Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence.
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ABSTRACT:
SUBMITTER: Moon SJ
PROVIDER: S-EPMC8640161 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionDespite important advances in many areas of hepatobiliary surgical practice during the past decades, posthepatectomy liver failure (PHLF) still represents an important clinical challenge for the hepatobiliary surgeon. The aim of this review is to present the current body of evidence regarding different aspects of PHLF.MethodsA literature review was conducted to identify relevant articles for each topic of PHLF covered in this review. The literature search was performed using Medical Subject Heading terms on PubMed for articles on PHLF in English until May 2022.ResultsUniform reporting on PHLF is lacking due to the use of various definitions in the literature. There is no consensus on optimal preoperative assessment before major hepatectomy to avoid PHLF, although many try to estimate future liver remnant function. Once PHLF occurs, there is still no effective treatment, except liver transplantation, where the reported experience is limited.DiscussionStrict adherence to one definition is advised when reporting data on PHLF. The use of the International Study Group of Liver Surgery criteria of PHLF is recommended. There is still no widespread established method for future liver remnant function assessment. Liver transplantation is currently the only effective way to treat severe, intractable PHLF, but for many indications, this treatment is not available in most countries.
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Project description:An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration.We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, n(subjects) = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or "brain," can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates.Initial glucose values ranged from 84-251 mg/dL. Blood glucose was kept in the near-normal range (80-180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively.These encouraging short-term results reveal the ability of a control algorithm tailored to an individual's glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.
| S-EPMC3609541 | biostudies-literature
Project description:Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal.
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Project description:OBJECTIVE:The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose. METHODS:Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, with and without meal announcement, on the full version of the US-FDA accepted UVA/Padova metabolic simulator. RESULTS:The event-based controller remains on for 18 h of 41 h in closed loop with unannounced meals, while maintaining glucose in 70-180 mg/dL for 25 h, compared to 27 h for a standard MPC controller. With meal announcement, the time in 70-180 mg/dL is almost identical, with the controller operating a mere 25.88% of the time in comparison with a standard MPC. CONCLUSION:A novel control architecture for AP systems enables safe glycemic regulation with reduced processor computations. SIGNIFICANCE:Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.
| S-EPMC5839516 | biostudies-literature
Project description:Glioblastoma (GBM) is the most common primary malignant brain tumor in adults with an extremely poor prognosis. There is a dire need to develop effective therapeutics to overcome the intrinsic and acquired resistance of GBM to current therapies. The process of developing novel anti-neoplastic drugs from bench to bedside can incur significant time and cost implications. Drug repurposing may help overcome that obstacle. A wide range of drugs that are already approved for clinical use for the treatment of other diseases have been found to target GBM-associated signaling pathways and are being repurposed for the treatment of GBM. While many of these drugs are undergoing pre-clinical testing, others are in the clinical trial phase. Since GBM stem cells (GSCs) have been found to be a main source of tumor recurrence after surgery, recent studies have also investigated whether repurposed drugs that target these pathways can be used to counteract tumor recurrence. While several repurposed drugs have shown significant efficacy against GBM cell lines, the blood-brain barrier (BBB) can limit the ability of many of these drugs to reach intratumoral therapeutic concentrations. Localized intracranial delivery may help to achieve therapeutic drug concentration at the site of tumor resection while simultaneously minimizing toxicity and side effects. These strategies can be considered while repurposing drugs for GBM.
| S-EPMC8699739 | biostudies-literature
Project description:Alzheimer's disease (AD) is the most common type of dementia responsible for more than 121,499 deaths from AD in 2019 making AD the sixth-leading cause in the United States. AD is a progressive neurodegenerative disorder characterized by decline of memory, behavioral impairments that affects a person's ability to function independently ultimately leading to death. The current pressing need for a treatment for (AD) and advances in the field of cell therapy, has rendered stem cell therapeutics a promising field of research. Despite advancements in stem cell technology, confirmed by encouraging pre-clinical utilization of stem cells in AD animal models, the number of clinical trials evaluating the efficacy of stem cell therapy is limited, with the results of many ongoing clinical trials on cell therapy for AD still pending. Mesenchymal stem cells (MSCs) have been the main focus in these studies, reporting encouraging results concerning safety profile, however their efficacy remains unproven. In the current article we review the latest advances regarding different sources of stem cell therapy and present a comprehensive list of every available clinical trial in national and international registries. Finally, we discuss drawbacks arising from AD pathology and technical limitations that hinder the transition of stem cell technology from bench to bedside. Our findings emphasize the need to increase clinical trials towards uncovering the mode of action and the underlying therapeutic mechanisms of transplanted cells as well as the molecular mechanisms controlling regeneration and neuronal microenvironment.
| S-EPMC8898169 | biostudies-literature
Project description:ObjectiveOur aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).MethodsStructural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion.ResultsA reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours.ConclusionThis study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.
| S-EPMC6134618 | biostudies-literature
Project description:BACKGROUND:In the context of the COVID-19 worldwide pandemic, an up-to-date review of current challenges in addictions is necessary. While large scale disasters may have an impact on substance use and addictions, the use of some substances is also likely to modify the risk of COVID-19 infection or course. Many countries have imposed lockdowns. Whether this quarantine or the end of lockdown measures will have an impact on substance use is discussed. The aim of this review is to gather knowledge for clinicians and to guide public health policies during/after lockdown. METHODS:PubMed was reviewed in August 6th (2020), to determine the current evidences and observations concerning the addictions and SARS-CoV2. We used all the names of the severe acute respiratory syndrome of coronavirus 2 (SARS-CoV2 previously 2019 nCoV), the name of the coronavirus disease 2019 (COVID-19), and common substances of abuse. For the physiopathological parts, searches were conducted using key words such as "infection" or "pneumonia". For the lockdown effects, key words such as "quarantine", "disaster" or "outbreak" were used. RESULTS:Overall, pathophysiological data showed an increased risk of infections for individuals with Substance Use Disorders (SUD) and a possible protective role of nicotine. During lockdown, there is a substantial risk of increasing SUDs. Individuals with opioid use disorder are particularly at risk of relapse or of involuntary withdrawal. After lockdown, increase of use may be observed as far as years after. Individuals with addictions are at higher risk of multimorbidity and mortality during COVID outbreak. CONCLUSION:This review describes useful strategies in clinical practice, including a systematic assessment of addiction comorbidity during this almost worldwide lockdown/pandemic. This review also highlights important areas for future research.
| S-EPMC7420609 | biostudies-literature
Project description:The use of food supplements for weight loss purposes has rapidly gained popularity as the prevalence of obesity increases. Navigating through the vast, often low quality, literature available is challenging, as is providing informed advice to those asking for it. Herein, we provide a comprehensive literature revision focusing on most currently marketed dietary supplements claimed to favor weight loss, classifying them by their purported mechanism of action. We conclude by proposing a combination of supplements most supported by current evidence, that leverages all mechanisms of action possibly leading to a synergistic effect and greater weight loss in the foreseen absence of adverse events. Further studies will be needed to confirm the weight loss and metabolic improvement that may be obtained through the use of the proposed combination.
| S-EPMC7551574 | biostudies-literature
Project description:PurposeOver the last couple of years, we have witnessed the availability of a wide variety of different therapeutic agents and the identification of effective combinations of existing ones that have transformed the way we approach and treat pancreatic cancer. Proof of this are the recent validations that combinations of conventional chemotherapy drugs, the FOLFIRINOX regimen and gemcitabine plus nab-paclitaxel, significantly improves clinical outcomes in patients with metastatic disease. However, deeper and more sophisticated understanding of the biology of this cancer as well as the ability to develop better and perhaps more precise drugs predict that the landscape may be changing even more.Methodology and resultsIn this review, we will summarize the most recent treatment advances including FOLFIRINOX, gemcitabine plus nab-paclitaxel and discuss novel approaches such as immune-mediated therapies, drugs that disrupt the tumor-stromal compartment, PARP inhibitors for BRCA pathway-deficient pancreatic cancer and new generations of conventional chemotherapeutics, which are in early phases of clinical development and have shown promising early results. We will also discuss some examples of drugs that failed, despite very good preliminary data, in order to appraise the lessons learned from these negative clinical trials. Lastly, we will comment on ongoing adjuvant and neoadjuvant trials.ConclusionWe hope that at least some of these will result in positive trials and add to our armamentarium for treating this challenging malignancy.
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