2022
RESEARCH SYMPOSIUM

NYC LSAMP 2022 Research Symposium

Welcome to the first annual NYC LSAMP Research Symposium! 

We would like to invite all members of the NYC LSAMP community - current and former Fellows, Faculty Coordinators and research mentors to join us. This will be a great opportunity for Fellows to meet their peers in other LSAMP campuses throughout CUNY, showcase their research and achievements through poster presentations, and connect with the greater LSAMP Alumni networks.

 

The symposium was held virtually on Friday, May 6, 2022, 1:00-3:00pm EST.

Event agenda

   1:00pm             Welcome and opening remarks

                              Laura Oliveira, Project Director NYC LSAMP

                              Peter Nwosu, Principal Investigator of NYC LSAMP, Provost & Senior VP of Academic Affairs, Lehman College

 

   1:15pm             Meet-and-greet session (Breakout rooms)

 

   1:30pm             Research poster session (Breakout rooms)

Room 1

In Silico Discovery of SARS-CoV-2 Papain-Like Protease Inhibitors. Ronald Renold Salnave, Natural Sciences Department, Hostos Community College, Bronx, NY

 

Occurrence and Characterization of Perfluoroalkyl Substances (PFASs) in the Atmosphere and Surface Water around Waste Water Treatment Plants in NY & NJ. Jahsun Hurley, Department of Chemistry & Environmental Science, Medgar Evers College, Brooklyn, NY.

 

Big data analytics on social media regarding the COVID-19 vaccines. Serigne Mbaye,Natural Sciences Department, Hostos Community College, Bronx, NY.

Room 2

Quercetin and Green Tea Extract Supplement Regime Proposal for treating Covid-19 patients. Brian Law, Department of Natural Sciences, Baruch College, New York, NY.

 

The Optimization of Nanogels for Targeted Drug Delivery against Glioblastomas. Alaa Hamdan, Department of Biology, CUNY College of Staten Island, Staten Island, NY

 

Computational Discovery of SARS-CoV-2 Main Protease (Mpro) Inhibitors. Dawrys Munoz and Kevin Rivadeneira, Natural Sciences Department, Hostos Community College, Bronx, NY.

 

Room 3

Mouse Fed a Western Diet Develop Obesity and NonAlcoholic Fatty Liver Disease. Jasmine Williams, Department of Health and Nutrition Sciences, Brooklyn College, Brooklyn, NY.

 

How can technology be used to fight against sex trafficking?. Elisa Agosto, Information Technology, Guttman Community College, New York, NY.

 

In Silico Discovery of Neutralizing Agents Targeting SARS-CoV-2 Spike Glycoprotein. Scarlet Martínez Cardoze and Onyinyechi Winner Obineche, Natural Sciences Department, Hostos Community College, Bronx, NY.

Asynchronous presentations

A machine learning model to predict confirmed COVID-19 cases using linear regression. Baffour Amponsah-Antwi, Department of Biology, Lehman College, Bronx, NY.

A Survey for Understanding How Undergraduate Organic Chemistry Students Learn. Aneeza Hussain, Olorundamilola Okemeta, Peter Spellane, Department of Applied Chemistry, New York City College of Technology, Brooklyn, NY.

Ethnopharmacology of Bejuco de Indio (Gouania lupuloides), a Caribbean Medicinal Plant Used for Oral Health. Jood Abuali, Department of Biological Sciences, Lehman College, Bronx, NY.

   2:00pm            Keynote speech                  

                             Paul Faronbi, Senior Process Engineer for Nestle Purina and Founder of the National Association of LSAMP Alumni (NALA)

 

   3:00pm            Closing

Meet our keynote speaker!
Paul.jpg

Paul Faronbi graduated with his B.S. in Chemical Engineering and minor in Biomedical Engineering from Iowa State University in December 2016. He is an IINSPIRE-LSAMP alumni who conducted academic research with IINSPIRE funding for one summer and two semesters in Biomedical research for

NALA_logo.png

regenerating severed nerve cells, sponsored by the NSF and US Army.  He is a co-founder of the IINSPIRE-LSAMP Alumni Committee (ILAC) that created a network of past IINSPIRE students to help host events for current LSAMP students. He is the founder of the National Association of LSAMP Alumni or NALA for short, which serves to unite all LSAMP alumni across all alliances all over the nation. He is currently the President of NALA. Paul works for Nestle Purina as a Senior Process Engineer in St. Louis, Missouri and has a business called IncrediPaul where he empowers the professional development of students and professionals in STEM via speaking, workshops, and mentoring activities. He has a passion for leadership and considers it a great honor to be an LSAMP alumni.

Poster session presentations

We are proud to share the research achievements of our NYC LSAMP Fellows with the LSAMP community! To view full posters, click the thumbnails.

1. In Silico Discovery of SARS-CoV-2 Papain-Like Protease Inhibitors. Ronald Renold Salnave, Natural Sciences Department, Hostos Community College, Bronx, NY.

The papain-like protease (PLpro) is a pivotal coronavirus enzyme needful for the polyproteins viral process to produce a function replicase complex and enable viral spread. PLpro plays a key role in facilitating coronavirus replication in target cells. Therefore, with a blocked protease, SARS-CoV-2 cannot be activated and replicated in human cells. As of 24 April 2022, the World Health Organization had reported that over

Ronald_poster.png

184 countries have been affected by the coronavirus disease 2019 (COVID-19) and recorded over 500 million cases and over 6 million deaths. Due to still lack of effective drugs for COVID-19 treatment, there is a necessity to discover high-affinity-selective non-covalent small molecule inhibitors of PLpro that can prevent SARS-CoV-2 from replication. To identify PLpro inhibitors, we have used computer-aided molecular design tools to examine the molecular basis for such inhibitors’ interaction with SARS-CoV-2 PLpro. We also conducted structure-based molecular virtual screening of a non-covalent small-molecule commercially available database (eMolecules ~4.3 million compounds) against two X-ray crystal structures of SARS-CoV-2 PLpro (6WX4 and 7KOL) using the OEDocking-FRED-4.0.0.0 software. To select the top 10,000 non-covalent small molecules, we used the OEDocking-FRED-4.0.0.0 Chemguass4 scoring function. These top-ranked potential small-molecule inhibitors were inspected and analyzed visually and regrouped based on their scaffold to define common interaction designs. The highest-ranked candidate compounds (~50) have been prioritized to be tested experimentally using the fluorescence-based SARS-CoV-2 PLpro enzymatic assay. Among these compounds, we could eventually lead to relevant drugs that can selectively inhibit SARS-CoV-2 PLpro and be used as effective COVID-19 treatment.

Yoel Rodríguez, Ph.D. (Faculty mentor)

Natural Sciences Department, Hostos Community College

2. Occurrence and Characterization of Perfluoroalkyl Substances (PFASs) in the Atmosphere and Surface Water around Waste Water Treatment Plants in NY & NJ. Jahsun Hurley, Department of Chemistry & Environmental Science, Medgar Evers College, Brooklyn, NY.

Per- and polyfluoroalkyl substances (PFASs) are human-made substances growing concerns over their persistence, bioaccumulation, and potential adverse effects in animals and humans. The effluents of wastewater treatment plants

Jahsun_poster.png

(WWTPs) are a well-known significant source of PFASs into surface water and ambient air. Although monitoring is currently ongoing, the knowledge on PFASs occurrence from WWTP is still relatively low due to a lack of data and many source interferences. The purpose of this study is to observe the effect of WWTPs on local ambient air PFASs concentrations and ultimately assess any correlation between surface water and ambient air PFASs concentrations depending on the distance from the WWTPs. Surface water and ambient air samples were collected from the three urban streams (Raritan River in Northern New Jersey and Newton Creek in New York City) running along the WWTPs and analyzed LC-MS/MS for 26 PFAS. The most widely detected compound was found to be PFBA, with a maximum concentration in water of 150 ng/L and air of 61 ng/m3. And other dominant species of PFASs in this study were perfluorohexanoate (PFHxA) > perfluoroheptanoate(PFHpA) >, PFOA > perfluorododecanoate (PFDoDA) > perfluorononanoate (PFNA) > PFOS, perfluorodecanoate(PFDA), perfluoroundecanoate (PFUnDA), perfluorododecanoate (PFDoDA) and, perfuorohexanesulfonate (PFHxS).  Our results show that the distance from WWTPs in the river greatly influences the concentrations of PFASs in water and air quality. This result will characterize sources of PFAS affecting these communities and provide a baseline for planners and engineers.

 

Jin Shin (Faculty mentor)

Department of Chemistry & Environmental Science, Medgar Evers College

3. Big data analytics on social media regarding the COVID-19 vaccines. Serigne Mbaye, Natural Sciences Department, Hostos Community College, Bronx, NY.

The COVID-19 pandemic has significantly changed people's lives in a sudden and dramatic way leaving them with an uncertain course of when the situation could be back to normal. People have lost their houses, jobs, money and most importantly the lost loved ones. The government has work on different ways to grasp a solution for the people, most importantly have been the reliefs packages and the approval of the Covid-19 vaccines. Since there was an abrupt closure of

Serigne_poster.jpg

the cities as response measure to control the spread of the virus, social media have gained more popularity for people to express and share their views, what they think, feel, like and dislike. In the last decades, social media platforms have been very popular for users to express their opinion about different topics. It is important, or even crucial to know the trend or people’s thoughts in a timely manner towards political and social events developing or already developed in the economy. In this project, we have collected classified Twitter data, one of the biggest social media out there, using a program developed by our team, a program configurated in Pycharm to collect hashtags called ‘Rambo’, and analyzed the semantic meaning of this Tweets post using Natural Language Processing technology and other sophisticated Programing libraries to examinate how people express on this platform concerning the economic impact COVID-19 pandemic, the reaction to the Covid-19 Vaccine as a solution, how people react to government decisions, economy in general and the emotional influence users post generate. Rambo was designed to search, extract, and analyze data based on a specific trending topic which is the COVID-19, from twitter users without evading their privacy.

Biao Jiang (Faculty mentor)

Natural Sciences Department, Hostos Community College

4. Quercetin and Green Tea Extract Supplement Regime Proposal for treating Covid-19 patients. Brian Law, Department of Natural Sciences, Baruch College, New York, NY.

Covid 19 is an ongoing pandemic and more readily available effective treatments are still needed. A Quercetin supplements and Green Tea Extract supplements (EGCG) regime may be a potential synergistic treatment protocol, but more studies are required to determine their effect. This paper discusses of a theoretical clinical study that compares this supplement regime versus the antiviral medications (Remdesivir, Paxlovid, and Molnupiravir) available. Studies have shown that Quercetin can target multiple parts (3CL-protease, sPLA2-II, TGF-β1, MMP-9, GPIIb/IIIa receptors, ALT, and AST) of the Covid-19 virus which could disturb the replication of the virus and reduce the damage to the body caused by Covid-19. EGCG has the potential to preventing the spread of the virus by activating Nrf2 and suppresses the replication of SARS-CoV-2 by inhibiting 3CL-protease. 

Pablo Peixoto (Faculty mentor)

Department of Natural Sciences

Brian_poster.png

5. The Optimization of Nanogels for Targeted Drug Delivery against Glioblastomas. Alaa Hamdan, Department of Biology, CUNY College of Staten Island, Staten Island, NY.

Hydrophilic nanogels continue to be optimized for the purpose of targeted drug delivery of curcumin and a chemotherapeutic agent against glioblastoma. The optimization of nanoparticles into ideal drug delivery nanogels is underworks through the use of an organic polymer known as Chitosan. A nanoparticle is made out of a certain polymer with all three of its dimensions in the nanoscale, 10-9 m. A particle in this range of 1-100 nm exhibits properties that are not seen in any other size, and these properties allow them to be used in multiple fields. The nanoparticles are being optimized for targeted and controlled drug delivery of the curcumin (CC) and

Alaa_poster.png

chemotherapeutic agent adduct (CC-CA) to multiple standards. The first is biocompatibility which is aimed to be achieved through optimizing the pH and temperature of the nanogels, this coincides with cytotoxicity, the nanogels must be highly toxic to the tumor cells while simultaneously harmless to the rest of the body. The nanogels must also be biodegradable when placed in vivo in the following phase of the research as the CC-CA is used. The nanogels are favored to be monodispersed which was achieved through the use of a filtration apparatus and dialysis procedures. Promising results were acquired through the recording of nanogel size and distribution in dynamic light scattering (DLS). The nanogels were optimized for effective drug delivery and controlled drug release through in vitro procedures and have yet to be applied in vivo, this will be the next phase in the research as optimization concludes.

 

Probal Banerjee (Faculty mentor)

Department of Biology, College of Staten Island

6. Computational Discovery of SARS-CoV-2 Main Protease (Mpro) Inhibitors. Dawrys Munoz and Kevin Rivadeneira, Natural Sciences Department, Hostos Community College, Bronx, NY.

SARS-CoV-2 is the virus responsible for causing the coronavirus disease 2019 (COVID-19) pandemic. There are currently three attractive drug targets to fight SARS-CoV-2: the spike glycoprotein, the papain-like protease and the main protease (M pro ). Here, we focus on M pro (3CL pro ) due to its essential role in processing the polyproteins that are translated from the viral RNA and as a key enzyme in the SARS-CoV-2 viral replication cycle. Although there have been discovered SARS-CoV-2 M pro enzymatic inhibitors, there is still an urgency to develop more potent-diverse M pro inhibitors due to the SARS-CoV-2 lack effective treatment methods. If small-molecule inhibitors bind with high affinity-selectivity, and low cytotoxicity to the catalytic site of SARS-CoV-2 M pro , virus replication could be prevented, making these compounds potential candidates to be used for therapeutic treatment. By using computational biophysics methods, we aim to discover effective enzymatic SARS-CoV-2 M pro inhibitors. To this end, we performed structural analysis that focuses on exploring the available X-ray crystal structure of the M pro in complexed with non-covalent inhibitor X7V (7KX5); and structure-based virtual screening of eMolecules commercially available database (~4.3M Compounds) against SARS-CoV-2 M pro X-ray crystal structure (7KX5) using OEDocking-FRED-4.0.0.0 program. The Chemguass4 scoring function was used to rank the screened small molecules based on their affinities towards SARS-CoV-2 M pro. Visual inspection, ligand shape complementary and chemical feature alignment were used to select the best candidates (~50) to be tested experimentally using the 3CL Protease, MBP-tagged (SARS-CoV-2) assay kit. These compounds could ultimately provide better therapeutic efficacy for the treatment of COVID-19.

Yoel Rodríguez (Faculty mentor)

Natural Sciences Department, Hostos Community College

7. Mouse Fed a Western Diet Develop Obesity and NonAlcoholic Fatty Liver Disease. Jasmine Williams, Department of Health and Nutrition Sciences, Brooklyn College, Brooklyn, NY.

The Dietary and hereditary factors that affect the development of obesity and Non-Alcoholic Fatty Liver Disease (NAFLD) in mice. Creating an animal model that mimics the diet and health development of people suffering from obesity and on the path to developing Non-Alcoholic Fatty Liver Disease.

Jorge Matias Caviglia (Faculty mentor)

Department of Health and Nutrition Sciences, Brooklyn College

Jasmine_poster.png

8. How can technology be used to fight against sex trafficking?. Elisa Agosto, Information Technology, Guttman Community College, New York, NY.

It has been known that minors have been trafficked for years. This involved family members or friends misleading their victims into a false sense of hope. However, as technology advances, the approach predators have taken to lure at-risk teens has become more complex, and even harder for law enforcement to keep up with. Websites like Craigslist, Backpage, and even Facebook have all been named top websites used by traffickers to lure and traffick vulnerable youth. This presentation will focus specifically on how young minors are being trafficked within America and through the surface web.

Dalvin Hill (Faculty mentor)

Information Technology, Guttman Community College

9. In Silico Discovery of Neutralizing Agents Targeting SARS-CoV-2 Spike Glycoprotein. Scarlet Martínez Cardoze and Onyinyechi Winner Obineche, Natural Sciences Department, Hostos Community College, Bronx, NY.

A novel coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the coronavirus diseases 2019 (COVID-19) pandemic. The SARC-CoV-2 Spike (S) glycoprotein via its receptor-binding domain (RBD) recognizes the hACE2 receptor facilitating viral entry into target cells. Several variants of SARS-CoV-2 have been identified globally. These variants show different transmissibility-infectivity due to mutations in the viral SARS-CoV-2 S glycoprotein. Despite the available SARC-CoV-2 vaccines, there is still a keen need to identify selective antivirals against this virus and the S glycoprotein RBD is a

ScarletOnyinyechi_poster.png

key target to neutralize the virus. We aim to identify small molecules with high-affinity-selectivity for the SARS-CoV-2 S glycoprotein RBD. We hypothesize that these small molecules could disrupt its interaction with hACE2, and therefore affect SARS-CoV-2 replication. Towards this end, we first analyzed the available SARS-CoV-2 S glycoprotein-hACE2 complex structure (PDBID-7DF4) and created models for different variants using VMD and MOE programs. We then started the hits search by performing structure-based molecular docking virtual screening of a commercially available small molecules database (eMolecules ~4.5M) against the four SARS-CoV-2 S glycoprotein RBD models using FRED-4.0.0.0 software. The FRED-4.0.0.0-Chemguass4 scoring function was used to rank the screened molecules based on their affinities toward the SARS-CoV-2 S glycoprotein. The best candidate compounds (~30) were purchased and tested using the SARS-CoV-2 Spike pseudotyped lentivirus assays. Three of these compounds, alone or in combination, showed antiviral selectivity. These small molecules could eventually lead to effective antiviral therapeutic treatment against COVID-19 or serve as probes to better understand SARS-CoV-2 biology.

Yoel Rodríguez (Faculty mentor)

Natural Sciences Department, Hostos Community College

Asynchronous presentations

1. A machine learning model to predict confirmed COVID-19 cases using linear regression. Baffour Amponsah-Antwi, Department of Biology, Lehman College, Bronx, NY.

The Severe Acute Respiratory Syndrome (SARS) Coronavirus 2 (SARS-CoV-2), provisionally named 2019-nCoV, but now SARS-CoV-2 according to the Coronavirus Study Group of the International Committee on Taxonomy of Viruses, has racked a lot of havoc on this planet of ours for a while now. And by a while, it indeed feels like forever, having to live through the harsh realities of the world today and the undulating effects it has had, and still having, on our day-to-

day lifestyles. Originating from Wuhan, Hubei Province in China, SARS-CoV-2 belongs to the Coronaviridae family, Betacoronavirus genus, subgenus Sarbecovirus. Its existence has brought with it lessons that must be learnt and measures that must be put in place to either prevent future calamities as such, or to much better manage and to drastically reduce the mind-blowing human casualties and lives that have been lost and negatively impacted due to poorly advised decisions. Reasons like these sparked my interest in developing a machine learning model to predict confirmed COVID-19 cases (Coronavirus disease 2019) in the United States of America and to discuss such matters from a computer science and data analysis background in an effort to encourage more discussion around these matters of urgency and pitfalls of action and inaction. Overall, the aim of my research is to hammer down and raise awareness that we need to be better and do better as a scientific community, bringing about fresh new perspectives on the uprising of COVID-19 and the way forward.

Stephen Redenti (Faculty Mentor)

Department of Biology

2. A Survey for Understanding How Undergraduate Organic Chemistry Students Learn. Aneeza Hussain, Olorundamilola Okemeta, Peter Spellane, Department of Applied Chemistry, New York City College of Technology, Brooklyn, NY.

This research aimed to improve the laboratory learning experiences for undergraduate science students, focusing on organic chemistry-I students at City Tech. Organic Chemistry Laboratory classes should provide students with first-hand learning experience with course concepts and with the opportunity to explore methods used by scientists in their discipline. This exposure is to prepare them for their potential laboratory jobs. We strongly believe that instead of handing over full procedural directions and expected results to students, it is a better idea to demonstrate some key techniques and equipment operation by describing the location and handling of special materials to let them explore

Aneeza_poster.png

the experiment on their own rather than spoiling the entire experience. This means the educator will push them to take responsibility for their own learning or may answer a question with a question to get them to think about an idea, or even the educator may tell them “try and see what happens…” to foster learning. Reminding them that they are doing this to help learn and to develop their expertise for when they enter the real world field of chemistry. We created a survey to analyze what the students thought about this learning experience. Most of the responses were based on receiving directions from the professor because they were afraid to be lost in the procedural process. We also observed during lab sessions to see how the students reacted when the professor asked them to explore on their own after giving a brief background of the laboratory activity.

 

Peter Spellane (Faculty Mentor)

Department of Applied Chemistry

3. Ethnopharmacology of Bejuco de Indio (Gouania lupuloides), a Caribbean Medicinal Plant Used for Oral Health. Jood Abuali, Department of Biological Sciences, Lehman College, Bronx, NY.

Ethnopharmacology of Bejuco de Indio (Gouania lupuloides), a Caribbean Medicinal Plant Used for Oral Health,” Jood Abuali, Department of Biological Sciences, Lehman College, Bronx, NY.

Throughout the Caribbean, Gouania lupuloides (L.) Urb. (Rhamnaceae), is commonly used as a chew stick to clean teeth, remove plaque, and massage gums. Previous research has established that G. lupuloides contains antimicrobial compounds that support its traditional use. Gouania lupuloides is frequently sold as bejuco de Indio in Spanish language Caribbean herbal markets (botánicas) in New York

Jood_poster.png

City (NYC). However, as with other herbs of commerce, there is a possibility that chew sticks sold as bejuco de Indio are not actually G. lupuloides. The overall aim of this research is to understand the phytochemistry and traditional knowledge of G. lupuloides as it is used in NYC, and to authenticate its botanical identity in commerce. We observed morphological differences between G. lupuloides type specimens and chew sticks sold as bejuco de Indio that may indicate adulteration. Dammarane saponins, such as gouanoside B, which are distinctive of G. lupoloides, have been tentatively identified in type specimens using ultra-high performance liquid chromatography-quadrupole time-of-flight mass

spectrometry (UPLC-QTOF-MS). Principle component analysis of UPLC-QTOF-MS data indicates that at least three of the chew stick samples collected as bejuco de Indio are chemically distinct from G. lupuloides type specimens. Our phytochemical analysis aims to characterize a chemical fingerprint typical of G. lupuloides to aid in the chemotaxonomic identification of unknown chew sticks. Additionally, G. lupuloides extracts are being screened against other microbes related to its other documented medicinal uses in the Caribbean. A small ethnobotanical survey is also being conducted to understand how G. lupuloides, and other herbs of commerce, are used in the NYC area for oral health.

Edward Kennelly (Faculty mentor)

Department of Biological Sciences, Lehman College

Thank you to all our presenters and attendees!