Special issue in SLAS Technology – completed manuscripts accepted until July 30, 2024.
Guest Editors:
Chinmay Chakraborty, Ph.D.
Birla Institute of Technology, Jharkhand, India
Bhuvan Unhelkar, Ph.D.
University of South Florida, USA
Saïd Mahmoudi, Ph.D.
University de Mons, Belgium
The Special Issue proposes bio-inspired computing and machine learning analytics for mental well-being in the field of life sciences innovation. It justifies the need for this special issue by highlighting the emerging behavioral health technologies that integrate big biological data, artificial intelligence, cloud/fog computing, machine learning, bio-inspired computation, the Internet of Medical Things, wireless technologies, security and privacy, semantic databases and content. The large volume of behavioral health data, often referred to as big data, requires scalable machine learning, deep learning and intelligent algorithms to process it effectively. By intelligently investigating and collecting vast amounts of biological data, sensors can improve the decision-making process and facilitate early disease diagnosis. The ultimate goal is to revolutionize the delivery of biological services through a medical assistive environment and facilitate the independent living of patients.
Keywords include:
Submit your manuscript before July 30, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Technology Special Issue.
Publication note: After submissions are accepted, they typically publish online ahead-of-print within 30 days and become immediately searchable and citable with a DOI.
Questions? Please e-mail: cchakrabarty@bitmesra.ac.in
Special issue in SLAS Discovery – completed manuscripts accepted until August 31, 2024.
Guest Editors:
Glauco Souza, Ph.D.
Greiner Bio-One
Madhu Nag, M.B.S., Ph.D.
InSphero
Evan Cromwell, Ph.D.
Protein Fluidics
This special issue will focus on protocols related to technologies and methodologies that are reshaping the field of 3D biology. It aims to publish detailed protocols in 3D cell culture, including protocols applying novel tools, organ-on-a-chip, and/or the integration of AI to augment throughput, automation, analysis, and control in 3D biology.
The Importance of Protocols
Protocols provide step-by-step instructions, methods and other criteria that enable investigators to conduct and expand research. Protocols published in SLAS journals benefit from:
Keywords
Please use this template when submitting your paper to avoid automatic revision requests before the paper can proceed through the peer review process.
Submit your manuscript before August 31, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Discovery Special Issue.
Questions? Please e-mail SLAS Publishing Manager Jenny Cunningham (jcunningham@slas.org).
Special issue in SLAS Technology – completed manuscripts accepted until September 1, 2024.
Guest Editors:
Chang Liu, Ph.D.
SCIEX
The rapid development of high-throughput mass spectrometry technologies has enabled MS to be an invaluable analytical tool in various disciplines, industries and research fields. It has become particularly central to new drug discovery and development, broadly deploying mass spectrometry at every phase. The pharmaceutical industry has become one of the main drivers of technological development in mass spectrometry.
In the proposed special issue, we would like to present current technical developments in the fields, including AEMS, IR-MALDESI, LAP-MALDI, DESI, nano-DESI, MALDI, etc. and their applications in high-throughput drug discovery workflows.
Keywords include:
Submit your manuscript before September 1, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Technology Special Issue.
Special issue in SLAS Discovery – completed manuscripts accepted until September 30, 2024.
Biomolecular condensates are membraneless organelles that form dynamically throughout the cell via the process of liquid:liquid phase separation. Proteins, peptides and RNAs are primary components of condensates. These subcellular compartments organize and concentrate molecules within cells to compartmentalize key biochemical processes. The dysregulation of biomolecular condensates is observed in many pathophysiological states, including neurodegenerative diseases, cancer, inflammation, metabolic diseases and viral infections. Drugs that modify biomolecular condensates formation, stability or dissolution are novel therapeutic strategies for a wide spectrum of diseases. A variety of pharmacological mechanisms of action including direct activity on condensates, as well as indirect modulation of biochemical processes and signally pathways, are being pursued in both academia and biotechnology and pharmaceutical companies.
All interested researchers are invited to submit your manuscript at https://www.editorialmanager.com/slasdisc/default2.aspx. The Journal’s submission system is open for receiving submissions to our Special Issue. To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important to select “VSI: biomolecular condensates drug discovery” when you reach the “Article Type” step in the submission process.
Inquiries related to the special issue, including questions about appropriate topics, may be sent electronically to the Guest Editor Charles P. Hart, Ph.D. - Charles.Hart@ucsf.edu.
Special issue in SLAS Technology – completed manuscripts accepted until October 1, 2024.
Guest Editors:
Roman Voronov, M.S., Ph.D.
New Jersey Institute of Technology
Sergei Chapek, Ph.D.
Ergometa, Inc.
SLAS Technology Guest Editors Roman Voronov, M.S., Ph.D. (New Jersey Institute of Technology) and Sergei Chapek, Ph.D. (Ergometa, Inc.), invite the submission of manuscripts about novel advances and applications in “3D Printing Microfluidics” for publication in a 2023-2024 Special Issue. SLAS Technology has a 2.8 Impact Factor / 3.1 5-Year Impact Factor / 5.4 CiteScore and authors participating in the Special Issue will receive a 30% discount on the Open Access fee.
The Special Issue arrives at a time when 3D printing has evolved beyond a mere prototyping method to become a futuristic approach to the fabrication of microfluidic devices. In this special issue, we delve into the captivating world of 3D-printed microfluidics. Over the years, 3D printing technology has facilitated revolutionary breakthroughs in microfluidics. This emerging discipline marries the precision of 3D printing with the complexity of microfluidic devices, empowering scientists, and engineers to craft highly customizable and efficient tools for a range of research and applications. In this edition, we explore the exciting developments, novel designs and forefront applications at the juncture of 3D printing and microfluidics. Join us as we unveil the extraordinary potential harbored in this transformative field and learn how it is sculpting the future of 3D-printed microfluidic devices.
Voronov and Chapek seek high-quality, short or full-length research reports, reviews and perspectives related to novel advances and applications in 3D Printing Microfluidics. Manuscripts are welcome from academia, industry and vendors. Areas of particular interest include, but are not limited to:
Submit your manuscript before October 1, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Technology Special Issue.
Publication note: After submissions are accepted, they typically publish online ahead-of-print within 30 days and become immediately searchable and citable with a DOI.
Questions? Please e-mail rvoronov@njit.edu AND sergei@ergometa.com.
Special issue in SLAS Technology – completed manuscripts accepted until October 31, 2024.*
*All published special issue submissions will receive 30% off the APC fee!
Guest Editors:
Bikash Behera, Ph.D.
Indian Institute of Science Education and Research
Azeem Irshad, Ph.D.
Department of Computer Science and Software Engineering, International Islamic University
Imad Rida, Ph.D.
University of Technology of Compiègne
Mohammad Shabaz, Ph.D.
Model Institute of Engineering and Technology
Traditional methods of diagnosis and prediction often rely on observable symptoms and historical data, which may not be sufficiently accurate or timely. With artificial intelligence (AI), healthcare providers can analyze an array of variables, from genetic markers to lifestyle choices and even environmental factors, to predict the likelihood of disease occurrence and progression. Predictive modeling using AI is increasingly becoming an indispensable tool for disease prevention and early detection. By leveraging machine learning algorithms and predictive analytics techniques, this approach can identify patterns and trends that might not be apparent through traditional statistical methods. By leveraging AI's predictive capabilities, the healthcare sector can shift from a predominantly reactive model to a more proactive and preventative one. Furthermore, AI-driven predictive modeling opens the door to personalized medicine on an unprecedented scale.
Using machine learning algorithms that analyze genetic, epigenetic and proteomic data, personalized treatment plans can be devised for individual patients. However, implementing AI-driven predictive modeling is experienced with complex challenges such as data sensitivity, incomplete and inconsistent data and missing values. Addressing these difficulties is crucial for the successful implementation and create a healthcare system that truly harnesses the power of AI for the benefit of all.
This special issue will serve as a platform for researchers, clinicians, policymakers and scholars to share their cutting-edge research, findings, methodologies, case studies and advancements that explore the transformative impact of AI in the realm of disease prevention and early detection.
Submit your manuscript before October 31, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Technology Special Issue.
Publication note: After submissions are accepted, they typically publish online ahead-of-print within 30 days and become immediately searchable and citable with a DOI.
Questions? Please e-mail Bikash Behera, dr.bikash.behera@ieee.org
Special issue in SLAS Technology – completed manuscripts accepted until November 1, 2024.
Special issue scope: Exploration of the versatile applications of NLP and LLMs across various domains in the life sciences, including biomedical research, diagnostics, drug discovery, and personalized medicine.
Guest Editors:
Akshi Kumar, Ph.D.
Goldsmiths University of London
Mohinder Pal Singh Bhatia, Ph.D.
Netaji Subhas University of Technology
This special issue of SLAS Technology is dedicated to highlighting the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in the life sciences. These computational technologies are transforming the landscape of biomedical research, diagnostics, drug discovery, and personalized medicine by enabling more efficient data processing, enhancing analytical precision, and facilitating a deeper understanding of complex biological information. For instance, researchers can use NLP to automatically extract and categorize information about gene-disease associations from a vast array of biomedical texts, such as research papers and clinical trial reports. This process accelerates the identification of potential therapeutic targets and supports the development of treatments. Similarly, LLMs like GPT-3 have been applied to predict the structure and function of proteins based on textual descriptions in research articles, which can significantly speed up the drug discovery process by identifying promising compounds before empirical testing begins.
NLP and LLMs have the potential to revolutionize the life sciences by extracting and interpreting the vast amounts of unstructured textual data generated in these fields. This includes data from research papers, clinical reports, patient records, and genetic information. By automating the extraction of meaningful insights from this data, these technologies improve the accuracy of diagnostic tools, accelerate the pace of drug discovery by predicting molecular behaviour, and enable the customization of medical treatments to individual patient profiles based on their genetic and clinical data.
Submit your manuscript before November 1, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Discovery Special Issue.
Questions? Please e-mail SLAS Publishing Manager Jenny Cunningham (jcunningham@slas.org).
Special issue in SLAS Technology – completed manuscripts accepted until November 30, 2024.
We invite researchers, scientists, and industry experts to submit articles on work surrounding the topic of lab of the future, cloud lab or walk-away lab.
Guest Editors:
Kalpesh Gupta, Ph.D.
Moderna
Mario Richter, Ph.D.
Abbvie
We are excited to announce a special edition on "Lab of the Future" in the field of lab automation. This edition aims to explore cutting-edge advancements, innovative technologies, and visionary concepts that are shaping the future of laboratories.We invite researchers, scientists, and industry experts to submit their original research papers, case studies, and review articles.
Topics of interest include, but are not limited to:
Submit your manuscript before November 1, 2024. All submitted papers will be subject to peer-review to ensure scientific rigor, clarity of expression and integration with other contributions in the SLAS Discovery Special Issue.
Questions? Please e-mail SLAS Publishing Manager Jenny Cunningham (jcunningham@slas.org).
