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  1. Article ; Online: Deciphering expression and variants in cardiovascular disease genes among heart failure population for precision medicine.

    Ahmed, Zeeshan

    ESC heart failure

    2023  Volume 11, Issue 1, Page(s) 606–609

    MeSH term(s) Humans ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/genetics ; Precision Medicine ; Heart Failure/genetics ; Heart Failure/therapy ; Genetic Predisposition to Disease
    Language English
    Publishing date 2023-12-22
    Publishing country England
    Document type Letter
    ZDB-ID 2814355-3
    ISSN 2055-5822 ; 2055-5822
    ISSN (online) 2055-5822
    ISSN 2055-5822
    DOI 10.1002/ehf2.14653
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Editorial: Artificial intelligence for personalized and predictive genomics data analysis.

    Ahmed, Zeeshan / Zeeshan, Saman / Lee, Donghyung

    Frontiers in genetics

    2023  Volume 14, Page(s) 1162869

    Language English
    Publishing date 2023-03-03
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2023.1162869
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Recent Trends in Computational Fluid Dynamics

    Mubashir Bhatti, Muhammad / Marin, Marin I. / Zeeshan, Ahmed / Abdelsalam, Sara I.

    2020  

    Keywords Science: general issues ; Physics ; computational fluid dynamics ; non-Newtonian/Newtonian fluids ; heat and mass transfer ; multiphase flow simulations ; thermodynamics ; nanofluids
    Size 1 electronic resource (215 pages)
    Publisher Frontiers Media SA
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021230894
    ISBN 9782889661930 ; 2889661938
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  4. Article: Physics-based Models for photonic thermometers.

    Ahmed, Zeeshan

    Sensors and actuators. A, Physical

    2023  Volume 348

    Abstract: Resistance thermometry, meticulously developed over the last century, provides a time-tested method for taking temperature measurements. However, fundamental limits to resistance-based approaches along with a desire to reduce the cost of sensor ownership, ...

    Abstract Resistance thermometry, meticulously developed over the last century, provides a time-tested method for taking temperature measurements. However, fundamental limits to resistance-based approaches along with a desire to reduce the cost of sensor ownership, increase sensor stability and meet the growing needs of emerging economy has produced considerable interest in developing photonic temperature sensors. In this study we utilize Della-Corte-Varshni treatment for thermo-optic coefficient to derive models for temperature-wavelength relationships in silicon ring resonators and Fiber Bragg gratings. Model evaluation is carried out using a Bayesian criteria that selects models for superior out-of-sample predictive accuracy whilst minimizing model complexity. Our work presents physics-based framework for photonic thermometry reference functions, putting constraints on model complexity and parameter bounds, pointing the way towards a reference function that can be utilized for future standardization and inter-comparison of photonic thermometers.
    Language English
    Publishing date 2023-05-03
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1026063-8
    ISSN 0924-4247
    ISSN 0924-4247
    DOI 10.1016/j.sna.2022.113987
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  5. Article ; Online: Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches.

    Ahmed, Zeeshan / Degroat, William / Abdelhalim, Habiba / Zeeshan, Saman / Fine, Daniel

    Clinical oral investigations

    2024  Volume 28, Issue 1, Page(s) 52

    Abstract: Objectives: Periodontal diseases are chronic, inflammatory disorders that involve the destruction of supporting tissues surrounding the teeth which leads to permanent damage and substantially heightens systemic exposure. If left untreated, dental, oral, ...

    Abstract Objectives: Periodontal diseases are chronic, inflammatory disorders that involve the destruction of supporting tissues surrounding the teeth which leads to permanent damage and substantially heightens systemic exposure. If left untreated, dental, oral, and craniofacial diseases (DOCs), especially periodontitis, can increase an individual's risk in developing complex traits including cardiovascular diseases (CVDs). In this study, we are focused on systematically investigating causality between periodontitis with CVDs with the application of artificial intelligence (AI), machine learning (ML) algorithms, and state-of-the-art bioinformatics approaches using RNA-seq-driven gene expression data of CVD patients.
    Materials and methods: In this study, we built a cohort of CVD patients, collected their blood samples, and performed RNA-seq and gene expression analysis to generate transcriptomic profiles. We proposed a nexus of AI/ML approaches for the identification of significant biomarkers, and predictive analysis. We implemented recursive feature elimination, Pearson correlation, chi-square, and analysis of variance to detect significant biomarkers, and utilized random forest and support vector machines for predictive analysis.
    Results: Our AI/ML analyses have led us to the preliminary conclusion that GAS5, GPX1, HLA-B, and SNHG6 are the potential gene markers that can be used to explain the causal relationship between periodontitis and CVDs.
    Conclusions: CVDs are relatively common in patients with periodontal disease, and an increased risk of CVD is associated with periodontal disease independent of gender. Genetic susceptibility contributing to periodontitis and CVDs have been suggested to some extent, based on the similar degree of heritability shared between both complex diseases.
    MeSH term(s) Humans ; Cardiovascular Diseases/complications ; Cardiovascular Diseases/genetics ; Artificial Intelligence ; Periodontitis/complications ; Periodontal Diseases/complications ; Genomics ; Biomarkers ; Machine Learning
    Chemical Substances Biomarkers
    Language English
    Publishing date 2024-01-01
    Publishing country Germany
    Document type Letter
    ZDB-ID 1364490-7
    ISSN 1436-3771 ; 1432-6981
    ISSN (online) 1436-3771
    ISSN 1432-6981
    DOI 10.1007/s00784-023-05406-3
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  6. Article: Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities.

    Ahmed, Zeeshan

    Emerging topics in life sciences

    2022  Volume 6, Issue 2, Page(s) 215–225

    Abstract: Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and ...

    Abstract Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.
    MeSH term(s) Biomarkers ; Genomics ; Humans ; Metabolomics ; Precision Medicine ; Proteomics
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-03-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2882721-1
    ISSN 2397-8554 ; 2397-8554 ; 2397-8562
    ISSN (online) 2397-8554
    ISSN 2397-8554 ; 2397-8562
    DOI 10.1042/ETLS20210244
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  7. Article ; Online: Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis.

    Ahmed, Zeeshan

    Progress in molecular biology and translational science

    2022  Volume 190, Issue 1, Page(s) 101–125

    Abstract: Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and to improve routine medical and public health practice. Understanding patients' multi-omics ...

    Abstract Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and to improve routine medical and public health practice. Understanding patients' multi-omics make-up in conjunction with the clinical data will lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and to optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. Precision medicine promotes integrating collective and individualized clinical data with patient-specific multi-omics data to develop therapeutic strategies and knowledge bases for predictive and personalized medicine in diverse populations. Artificial intelligence approaches and machine learning algorithms will add additional capabilities to precision medicine that will leverage and extend the information contained within the original data and facilitate modeling patient-specific multi-omics data against publicly available annotation data for better understanding disease mechanisms. This chapter discusses emerging, significant, and recently reported multi-omics, deep phenotyping, and translational approaches to facilitate the implementation of precision medicine, as well as innovative, smart, and robust big-data platforms that are necessary to improve the quality and transition of healthcare by analyzing heterogeneous healthcare and multi-omics data.
    MeSH term(s) Artificial Intelligence ; Genomics ; Humans ; Machine Learning ; Precision Medicine ; Proteomics
    Language English
    Publishing date 2022-03-07
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2471995-X
    ISSN 1878-0814 ; 0079-6603 ; 1877-1173
    ISSN (online) 1878-0814
    ISSN 0079-6603 ; 1877-1173
    DOI 10.1016/bs.pmbts.2022.02.002
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  8. Book ; Online: Hysteresis Compensation in Temperature Response of Fiber Bragg Grating Thermometers Using Dynamic Regression

    Ahmed, Zeeshan

    2023  

    Abstract: In recent years there has been considerable interest in using photonic thermometers such as Fiber Bragg grating (FBG) and silicon ring resonators as an alternative technology to resistance-based legacy thermometers. Although FBG thermometers have been ... ...

    Abstract In recent years there has been considerable interest in using photonic thermometers such as Fiber Bragg grating (FBG) and silicon ring resonators as an alternative technology to resistance-based legacy thermometers. Although FBG thermometers have been commercially available for decades their metrological performance remains poorly understood, hindered in part by complex behavior at elevated temperatures. In this study we systematically examine the temporal evolution of the temperature response of 14 sensors that were repeatedly cycled between 233 K and 393 K. Data exploration and modelling indicate the need to account for serial-correlation in model selection. Utilizing the coupled-mode theory treatment of FBG to guide feature selection we evaluate various calibration models. Our results indicates that a dynamic regression model can effectively reduce measurement uncertainty due to hysteresis by up to $\approx 70 \%$
    Keywords Physics - Applied Physics ; Physics - Data Analysis ; Statistics and Probability
    Subject code 621
    Publishing date 2023-03-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19.

    Ahmed, Zeeshan / Renart, Eduard Gibert / Zeeshan, Saman

    Personalized medicine

    2022  Volume 19, Issue 3, Page(s) 229–250

    Abstract: Aim: ...

    Abstract Aim:
    MeSH term(s) Angiotensin-Converting Enzyme 2/genetics ; COVID-19/genetics ; Genome ; Humans ; Membrane Transport Proteins/genetics ; SARS-CoV-2/genetics ; Whole Exome Sequencing
    Chemical Substances Membrane Transport Proteins ; SLC6A20 protein, human ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
    Language English
    Publishing date 2022-03-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2299146-3
    ISSN 1744-828X ; 1741-0541
    ISSN (online) 1744-828X
    ISSN 1741-0541
    DOI 10.2217/pme-2021-0132
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  10. Article ; Online: Digital Morphology: Bridging the Final Gap in Automated Haematology Testing.

    Shaikh, Muhammad Shariq / Ahmed, Zeeshan Ansar

    Journal of the College of Physicians and Surgeons--Pakistan : JCPSP

    2023  Volume 33, Issue 8, Page(s) 949

    Abstract: Null. ...

    Abstract Null.
    MeSH term(s) Humans ; Hematologic Tests ; Hematology
    Language English
    Publishing date 2023-07-31
    Publishing country Pakistan
    Document type Journal Article
    ZDB-ID 2276646-7
    ISSN 1681-7168 ; 1022-386X
    ISSN (online) 1681-7168
    ISSN 1022-386X
    DOI 10.29271/jcpsp.2023.08.949
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