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Our History
Founder & CEO
Our History
As a young boy growing up in rural India, most of what I knew of the world was what I could see around me. But each night, I would look at the Moon...
Founder & CEO
Founder & CEO
Founder & CEO
NATURE INDIA | INDIGENUS
Away from home: Using science for societal good
Publications
- Mattapally S, Z Mattapally S, Pawlik KM, Fast VG, Zumaquero E, Lund FE, Randall TD, Townes TM, Zhang J.. Human Leukocyte Antigen Class I and II Knockout Human Induced Pluripotent Stem Cell-Derived Cells: Universal Donor for Cell Therapy. J Am Heart Assoc. 2018 Dec 4;7(23):e010239. doi: 10.1161/JAHA.118.010239.
- Mattapally S, Zhu W, Fast VG, Gao L, Worley C, Kannappan R, Borovjagin A, Zhang J. Spheroids of cardiomyocytes derived from human induced-pluripotent stem cells improve recovery from myocardial injury in mice. Am J Physiol Heart Circ Physiol. 2018 Aug 1;315 (2):H327-H339. https://doi.org/10.1152/ajpheart.00688.2017
- Mattapally S, Singh M, Murthy KS, Asthana S, Banerjee SK. Computational modeling suggests impaired interactions between NKX2.5 and GATA4 in individuals carrying a novel pathogenic D16N NKX2.5 mutation. Oncotarget 2018_ 9 (17), 13713. doi: 10.18632/oncotarget.24459
- Zhao M , Fan C, Ernst PJ , Tang Y, Zhub H , Mattapally S , Oduk Y , Borovjagin AV , Zhou L , Zhang J, , Zhu W. Y-27632 Preconditioning Enhances Transplantation of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes in Myocardial Infarction Mice. Cardiovascular Research, cvy207,https://doi.org/10.1093/cvr/cvy207.
- Gao L, Gregorich Z, Zhu W, Mattapally S, Oduk Y, lou X, Kannappan R, Borovjagin A, Walcott G, Pollard A, Fast V, Hu X, Lloud SG, Ge Y, Zhang J: Large Cardiac-muscle Patches Engineered from Human Induced-pluripotent Stem-cell derived Cardiac Cells Improve Recovery from Myocardial Infarction in Swine. Circulation 2017.137(16):1712-1730.DOI: 10.1161/CIRCULATIONAHA.117.030785
- Zhu W, Zhao M, Mattapally S, Zhang J: CCND2 Overexpression Enhances the Regenerative Potency of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes: Remuscularization of Injured Ventricle. Circulation Research. 2017;122:88-96. doi: 10.1161/CIRCRESAHA.117.311504
- Mattapally S, Nizamuddin S, Murthy KS, Thangaraj K, Banerjee SK. c.620C>T mutation in GATA4 is associated with congenital heart disease in South India. BMC Medical Genetics 2015, 16:7.https://doi.org/10.1186/s12881-015-0152-7
- Mattapally S, Banerjee SK: Nitric oxide: Redox balance, protein modification and therapeutic potential in cardiovascular system. IIOAB Journal 2011, 2:29-38.doi=10.1.1.674.960&rep=rep1&type=pdf.
- Kannappan R, Mattapally S, Pooja S, Zhang J. Transactivation Domain of P53 Regulates DNA Integrity in Human Cardiac Fibroblast Derived iPS Cells. AJP-Heart and Circ-2018 (In Press).https://doi.org/10.1152/ajpheart.00160.2018.
- Pala RS, Pathipati UR, Mattapally S, Banerjee SK. Ultra-small silver nanoparticles induced ROS activated Toll-pathway against Staphylococcus aureus disease in silkworm model. Materials Science and Engineering, 2017: C 77, 990-1002.https://doi.org/10.1016/j.msec.2017.04.026
- Bagul PK, Middela H, Matapally S, Padiya R, Bastia T, Madhusudana K, Reddy BR, Chakravarty S, Banerjee SK. Attenuation of Insulin resistance, metabolic syndrome and hepatic oxidative stress by resveratrol in fructose-fed rats. Pharmacological Research, 2012, 66(3): 260-268.https://doi.org/10.1016/j.phrs.2012.05.003
- Adela R, Nethi SK, Bagul PK, Barui AK, Mattapally S, Kuncha M, Patra CR, Reddy PN, Banerjee SK. Hyperglycaemia Enhances Nitric Oxide Production in Diabetes: A study from Indian Patients. PLoS One. 2015 Apr 20;10(4). https://doi.org/10.1371/journal.pone.0125270
- Nethi SK, Veeriah V, Barui AK, Rajendran S, Mattapally S, Misra S, Chatterjee S and Patra CR. Investigation of molecular mechanisms and regulatory pathways of pro-angiogenic nanorods. Nanoscale. 2015,7, 9760-9770.doi: 10.1039/c5nr01327e
- Santhoshi A, Mahendar B, Mattapally S, Sadhua P, Banerjee SK, Rao VJ. Synthesis of Thio-HeterocyclicAnaloguesfrom Baylis-Hillmann Bromides as Potent Cyclooxygenase-2 Inhibitors. Bioorg Med Chem Lett, 2014, 24 (8), 1952-1957. https://doi.org/10.1016/j.bmcl.2014.02.073
- Ravinder M, Mahendar B, Mattapally S, Hamsini KV, Reddy TN, Rohit C, Srinivas K, Banerjee SK, Rao VJ. Synthesis and evaluation of novel 2-pyridone derivatives as inhibitors of phosphodiestarase3 (PDE3): A target for heart failure and platelet aggregation. Bioorg Med Chem Lett, 2012, 22(18): 6010-6015.https://doi.org/10.1016/j.bmcl.2012.05.019
Abstract in Journals:
- Mattapally S, Zhang J. hiPSC of CIITA and B2M KO Derived Cardiac Cells Engineered 3D Spheroid Transplantation Induce Tachycardia in Myocardial Infarction in Swine. Circulation 142 (Suppl_3), A13381-A13381. https://www.ahajournals.org/doi/abs/10.1161/circ.142.suppl_3.13381
- Mattapally S, E Zhang E, Fast VG, Pollard AE, Mattapally C, Zhang J. CRISPR Guide RNA Design is Able to Use for Studying Splicing and Circadian Gene Expression. Circulation. 2021;144:A13426. https://www.ahajournals.org/doi/abs/10.1161/circ.144.suppl_1.13426
- Mattapally S, Antony VB. Computational Modeling of Aldehyde Dehydrogenase 2 (ALDH2) a Marker for Damaged Mitochondria in Extracellular Vesicles of EVALI. TP083 Hey jude-novel findings from omic and bioinformatic approaches in pulmonary hypertension, Am J Respir Crit Care Med 2021;203: A3626. https://www.atsjournals.org/doi/pdf/10.1164/ajrccm-conference.2021.203.1_MeetingAbstracts.A3626
- Mattapally S, Surolia R, Li FJ, Wang Z, Guo Y, Antony VB. Role of Peptidyl Arginine Deiminases in the Pathogenesis of Calcified Pleural Plaque Formation Following Asbestos Exposure. A39 - Diagnosis and Treatment In Pleural Disease, Am J Respir Crit Care Med 2020;201: A1572. https://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A1572
- Mattapally S, Z Mattapally S, Pawlik KM, Fast VG Zhang J. HLA Class I and II Knockout Human Induced Pluripotent Stem Cell-Derived Cells: Universal Donor for Regeneration. Circulation 138 (Suppl_1), A13210- A13210.https://www.ahajournals.org/doi/abs/10.1161/circ.138.suppl_1.13210
- Mattapally S, Zhu W, Fast VG, Worley C, Gao L, Kannappan R, Zhang J. A Scaffold Free 3D Spheroid tissue derived from hipsc-differentiated cardiomyocytes for myocardial infarction therapy applications. Circulation, 136 (Suppl 1), A20877-A20877.doi/abs/10.1161/circ.136.suppl_1.20877
- Zhu W, Zhao M, Mattapally S, Zhang J: Cyclin-D2 Overexpression Enhances the Regenerative Potency of Human Induced Pluripotent Stem Cell Derived Cardiomyocytes. Circulation 136 (Suppl 1), A18679-A18679.doi/10.1161/circ.136.suppl_1.18679
- LaBarge W, Mattapally S, Berry J, Zhang J. Development of a Scaffold-Free, Three Dimensional Bioprinter that Utilizes Cellular Spheroids. 2017 BMES Annual Meeting, USA.
- Kannappan R, Mattapally S, Zhu W, Zhang J. Transactivation Domain of P53 Regulates DNA Integrity in Human Cardiac Fibroblast Derived iPS Cells. Circulation, 2016, 134 (Suppl 1), A19566-A19566.doi/10.1161/circ.134.suppl_1.19566
- Zhu W, Zhao M, Mattapally S, Gao L, Zhang J: Cell Cycle Activation of Cardiomyocytes Derived From Induced Pluripotent Stem Cells for Cardiac Regeneration. Circulation Research, 2016, 119 (Suppl 1), A41-A41.doi/10.1161/res.119.suppl_1.41
- Mattapally S, Murthy KS, Nizamuddin S, Thangaraj K, Banerjee SK; (Abstract/Program # 2039S) Novel NKX2.5 mutation associated with congenital heart disease in south Indian patients. American Society of Human Genetics, October 19, 2014 in San Diego, CA. abstracts/fulltext/f140121487.htm
- Mattapally S., Murthy KS., Nizamuddin S., Thangaraj K., Banerjee SK; (Abstract/Program #2722T) Novel GATA4 promoter polymorphism associated with congenital heart disease in south Indian patients. American Society of Human Genetics, October 23, 2013 in Boston, MA.abstracts/fulltext/f130121691.htm
- Mattapally S, Adela R, Rao V, Kona SM, Banerjee SK: Congenital heart disease: its prevalence and importance in Indian population. IJPPAZ 55 (5) 1-336 (2011),ISSN 0019-5499.
Patent Granted/Filed (US, French & India):
1. Mahendar B, Mattapally S, Ravinder M, Banerjee SK, Rao VJ. Pyridopyrimidine based derivatives as potential phosphodiesterase 3 (pde3) inhibitors and a process for the preparation thereof. US Patent App. 14/168,411, 2014.( US20140221651 A1 ) patent/US9242982B2/en
2. Mahendar B, Mattapally S, Ravinder M, Banerjee SK, Rao VJ. Indolizinone based derivatives as potential phosphodiesterase 3 (pde3) inhibitors and a process for the preparation thereof. US Patent App. 14/168,370, 2014.( US20140296530 A1 ) patent/US9249139B2/en
3. Mahendar B, Mattapally S, Ravinder M, Banerjee SK, Rao VJ. Pyridopyrimidine based derivatives as potential phosphodiesterase 3 (pde3) inhibitors and a process for the preparation thereof. IN Patent App. 287/DEL/2013.
4. Mahendar B, Mattapally S, Ravinder M, Banerjee SK, Rao VJ. Indolizinone based derivatives as potential phosphodiesterase 3 (pde3) inhibitors and a process for the preparation thereof. IN Patent App. 288/DEL/2013.
5. G. Jagadeesh KUMAR, B. Mahendar, M. Saidulu, S K Banerjee, V. Jayathirtha RAO. 2-pyridone based compounds useful as potential phosphodiesterase3a (pde3a) inhibitors and a process for the preparation thereof. WO2017072796A1 (USA, French). patent/WO2017072796A1/en
Book Chapter:
1. Mattapally S, Cukier-Meisner W.K, Zhang J. (2017) Differentiation and Use of Induced Pluripotent Stem Cells for Cardiovascular Therapy and Tissue Engineering. In: Ieda M., Zimmermann WH. (eds) Cardiac Regeneration. Cardiac and Vascular Biology. Springer. DOI https://doi.org/10.1007/978-3-319-56106-6_5.
Projects
I am currently worked on the project “COVID-19 role immunosuppresion and arrhythmias in solid-organ transplant recipient.”
The project involves understanding the clinical highlights and results of COVID-19 among immunosuppressed patients, who are at assumed risk of more serious infection.....
Colleges, Universities, and Professional Schools
Our UAB.AI Course’s
Professional Schools Services
Professional Schools Services
The integration of artificial intelligence (AI) into clinical workflows has the potential to enhance patient care, improve efficiency, and optimize resource allocation. Here are some ways AI can be seamlessly integrated into clinical practice:
1. Automated Documentation and Data Entry: o AI-powered tools can automate routine tasks such as data entry, note-taking, and documentation. This allows healthcare professionals to focus more on patient interactions and decision-making. o Natural language processing (NLP) algorithms can extract relevant information from clinical notes, lab reports, and other documents, reducing administrative burden.
2. Predictive Analytics and Early Warning Systems: o AI models can analyze patient data (such as vital signs, lab results, and historical records) to predict adverse events or deteriorating conditions. o Early warning systems can alert clinicians to potential issues, enabling timely interventions.
3. Clinical Decision Support: o AI algorithms can provide evidence-based recommendations to guide clinical decisions. o For example, when interpreting diagnostic images, AI can highlight suspicious areas or suggest differential diagnoses based on patterns in the data.
4. Personalized Treatment Plans: o AI can analyze patient-specific data (including genetics, medical history, and comorbidities) to tailor treatment plans. o Personalized medicine, driven by AI, ensures that treatments are optimized for individual patients.
5. Drug Interaction and Adverse Event Detection: o AI can identify potential drug interactions, allergies, and adverse events by analyzing patient profiles and medication histories. o This helps prevent harmful interactions and improves medication safety.
6. Resource Allocation and Workflow Optimization: o AI algorithms can optimize hospital resource allocation, such as bed management, staff scheduling, and inventory control. o Predictive models can estimate patient admission rates, allowing hospitals to allocate resources efficiently.
7. Telemedicine and Remote Monitoring: o AI-powered telemedicine platforms facilitate remote consultations, diagnostics, and follow-ups. o Remote monitoring devices (e.g., wearables) collect real-time data, which AI can analyze to track patient health and detect anomalies.
8. Radiology and Pathology Assistance: o AI assists radiologists and pathologists by automating image analysis. o Deep learning models can detect abnormalities in X-rays, MRIs, and histopathology slides, improving diagnostic accuracy.
9. Privacy: o Definition: Privacy refers to an individual’s ability to keep certain personal health information free from unauthorized access and the ability to access and share the information themselves1 . o Importance: Protecting patient privacy is essential to maintain trust and confidentiality. Patients should have control over who accesses their health information. o HIPAA: The Health Insurance Portability and Accountability Act (HIPAA) is the main federal law that protects health information.
10. Security: o Definition: Security encompasses the measures and controls put in place to protect health information. It includes safeguarding data from accidental or intentional disclosure. o Access Controls: Healthcare organizations define workforce roles that require access to patient records. They also monitor for unauthorized access to prevent breaches. o HIPAA Security Rule: In addition to the Privacy Rule, HIPAA includes the Security Rule, which focuses on electronic protected health information (e-PHI). It outlines security standards for electronic health records, data encryption, and access controls
Professional Schools Services
Professional Schools Services
Professional Schools Services
- Disease Detection and Diagnosis AI algorithms can analyze medical data, such as health records, to detect and diagnose diseases more accurately. For instance, machine learning models can categorize information or predict outcomes based on patient data1 . o Personalized disease diagnosis is another area where AI shines. By analyzing individual patient data, AI can tailor diagnostic recommendations and treatment plans to each person’s unique needs.
- Personalized Disease Treatment AI helps create personalized treatment plans for patients. By considering a patient’s medical history, genetic information, and other relevant factors, AI algorithms can recommend the most effective treatments. o For critical illnesses like cancer, AI can identify optimal treatment options based on a patient’s specific condition and genetic makeup
- Medical Imaging: AI plays a crucial role in medical imaging, including tasks like interpreting X-rays, MRIs, and CT scans. o Deep learning models can analyze images to detect abnormalities, tumors, or other signs of disease. This enhances radiologists’ accuracy and speeds up the diagnostic process.
- Clinical Trial Efficiency AI streamlines clinical trials by identifying suitable candidates for trials and predicting patient responses to new treatments. o Researchers can use AI to analyze large datasets and identify potential drug candidates more efficiently.
- Accelerated Drug Development AI assists in drug discovery by analyzing vast amounts of biological and chemical data. o Machine learning models predict the effectiveness of potential drug compounds, helping researchers focus on the most promising candidates. In summary, AI is transforming health care by enhancing diagnosis, treatment, and patient care across various settings. Rather than merely automating tasks, AI technologies aim to improve patient outcomes and revolutionize the medical field2 . The ethical considerations surrounding artificial intelligence (AI) in medicine are crucial as we integrate these technologies into patient care. Let’s explore some key aspects related to AI ethics in the medical field:
- Shared Decision-Making and Collaboration Recent policy documents and research literature emphasize the importance of collaboration between AI developers, medical doctors, and other stakeholders1 . o The collaborative model suggests that AI can be ethically acceptable when there is increased cooperation among these parties. In this model, shared decision-making involves input from both human experts and AI systems.
- Challenges and Concerns While AI can enhance efficiency and accuracy in medical decision-making, it also presents ethical challenges. o Some of these challenges include: Risk of Error and Bias: AI algorithms may introduce errors or biases, affecting patient outcomes. Lack of Transparency: The opacity of machine learning algorithms can hinder proper interaction between doctors and patients.
- Global Guidelines and Regulations: o The World Health Organization (WHO) and the European Union have issued guidelines to address the ethical impact of medical AI2 . o These guidelines help healthcare professionals stay informed about evolving AI technologies and mitigate moral concerns. 4. Public Deliberation Model: o When AI fundamentally transforms shared decision-making conditions, the public deliberation model becomes relevant. o This model involves broader societal discussions about the ethical implications of AI in healthcare.
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