Breathing Pattern Monitoring by Using Remote Sensors.

The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal cameras. The recordings were used to develop and compare several respiratory signal extraction algorithms. An algorithm for the robust extraction of multiple respiratory features was developed and evaluated, capable of differentiating a wide range of respiratory patterns. These features were used to train a one vs. one multiclass support vector machine, which can distinguish between breathing patterns with an accuracy of 95.79 %. The recorded dataset was published to enable further improvement of contactless breathing pattern classification, especially for complex breathing patterns.

Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging.

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient's consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims' mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies' longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images.

Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

Genetic Variations in Prostaglandin E2 Pathway Identified as Susceptibility Biomarkers for Gastric Cancer in an Intermediate Risk European Country.

The cyclooxygenase-2 (COX-2)/prostaglandin E2 (PGE2) pathway exerts deleterious pleiotropic effects in inflammation-induced gastric carcinogenesis. We aimed to assess the association of genetic variants in prostaglandin-endoperoxide synthase 2 (PTGS2), ATP binding cassette subfamily C member 4 (ABCC4), hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD), and solute carrier organic anion transporter family member 2A1 (SLCO2A1) PGE2 pathway-related genes with gastric cancer (GC) risk in a European Caucasian population. A hospital-based case-control study gathering 260 GC cases and 476 cancer-free controls was implemented. Using a tagSNP approach, 51 single nucleotide polymorphisms (SNPs) were genotyped through MassARRAY® iPLEX Gold Technology or allelic discrimination by real-time polymerase chain reaction (PCR). Homozygous carriers of the minor allele for both rs689466 and rs10935090 SNPs were associated with a 2.98 and 4.30-fold increased risk for GC, respectively (95% confidence interval (CI): 1.14-7.74, p = 0.027; 95% CI: 1.22-15.16, p = 0.026), with the latter also being associated with an anticipated diagnosis age. A multifactor dimensionality reduction analysis identified an overall three-factor best interactive model composed of age, rs689466, and rs1678374 that was associated with a 17.6-fold GC increased risk (95% CI: 11.67-26.48, p < 0.0001, (cross-validation) CV consistency of 8/10 and accuracy of 0.807). In this preliminary study, several tagSNPs in PGE2 pathway-related genes were identified as risk biomarkers for GC development. This approach may help to identify higher-risk individuals and may contribute to the tailoring screening of GC in intermediate-risk European countries.

Prostaglandin E2 Pathway Is Dysregulated in Gastric Adenocarcinoma in a Caucasian Population.

Gastric cancer (GC) represents the third leading cause of cancer-related deaths worldwide. The levels of prostaglandin E2, a key player in the hallmarks of cancer, are mainly regulated by prostaglandin-endoperoxide synthase 2 (PTGS2) and ATP-binding cassette subfamily C member 4 (ABCC4), involved in its synthesis and exportation, respectively, and 15-hydroxyprostaglandin dehydrogenase (15-PGDH) and solute carrier organic anion transporter family member 2A1 (SLCO2A1), responsible for its inactivation. Even though there are distinct molecular signatures across ethnic populations, most published studies focus on Asian populations. Our main aim was to explore the genetic expression of the aforementioned molecules in a Caucasian population. 94 "Normal" and 89 tumoral formalin-fixed paraffin-embedded (FFPE) samples from GC patients were used to assess the mRNA expression of PTGS2, ABCC4, hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD), SLCO2A1 by Real-Time PCR. We found an upregulation for the PTGS2 gene mean factor of 2.51 and a downregulation for the HPGD and SLCO2A1 genes (mean factor of 0.10 and 0.37, respectively) in tumorous mucosa in a gender-independent manner. In females, we observed an ABCC4 downregulation and a PTGS2 mRNA upregulation compared to males in tumoral mucosa (mean factor of 0.61 and 1.64, respectively). We reported dysregulation of the inflammation triggered PGE2 pathway in a Caucasian population with an intermediate risk for GC, which might highlight the applicability of aspirin in the treatment of GC patients.

Pharmacogenomics in epithelial ovarian cancer first-line treatment outcome: validation of GWAS-associated NRG3 rs1649942 and BRE rs7572644 variants in an independent cohort.

The identification of predictive biomarkers for the first-line treatment of epithelial ovarian cancer (EOC) remains a challenge. Although genome-wide association studies (GWAS) have identified several genetic polymorphisms as predictors of EOC clinical outcome, the subsequent validation has not yet been performed. This study aims to validate the influence of Neuregulin 3 (NRG3) rs1649942 and Brain and reproductive organ-expressed (TNFRSF1A modulator) (BRE) rs7572644 GWAS-identified variants in an independent cohort of EOC patients from the North region of Portugal (n = 339) submitted to first-line treatment. Polymorphism genotypes were determined by real-time PCR using validated assays. Patients carrying the NRG3 rs1649942 A allele presented a significantly longer overall survival (OS) when compared to GG-genotype patients (log-rank test, P = 0.011) in the FIGO IV stage subgroup. No impact was observed for early-stage patients or considering disease-free survival (DFS) as an outcome. For FIGO I/II stage patients, BRE rs7572644 C allele carriers exhibit a decreased OS (P = 0.014) and DFS (P = 0.032) when compared to TT-homozygous patients. Furthermore, a Multivariate Cox regression analysis revealed a three-fold increase in the risk of death (HR, 3.09; P = 0.015) and recurrence (HR, 3.33; P = 0.009) for FIGO I/II C allele carriers. No significant impact was observed for late-stage patients. The BRE rs7572644 and NRG3 rs1649942 genetic variants were validated in an independent cohort of EOC Portuguese patients, particularly in specific subgroups considering FIGO staging. Further functional post-GWAS analyses are indispensable to understand the biological mechanisms underlying the observed results.

Evaluation of a bronchoscopy guidance system for bronchoscopy training, a randomized controlled trial.

BACKGROUND: Conventional training in bronchoscopy is performed either on patients (apprenticeship model) or phantoms. While the former is associated with increased rate of patient complications, procedure time, and amount of sedation, the latter does not offer any form of feedback to the trainee. This paper presents a study which investigates whether a bronchoscopy guidance system may be a helpful tool for training of novice bronchoscopists. METHODS: A randomized controlled study with 48 medical students was carried out with two different groups (control and test group, each N = 24). Whereas the control group performed a conventional bronchoscopy on phantom the test group carried out an Electromagnetic Navigation Bronchoscopy (ENB) for tracking of the bronchoscopal tip in the bronchial system. All volunteers had a common task: to perform a complete and systematic diagnostic bronchoscopy within 10 min. RESULTS: The test group examined significantly more lobes than the control group (p = 0.009). Due to the real-time feedback of the system, all students of test group felt more confident having analyzed the entire lung. Additionally, they were unanimous that the system would be helpful during the next bronchoscopy. CONCLUSIONS: In sum, this technology may play a major role in unsupervised learning by improving accuracy, dexterity but above all by increasing the confidence of novices, students as well as physicians. Due to good acceptance, there may be a great potential of this tool in clinical routine.

CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions versus Filtered Back Projection.

Purpose To investigate performance in detectability of small (≤1 cm) low-contrast hypoattenuating focal lesions by using filtered back projection (FBP) and iterative reconstruction (IR) algorithms from two major CT vendors across a range of 11 radiation exposures. Materials and Methods A low-contrast detectability phantom consisting of 21 low-contrast hypoattenuating focal objects (seven sizes between 2.4 and 10.0 mm, three contrast levels) embedded into a liver-equivalent background was scanned at 11 radiation exposures (volume CT dose index range, 0.5-18.0 mGy; size-specific dose estimate [SSDE] range, 0.8-30.6 mGy) with four high-end CT platforms. Data sets were reconstructed by using FBP and varied strengths of image-based, model-based, and hybrid IRs. Sixteen observers evaluated all data sets for lesion detectability by using a two-alternative-forced-choice (2AFC) paradigm. Diagnostic performances were evaluated by calculating area under the receiver operating characteristic curve (AUC) and by performing noninferiority analyses. Results At benchmark exposure, FBP yielded a mean AUC of 0.79 ± 0.09 (standard deviation) across all platforms which, on average, was approximately 2% lower than that observed with the different IR algorithms, which showed an average AUC of 0.81 ± 0.09 (P = .12). Radiation decreases of 30%, 50%, and 80% resulted in similar declines of observer detectability with FBP (mean AUC decrease, -0.02 ± 0.05, -0.03 ± 0.05, and -0.05 ± 0.05, respectively) and all IR methods investigated (mean AUC decrease, -0.00 ± 0.05, -0.04 ± 0.05, and -0.04 ± 0.05, respectively). For each radiation level and CT platform, variance in performance across observers was greater than that across reconstruction algorithms (P = .03). Conclusion Iterative reconstruction algorithms have limited radiation optimization potential in detectability of small low-contrast hypoattenuating focal lesions. This task may be further complicated by a high degree of variation in radiologists' performances, seemingly exceeding real performance differences among reconstruction algorithms. © RSNA, 2018 Online supplemental material is available for this article.

Influenza A Virus NS1 Protein Promotes Efficient Nuclear Export of Unspliced Viral M1 mRNA.

Influenza A virus mRNAs are transcribed by the viral RNA-dependent RNA polymerase in the cell nucleus before being exported to the cytoplasm for translation. Segment 7 produces two major transcripts: an unspliced mRNA that encodes the M1 matrix protein and a spliced transcript that encodes the M2 ion channel. Export of both mRNAs is dependent on the cellular NXF1/TAP pathway, but it is unclear how they are recruited to the export machinery or how the intron-containing but unspliced M1 mRNA bypasses the normal quality-control checkpoints. Using fluorescent in situ hybridization to monitor segment 7 mRNA localization, we found that cytoplasmic accumulation of unspliced M1 mRNA was inefficient in the absence of NS1, both in the context of segment 7 RNPs reconstituted by plasmid transfection and in mutant virus-infected cells. This effect was independent of any major effect on steady-state levels of segment 7 mRNA or splicing but corresponded to a ∼5-fold reduction in the accumulation of M1. A similar defect in intronless hemagglutinin (HA) mRNA nuclear export was seen with an NS1 mutant virus. Efficient export of M1 mRNA required both an intact NS1 RNA-binding domain and effector domain. Furthermore, while wild-type NS1 interacted with cellular NXF1 and also increased the interaction of segment 7 mRNA with NXF1, mutant NS1 polypeptides unable to promote mRNA export did neither. Thus, we propose that NS1 facilitates late viral gene expression by acting as an adaptor between viral mRNAs and the cellular nuclear export machinery to promote their nuclear export.IMPORTANCE Influenza A virus is a major pathogen of a wide variety of mammalian and avian species that threatens public health and food security. A fuller understanding of the virus life cycle is important to aid control strategies. The virus has a small genome that encodes relatively few proteins that are often multifunctional. Here, we characterize a new function for the NS1 protein, showing that, as well as previously identified roles in antagonizing the innate immune defenses of the cell and directly upregulating translation of viral mRNAs, it also promotes the nuclear export of the viral late gene mRNAs by acting as an adaptor between the viral mRNAs and the cellular mRNA nuclear export machinery.

Monitoring of Cardiorespiratory Signals Using Thermal Imaging: A Pilot Study on Healthy Human Subjects.

Heart rate (HR) and respiratory rate (RR) are important parameters for patient assessment. However, current measurement techniques require attachment of sensors to the patient’s body, often leading to discomfort, stress and even pain. A new algorithm is presented for monitoring both HR and RR using thermal imaging. The cyclical ejection of blood flow from the heart to the head (through carotid arteries and thoracic aorta) leads to periodic movements of the head; these vertical movements are used to assess HR. Respiratory rate is estimated by using temperature fluctuations under the nose during the respiratory cycle. To test the viability and feasibility of this approach, a pilot study was conducted with 20 healthy subjects (aged 18⁻36 and 1 aged 50 years). The study consisted of two phases: phase A (frontal view acquisitions) and phase B (side view acquisitions). To validate the results, photoplethysmography and thoracic effort (piezoplethysmography) were simultaneously recorded. High agreement between infrared thermography and ground truth/gold standard was achieved. For HR, the root-mean-square errors (RMSE) for phases A and B were 3.53 ± 1.53 and 3.43 ± 1.61 beats per minute, respectively. For RR, the RMSE between thermal imaging and piezoplethysmography stayed around 0.71 ± 0.30 breaths per minute (phase A). This study demonstrates that infrared thermography may be a promising, clinically relevant alternative for the assessment of HR and RR.

Estimating Respiratory Rate in Post-Anesthesia Care Unit Patients Using Infrared Thermography: An Observational Study.

The post-anesthesia care unit (PACU) is the central hub for recovery after surgery, especially when the surgery is performed under general anesthesia. Aside from clinical aspects, respiratory impairment is one of the major causes of morbidity and affected recovery in the PACU and should therefore be monitored. In previous studies, infrared thermography was applied to assess the breathing rate (BR) of healthy volunteers. Here, the transferability of published methods for postoperative patients in the PACU was examined. Video recordings of 28 patients were acquired using a long-wave infrared camera, and analyzed offline. For validation purposes, BRs derived from body surface electrocardiography were measured simultaneously. In general, a close agreement between the two techniques (r = 0.607, p = 0.002 upon arrival, and r = 0.849, p < 0.001 upon discharge from the PACU) was obtained. In conclusion, the algorithm was demonstrated to be feasible and reliable under these challenging conditions.

Remote Welfare Monitoring of Rodents Using Thermal Imaging.

Animal research has always played a crucial role in various medical and scientific breakthroughs. They offer, inter alia, insights into diseases mechanisms, genetic predisposition to a disease, and drug therapy. However, the use of animals for medical research is a cause of major controversies and debates in modern science. To warrant high bioethical standards, new directives have been being adopted to replace animal research whenever possible, to reduce the number of animals, and to refine the procedures to minimize stress and pain. Here, we present two new approaches, based on thermal imaging (a remote and passive technology), to assess respiratory rate (RR) as well as exploratory behavior and general activity in rodents. In animal research, these parameters are gold standards for welfare assessment. The approaches were validated in a study conducted with both rats and mice. To test the feasibility of our algorithm to estimate RR, thermal videos from anesthetized rodents were acquired. The capability of the second approach to monitor activity was tested with videos of Open Field tests. Regarding RR, a high agreement between thermal imaging and gold standard (electrocardiography-derived RR) was achieved. The mean relative error averaged 0.50 ± 0.15 breaths/min and 4.55 ± 2.94 breaths/min for rats and mice, respectively. The second approach was capable of monitoring and tracking the activity of the rodents very well. This paper demonstrates that thermal imaging is a promising and relevant alternative for monitoring of RR and activity in rodents, thus contributing to the remote assessment of animal welfare.

Active and Passive Optical Imaging Modality for Unobtrusive Cardiorespiratory Monitoring and Facial Expression Assessment.

Because of their obvious advantages, active and passive optoelectronic sensor concepts are being investigated by biomedical research groups worldwide, particularly their camera-based variants. Such methods work noninvasively and contactless, and they provide spatially resolved parameter detection. We present 2 techniques: the active photoplethysmography imaging (PPGI) method for detecting dermal blood perfusion dynamics and the passive infrared thermography imaging (IRTI) method for detecting skin temperature distribution. PPGI is an enhancement of classical pulse oximetry. Approved algorithms from pulse oximetry for the detection of heart rate, heart rate variability, blood pressure-dependent pulse wave velocity, pulse waveform-related stress/pain indicators, respiration rate, respiratory variability, and vasomotional activity can easily be adapted to PPGI. Although the IRTI method primarily records temperature distribution of the observed object, information on respiration rate and respiratory variability can also be derived by analyzing temperature change over time, for example, in the nasal region, or through respiratory movement. Combined with current research areas and novel biomedical engineering applications (eg, telemedicine, tele-emergency, and telemedical diagnostics), PPGI and IRTI may offer new data for diagnostic purposes, including assessment of peripheral arterial and venous oxygen saturation (as well as their differences). Moreover, facial expressions and stress and/or pain-related variables can be derived, for example, during anesthesia, in the recovery room/intensive care unit and during daily activities. The main advantages of both monitoring methods are unobtrusive data acquisition and the possibility to assess vital variables for different body regions. These methods supplement each other to enable long-term monitoring of physiological effects and of effects with special local characteristics. They also offer diagnostic advantages for intensive care patients and for high-risk patients in a homecare/outdoor setting. Selected applications have been validated at our laboratory using optical PPGI and IRTI techniques in a stand-alone or hybrid configuration. Additional research and validation is required before these preliminary results can be introduced for clinical applications.

Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects.

Diverse studies have demonstrated the importance of monitoring breathing rate (BR). Commonly, changes in BR are one of the earliest and major markers of serious complications/illness. However, it is frequently neglected due to limitations of clinically established measurement techniques, which require attachment of sensors. The employment of adhesive pads or thoracic belts in preterm infants as well as in traumatized or burned patients is an additional paramount issue. The present paper proposes a new robust approach, based on data fusion, to remotely monitor BR using infrared thermography (IRT). The algorithm considers not only temperature modulation around mouth and nostrils but also the movements of both shoulders. The data of these four sensors/regions of interest need to be further fused to reach improved accuracy. To investigate the performance of our approach, two different experiments (phase A: normal breathing, phase B: simulation of breathing disorders) on twelve healthy volunteers were performed. Thoracic effort (piezoplethysmography) was simultaneously acquired to validate our results. Excellent agreements between BR estimated with IRT and gold standard were achieved. While in phase A a mean correlation of 0.98 and a root-mean-square error (RMSE) of 0.28 bpm was reached, in phase B the mean correlation and the RMSE hovered around 0.95 and 3.45 bpm, respectively. The higher RMSE in phase B results predominantly from delays between IRT and gold standard in BR transitions: eupnea/apnea, apnea/tachypnea etc. Moreover, this study also demonstrates the capability of IRT to capture varied breathing disorders, and consecutively, to assess respiratory function. In summary, IRT might be a promising monitoring alternative to the conventional contact-based techniques regarding its performance and remarkable capabilities.

Thermoregulation in premature infants: A mathematical model.

PURPOSE: In 2010, approximately 14.9 million babies (11.1%) were born preterm. Because preterm infants suffer from an immature thermoregulatory system they have difficulty maintaining their core body temperature at a constant level. Therefore, it is essential to maintain their temperature at, ideally, around 37°C. For this, mathematical models can provide detailed insight into heat transfer processes and body-environment interactions for clinical applications. METHODS: A new multi-node mathematical model of the thermoregulatory system of newborn infants is presented. It comprises seven compartments, one spherical and six cylindrical, which represent the head, thorax, abdomen, arms and legs, respectively. The model is customizable, i.e. it meets individual characteristics of the neonate (e.g. gestational age, postnatal age, weight and length) which play an important role in heat transfer mechanisms. The model was validated during thermal neutrality and in a transient thermal environment. RESULTS: During thermal neutrality the model accurately predicted skin and core temperatures. The difference in mean core temperature between measurements and simulations averaged 0.25±0.21°C and that of skin temperature averaged 0.36±0.36°C. During transient thermal conditions, our approach simulated the thermoregulatory dynamics/responses. Here, for all infants, the mean absolute error between core temperatures averaged 0.12±0.11°C and that of skin temperatures hovered around 0.30°C. CONCLUSIONS: The mathematical model appears able to predict core and skin temperatures during thermal neutrality and in case of a transient thermal conditions.

The -1195G allele increases the transcriptional activity of cyclooxygenase-2 gene (COX-2) in colon cancer cell lines.

Up-regulation of cyclooxygenase-2 (COX-2) is an early and key event in human colorectal carcinogenesis (CRC). Nevertheless, the molecular mechanisms leading to this over-expression are largely unknown. We previously reported an association between the -1195G allele and higher predisposition for CRC in a Caucasian population. The biological explanation for the involvement of this polymorphism in CRC remains elusive. We aimed to functionally characterize the influence of the -1195A>G promoter region polymorphism on COX-2 transcription activity in colon cancer cell lines. Luciferase reporter assays were performed to assess whether the -1195A/G alleles influenced COX-2 transcription. The COX-2 promoter's region containing either the -1195A or -1195G alleles was cloned into pGL3-basic reporter vector. The reporter vectors were transiently co-transfected with the pGL4.73 control plasmid to HCT-116 and HCA-7 colon cancer cell lines. The levels of reporter gene expression driven by the -1195G allele-containing COX-2 promoter were significantly higher in both colon cancer cell lines. A 2.2-fold increase in promoter activity was observed in the HCT-116 cell line (P < 0.001), and this over-expression was even more noticeable in the HCA-7 COX-2 expressing cell line with a threefold higher transcriptional activity (P = 0.001). The -1195G allele appeared to enhance COX-2 transcription, providing a molecular basis underlying the increased susceptibility for CRC and potentially a new mechanism for COX-2 overexpression.

Gastric cancer detection by non-blood-based liquid biopsies: A systematic review looking into the last decade of research.

Gastric cancer (GC) screening is arguable in most Western countries. Liquid biopsies are a great promise to answer the unmet need for less invasive diagnostic biomarkers in GC. Thus, we aimed at systematically reviewing the current knowledge on liquid biopsy-based biomarkers in GC screening. A systematic search on PubMed/MEDLINE and Scopus databases was performed on published articles reporting the use of non-blood specimen (saliva, gastric juice [GJ], urine and stool) on GC diagnosis. 3208 records were retrieved by June 2022. After removal of duplicate records, 2379 abstracts were screened, and 84 full texts included in this systematic review. More than 90% of studies were reported on Asian populations. Overall, 9 studies explored stool-, 12 saliva-, and 29 urine-derived biomarkers for GC detection. Additionally, 37 studies, representing the majority, analyzed GJ, focusing on nucleic acid molecules. Several miRNAs and lncRNA molecules have been associated with GC risk, particularly miR-21 (area under the curve [AUC] = 0.97, 95% CI: 0.94-1.00). Considering salivary biomarkers, the best described model in validation sets included the soybean agglutinin and Vicia villosa agglutinin lectins (AUC = 0.89, 95% CI: 0.80-0.99). Most studies in urine carried out metabolomic approaches, with two discriminatory models presenting AUC values superior to 0.97. This systematic review emphasizes the potential role of non-blood-based biomarkers, although further validation, particularly in Western countries, is mandatory, namely for non-invasive screening and/or monitoring, as well as the use of GJ as a tool to enhance upper gastrointestinal endoscopy accuracy.

Linking dysbiosis to precancerous stomach through inflammation: Deeper than and beyond imaging.

Upper gastrointestinal endoscopy is considered the gold standard for gastric lesions detection and surveillance, but it is still associated with a non-negligible rate of missing conditions. In the Era of Personalized Medicine, biomarkers could be the key to overcome missed lesions or to better predict recurrence, pushing the frontier of endoscopy to functional endoscopy. In the last decade, microbiota in gastric cancer has been extensively explored, with gastric carcinogenesis being associated with progressive dysbiosis. Helicobacter pylori infection has been considered the main causative agent of gastritis due to its interference in disrupting the acidic environment of the stomach through inflammatory mediators. Thus, does inflammation bridge the gap between gastric dysbiosis and the gastric carcinogenesis cascade and could the microbiota-inflammation axis-derived biomarkers be the answer to the unmet challenge of functional upper endoscopy? To address this question, in this review, the available evidence on the role of gastric dysbiosis and chronic inflammation in precancerous conditions of the stomach is summarized, particularly targeting the nuclear factor-κB (NF-κB), toll-like receptors (TLRs) and cyclooxygenase-2 (COX-2) pathways. Additionally, the potential of liquid biopsies as a non-invasive source and the clinical utility of studied biomarkers is also explored. Overall, and although most studies offer a mechanistic perspective linking a strong proinflammatory Th1 cell response associated with, but not limited to, chronic infection with Helicobacter pylori, promising data recently published highlights not only the diagnostic value of microbial biomarkers but also the potential of gastric juice as a liquid biopsy pushing forward the concept of functional endoscopy and personalized care in gastric cancer early diagnosis and surveillance.

Camera-Based Respiration Monitoring of Unconstrained Rodents.

Animal research has always been crucial for various medical and scientific breakthroughs, providing information on disease mechanisms, genetic predisposition to diseases, and pharmacological treatment. However, the use of animals in medical research is a source of great controversy and ongoing debate in modern science. To ensure a high level of bioethics, new guidelines have been adopted by the EU, implementing the 3R principles to replace animal testing wherever possible, reduce the number of animals per experiment, and refine procedures to minimize stress and pain. Supporting these guidelines, this article proposes an improved approach for unobtrusive, continuous, and automated monitoring of the respiratory rate of laboratory rats. It uses the cyclical expansion and contraction of the rats' thorax/abdominal region to determine this physiological parameter. In contrast to previous work, the focus is on unconstrained animals, which requires the algorithms to be especially robust to motion artifacts. To test the feasibility of the proposed approach, video material of multiple rats was recorded and evaluated. High agreement was obtained between RGB imaging and the reference method (respiratory rate derived from electrocardiography), which was reflected in a relative error of 5.46%. The current work shows that camera-based technologies are promising and relevant alternatives for monitoring the respiratory rate of unconstrained rats, contributing to the development of new alternatives for a continuous and objective assessment of animal welfare, and hereby guiding the way to modern and bioethical research.

For Heart Rate Assessments from Drone Footage in Disaster Scenarios.

The ability to use drones to obtain important vital signs could be very valuable for emergency personnel during mass-casualty incidents. The rapid and robust remote assessment of heart rates could serve as a life-saving decision aid for first-responders. With the flight sensor data of a specialized drone, a pipeline was developed to achieve a robust, non-contact assessment of heart rates through remote photoplethysmography (rPPG). This robust assessment was achieved through adaptive face-aware exposure and comprehensive de-noising of a large number of predicted noise sources. In addition, we performed a proof-of-concept study that involved 18 stationary subjects with clean skin and 36 recordings of their vital signs, using the developed pipeline in outdoor conditions. In this study, we could achieve a single-value heart-rate assessment with an overall root-mean-squared error of 14.3 beats-per-minute, demonstrating the basic feasibility of our approach. However, further research is needed to verify the applicability of our approach in actual disaster situations, where remote photoplethysmography readings could be impacted by other factors, such as blood, dirt, and body positioning.