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Tourism still represents a means of generating revenues in the coastal areas in the Mexican Caribbean, despite the growing concern about the social and environmental impacts. The Nichupte Lagoon System (NLS), the most representative lagoon of Quintana Roo State for being in the middle of Cancun’s hotel development, has experienced a continuous drop-off in its water quality due to several factors, including dredging and wastewater discharges from different anthropogenic activities, which modify the flux of nutrients, increase the number of pathogenic microorganisms, and promote physicochemical changes in this ecosystem. Three sampling campaigns (2018, 2019, and 2020) were carried out in the NLS in August, which is the month of greatest tourist occupancy. To evidence the presence of anthropogenic wastewater in the NLS, the caffeine tracer was used, and to determine the water quality, 43 sampling stations were monitored for “in situ” physicochemical parameters (salinity and dissolved oxygen), and water samples were collected for the quantification of nutrients (NO₂⁻ + NO₃⁻, NH₄⁺, SRP and SRSi) and chlorophyll-a (Chl-a). For data analysis, the lagoon was subdivided into five zones (ZI, ZII, ZIII, ZIV, and ZV). Caffeine spatial and time variation evidence (1) the presence of anthropogenic wastewater in all areas of the NLS probably resulting from the tourist activity, and (2) wastewater presence is directly influenced by the coupling of the hydrological changes driven by anomalous rain events and the number of tourists. This same tendency was observed for nutrients that increased from 2018 to 2019 and the trophic state changed from oligotrophic to hypertrophic in all areas, as a result of previous anomalous precipitations in 2018, followed by normal precipitations in 2019. From 2019 to 2020, the nutrients decreased due to the drop in tourism due to COVID-19, promoting fewer nutrients in the lagoon, but, also coupled with an anomalous precipitation event (Cristobal storm), resulted in a dilution phenomenon and an oligotrophic state. The cluster analysis indicated that the least similar zones in the lagoon were the ZI and ZV due to their geomorphology that restricts the connection with the rest of the system. Principal component analysis revealed that wastewater presence evidenced by the caffeine tracer had a positive association with dissolved oxygen and chlorophyll-a, indicating that the arrival of nutrients from wastewater amongst other sources promotes algal growth, but this could develop into an eutrophic or hypertrophic state under normal precipitation conditions as seen in 2019. This study shows the relevance of monitoring in time of vulnerable karstic systems that could be affected by anthropogenic contamination from wastewater inputs, stressing the urgent need for efficient wastewater treatment in the area. The tourist industry in coastal karstic lagoons such as the NLS must have a Wastewater Treatment Program as a compensation measure for the anthropic pressure that is negatively changing the water quality of this highly relevant socio-environmental system. Full article

Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain–computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices. Full article

(1) Background: Delirium is a serious condition in patients undergoing treatment for somatic diseases, leading to poor prognosis. However, the pathophysiology of delirium is not fully understood and should be clarified for its adequate treatment. This study analyzed the relationship between confusion symptoms in delirium and resting-state electroencephalogram (EEG) power spectrum (PS) profiles to investigate the heterogeneity. (2) Methods: The participants were 28 inpatients in a general hospital showing confusion symptoms with an Intensive Care Delirium Screening Checklist (ICDSC) score of 4 or above. EEG was measured at Pz in the daytime awake state for 100 s with the eyes open and 100 s with the eyes closed on the day of the ICDSC evaluation. PS analysis was conducted consecutively for each 10 s datum. (3) Results: Two resting EEG PS patterns were observed regarding the dominant rhythm: the presence or absence of a dominant rhythm, whereby the PS showed alpha or theta peaks in the former and no dominant rhythm in the latter. The patients showing a dominant EEG rhythm were frequently accompanied by hallucination or delusion (p = 0.039); conversely, those lacking a dominant rhythm tended to exhibit fluctuations in the delirium symptoms (p = 0.020). The other ICDSC scores did not differ between the participants with these two EEG patterns. (4) Discussion: The present study indicates that the presence and absence of a dominant EEG rhythm in delirious patients are related to different symptoms of delirium. Using EEG monitoring in the care of delirium will help characterize its heterogeneous pathophysiology, which requires multiple management strategies. Full article

Electromyographic (EMG) sensors are essential tools for analyzing muscle activity, but traditional designs often face challenges such as motion artifacts, signal variability, and limited wearability. This study introduces a novel EMG sensor fabricated using Aerosol Jet Printing (AJP) technology that addresses these limitations with a focus on precision, flexibility, and stability. The innovative sensor design minimizes air interposition at the skin–electrode interface, thereby reducing variability and improving signal quality. AJP enables the precise deposition of conductive materials onto flexible substrates, achieving a thinner and more conformable sensor that enhances user comfort and wearability. Performance testing compared the novel sensor to commercially available alternatives, highlighting its superior impedance stability across frequencies, even under mechanical stress. Physiological validation on a human participant confirmed the sensor’s ability to accurately capture muscle activity during rest and voluntary contractions, with clear differentiation between low and high activity states. The findings highlight the sensor’s potential for diverse applications, such as clinical diagnostics, rehabilitation, and sports performance monitoring. This work establishes AJP technology as a novel approach for designing wearable EMG sensors, providing a pathway for further advancements in miniaturization, strain-insensitive designs, and real-world deployment. Future research will explore optimization for broader applications and larger populations. Full article

In this letter, we highlight the structure and main properties of an abstract approach to quantum cosmology and gravity, dubbed $SU(\infty )$-QGR. Beginning from the concept of the Universe as an isolated quantum system, the main axiom of the model is the existence of an infinite number of mutually commuting observables. Consequently, the Hilbert space of the Universe represents $SU(\infty )$ symmetry. This Universe as a whole is static and topological. Nonetheless, quantum fluctuations induce local clustering in its quantum state and divide it into approximately isolated subsystems representing $G\times SU(\infty )$, where G is a generic finite-rank internalsymmetry. Due to the global $SU(\infty )$ each subsystem is entangled to the rest of the Universe. In addition to parameters characterizing the representation of G, quantum states of subsystems depend on four continuous parameters: two of them characterize the representation of $SU(\infty )$, a dimensionful parameter arises from the possibility of comparing representations of $SU(\infty )$ by different subsystems, and the fourth parameter is a measurable used as time registered by an arbitrary subsystem chosen as a quantum clock. It introduces a relative dynamics for subsystems, formulated by a symmetry-invariant effective Lagrangian defined on the (3+1)D space of the continuous parameters. At lowest quantum order, the Lagrangian is a Yang–Mills field theory for both $SU(\infty )$ and internal symmetries. We identify the common $SU(\infty )$ symmetry and its interaction with gravity. Consequently, $SU(\infty )$-QGR predicts a spin-1 mediator for quantum gravity (QGR). Apparently, this is in contradiction with classical gravity. Nonetheless, we show that an observer who is unable to detect the quantumness of gravity perceives its effect as curvature of the space of average values of the continuous parameters. We demonstrate Lorentzian geometry of this emergent classical spacetime. Full article

Objective: This study aimed to explore longitudinal relationships between neurophysiological biomarkers and upper limb motor function recovery in stroke patients, focusing on electroencephalography (EEG) and transcranial magnetic stimulation (TMS) metrics. Methods: This longitudinal cohort study analyzed neurophysiological, clinical, and demographic data from 102 stroke patients enrolled in the DEFINE cohort. We investigated the associations between baseline and post-intervention changes in the EEG theta/alpha ratio (TAR) and TMS metrics with upper limb motor functionality, assessed using the outcomes of five tests: the Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Pinch Strength Test (PST), Finger Tapping Test (FTT), and Nine-Hole Peg Test (9HPT). Results: Our multivariate models identified that a higher baseline TAR in the lesioned hemisphere was consistently associated with poorer motor outcomes across all five assessments. Conversely, a higher improvement in the TAR was positively associated with improvements in FMA and 9HPT. Additionally, an increased TMS motor-evoked potential (MEP) amplitude in the non-lesioned hemisphere correlated with greater FMA-diff, while a lower TMS Short Intracortical Inhibition (SICI) in the non-lesioned hemisphere was linked to better PST improvements. These findings suggest the potential of the TAR and TMS metrics as biomarkers for predicting motor recovery in stroke patients. Conclusion: Our findings highlight the significance of the TAR in the lesioned hemisphere as a predictor of motor function recovery post-stroke and also a potential signature for compensatory oscillations. The observed relationships between the TAR and motor improvements, as well as the associations with TMS metrics, underscore the potential of these neurophysiological measures in guiding personalized rehabilitation strategies for stroke patients. Full article

Background/Objectives: To examine the relationships between state affect and sleep health at within- and between-person levels among health science students. Methods: A correlational design was used and 54 health science students were included. The participants completed baseline and 7-day ambulatory assessments in a free-living setting. Daily sleep and affect were measured using the Consensus Sleep Diary and Positive and Negative Affect Schedule. Mixed-effect models were used to examine the effects of affect on sleep health. Results: The participants were 19.8 (SD, 0.6) years and 92.6% were females. Approximately 40% had poor sleep quality. Controlling for the potential confounders (e.g., age, sex, and bedtime procrastination), higher within-person negative affect predicted shorter sleep duration, lower sleep efficiency, longer sleep onset latency, and less feeling rested. Higher between-person negative affect predicted shorter sleep duration. Higher within-person positive affect predicted longer sleep onset latency. Higher within- and between-person positive affect predicted more feeling rested. Conclusions: Negative affect was most consistently associated with sleep health at the individual level. Affect regulation should be considered when delivering personalized interventions targeting sleep health among health science students. Full article

Background: Coffee and tea are widely consumed beverages, but their long-term effects on cognitive function and aging remain largely unexplored. Lifestyle interventions, particularly dietary habits, offer promising strategies for enhancing cognitive performance and preventing cognitive decline. Methods: This study utilized data from the UK Biobank cohort (n = 12,025) to examine the associations between filtered coffee, green tea, and standard tea consumption and neural network functional connectivity across seven resting-state networks. We focused on networks spanning prefrontal and occipital areas that are linked to complex cognitive and behavioral functions. Linear mixed models were used to assess the main effects of coffee and tea consumption, as well as their interactions with Apolipoprotein E (APOE) genetic risk—the strongest genetic risk factor for Alzheimer’s disease (AD). Results: Higher filtered coffee consumption was associated with increased functional connectivity in several networks, including Motor Execution, Sensorimotor, Fronto-Cingular, and a Prefrontal + ‘What’ Pathway Network. Similarly, greater green tea intake was associated with enhanced connectivity in the Extrastriate Visual and Primary Visual Networks. In contrast, higher standard tea consumption was linked to reduced connectivity in networks such as Memory Consolidation, Motor Execution, Fronto-Cingular, and the “What” Pathway + Prefrontal Network. The APOE4 genotype and family history of AD influenced the relationship between coffee intake and connectivity in the Memory Consolidation Network. Additionally, the APOE4 genotype modified the association between standard tea consumption and connectivity in the Sensorimotor Network. Conclusions: The distinct patterns of association between coffee, green tea, and standard tea consumption and resting-state brain activity may provide insights into AD-related brain changes. The APOE4 genotype, in particular, appears to play a significant role in modulating these relationships. These findings enhance our knowledge of how commonly consumed beverages may influence cognitive function and potentially AD risk among older adults. Full article

Many biological markers of normal and disease states can be detected in saliva. The benefits of saliva collection for research include being non-invasive, ease of frequent sample collection, saving time, and being cost-effective. A small volume (≈1 mL) of saliva is enough for these analyses that can be collected in just a few minutes. For “dry” saliva paper matrices, additional drying times (about 30 min) may be needed, but this can be performed at room temperature without the need for freezers and specialized equipment. Together, these make saliva an ideal choice of body fluid for many clinical studies from diagnosis to monitoring measurable biological substances in hospital settings, remote, and other general locations including disaster areas. For these reasons, we have been using saliva (dry as well as wet) from astronauts participating in short- and long-duration space missions for over two decades to conduct viral, stress, and immunological studies. We have also extended the use of saliva to space analogs including bed rest, Antarctica, and closed-chamber studies. Saliva is a biomarker-rich and easily accessible body fluid that could enable larger and faster public health screenings, earlier disease detection, and improved patient outcomes. This review summarizes our lessons learned from utilizing saliva in spaceflight research and highlights the advantages and disadvantages of saliva in clinical diagnostics. Full article

This work presents an original and very interesting approach to a calculus problem involving beams with intermediate supports through the transfer-matrix method, a very easy method to program to quickly obtain good results. To exemplify the applicability of this approach in dentistry, the calculus of a dental bridge on three poles is explored. Dental restorations are very important for improving a person’s general state of health as a result of improving mastication and esthetic appearance. The approach used in this study consists of presenting a theoretical study about an indeterminate beam with an intermediate support and then particularizing it for application in a dental restoration case, with a dental bridge on three poles and two missing teeth between the three poles. The bridge is assimilated to a simple static indeterminate beam. This paper is unique in that it involves the application of the transfer-matrix method for a case study in dental restoration. The assimilation of a dental bridge with a statically undetermined beam, resting on the extremities and on an intermediate support, is an original approach. The results obtained in the presented case study were validated by comparison with those obtained through the classical calculation of the Resistance of Materials, with Clapeyron’s equation of three moments. Due to the ease and elegance of solving various problems with the TMM, this approach will continue to be relevant to other original case studies with different modeling requirements, and these applications will be presented in future research. Full article

Background: Spinal cord injury (SCI) affects approximately 250,000 to 500,000 individuals annually. Current therapeutic interventions predominantly focus on mitigating the impact of physical and neurological impairments, with limited functional recovery observed in many patients. Electroencephalogram (EEG) oscillations have been investigated in this context of rehabilitation to identify effective markers for optimizing rehabilitation treatments. Methods: We performed an exploratory cross-sectional study assessing the baseline EEG resting state of 86 participants with SCI as part of the Deficit of Inhibitory as a Marker of Neuroplasticity in Rehabilitation Cohort Study (DEFINE). Results: Our multivariate models demonstrated a positive correlation between frontal delta asymmetry and depression symptoms, while the frontal alpha asymmetry band and anxiety symptoms were negatively correlated. Theta oscillations were negatively associated with motor-evoked potential (MEP), whereas alpha oscillations were positively associated with MEP in all regions of interest and with CPM response as a negative correlation. Based on the potential role of lower-frequency oscillations in exerting a salutogenic compensatory effect, detrimental clinical and neurophysiological markers, such as depression and lower ME, likely induce slow oscillatory rhythms. Alpha oscillations may indicate a more salutogenic state, often associated with various cognitive functions, such as attention and memory processing. Conclusions: These results show an attempt by the CNS to reorganize and restore function despite the disruption caused by SCI. Indeed, this finding also challenges the notion that low-frequency EEG rhythms are associated with cortical lesions. These results may contribute to the development of rehabilitation strategies and potentially improve the clinical outcomes of patients with SCI. Full article

Objectives: This study investigates disruptions in functional brain networks in Parkinson’s Disease (PD), using advanced modeling and machine learning. Functional networks were constructed using the Nonlinear Autoregressive Distributed Lag (NARDL) model, which captures nonlinear and asymmetric dependencies between regions of interest (ROIs). Key network metrics and information-theoretic measures were extracted to classify PD patients and healthy controls (HC), using deep learning models, with explainability methods employed to identify influential features. Methods: Resting-state fMRI data from the Parkinson’s Progression Markers Initiative (PPMI) dataset were used to construct NARDL-based networks. Metrics, such as Degree, Closeness, Betweenness, and Eigenvector Centrality, along with Network Entropy and Complexity, were analyzed. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models, classified PD and HC groups. Explainability techniques, including SHAP and LIME, identified significant features driving the classifications. Results: PD patients showed reduced Closeness (22%) and Betweenness Centrality (18%). CNN achieved 91% accuracy, with Network Entropy and Eigenvector Centrality identified as key features. Increased Network Entropy indicated heightened randomness in PD brain networks. Conclusions: NARDL-based analysis with interpretable deep learning effectively distinguishes PD from HC, offering insights into neural disruptions and potential personalized treatments for PD. Full article

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to measure functional connectivity between 55 schizophrenia patients and 63 healthy controls across 246 regions of interest (ROIs) and extracted the disease-related connectivity patterns using energy landscape (EL) analysis. EL analysis captures the complexity of brain function in schizophrenia by focusing on functional brain state stability and region-specific dynamics. Age, sex, and smoker demographics between patients and controls were not significantly different. However, significant patient and control differences were found for the brief psychiatric rating scale (BPRS), auditory perceptual trait and state (APTS), visual perceptual trait and state (VPTS), working memory score, and processing speed score. We found that the brains of individuals with schizophrenia have abnormal energy landscape patterns between the right and left rostral lingual gyrus, and between the left lateral and orbital area in 12/47 regions. The results demonstrate the potential of the proposed imaging analysis workflow to identify potential connectivity biomarkers by indexing specific clinical features in schizophrenia patients. Full article

Background: It is well known from cross-sectional studies that pain intensity affects brain activity as measured by electroencephalography (EEG) in people with neuropathic pain (NP). However, quantitative characterisation is scarce. Methods: In this longitudinal study, ten people with spinal cord injury-related NP recorded their home EEG activity ten days before and after taking medications over a period of several weeks. Results: The reduction in pain due to medications was accompanied by changes in the resting state EEG and its reactivity to eyes opening (EO) and closing (EC). There was a significant positive correlation between the frontal theta band and the intensity of pain (visual numerical scale) pre-medication (p = 0.007, Pearson R = 0.29) and theta, alpha, and lower beta (6–15 Hz) band power and the intensity of pain after post-medication over the frontal, central, and parietal cortices. Reactivity had a negative correlation with pain intensity at all locations and frequency bands and showed similar behaviour in wider frequency bands like 8–15 Hz at the occipital cortex and 2–12 Hz at the frontal cortex. Conclusions: EEG could be used to detect the intensity of NP to serve as a surrogate or pharmacodynamic marker. Full article

Background: Among all professions, teaching is significantly affected by psycho-social risks with approximately 33.33% of educators reporting work-related fatigue. Suggestopedia, an effective pedagogical approach developed in Bulgaria, claims to induce positive psychological and cognitive benefits in both teachers and students. In order to gather scientific evidence, given the above statement, we designed a methodology to detect fatigue in Suggestopedia teachers based on neurocognitive analysis and psychological assessment. Methods: An increase in the EEG theta and alpha band powers is considered among the most reliable markers of fatigue. The proposed methodology introduces a robust framework for fatigue analysis. Initially, the changes in EEG band powers using the resting state EEG activity before and after teaching are measured. Subsequently, validated psychological questionnaires are used to gain subjective feedback on fatigue. The study participants include a control group (traditional teachers) and the test group (suggestopedia teachers) to assess whether suggestopedia practice mitigates fatigue among teachers. Observations: In a pilot study, the EEG data was analyzed by evaluating the interrelations between EEG bands and the alpha–beta ratio. The results of the proposed study are expected to provide comprehensive analysis for the fatigue levels of teachers. In future research, our goal is to position the described methodology as a robust approach for evaluating cognitive and emotional states. Full article