Penyakit jantung koroner (PJK) merupakan penyebab kematian utama di dunia. Penelitian lain yang dilakukan sebelumnya memberikan hasil bahwa dislipidemia merupakan faktor risiko tersering penyakit jantung koroner. TujuanTujuan penelitian ini untuk mengetahui hubungan antara dislipidemia (LDL) dan kejadian penyakit jantung koroner pada penderita di RS PKU Muhammadiyah Yogyakarta periode 1 Januari 2010 -31 Desember 2011. Penelitian ini menggunakan desain cross-sectional, dan pengambilan sampel menggunakan metode konsekutif sampling. Subyek penelitian adalah laki-laki dan perempuan yang berumur lebih dari 45 tahun dan mempunyai data profil lipid yang lengkap. Subyek penelitian dibagi menjadi 2 kelompok, 32 subyek dengan penyakit jantung koroner dan 32 subyek tanpa penyakit jantung koroner. Analisis data dilakukan menggunakan uji chi-square. HasilPersentase pasien dengan kadar LDL >130 mg/dL pada kelompok PJK adalah 65,6% dan pada kelompok non-PJK adalah 40,6% (p=0,045 dan RP=1,68; 95% CI=1,01-7,7). KesimpulanPenelitian ini menunjukkan hasil bahwa kadar LDL >130mg/dL berhubungan dengan faktor risiko kejadian penyakit jantung koroner pada subyek penelitian Kata kunci : Penyakit jantung koroner, dislipidemia, LDL

Penelitian ini mengusulkan salah satu pendekatan pengolahan citra dan klasifikasi dalam analisis urin (urinalisis) dengan metode carik celup menggunakan dipstik urin sepuluh parameter (dipstik 10P). Adapun yang diurinalisis dalam pemeriksaan urin meliputi leukosit, nitrit, urobilinogen, protein, keasaman, darah, berat jenis, keton, bilirubin dan glukosa pada urine. Penggunaan kamera yang disematkan pada smartphone dapat menjadi solusi dalam akuisisi citra untuk data reference dan data uji dipstik. Setelah akuisi citra dilanjutkan dengan skema pemrosesan citra dipstik. Citra hasil tangkapan kamera smartphone menempati ruang warna RGB yang selanjutnya digunakan sebagai nilai ekstraksi fitur. Hasil dari ekstraksi fitur warna RGB digunakan sebagai nilai untuk mengukur jarak kedekatan antara reference dan data uji. Metode yang digunakan adalah Jarak Manhattan. Nilai jarak terdekat menjadi solusi dalam masalah klasifikasi hasil urinalisis ini. Perancangan sistem menggunakan bahasa pemrograman Python dengan package  OpenCV. Hasil dari perancangan ini menunjukkan sistem dapat melakukan klasifikasi.

Each year more than 41,000 blood donations are needed every day and 30 million blood components are transfused. Blood products that can be transfused include Packed Red Cells (PRC), Whole Blood (WB), Thrombocyte Concentrate (TC), Fresh Frozen Plasma (FFP). Monitoring Hemoglobin (Hb) after transfusion is essential for assessing the success of a transfusion. The time factor after transfusion for Hemoglobin (Hb) examination needs to be established, analyze to judge the success of a blood transfusion which is performed. The aim of this study was to analyze the differences in changes of hemoglobin between 6-12 hours, and 12-24 hours after-transfusion. This study was retrospective observational using secondary data. The subjects were patients who received PRC, and WBC transfusion. At 6-12, and 12-24 hours after-transfusion, hemoglobin, RBC, and hematocrit were measured. Then the data were analyzed by unpaired t-test. The collected data included the results of the Hb pre-transfusion, 6-12, and 12-24 hours after-transfusion. The subjects of this study were 98 people. The administration of transfusion increased by 10-30% in hemoglobin concentration at 6-12 hours after-transfusion. While at 12-24 hours after-transfusion, hemoglobin after-transfusion increased 15-37% from the baseline. Hemoglobin values were not different at any of the defined after-transfusion times (p = 0.76 (p>0.05)). Hemoglobin values were not different at 6-12 hours, and 12-24 hours after-transfusion.    Keywords: Hemoglobin, measurement, after-transfusion

It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.

Background: Anemia is the most common complication due to CKD. Normocytic normochromic is the most common type of anemia in CKD is, but there is also some microcytic hypochromic. Clinical examination such as erythrocyte indices examination and monitoring serum ferritin levels can help diagnose anemia in CKD. The purpose of this study is to determine the correlation between erythrocyte indices and ferritin levels in CKD patients in the hemodialysis unit of PKU Hospital Bantul.Methods: This research involved 50 CKD patients at PKU Hospital Bantul. Blood samples were taken to check ferritin levels and erythrocyte indices consisting of mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC). This study was conducted by cross-sectional study design. Data were analyzed using SPSS version 20 for Windows.Results: The average hemoglobin level was 8.46±1.31 g/dL in males and 7.92±0.90 g/dL in females. Most of the hematological profile was predominant in male patients such as in MCV (89.36±6.72 fl), MCH (29.12±2.76 pg), and MCHC (32.95±0.96 g/dL). However, the average ferritin level (352.51±544.74 ng/mL) was predominant in female patients (399.99±680.96 ng/mL). There was a significant correlation between MCH and ferritin levels (r = 0.364; p = 0.009) and MCHC with ferritin levels (r = 0.295; p = 0.038). However, there was no significant correlation between MCV and ferritin levels (r = -0.059, p = 0.683).Conclusion: There is a significant correlation between MCH and MCHC levels with ferritin levels. In addition, the patients had normal MCV, MCH, and MCHC values with normocytic normochromic anemia.