Revolutionizing Blood Collection: Innovations, Applications, and the Potential of Microsampling Technologies for Monitoring Metabolites and Lipids

Bossi, Eleonora; Limo, Elena; Pagani, Lisa; Monza, Nicole; Serrao, Simone; Denti, Vanna; Astarita, Giuseppe; Paglia, Giuseppe

doi:10.3390/metabo14010046

Cited by 6 publications

(2 citation statements)

References 112 publications

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“…The pathophysiology of RD is thought to involve several pathogenic processes, including inflammation [ 17 ], blood circulation disorders [ 18 ], and metabolic disturbances [ 19 ]. Routine blood indices, biochemical indices, and coagulation indices can reflect a wide range of physiological and pathological states in the body [ 20 ], providing information on aspects such as inflammation, blood circulation, metabolic status, and tissue injury. Research indicates that blood markers of inflammation [ 21 ], glucose levels [ 22 ], and lipid levels [ 23 ] are associated with an increased risk of ocular diseases, including HM, and RD.…”

Section: Introductionmentioning

confidence: 99%

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia

Li,

et al. 2024

J Transl Med

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Background Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. Methods We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. Results Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322–0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250–0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060–0.8966) and 0.5355 (95%CI = 0.4988–0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340–0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345–0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. Conclusion GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

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Section: Introductionmentioning

confidence: 99%

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia

Li,

et al. 2024

J Transl Med

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“…Some new collection and sampling technologies pertaining to blood are discussed in [64]. The focus of the study is on blood microsampling technologies, devices, and applications.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an Electronic Controller for Accurate Temperature Management for Storage of Biological and Chemical Samples in Healthcare

Ilchev

2024

Computation

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This paper presents the design and development of an electronic controller for accurate temperature management for the storage of biological and chemical samples in healthcare applications. In the introduction, some important application aspects related to the use of temperature control devices in healthcare are discussed. Keeping these aspects in mind, a brief overview of some related works is presented. The findings are then translated to specific requirements for an electronic controller, which is to be used in a temperature control device. These requirements made necessary the development of a custom controller, as no readily available solutions could be obtained. The paper proceeds with the design of a suitable architecture and discusses some of the design choices. Then, some implementation details are presented and the prototype controller, together with its user interface, is illustrated. Experiments are conducted and several points for improvement are identified. Overall, the main task of keeping accurate, traceable temperature at all times is accomplished successfully, and the electronic controller proves to be a viable solution that conforms to the identified requirements. Future versions will improve the speed of the temperature adaptation and include better user interface and wireless connectivity for remote monitoring and control.

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A Bioinspired and Cost‐Effective Device for Minimally Invasive Blood Sampling

Zoratto,

Klein‐Cerrejon,

Gao

et al. 2024

Advanced Science

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Conventional venipuncture is invasive and challenging in low and middle‐income countries. Conversely, point‐of‐care devices paired with fingersticks, although less invasive, suffer from high variability and low blood volume collection. Recently approved microsampling devices address some of these issues but remain cost‐prohibitive for resource‐limited settings. In this work, a cost‐effective microsampling device is described for the collection of liquid blood with minimal invasiveness and sufficient volume retrieval for laboratory analyses or immediate point‐of‐care testing. Inspired by the anatomy of sanguivorous leeches, the single‐use device features a storage compartment for blood collection and a microneedle patch hidden within a suction cup. Finite Element Method simulations, corroborated by mechanical analyses, guide the material selection for device fabrication and design optimization. In piglets, the device successfully collects ≈195 µL of blood with minimal invasiveness. Additionally, a tailor‐made lid and adapter enable safe fluid transportation and integration with commercially available point‐of‐care systems for on‐site analyses, respectively. Taken together, the proposed platform holds significant promise for enhancing healthcare in the pediatric population by improving patient compliance and reducing the risk of needlestick injuries through concealed microneedles. Most importantly, given its cost‐effective fabrication, the open‐source microsampling device may have a meaningful impact in resource‐limited healthcare settings.

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Revolutionizing Blood Collection: Innovations, Applications, and the Potential of Microsampling Technologies for Monitoring Metabolites and Lipids

Cited by 6 publications

References 112 publications

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia

Design and Development of an Electronic Controller for Accurate Temperature Management for Storage of Biological and Chemical Samples in Healthcare

A Bioinspired and Cost‐Effective Device for Minimally Invasive Blood Sampling

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