Non-contact immunological signaling for highly-efficient regulation of the transcriptional map of human monocytes.

The different immune system cells communicate and coordinate a response using a complex and evolved language of cytokines and chemokines. These cellular interactions carry out multiple functions in distinct cell types with numerous developmental outcomes. Despite the plethora of different cytokines and their cognate receptors, there is a restricted number of signal transducers and activators to control immune responses. Herein, we report on a new class of immunomodulatory signaling molecules based on volatile molecules (VMs, namely, volatile organic compounds [VOCs]), by which they can affect and/or control immune cell behavior and transcriptomic profile without any physical contact with other cells. The study demonstrates the role of VMs by analyzing non-contact cell communication between normal and cancerous lung cells and U937 monocytes, which are key players in the tumor microenvironment. Integrated transcriptome and proteome analyses showed the suggested regulatory role of VMs released from normal and cancer cells on neighboring monocytes in several molecular pathways, including PI3K/AKT, PPAR, and HIF-1. Presented data provide an initial platform for a new class of immunomodulatory molecules that can potentially mirror the genomic and proteomic profile of cells, thereby paving the way toward non-invasive immunomonitoring.

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring.

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.

Consensus protocol for management of early and late twin-twin transfusion syndrome: Delphi study.

OBJECTIVE: Fetoscopic laser photocoagulation (FLP) is a well-established treatment for twin-twin transfusion syndrome (TTTS) between 16 and 26 weeks' gestation. High-quality evidence and guidelines regarding the optimal clinical management of very early (prior to 16 weeks), early (between 16 and 18 weeks) and late (after 26 weeks) TTTS are lacking. The aim of this study was to construct a structured expert-based clinical consensus for the management of early and late TTTS. METHODS: A Delphi procedure was conducted among an international panel of experts. Participants were chosen based on their clinical expertise, affiliation and relevant publications. A four-round Delphi survey was conducted using an online platform and responses were collected anonymously. In the first round, a core group of experts was asked to answer open-ended questions regarding the indications, timing and modes of treatment for early and late TTTS. In the second and third rounds, participants were asked to grade each statement on a Likert scale (1, completely disagree; 5, completely agree) and to add any suggestions or modifications. At the end of each round, the median score for each statement was calculated. Statements with a median grade of 5 without suggestions for change were accepted as the consensus. Statements with a median grade of 3 or less were excluded from the Delphi process. Statements with a median grade of 4 were modified according to suggestions and reconsidered in the next round. In the last round, participants were asked to agree or disagree with the statements, and those with more than 70% agreement without suggestions for change were considered the consensus. RESULTS: A total of 122 experts met the inclusion criteria and were invited to participate, of whom 53 (43.4%) agreed to take part in the study. Of those, 75.5% completed all four rounds. A consensus on the optimal management of early and late TTTS was obtained. FLP can be offered as early as 15 weeks' gestation for selected cases, and can be considered up to 28 weeks. Between 16 and 18 weeks, management should be tailored according to Doppler findings. CONCLUSIONS: A consensus-based treatment protocol for early and late TTTS was agreed upon by a panel of experts. This protocol should be modified at the discretion of the operator, according to their experience and the specific demands of each case. This should advance the quality of future studies, guide clinical practice and improve patient care. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Continuous Monitoring of Psychosocial Stress by Non-Invasive Volatilomics.

Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.

Using Zodiacal Light For Spaceborne Calibration Of Polarimetric Imagers.

We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant target in realistic calibration sessions. We derive a forward model for polarimetric image formation. Based on it, we formulate an inverse problem for polarimetric calibration and self-calibration, as well as an algorithm for the solution. The methods here are demonstrated in simulations. Towards these simulations, we render polarized images of the sky, including ZL from space, polarimetric disturbances, and imaging noise.

Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management.

As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.

Revitalizing Hands: A Comprehensive Review of Anatomy and Treatment Options for Hand Rejuvenation.

Dorsal hand rejuvenation is gaining popularity as a solitary procedure and adjunct to face and neck rejuvenation treatments. As the hands age, the skin loses elasticity and becomes more translucent, the veins, joints, and tendons appear more prominent, and the bones become more noticeable. These changes are due to intrinsic and extrinsic factors. Current treatment methods include the injection of dermal fillers and autologous fat grafting. Anatomic studies to ensure the successful implementation of rejuvenation procedures identified three separate fascial layers in the dorsum, from superficial to deep. More recent re-evaluations revealed a less distinct, inseparable, sponge-like fascial layer. All authors agree that the superficial dermal layer is probably the optimal location for the injection of volumizing materials because it is free of anatomical structures. Many methods for harvesting, preparing, and injecting fat grafts to the dorsum of the hand have been described in the past three decades. Both filler and fat-graft procedures are performed on an ambulatory basis under local anesthesia. Good results with low postoperative and long-term complication rates and high patient satisfaction have been reported.

Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer.

Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.

Noninvasive Detection of Stress by Biochemical Profiles from the Skin.

Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group (n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.

Noninvasive Pregestational Genetic Testing of Embryos Using Smart Sensors Array.

Pregestational genetic testing of embryos is the conventional tool in detecting genetic disorders (fetal aneuploidy and monogenic disorders) for in vitro fertilization (IVF) procedures. The accepted clinical practice for genetic testing still depends on biopsy, which has the potential to harm the embryo. Noninvasive genetic prenatal testing has not yet been achieved. In this study, embryos with common genetic disorders created through IVF were tested with an artificially intelligent nanosensor array. Volatile organic compounds emitted by the culture fluid of embryos were analyzed with chemical gas sensors. The obtained results showed significant discrimination between the embryos with different genetic diseases and their wild-types. Embryos were obtained from the same clinical center for avoiding differences based on clinical and demographical characteristics. The achieved discrimination accuracy was 81% for PKD disease, 90% for FRAX disease, 85% for HOCM disease, 90% for BRCA disease, and 100% for HSCR disease. These proof-of-concept findings might launch the development of a noninvasive approach for early assessment of embryos by examining the culture fluid of the embryos, potentially enabling noninvasive diagnosis and screening of genetic diseases for IVF.

Variable Imaging Projection Cloud Scattering Tomography.

Scattering-based computed tomography (CT) recovers a heterogeneous volumetric scattering medium using images taken from multiple directions. It is a nonlinear problem. Prior art mainly approached it by explicit physics-based optimization of image-fitting, being slow and difficult to scale. Scale is particularly important when the objects constitute large cloud fields, where volumetric recovery is important for climate studies. Besides speed, imaging and recovery need to be flexible, to efficiently handle variable viewing geometries and resolutions. These can be caused by perturbation in camera poses or fusion of data from different types of observational sensors. There is a need for fast variable imaging projection scattering tomography of clouds (VIP-CT). We develop a learning-based solution, using a deep-neural network (DNN) which trains on a large physics-based labeled volumetric dataset. The DNN parameters are oblivious to the domain scale, hence the DNN can work with arbitrarily large domains. VIP-CT offers much better quality than the state of the art. The inference speed and flexibility of VIP-CT make it effectively real-time in the context of spaceborne observations. The paper is the first to demonstrate CT of a real cloud using empirical data directly in a DNN. VIP-CT may offer a model for a learning-based solution to nonlinear CT problems in other scientific domains. Our code is available online.

Detecting Coronary Artery Disease Using Exhaled Breath Analysis.

INTRODUCTION: Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide, and there is an unmet need for a simple, inexpensive, noninvasive tool aimed at CAD detection. The aim of this pilot study was to evaluate the possible use of breath analysis in detecting the presence of CAD. MATERIALS AND METHODS: In a prospective study, breath from patients with no history of CAD who presented with acute chest pain to the emergency room was sampled using a designated portable electronic nose (eNose) system. First, breath samples from 60 patients were analyzed and categorized as obstructive, nonobstructive, and no-CAD according to the actual presence and extent of CAD as was demonstrated on cardiac imaging (either computerized tomography angiography or coronary angiography). Classification models were built according to the results, and their diagnostic performance was then examined in a blinded manner on a new set of 25 patients. The data were compared with the actual results of coronary arteries evaluation. Sensitivity, specificity, and accuracy were calculated for each model. RESULTS: Obstructive CAD was correctly distinguished from nonobstructive and no-CAD with 89% sensitivity, 31% specificity, 83% negative predictive value (NPV), 42% positive predictive value (PPV), and 52% accuracy. In another model, any extent of CAD was successfully distinguished from no-CAD with 69% sensitivity, 67% specificity, 54% NPV, 79% PPV, and 68% accuracy. CONCLUSION: This proof-of-concept study shows that breath analysis has the potential to be used as a novel rapid, noninvasive diagnostic tool to help identify presence of CAD in patients with acute chest pain.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers.

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

The Utility of Breath Analysis in the Diagnosis and Staging of Parkinson's Disease.

BACKGROUND: The analysis of volatile organic compounds (VOCs) collected in breath samples has the potential to be a rapid, non-invasive test to aid in the clinical diagnosis and tracking of chronic conditions such as Parkinson's disease (PD). OBJECTIVE: To assess the feasibility and utility of breath sample analysis done, both at point of collection in clinic and when sent away to be analyzed remotely, to diagnose, stratify and monitor disease course in a moderately large cohort of patients with PD. METHODS: Breath samples were collected from 177 people with PD and 37 healthy matched control individuals followed over time. Standard clinical data (MDS-UPDRS & cognitive assessments) from the PD patients were collected at the same time as the breath sample was taken, these measures were then correlated with the breath test analysis of exhaled VOCs. RESULTS: The breath test was able to distinguish patients with PD from healthy control participants and correlated with disease stage. The off-line system (remote analysis) gave good results with overall classification accuracies across a range of clinical measures of between 73.6% to 95.6%. The on-line (in clinic) system showed comparable results but with lower levels of correlation, varying between 33.5% to 82.4%. Chemical analysis identified 29 potential molecules that were different and which may relate to pathogenic pathways in PD. CONCLUSION: Breath analysis shows potential for PD diagnostics and monitoring. Both off-line and on-line sensor systems were easy to do and provided comparable results which will enable this technique to be easily adopted in clinic if larger studies confirm our findings.

Computational Imaging on the Electric Grid.

Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a dataset of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.

Biodiagnostics in an era of global pandemics-From biosensing materials to data management.

The novel corona virus SARS-CoV-2 (COVID-19) has exposed the world to challenges never before seen in fast diagnostics, monitoring, and prevention of the outbreak. As a result, different approaches for fast diagnostic and screening are made and yet to find the ideal way. The current mini-review provides and examines evidence-based innovative and rapid chemical sensing and related biodiagnostic solutions to deal with infectious disease and related pandemic emergencies, which could offer the best possible care for the general population and improve the approachability of the pandemic information, insights, and surrounding contexts. The review discusses how integration of sensing devices with big data analysis, artificial Intelligence or machine learning, and clinical decision support system, could improve the accuracy of the recorded patterns of the disease conditions within an ocean of information. At the end, the mini-review provides a prospective on the requirements to improve our coping of the pandemic-related biodiagnostics as well as future opportunities.

A Wearable Microneedle-Based Extended Gate Transistor for Real-Time Detection of Sodium in Interstitial Fluids.

Sodium is a prominent prognostic biomarker for assessing health status, such as dysnatremia. As of now, detection and monitoring of sodium levels in the human body is carried out by means of laborious and bulky laboratory equipmentand/or by offline analysis of various body fluids. Herein, an innovative stretchable, skin-conformal and fast-response microneedle extended-gate FET biosensor is reported for real-time detection of sodium in interstitial fluids for minimally invasive health monitoring along with high sensitivity, low limit of detection, excellent biocompatibility, and on-body mechanical stability. The integration of the reported device with a wireless-data transmitter and the Internet-of-Things cloud for real-time monitoring and long-term analysis is reported and discussed. This platform would eventually help bringing unlimited possibilities for effecient medical care and accurate clinical decision-making.

Plankton reconstruction through robust statistical optical tomography.

Plankton interact with the environment according to their size and three-dimensional (3D) structure. To study them outdoors, these translucent specimens are imaged in situ. Light projects through a specimen in each image. The specimen has a random scale, drawn from the population's size distribution and random unknown pose. The specimen appears only once before drifting away. We achieve 3D tomography using such a random ensemble to statistically estimate an average volumetric distribution of the plankton type and specimen size. To counter errors due to non-rigid deformations, we weight the data, drawing from advanced models developed for cryo-electron microscopy. The weights convey the confidence in the quality of each datum. This confidence relies on a statistical error model. We demonstrate the approach on live plankton using an underwater field microscope.

Fabricating and printing chemiresistors based on monolayer-capped metal nanoparticles.

Chemiresistors that are based on monolayer-capped metal nanoparticles (MCNPs) have been used in a wide variety of innovative sensing applications, including detection and monitoring of diagnostic markers in body fluids, explosive materials, environmental contaminations and food quality control. The sensing mechanism is based on reversible swelling or aggregation and/or changes in dielectric constant of the MCNPs. In this protocol, we describe a procedure for producing MCNP-based chemiresistive sensors that is reproducible from device to device and from batch to batch. The approach relies on three main steps: (i) controlled synthesis of gold MCNPs, (ii) fabrication of electrodes that are surrounded with a microbarrier ring to confine the deposited MCNP solution and (iii) a tailor-made drying process to enable evaporation of solvent residues from the MCNP sensing layer to prevent a coffee-ring effect. Application of this approach has been shown to produce devices with ±1.5% variance-a value consistent with the criterion for commercial sensors-as well as long shelf life and stability. Fabrication of chemical sensors based on dodecanethiol- or 2-ethylhexanethiol-capped MCNPs with this approach provides high sensitivity and accuracy in the detection of volatile organic compounds (e.g., octane and decane), toxic gaseous species (e.g., HCl and NH3) in air and simulated mixtures of lung and gastric cancer from exhaled breath.