Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review

The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural networks and the deployment of deep learning models on ultra-low power microcontrollers (MCUs) for TinyML applications. It begins by introducing neural networks and discussing their architectures and resource requirements. It then explores MEMS-based applications on ultra-low power MCUs, highlighting their potential for enabling TinyML on resource-constrained devices. The core of the review centres on efficient neural networks for TinyML. It covers techniques such as model compression, quantization, and low-rank factorization, which optimize neural network architectures for minimal resource utilization on MCUs. The paper then delves into the deployment of deep learning models on ultra-low power MCUs, addressing challenges such as limited computational capabilities and memory resources. Techniques like model pruning, hardware acceleration, and algorithm-architecture co-design are discussed as strategies to enable efficient deployment. Lastly, the review provides an overview of current limitations in the field, including the trade-off between model complexity and resource constraints. Overall, this review paper presents a comprehensive analysis of efficient neural networks and deployment strategies for TinyML on ultra-low-power MCUs. It identifies future research directions for unlocking the full potential of TinyML applications on resource-constrained devices.Comment: 39 pages, 9 figures, 5 table

Thrombocytopenia and platelet transfusions in ICU patients: an international inception cohort study (PLOT-ICU)

Purpose Thrombocytopenia (platelet count < 150 × 109/L) is common in intensive care unit (ICU) patients and is likely associated with worse outcomes. In this study we present international contemporary data on thrombocytopenia in ICU patients. Methods We conducted a prospective cohort study in adult ICU patients in 52 ICUs across 10 countries. We assessed frequencies of thrombocytopenia, use of platelet transfusions and clinical outcomes including mortality. We evaluated pre-selected potential risk factors for the development of thrombocytopenia during ICU stay and associations between thrombocytopenia at ICU admission and 90-day mortality using pre-specified logistic regression analyses. Results We analysed 1166 ICU patients; the median age was 63 years and 39.5% were female. Overall, 43.2% (95% confidence interval (CI) 40.4–46.1) had thrombocytopenia; 23.4% (20–26) had thrombocytopenia at ICU admission, and 19.8% (17.6–22.2) developed thrombocytopenia during their ICU stay. Non-AIDS-, non-cancer-related immune deficiency, liver failure, male sex, septic shock, and bleeding at ICU admission were associated with the development of thrombocytopenia during ICU stay. Among patients with thrombocytopenia, 22.6% received platelet transfusion(s), and 64.3% of in-ICU transfusions were prophylactic. Patients with thrombocytopenia had higher occurrences of bleeding and death, fewer days alive without the use of life-support, and fewer days alive and out of hospital. Thrombocytopenia at ICU admission was associated with 90-day mortality (adjusted odds ratio 1.7; 95% CI 1.19–2.42). Conclusion Thrombocytopenia occurred in 43% of critically ill patients and was associated with worse outcomes including increased mortality. Platelet transfusions were given to 23% of patients with thrombocytopenia and most were prophylactic.publishedVersio

Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review

The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural networks and the deployment of deep learning models on ultra-low power microcontrollers (MCUs) for TinyML applications. It begins by introducing neural networks and discussing their architectures and resource requirements. It then explores MEMS-based applications on ultra-low power MCUs, highlighting their potential for enabling TinyML on resource-constrained devices. The core of the review centres on efficient neural networks for TinyML. It covers techniques such as model compression, quantization, and lowrank factorization, which optimize neural network architectures for minimal resource utilization on MCUs. The paper then delves into the deployment of deep learning models on ultra-low power MCUs, addressing challenges such as limited computational capabilities and memory resources. Techniques like model pruning, hardware acceleration, and algorithm-architecture co-design are discussed as strategies to enable efficient deployment. Lastly, the review provides an overview of current limitations in the field, including the trade-off between model complexity and resource constraints. Overall, this review paper presents a comprehensive analysis of efficient neural networks and deployment strategies for TinyML on ultra-low-power MCUs. It identifies future research directions for unlocking the full potential of TinyML applications on resource-constrained devices

Current Advances in Lung Ultrasound in COVID-19 Critically Ill Patients: A Narrative Review

Lung ultrasound (LUS) has a relatively recent democratization due to the better availability and training of physicians, especially in intensive care units. LUS is a relatively cheap and easy-to-learn and -use bedside technique that evaluates pulmonary morphology when using simple algorithms. During the global COVID-19 pandemic, LUS was found to be an accurate tool to quickly diagnose, triage and monitor patients with COVID-19 pneumonia. This paper aims to provide a comprehensive review of LUS use during the COVID-19 pandemic. The first section of our work defines the technique, the practical approach and the semeiotic signs of LUS examination. The second section exposed the COVID-19 pattern in LUS examination and the difference between the differential diagnosis patterns and the well-correlation found with computer tomography scan findings. In the third section, we described the utility of LUS in the management of COVID-19 patients, allowing an early diagnosis and triage in the emergency department, as the monitoring of pneumonia course (pneumonia progression, alveolar recruitment, mechanical ventilation weaning) and detection of secondary complications (pneumothorax, superinfection). Moreover, we describe the usefulness of LUS as a marker of the prognosis of COVID-19 pneumonia in the fourth section. Finally, the 5th part is focused on describing the interest of the LUS, as a non-ionized technique, in the management of pregnant COVID-19 women

Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review

The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural networks and the deployment of deep learning models on ultra-low power microcontrollers (MCUs) for TinyML applications. It begins by introducing neural networks and discussing their architectures and resource requirements. It then explores MEMS-based applications on ultra-low power MCUs, highlighting their potential for enabling TinyML on resource-constrained devices. The core of the review centres on efficient neural networks for TinyML. It covers techniques such as model compression, quantization, and lowrank factorization, which optimize neural network architectures for minimal resource utilization on MCUs. The paper then delves into the deployment of deep learning models on ultra-low power MCUs, addressing challenges such as limited computational capabilities and memory resources. Techniques like model pruning, hardware acceleration, and algorithm-architecture co-design are discussed as strategies to enable efficient deployment. Lastly, the review provides an overview of current limitations in the field, including the trade-off between model complexity and resource constraints. Overall, this review paper presents a comprehensive analysis of efficient neural networks and deployment strategies for TinyML on ultra-low-power MCUs. It identifies future research directions for unlocking the full potential of TinyML applications on resource-constrained devices

Once‐daily darunavir/ritonavir 400/100 mg in triple therapy: efficacy and penetration in seminal compartment in ANRS ‐165 DARULIGHT study

International audienceA key concern for reduced-dose antiretroviral regimens in maintenance strategies in HIV-infected patients is virological efficacy, as assessed by the suppression of HIV-RNA in blood and reservoirs, such as the genital tract. The risk of minority variants emerging in reservoirs with different antiretroviral drug penetrations and immune pressures is also a matter of concern [1–4]. Thus, antiviral activity and drug penetration into deep compartments must be considered in reduced-dose strategies

Effectiveness of a ‘do not interrupt’ vest intervention to reduce medication errors during medication administration: a multicenter cluster randomized controlled trial

International audienceAbstract Background The use of a ‘do not interrupt’ vest during medication administration rounds is recommended but there have been no controlled randomized studies to evaluate its impact on reducing administration errors. We aimed to evaluate the impact of wearing such a vest on reducing such errors. The secondary objectives were to evaluate the types and potential clinical impact of errors, the association between errors and several risk factors (such as interruptions), and nurses’ experiences. Methods This was a multicenter, cluster, controlled, randomized study (March–July 2017) in 29 adult units (4 hospitals). Data were collected by direct observation by trained observers. All nurses from selected units were informed. A ‘Do not interrupt’ vest was implemented in all units of the experimental group. A poster was placed at the entrance of these units to inform patients and relatives. The main outcome was the administration error rate (number of Opportunities for Error (OE), calculated as one or more errors divided by the Total Opportunities for Error (TOE) and multiplied by 100). Results We enrolled 178 nurses and 1346 patients during 383 medication rounds in 14 units in the experimental group and 15 units in the control group. During the intervention period, the administration error rates were 7.09% (188 OE with at least one error/2653 TOE) for the experimental group and 6.23% (210 OE with at least one error/3373 TOE) for the control group ( p = 0.192). Identified risk factors (patient age, nurses’ experience, nurses’ workload, unit exposition, and interruption) were not associated with the error rate. The main error type observed for both groups was wrong dosage-form. Most errors had no clinical impact for the patient and the interruption rates were 15.04% for the experimental group and 20.75% for the control group. Conclusions The intervention vest had no impact on medication administration error or interruption rates. Further studies need to be performed taking into consideration the limitations of our study and other risk factors associated with other interventions, such as nurse’s training and/or a barcode system. Trial registration The PERMIS study protocol (V2–1, 11/04/2017) was approved by institutional review boards and ethics committees (CPP Ile de France number 2016-A00211–50, CNIL 21/03/2017, CCTIRS 11/04/2016). It is registered at ClinicalTrials.gov (registration number: NCT03062852 , date of first registration: 23/02/2017)

Tenofovir plasma concentrations related to estimated glomerular filtration rate changes in first-line regimens in African HIV-infected patients: ANRS 12115 DAYANA substudy

International audienceOBJECTIVES:An open-label randomized trial (DAYANA) was conducted in sub-Saharan settings to evaluate four different regimens containing tenofovir disoproxil fumarate as first-line treatment for HIV infection. The objectives of the present substudy were to assess the relationship between trough concentrations of tenofovir in plasma collected after 24 h (C24) and estimated glomerular filtration rates (eGFR) calculated by the different formulae that are available.METHODS:The criteria for eligibility were those of the DAYANA trial, recruiting naive patients. The four tenofovir regimens were: Group 1, tenofovir/emtricitabine/nevirapine; Group 2, tenofovir/lopinavir/ritonavir; Group 3, tenofovir/emtricitabine/zidovudine; and Group 4, tenofovir/emtricitabine/efavirenz. The C24 of tenofovir was determined using LC-MS/MS. The eGFR was calculated using the Cockcroft-Gault, Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulae.RESULTS:The median C24 of tenofovir was 42 ng/mL. The C24 of tenofovir was higher with lopinavir/ritonavir than with the other three regimens: at Week 4, 84 ng/mL versus 25 ng/mL; and at Week 48, 81 ng/mL versus 52 ng/mL. The baseline merged eGFR was 98.2 mL/min/1.73 m(2) with the CKD-EPI equation. Only the mean changes in eGFR in Group 2 differed from the absolute value of zero (-8.2 mL/min/1.73 m(2)) with the CKD-EPI equation between baseline and Week 48. The Cockcroft-Gault formula is inappropriate for these African patients because it underestimated the baseline eGFR and overestimated the changes in eGFR between baseline and Week 48.CONCLUSIONS:In this population of mostly female HIV-1-infected African patients, tenofovir plasma overexposure was associated with PI/ritonavir and a time-dependent decrease in eGFR, probably via an inhibition of MRP2/MRP4 efflux transporters. The close monitoring over time of the eGFR using MDRD or CKD-EPI calculations and by using other biomarkers of renal disorder should be proposed as an alternative to therapeutic drug monitoring in resource-limited countries