Quality assurance of add-on testing in plasma samples: stability limit for 29 biochemical analytes.

Introduction: Clinical laboratories should guarantee sample stability in specific storage conditions for further analysis. The aim of this study is to evaluate the stability of plasma samples under refrigeration for 29 common biochemical analytes usually ordered within an emergency context, in order to determine the maximum allowable period for conducting add-on testing. Materials and methods: A total of 20 patient samples were collected in lithium heparin tubes without gel separator. All analyses were performed using Alinity systems (Abbott Laboratories, Abbott Park, USA) and samples were stored at 2-8 °C. Measurements were conducted in primary plasma tubes at specific time points up to 48 hours, with an additional stability study in plasma aliquots extending the time storage up to 96 hours. The stability limit was estimated considering the total limit of change criteria. Results: Of the 29 studied parameters, 24 demonstrated stabilities within a 48-hour storage period in primary plasma tubes. However, five analytes: aspartate aminotransferase, glucose, lactate dehydrogenase, inorganic phosphate and potassium evidenced instability at different time points (7.9 hours, 2.7 hours, 2.9 hours, 6.2 hours and 4.7 hours, respectively). The stability study in plasma aliquots showed that all parameters remained stable for 96 hours, except lactate dehydrogenase, with a stability limit of 63 hours. Conclusions: A reduced stability of primary plasma samples was observed for five common biochemical analytes ordered in an emergency context. To ensure the quality of add-on testing for these samples, plasma aliquots provide stability for a longer period.

The Phenix-AlphaFold webservice: Enabling AlphaFold predictions for use in Phenix.

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

High-Throughput DNA-Encoded Libraries Affinity Selection Platform for Binder Identification with Solid Support Protein Immobilization.

DNA-encoded libraries (DELs) have demonstrated to be one of the most powerful technologies within the ligand identification toolbox, widely used either in academia or biotech and pharma companies. DEL methodology utilizes affinity selection (AS) as the approach to interrogate the protein of interest for the identification of binders. Here we present a high-throughput, fully automated AS platform developed to fulfill industrial standards and compatible with different assay formats to improve the reproducibility of the AS process for DEL binders identification. This platform is flexible enough to virtually set aside all kinds of DELs and AS methods and conditions using immobilized proteins. It bears the two main immobilization methods to support of the proteins of interest: magnetic beads or resin tip columns. A combination of a broad variety of protocol options with a wide range of different experimental conditions can be set up with a throughput of 96 samples at the same time. In addition, small modifications of the protocols provide the platform with the versatility to run not only the routine DEL screens, but also test covalent libraries, the successful immobilization of the proteins of interest, and many other experiments that may be required. This versatile AS platform for DEL can be a powerful instrument for direct application of the technology in academic and industry settings.

Magnetically functionalized hydrogels for high-throughput genomic applications.

Single-cell genomics has revolutionized tissue analysis by revealing the genetic program of individual cells. The key aspect of the technology is the use of barcoded beads to unambiguously tag sequences originating from a single cell. The generation of unique barcodes on beads is mainly achieved by split-pooling methods, which are labor-intensive due to repeated washing steps. Towards the automation of the split-pooling method, we developed a simple method to magnetize hydrogel beads. We show that these hydrogel beads provide increased yields and washing efficiencies for purification procedures. They are also fully compatible with single-cell sequencing using the BAG-Seq workflow. Our work opens the automation of the split-pooling technique, which will improve single-cell genomic workflows.

The National Healthcare Safety Network's digital quality measures: CDC's automated measures for surveillance of patient safety.

OBJECTIVE: This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. CONCLUSIONS: The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network.

Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and high-throughput alternative for protein extraction and digestion. Here, a refined paramagnetic bead-based digestion protocol is described for Opentrons® OT-2 platform (OT-2) as a versatile, reproducible, and affordable alternative for the automatic sample preparation for MS analysis. For this purpose, an artificial neural network (ANN) was applied to maximize the number of peptides without missed cleavages identified in HeLa extract by combining factors such as the quantity (µg) of trypsin/Lys-C and beads (MagReSyn® Amine), % (w/v) SDS, % (v/v) acetonitrile, and time of digestion (h). ANN model predicted the optimal conditions for the digestion of 50 µg of HeLa extract, pointing to the use of 2.5% (w/v) SDS and 300 µg of beads for sample preparation and long-term digestion (16h) with 0.15 µg Lys-C and 2.5 µg trypsin (≈1:17 ratio). Based on the results of the ANN model, the manual protocol was automated in OT-2. The performance of the automatic protocol was evaluated with different sample types, including human plasma, Arabidopsis thaliana leaves, Escherichia coli cells, and mouse tissue cortex, showing great reproducibility and low sample-to-sample variability in all cases. In addition, we tested the performance of this method in the preparation of a challenging biological fluid such as rat bile, a proximal fluid that is rich in bile salts, bilirubin, cholesterol, and fatty acids, among other MS interferents. Compared to other protocols described in the literature for the extraction and digestion of bile proteins, the method described here allowed identify 385 unique proteins, thus contributing to improving the coverage of the bile proteome.

The prospects of automation in drug discovery research using silkworms.

We have established several models of infectious diseases in silkworms to explore disease-causing mechanisms and identify new antimicrobial substances. These models involve injecting laboratory-cultured pathogens into silkworms and monitoring their survival over a period of days. The use of silkworms is advantageous because they are cost-effective and raise fewer ethical concerns than mammalian subjects, allowing for larger experimental group sizes. To capitalize on these benefits, there is a growing importance in mechanizing and automating the experimental processes that currently require manual labor. This paper discusses the future of laboratory automation, specifically through the mechanization and automation of silkworm-based experimental procedures.

Filling the gap: the workforce of tomorrow for CGT manufacturing as the sector advances.

Workforce education and development are key cornerstones in advancing and maturing the Cell & Gene Therapy sector. A skilled worker shortage can significantly impact and delay progress as well as the quality of output for any developer, thereby negatively impacting a patient's access to life-saving treatments. Several roundtable discussions were held at the International Society for Cell & Gene Therapy (ISCT) 2023 Annual Meeting to dive deeper into the current state of workforce development and solutions to address this bottleneck. One roundtable discussion was co-hosted by the Alliance for Regenerative Medicine (ARM) and ISCT, which focused on the gap analysis provided for the United States Cell & Gene Therapy (CGT) sector, highlighting the lack of skilled workers in manufacturing and quality control. In this manuscript, the roundtable participants continue this conversation, review the roles and staffing requirements in both academic and industry as well as small and large company settings. The adoption of increased manufacturing automation is one promising solution to propel the sector forward. However, automation alone won't replace on-site staff, but will lower the bar to entry for a larger pool of people and require different training. This paper also addresses the workforce development and training paradigm shift as advanced manufacturing techniques are implemented, which will differ considerably based on the type of manufacturing efforts, thus emphasizing the need for a well-thought-out strategy to up-skill and re-skill the technical workforce to adapt to these advancements. Organizations such as ISCT and ARM have a role to play in propelling the field forward, providing awareness and education to stakeholders at all levels, as well as acting as a convener and participating as a key stakeholder in discussions and partnerships between academia and industry towards solutions for training the best personnel for CGT manufacturing. This scope includes novel digital tools and technologies to simplify training to increase access to new talent pools interested in careers in a rapidly advancing sector.

Improved automated one-pot two-step radiosynthesis of (S)-[18F]FETrp, a radiotracer for PET imaging of indoleamine 2,3-dioxygenase 1 (IDO1).

BACKGROUND: (S)-[18F]FETrp is a promising PET radiotracer for imaging IDO1 activity, one of the main enzymes involved in the tryptophan metabolism that plays a key role in several diseases including cancers. To date, the radiosynthesis of this tryptophan analogue remains highly challenging due to partial racemization occurring during the nucleophilic radiofluorination step. This work aims to develop a short, epimerization-free and efficient automated procedure of (S)-[18F]FETrp from a corresponding enantiopure tosylate precursor. RESULTS: Enantiomerically pure (S)- and (R)-FETrp references as well as tosylate precursors (S)- and (R)-3 were obtained from corresponding Na-Boc-(L and D)-tryptophan in 2 and 4 steps, respectively. Manual optimisation of the radiolabelling conditions resulted in > 90% radiochemical conversion with more than 99% enantiomeric purity. Based on these results, the (S)-[18F]FETrp radiosynthesis was fully automated on a SynChrom R&D EVOI module to produce the radiotracer in 55.2 ± 7.5% radiochemical yield, 99.9% radiochemical purity, 99.1 ± 0.5% enantiomeric excess, and molar activity of 53.2 ± 9.3 GBq/µmol (n = 3). CONCLUSIONS: To avoid racemisation and complicated purification processes, currently encountered for the radiosynthesis of (S)-[18F]FETrp, we report herein significant improvements, including a versatile synthesis of enantiomerically pure tosylate precursor and reference compound and a convenient one-pot two-step automated procedure for the radiosynthesis of (S)-[18F]FETrp. This optimised and robust production method could facilitate further investigations of this relevant PET radiotracer for imaging IDO1 activity.

High-throughput semi-automated emulsive liquid-liquid microextraction for detecting SDHI fungicides in water, juice, and alcoholic beverage samples via UHPLC-MS/MS.

Herein, a High-Throughput Semi-automated Emulsive Liquid-Liquid Microextraction (HTSA-ELLME) method was developed to detect Succinate Dehydrogenase Inhibitor (SDHI) fungicides in food samples via UHPLC-MS/MS. The Oil-in-Water (O/W) emulsion comprising a hydrophobic extractant and water was dilutable with the aqueous sample solution. Upon injecting the primary emulsion into the sample solution, a secondary O/W emulsion was formed, allowing SDHI fungicides to be extracted. Subsequently, a NaCl-saturated solution was injected in the secondary O/W emulsion as a demulsifier to rapidly separate the extractant, eliminating the need for centrifugation. A 12-channel electronic micropipette was used to achieve a high-throughput semi-automation of the novel sample pretreatment. The linear range was 0.003-0.3 µg L-1 with R2 > 0.998. The limit of detection was 0.001 µg L-1. The HTSA-ELLME method successfully detected SDHI fungicides in water, juice, and alcoholic beverage samples, with recoveries and relative standard deviations of 82.6-106.9% and 0.8-5.8%, respectively. Unlike previously reported liquid-liquid microextraction approaches, the HTSA-ELLME method is the first to be both high-throughput and semi-automated and may aid in designing pesticide pretreatment processes in food samples.

Automated Extraction and LC-MS/MS Analysis of 11-Nor-9-carboxy-tetrahydrocannabinol Isomers and Prevalence in Authentic Urine Specimens.

11-Nor-9-carboxy-Δ9-tetrahydrocannabinol (Δ9-THCCOOH) is the most frequently detected illicit drug metabolite in the military drug testing program. An increasing number of specimens containing unresolved Δ8-THCCOOH prompted the addition of this analyte to the Department of Defense (DoD) drug testing panel. A method was developed and validated for the quantitative confirmation of the carboxylated metabolites of Δ8- and Δ9-THC in urine samples utilizing automated pipette tip dispersive solid phase extraction and analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS). Analytes were separated isocratically over an 8.5 min runtime and detected on an MS/MS system equipped with an electrospray ionization source operating in negative mode. A single point calibrator (15 ng/mL) forced through zero demonstrated linearity from 3 to 1,000 ng/mL. Intra- and inter-day precision were ≤9.1% CV, and bias was within ±14.1% for Δ8-THCCOOH and Δ9-THCCOOH. No interferences were found after challenging the method with different over-the-counter drugs, prescription pharmaceuticals, drugs of abuse, and several cannabinoids and cannabinoid metabolites, including Δ1°-THCCOOH. Urine specimens presumptively positive by immunoassay (n=2939; 50 ng/mL Δ9-THCCOOH cutoff) were analyzed with this confirmation method. Specimens that contained Δ8-THCCOOH often had Δ9-THCCOOH above the 15 ng/mL cutoff. However, nearly one-third of the specimens analyzed were positive for Δ8-THCCOOH only. This manuscript describes the first validated automated extraction and confirmation method for Δ8- and Δ9-THCCOOH in urine that provides adequate analyte separation in urine specimens with extreme isomer abundance ratios.

Automated detection of methicillin-resistant Staphylococcus aureus with the MRSA CHROM imaging application on BD Kiestra Total Lab Automation System.

The virulence of methicillin-resistant Staphylococcus aureus (MRSA) and its potentially fatal outcome necessitate rapid and accurate detection of patients colonized with MRSA in healthcare settings. Using the BD Kiestra Total Lab Automation (TLA) System in conjunction with the MRSA Application (MRSA App), an imaging application that uses artificial intelligence to interpret colorimetric information (mauve-colored colonies) indicative of MRSA pathogen presence on CHROMagar chromogenic media, anterior nares specimens from three sites were evaluated for the presence of mauve-colored colonies. Results obtained with the MRSA App were compared to manual reading of agar plate images by proficient laboratory technologists. Of 1,593 specimens evaluated, 1,545 (96.98%) were concordant between MRSA App and laboratory technologist reading for the detection of MRSA growth [sensitivity 98.15% (95% CI, 96.03, 99.32) and specificity 96.69% (95% CI, 95.55, 97.60)]. This multi-site study is the first evaluation of the MRSA App in conjunction with the BD Kiestra TLA System. Using the MRSA App, our results showed 98.15% sensitivity and 96.69% specificity for the detection of MRSA from anterior nares specimens. The MRSA App, used in conjunction with laboratory automation, provides an opportunity to improve laboratory efficiency by reducing laboratory technologists' labor associated with the review and interpretation of cultures.

Compositional Analysis of Complex Mixtures using Automatic MicroED Data Collection.

Quantitative analysis of complex mixtures, including compounds having similar chemical properties, is demonstrated using an automatic and high throughput approach to microcrystal electron diffraction (MicroED). Compositional analysis of organic and inorganic compounds can be accurately executed without the need of diffraction standards. Additionally, with sufficient statistics, small amounts of compounds in mixtures can be reliably detected. These compounds can be distinguished by their crystal structure properties prior to structure solution. In addition, if the crystals are of good quality, the crystal structures can be generated on the fly, providing a complete analysis of the sample. MicroED is an effective method for analyzing the structural properties of sub-micron crystals, which are frequently found in small-molecule powders. By developing and using an automatic and high throughput approach to MicroED, and with the use of SerialEM for data collection, data from thousands of crystals allow sufficient statistics to detect even small amounts of compounds reliably.

Molecular architecture of the actin cytoskeleton: From single cells to whole organisms using cryo-electron tomography.

Cryo-electron tomography (cryo-ET) has begun to provide intricate views of cellular architecture at unprecedented resolutions. Considerable efforts are being made to further optimize and automate the cryo-ET workflow, from sample preparation to data acquisition and analysis, to enable visual proteomics inside of cells. Here, we will discuss the latest advances in cryo-ET that go hand in hand with their application to the actin cytoskeleton. The development of deep learning tools for automated annotation of tomographic reconstructions and the serial lift-out sample preparation procedure will soon make it possible to perform high-resolution structural biology in a whole new range of samples, from multicellular organisms to organoids and tissues.

A conceptual framework for automation disengagements.

A better understanding of automation disengagements can lead to improved safety and efficiency of automated systems. This study investigates the factors contributing to automation disengagements initiated by human operators and the automation itself by analyzing semi-structured interviews with 103 users of Tesla's Autopilot and FSD Beta. The factors leading to automation disengagements are represented by categories. In total, we identified five main categories, and thirty-five subcategories. The main categories include human operator states (5), human operator's perception of the automation (17), human operator's perception of other humans (3), the automation's perception of the human operator (3), and the automation incapability in the environment (7). Human operators disengaged the automation when they anticipated failure, observed unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believed the automation is not capable to operate safely in certain environments (e.g., inclement weather, non-standard roads). Negative experiences of human operators, such as frustration, unsafe feelings, and distrust represent some of the adverse human operate states leading to automation disengagements initiated by human operators. The automation, in turn, monitored human operators and disengaged itself if it detected insufficient vigilance or speed rule violations by human operators. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage the automation if they sensed discomfort, anger, or embarrassment due to the automation's actions. The results of the analysis are synthesized into a conceptual framework for automation disengagements, borrowing ideas from the human factor's literature and control theory. This research offers insights into the factors contributing to automation disengagements, and highlights not only the concerns of human operators but also the social aspects of this phenomenon. The findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.

Enhancing agricultural automation through weather invariant soil parameter prediction using machine learning.

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.

Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans.

Background and purpose: Retrospective dose evaluation for organ-at-risk auto-contours has previously used small cohorts due to additional manual effort required for treatment planning on auto-contours. We aimed to do this at large scale, by a) proposing and assessing an automated plan optimization workflow that used existing clinical plan parameters and b) using it for head-and-neck auto-contour dose evaluation. Materials and methods: Our automated workflow emulated our clinic's treatment planning protocol and reused existing clinical plan optimization parameters. This workflow recreated the original clinical plan (POG) with manual contours (PMC) and evaluated the dose effect (POG-PMC) on 70 photon and 30 proton plans of head-and-neck patients. As a use-case, the same workflow (and parameters) created a plan using auto-contours (PAC) of eight head-and-neck organs-at-risk from a commercial tool and evaluated their dose effect (PMC-PAC). Results: For plan recreation (POG-PMC), our workflow had a median impact of 1.0% and 1.5% across dose metrics of auto-contours, for photon and proton respectively. Computer time of automated planning was 25% (photon) and 42% (proton) of manual planning time. For auto-contour evaluation (PMC-PAC), we noticed an impact of 2.0% and 2.6% for photon and proton radiotherapy. All evaluations had a median ΔNTCP (Normal Tissue Complication Probability) less than 0.3%. Conclusions: The plan replication capability of our automated program provides a blueprint for other clinics to perform auto-contour dose evaluation with large patient cohorts. Finally, despite geometric differences, auto-contours had a minimal median dose impact, hence inspiring confidence in their utility and facilitating their clinical adoption.

Sequential Treatment Application Robot (STAR) for high-replication marine experimentation.

Marine organisms are often subject to numerous anthropogenic stressors, resulting in widespread ecosystem degradation. Physiological responses to these stressors, however, are complicated by high biological variability, species-specific sensitivities, nonlinear relationships, and countless permutations of stressor combinations. Nevertheless, quantification of these relationships is paramount for parameterizing predictive tools and ultimately for effective management of marine resources. Multi-level, multi-stressor experimentation is therefore key, yet the high replication required has remained a logistical challenge and a financial barrier. To overcome these issues, we created an automated system for experimentation on marine organisms, the Sequential Treatment Application Robot (STAR). The system consists of a track-mounted robotic arm that sequentially applies precision treatments to independent aquaria via syringe and peristaltic pumps. The accuracy and precision were validated with dye and spectrophotometry, and stability was demonstrated by maintaining corals under treatment conditions for more than a month. The system is open source and scalable in that additional treatments and replicates may be added without incurring multiplicative costs. While STAR was designed for investigating the combined impacts of nutrients, warming, and disease on reef-building corals, it is highly customizable and may be used for experimentation involving a diverse array of treatments and species.

Digitalization concepts in academic bioprocess development.

Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT-experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT-experts in order for a well-developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory.

Artificial intelligence powered glucose monitoring and controlling system: Pumping module.

BACKGROUND: Diabetes, a globally escalating health concern, necessitates innovative solutions for efficient detection and management. Blood glucose control is an essential aspect of managing diabetes and finding the most effective ways to control it. The latest findings suggest that a basal insulin administration rate and a single, high-concentration injection before a meal may not be sufficient to maintain healthy blood glucose levels. While the basal insulin rate treatment can stabilize blood glucose levels over the long term, it may not be enough to bring the levels below the post-meal limit after 60 min. The short-term impacts of meals can be greatly reduced by high-concentration injections, which can help stabilize blood glucose levels. Unfortunately, they cannot provide long-term stability to satisfy the post-meal or pre-meal restrictions. However, proportional-integral-derivative (PID) control with basal dose maintains the blood glucose levels within the range for a longer period. AIM: To develop a closed-loop electronic system to pump required insulin into the patient's body automatically in synchronization with glucose sensor readings. METHODS: The proposed system integrates a glucose sensor, decision unit, and pumping module to specifically address the pumping of insulin and enhance system effectiveness. Serving as the intelligence hub, the decision unit analyzes data from the glucose sensor to determine the optimal insulin dosage, guided by a pre-existing glucose and insulin level table. The artificial intelligence detection block processes this information, providing decision instructions to the pumping module. Equipped with communication antennas, the glucose sensor and micropump operate in a feedback loop, creating a closed-loop system that eliminates the need for manual intervention. RESULTS: The incorporation of a PID controller to assess and regulate blood glucose and insulin levels in individuals with diabetes introduces a sophisticated and dynamic element to diabetes management. The simulation not only allows visualization of how the body responds to different inputs but also offers a valuable tool for predicting and testing the effects of various interventions over time. The PID controller's role in adjusting insulin dosage based on the discrepancy between desired setpoints and actual measurements showcases a proactive strategy for maintaining blood glucose levels within a healthy range. This dynamic feedback loop not only delays the onset of steady-state conditions but also effectively counteracts post-meal spikes in blood glucose. CONCLUSION: The WiFi-controlled voltage controller and the PID controller simulation collectively underscore the ongoing efforts to enhance efficiency, safety, and personalized care within the realm of diabetes management. These technological advancements not only contribute to the optimization of insulin delivery systems but also have the potential to reshape our understanding of glucose and insulin dynamics, fostering a new era of precision medicine in the treatment of diabetes.