Publications by authors named "Ali Yavari"

3 Publications

  • Page 1 of 1

The natural-based optimization of kojic acid conjugated to different thio-quinazolinones as potential anti-melanogenesis agents with tyrosinase inhibitory activity.

Bioorg Med Chem 2021 Apr 27;36:116044. Epub 2021 Jan 27.

Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Medicinal Chemistry, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address:

Melanin pigment and melanogenesis are a two-edged sword. Melanin has a radioprotection role while melanogenesis has undesirable effects. Targeting the melanogenesis pathway, a series of kojyl thioether conjugated to different quinazolinone derivatives were designed, synthesized, and evaluated for their inhibitory activity against mushroom tyrosinase. All the synthesized compounds were screened for their anti-tyrosinase activity and all derivatives displayed better potency than kojic acid as the positive control. In this regard, 5j and 5h as the most active compounds showed an IC value of 0.46 and 0.50 µM, respectively. In kinetic evaluation against tyrosinase, 5j depicted an uncompetitive inhibition pattern. Designed compounds also exhibited mild antioxidant capacity. Moreover, 5j and 5h achieved good potency against the B16F10 cell line to reduce the melanin content, whilst showing limited toxicity against malignant cells. The proposed binding mode of new inhibitors evaluated through molecular docking was consistent with the results of structure-activity relationship analysis.
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http://dx.doi.org/10.1016/j.bmc.2021.116044DOI Listing
April 2021

Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases.

J Biomed Inform 2021 Feb 4;116:103695. Epub 2021 Feb 4.

Department of Electrical and Computer Engineering, Razi University, Kermanshah, Iran. Electronic address:

The existing data mining solutions to identify risk factors associated with diseases are burdened with quite a few shortcomings. They usually use crisp partitions for numerical features and also do not use patient-specific profiles. These shortcomings create limitations for solving real problems. Discretizing a numerical feature through crisp partitions can also generate substantial partitioning errors, particularly for features whose values are closer to crisp boundaries. Since the normal range of each numerical feature varies according to the age, gender, and medical conditions of the patients, then ignoring these differences can undermine the accuracy of the extracted itemsets and rules. This paper presents a profile-based fuzzy association rule mining (PB-FARM) approach for the assessment of risk factors highly correlated with diseases. The proposed approach has three phases. Phase I involves creating profiles for patients based on their age, gender, and medical conditions, to determine a normal range of each numerical feature. Then fuzzy partitioning is done for all features (namely, numerical and categorical), and consequently, a structure, called FirstScan, is created. In Phase II, the FirstScan structure is utilized to mine for large fuzzy k-itemsets. Ultimately, in Phase III, the given k-itemsets are employed to generate fuzzy rules for associations between risk factors and diseases. To evaluate the performance of the proposed method the Z-Alizadeh Sani coronary artery disease (CAD) dataset, containing 303 records and 54 features, was used. The results show a positive correlation between typical chest pain and old age with the incidence of CAD. The comparisons made in this study showed that, firstly, the proposed algorithm has a higher partitioning accuracy than other methods, and secondly, it has a reasonably short execution time.
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http://dx.doi.org/10.1016/j.jbi.2021.103695DOI Listing
February 2021

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

Sensors (Basel) 2016 Nov 9;16(11). Epub 2016 Nov 9.

Data 61, CSIRO, Melbourne 3168, Australia.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.
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http://dx.doi.org/10.3390/s16111884DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134543PMC
November 2016