About

Dr. Nilesh P. Ingle is an adjunct assistant professor at the Wilson College of Textiles at NC State University, with a multidisciplinary background encompassing textiles, biomedical engineering, and data science. His academic journey began in India, where he studied natural fibers, and progressed to research on barbed surgical sutures and medical devices during his Ph.D. at NC State under Dr. Martin King. His postdoctoral work involved investigating gene therapy applications, specifically how therapeutic polymeric nanoparticles deliver DNA and RNA to cells, working with Dr. Theresa Reineke at Virginia Tech and the University of Minnesota. Transitioning into data science, Dr. Ingle applied machine learning and deep learning techniques across various industries, including biodiagnostics, market research, and gaming. His current research interests focus on integrating artificial intelligence to solve manufacturing challenges, optimize quality, and foster product innovation within textiles. He collaborates with Dr. Warren Jasper to develop predictive models for fabric dyeing and explore neural network architectures to advance textile and biomedical applications. Beyond his academic pursuits, Dr. Ingle is the creator of 'learndataa,' a YouTube channel with over 500 educational videos on Python and data science, reflecting his dedication to teaching and knowledge sharing globally.

Research topics

• Computer Science
• Artificial Intelligence
• Materials science
• Engineering
• Physical medicine and rehabilitation
• Composite material
• Process engineering
• Cell biology
• Biophysics
• Manufacturing engineering
• Chemistry
• Medicine
• Biology
• Anatomy
• Surgery
• Nanotechnology
• Electronic engineering
• Biochemistry

Selected publications

• A review of the evolution and concepts of deep learning and AI in the textile industry

  Textile Research Journal · 2025-01-16 · 13 citations

  review1st authorCorresponding

  Machine learning (ML) and deep learning (DL) are transforming the textile industry by integrating advanced technologies into various processes. Textiles, once seen as passive materials, are now essential components of complex systems due to automation and innovative materials. This review focuses on articles that utilized AI, ML, or DL in textile research and industry. The review presents bibliometric analysis of AI methods in textiles. Later, the review is structured into sections that examine the effect of ML and DL across the textile sector. We outline key ML and DL methods applied in textiles, discussing their main uses and potential applications. This overview aims to clarify the working principles behind these methods, which are explored in greater detail. The methods analyzed range from basic linear regression to ensemble techniques such as XGBoost. DL techniques include convolutional neural networks for image analysis and long short-term memory networks for time-series analysis. In addition, a bibliometric review identifies trends and gaps in the literature, highlighting areas for future research. We also provide a detailed examination of how these methods are implemented in textiles.

  PublisherDOI

• A review of deep learning and artificial intelligence in dyeing, printing and finishing

  Textile Research Journal · 2024 · 24 citations

  1st authorCorresponding
  • Artificial Intelligence
  • Computer Science
  • Artificial Intelligence

  This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.

  PublisherDOI

• ULTRASONOGRAPHIC EVALUATION IN ACHILLES TENDON RUPTURE

  Global Journal For Research Analysis · 2024

  Senior authorCorresponding
  • Medicine
  • Physical medicine and rehabilitation
  • Anatomy

  Sports injuries involving the Achilles tendon are among the most frequent (1). It usually results from overuse over time, which is common in athletes who perform long-term repetitive motions like running and jumping which involve repetitive tensile forces (2). Achilles tendon ruptures usually affect males in their 30s to 50s who have never had an injury to the leg in question (3). The most frequently mentioned causes of Achilles tendon rupture are violent dorsiexion of a plantarly exed ankle, unanticipated abrupt dorsiexion of the ankle, and pushing off the weight-bearing foot with the knee extended. Due to the short cross-sectional area, high eccentric stresses, and hypovascularity, the majority of Achilles tendon ruptures happen 3 to 6 cm proximal to the tendon's calcaneal insertion (1). Over 20% of cases of acute Achilles tendon rupture have been documented to go undiagnosed, most likely due to pain and edema that interfere with a physical examination (4).

  PublisherDOI

• A review of deep learning within the framework of artificial intelligence for enhanced fiber and yarn quality

  Textile Research Journal · 2024-09-18 · 11 citations

  review1st authorCorresponding

  In the textile production chain, fibers serve as the foundational units for yarn, and yarn, in turn, acts as a fundamental component for woven or knitted fabrics. The quality control of fabrics is intricately tied to the management of fibers and yarns. Traditional laboratory methods have been utilized to assess their quality, but the advent of machine learning and deep learning introduces a transformative approach. This review explores the application of machine learning methods such as principal component analysis, support vector machine, and deep learning methods such as artificial neural networks, convolutional neural networks, you look only once, and genetic algorithms to predict various properties of fibers and yarns. In the context of fibers, the review delves into topics such as cotton fiber grading based on color, characterization of jute fiber, and the identification of medullation in alpaca fibers. For yarns, the focus shifts to predicting parameters such as yarn tenacity, evenness, abrasion index of spun yarns, inspection of false twist textured yarn packages, breaking elongation of ring-spun cotton yarns, tensile properties of cotton/spandex yarns, yarn thickness, and yarn hairiness. The review also provides insights into the advantages and limitations of the discussed studies. Despite the comprehensiveness of this review, it is acknowledged that there might be additional relevant work not covered. The review encourages the sharing of data to expedite the integration of these technologies in future applications within the field.

  PublisherDOI

• Materials Technology Co-Optimization of Self-Aligned Gate Contact for Advanced CMOS Technology Nodes

  2020 · 1 citations

  • Computer Science
  • Materials science
  • Electronic engineering

  Materials technology co-optimization (MTCO) modeling is used for the first time to simulate Performance-Power-Area (PPA) benefits of self-aligned gate contact (SAGC) technology. We also demonstrate a process flow to integrate novel CMOS compatible materials and processes to enable SAGC at the 3nm node and below.

  PublisherDOI

• Polyplexes Are Endocytosed by and Trafficked within Filopodia

  Biomacromolecules · 2020 · 16 citations

  1st authorCorresponding
  • Cell biology
  • Chemistry
  • Biophysics

  The improvement of nonviral gene therapies relies to a large extent on understanding many fundamental physical and biological properties of these systems. This includes interactions of synthetic delivery systems with the cell and mechanisms of trafficking delivery vehicles, which remain poorly understood on both the extra- and intracellular levels. In this study, the mechanisms of cellular internalization and trafficking of polymer-based nanoparticle complexes consisting of polycations and nucleic acids, termed polyplexes, have been observed in detail at the cellular level. For the first time evidence has been obtained that filopodia, actin projections that radiate out from the surface of cells, serve as a route for the direct endocytosis of polyplexes. Confocal microscopy images demonstrated that filopodia on HeLa cells detect external polyplexes and extend into the extracellular milieu to internalize these particles. Polyplexes are observed to be internalized into membrane-bound vesicles (i.e., clathrin-coated pits and caveolae) directly within filopodial projections and are subsequently transported along actin to the main cell body for potential delivery of the nucleic acids to the nucleus. The kinetics and speed of polyplex trafficking have also been measured. The polyplex-loaded vesicles were also discovered to traffic between two cells within filopodial bridges. These findings provide novel insight into the early events of cellular contact with polyplexes through filopodial-based interactions in addition to endocytic vesicle trafficking-an important fundamental discovery to enable advancement of nonviral gene editing, nucleic acid therapies, and biomedical materials.

  PublisherDOI

• Correction to Trehalose-Based Block Copolycations Promote Polyplex Stabilization for Lyophilization and in Vivo pDNA Delivery

  ACS Biomaterials Science & Engineering · 2017-02-10

  articleOpen access

  ADVERTISEMENT RETURN TO ISSUEPREVAddition & Corre...Addition & CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to Trehalose-Based Block Copolycations Promote Polyplex Stabilization for Lyophilization and in Vivo pDNA DeliveryZachary P. TolstykaZachary P. TolstykaMore by Zachary P. Tolstyka, Haley PhillipsHaley PhillipsMore by Haley Phillips, Mallory CortezMallory CortezMore by Mallory Cortez, Yaoying WuYaoying WuMore by Yaoying Wu, Nilesh IngleNilesh IngleMore by Nilesh Ingle, Jason B. BellJason B. BellMore by Jason B. Bell, Perry B. HackettPerry B. HackettMore by Perry B. Hackett, and Theresa M. Reineke*Theresa M. ReinekeMore by Theresa M. Reinekehttp://orcid.org/0000-0001-7020-3450Cite this: ACS Biomater. Sci. Eng. 2017, 3, 3, 495Publication Date (Web):February 10, 2017Publication History Received21 December 2016Published online10 February 2017Published inissue 13 March 2017https://doi.org/10.1021/acsbiomaterials.6b00803Copyright © 2017 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views482Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (4 MB) Get e-AlertsSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Genetics,Organic compounds,Polymers,Stabilization,Transmission electron microscopy Get e-Alerts

  PublisherOA PDFDOI

• Fast, Efficient, and Gentle Transfection of Human Adherent Cells in Suspension

  ACS Applied Materials & Interfaces · 2016-04-01 · 12 citations

  article

  We demonstrate a highly efficient method for gene delivery into clinically relevant human cell types, such as induced pluripotent stem cells (iPSCs) and fibroblasts, reducing the protocol time by one full day. To preserve cell physiology during gene transfer, we designed a microfluidic strategy, which facilitates significant gene delivery in a short transfection time (<1 min) for several human cell types. This fast, optimized and generally applicable cell transfection method can be used for rapid screening of different delivery systems and has significant potential for high-throughput cell therapy applications.

  PublisherDOI

• Trehalose-Based Block Copolycations Promote Polyplex Stabilization for Lyophilization and in Vivo pDNA Delivery

  ACS Biomaterials Science & Engineering · 2015-12-22 · 50 citations

  articleOpen access

  -AEMA-2 polyplexes were evaluated in mice via slow tail vein infusion. The vehicle displayed minimal toxicity and discouraged nonspecific internalization in the liver, kidney, spleen, and lungs as determined by quantitative polymerase chain reaction (qPCR) and fluorescence imaging experiments. Hydrodynamic infusion of the polyplexes, however, led to very specific localization of the polyplexes to the mouse liver and promoted excellent gene expression in vivo.

  PublisherDOI

• Spatiotemporal Cellular Imaging of Polymer–pDNA Nanocomplexes Affords in Situ Morphology and Trafficking Trends

  Molecular Pharmaceutics · 2013-09-05 · 18 citations

  article1st authorCorresponding

  Synthetic polymers are ubiquitous in the development of drug and polynucleotide delivery vehicles, offering promise for personalized medicine. However, the polymer structure plays a central yet elusive role in dictating the efficacy, safety, mechanisms, and kinetics of therapeutic transport in a spatial and temporal manner. Here, we decipher the intracellular pathways pertaining to shape, size, location, and mechanism of four structurally divergent polymer vehicles (Tr455, Tr477, jetPEI, and Glycofect) that create colloidal nanoparticles (polyplexes) when complexed with fluorescently labeled plasmid DNA (pDNA). Multiple high resolution tomographic images of whole HeLa (human cervical adenocarcinoma) cells were captured via confocal microscopy at 4, 8, 12, and 24 h. The images were reconstructed to visualize and quantify trends in situ in a four-dimensional spatiotemporal manner. The data revealed heretofore-unseen images of polyplexes in situ and structure-function relationships, i.e., Glycofect polyplexes are trafficked as the smallest polyplex complexes and Tr455 polyplexes have expedited translocation to the perinuclear region. Also, all of the polyplex types appeared to be preferentially internalized and trafficked via early endosomes affiliated with caveolae, a Rab-5-dependent pathway, actin, and microtubules.

  PublisherDOI

Frequent coauthors

• Theresa M. Reineke

  University of Minnesota System

  17 shared

• Martin W. King

  13 shared

• S. J. Chung

  4 shared

• Randolph Guzman

  St. Boniface Hospital

  3 shared

• Yaoying Wu

  SUNY Upstate Medical University

  3 shared

• Mallory A. Cortez

  Nicholls State University

  3 shared

• Lian Xue

  3 shared

• Tieying Yin

  Chongqing University

  3 shared

Labs

• Wilson College of TextilesPI

Education

• Ph.D., Textiles, Biomedical Engineering, and Data Science

  NC State University

• B.S., Natural Fibers

  India