Hasil Pencarian  ::  Simpan CSV :: Kembali

"This handbook gives a comprehensive overview about Raman spectroscopy for the characterization of nanomaterials. Modern applications and state-of-the-art techniques are covered and make this volume essential reading for research scientists in academia and industry."

"Raman imaging has long been used to probe the chemical nature of a sample, providing information on molecular orientation, symmetry and structure with sub-micron spatial resolution. Recent technical developments have pushed the limits of micro-Raman microscopy, enabling the acquisition of Raman spectra with unprecedented speed, and opening a pathway to fast chemical imaging for many applications from material science and semiconductors to pharmaceutical drug development and cell biology, and even art and forensic science. The promise of tip-enhanced raman spectroscopy (TERS) and near-field techniques is pushing the envelope even further by breaking the limit of diffraction and enabling nano-Raman microscopy."

"Diabetes Melitus (DM) merupakan salah satu penyebab utama penyakit ginjal kronis (CKD), dengan komplikasi umum berupa penyakit ginjal diabetik (DKD). Diagnosis DKD secara tradisional mengandalkan biopsi ginjal, metode invasif yang memiliki risiko dan keterbatasan. Penelitian ini bertujuan untuk mengembangkan metode diagnostik non-invasif berbasis spektroskopi Raman dan machine learning guna membedakan DKD dari penyakit glomerular lainnya melalui analisis sampel urin. Spektroskopi Raman digunakan untuk menganalisis 320 spektra urin dari 32 pasien, yang direplikasi 10 kali per pasien. Hasil menunjukkan bahwa algoritma support vector machine mampu mengklasifikasikan DKD dengan tingkat akurasi sebesar 0,91, presisi 0,889, sensitivitas (recall) 0,889, dan spesifisitas 0,925. Selain itu, analisis spektral mengidentifikasi puncak-puncak utama seperti 898, 931, dan 1060 cm sebagai komponen yang terkait dengan karbohidrat, protein, dan urea. Penelitian ini menunjukkan bahwa metode berbasis spektroskopi Raman dan support vector machine tidak hanya meningkatkan akurasi diagnosis, tetapi juga memberikan altematif yang lebih aman dan nyaman bagi pasien sekaligus membuka peluang pengembangan alat diagnostik cepat di masa depan.

---

Diabetes Mellitus (DM) is one of the leading causes of chronic kidney disease (CKD), witha common complication being Diabetic Kidney Disease (DKD). Diagnosis of DKD has traditionally relied on kidney biopsy, an invasive method that has risks and limitations. This study aims to develop a non-invasive diagnostic method based on Raman spectroscopy and machine learning to distinguish DKD from other glomerular diseases through urine sample analysis. Raman spectroscopy was used to analyze 320 urine spectra from 32 patients, replicated 10 times per patient. Results showed that the support vector machine algorithm was able to classify DKD with an accuracy of 0.91, precision of 0.889, sensitivity (recall) of 0.889, and specificity of 0.925. In addition, spectral analysis identified major peaks such as 898, 931, and 1060 cm 1 as components related to carbohydrates, proteins, and urea. This study shows that the method based on Raman spectroscopy and support vector machine not only improves the accuracy of diagnosis, but also providesa safer and more convenient alternative for patients while opening up opportunities for the development of rapid diagnostic tools in the future."