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Id	Authors	Author S Id	Title	Year	Source Title	Volume	Issue	Art No	Page Start	Page End	Cited By	Link	Affiliations	Authors With Affiliations	Abstract	Author Keywords	Index Keywords	Publisher	Issn	Document Type	Sjr Best Quartile	Sjr	Sjr Rank
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11202(2)
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139	Harzif A., Anggraeni T., Syaharutsa D., Hellyanti T.	57191493435;57192894826;57204145265;57217993236;	Hysteroscopy role for female genital tuberculosis	2021	Gynecology and Minimally Invasive Therapy	10	4		243	246		https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119285063&doi=10.4103%2fGMIT.GMIT_151_20&partnerID=40&md5=2dea6cbfe3f4a65bb9bc6b632d3cc573	Department of Obstetrics and Gynecology, Faculty of Medicine, University of Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Department of Anatomical Pathology, Faculty of Medicine, University of Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia	Harzif, A., Department of Obstetrics and Gynecology, Faculty of Medicine, University of Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Anggraeni, T., Department of Obstetrics and Gynecology, Faculty of Medicine, University of Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Syaharutsa, D., Department of Obstetrics and Gynecology, Faculty of Medicine, University of Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Hellyanti, T., Department of Anatomical Pathology, Faculty of Medicine, University of Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia	Female genital tuberculosis affects the quality of women's lives. One of the symptoms is amenorrhea. In our country, it is still underdiagnosed due to limited resources. Hysteroscopy is known as one of the diagnostic tools for this condition. We performed hysteroscopy and endometrial biopsy in four cases. Hysteroscopy findings show various signs. Histopathological examination showed typical features of tuberculosis in some cases. We also learned that hysteroscopy could evaluate the condition of the endometrium when ongoing and after treatment is accomplished. It is useful for further explanation to the client. Hysteroscopy can be utilized as a diagnostic tool for endometrial sampling, evaluate intracavity condition after treatment, and prognostic tool for future reproductive function. © 2021 Wolters Kluwer Medknow Publications. All rights reserved.	Amenorrhea; caseous necrosis; genital tuberculosis; hysteroscopy	tuberculostatic agent; adolescent; adult; Article; case report; caseation; chronic inflammation; clinical article; clinical feature; diagnostic value; echography; endometritis; endometrium; endometrium biopsy; female; female genital tuberculosis; granulomatous inflammation; histopathology; human; human tissue; hysteroscopy; laparotomy; primary amenorrhea; secondary amenorrhea; sister; tuberculous peritonitis; tuberculous spondylitis	Wolters Kluwer Medknow Publications	22133070	Article	Q3	441	11202	View
881	Harzif A., Hyaswicaksono P., Kurniawan R., Wiweko B.	57191493435;57221907604;57196055951;43061741400;	Heterotopic pregnancy: Diagnosis and pitfall in ultrasonography	2021	Gynecology and Minimally Invasive Therapy	10	1		53	56	1	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100537776&doi=10.4103%2fGMIT.GMIT_92_19&partnerID=40&md5=ba933e51def94298b03a38705981ed87	Department of Obstetrics and Gynecology, Reproductive Immunology and Endocrinology Division, Faculty of Medicine, Dr. Cipto Mangunkusumo General Hospital, Universitas Indonesia, Jakarta, Indonesia; Department of Obstetrics and Gynecology, Faculty of Medicine, Dr. Cipto Mangunkusumo General Hospital, Universitas Indonesia, Jl. Diponegoro No. 71, Jakarta Pusat, 10340, Indonesia	Harzif, A., Department of Obstetrics and Gynecology, Reproductive Immunology and Endocrinology Division, Faculty of Medicine, Dr. Cipto Mangunkusumo General Hospital, Universitas Indonesia, Jakarta, Indonesia; Hyaswicaksono, P., Department of Obstetrics and Gynecology, Faculty of Medicine, Dr. Cipto Mangunkusumo General Hospital, Universitas Indonesia, Jl. Diponegoro No. 71, Jakarta Pusat, 10340, Indonesia; Kurniawan, R., Department of Obstetrics and Gynecology, Faculty of Medicine, Dr. Cipto Mangunkusumo General Hospital, Universitas Indonesia, Jl. Diponegoro No. 71, Jakarta Pusat, 10340, Indonesia; Wiweko, B., Department of Obstetrics and Gynecology, Reproductive Immunology and Endocrinology Division, Faculty of Medicine, Dr. Cipto Mangunkusumo General Hospital, Universitas Indonesia, Jakarta, Indonesia	Heterotopic pregnancy (HP) is the coexistence of extrauterine and intrauterine pregnancies. This case is rare, difficult to diagnose, and threatening if left untreated. Incidental rate is estimated 1 in 30,000 spontaneous pregnancies and higher in assisted reproductive techniques. HP is often missed because of the detection of intrauterine sacs; therefore, comprehensive and systematic ultrasonography (USG) is needed, especially when there is ectopic pregnancy suspicion or when there is free fluid in the pelvis. A 46-year nulligravida with 13-year primary infertility history underwent frozen embryo transfer process with positive beta-human chorionic gonadotropin 2 weeks after the procedure. Clinical pregnancy is expressed by gestational sac findings at 6-week gestation. Two weeks later, she complained of lower right abdominal pain accompanied by spots from the birth canal. USG showed intrauterine pregnancy and sac appropriate to 8-week gestation and adnexal mass accompanied by a ring of fire image. The patient underwent right salpingectomy, recovered well, and continued her pregnancy. In vitro fertilization is the main risk factor for multiple and ectopic pregnancies. Clinical manifestations are similar to pregnancy loss or ectopic pregnancy. Specific risk factor must be acknowledged by the physician prior initial examination to rule out HP. Transvaginal ultrasound is useful in making the diagnosis of HP, especially in early pregnancy. © 2021 Wolters Kluwer Medknow Publications. All rights reserved.	Heterotopic; laparoscopy; ultrasound	chorionic gonadotropin beta subunit; progesterone; abdominal pain; adnexa disease; adult; Article; case report; clinical article; ectopic pregnancy; embryo transfer; female; female infertility; first trimester pregnancy; gestational sac; human; in vitro fertilization; middle aged; priority journal; risk factor; salpingectomy; transvaginal echography; uterine adnexa	Wolters Kluwer Medknow Publications	22133070	Article	Q3	441	11202	View
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803	Nova R., Nurmaini S., Partan R.U., Putra S.T.	57210234221;26639610000;57190664693;6603587929;	Automated image segmentation for cardiac septal defects based on contour region with convolutional neural networks: A preliminary study	2021	Informatics in Medicine Unlocked	24		100601				https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106348104&doi=10.1016%2fj.imu.2021.100601&partnerID=40&md5=37e5f3bcab0ef015f7441efa32757710	Department of Child Health, Division of Pediatric Cardiology, Dr. Moh Hoesin Hospital, Faculty of Medicine, Universitas Sriwijaya, Palembang, 30126, Indonesia; Intelligent System Research Group, Universitas Sriwijaya, Palembang, 30139, Indonesia; Department of Medicine, Dr. MohHoesin Hospital, Faculty of Medicine, Universitas Sriwijaya, Palembang, 30126, Indonesia; Department of Child Health, Division of Pediatric Cardiology, Dr. CiptoMangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia	Nova, R., Department of Child Health, Division of Pediatric Cardiology, Dr. Moh Hoesin Hospital, Faculty of Medicine, Universitas Sriwijaya, Palembang, 30126, Indonesia; Nurmaini, S., Intelligent System Research Group, Universitas Sriwijaya, Palembang, 30139, Indonesia; Partan, R.U., Department of Medicine, Dr. MohHoesin Hospital, Faculty of Medicine, Universitas Sriwijaya, Palembang, 30126, Indonesia; Putra, S.T., Department of Child Health, Division of Pediatric Cardiology, Dr. CiptoMangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia	Echocardiogram examination is important for diagnosing cardiac septal defects. With the development of AI-based technology, an echocardiogram examination previously performed manually by cardiologists can be done automatically. Automatic segmentation of cardiac septal defects can help a physician to make an initial diagnosis before referring a pediatric cardiologist for further treatment. In previous studies, automatic object segmentation using convolutional neural networks (CNNs) was one of the DL applications that have been developed for cardiac abnormalities. In this study, we propose a CNN-based U-Net architecture to automatically segment the cardiac chamber to detect abnormalities (holes) in the heart septum. In this study, echocardiogram examinations were performed on atrial septal defects (ASDs), ventricular septal defects (VSDs), atrioventricular septal defects (AVSDs), and normal hearts with patients undergoing echocardiogram examination at Moh Hoesin Hospital in Palembang. The results show that even for the relatively small number of datasets, the proposed technique can produce superior performance in the detection of the cardiac septal defects. Using the proposed segmentation model for four classes produces a pixel accuracy of 99.15%, mean intersection over union (IoU) of 94.69%, mean accuracy of 97.73%, sensitivity of 96.02%, and F1 score of 94.88%, respectively. The plots of the loss and accuracy curve show that all the errors were small, with accuracy rates reaching 99.05%, 98.62%, 99.39%, and 98.97% for ASD, VSD, AVSD, and normal heart, respectively. The comparison accuracy of contour prediction for U-Net was 99.01%, while V-Net was 93.70%. This shows that the U-Net has better accuracy than the V-Net model architecture. It can be proven that the architecture of CNNs has been successful in segmenting the cardiac chamber to detect defects in the heart septum and support the work of cardiologists. © 2021 The Authors	Cardiac septal defect; CNNs; Contour segmentation; Deep learning	adult; Article; atrioventricular septal defect; child; clinical article; clinical examination; computer assisted diagnosis; controlled study; convolutional neural network; deconvolution; deep learning; diagnostic accuracy; diagnostic test accuracy study; heart atrium septum defect; heart ventricle septum defect; human; image processing; image segmentation; pediatric patient; two dimensional echocardiography	Elsevier Ltd	23529148	Article	Q3	440	11223	View
827	Haryanto T., Suhartanto H., Arymurthy A.M., Kusmardi K.	57193869197;16423632300;36815724000;56966625300;	Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification	2021	Informatics in Medicine Unlocked	23		100565				https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104075953&doi=10.1016%2fj.imu.2021.100565&partnerID=40&md5=dceb8084f0a81f31189f09578a878d62	Faculty of Computer Science, Universitas Indonesia, Kampus Universitas Indonesia, Depok, 16424, Indonesia; Department of Computer Science, IPB University, Kampus IPB Dramaga, Bogor, 16680, Indonesia; Department of Pathology Anatomy, Faculty of Medicine, Universitas Indonesia, Kampus Universitas Indonesia, Jakarta, Indonesia	Haryanto, T., Faculty of Computer Science, Universitas Indonesia, Kampus Universitas Indonesia, Depok, 16424, Indonesia, Department of Computer Science, IPB University, Kampus IPB Dramaga, Bogor, 16680, Indonesia; Suhartanto, H., Faculty of Computer Science, Universitas Indonesia, Kampus Universitas Indonesia, Depok, 16424, Indonesia; Arymurthy, A.M., Faculty of Computer Science, Universitas Indonesia, Kampus Universitas Indonesia, Depok, 16424, Indonesia; Kusmardi, K., Department of Pathology Anatomy, Faculty of Medicine, Universitas Indonesia, Kampus Universitas Indonesia, Jakarta, Indonesia	Large amounts of data are required for the training process with a convolutional neural network (CNN) because small datasets with low variation will cause over-fitting, and the model cannot predict new data with high accuracy. Additionally, the non-availability of histopathological medical data presents an issue because without ethical permission, such data cannot be obtained easily. Therefore, this study proposes a conditional sliding window algorithm to obtain sub-sample data on images of histopathology. Two sets of original data were used, one from the Warwick dataset with dimensions of 775 × 522 pixels and the other from the Department of Pathology and Anatomy, Faculty of Medicine Universitas Indonesia. The algorithm used was inspired by the conventional sliding window method, but implemented with added conditions, such as sliding the window algorithm from the left on (x,y) pixel coordinates, thereby moving from left to right, then up to down until the entire image was covered. Consequently, the new image was produced with two dimensions: 200 × 200 and 300 × 300 pixels. However, to avoid loss of information, the 25 and 50 pixels overlap were used. In this study, CNN 7-5-7 was designed and proposed to perform the process. The conditional sliding window algorithm can produce various sub-samples depending on the image and window size. Furthermore, the images produced were used to develop a CNN and were proven to accurately predict benign and malignant tissues compared to the model from the original dataset. Moreover, the sensitivity values of the Warwick public dataset and the one generated in this study are above 0.80, which shows that the proposed CNN architecture is more stable compared to the existing methods such as AlexNet and DenseNet121. This study succeeded in solving the limitations of colorectal histopathological training data by developing a conditional sliding window algorithm. This algorithm can be applied to generate other histopathological data. Moreover, our proposed CNN 7-5-7 is the fastest architecture for training, comparable to state-of-the-art methodologies. Furthermore, the dataset was used to develop the model for colorectal cancer identification and integrated on the web-based application for further implementation. © 2021 The Authors	Augmentation; Conditional sliding windows; Convolutional neural network; Histopathology	Article; colorectal cancer; computer language; convolutional neural network; deep learning; diagnostic accuracy; entropy; feature extraction; histopathology; human; image processing; information processing; learning algorithm; sensitivity and specificity	Elsevier Ltd	23529148	Article	Q3	440	11223	View
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398	Setiabudiawan B., Sitaresmi M.N., Sapartini G., Sumadiono S., Citraresmi E., Sekartini R., Putra A.M., Jo J.	57189310070;16040647800;57203168733;55581650500;57224512641;50162572300;57224522586;55345586200;	Growth Patterns of Indonesian Infants with Cow's Milk Allergy and Fed with Soy-Based Infant Formula	2021	Pediatric Gastroenterology, Hepatology and Nutrition	24	3		316	324	1	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107767665&doi=10.5223%2fPGHN.2021.24.3.316&partnerID=40&md5=426ce2a647c7efc4e412e462e9a020a5	Department of Child Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia; Department of Child Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia; Department of Child Health, Harapan Kita Child and Mother Hospital, Jakarta, Indonesia; Department of Child Health, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Child Health, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Danone Nutricia Research, Singapore	Setiabudiawan, B., Department of Child Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia; Sitaresmi, M.N., Department of Child Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia; Sapartini, G., Department of Child Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia; Sumadiono, S., Department of Child Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia; Citraresmi, E., Department of Child Health, Harapan Kita Child and Mother Hospital, Jakarta, Indonesia; Sekartini, R., Department of Child Health, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Putra, A.M., Department of Child Health, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Jo, J., Danone Nutricia Research, Singapore	Purpose: The use of soy-based infant formula has increased widely in infants with cow's milk allergy (CMA). This study aimed to provide evidence on the growth pattern of CMA infants fed with soy-based infant formula in an Indonesian setting. Methods: A multi-site, intervention study was conducted among full-term and normal birth weight CMA infants. Within six months, the subjects were provided with a soy-based infant formula. Weight, height, and head circumference were measured at baseline, weeks 4, 8, 12, 16, 20, and 24. Adverse events were recorded by scoring atopic dermatitis and symptom-based clinical scores. Results: Based on the World Health Organization growth chart, we found that most of subjects had normal nutritional status for weight-for-age, length-for-age, weight-for-length, and head-circumference-for-age. There were statistically significant differences between baseline and end-line for weight-for-age, length-for-age, weight-for-length, and head circumference-for-age nutritional status. No allergic symptoms or intolerance toward soy formula were observed at the end of the intervention period. Conclusion: These results show that infants fed with soy-based infant formula have a normal pattern of growth. Copyright © 2021 by The Korean Society of Pediatric Gastroenterology, Hepatology and Nutrition This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.	Body height; Body weight; Milk hypersensitivity; Soy milk		Korean Society of Pediartic Gastroenterology, Hepatology and Nutrition	22348646	Article	Q3	440	11229	View
651	Sudarma V., Hegar B., Hidayat A., Agustina R.	55257657600;6506273944;7006069132;57214141404;	Human Milk Oligosaccharides as a Missing Piece in Combating Nutritional Issues during Exclusive Breastfeeding	2021	Pediatric Gastroenterology, Hepatology and Nutrition	24	6		501	509		https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121005273&doi=10.5223%2fpghn.2021.24.6.501&partnerID=40&md5=55bd98613f1bc0613d5d9ff96f327b95	Department of Nutrition, Faculty of Medicine, Universitas Indonesia - Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Department of Nutrition, Faculty of Medicine, Trisakti University, Jakarta, Indonesia; Department of Child Health, Faculty of Medicine, Universitas Indonesia - Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Department of Public Health, Faculty of Medicine, Trisakti University, Jakarta, Indonesia; Human Nutrition Research Center, Indonesia Medical Education and Research Institute, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia	Sudarma, V., Department of Nutrition, Faculty of Medicine, Universitas Indonesia - Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia, Department of Nutrition, Faculty of Medicine, Trisakti University, Jakarta, Indonesia; Hegar, B., Department of Child Health, Faculty of Medicine, Universitas Indonesia - Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; Hidayat, A., Department of Public Health, Faculty of Medicine, Trisakti University, Jakarta, Indonesia; Agustina, R., Department of Nutrition, Faculty of Medicine, Universitas Indonesia - Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia, Human Nutrition Research Center, Indonesia Medical Education and Research Institute, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia	Extensive studies have shown that breast milk is the best source of nutrition for infants, especially during the first six months, because it fulfills almost all of their nutritional needs. Among the many functional building blocks in breast milk, human milk oligosaccharides (HMOs) have been receiving more attention recently. Furthermore, it is the third most common group of compounds in human milk, and studies have demonstrated the health benefits it provides for infants, including improved nutritional status. HMOs were previously known as the ‘bifidus factor' due to their ‘bifidogenic' or prebiotic effects, which enabled the nourishment of the gastrointestinal microbiota. Healthy gastrointestinal microbiota are intestinal health substrates that increase nutrient absorption and reduce the incidence of diarrhea. In addition, HMOs, directly and indirectly, protect infants against infections and strengthen their immune system, leading to a positive energy balance and promoting normal growth. Non-modifiable factors, such as genetics, and modifiable factors (e.g., maternal health, diet, nutritional status, environment) can influence the HMO profile. This review provides an overview of the current understanding of how HMOs can contribute to the prevention and treatment of nutritional issues during exclusive breastfeeding. © 2021. The Korean Society of Pediatric Gastroenterology, Hepatology and Nutrition. All Rights Reserved.	Breast feeding; Human milk; Nutritional status; Oligosaccharides		Korean Society of Pediartic Gastroenterology, Hepatology and Nutrition	22348646	Article	Q3	440	11229	View
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137	Aditianingsih D., Hidayat J., Ginting V.M.	56312263600;57221444286;57377973500;	Comparison of bioimpedance versus pulse contour analysis for intraoperative cardiac index monitoring in patients undergoing kidney transplantation	2021	Anesthesiology and Pain Medicine	11	5	e117918				https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121465922&doi=10.5812%2fAAPM.117918&partnerID=40&md5=cc44c95f4c7c6f8cc3dd0d4b279457ff	Department of Anesthesiology and Intensive Therapy, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia	Aditianingsih, D., Department of Anesthesiology and Intensive Therapy, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia; Hidayat, J., Department of Anesthesiology and Intensive Therapy, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia; Ginting, V.M., Department of Anesthesiology and Intensive Therapy, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia	Background: Cardiac index (CI; cardiac output indexed to body surface area) is routinely measured during kidney transplant surgery. Bioimpedance cardiometry is a transthoracic impedance as the non-invasive alternative for hemodynamic monitoring, using semi-invasive uncalibrated pulse wave or contour (UPC) analysis. Objectives: We performed a cross-sectional observational study on 50 kidney transplant patients to compare the CI measurement agreement, concordance rate, and trending ability between bioimpedance and UPC analysis. Methods: For each patient, CI was measured by bioimpedance analysis (ICON™) and UPC analysis (EV1000™) devices at three time points: after induction, during incision, and at reperfusion. The device measurement accuracy was assessed by the bias value, limit of agreement (LoA), and percentage error (PE) using Bland-Altman analyses. Trending ability was assessed by angular bias and polar concordance through four-quadrant and polar plot analyses. Results: From each time point and pooled measurement, the correlation coefficients were 0.267, 0.327, 0.321, and 0.348. BlandAltman analyses showed mean bias values of 1.18, 1.06, 1.48, and 1.30, LoA of-1.35 to 3.72,-1.39 to 3.51,-1.07 to 4.04, and-1.17 to 3.78, and PE of 82.21, 78.50, 68.74, and 74.58%, respectively. Polar plot analyses revealed angular bias values of-10.37º,-15.01º,-18.68º, and-12.62º, with radial LoA of 89.79º, 85.86º, 83.38º, and 87.82º, respectively. The four-quadrant plot concordance rates were 70.77, 67.35, 65.90, and 69.79%. These analyses showed poor agreement, weak concordance, and low trending ability of bioimpedance cardiometry to UPC analysis. Conclusions: Bioimpedance and UPC analysis for CI measurements were not interchangeable in patients undergoing kidney transplant surgery. Cardiac index monitoring using bioimpedance cardiometry during kidney transplantation should be interpreted cautiously because it showed poor reliability due to low accuracy, precision, and trending ability for CI measurement. © 2021, Author(s).	Cardiac Output; Intraoperative Monitoring; Kidney Transplantation; Pulse Wave Analysis; Transthoracic Impedance	atracurium besilate; dobutamine; fentanyl; noradrenalin; propofol; sevoflurane; adult; aged; agitation; anesthesia induction; arterial pressure; Article; body mass; bradycardia; cardiac index; cardiopulmonary bypass; central venous pressure; chronic kidney failure; correlation coefficient; cross-sectional study; female; heart arrhythmia; heart output; heart rate; heart stroke volume; hemodynamic monitoring; human; hypertension; intraoperative monitoring; kidney transplantation; major clinical study; male; mean arterial pressure; measurement accuracy; nausea and vomiting; observational study; outcome assessment; pleura effusion; prospective cost; prospective study; pulse oximetry; pulse wave; systolic blood pressure; tachycardia; transesophageal echocardiography; uncalibrated pulse contour	Kowsar Medical Institute	22287523	Article	Q2	438	11251	View
11324(3)
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38	Dhelika R., Hadi A.F., Yusuf P.A.	55536628000;57368046500;57192156597;	Development of a motorized hospital bed with swerve drive modules for holonomic mobility	2021	Applied Sciences (Switzerland)	11	23	1356				https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120877522&doi=10.3390%2fapp112311356&partnerID=40&md5=7c16eb6fe630116772c69c4d02b4bd9f	Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia; Department of Medical Physics/Medical Technology Core Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia	Dhelika, R., Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia; Hadi, A.F., Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia; Yusuf, P.A., Department of Medical Physics/Medical Technology Core Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia	In hospitals; transferring patients using hospital beds is time consuming and inefficient. Additionally; the task of frequently pushing and pulling beds poses physical injury risks to nurses and caregivers. Motorized hospital beds with holonomic mobility have been previously proposed. However; most such beds come with complex drivetrain which makes them costly and hinders larger‐scale adoption in hospitals. In this study; a motorized hospital bed that utilizes a swerve drive mechanism is proposed. The design takes into account simplicity which would allow for minimum modification of the existing beds. Two DC motors for steering and propulsion are used for a single swerve drive module. The control of the propulsion motor is achieved by a combination of trajectory planning based on quintic polynomials and PID control. Further; the control performance of the proposed bed was evaluated; and the holonomic mobility of its prototype was successfully demonstrated. An average error of less than 3% was obtained for motion with a constant velocity; however; larger values in the range of 15% were observed for other conditions, such as accelerating and decelerating. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.	Hospital bed; Mechatronics; PID control; Swerve drive; Trajectory planning		MDPI	20763417	Article	Q2	435	11324	View
210	Nurhayati R.W., Cahyo R.D., Pratama G., Anggraini D., Mubarok W., Kobayashi M., Antarianto R.D.	55748436600;57212460506;57195959221;57221606578;57208440063;20835016200;57190862806;	Alginate-chitosan microencapsulated cells for improving cd34+ progenitor maintenance and expansion	2021	Applied Sciences (Switzerland)	11	17	7887				https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114106674&doi=10.3390%2fapp11177887&partnerID=40&md5=8aa18a5d52c69e6eb72dcb38252f4489	Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Jl. Prof. Soemantri Brojonegoro, Kampus UI, Depok, 16424, Indonesia; Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia; Department of Biotechnology, Faculty of Engineering, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan; Department of Obstetric and Gynecology, Faculty of Medicine, Universitas Indonesia—Dr. Cipto Mangunkusumo General Hospital, Jl. Diponegoro No. 71, Central Jakarta, 10430, Indonesia; Integrated Service Unit of Stem Cell Medical Technology (IPT TK Sel Punca), Dr. Cipto Mangunkusumo General Hospital (RSCM), Jl. Diponegoro No. 71, Salemba, Central Jakarta, 10430, Indonesia; Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Division of Chemical Engineering, Department of Materials Engineering Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan; Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Department of Histology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia	Nurhayati, R.W., Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Jl. Prof. Soemantri Brojonegoro, Kampus UI, Depok, 16424, Indonesia, Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia; Cahyo, R.D., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Department of Biotechnology, Faculty of Engineering, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan; Pratama, G., Department of Obstetric and Gynecology, Faculty of Medicine, Universitas Indonesia—Dr. Cipto Mangunkusumo General Hospital, Jl. Diponegoro No. 71, Central Jakarta, 10430, Indonesia, Integrated Service Unit of Stem Cell Medical Technology (IPT TK Sel Punca), Dr. Cipto Mangunkusumo General Hospital (RSCM), Jl. Diponegoro No. 71, Salemba, Central Jakarta, 10430, Indonesia; Anggraini, D., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Mubarok, W., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Division of Chemical Engineering, Department of Materials Engineering Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan; Kobayashi, M., Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Antarianto, R.D., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Department of Histology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia	Protocols for isolation, characterization, and transplantation of hematopoietic stem cells (HSCs) have been well established. However, difficulty in finding human leucocyte antigens (HLA)-matched donors and scarcity of HSCs are still the major obstacles of allogeneic transplanta-tion. In this study, we developed a double-layered microcapsule to deliver paracrine factors from non-matched or low-matched HSCs to other cells. The umbilical cord blood-derived hematopoietic progenitor cells, identified as CD34+ cells, were entrapped in alginate polymer and further protected by chitosan coating. The microcapsules showed no toxicity for surrounding CD34+ cells. When CD34+ cells-loaded microcapsules were co-cultured with bare CD34+ cells that have been collected from unrelated donors, the microcapsules affected surrounding cells and increased the percentage of CD34+ cell population. This study is the first to report the potency of alginate-chitosan microcap-sules containing non-HLA-matched cells for improving proliferation and progenitor maintenance of CD34+ cells. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.	Alginate; CD34; Chitosan; Hematopoietic; Megakaryocyte; Microencapsulation; Progenitor; Proliferation; Stem cells		MDPI	20763417	Article	Q2	435	11324	View
585	Silitonga P., Bustamam A., Muradi H., Mangunwardoyo W., Dewi B.E.	57219406661;36815737800;57188977950;24544449900;24076058600;	Comparison of dengue predictive models developed using artificial neural network and discriminant analysis with small dataset	2021	Applied Sciences (Switzerland)	11	3	943	1	16	3	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099695109&doi=10.3390%2fapp11030943&partnerID=40&md5=3a9bba4cf9ba9dacfef04d7349c81f4b	Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Department of Mathematics, Faculty of Science and Information Technology, Institut Sains dan Teknologi Nasional, Jl.Moh Kahfi II Srengseng Sawah Jagakarsa, Jakarta Selatan, 12640, Indonesia; Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Department of Microbiology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya no. 5, Kota Jakarta Pusat, Daerah Khusus Ibu Kota Jakarta, 10430, Indonesia	Silitonga, P., Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Bustamam, A., Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Muradi, H., Department of Mathematics, Faculty of Science and Information Technology, Institut Sains dan Teknologi Nasional, Jl.Moh Kahfi II Srengseng Sawah Jagakarsa, Jakarta Selatan, 12640, Indonesia; Mangunwardoyo, W., Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Dewi, B.E., Department of Microbiology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya no. 5, Kota Jakarta Pusat, Daerah Khusus Ibu Kota Jakarta, 10430, Indonesia	In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.	Artificial neural network; Dengue; Discriminant analysis		MDPI AG	20763417	Article	Q2	435	11324	View

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