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arXiv 2025年

作者： Huang, Yepeng Su, Xiaorui Ullanat, Varun Liang, Ivy Clegg, Lindsay Olabode, Damilola Ho, Nicholas John, Bino Gibbs, Megan Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Harvard College CambridgeMA United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca GaithersburgMD United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Program in Computational Biology Carnegie Mellon University PittsburghPA United States Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference—a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance. Copy

关键词： Diseases

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Antioxidant and Antidiabetic Activities of Melastoma Malabathricum Leaves Extracts

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Hunan Daxue Xuebao/Journal of Hunan University Natural sciences 2022年第7期49卷 144-153页

作者： Idris, Muddatstsir Purnomo, Adi Setyo Martak, Fahimah Fatmawati, Sri Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jalan Arif Rahman Hakim Sukolilo Surabaya East Java60111 Indonesia Program of Pharmacy Study Faculty of Mathematics and Natural Sciences University of Lambung Mangkurat A. Yani KM. 35.8 Banjarbaru South Kalimantan70714 Indonesia Agri-Food and Biotechnology Research Center Institut Teknologi Sepuluh Nopember Jalan Arif Rahman Hakim Sukolilo Surabaya East Java60111 Indonesia

Currently, no effective medication is available to treat diabetes despite this disease is a serious health problem. As part of our project to explore Indonesian medicinal plants for antidiabetic agents, this study aimed to investigate the total phenolic and flavonoid contents, antioxidant and antidiabetic properties of Melastoma malabathricum leaves extracts. Spectrophotometric methods were used to determine the total phenolic and flavonoid contents. Antioxidant activity was performed using DPPH, ABTS, and FRAP methods. The in vitro antidiabetic test was conducted through an inhibitory evaluation of α-glucosidase and α-amylase, while STZ-induced diabetic rats were used for in vivo study. The highest value of total phenolic (183.71 ± 0.11 mg GAE/g Extract) was recorded in the methanolic extract and flavonoid (24.10 ± 0.04 mg QE/g Extract) contents were recorded in the EtOAc extract. The methanolic extract has the highest DPPH and ABTS activities with IC50 values of 8.58 ± 0.03 and 4.59 ± 0.03 μg/mL, respectively. It also showed the highest FRAP activity with a 51.15 ± 0.10 µM Fe2+/g. In vitro antidiabetic testing of the methanolic extract of leaves against α-glucosidase and α-amylase was reported for the first time. This novel result showed that the methanolic extract inhibited α-glucosidase and α-amylase with IC50 values of 75.25±1.60 and 52.38±1.32 μg/mL, respectively. A dose of 200 mg/Kg body weight of the methanolic extract reduced rats’ blood glucose rate and serum blood glucose by 51.01% and 37.82%, respectively, after 15 days of treatments. These findings suggested that the methanolic extract of M. malabathricum leaves can be used as a potential source of antioxidant and antidiabetic agents. © 2022 Hunan University. All rights reserved.

关键词： Antioxidants

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Recent trend of metal promoter role for CO₂ hydrogenation to C₁ and C₂₊ products

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South African Journal of Chemical Engineering 2023年 44卷 14-30页

作者： Sholeha, Novia Amalia Holilah, Holilah Bahruji, Hasliza Ayub, Athirah Widiastuti, Nurul Ediati, Ratna Jalil, Aishah Abdul Ulfa, Maria Masruchin, Nanang Nugraha, Reva Edra Prasetyoko, Didik College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Department Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Keputih Surabaya Sukolilo 60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Darussalam Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Johor Bahru Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia Skudai 81310 UTM Skudai Johor Johor Bahru Malaysia Chemistry Education Study Program Faculty of Teacher Training and Education Sebelas Maret University Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Research Collaboration Center for Biomass and Biorefinery between BRIN and Universitas Padjadjaran Jatinangor 45363 Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Pembangunan Nasional “Veteran” Jawa Timur East Java Surabaya 60294 Indonesia

CO2 hydrogenation as sustainable route for generation of value-added carbon feedstock is identified as green pathway for mitigation of greenhouse gasses emission. CO2 methanation is one of the promising solutions, whi... 详细信息

CO₂ hydrogenation as sustainable route for generation of value-added carbon feedstock is identified as green pathway for mitigation of greenhouse gasses emission. CO₂ methanation is one of the promising solutions, which only requires reactions at atmospheric pressure while utilizing metal catalysts to overcome kinetic limitations. Metal catalysts can be promoted to alter reducibities, CO₂ adsorption capacities, and H_2[sbnd]CO₂ dissociation potential. The role of metal promoter such as rare earth elements (Ce, Mn, Co, and La) and alkali and alkali earth metal (Li, Na, Ca, and K) will be discussed within the scope of CO₂ methanation. The aspect of catalysts modification towards hydrogen dissociation potential and surface oxygen vacancy will be emphasized to enhance selectivity for methane. Another pathway for CO₂ hydrogenation is via further conversion into longer chain molecules such as olefin and ethanol. The benefit of metal promoter will be discussed in this review on the effect towards promoting C[sbnd]C coupling reaction for producing longer chain alcohol and light olefin. The strategies to develop active catalysts for the coupling reaction of C[sbnd]C will be emphasized with the promoter introduction. For the hydrogenation of CO₂ to longer chain molecules, the main metal catalysts Ni, Pd, Rh, and Co, and their modification with promoter such as Ga, Cu, and alkali metal Na, K will be discussed. A critical analysis of the CO₂ methanation mechanism and further C[sbnd]C reaction to longer chain molecules will be discussed, particularly the effect of metal promoters to stabilize the intermediate and maneuver the catalytic reaction pathway into the desired products. © 2023 The Author(s)

关键词： C1 products C2+ products CO2 Hydrogenation Metal promoter

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study on structure and morphology of silicon-substituted LiFePO4/C doped material

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AIP Conference Proceedings 2023年第1期2604卷

作者： Fahmi Astuti Vera L. Maghfirohtuzzoimah Sahara H. Intifadhah Pelangi Az-Zahra Muhammad R. Ramadhan Mochamad Zainuri Darminto Darminto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Study Program of Physics Faculty of Mathematics and Natural Sciences Republic of Indonesia Defense University Bogor Indonesia

Elemental doping and carbon coating on the electrode are able to increase the battery’s performance to be a good energy storage. In this case, some studies are necessary to confirm the quality of the cathode material, LiFePO4 on the Li-ion based battery material (LIB). Here we report the study of structure and morphology of LiFePO4 by silicon doping at the P-atom atomic position and carbon coating (henceforth called as LFP-Si) utilizing the X-Ray diffractometer, the Fourier-transform infrared technique, and both the scanning and transmission electron microscopy (SEM & TEM), as a preliminary investigation to know the structure and morphology of LiFePO4 after elemental doping and carbon coating. All characterization results are essential for the improvement of the performance of LiFePO4/C doped material.

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Estimation of nonparametric ordinal logistic regression model using generalized additive models (GAM) method based on local scoring algorithm

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AIP Conference Proceedings 2022年第1期2668卷

作者： Marisa Rifada Nur Chamidah Ratih Ardiati Ningrum 1Statistics Study Program Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 3Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 2Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia

One of the statistical methods used to describe the relationship between categorical scale response variable and categorical or continuous variable of predictors is logistic regression analysis. If the response variable has ordinal scale, it is called ordinal logistic regression. The ordinal response variables is common used in scientific research. There are two approaches to the regression model, i.e. parametric and nonparametric approach. We develop the nonparametric ordinal logistic regression which is an expansion of the ordinal logistic regression model where the regression function is estimated using a nonparametric approach. The aim of this study is determine the regression function estimators of the nonparametric ordinal logistic regression model using Generalized Additive Models (GAM) method based on local scoring algorithm. The GAM method assumes that the regression function is expressed as the sum of the regression functions of each component predictor variables. The local scoring algorithm consists of two loops, namely the scoring step (outer loop) which is iterated until the deviance value converges and the backfitting step (inner loop) is iterated until the Residual Sum of Squares (RSS) value converges.

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Effect of foliar nutrients application to the growth and yield of Physalis peruviana and Physalis alkekengi

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IOP Conference Series: Earth and Environmental science 2022年第1期978卷

作者： Diana Nurus Sholehah Dini Ermavitalini Miratul Khasanah Edy Setiti Wida Utami Hery Purnobasuki Doctoral Study Program of Mathematics and Natural Science Faculty of Science and Technology Airlangga University Indonesia Study Program of Agroecotechnology Faculty of Agriculture University of Trunojoyo Madura Indonesia Departement of Biology Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Indonesia Departement of Chemistry Faculty of Science and Technology Airlangga University Indonesia Departement of Biology Faculty of Science and Technology Airlangga University Indonesia

Physalis Sp. is a potential medicinal plant and functional food. The use of appropriate nutrients can gain optimal plant growth and productivity. This study aimed to determine the macro and micronutrients that can provide optimal growth and yield of Physalis sp. This research was conducted at a greenhouse in Bangkalan Madura. A factorial randomized block design with four replications was used in this study. The first factor was the species of Physalis Sp., namely P. peruviana and P. alkekengi. The second factor is nutrition at four levels: a spray of water only as control; foliar application of macronutrients at a dose of 2 g/L; foliar application of micronutrients at a dose of 0,125 g/L; foliar Application of a combination of macronutrients 1 g/L and micronutrients 0,0625 g/L. The data obtained were analyzed using analysis of variance (ANOVA) followed by Least Significant Difference (LSD) at the 5 % level. Nutrition significantly affected growth (plant height, stem diameter, and number of leaves) and yield (number of fruit). The interaction between treatments occurred in the number of fruits. Micronutrients gave the highest number of fruit, both species of Physalis sp.

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Investigation of structural and electronic properties of boron carbide based on SCAN meta-GGA functional

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AIP Conference Proceedings 2023年第1期2604卷

作者： Muhammad R. Ramadhan Imam M. Al-Khafiiz Jihan Anavisha Jofim Y. Napitupulu Wanda R. Tiana Fahmi Astuti M. Zuhnir Piliang Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor 16810 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

Boron Carbide (B4C) is one of the known superhard material that can be utilized in many engineering and scientific applications. Here we investigated B4C system in its rhombohedral structure (a=5.17 Å, α=65.52°) by using the meta-GGA functional, SCAN, within density functional theory framework. We observed that the optimized lattice parameters given by the SCAN functional is more realistic compared to the LDA and GGA calculations. From the optimized structure of B4C, we determined the bulk modulus value of 255 GPa which only differs by ∼3 % from experimental data. Furthermore, the underestimation of band gap in LDA and GGA implementations is also remedied, where the meta-GGA, SCAN, functional estimates 1.98 eV insulating gap from density of states calculation. This value is very close to the known experimental observations (2.09 eV).

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Graph AI in Medicine

arXiv

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arXiv 2023年

作者： Johnson, Ruth Li, Michelle M. Noori, Ayush Queen, Owen Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Berkowitz Family Living Laboratory Harvard Medical School BostonMA02115 United States Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal to no re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions. © 2023, CC BY.

关键词： Knowledge graph

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Assisting smart disaster management for smart city program, case study: Pringgokusuman village, Yogyakarta

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IOP Conference Series: Earth and Environmental science 2021年第1期683卷

作者： I A Suherningtyas A L Permatasari P P K Wiguna G B Adninda Geography Study Program Faculty of Science and Technology Universitas Amikom Yogyakarta Center for Spatial Data Infrastructure Development (PPIDS) Universitas Udayana Bali Study Program of Urban and Regional Planning Faculty of Science and Technology Universitas Amikom Yogyakarta

The smart city is a concept of creating a comfortable, safe, and accessible living place. Furthermore, a smart city is built to raise the value of the city in economic, social, and technology. One dimension of all seven dimensions planned by Yogyakarta Smart City program is smart disaster management. Yogyakarta located in the disaster – prone area, with disaster potentials such as earthquake, fire, tornado, and flood. This particular dimension becoming essential to reduce the effects of a disaster. One of the effects of a disaster is the loss of human life. Citizen in Yogyakarta especially in Pringgokusuman is one of the group of element at risk in disaster. Education about disaster is important to them. This research aims to analyse citizen knowledge of the disaster information. This research is using qualitative research methodology including an indepth interview with descriptive analysis. This study shows that information about disaster management is not optimized, even though the information is needed. A suitable dissemination strategy needs to be developed in order to educate citizens and enhance their awareness in applying smart disaster management technology.

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Evaluation of lahar flood hazard management policy using participatory planning in Putih watershed, Magelang regency, central java, Indonesia

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IOP Conference Series: Earth and Environmental science 2021年第1期683卷

作者： A L Permatasari I A Suherningtyas P P K Wiguna Geography Study Program Faculty of Science and Technology Universitas Amikom Yogyakarta Center for Spatial Data Infrastructure Development Universitas Udayana Bali

The occurrence of Lahar floods after Merapi Volcano eruption in 2010 still continue to be a concern for the government and the community. Kali Putih is one of the rivers that originates in Merapi Volcano and is one of the Lahar flood hazard area. Non-structural mitigation efforts have been carried out by the government. Until now, there are still community activities in the forbidden hazard area. Regulations regarding to spatial planning regulation in the hazard area need to be evaluated. The aim of the study is to evaluate the Lahar flood hazard mitigation based on participatory mapping and Focus Group Discussions (FGD). The research area is Jumoyo Village, which is the highest Lahar hazard flood area. The results indicate that there are still residential areas and community activities in the Lahar flood hazard area. The community does not want to lose their livelihood or relocation of their homes. The community expect that the government is able to revive the economic activities of the community through the establishment of new economic centres without relocation of their homes. The community also expect that the government able to creating new employment.

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