Sex: Male
Education:

• Doctor of Philosophy in Chemical Science, Hokkaido University
• Master of Science in Chemistry, De La Salle University
• Bachelor of Science in Biochemistry, De La Salle University

Field of Specialization:
Biochemistry
Peptides
Biomimetic materials
Chemical Ecology
Agricultural Chemistry

Researches:

Article title: A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors
Authors: GabrielaIlona B. Janairo, Derrick Ethelbhert C. Yu, Jose Isagani B. Janairo
Publication title: Network Modeling Analysis in Health Informatics and Bioinformatics 10(1), December 2021

Abstract:
The widespread infection caused by the 2019 novel corona virus (SARS-CoV-2) has initiated global efforts to search for antiviral agents. Drug discovery is the first step in the development of commercially viable pharmaceutical products to deal with novel diseases. In an effort to accelerate the screening and drug discovery workflow for potential SARS-CoV-2 protease inhibitors, a machine learning model that can predict the binding free energies of compounds to the SARS-CoV-2 main protease is presented. The optimized multiple linear regression model, which was trained and tested on 226 natural compounds demonstrates reliable prediction performance (r 2 test = 0.81, RMSE test = 0.43), while only requiring five topological descriptors. The externally validated model can help conserve and maximize available resources by limiting biological assays to compounds that yielded favorable outcomes from the model. The emergence of highly infectious diseases will always be a threat to human health and development, which is why the development of computational tools for rapid response is very important. Supplementary information: The online version contains supplementary material available at 10.1007/s13721-021-00326-2.
Full text link https://tinyurl.com/2p8dc68y

Article title: Machine Learning for the Cleaner Production of Antioxidant Peptides
Authors: Jose Isagani B. Janairo
Publication title: International Journal of Peptide Research and Therapeutics 27(3), September 2021

Abstract:
Antioxidant peptides (AP) are promising functional foods that have the potential to provide multitude health benefits. They are found in a wide variety of sources, but current methods of discovery and extraction dramatically increases the cost of production which hampers the commercial competitiveness of APs. Focusing on the search and development of short AP sequences that can be easily synthesized through synthetic chemical methods may be able to decrease the cost of production and accelerate lead discovery. However, the traditional method of peptide synthesis that relies on solid-phase chemistry adversely impacts the environment. Thus, minimizing trial-and-error will not only shorten AP discovery but can also make the entire process greener and more cost-effective. In this study, the formulation of a machine learning model that can predict the trolox equivalent antioxidant capacity (TEAC) of tripeptides is presented. It was found that the combination of support vector regression with a polynomial kernel and Blosum indices can accurately predict AP TEAC. The optimized regression model was trained, tested, and externally validated on 121 sequences curated from three different publications. The optimized model demonstrates a 7 % average percent error based on external validation.
Full text link https://tinyurl.com/3zwnuk43

Article title: Green Fluorescent Protein-Mediated Biomineralization of Silver Microparticles
Authors: Ma. Monica M. Cabiles, Brandon Cyril S. Lira, Jose Isagani B. Janairo
Publication title: Orbital - The Electronic Journal of Chemistry 13(1), March 2021

Abstract:
Biomineralization is a bio-inspired technique of creating inorganic nanostructures using peptides or proteins. An important consideration in selecting a biomineralization agent is the overall shape or geometry of the protein since this can influence the properties of the produced nanostructures. The green fluorescent protein (GFP) from the jellyfish Aequorea victoria is a promising biomineralization agent due to its distinct structure, characterized by having a barrel-like structure. In this study, silver microparticles (AgMPs) with a diameter of 500 nm were produced through GFP-mediated biomineralization under ambient reaction conditions. In the absence of GFP, aggregated and disordered silver structures were formed. A proposed model to account for the observation involves the formation of GFP networks to which growing silver particles may become adsorbed to. The presented study provides the motivation for the further study of using GFP towards nanostructure synthesis.
Full text link https://tinyurl.com/2p8rt5zr

Article title: Unsustainable plastic consumption associated with online food delivery services in the new normal
Authors: Jose Isagani B. Janairo
Publication title: Cleaner and Responsible Consumption 2:100014, June 2021

Abstract:
The restaurant industry is one of the hardest hit sectors of the COVID-19 pandemic. The prolonged closures and declining patrons brought about by community lockdowns have imposed financial struggles to numerous restaurants and food establishments. As people were forced to remain indoors in order to curb the spread of the virus, demand for online food delivery service has surged. The growing demand for this type of food service is predicted to significantly alter the consumption pattern of restaurant patrons, which may accelerate the consumption of single-use plastics. In this paper, sustainability challenges relating to plastic consumption associated with online food delivery services are presented together with recommendations on how to address them. From the proposed actions to be implemented, it appears that the online food delivery service providers are in a central position to implement potentially high-impact actions within a relatively shorter time horizon compared with different stakeholders, such as the consumers, restaurants, and governments. Thus, encouraging greater accountability and initiatives from online food delivery service providers will be crucial in the drive for cleaner and responsible consumption of plastics derived from food deliveries.
Full text available upon request to the author

Article title: What University Attributes Predict for Graduate Employability?
Authors: K. B. Aviso, F. P. A. Demeterio III, J. I. B. Janairo, R. I. G. Lucas, M. A. B. Promentilla, R. R. Tan, D. E. C. Yu
Publication title: Cleaner Engineering and Technology 2:100069, June 2021

Abstract:
Research universities play an important role in developing new technologies that can be the basis for economic development, improved quality of life, and reduced environmental impacts. In developing countries, there have been accelerated efforts to transform teaching-oriented higher education institutions into research-intensive universities that can contribute to social development through the generation of new knowledge. There have also been parallel efforts to use internationalization to enrich both education and research. However, the effect of such reforms on the employability of university graduates is unclear. In this work, the influence of different institution attributes on graduate employability is investigated using the hyperbox machine learning technique, which is capable of generating classification models in the form of if/then rules. The analysis focuses on Southeast Asian universities listed in the 2020 Quacquarelli Symonds Asian University Rankings and uses the normalized scores across the different ranking criteria. Five plausible rule-based classifiers are derived and validated. The results show notable association between research and internationalization metrics with employability.
Full text available upon request to the author

Article title: A principal component regression model for predicting phytochemical binding to the H. pylori CagA protein
Authors: Jose Isagani B. Janairo
Publication title: Network Modeling Analysis in Health Informatics and Bioinformatics 9(1), December 2020

Abstract:
Helicobater pylori is an important causative factor in the pathogenesis of multiple gastrointestinal diseases. One of the factors responsible for the virulence of H. pylori is the cagA protein, which can interfere with a number of cellular signaling processes once this protein is transferred inside the host cell. Thus, inhibiting the interaction of the cagA protein with the host cell membrane using small molecular inhibitors appears to be a promising pharmacological strategy. In this study, a predictive model for the binding free energy of natural compounds towards the cagA protein is presented. The formulated model which is built on principal component—multiple linear regression demonstrates reliable accuracy (r2test = 0.92, RMSEtest = 0.483), while only requiring five independent variables for the prediction. It was further noted that topological descriptors had the greatest influence on the generated principal components which served as the predictors. The created regression model can help promote and accelerate the discovery of natural compounds as cagA binders for the development of anti-H. pylori agents.
Full text link https://tinyurl.com/2ypppsun

Article title: Physicochemical properties and in vitro digestibility of flours and starches from taro cultivated in different regions of Thailand
Authors: Rungtiwa Wongsagonsup,Thanupong Nateelerdpaisan, Chayapon Gross, Manop Suphantharika, Prasanna D. Belur, Esperanza Maribel G. Agoo, Jose Isagani Belen Janairo
Publication title: International Journal of Food Science & Technology 56(5), November 2020

Abstract:
There was a significant difference in the physicochemical properties and in vitro digestibility of flours and water‐ and alkaline‐extracted starches from taro cultivated in different regions of Thailand. This research aimed to study physicochemical properties and in vitro digestibility of flours and starches from taro cultivated in different regions of Thailand, that is, Kanchanaburi (KB), Chiang Mai (CM), Phetchaburi (PB) and Saraburi (SB). Taro starches were extracted from taro flours using either water or alkaline extraction. The taro flours had significantly (P ≤ 0.05) larger particle size, higher pasting and gelatinisation temperatures, and resistant starch content but lower total starch content, whiteness (L* value), paste viscosities and clarity than their corresponding extracted starches. All the taro starches exhibited polygonal and irregular granules and gave A‐type X‐ray diffraction pattern. The alkaline‐extracted taro starches had significantly (P ≤ 0.05) higher extraction yield, total starch content, L* value, pasting and gelatinisation temperatures, and paste clarity but lower granular size, amylose content, resistant starch content, paste viscosities and relative crystallinity than their water‐extracted counterparts.
Full text available upon request to the author

Article title: Predicting Peptide Oligomeric State Through Chemical Artificial Intelligence
Authors: Jose Isagani B. Janairo & Gerardo C. Janairo
Publication title: International Journal of Peptide Research and Therapeutics 27(1), October 2020

Abstract:
Oligomerization plays a crucial role in the structure and function of peptides and proteins, wherein sequence variations can affect the oligomeric stability of the biomolecule. In this study, an artificial neural network classifier that can predict the oligomeric state of peptides is presented, using the p53 tetramerization domain and associated mutants as the model system. The FASGAI vectors were utilized as the peptide descriptors, and the resulting binary classifier exhibits satisfactory predictive ability as demonstrated by a test set accuracy of 86%.
Full text available upon request to the author

Article title: Volatile Chemical Profiling and Microplastic Inspection of Fish Pastes from Balayan, Batangas, Philippines
Authors: Brandon Cyril S. Lira, Andrei Carlos Cresencia, Mary Angelique Tavera, Jose Isagani Janairo
Publication title: Asian Fisheries Science 33:213-221, 2020

Abstract:
Fermented fish pastes (Bagoong) are one of the most commonly used liquid condiments among Asian countries, wherein the production of fish pastes may vary from one Asian country to another. In the Philippines, Balayan is one of the municipalities in the province of Batangas that is popular for its Bagoong Balayan. Chemical profiling from volatile organic compounds (VOCs) can be used to determine aroma-inducing compounds that are specific to this local variant and for quality assessment for food safety. In the meantime, the emerging pollution in the marine environment from persistent organic pollutants (POPs) and microplastics are alarming and threats to food safety. These marine-derived commodities, such as fish pastes may therefore harbour these kinds of pollutants. In this study, Bagoong Balayan samples were subjected to solid-phase microextraction coupled with gas chromatography-mass spectrometry. A total of 29 compounds were detected that passed the minimum match factor of 80 and 14 of them were common in all collected fish paste samples. Some of these compounds were also reported to be in fish paste samples produced in other Asian countries. However, five of them were observed to be found only in Bagoong Balayan, namely 1-octen-3-ol, 1-octen-3-one, 2-nonanone, tridecane, and 2,6,10,14-tetramethylpentadecane. No traces of POPs were found in Bagoong Balayan samples. The presence of microplastics was seen in all of the samples after centrifugation, vacuum filtration, and inspection using a microscope. Most of the microplastics that are present appeared to be fibrous in structure and coloured red or blue.
Full text link https://tinyurl.com/5n7hf5td

Article title: Design of fragrant molecules through the incorporation of rough sets into computer-aided molecular design
Authors: Kirridharhapany T. Radhakrishnapany, Chee Yan Wong, Fang Khai Tan, Jia Wen Chong, Raymond R. Tan, Kathleen B. Aviso, Jose Isagani B. Janairoc and Nishanth G. Chemmangattuvalappil
Publication title: Molecular Systems Design & Engineering 5(8), August 2020

Abstract:
Design and screening of fragrances based on experiments or experiences of specialists can overlook potentially better fragrance products. To overcome this issue, a systematic mathematical programming-based approach is developed for the design of fragrant molecules. A novel data-driven rough set-based machine learning (RSML) model is utilised as a predictive or diagnostic modelling tool for odour properties. RSML generates deterministic rules based on the relationship between topology of fragrant molecules and their odour characters elicited from existing odour database. The rules generated are then integrated as constraints into a Computer-Aided Molecular Design (CAMD) problem. The CAMD framework also involves other relevant properties such as diffusion coefficient, vapour pressure, viscosity, LC50 and solubility parameter which are predicted using group contribution (GC) method. Since there are different types of models involved in the prediction of various attributes, molecular signature descriptors are utilised as the common platform that links machine learning and other predictive models in a CAMD problem. The application of the new design method is demonstrated through a case study to design fragrant molecules for shampoo additives with desirable physical and environmental properties. The results indicate the ability of the novel method in identifying non-intuitive and promising fragrant molecules that can be used for various applications.
Full text available upon request to the author

Article title: Metal-dependent Ser/Thr protein phosphatase PPM family: Evolution, structures, diseases and inhibitors
Authors: Rui Kamada, Fuki Kudoh, Shogo Ito, Itsumi Tani, Jose Isagani B. Janairo, James G. Omichinski, Kazuyasu Sakaguchi
Publication title: Pharmacology & Therapeutics 214:107622, November 2020

Abstract:
Protein phosphatases and kinases control multiple cellular events including proliferation, differentiation, and stress responses through regulating reversible protein phosphorylation, the most important post-translational modification. Members of metal-dependent protein phosphatase (PPM) family, also known as PP2C phosphatases, are Ser/Thr phosphatases that bind manganese/magnesium ions (Mn2+/Mg2+) in their active center and function as single subunit enzymes. In mammals, there are 20 isoforms of PPM phosphatases: PPM1A, PPM1B, PPM1D, PPM1E, PPM1F, PPM1G, PPM1H, PPM1J, PPM1K, PPM1L, PPM1M, PPM1N, ILKAP, PDP1, PDP2, PHLPP1, PHLPP2, PP2D1, PPTC7, and TAB1, whereas there are only 8 in yeast. Phylogenetic analysis of the DNA sequences of vertebrate PPM isoforms revealed that they can be divided into 12 different classes: PPM1A/PPM1B/PPM1N, PPM1D, PPM1E/PPM1F, PPM1G, PPM1H/PPM1J/PPM1M, PPM1K, PPM1L, ILKAP, PDP1/PDP2, PP2D1/PHLPP1/PHLPP2, TAB1, and PPTC7. PPM-family members have a conserved catalytic core region, which contains the metal-chelating residues. The different isoforms also have isoform specific regions within their catalytic core domain and terminal domains, and these regions may be involved in substrate recognition and/or functional regulation of the phosphatases. The twenty mammalian PPM phosphatases are involved in regulating diverse cellular functions, such as cell cycle control, cell differentiation, immune responses, and cell metabolism. Mutation, overexpression, or deletion of the PPM phosphatase gene results in abnormal cellular responses, which lead to various human diseases. This review focuses on the structures and biological functions of the PPM-phosphatase family and their associated diseases. The development of specific inhibitors against the PPM phosphatase family as a therapeutic strategy will also be discussed.
Full text available upon request to the author

Article title: A sequence-dependent classification algorithm for Crohn’s Disease – causing NOD2 protein mutations
Authors: Jose Isagani B. Janairo and Marianne Linley L. Sy-Janairo
Publication title: Nova Biotechnologica et Chimica 19(1):52-60, June 2020

Abstract:
Certain NOD2 protein mutations have been associated with the onset of the inflammatory bowel disease, Crohn’s Disease (CD). NOD2 is involved in the inflammatory response of the gut to the microbial community, wherein its functional impairment through mutations may lead to CD progression. Considering the significant role that NOD2 plays in CD pathogenesis, predicting whether a specific type of NOD2 mutation is the cause of CD can greatly aid the accuracy of the disease diagnosis. Hence, a novel sequence-based classification algorithm built on artificial neural network (ANN) is herein presented that can predict whether a specific NOD2 mutation can cause CD or not. The NOD2 mutant types and their association with CD were taken from literature, and the calculated sequence-order coupling numbers were used as the classification predictors. The formulated ANN classifier exhibited satisfactory predictive ability, with 82.4 % accuracy, 62.5 % sensitivity, 100 % specificity, 100 % positive predictive value, and 75 % negative predictive value. The presented ANN classifier provides a proof-of-concept that predicting the onset of CD from NOD2 protein variant is possible.
Full text available upon request to the author

Article title: Enhanced Hyperbox Classifier Model for Nanomaterial Discovery
Authors: Jose Isagani B. Janairo, Kathleen B. Aviso, Michael Angelo B. Promentilla, and Raymond R. Tan
Publication title: AI 1(2):299-311, June 2020

Abstract:
Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis.
Full text link https://tinyurl.com/yc8n5avj

Article title: A Screening Algorithm for Gastric Cancer-Binding Peptides
Authors: Jose Isagani B. Janairo and Marianne Linley L. Sy‑Janairo
Publication title: International Journal of Peptide Research and Therapeutics 26(2), June 2020

Abstract:
Gastric cancer-binding peptides (GCBP) are promising diagnostic and therapeutic agents for gastric cancer management. Their utility lies in their ability to facilitate the early detection of gastric cancer, prevent metastasis, and prevent tumor angiogenesis. In order to promote and accelerate the discovery of more GCBP, this study aims to create a machine-learning classification model that can predict if a given sequence can bind with gastric cancer cells. A systematic literature search was conducted to extract peptides that can and cannot bind with gastric cancer cells. Nine descriptor classes were then calculated for each sequence. The resulting dataset was used to create classifiers using five machine-learning algorithms. Rigorous model optimizations were conducted which included descriptor selection and probability threshold tuning. The combination of the topological descriptor T-scales, and logistic regression were found to satisfactorily predict GCBP class. The optimized classification model exhibited satisfactory accuracy with balanced sensitivity and specificity, and excellent precision. The results brought forward provide the foundation for an alternative screening method for GCBPs. This system is expected to positively contribute in the discovery of new GCBPs, thereby potentially enhancing GC disease diagnostics and management.
Full text link https://tinyurl.com/293fw9hw

Article title: A hyperbox classifier model for identifying secure carbon dioxide reservoirs
Authors: Raymond R. Tan, Kathleen B. Aviso, Jose Isagani B. Janairo, Michael Angelo B. Promentilla
Publication title: Journal of Cleaner Production 272:122181, June 2020

Abstract:
Carbon management technologies such as carbon dioxide capture and storage and direct air capture systems will be needed to mitigate climate change in the coming decades. Both of these technologies will depend on the availability of secure geological storage sites that can permanently hold carbon dioxide with minimal risk of leakage. Machine learning tools that can characterize candidate storage sites based on geological data can aid decision-makers in planning carbon management networks. In this work, a mixed integer linear programming model is developed to generate a binary hyperbox classifier for determining the integrity of a candidate storage site. The model is calibrated and validated using literature data on natural carbon dioxide reservoirs, resulting in a set of IF-THEN rules that are readily interpreted by decision-makers. The approach developed here also includes rule simplification features and the capability to account for statistical Type I (false positive) and Type II (false negative) errors. Different sets of rules can be generated using the model based on user-defined number of hyperboxes. The best set of rules can be selected based on a combination of its performance with the validation data and consistency with expert knowledge. Using the case study for identifying secure CO2 reservoirs, the set of rules which resulted in zero false positives using the validation data was generated using three hyperboxes. However, an alternative set of rules which falsely predicted two out of three insecure sites as positive provides simpler rules indicating CO2 density and reservoir depth as the most important criteria.
Full text available upon request to the author

Article title: Estimating the Effectiveness of Gold and Iron Oxide Nanoparticles for Hepatocellular Carcinoma Ablation Therapy: a Meta-Analysis
Authors: Jose Isagani B. Janairo & Marianne Linley L. Sy-Janairo
Publication title: BioNanoScience 10(1), March 2020

Abstract:
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide, wherein treatment still remains a challenge. A promising experimental treatment for tumors that can potentially overcome some of the limitations faced by conventional therapies for HCC is nanoparticle – mediated hyperthermia (NMH). Currently at the developmental stage, NMH usually utilizes gold or iron oxide nanoparticles to convert radiofrequency or alternating magnetic field into heat that causes targeted cell death. In order to assess the effectiveness of NMH on a greater scale, and in the face of varying levels of effectiveness in published literature, this meta-analysis was conducted. A systematic literature search identified relevant studies in gold and iron oxide NMH for HCC cell lines published from 2008 – January 2019. Seven studies met the inclusion criteria, wherein three studies used gold NMH, and the remaining four used iron oxide NMH. Outcome of interest is cell viability after irradiation in the presence of the nanoparticles. A pooled risk ratio with 95% confidence interval (CI) was calculated using a random effects model. Iron oxide – based NMH was able to decrease HCC cell viability by 87% while 19% for gold-based NMH. Moderate to high heterogeneity is attributed to the different classes of nanoparticles, concentration, cell lines, and irradiation duration used in the pooled studies. The results provide encouraging insights for the continuous development of NMH for cancer treatment. Considering that iron oxide is more cost-effective than gold nanoparticles, focusing on the development of iron oxide nanoparticles for this nanomedical technology may accelerate the development and clinical application of NMH.
Full text available upon request to the author

Article title: Physical Characterization of Latex from Artocarpus heterophyllus Lam. (Jackfruit) and Four Related Artocarpus spp.
Authors: Maria Rejane J. Nepacina, Virgilio C. Linis, Jose Isagani B. Janairo
Publication title: Key Engineering Materials 833:107-117, March 2020

Abstract:
This study focused on the physical properties of latex extracted from five species of Artocarpus J.R.Forster & G.Forster, namely: A. altilis (Parkinson) Fosberg, A. blancoi Merr, A. camansi Blanco, A. heterophyllus Lam., and A. ovatus Blanco as potential natural adhesives. Surface morphology showed that all five Artocarpus spp. have no specific forms, but otherwise flexible and viscoelastic. Contact angle measurements showed that all samples of Artocarpus spp. were hydrophilic with low contact angle values owing to the contents of natural source of the latex. FTIR analysis matched all Artocarpus latex samples to that of Polyvinyl acetate. Highest resin content was found on A. ovatus with all the species containing natural resin. It was also confirmed that out of the three stress strain analyses, A. camansi had the highest values for tensile strength, A. ovatus had the highest values for Young's modulus of elasticity and the highest percentage elongation values belonged to A. heterophyllus. Adhesive shear strengths with maximum force values were highest in A. ovatus. Through cluster analysis, out of the eight variables tested A. heterophyllus was the outgroup being attributed to its latex gum-like property. All the above tests and analyses suggested that latex of all five Artocarpus spp. were similar in characteristics to polymer adhesive. Among which A. camansi and A. ovatus exhibited high results on adhesive strength tests.
Full text available upon request to the author

Article title: Data on the sequence-derived properties of gastric cancer – binding peptides
Authors: Jose Isagani B. Janairo and Marianne Linley L. Sy-Janairo
Publication title: Data in Brief 29:105351, February 2020

Abstract:
The article presents a dataset containing nine classes of calculated sequence-derived descriptors for 78 peptide sequences, 21 of which demonstrate the ability to bind with gastric cancer cells. The datasaet was used in the paper “A screening algorithm for gastric cancer binding peptides”[1] for the creation of a classification model that can predict the ability of a given peptide sequence to bind with gastric cancer cells. The 78 peptide sequences were extracted from a systematic literature search, and the various peptide descriptors were calculated using the R package “Peptides”. The nine calculated sequence-derived descriptor classes are the Blosum indices, Cruciani properties, FASGAI vectors, Kidera factors, ProtFP, ST-scales, T-scales, VHSE scales, and Z-scales. The resulting dataset, which is composed of over 4,000 data points, offers a rich resource for further protochemometric analyses of the curated peptide sequences relevant to cancer diagnostics and therapeutics.
Full text link https://tinyurl.com/255ttk9j

Article title: Coal Fly Ash-based Geopolymer Spheres Coated with Amoxicillin and Nanosilver for Potential Antibacterial Applications
Authors: Brandon Cyril S. Lira, Sophia Bianca A. Dellosa, Casey Irene L. Toh, Al Patrick A. Quintero, Andre Leopold S. Nidoy, Kimmie Dela Cerna, Derrick Ethelbhert C. Yu, Jose Isagani B. Janairo, Michael Angelo B. Promentilla
Publication title: ASEAN Journal of Chemical Engineering 19(1):25, October 2019

Abstract:
Geopolymers are emerging “green” materials for its low embodied energy and carbon footprint, and its potential to valorize wastes, such as coal fly ash. It is an inorganic cementitious material formed from the polymerization of aluminosilicates in an activating solution such as that of alkali hydroxides or silicates. Their superior mechanical properties, including heat and fire resistance make them an excellent material for diverse applications. Recent studies have also exploited the tunable open porosity and adsorbing properties of geopolymers. Our work thus explores the potential of porous geopolymer spheres for antibacterial applications. These spheres were synthesized using coal fly ash as the geopolymer precursor and the porous surface is coated with either amoxicillin or silver nanoparticles (AgNPs) adsorbed in the matrix. For the AgNP geopolymer spheres, SEM images show spherical nanostructures when using ascorbic acid as a reducing agent, while spherical, cubical, and wire structures were observed when reduced using NaBH4. Indication from UV-Vis results also suggests the gradual release of both amoxicillin and AgNPs in the solution from the functionalized geopolymer spheres. Using E. Coli as the test organism for a modified disk diffusion assay, both showed zones of inhibition against the bacteria. Further tests on antibacterial application of AgNP geopolymer spheres show their effectiveness to kill at least 95% of the E. coli in a water sample initially containing 105 cfu/mL in just 30 minutes.
Full text link https://tinyurl.com/yhw4e5zk

Article title: Prediction of CO2 storage site integrity with rough set-based machine learning
Authors: Kathleen B. Aviso, Jose Isagani B. Janairo, Michael Angelo B. Promentilla, Raymond R. Tan
Publication title: Clean Technologies and Environmental Policy 21(8), October 1019

Abstract:
CO2 capture and storage (CCS) and negative emissions technologies (NETs) are considered to be essential carbon management strategies to safely stabilize climate. CCS entails capture of CO2 from combustion products from industrial plants and subsequent storage of this CO2 in geological formations or reservoirs. Some NETs, such as bioenergy with CCS and direct air capture, also require such CO2 sinks. For these technologies to work, it is essential to identify and use only secure geological reservoirs with minimal risk of leakage over a timescale of multiple centuries. Prediction of storage integrity is thus a difficult but critical task. Natural analogues or naturally occurring deposits of CO2, can provide some information on which geological features (e.g., depth, temperature, and pressure) are predictive of secure or insecure storage. In this work, a rough set-based machine learning (RSML) technique is used to analyze data from more than 70 secure and insecure natural CO2 reservoirs. RSML is then used to generate empirical rule-based predictive models for selection of suitable CO2 storage sites. These models are compared with previously published site selection rules that were based on expert knowledge. Graphic abstract Open image in new window
Full text link https://tinyurl.com/4sc8cu2m

Article title: Wetting Properties and Foliar Water Uptake of Tillandsia L.
Authors: Anna Rose C. Zambrano, Virgilio C. Linisa, Maria Rejane J. Nepacina, Mark Louie T. Silvestre, Juanito Raphael F. Foronda, Jose Isagani B. Janairo
Publication title: Biotribology 19:100103, September 2019

Abstract:
Quantitative dimensional analyses of the wetting property of selected Tillandsia L. were conducted. The wettability on the leaf surfaces of three Tillandsia species and one hybrid cultivar has significant variations (p < .05). This variation is influenced by their absorptive foliar trichomes. The structure, arrangement and density of their foliar trichomes on the leaf surfaces and the degree of corrugated trichome wings with variations on micro−/nano-protrusion allow the liquids to increase its spreading and/or liquid repellency. Among the Tillandsia species, T. schiedeana Steudel has the densest trichomes. The average trichome densities are as follows: T. schiedeana (61.20 mm2 ± 3.36) has the highest and T. Houston (T. stricta Sol. ex Sims T. recurvifolia Hook) hybrid (45.24 mm2 ± 5.93) has the lowest trichome density on the adaxial leaf surface; while T. schiedeana (63.55 mm2 ± 10.46) has the highest and T. xerographica Rohweder (40.66 mm2 ± 17.72) has the lowest trichome density found on the abaxial leaf surface (p < .0001). All examined Tillandsia exhibited foliar water uptake. One of them, T. schiedeana had significantly greater increase in leaf water content up to 115.9% followed by T. Houston (57.37%) > T. xerographica (36.63%) > T. caput-medusae E. Morren (35.91%). Based on the results of adhesion and surface free energy of the leaf surfaces, the desirable wetting properties of all four Tillandsia plants used in this study were determined. Among the four, T. schiedeana and T. caput medusae exhibited interesting liquid adhesion on the adaxial leaf surface which makes the two plants hydrophilic on this particular leaf surface. On the other hand, the highest water drop adherence to the leaf surface is observed in T. schiedeana which is necessary for its high foliar water uptake. In this study, it was proven that structure, arrangement and density of foliar trichomes found in Tillandsia affect the spreading of liquid and leaf surface wettability on their leaf surfaces which in turn improve the foliar water uptake of these plants.
Full text available upon request to the author

Article title: Surface morphological and wetting characterization of the hydrophobic and superhydrophobic leaves of Pistia stratiotes L., Salvinia molesta D.Mitch., Ananas comosus (L.) Merr. and Dyckia platyphylla L.B. Smith for bioinspired oil adsorbent materials
Authors: Mark Louie T. Silvestre, Anna Rose C. Zambrano, Virgilio C. Linis and Jose Isagani B. Janairo
Publication title: IOP Conference Series Materials Science and Engineering 479:012003, March 2019

Abstract:
In this paper, the surface morphology and wetting properties towards deionized water and pure oil samples with varying carbon chain lengths of adaxial and abaxial leaf surfaces of Pistia stratiotes L., Salvinia molesta D.Mitch., Ananas comosus (L.) Merr. and Dyckia platyphylla L.B. Smith were characterized. The surface morphological characterization showed that P. stratiotes L. has uniseriate trichomes on adaxial (ad) and abaxial (ab) surface and S. molesta D.Mitch. has multifaceted egg-beater shaped trichomesad and achlorophyllous filamentsab. Both surfaces of the bromeliads, A. comosus (L.) Merr. and D. platyphylla L.B. Smith have peltate scutiform trichomes. Overall, P. stratiotes L. has the greatest trichome density (no.of trichomes/mm²) of ad 36.77 and ab40.10 among A. comosus (L.) Merr. > D. platyphylla L.B. Smith> S. molesta D.Mitch. Contact angle measurement showed that P. stratiotes L. has the best water repellency having (154. 39 ± 3.26)ad > S. molesta D.Mitch. > A. comosus (L.) Merr. > D. platyphylla L.B. Smith and (147.90 ±3.17)ab > A. comosus (L.) Merr. > D. platyphylla L.B. Smith > S. molesta D.Mitch. Lastly, P. stratiotes L. showed the best common pure oil adsorption capacity among the four species. Therefore, the understanding on the fundamental concept on how the leaf surface of P. stratiotes L. adsorbs the oil and reacts in response to various solvents adsorbed on the leaf surface was established.
Full text link https://tinyurl.com/yck67dhb

Article title: Predictive Analytics for Biomineralization Peptide Binding Affinity
Authors: Jose Isagani B. Janairo
Publication title: BioNanoScience 9(32), March 2019

Abstract:
The rational design of biomineralization peptides for the synthesis of inorganic nanomaterials remains a challenging endeavor in biomimetics. The difficulty arises from the multiple factors that influence the affinity of the peptide towards a particular surface. This study presents classification and regression models of biomineralization peptide binding affinity for a gold surface using support vector machine. It was found that the Kidera factors, in particular those related to the extended structure preference, partial specific volume, flat extended preference, and pK-C of the peptide, are important descriptors to predict biomineralization peptide binding affinity. The classification model exhibited an overall prediction accuracy of 90% and 83% for the regression model. This highlights the reliability and accuracy of the formulated models, while requiring a reasonable number of descriptors. The created predictive models are steps in the right direction towards the further development of rational biomineralization peptide design.
Full text link https://tinyurl.com/4j5a8e85

Article title: Differentiation of Rubber Cup Coagulum Through Machine Learning
Authors: M.R.J. Nepacina, J.R.F. Foronda, K.J.F. Haygood, R.S. Tan, G.C. Janairo, F.F. Co, R.O. Bagaforo, T.A. Narvaez and J.I.B. Janairo
Publication title: Scientia Agriculturae Bohemica 50(1):51-55, March 2019

Abstract:
A support vector machine classification algorithm was formulated to differentiate rubber cup coagulum according to the type of acid coagulant used. Two classification models were established, a binary classification algorithm and a model that can identify if formic, acetic, sulfuric acid, or no acid was used to induce coagulation. The models were based on the properties of the rubber cup coagulum that are easy to measure, such as tensile strength, water contact angle, and density. The binary classification model, which differentiates the industry-accepted formic acid-coagulated rubber cup coagulum from those which are not, exhibited satisfactory reliability, as evidenced by a 92% overall prediction accuracy and 71.4% cross-validation accuracy. Moreover, it was also determined that the rubber properties density, and water contact angle were important contributors for the classification. Acid-induced rubber coagulation is an important post-harvest process that influences the resulting rubber quality. Thus, the accurate differentiation of the rubber samples is useful for quality assurance purposes, as well as in policy enforcement.
Full text link https://tinyurl.com/2p93n5mz

Article title: Nanocrystalline Titania Coated Metakaolin and Rice Hull Ash Based Geopolymer Spheres for Photocatalytic Degradation of Dyes in Wastewater
Authors: Patricia Isabel Bravo, Eiza Shimizu, Roy Alvin Malenab, ,April Anne Tigue, Kimmie Mae Dela Cerna, Jose Isagani Janairo, Michael Angelo Promentilla and Derrick Ethelbhert Yu
Publication title: Oriental Journal of Chemistry 35(1):167-172, February 2019

Abstract:
Geopolymer spheres made from metakaolin and rice hull ash have exceptional chemical and mechanical stability, making them promising catalyst support matrix for photoactive compounds. In this paper, titania was deposited on geopolymer sphere via horizontal vapor phase growth method, which produced nanotitania crystals embedded on the surface. The titania-coated geopolymer spheres resulted in 90% dye photodegradation activity that can be used repeatedly, thus qualifying the products as sustainable materials for wastewater treatment.
Full text link https://tinyurl.com/ttsjz6dv

Article title: Development of nanosilver-coated geopolymer beads (AgGP) from fly ash and baluko shells for antimicrobial applications
Authors: Kimmie Dela Cerna, Jose Isagani Janairo, and Michael Angelo Promentilla
Publication title: MATEC Web of Conferences 268(6):05003, January 2019

Abstract:
Geopolymers are a class of materials formed from treating alumina (Al2O3) and silica (SiO2) containing materials with an alkali activator. They are most notable for being environmentally-friendly substitutes to Ordinary Portland Cement; however, recent findings have shown that they may have potential as support matrices for antimicrobial agents such as nanosilver, particularly with the addition of foaming agents and setting time accelerators. In this study, nanosilver-coated geopolymer beads (AgGP) were made from fly ash (FA), calcined Baluko shells or pen shells (BS), and hydrogen peroxide (H). Addition of BS and H reduces the setting time and increases the porosity of the geopolymer beads. The beads were then dipped in AgNO3 and NaBH4 respectively to provide the nanosilver coating. When immersed in water, a controlled release of silver ions leaches out from the beads, neutralizing any bacteria in the water. It was found that the AgGP removed as much as 99.96% of the E. coli in a suspension originally at 105 CFU/mL.
Full text link https://tinyurl.com/4kwru9hj

Article title: Screening of Silver-Tolerant Bacteria from a Major Philippine Landfill as Potential Bioremediation Agents
Authors: Joan S. Adriano, Glenn G. Oyong, Esperanza C. Cabrera and Jose Isagani B. Janairo
Publication title: Ecological Chemistry and Engineering. S = Chemia i Inżynieria Ekologiczna. S 25(3):469-485, September 2018

Abstract:
The field of microbial biotechnology has revolutionized the utilization of microorganisms to overcome the problems of environmental pollutions. The present study aimed to identify silver-tolerant isolates and screen their ability to synthesize silver nanoparticles for possible use as bioremediation agents. Seventeen bacterial isolates from soil collected from the Smokey Mountain landfill in Manila, Philippines, were found to tolerate 0.01 M AgNO 3 in the culture medium. Molecular and phylogenetic analyses using the 16S rRNA gene sequence identified the isolates as Bacillus cereus , Bacillus subtilis , Bacillus flexus , Bacillus thuringiensis , Alcaligenes faecalis , Achromobacter sp. and Ochrobactrum sp. The formation of silver nanoparticles was evident in the change in color of the reaction mixtures, and was detected through UV-VIS spectroscopy with absorbance peaks at 250-300 nm and 400-450 nm. Scanning electron microscopy revealed the aggregation of diverse shapes of silver nanoparticles with sizes ranging from 70 to 200 nm. The best silver nanoparticle-synthesizing isolates were Alcaligenes faecalis and Bacillus cereus . The results denote the promising microbial technology application of the 17 silver-tolerant isolates in combating the adverse effects of metals and other pollutants in the environment.
Full text https://tinyurl.com/4993xmwv

Article title: Synthesis of Bimetallic PdAg Nanoparticles through an Oligomerization- Controlled Biomineralization Peptide
Authors: Jose Isagani B. Janairo and Kazuyasu Sakaguchi
Publication title: Materials Science Forum 928:77-82, August 2018

Abstract:
Peptide – mediated biomineralization is an emerging and promising biomimetic approach for the synthesis of nanomaterials. This nature – inspired technique of producing inorganic nanostructures depends on the biomineralization peptide to control the shape and morphology of the prevailing inorganic nanostructure. One of the challenges in peptide – mediated biomineralization is controlling the 3D arrangement and orientation of the peptide. Recently, we have developed a peptide platform that can specify and direct the geometric arrangement and spatial orientation of the biomineralization peptide. The peptide platform is composed of two segments: a metal binding sequence, and the tetramerization domain of the tumor suppressor p53 protein, which acts as the oligomerization control element. The resulting fusion peptide exhibits a spatially – fixed and well – controlled assembly of the palladium binding sequence. This present study demonstrates the utility and efficacy of this peptide platform to bimetallic materials. Monodispersed 5 nm bimetallic PdAg nanoparticles were synthesized using the oligomerization – controlled biomineralization peptide. The synthesis was carried out in an aqueous environment, void of harsh reagents. When other fusion biomineralization peptides were used to synthesize bimetallic PdAg nanoparticles, less ordered nanoparticles were yielded. The results highlight the importance of controlled assembly on bimetallic nanoparticle formation through biomineralization. The presented method offers a straightforward manner of creating monodispersed and extremely small nanoparticles, which are useful in a wide array of applications.
Full text available upon request to the author

Article title: A machine learning approach in predicting mosquito repellency of plant – derived compounds
Authors: Jose Isagani B. Janairo, Gerardo C. Janairo and Frumencio F. Co
Publication title: Nova Biotechnologica et Chimica 17(1):58-65, July 2018

Abstract:
The increasing prevalence of mosquito – borne diseases has prompted intensified efforts in the prevention of being bitten by the vector. Among the various strategies of vector control, the application of repellents provides instant and effective protection from mosquitoes. However, emerging concerns regarding the safety of the widely used repellent, DEET, has led to initiatives to explore natural alternatives. In order to fully realize the potential of natural repellents, focusing on the discovery of natural compounds eliciting repellency is of paramount importance. In this paper, machine learning was utilized to establish association between the mosquito repellent activity of 33 natural compounds using 20 chemical descriptors. Individually, the descriptors had insignificant monotonic relationship with the response variable. But when optimized, the formulated model through boosted trees regression exhibited reliable predictive ability ( r ²train = 0.93, r ²test = 0.66, r ²overall = 0.87). The findings presented have also introduced new descriptors that exhibited association with repellency through ensemble learning such as heat capacity, Log P , entropy, enthalpy, Gibb’s free energy, energy, and zero-point energy.
Full text link https://tinyurl.com/4kbc9ehv

Article title: Call to restrict neonicotinoids
Authors: Jose Isagani Janairo
Publication title: Science Editing 5(1): 59-61, 2018

Abstract:
The revival of a scientific journal presents unique challenges in comparison with starting a new journal. In this case study, the experiences encountered in the recent revival of the Manila Journal of Science are outlined and discussed. The Manila Journal of Science is a general science journal published by De La Salle University, Philippines. The challenges faced during the revival of the journal included competition for submissions, restricted budget allocations, peer review, and improving the journal’s reputation. Several strategies were adopted to address these challenges, and the journal’s performance thus far is promising.
Full text link https://tinyurl.com/429wt3z6

Article title: A stochastic fuzzy multi-criteria decision-making model for optimal selection of clean technologies
Authors: Michael Angelo B. Promentilla, Jose Isagani B. Janairo, Derrick Ethelbhert C. Yu, Carla Mae J. Pausta, Arnel B. Beltran, Aileen P. Huelgas-Orbecido, John Frederick D.Tapia, Kathleen B.Aviso, Raymond R. Tan
Publication title: Journal of Cleaner Production 183:1289-1299, May 2018

Abstract:
Selection of clean technology options requires systematic evaluation based on multiple criteria which are often conflicting. The optimal choice should consider not just technical performance but also the economic, environmental and social aspects of technologies. Furthermore, the interdependencies of these aspects should also be considered. The decision-maker often needs to make explicit trade-offs while ranking the alternatives. In addition, data gaps and imprecise information that are typical when dealing with emerging technologies make conventional methods ineffective. This work thus proposes a Stochastic Fuzzy Analytic Hierarchical Network Process decision model to address the complexity and uncertainty involved in the clean technology selection process. This method first decomposes the problem into a hierarchical network structure, and then derives the probability distribution of the priority weights needed for ranking. The capabilities of the methodology are demonstrated with three case studies, involving the comparison of different carbon nanotube synthesis methods, nutrient removal treatment technology options for municipal wastewater, and low-carbon electricity sources in the Philippines.
Full text available upon request to the author

Article title: Dipole Moment, Solvation Energy, and Ovality Account for the Variations in the Biological Activity of HIV-1 Reverse Transcriptase Inhibitor Fragments
Authors: Derick Erl P. Sumalapao, Jose Isagani B. Janairo and Nina G. Gloriani
Publication title: Annual Research & Review in Biology 22(5):1-8, January 2018

Abstract:
Objective: A computational approach was employed to determine the interaction of molecular descriptors and the biological activity of the different fragments of HIV-1 reverse transcriptase inhibitors (RTIs). Methods: Using multiple linear regression analysis and leave-one-out validation method, a quantitative structure activity relationship (QSAR) model was developed to relate the biological activity (log IC50) of the different fragment-sized compounds against HIV-1 RT(WT) DNA-dependent DNA polymerase and molecular descriptors of these compounds. Results: QSAR model identified dipole moment, solvation energy, and ovality of fragment-sized compounds to confer reverse transcriptase inhibitory action. A highly significant correlation with log P, molecular weight, polarizability, molecular energy, zero-point energy, constant volume heat capacity at 298 K, and entropy was identified to account for the variations in the potency of RTIs. An increase in ovality, log P, and molecular weight of the fragment-sized compound renders a more active reverse transcriptase inhibition. Conclusion: The quality of the established QSAR model has been validated and demonstrates its potential as a tool for computational design and synthesis of next generation RTIs.
Full text link https://tinyurl.com/mrrtxnsp

Article title: Effect of Aspidiotus rigidus infestation on the volatile chemical profile of the host plant Garcinia mangostana
Authors: M.A.A. Tavera, J.C.A. Lago, V.K.D. Magalong, G.A.V. Vidamo, J.S.R. Carandang VI1, D.M. Amalin and J.I.B. Janairo
Publication title: Hellenic Plant Protection Journal 11(1):1-8, January 2018

Abstract:
Plants respond to stress or damage by releasing volatile compounds, primarily for defense purposes. These volatiles function as signals for different interactions of the plant with its environment. In this study, the volatile chemical profile of healthy Garcinia mangostana L. (mangosteen) leaves was compared against leaves infested with the scale insect, Aspidiotus rigidus Reyne (Hemiptera: Diaspididae) through solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Analyses revealed that leaves emit the terpene kaur-16-ene in response to A. rigidus infestation. Kaur-16-ene is a precursor of gibberellin, a plant hormone for growth and development. The results suggest that the emission of kaur-16-ene in infested G. mangostana may play a role relevant in increasing the resistance of the plant towards infestations by herbivores.
Full text link https://tinyurl.com/bddb4mx2

Article title: Volatile chemical profile of cacao liquid smoke
Authors: J.I.B. Janairo and D.M. Amalin
Publication title: International Food Research Journal 25(1):213-216, January 2018

Abstract:
Liquid smoke is a food additive derived from pyrolysed biomass which conveniently provides a smoked flavor and aroma to marinated food items. A variety of plant sources has been utilized to develop liquid smoke since the properties, such as the odor, are dependent on the source. Recently, liquid smoke derived from discarded cacao pod husks was produced in an effort to utilize this major agricultural waste from cacao farming. In this paper, we report the volatile chemical profile of cacao liquid smoke using gas chromatography mass-spectrometry. Chemical composition analysis of liquid smoke is an integral part of ensuring compliance to regulatory standards, which ultimately leads to consumer safety and protection. The major components of the cacao liquid smoke were found to be the typical biomass pyrolysis products. In addition, functional compounds such as an arenofuran and a pyrazine were observed which may provide antifungal properties to the cacao liquid smoke, in addition to a distinct flavor and aroma. Efforts must be made during processing in order to lessen the presence of the polyaromatic hydrocarbons detected in order to further promote the development of liquid smoke out of the cacao pod husk as value added products from agricultural waste.
Full text available upon request to the author

Article title: Synergic Strategies for the Enhanced Self-Assembly of Biomineralization Peptides for the Synthesis of Functional Nanomaterials
Authors: J.I.B. Janairo, Tatsuya Sakaguch, Kenta Mine, Rui Kamada, et al.
Publication title: Protein and Peptide Letters 25(1), December 2017

Abstract:
Introduction: Peptide-mediated biomineralization is a promising bioinspired technique of nanostructure formation. The biomineralization peptide is responsible for the regulation of the growth and morphology of the inorganic nanostructure. The 3D properties of the biomineralization peptide is a crucial factor in which the success of creating functional nanomaterials depends on. However, given the relatively short sequence of most biomineralization peptides, controlling the multivalency and spatial orientation of the peptide can be a challenging endeavor.

Objective: This mini-review details recent advances in enhancing the self-assembly and 3D properties of the biomineralization peptide. The design and creation of fusion peptides is highlighted, which involves the conjugation of the biomineralization peptide with a control element. The control element is responsible for directing the self-assembly of the biomineralization peptide.

Conclusion: A variety of control elements that can direct the self-assembly of biomineralization peptides are available. The control element can be a small organic molecule such as a biphenyl, or a peptide segment such as the p53 tetramerization domain or the amyloid peptide. The high diversity of existing control elements further increases the ability of peptide-mediated biomineralization to create functional nanomaterials.
Full text available upon request to the author

Article title: Oligomerization enhances the binding affinity of a silver biomineralization peptide and catalyzes nanostructure formation
Authors: Tatsuya Sakaguchi, Jose Isagani B. Janairo, Mathieu Lussier-Price, Junya Wada1, James G. Omichinski & Kazuyasu Sakaguchi
Publication title: Scientific Reports 7(1), December 2017

Abstract:
Binding affinity and specificity are crucial factors that influence nanostructure control by biomineralization peptides. In this paper, we analysed the role that the oligomeric state of a silver biomineralization peptide plays in regulating the morphology of silver nanostructure formation. Oligomerization was achieved by conjugating the silver specific TBP biomineralization peptide to the p53 tetramerization domain peptide (p53Tet). Interestingly, the TBP–p53Tet tetrameric peptide acted as a growth catalyst, controlling silver crystal growth, which resulted in the formation of hexagonal silver nanoplates without consuming the peptide. The TBP–p53Tet peptide caps the surface of the silver crystals, which enhances crystal growth on specific faces and thereby regulates silver nanostructure formation in a catalytic fashion. The present findings not only provide an efficient strategy for controlling silver nanostructure formation by biomineralization peptides, but they also demonstrate that in this case the oligomeric peptides play a unique catalytic role.
Full text link https://tinyurl.com/yc3tz7rd

Article title: Bioaccumulation of Cadmium, Copper, Lead, and Zinc in Water Buffaloes (Bubalus bubalis) Infected with Liver Flukes (Fasciola gigantica)
Authors: Adrian Gabriel D. Acosta, Camille Noelle M. Camara, Juan Rocco Martin J. Ongsiako, Joachim N. Tsoi, Mary Jane C. Flores, Jose Isagani B. Janairo, Jose Santos R. Carandang Vi, Rodante G. Flores, Divina M. Amalin, Nancy S. Abes and Derick Erl P. Sumalapao
Publication title: Oriental Journal of Chemistry 33(4):1684-1688, August 2017

Abstract:
Exposure of living organisms to heavy metals can lead to bioaccumulation and can have some detrimental health effects. This study identified the species of liver flukes present in the liver tissues of water buffaloes, determined the concentration and bioconcentration factor of cadmium, copper, lead, and zinc present in both the liver tissues and liver flukes using atomic absorption spectrophotometry. Of the 1,329 liver flukes extracted from the 14 livers, Fasciola gigantica (F. gigantica) was the only species present in the collected liver tissues. The median heavy metal concentrations (μg/g) in the liver tissues were 0.93, 9.13, 4.75, and 48.95 for cadmium, copper, lead, and zinc, respectively. F. gigantica had median heavy metal concentrations (μg/g) of 3.32, 72.26, 20.82, and 159.37 for cadmium, copper, lead, and zinc, respectively. Both the liver tissues and F. gigantica were identified to contain varying concentrations of these heavy metals (p<0.05). The presence of these heavy metals in both the liver tissues and F. gigantica suggests heavy metal contamination of the areas where the carabaos graze. The bioconcentration factors for cadmium, copper, lead, and zinc were greater than 1.0 indicating that liver flukes are good bioaccumulators and bioindicators of environmental pollution.
Full text link https://tinyurl.com/2p88vr9a

Article title: Facile synthesis of highly active Pd nanocatalysts using biological buffers
Authors: Jose Isagani B. Janairo, Jose Santos Carandang VI, Divina Medina Amalin
Publication title: Chiang Mai Journal of Science 44(1), January 2017

Abstract:
A simple and straightforward approach in the synthesis of highly active palladium nanocatalysts aided by biological buffers is herein presented. Using HEPES and Tris as the buffers, highly active sub-10 nm palladium nanoparticles were formed. These nanocatalysts exhibited high catalytic activity toward the reduction of nitroaminophenol isomers. Moreover, this approach involves ambient conditions and a one-pot setup void of harsh reagents. The presented method highlights practicality without compromising catalytic activity which is very attractive for routine preparation of palladium nanocatalysts.
Full text link https://tinyurl.com/bdhtpb4c