A portable metabolomics-on-CMOS platform for point-of-care testing

Metabolomics is the study of the metabolites, small molecules produced during the metabolism. Metabolite levels mirror the health status of an individual and therefore have enormous potential in medical point-of-care (POC) applications. POC platforms are miniaturised and portable systems integrating all steps from sample collection to result of a medical test. POC devices offer the possibility to reduce the diagnostic costs, shorten the testing time, and, ultimately, save lives for several applications. The glucose meter, arguably the most successful example of metabolomics POC platform, has already demonstrated the dramatic impact that such platforms can have on the society. Nevertheless, other relevant metabolomic tests are still relegated to centralised laboratories and bulky equipment. In this work, a metabolomics POC platform for multi-metabolite quantification was developed. The platform aims to untap metabolomics for the general population. As case studies, the platform was designed and evaluated for prostate cancer and ischemic stroke. For prostate cancer, new affordable diagnostic tools to be used in conjunction with the current clinical standard have are needed to reduce the medical costs due to overdiagnosis and increase the survival rate. Thus, a novel potential metabolic test based on L-type amino acids (LAA) profile, glutamate, choline, and sarcosine blood concentrations was developed. For ischemic stroke, where the portable and rapid test can make a difference between life and death, lactate and creatinine blood levels were chosen as potential biomarkers. All the target metabolites were quantified using an optical method (colorimetry). The platform is composed of three units: the cartridge, the reader, and the graphical user interface (GUI). The cartridge is the core of the platform. It integrates a CMOS 16x16 array of photodiodes, capillary microfluidics, and biological receptors onto the same ceramic package. To measure multiple metabolites, a novel method involving a combination of replica moulding and injection moulding was developed for the monolithic integration of microfluidics onto integrated chips. The reader is composed of a custom PCB and a microcontroller board. It is used for addressing, data digitisation and data transfer to the GUI. The GUI - a software running on a portable electronic device - is used for interfacing the system, visualise, acquire, process, and store the data. The analysis of the microfluidic structures showed successful integration. The selection of the specific chemistry for detecting the analytes of interest was demonstrated to be suitable for the performance of the sensors. Quick and reliably capillary flow of human plasma, serum and blood was demonstrated. On-chip quantification of the target metabolites was demonstrated in diluted human serum and human plasma. Calibration curves, kinetics parameter and other relevant metrics were determined. For all the metabolites, the limits of detection were lower than the physiological range, demonstrating the capability of the platform to be used in the target applications. Multi-metabolite testing capability was also demonstrated using commercially and clinically sourced human plasma. For multiplexed assays, reagents were preloaded in the microfluidic channel and lyophilised. Lyophilisation also improved the shelf-life of the reagents. Alternative configurations, involving the use of paper microfluidics, integration of passive blood filter and use of whole blood, were investigated. The chracterisation of the platform culminated with a clinical evaluation for both the target applications. The same platform with minimal modification of the cartridge was able to provide clinically relevant information for both the distinct applications, highlighting the versatility of the platform for POC determination of metabolic biomarkers. For prostate cancer, the platform was used for the quantification of the potential metabolic biomarker in 10 healthy samples and 16 patients affected by prostate cancer. LAA, glutamate and choline average concentrations were elevated in the cancer group with respect to the control group and were therefore regarded as metabolic biomarkers in this population. Metabolomic profiles were used to train a classifier algorithm, which improved the performance of the current clinical blood test, for this population. For ischemic stroke, lactate determination was performed in clinically sourced samples. Clinical evaluation for ischemic stroke was performed using 10 samples from people diagnosed with ischemic stroke. Results showed that the developed platform provided comparable results with an NHS-based gold standard method in this population. This comparison demonstrated the potential of the platform for its on-the-spot use. The developed platform has the potential to lead the way to a new generation of low-cost and rapid POC devices for the early and improved diagnosis of deadly diseases

Micromolar metabolite measurement in an electronically multiplexed format

The detection of metabolites such as choline in blood are important in clinical care for patients with cancer and cardiovascular disease. Choline is only present in human blood at low concentrations hence accurate measurement in an affordable point-of-care format is extremely challenging. Integration of microfluidics on to complementary metal-oxide semiconductor (CMOS) technology has the potential to enable advanced sensing technologies with extremely low limit of detection that are well suited to multiple clinical metabolite measurements. Although CMOS and microfluidics are individually mature technologies, their integration has presented challenges that we overcome in a novel, cost-effective, single-step process. To demonstrate the process, we present the microfluidic integration of a metabolomics-on-CMOS point-of-care platform with four capillary microfluidic channels on top of a CMOS optical sensor array. The fabricated device was characterised to verify the required structural profile, mechanical strength, optical spectra, and fluid flow. As a proof of concept, we used the device for the in-vitro quantification of choline in human blood plasma with a limit of detection of 3.2 M and a resolution of 1.6 M

Off-label long acting injectable antipsychotics in real-world clinical practice: a cross-sectional analysis of prescriptive patterns from the STAR Network DEPOT study

Introduction Information on the off-label use of Long-Acting Injectable (LAI) antipsychotics in the real world is lacking. In this study, we aimed to identify the sociodemographic and clinical features of patients treated with on- vs off-label LAIs and predictors of off-label First- or Second-Generation Antipsychotic (FGA vs. SGA) LAI choice in everyday clinical practice. Method In a naturalistic national cohort of 449 patients who initiated LAI treatment in the STAR Network Depot Study, two groups were identified based on off- or on-label prescriptions. A multivariate logistic regression analysis was used to test several clinically relevant variables and identify those associated with the choice of FGA vs SGA prescription in the off-label group. Results SGA LAIs were more commonly prescribed in everyday practice, without significant differences in their on- and off-label use. Approximately 1 in 4 patients received an off-label prescription. In the off-label group, the most frequent diagnoses were bipolar disorder (67.5%) or any personality disorder (23.7%). FGA vs SGA LAI choice was significantly associated with BPRS thought disorder (OR = 1.22, CI95% 1.04 to 1.43, p = 0.015) and hostility/suspiciousness (OR = 0.83, CI95% 0.71 to 0.97, p = 0.017) dimensions. The likelihood of receiving an SGA LAI grew steadily with the increase of the BPRS thought disturbance score. Conversely, a preference towards prescribing an FGA was observed with higher scores at the BPRS hostility/suspiciousness subscale. Conclusion Our study is the first to identify predictors of FGA vs SGA choice in patients treated with off-label LAI antipsychotics. Demographic characteristics, i.e. age, sex, and substance/alcohol use co-morbidities did not appear to influence the choice towards FGAs or SGAs. Despite a lack of evidence, clinicians tend to favour FGA over SGA LAIs in bipolar or personality disorder patients with relevant hostility. Further research is needed to evaluate treatment adherence and clinical effectiveness of these prescriptive patterns

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS < 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders

Integrating microfluidics and electronics in point-of-care diagnostics: current and future challenges

No abstract available

Cyber-Physical System for Movement Related Potentials Early Detection by Synchronized EEG and EMG Signals

We present a study based on time-frequency analysis to identify the correlation between synchronized EEG and EMG signals during gait for the detection of premotor brain activity, focusing on Bereitschaftspotential and mu-rhythm detection – directly related to voluntary movements. We demonstrate this correlation in the bands of interest (2-5 Hz and 7.5-12.5 Hz) based on a large dataset of signals coming from nine subjects. This experiment identifies whether a movement is voluntary even before the movement is initiated. Starting from these results, the study and design a non-invasive, wireless system for the synchronous acquisition of EEG and EMG signals, is outlined. The overall system is suitable for health and life-care applications in ambient assisted living such as gait monitoring and fall prevention in neurodegenerative diseases

Cyber-Physical System for Gait Analysis and Fall-risk Evaluation by Embedded Cortico-muscular Coupling Computing

The paper describes the architecture of a non-invasive, wireless embedded system for gait analysis and preventing involuntary movements including fall. The system operates with synchronized and digitized data samples from 8 EMG (limbs) and 8 EEG (motor-cortex) channels. An embedded Altera Cyclone V FPGA operates the real-time signal pre-processing and the computation (resource utilization: 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory; processing latency < 1ms). The system has been tested on patients affected by Parkinson disease (PD) under physician guide and compared with healthy subjects’ results. Both PD and healthy subjects have been involved in the standard diagnostic protocol (normal gait and pull test). The developed cyber-physical system detects differences between the PD and the healthy subjects in terms of walking pattern, i.e., agonist-antagonist co-contractions (Typ time: PD’s 148ms vs Healthy 88ms; Max: PD’s 388ms vs Healthy 314ms). The PD’s cerebral Movement Related Potentials (i.e., Bereitschaft) analysis during the pull-test showed an increasing from 59dBμ to 66dBμ after 3 settling steps while measurements on healthy subject return, respectively, 57dBμ, 62dBμ in 1 settling step. The system is able to prevent fall enabling the actuator in 168ms, i.e., better than the normal human time reaction (300ms)

Remote neuro-cognitive impairment sensing based on P300 spatio-temporal monitoring

A novel mobile healthcare solution for remotely monitoring neuro-cognitive efficiency is here presented. The method is based on the spatio-temporal characterization of a specific event-related potential, called P300, induced in our brain by a target stimulus. P300 analysis is used as a biomarker: the amplitude and latency of the signal are quality indexes of the brain activity. Up to now, the P300 characterization has been performed in hospital through EEG analysis and it has not been experimented an algorithm that can work remotely and learn from the subject performance. The proposed m-health service allows remote EEG monitoring of P300 through a “plug and play” system based on the video game reaction of the subject under test. The signal processing is achieved by tuned residue iteration decomposition (t-RIDE). The methodology has been tested on the parietal-cortex area (Pz, Fz, and Cz) of 12 subjects involved in three different cognitive tasks with increasing difficulty. For the set of considered subjects, a P300 deviation has been detected: the amplitude ranges around 2.8-8 μV and latency around 300-410 ms. To demonstrate the improvement achieved by the proposed algorithm respect the state of the art, a comparison between t-RIDE, RIDE, independent component analysis (ICA) approaches, and grand average method is here reported. t-RIDE and ICA analyses report the same results (0.1% deviation) using the same data set (game with a detection of 40 targets). Nevertheless, t-RIDE is 1.6 times faster than ICA since converges in 79 iterations (i.e., t-RIDE: 1.95s against ICA: 3.1s). Furthermore, t-RIDE reaches 80% of accuracy after only 13 targets (task time can be reduced to 65s); differently from ICA, t-RIDE can be performed even on a single channel. The procedure shows fast diagnosis capability in cognitive deficit, including mild and heavy cognitive impairment

Wireless Remote Environmental Monitoring and Control of Perishable Goods in Maritime Transportation

Global trade has reached an ever seen level thanks to globalization. Maritime transportation covers an important role in responding to the growing demand of products from all around the globe. Due to the global travelling, products experienced several environment conditions; in the case of perishable goods, significant variations of transport conditions can spoil their quality, causing huge losses to the vendor. Thus, the environment control in freight car-go is a compelling solution to guarantee product quality during the transporta-tion phase. In this paper, Wireless Sensor Network (WSN) systems are present-ed as an effective and low cost solution to identify not adequate transportation conditions, i.e. temperature, humidity, light exposition; moreover, collected da-ta can be processed to control product quality, ensuring product safety to users and certification to vendors

Wireless Sensor Network for Real Time Sea Water Quality Monitoring

In this paper, it is proposed the concept of a wireless sensor network (WSN) designed for real time remote sea water quality monitoring. Each network node is equipped by sensors measuring temperature, ambient light, conductivity, dissolved oxygen, pH, dissolved ions and turbidity for an automated diagnosis that enables the early identification of critical situations in the water quality, allowing an immediate intervention favoring pollution control