Preterm birth represents a public health problem, with prematurity being the leading cause of infant mortality. An objective brain maturation and oxygenation measurement are necessary. The objective has been To test the feasibility of EEG and NIRS combination in the assessment of physiological brain maturation and oxygenation in preterm and non-preterm babies. A systematic review in Pubmed, Web of Science, MEDLINE, Cochrane, Dialnet, CINAHL, Scopus, Lilacs and PEDro databases until December 2022 was developed. 598 registers were found, finally 5 of them reached the inclusion criteria. Two independent reviewers analyzed data and a third reviewer were available for discrepancies. All articles combined EEG and NIRS to assess brain oxygenation and maturation in healthy new-born babies. There is an agreement on the electrode\'s placement for EEG at P3 and P4; besides, these regions have been shown to be a development predictive area, as well as the frontoparietal region for the NIRS region and comparison between regions. There is little evidence about the physiological brain electrical activity and oxygenation without stimuli.Conclusion: EEG and NIRS have been useful to assess brain electrical activity and oxygenation in preterm and non-preterm. The combined measurement of these instruments could be essential in neurological disorders diagnosis or their sequels. Unfortunately, the heterogeneity of the results found prevents a consensus on which variables are the most appropriate for the assessment of this population. What is Known • Brain assessment could help clinicians to prevent sequels. • There is an agreement for EEG electrodes placement at P3 and P4 region. What is New • EEG and NIRS assessment are effective measurements for preterm babies. • P3 and P4 regions have shown to be a predictive area of development, as well as the frontoparietal region for NIRS assessment.

Engaging intracoronary imaging (IC) techniques such as intravascular ultrasound or optical coherence tomography enables the precise description of vessel architecture. These imaging modalities have well-established roles in providing guidance and optimizing percutaneous coronary intervention (PCI) outcomes. Furthermore, IC is increasingly recognized for its diagnostic capabilities, as it has the unique capacity to reveal vessel wall characteristics that may not be apparent through angiography alone. This manuscript thoroughly reviews the contemporary landscape of IC in clinical practice. Focused on current methodologies, the review explores the utility and advancements in IC techniques. Emphasizing their role in clarifying coronary pathophysiology, guiding PCI, and optimizing patient outcomes, the manuscript critically evaluates the strengths and limitations of each modality. Additionally, the integration of IC into routine clinical workflows and its impact on decision-making processes are discussed. By synthesizing the latest evidence, this review provides valuable insights for clinicians, researchers, and healthcare professionals involved in the dynamic field of interventional cardiology.

(1) Background: Adequate organ perfusion during cardiopulmonary bypass (CPB) requires accurate estimation and adjustment of flow rates which conventional methods may not always achieve. Perioperative monitoring of cerebral oxygen saturation (ScO2) may detect changes in oxygen transport. This study aims to compare estimated and measured perfusion flow rates and assess the capacity of ScO2 to detect subtle changes in oxygen transport during CPB. (2) Methods: This observational study included 50 patients scheduled for elective coronary artery bypass grafting (CABG) surgery, all of whom provided written informed consent. Perfusion flow rates were estimated using the DuBois formula and measured using echocardiography and a flow probe in the arterial line of the CPB system. ScO2 was continuously monitored, alongside intermittent measurements of oxygen delivery and extraction ratios. (3) Results: Significant discrepancies were found between estimated flow rates (5.2 [4.8-5.5] L/min) and those measured at the start of the surgery (4.6 [4.0-5.0] L/min). These discrepancies were flow rate-dependent, being more pronounced at lower perfusion rates and diminishing as rates increased. Furthermore, ScO2 showed a consistent correlation with both oxygen delivery (r = 0.48) and oxygen extraction ratio (r = 0.45). (4) Conclusions: This study highlights discrepancies between estimated and actual perfusion flow rates during CPB and underscores the value of ScO2 monitoring as a continuous, noninvasive tool for maintaining adequate organ perfusion, suggesting a need for improved, patient-tailored perfusion strategies.

A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models.

Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings.

Monitoring of changes in skeletal muscle oxygenation during exercise has increased in recent years. Tissue oxygenation, which is related to fatigue and muscle hypertrophy, is often measured using near-infrared spectroscopy (NIRS).
OBJECTIVE: This study aimed to determine the test-retest reliability of a non-portable NIRS (NIRO200Nx) during the full-squat exercise and recovery in young healthy men.
METHODS: Twenty-five male participants (21.8 ± 2.6 years) were recruited for this original research. Each participant completed an 8-repetition test with a load that elicited a velocity of 1 m·s-1. The test was conducted twice, with a 48-hour washout period between sessions.
METHODS: The NIRS measured the changes of oxygenated-Hemoglobin (O2Hb), deoxygenated-Hemoglobin (HHb) and Tissue Oxygenation Index (TOI) in both Vastus Lateralis and Vastus Medialis during rest, exercise, and recovery. Coefficient of Variation (CV), Standard Error Measurement (SEM) and Intraclass Correlation Coefficient (ICC) were used to evaluate the reliability of the data. Significance was set at p < 0.05.
RESULTS: The results indicated that TOI had good to acceptable absolute reliability (CVTOI = 2.7-10.2 %). A good relative relativity for the overall test was found for Vastus Medialis O2Hb (ICC = 0.851), HHb (ICC = 0.852), and TOI (ICC = 0.864), and Vastus Lateralis O2Hb (ICC = 0.898), HHb (ICC = 0.899), and TOI (ICC = 0.897).
CONCLUSIONS: We conclude that NIRO200Nx is a reliable instrument for measuring muscle oxygen saturation through the TOI parameter in not-to-failure dynamic resistance exercises (1 set of 8 reps against ∼40 % 1 repetition maximum). Tissue oxygenation assessment could be a new way of individualizing exercise through dynamic resistance exercises.

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties \'Chardonnay\', \'Riesling\', \'Dornfelder\', and \'Pinot Noir\'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.

UNASSIGNED: Cerebrovascular complications are feared but also commonly reported in patients with COVID-19 requiring extracorporeal membrane oxygenation (ECMO) support therapy. Besides other reasons, a connection between impaired cerebral autoregulation and SARS-CoV-2 infection as a mechanism for an increase in cerebrovascular complications has been hypothesized.
UNASSIGNED: In an observational single-center study, we investigated a cohort of 48 patients requiring veno-venous ECMO support therapy with (n = 31) and without SARS-CoV-2 infection (n = 17). Cerebral autoregulation was assessed with the cerebral oximetry-derived autoregulation index (ORx) based on a moving correlation between arterial pressure and cerebral oximetry.
UNASSIGNED: Patients with ECMO support therapy and SARS-CoV-2 experienced more time with impaired cerebral autoregulation than without SARS-CoV-2 [17 ± 9 vs. 13 ± 9% (p = 0.027)]. Patients with SARS-CoV-2 suffering from cerebrovascular complications had more time with impaired autoregulation than non SARS-CoV-2 patients with these complications (19 ± 9 vs. 10 ± 4%, p = 0.032).
UNASSIGNED: Our results suggest a connection between SARS-CoV-2 and impaired cerebral autoregulation as well as cerebrovascular complications in SARS-CoV-2 patients.

Currently, coffee fermentation is visually operated, which results in incomplete or excessive processes and coffees with undesirable characteristics. In front of it, pH and total soluble solids (TSS) have been shown to be good fermentation indicators, although this requires rapid, accurate, and chemical-free measurement techniques such as NIR spectroscopy. However, the complexity of the NIR spectra requires optimization steps in which variable selection techniques simplify profiles and subsequent models. This work tests a new covering array feature selection (CAFS) approach on NIR spectra to optimize prediction models in coffee samples during fermentation. Spectral profiles in the range 1100-2100 nm were extracted from coffee beans (Typica, Caturra, and Catimor varieties) raw and during fermentation (4, 8, 12, 16, 20, and 24 h). Partial least-squares regressions (PLSR) were performed using full spectra using a five-fold cross-validation strategy for training and validation. The relevant wavelengths were then selected using the β coefficients, the important projection of variables (VIP), and the CAFS method. Finally, optimized models were performed using the relevant wavelengths and compared among these using their statistical metrics. The models performed using the selected variables (22-47) of CAFS showed the best performance in predicting pH (R 2 = 0.825-0.903, RMSE = 0.096-0.158, RPD = 6.33-10.38) and TSS (R 2 = 0.865-0.922, RMSE = 0.688-1.059, RPD = 0.94-1.45) compared to the other methods. These findings suggest that simple and efficient models could be performed and implemented in routine analysis due to the maximum coverage and minimum cardinality of CAFS.

The study assessed vastus lateralis oxygen desaturation kinetics (SmO2) in 32 male cyclists (16 Seniors, 16 Juniors) during a 30 s sprint, examining effects of age and performance. An incremental test was used to determine ventilatory thresholds (VT1, VT2) and maximal oxygen uptake (VO2kg), followed by a sprint test to evaluate anaerobic performance. Cyclists\' performance phenotype was determined as the ratio of power at VT2 to 5 s peak sprint power. Juniors exhibited sprinter-like traits, excelling in all functional tests except for lactate levels post-sprint. SmO2 data showed no age-related or bilateral differences across participants. The combined mean response time (MRT) revealed stronger bilateral goodness of fit (R2 = 0.64) than individual time delay (TD) and time constant (τ). Higher VO2kg at VT2, peak power, and maximal uptake were linked to longer TD, while shorter TD correlated with higher lactate production and increased fatigue. Bilaterally averaged SmO2 kinetics distinguished between sprint and endurance athletes, indicating the potential to reflect the alactic anaerobic system\'s capacity and depletion. Age did not affect desaturation rates, but younger cyclists showed greater response amplitude, attributed to a higher initial baseline rather than maximal desaturation at the end of the exercise.