EFEK PEMBERIAN METFORMIN TERHADAP HIPERTROFI JANTUNG DAN KESINTASAN PADA MENCIT PASCAINFARK MIOKARDIUM YANG DIINDUKSI ISOPROTERENOL

Latar belakang Cardiac remodeling pasca-infark miokardium (IM) merupakan peristiwa penentu terjadinya gagal jantung. Salah satu bentuk remodeling ini yaitu hipertrofi jantung. Hipertrofi jantung merupakan faktor risiko yang independen terhadap kejadian gagal jantung. Metformin, obat golongan biguanida yang lazim digunakan dalam tatalaksana diabetes melitus tipe 2, dibuktikan memiliki efek kardioprotektif yang independen dari efek antidiabetesnya melalui mekanisme aktivasi AMPK. Meskipun demikian, penelitian yang dipublikasi mengenai pengaruh pemberian metformin terhadap hipertrofi jantung dan kesintasan pasca- IM masih sangat terbatas. Tujuan Diketahuinya efek pemberian metformin terhadap luas penampang kardiomiosit (sebagai indikator hipertrofi jantung) dan kesintasan pada mencit pasca-IM yang diinduksi isoproterenol. Metode Empat puluh empat mencit Swiss betina dialokasikan secara acak menjadi empat kelompok (sebelas mencit pada tiap kelompok yaitu kontrol, metformin, isoproterenol, metformin+isoproterenol). Hipertrofi jantung pasca-IM diinduksi dengan injeksi subkutan isoproterenol 10 mg/kg/hari selama 2 hari. Metformin 300 mg/kg/hari selama 28 hari periode pasca-IM diberikan secara per oral menggunakan sonde lambung untuk dilihat efeknya terhadap luas penampang kardiomiosit dan kesintasan. Hasil Pemberian metformin mampu menurunkan luas penampang kardiomiosit hingga 29,4% (p = 0,001) pada mencit dengan hipertrofi jantung pasca-IM. Meskipun demikian, model hipertrofi pasca-IM pada mencit ini belum adekuat untuk digunakan dalam penilaian survival karena tidak adanya perbedaan kesintasan antara kelompok hipertrofi dengan kelompok kontrol (p = 0,098). Simpulan Metformin mampu menurunkan luas penampang kardiomiosit pada mencit pasca-IM yang diinduksi isoproterenol. Namun, efek pemberian metformin terhadap kesintasan belum dapat diketahui. Kata kunci: metformin, hipertrofi jantung, kesintasan, pasca-infark miokardium/pasca-IM, isoproterenol, luas penampang kardiomiosit

Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods

Background and Hypothesis: It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design: We used data from a large multi-center study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularization by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results: Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC=0.84-0.86). Specificity (range=73.9-78.0%) and sensitivity (range=75.6-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions: The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability.</p

Development and validation of predictive model for a diagnosis of first episode psychosis using the multinational EU-GEI case-control study and modern statistical learning methods.

Background and Hypothesis It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design We used data from a large multi-centre study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularisation by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC=0.84-0.86). Specificity (range=73.9%-78.0%) and sensitivity (range=75.6%-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizabilit

Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods

Background and Hypothesis: It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design: We used data from a large multi-center study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularization by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results: Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC=0.84-0.86). Specificity (range=73.9-78.0%) and sensitivity (range=75.6-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions: The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability

KUALITAS SEMEN CAIR SAPI BALI SELAMA PENYIMPANAN SUHU RUANG MENGGUNAKAN PENGENCER SKIM MILK DENGAN PENAMBAHAN FILTRAT KECAMBAH KACANG HIJAU

Penelitian ini bertujuan untuk mengetahui kualitas semen cair sapi Bali yang menggunakan pengencer skim milk dengan penambahan filtrat kecambah kacang hijau selama penyimpanan di suhu ruang (29oC). Hasil penelitian diharapkan dapat menjadi kajian ilmiah serta sumber informasi bagi akademisi dan Balai Inseminasi Buatan. Materi penelitian yang digunakan adalah semen segar sapi Bali yang dipelihara secara intensif di BBIB Singosari Malang dengan kriteria umur 6-11 tahun, motilitas massa ++ dan motilitas individu 50-70%. Metode penelitian yang digunakan adalah percobaan laboratorium menggunakan pola Rancangan Acak Kelompok (RAK) dengan 4 perlakuan dan 5 kali ulangan, P0 sebagai kontrol (100% Skim Milk + 0% Filtrat Kecambah Kacang hijau); P1 (98% SM + 2% FKKH); P2 (96% SM + 4% FKKH) dan P3 (94% SM + 6% FKKH). Analisis data yang yang digunakan adalah Analisis Ragam atau Analysis of Variant (ANOVA), apabila terdapat perbedaan yang nyata atau sangat nyata maka dilakukan uji jarak berganda Duncan. Hasil penelitian menunjukkan bahwa penambahan filtrat kecambah kacang hijau ke dalam pengencer skim milk memberikan perbedaan yang tidak nyata (P>0,05) terhadap kualitas semen cair sapi Bali selama penyimpanan suhu ruang (29oC) baik motilitas individu, viabilitas maupun abnormalitas spermatozoa.   Kata kunci: antioksidan, motilitas, viabilitas, abnormalita

HEALTH POLITICS OBJECTIVES: DISCURSIVE TRANSLATION STRATEGIES OF INDONESIAN COVID-19 GUIDANCE TEXT

This study aims to reveal discursive strategis in the translation of Indonesian COVID-19 Medical Guidance. The medical terms were collected from COVID-19 GUIDANCE, 13 May 2020, by (Rights United, 2020). There were 21 medical terms as the data to translate and analyze in the SL into the TL, the authors applied the contextual research and supported by semantic and morphological analyses. Physical distancing and self-isolation were not commonly called medical terms but since the Covid 19 pandemic, the English words physical distancing and self-isolation are as understood as medical terms which mean preventing the spread of the virus by enforcing a distance between one person and another. The results show that literal, adaptation, borrowing, modulation, and amplification translation techniques are workable in transferring the messages from the SL into the TL. The authors propose a multidisciplinary approach in translating the English medical terms for Indonesian.

Pandemic inequity in a megacity: a multilevel analysis of individual, community and healthcare vulnerability risks for COVID-19 mortality in Jakarta, Indonesia

Introduction Worldwide, the 33 recognised megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. We assessed individual, community-level and healthcare factors associated with COVID-19-related mortality in a megacity of Jakarta, Indonesia, during two epidemic waves spanning 2 March 2020 to 31 August 2021. Methods This retrospective cohort included residents of Jakarta, Indonesia, with PCR-confirmed COVID-19. We extracted demographic, clinical, outcome (recovered or died), vaccine coverage data and disease prevalence from Jakarta Health Office surveillance records, and collected subdistrict level sociodemographics data from various official sources. We used multilevel logistic regression to examine individual, community and subdistrict-level healthcare factors and their associations with COVID-19 mortality. Results Of 705 503 cases with a definitive outcome by 31 August 2021, 694 706 (98.5%) recovered and 10 797 (1.5%) died. The median age was 36 years (IQR 24–50), 13.2% (93 459) were <18 years and 51.6% were female. The subdistrict level accounted for 1.5% of variance in mortality (p<0.0001). Mortality ranged from 0.9 to 1.8% by subdistrict. Individual-level factors associated with death were older age, male sex, comorbidities and age <5 years during the first wave (adjusted OR (aOR)) 1.56, 95% CI 1.04 to 2.35; reference: age 20–29 years). Community-level factors associated with death were poverty (aOR for the poorer quarter 1.35, 95% CI 1.17 to 1.55; reference: wealthiest quarter) and high population density (aOR for the highest density 1.34, 95% CI 1.14 to 2.58; reference: the lowest). Healthcare factor associated with death was low vaccine coverage (aOR for the lowest coverage 1.25, 95% CI 1.13 to 1.38; reference: the highest). Conclusion In addition to individual risk factors, living in areas with high poverty and density, and low healthcare performance further increase the vulnerability of communities to COVID-19-associated death in urban low-resource settings

Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case–control Study and Modern Statistical Learning Methods

Funder: National Institute for Health Research; DOI: https://doi.org/10.13039/501100000272Background and Hypothesis: It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design: We used data from a large multi-center study encompassing 2627 phenotypically well-defined participants (aged 18–64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularization by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results: Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC = 0.84–0.86). Specificity (range = 73.9–78.0%) and sensitivity (range = 75.6–79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions: The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability