Objective: To compare the efficacy of SSRI medication alone and SSRI+CBT combined. Methods: NCBI Pubmed, DARE, CSDR and NGC were searched October-November 2019. The population size, as well as the base and endpoint CGAS mean and standard deviation from the three studies included, are recorded. Statistical analysis was done in RStudio with the "meta" package. Results: For the SSRI only, the effect size was -1.82 with a 95% confidence interval between -2.28 and -1.37.  For the SSRI and CBT combined, the effect size was -1.68 with a 95% confidence interval between -2.39 and -0.98. The effect size for both SSRI and SSRI + CBT didn't cross the null effect line, but the heterogeneity exceeds 50%. The result for the comparison of post SSRI vs. SSRI + CBT showed the effect size of -0.05 with a 95% confidence interval between -0.23 and 0.12. The size effect did cross the null effect line, but the heterogeneity was less than 50%. Conclusion: Both methods were shown to be effective. However, due to statistical inconsistencies, it couldn’t be concluded whether the combination of SSRI and CBT is better than treatment with SSRI alone.

Autism Spectrum Disorder (ASD) is a neurologic development disorder; it is listed in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Early diagnosis is critical in improving the life quality of individuals affected by ASD. Several studies show that Artificial Intelligence can be used in the diagnosis of ASD through biological means such as observing patient EEG data and surveying their genome. Articles were searched in the PUBMED database, ScienceDirect, and Springer Link between 2019 - 2020. Four papers were selected for review. The papers devised models that can accurately predict ASD in affected individuals, though some are based on old data and/or require testing on larger datasets to determine accuracy. As ASD diagnosis usually cannot be achieved before the individual shows symptoms, AI has the potential to improve ASD diagnosis in affected individuals. Further study to confirm the models and test on larger, more recent datasets would be required to develop more accurate models and achieve even better results. Autism spectrum disorder (ASD) merupakan salah satu gangguan perkembangan saraf yang tercantum pada Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Diagnosis dini sangat penting untuk meningkatkan kualitas hidup individu ASD. Beberapa penelitian menunjukkan bahwa kecerdasan buatan dapat digunakan untuk diagnosis ASD melalui metode berbasis biologis seperti mengamati data EEG pasien dan mensurvei genomnya. Review ini berbasis pencarian data antara tahun 2019 – 2020 di database PUBMED, ScienceDirect, dan Springer Link. Empat makalah kunci dipilih untuk ditinjau. Makalah-makalah tersebut mampu merancang model yang dapat memprediksi ASD secara akurat, meskipun beberapa aspek implementasinya didasarkan pada data usang dan/ atau memerlukan pengujian pada kumpulan data yang lebih besar untuk menentukan akurasi. Mengingat diagnosis ASD biasanya tidak dapat dilakukan sebelum individu menunjukkan gejala, kecerdasan buatan berpotensi meningkatkan ketepatan diagnosis ASD. Masih diperlukan studi lanjutan untuk mengkonfirmasi model dan pengujian pada kumpulan data yang lebih besar dan lebih baru untuk mengembangkan model yang memiliki presisi lebih baik dan hasil lebih akurat.

<p>Autism Spectrum Disorder (ASD) is a neurologic development disorder; it is listed in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Early diagnosis is critical in improving the life quality of individuals affected by ASD. Several studies show that Artificial Intelligence can be used in the diagnosis of ASD through biological means such as observing patient EEG data and surveying their genome. Articles were searched in the PUBMED database, ScienceDirect and Springer Link between 2019 - 2020. Four papers were selected for review. The papers were able to devise models that can accurately predict ASD in affected individuals, though some are based on old data and/or require testing on larger datasets to determine accuracy. As ASD diagnosis usually cannot be achieved before the individual shows symptoms, AI has the potential to improve ASD diagnosis in affected individuals. Further study to confirm the models and test on larger, more recent datasets would be required to develop more accurate models and achieve even better results.</p><p>Autism Spectrum Disorder (ASD) merupakan salah satu gangguan perkembangan saraf yang tercantum pada Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Diagnosis dini sangat penting untuk meningkatkan kualitas hidup individu ASD. Beberapa penelitian menunjukkan bahwa Kecerdasan Buatan dapat digunakan untuk diagnosis ASD melalui metode berbasis biologis seperti mengamati data EEG pasien dan mensurvei genomnya. Review ini berbasis pencarian data antara 2019 – 2020 di database PUBMED, ScienceDirect dan Springer Link. Empat makalah kunci dipilih untuk ditinjau. Makalah-makalah tersebut mampu merancang model yang dapat memprediksi ASD secara akurat, meskipun beberapa aspek implementasinya didasarkan pada data usang dan/atau memerlukan pengujian pada kumpulan data yang lebih besar untuk menentukan akurasi. Mengingat diagnosis ASD biasanya tidak dapat dilakukan sebelum individu menunjukkan gejala sekunder, kecerdasan buatan berpotensi meningkatkan keandalan diagnosis ASD. Masih diperlukan studi lanjutan untuk mengkonfirmasi model dan pengujian pada kumpulan data yang lebih besar dan lebih baru untuk mengembangkan model yang memiliki presisi lebih baik dan hasil lebih akurat.</p>

Leprosy is an infectious disease caused by Mycobacterium leprae that mainly affects the skin, peripheral nerve, mucosa of the upper respiratory tract, and eyes. There is no vaccine designed specifically to prevent leprosy. The most common vaccine strategy is Bacille Calmette-Guérin (BCG), however its efficacy is highly variable between studies. Current study utilized a computational method to predict antigenic epitopes from Mycobacterium leprae for peptide vaccine development. Molecular docking of top predicted peptides from 6 antigenic B-cell and 3 CTL epitopes were analyzed. These predicted antigenic epitopes might potentially be target peptides for future leprosy vaccines.