi histopatologi dari hasil biopsi endoskopi. Tetapi subjektivitas pemeriksan tersebut berpotensi menimbulkan kesalahan diagnosis terutama akibat kesalahan interpretasi ahli patologi. Untuk itu, diperlukan metode diagnostik kuantitatif yang dapat menilai secara objektif lesi prekanker atau inflamasi pada dinding lambung. Metode autofluoresensi sebelumnya sudah digunakan dalam upaya diagnostik kanker lambung. Namun, saat ini belum ada studi terkait penggunaan spektrofotometri autofluoresensi sebagai metode diagnostik kuantitatif dan objektif untuk kanker lambung. Tujuan: Studi ini dilakukan untuk mengetahui kemampuan spektrofotometri autofluoresensi dalam mengidentifikasi jaringan lambung normal, inflamasi dan prekanker berdasarkan intensitas fluoresensi jaringan.Metode: Studi ini menggunakan sediaan blok parafin jaringan lambung mencit (Mus musculus) normal, inflamasi dan prekanker. Intensitas fluoresensi jaringan diukur pada 640 panjang gelombang menggunakan spektrofotometer autofluoresensi sederhana dengan sumber cahaya ultraviolet. Analisis data dilakukan dengan SPSS untuk uji normalitas, homogenitas dan hipotesis. Dilanjutkan dengan pengelompokkan data secara kualitatif dengan Principal Component Analysis (PCA) dan secara kuantitatif dengan machine learning dengan 3-fold cross validation. Hasil analisis dengan PCA dinilai dengan scatter plot. Hasil pengolahan data secara kuantitatif dinilai dengan Area under the Curve (AUC),Classification Accuracy (CA), precision, recall, F1-score, sensitivitas dan spesifisitas. Hasil: Ditemukan dua panjang gelombang dengan intensitas fluoresensi bermakna untuk tiga kelompok jaringan dan 554 panjang gelombang yang bermakna untuk dua kelompok jaringan. Dalam pengelompokkan tiga variabel, ditemukan nilai AUC 0,900, CA 0,833, Skor F1 0,831, Precision 0,802, dan Recall 0,800. Dalam pengelompokkan dua variabel, ditemukan sensitivitas dan spesifisitas 100% untuk membedakan jaringan prekanker dengan normal. Sensitivitas 100% dan spesifisitas 80% untuk jaringan prekanker dengan inflamasi. Serta sensitivitas 80% dan spesifisitas 90% untuk jaringan inflamasi dengan normal. Kesimpulan: Spektrofotometeri autofluoresensi dapat membedakan jaringan lambung normal, inflamasi dan prekanker mencit Mus musculus dengan sensitivitas dan spesifisitas yang baik.

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Introduction: Gastric cancer was responsible for more than 1,000,000 new cancer cases in 2020 and an estimated 769,000 deaths or equal to one in every 13 deaths globally. Early detection is the key to reducing mortality and improving prognosis, with histopathological evaluation of endoscopic biopsy results as gold standard. However, the subjectivity of the examination has the potential to cause misdiagnosis, mainly due to the pathologist's misinterpretation. For this reason, quantitative diagnostic methods are needed that can objectively assess precancerous or inflammatory lesions in the gastric wall. The autofluorescence method has previously been used in the diagnostic effort of gastric cancer. However, there are currently no studies related to the use of autofluorescence spectrophotometry as a quantitative and objective diagnostic method for gastric cancer Objective: This study was conducted to determine the ability of autofluorescence spectrophotometry to identify normal, inflammatory and precancerous gastric tissue based on the intensity of tissue fluorescence.Method: This study used a paraffin block preparation of normal, inflammatory and precancerous mice (Mus musculus) gastric tissue. The intensity of tissue autofluorescence was measured at 640 wavelengths using simple autofluorescence spectrophotometer with ultraviolet light source. Data analysis was performed using SPSS to test for normality, homogeneity and hypotheses. Followed by grouping the data qualitatively with Principal Component Analysis (PCA) and quantitatively with machine learning with 3-fold cross validation. The results of the PCA analysis were assessed using a scatter plot. The results of quantitative data processing were assessed by Area under the Curve (AUC), Classification Accuracy (CA), precision, recall, F1-score, sensitivity and specificity. Result: Two wavelengths with significant fluorescence intensity were found for three tissue groups and 554 significant wavelengths for two tissue groups. In grouping the three variables, the AUC value was 0.900, CA 0.833, F1 score 0.831, Precision 0.802, and Recall 0.800. In grouping the two variables, 100% sensitivity and specificity were found to differentiate between precancerous and normal tissues. 100% sensitivity and 80% specificity for precancerous tissue with inflammation. As well as 80% sensitivity and 90% specificity for normal inflammatory tissue. Conclusion: Autofluorescence spectrophotometry can differentiate normal, inflammatory and precancerous gastric tissue in mice Mus musculus with good sensitivity and specificity."

"Latar belakang: Kasus kanker terus meningkat setiap tahunnya. Begitu pula dengan kanker kolon. Selain itu, belum terdapat penelitian mengenai pendeteksian kanker kolon menggunakan spektrofotometri autofluoresensi. Tujuan: Penelitian bertujuan untuk mengetahui perbedaan panjang gelombang dan intensitas cahaya reflektans pada sediaan preparat blok parafin jaringan kolon normal, radang, dan prekanker mencit menggunakan spektrofotometri autofluoresensi dengan menilai sensitivitas dan akurasinya. Metode: Penelitian ini mengukur panjang gelombang dan intensitas cahaya reflektans pada jaringan kolon normal, radang, dan prekanker mencit dengan spektrofotometri autofluoresensi bersumber cahaya ultraviolet (UV) pada panjang gelombang 420,2—762,9 nm. Kemudian dianalisis dengan menggunakan SPSS untuk menguji hipotesis dan normalitas data serta Orange Data Mining yang ditinjau dengan machine learning untuk mengetahui sensitivitas, spesifisitas, akurasi, precision, serta recall. Hasil: Tidak terdapat perbedaan signifikan panjang gelombang reflektans antara 3 kelompok jaringan kolon (normal, radang, dan prekanker) dengan akurasi 56,7% dan tidak ditemukan perbedaan signifikan panjang gelombang reflektans antara 2 kelompok jaringan (radang dengan prekanker) dengan sensitivitas 66,67% dan nilai diagnosis buruk. Namun, ditemukan 175 panjang gelombang reflektans dengan perbedaan signifikan dalam membedakan jaringan kolon normal dengan radang atau prekanker dengan sensitivitas 72,73%—100% dan nilai diagnosis baik hingga sangat baik. Kesimpulan: Spektrofotometri autofluoresensi bersumber cahaya ultraviolet (UV) dapat mengklasifikasikan 2 kelompok jaringan kolon, yakni jaringan kolon normal dengan jaringan kolon radang atau prekanker. Namun, tidak dapat mengklasifikasikan 3 kelompok jaringan kolon, yakni jaringan kolon normal, radang, dan prekanker serta 2 kelompok jaringan kolon radang dengan prekanker.

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Introduction: Cancer cases are increasing annually, including colon cancer. Furthermore, early detection of colon cancer using autofluorescence spectrophotometry also hasn't been done before. Objectives: This research aims to comprehend the difference between reflectance wavelength and light intensity in normal, inflammation, and precancerous mice's colon tissues in paraffin block samples using autofluorescence spectrophotometry by assessing its accuracy and sensitivity. Method: This research measured reflectance wavelength and light intensity of normal, inflammation, and precancerous mice's colon tissue using autofluorescence spectrophotometry with ultraviolet light, in the range of 420.2—762.9 nm. Afterward, it was analyzed by SPSS to test the hypothesis and data normality, also Orange Data Mining's machine learning to determine its sensitivity, specificity, accuracy, precision, and recall. Result: There was no significant difference in reflectance wavelength between 3 groups of colon tissues (normal, inflammation, and precancerous) with accuracy valued at 56.7%, also between 2 groups of colon tissues (inflammation and precancerous) with sensitivity valued at 66.67% and "poor" diagnostic value. Nonetheless, there were 175 significantly different reflectance wavelengths to differentiate normal with inflammation or precancerous colon tissue with sensitivity valued at 72.73%—100% and "good" to "excellent" diagnostic value. Conclusion: Autofluorescence spectrophotometry with ultraviolet (UV) light can classify 2 groups of colon tissue, i.e. normal with inflammation or precancerous colon tissue. Otherwise, it cannot classify 3 groups of colon tissue (normal, inflammation, precancerous) at a time and 2 groups of colon tissue (inflammation and precancerous)."

"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.

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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."