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"Pandemi Coronavirus Disease 2019 (COVID-19) merupakan pandemi disebabkan oleh virus SARS-CoV-2. Indonesia diketahui sebagai salah satu negara dengan tingkat infeksi COVID-19 paling tinggi di dunia. Deteksi cepat secara Real Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) merupakan salah satu langkah yang diperlukan untuk menekan laju penyebaran COVID-19. Kit deteksi BioCore 2019-nCoV Real Time PCR Kit adalah salah satu kit dignosis COVID-19 produksi BioCore. Ltd., Korea Selatan. Kit diagnosis BioCore telah beredar di Indonesia dan perlu diuji keakuratan diagnosis yang dihasilkan untuk menghindari hasil negatif palsu. Pengujian dilakukan menggunakan protokol Penjaminan Mutu Eksternal (PME) Kementerian Kesehatan Indonesia dengan melibatkan 30 sampel uji dan membandingkan hasil uji terhadap kit gold standard CDC dengan gen target N1, N2, dan HRP. Alur kerja penelitian dimulai dari proses pengambilan sampel, ekstraksi RNA, persiapan mastermix, adisi template RNA, dan amplifikasi template dengan metode rRT-PCR. Hasil penelitian menunjukkan adanya amplifikasi pada kontrol yang digunakan, sehingga proses diagnosis dapat dilakukan. Nilai Ct IC kit Biocore dan IC CDC menunjukkan perbedaan signifikan (P 0,05; CI=95%). Gen target SARS-CoV-2 tidak terdeteksi pada kit Biocore dengan nilai Ct>35, serta didapatkan nilai sensitivitas dan spesifisitas analitik kit Biocore berturut-turut sebesar 75% dan 100%. Hasil uji Kit Biocore terhadap pasien terinfeksi COVID-19 di Indonesia tidak memenuhi standar kit diagnosis yang ditetapkan oleh WHO, yaitu memiliki sensitivitas analitik sebesar 95%. Peninjauan ulang primer pada kit Biocore perlu dilakukan untuk memperbaiki mutu kit dalam deteksi awal virus SARS-CoV-2 di Indonesia.

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The Coronavirus Disease 2019 (COVID-19) pandemic is a pandemic caused by the SARS-CoV-2 virus. Indonesia is known as one of the countries with the highest COVID-19 infection rate in the world. Real Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) detection is one of the steps needed to accelerate the spread of COVID-19. The BioCore 2019-nCoV Real Time PCR Kit is one of the COVID-19 diagnosis kits produced by BioCore. Ltd., South Korea. The BioCore diagnostic kit has been circulating in Indonesia and needs to be tested for the accuracy of the resulting diagnosis to avoid false negative results. The test was carried out using the External Quality Assurance (PME) protocol of the Indonesian Ministry of Health involving 30 test samples and test results against the CDC gold standard kit with target genes N1, N2, and HRP. The research workflow starts from the sampling process, RNA extraction, mastermix preparation, RNA template addition, and template amplification using the rRT-PCR method. The results showed that there was amplification of the controls used, so that the diagnosis process could be carried out. The Ct values ​​of the Biocore IC kit and the CDC IC showed a significant difference (P 0.05; CI=95%). The SARS-CoV-2 target gene was not detected in the Biocore kit with a Ct value>35, ​​and the sensitivity and analytical specificity of the Biocore kit were 75% and 100%, respectively. The results of the Biocore Kit test on patients infected with COVID-19 in Indonesia do not meet the diagnostic kit standard set by WHO, which has an analytical sensitivity of 95%. Primary review on the Biocore kit needs to be done to improve the quality of the kit in early detection of the SARS-CoV-2 virus in Indonesia."

"Coronavirus Disease 2019 adalah penyakit menular yang disebabkan oleh Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Covid-19 telah dinyatakan sebagai pandemik sehingga perlu dilakukan upaya penanggulangan termasuk penguatan fungsi laboratorium yang berfungsi melakukan pemeriksaan spesimen, untuk menjamin kesinambungan pemeriksaan screening spesimen Coronavirus Disease 2019. Penelitian ini bertujuan untuk menganalisis implementasi hierarki pengendalian risiko dalam pencegahan penularan Covid-19 pada pekerja di Laboratorium Biomolekular PT X. Penelitian ini menggunakan metode penelitian kualitatif . Pengumpulan data dilakukan melalui data sekunder, wawancara dan observasi. Penelitian dilakukan dari November 2021-Juli 2022. Hasil penelitian menunjukkan proses kerja di PT. X terdiri enam jenis dari pengambilan sampel hingga pelaporan ke pasien. Masing-masing proses memiliki risiko masing-masing dalam pekerjaannya, dimana risiko tertinggi pada petugas pengambilan sampel. Pengetahuan dan perilaku pekerja terhadap hirarki pengendalian risiko dapat dikatakan sangat baik. Perilaku pencegahan Covid-19 yang dilakukan oleh pekerja di PT. X sudah ada upayanya, seperti mereka paham pentingnya bekerja dengan SOP dan menggunakan alat pelindung diri. PT.X telah mengimplementasikan hirarki pengendalian risiko meliputi pengendalian teknis seperti memberi pembatas dan pengaturan ventilasi, pengendalian administrasi seperti pembuatan SOP dan pengaturan shift kerja, dan penggunaan alat pelindung diri seperti masker, baju gown, sarung tangan, dsb. Terkait implementasi pengendalian risiko pada Laboratorium ada dua hal yang belum terpenuhi yaitu tidak adanya pengelolaan limbah padat B3 dan tidak ada manajemen biosecurity secara mandiri. Adapun saran yang dapat direkomendasikan adalah perlu memberikan edukasi, sosialisasi, maupun pelatihan secara berkala terkait manajemen pengendalian risiko dan melakukan upaya pengelolaan limbah B3 sendiri mengacu pada peraturan kementerian kesehatan untuk keamanaan baik para pekerja dan pelanggan yang berkunjung ke PT.X.

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Coronavirus Disease 2019 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Covid-19 has been declared a pandemic so it is necessary to take countermeasures including strengthening the laboratory function that functions to examine specimens, to ensure the continuity of the 2019 Coronavirus Disease specimen screening examination. This study aims to analyze the implementation of the risk control hierarchy in preventing the transmission of Covid-19 to workers in Biomolecular Laboratory of PT X. This research uses qualitative research methods. Data was collected through secondary data, interviews and observations. The research was conducted from November 2021-July 2022. The results showed that the work process at PT. X consists of six types from sampling to reporting to patients. Each process has its own risks in its work, where the risk is highest for the sampling officer. Knowledge and behavior of workers on the hierarchy of risk control can be said to be very good. Covid-19 prevention behavior carried out by workers at PT. X has made an effort, as they understand the importance of working with SOPs and using personal protective equipment. PT.X has implemented a risk control hierarchy including technical controls such as providing barriers and ventilation settings, administrative controls such as making SOPs and setting work shifts, and the use of personal protective equipment such as masks, gowns, gloves, etc. Regarding the implementation of risk control in the laboratory, there are two things that have not been fulfilled, namely the absence of B3 solid waste management and no independent biosecurity management. The suggestions that can be recommended are that it is necessary to provide education, socialization, and periodic training related to risk control management and make efforts to manage B3 waste itself referring to the regulations of the ministry of health for the safety of both workers and customers who visit PT.X.

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"Latar Belakang: Aktivasi komplemen dapat menyebabkan respon imun berlebihan dan merupakan salah satu faktor yang berpengaruh terhadap morbiditas serta mortalitas pasien COVID-19. Beberapa penghambat aktivasi komplemen saat ini sedang dipelajari untuk menghambat aktivasi sistem komplemen yang berlebihan pada pasien COVID-19. Resiko, keuntungan, waktu pemberian dan bagian dari sistem yang akan ditargetkan perlu dipertimbangkan pada saat akan menggunakan penghambat komplemen, oleh karena itu telaah sistematis ini dibuat untuk mengambil kesimpulan apakah pemberian terapi penghambat sistem komplemen dapat menurunkan mortalitas pasien COVID-19 yang dirawat di Rumah Sakit berdasarkan penelitian-penelitian yang tersedia.Tujuan: Mengetahui efek pemberian terapi penghambat sistem komplemen terhadap mortalitas pasien COVID-19 yang dirawat di Rumah Sakit.Metode: Dengan menggunakan kata kunci spesifik, dilakukan pencarian artikel potensial secara komprehensif pada PubMed, Embase, Cochrane, dan Scopus database dengan pembatasan waktu 2019 sampai dengan sampai 31 Desember 2022. Protokol studi ini telah diregistrasi di PROSPERO (CRD42022306632). Semua penelitian pemberian terapi penghambat komplemen pada pasien COVID-19 dimasukkan. Analisis statistik dilakukan dengan menggunakan perangkat lunak Review Manager 5.4.Hasil: 5 penelitian memenuhi kriteria dan dimasukkan dalam telaah sistematis serta meta-analisis dengan total 739 pasien COVID-19. Hasil analisis Forest plot menunjukan bahwa pemberian terapi penghambat sistem komplemen menurunkan mortalitas sebesar 28% pada pasien COVID-19 yang dirawat di Rumah Sakit (RR 0,72; 95% CI: 0,46 – 1,14, I2 = 61%, P-value = 0.16).Kesimpulan: Pemberian terapi penghambat sistem komplemen secara statistik tidak signifikan menurunkan mortalitas pada pasien COVID-19 yang dirawat di Rumah Sakit

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Background: Complement activation can cause an exaggerated immune response and is one of the factors that influence the morbidity and mortality of COVID-19 patients. Several complement activation inhibitors are currently being studied to inhibit excessive complement activation in COVID-19 patients. The risks, benefits, time of administration and the part of the system to be targeted need to be considered when using complement inhibition, therefore this systematic review was made to conclude whether the administration of complement system inhibition therapy can reduce the mortality of COVID-19 patients who are hospitalized based on available studies.

Objective: To determine the effect of complement system inhibitory therapy on the mortality of hospitalized COVID-19 patients

Methods: Using specific keywords, we comprehensively searched the PubMed, Embase, Cochrane, and Scopus databases for potential articles from 2019 to December 31, 2022. The research protocol was registered with PROSPERO (CRD42022306632). All studies administering complement inhibitory therapy to COVID-19 patients were processed. Statistical analysis was performed using Review Manager 5.4 software.

Result: 5 studies met the criteria and were included in a systematic review and meta-analysis of a total of 739 COVID-19 patients. The results of the Forest plot analysis showed that administration of complement system inhibitor therapy reduced mortality by 28% in hospitalized COVID-19 patients (RR 0.72; 95% CI: 0.46 – 1.14, I2 = 61%, P -value = 0.16).

Conclusion: Providing complement system inhibitor therapy did not statistically significantly reduce mortality in hospitalized COVID-19 patients"

"Coronavirus (CoV) adalah keluarga virus penyebab penyakit sistem pernapasan ringan hingga berat pada berbagai spesies hewan termasuk manusia. Salah satu spesies Coronavirus yang muncul pada akhir tahun 2019 yaitu SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) dan menimbulkan penyakit baru bernama Covid-19 (Coronavirus disease-2019) kemudian berstatus pandemi. Penyebaran Covid-19 yang cepat dan dengan tingkat kematian yang tinggi terus terjadi di berbagai negara. Oleh karena itu, deteksi dini patogen perlu dilakukan secara cepat dengan menggunakan data sekuens protein Coronavirus. Sekuens protein merupakan data struktur primer dari suatu protein yang memiliki 27 fitur berdasarkan discere. Dalam penerapannya, tidak semua fitur relevan dengan data yang digunakan sehingga perlu seleksi fitur untuk menghindari dimensi data yang tinggi dan tidak optimal. Seleksi fitur algoritma genetika memberikan fitur-fitur optimal pada data dan metode K-Nearest Neighbor (KNN) melakukan klasifikasi data sekuens protein Coronavirus dengan fitur hasil seleksi fitur algoritma genetika. Seleksi fitur algoritma genetika menghasilkan 11 fitur optimal yang meningkatkan performa running time metode klasifikasi KNN menjadi 0,0541 detik. Fitur optimal diperoleh dari karakteristik AA-count , secondary structure fraction , isoelectric point dan instability index. Hasil terbaik performa akurasi, spesifisitas beserta sensitifitas secara berurutan yaitu 96,68%, 98,7% dan 94,4% yang diperoleh pada nilai parameter K=3.

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Coronaviruses (CoV) are a family of viruses that cause mild to severe respiratory system diseases in various animal species including humans. One of the Coronavirus species that emerged at the end of 2019 was SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) and caused a new disease called Covid-19 (Coronavirus disease-2019) then had a pandemic status. The rapid spread of Covid-19 and with a high death rate continues to occur in most of countries. Therefore, early detection of pathogens needs to be done quickly using Coronavirus protein sequence data. Protein sequences are primary structural data of a protein that has 27 features but not all of the existing features are relevant to the data used, so feature selection is necessary to avoid high and suboptimal data dimensions. The genetic algorithm feature selection provides optimal features to the data and the K-Nearest Neighbor (KNN) method performs the classification of Coronavirus protein sequences data with features resulting from the genetic algorithm feature selection. The genetic algorithm feature selection produces 11 optimal features that improve the running time performance of the KNN classification method. The average result of running time is 0.0541 second. The best results were accuracy performance, specificity and sensitivity are 96.68%, 98.7% and 94.4% respectively which were obtained at the parameter value K=3."