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"Osteosarkoma merupakan salah satu tumor ganas tulang primer yang paling sering ditemukan. Kemoterapi neoadjuvan merupakan salah satu alternatif terapi yang dapat meningkatan luaran dan kesintasan pasien. Studi ini dilakukan untuk menilai luaran klinis, histopatologis, dan radiologis pada pasien osteosarkoma yang menjalani kemoterapi neoadjuvan beserta faktor-faktor yang mempengaruhinya. Penelitian ini merupakan suatu studi potong lintang yang menggunakan data pasien dengan diagnosis osteosarkoma yang telah menjalani kemoterapi neoadjuvan di RSUPN dr. Cipto Mangunkusumo pada bulan Januari 2017 hinggal Juli 2019. Terdapat 58 subjek dalam penelitian ini. Sebanyak 38 (65,5%) subjek berjenis kelamin laki-laki dengan median usia seluruh subjek 16 (5 hingga 67) tahun. Sebanyak 10 (17,2%) subjek merupakan good responder kemoterapi neoadjuvan. Dari hasil analisis data didaapatkan perbedaan bermakna kadar laboratoris ALP (p=0,002), LED (p=0,002), dan NLR (p<0,001) sebelum dan sesudah kemoterapi. Derajat nekrosis berkorelasi negatif dengan perubahan nilai LDH sebelum dan sesudah kemoterapi (r=-0,354; p=0,006), namun tidak didapatkan hubungan yang bermakna dengan parameter lain seperti perubahan kadar ALP (r=-0,186; p=0,162) dan LED (r=-0,104;  p=0,437). Secara radiologis didapatkan peningkatan nilai ADC yang bermakna (p=0,028) setelah pemberian kemoterapi neoadjuvan, namun perubahannya tidak berhubungan dengan persentase nekrosis tumor (r=-0,300; p=0,433). Pada pasien osteosarkoma yang menjalani kemoterapi neoadjuvan di RSUPN dr. Cipto Mangunkusumo bulan Januari 2017 hingga Juli 2019, didapatkan perbedaan bermakna kadar penanda inflamasi dan parameter radiologis berupa ADC sebelum dan sesudah pemberian kemoterapi adjuvan.

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Osteosarcoma is one of the most prevalent primary tumors of the bone. Neoadjuvant chemotherapy has been administered in osteosarcoma cases to increase the survival rate and improve outcomes. This study is conducted to investigate the clinical, histopathological, and radiological outcome of osteosarcoma patients who underwent neoadjuvant chemotherapy, as well as the various factors that contributes to said outcome. This study is a cross-sectional study that involves the data of patients diagnosed with osteosarcoma who underwent neoadjuvant chemotherapy in RSUPN dr. Cipto Mangunkusumo from January 2017 up to July2019. A total of 58 subjects was admitted in this study. Thirty-eight (65,5%) subjects are male, with the median age of all subjects being 16 years old (5 to 67). We found that 10 subjects (17,2%) is a good responder to neoadjuvant chemotherapy. From the data analysis, significant differences were observed in ALP (p=0,002), ESR (p=0,002) and NLR (p=<0,001) levels before and after neoadjuvant chemotherapy. The degree of necrosis is inversely correlated with the change in LDH level before and after neoadjuvant chemotherapy (r=-0,354; p=0,006), however, no significant correlation was observed in ALP (r=-0,186; p=0,162) dan ESR (r=-0,104;  p=0,437). Radiologically, there is an increase in ADC value (p=0,028) after neoadjuvant chemotherapy. However, this is not correlated with the degree of necrosis (r=-0,300; p=0,433) observed pathologically. There is a significant difference in inflammatory markers and radiological parameter (ADC) pre and post neoadjuvant chemotherapy among osteosarcoma patients in RSUPN dr. Cipto Mangunkusumo from January 2017 up to July 2019."

"Latar Belakang: Pandemi Corona Virus Disease 2019 (COVID-19) yang disebabkan oleh Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) berawal dari Wuhan, Cina sejak Desember 2019. Jakarta menjadi salah satu episentrum pandemic COVID-19 di Indonesia. Data penelitian COVID-19 di Indonesia masih sangat terbatas, sehingga diperlukan penelitian untuk mengetahui karakterisik klinis, radiologis, laboratorium dan derajat klinis. Metode Penelitian: Penelitian ini diambil dari 1070 pasien yang dirawat di Rumah Sakit Darurat Wisma Atlet yang menjalani skrining gejala klinis, radiologi toraks, laboratorium dan serologi SARS-CoV-2kemudian dilanjutkan dengan pemeriksaan swab RT-PCR. Hasil skrining swab RT PCR pada 1070 pasien terdapat 836pasien terkonfirmasiCOVID-19, lalu diskrining dari 836 pasien yang memiliki radiologi toraks dan laboratorium lengkap ada 413 pasien. Hasil Penelitian: Pasien terkonfirmasi COVID-19 derajat ringan-sedang didominasi oleh pasien laki-laki(55,4%) dengan kelompok usia < 60 tahun (91,8%) dan rerata umur 39,94±14,17 tahun. Sebagian besar pasien tidak memiliki komorbid, tetapi komorbid paling banyak adalah hipertensi (4,1%). Derajat klinis pasien yang dirawat paling banyak kasus asimtomatik (46%), sedang (31,5%) dan ringan (22,5%). Gejala yang sering muncul adalah batuk (22,5%), demam (14,3%),sesak napas (6,5%), nyeri tenggorok (5,3%) dan pilek (4,8%). Gambaran radiologis sebagian besar pasien normal (41,9%), sesuai pneumonia (31,5%) dan corakan meningkat (26,6%). Hasil pemeriksaan laboratorium didapatkan limfopenia (10,9%), trombositopenia (1,7%) dan peningkatan NLR (18,4%). Serologi SARS-CoV-2sebagian besar pasien reaktif (48,8%). Sebagian besar pasien dirawat ≥20 hari (63,9%), masa konversi ≥14 hari (52,5%) dan luaran akhir pasien sembuh (99,3%). Terdapat hubungan bermakna antara komorbid dengan lama rawat (p= 0,03) dan lama konversi (p= 0,03), status awal masuk RS dengan lama konversi (p= 0,00) dan lama rawat (p= 0,00). Kesimpulan: Proporsi pasien terkonfirmasi COVID-19 dari keseluruhan pasien yang dirawat sebesar 78,13%, sebagian besar laki-laki dan gambaran radiologis normal. Terdapat kenaikan NLR dan kebanyakan serologi SARS-CoV-2reaktif. Sebagian besar pasien yang dirawat kasus asimtomatik dan luaran akhir perawatannya sembuh.

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Background: Coronavirus disease 2019 (COVID-19) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated from Wuhan, China since December 2019. Jakarta is among of the epicenter of COVID-19 pandemic in Indonesia. Research data on COVID-19 in Indonesia is still very limited while there is an urgent need of disease characterization from the perspective of clinical features, radiological finding, laboratory profile, and severity.

Methods: This retrospective cohort study involved 1070 patients treated at an emergency hospital in Jakarta, Indonesia. Patients were screened for their clinical symptoms, radiological finding, laboratory profile, including the SARS-CoV-2 immunoserology, and then proceed with the SARS-CoV-2 RT-PCR examination. The screening resulted in 836 patients were confirmed COVID-19, and 413 patients had a complete medical record to be further studied.

Results: The mild-moderate cases were dominated by males (55.4%) of age groups <60 years-old (91.8%). The mean age was 39.94±14.17 years-old. Most subjects presented without comorbidities, although hypertension was common (4.1%). Most subjects were asymptomatic (46%) followed by moderate case (31.5%), and mild case (22.5%). Symptoms were cough (22.5%), fever (14.3%), shortness of breath (6.5%), sore throat (5.3%), and runny nose (4.8%). Radiological findings were normal (41.9%), pneumonia (31.5%), and increased opacity (26.6%). Laboratory tests showed lymphopenia (10.9%), thrombocytopenia (1.7%), and increased NLR (18.4%). The SARS-CoV-2 immunoserology was mostly reactive (48.9%). Length of stay (LoS) was ≥20 days (63.9%), conversion period was ≥14 days (52.5%), and most were recovered (99.3%). There were correlations between existing comorbidities and LoS (p=0.03) and conversion time (p=0.03). There were correlations between initial condition during hospital admission with conversion time (p=0.00) and LoS (p=0.00).

Conclusion: The proportion of patients with COVID-19 confirmed from all patients treated was 78.13%, whom male, normal radiological finding, increased NLR, reactive SARS-CoV-2 immunoserology, and asymptomatic predominated. Most of the patients were moderate cases and well recovered."

"This quick-reference handbook on clinical infectious diseases is intended for a broad medical audience. Written in outline format with 45 short focused chapters covering core infectious disease topics, the book provides a basic introduction to identifying and managing commonly encountered problems. Organized by system, each section begins with a general framework covering clinical presentation, laboratory and diagnostic evaluation, and empirical antibiotic therapy. Individual chapters within sections are devoted to particular problems and cover pathogenesis and risk factors, microbial causes, clinical manifestations, approach to the patient (history, examination, diagnostic studies), diagnostic criteria, and medical, antimicrobial, and surgical management. "

"Stroke merupakan penyebab utama kematian dan kecacatan di Indonesia. Mengingat sempitnya jendela waktu pengobatan stroke iskemik hiperakut dan potensi komplikasi yang terkait dengan intervensi trombolisis, prognostikasi yang akurat esensial dalam memastikan terapi yang cepat dan tepat. Penelitian ini memanfaatkan pembelajaran mesin, khususnya Random Forest (RF), bertujuan untuk mengembangkan model yang mampu memprediksi hasil klinis (Δ NIHSS) pasien stroke iskemik hiperakut setelah trombolisis, berdasarkan CT scan otak, data klinis, dan nilai laboratorium. Klasifikasi Δ NIHSS menggunakan tiga skenario berbeda —CT, CT + Data klinis, dan CT + Data klinis + Data lab— dan dikategorikan menjadi 2 dan 3 kelas yang akan digunakan dalam pemantauan model prediksi mana yang memberikan performa paling optimal. Pengumpulan data studi kohort ini diperoleh saat kedatangan awal pasien, terdiri dari data klinis, laboratorium, dan data CT otak non-kontras dari rekam medis dan Picture Archiving Communication System (PACS) Rumah Sakit Cipto Mangunkusumo Jakarta dengan periode 10 tahun sejak November 2014 hingga Februari 2023 dan total 145 pasien. Arsitektur dari Bacchi et al.1 yakni convolutional neural network (CNN) dan model pembelajaran mesin konvensional lainnya juga dianalisis sebagai pendekatan alternatif. Hasil penelitian menunjukkan bahwa algoritma RF (2 kelas) menggunakan data validasi dan skenario CT + Data klinis + Data lab menampilkan akurasi tertinggi (75%) dan unggul dalam sensitivitas dan spesifisitas (0,61 dan 0,59). Performa metrik juga menunjukkan tren peningkatan dari setiap skenario. Model ini diharapkan dapat meningkatkan efisiensi penatalaksanaan stroke iskemik hiperakut dengan memberikan informasi tambahan kepada klinisi dalam pengambilan keputusan terkait intervensi trombolisis.

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Stroke is the leading cause of both mortality and disability in Indonesia. Given the narrow time frame for treating acute ischemic stroke and the potential complications associated with thrombolysis intervention, accurate prognostication is essential to ensure a prompt and appropriate treatment. The National Institutes of Health Stroke Scale (NIHSS) can be utilized to identify individuals who may benefit from reperfusion therapy. The data for this cohort study acquired during the initial presentation, comprising clinical, laboratory, and non-contrast brain CT data from the medical records and Picture Archiving Communication System (PACS) of Cipto Mangunkusumo Hospital Jakarta. The study included 145 patients who experienced acute ischemic stroke and received thrombolysis treatment from November 2014 to February 2023. Currently, there is no clinical outcome prediction model for hyperacute ischemic stroke using data from Indonesia. By utilizing machine learning, specifically Random Forest, the author aims to develop a model capable of predicting the clinical outcome (Δ NIHSS) of hyperacute ischemic stroke patients following thrombolysis, based on brain CT scans, clinical data, and laboratory values. The classification of Δ NIHSS used three distinctive scenarios —CT, CT + Clinic, and CT + Clinic + Lab— and is categorized by 2 and 3 classes will be used in monitoring which prediction model gives optimal performance. Architecture derived from the research conducted by Bacchi et al.1 employed a convolutional neural network (CNN) and other conventional machine learning models were also analyzed as alternative approach. Result revealed that RF algorithm (2 classes) using data validation and CT + Clinic + Lab scenario displays the highest accuracy (75%) and excels in sensitivity and specificity (0,61 and 0,59). The performance metrics show continuous improvement, indicating that this model can enhance hyperacute ischemic stroke management by providing clinicians with additional decision-making support for thrombolysis intervention."