Introduction: Alzheimer’s Disease (AD) is the most common cause of dementia. It affects mostly the elderly and is directly impacted by the observed growth of life expectancy. AD manifests as a chronic and progressive neurodegenerative disease, gradually deteriorating memory and cognitive abilities, and diminishing day-to-day quality of life. As the global population ages, understanding and addressing the challenges of AD becomes increasingly important for public health. Early detection enables treatment planning and symptom management, becoming an important study subject. In that sense, the present study aims to develop an automatic Structural MRI-based tool for the detection of AD and early stages of the disease (Mild Cognitive Impairment—MCI).

Methods: 504 pre-processed sMRI images were decomposed into slices comprising the three anatomical planes (axial, coronal and sagittal) from where a set of 22 GLCM features were computed to feed 18 machine learning models, employing a hold-out method (80-20 train--test split). The analysis involved comparing three classes, HC (Healthy Controls), MCI and moderate AD in an All vs. All classification approach.

Results and Discussion: A wide set of metrics was used to evaluate the model's performance. Combining the three anatomical planes, the All vs. All classification with a Linear Support Vector Machine yielded the following results: 82.2% for Accuracy, 82.2% for Recall, 83.0% for Precision, 89.9% Specificity, 81.9% for F1-Score and 89.8% for AUC.

Conclusions: The results indicate that the proposed model distinguishes between AD, CN and MCI well. The methodology used provided a balanced performance across the seven metrics, highlighting the model's robustness and reliability in classifying the different groups. This approach shows significant potential for aiding in AD early detection and diagnosis and related cognitive impairments with an unusual approach.

Introduction: Emotional assessment has become a primary focus in multiple fields, thanks to its power to encompass the real-time status of individuals. One of the traits most affected by emotional status is the voice, recognized as a signal carrying a great deal of information. Effective computing through voice recordings holds significant importance in various fields, ranging from healthcare to human–computer interaction. In fact, by analyzing vocal cues, effective computing systems can detect emotional states, providing critical insights into a person's mental health and well-being. This study aims to develop a machine learning (ML) approach for emotion recognition from vocal recordings.

Methods: Emotion classification was performed using audio recordings from the EMOVO dataset (EMOVO Corpus: an Italian Emotional Speech Database), comprising syntactically neutral phrases spoken by six actors across seven emotions: neutral, disgust, anger, surprise, fear, sadness, and joy. The approach began with audio preprocessing, where a set of 20 power normalized cepstral coefficients was extracted. Crucially, the training and testing sets were divided in a manner to ensure an equal representation of each emotion class, maintaining balanced compositions, bolstering the reliability of the model proposed. Subsequently, a stacked ML model was employed, comprising kNN and SVM as base models, augmented with Extreme Gradient Boosting.

Results: This model achieved remarkable accuracies of 87% during training and 81% during testing, showcasing robustness and premises for novel and diverse applications. The methodology emphasized maintaining balanced distributions in predictions, ensuring unbiased and non-overfitted results.

Conclusion: This comprehensive approach integrated advanced features and a systematic classification strategy, contributing significantly to the advancement of emotion analysis in audio data, and fostering the development of more intuitive, responsive, and human-centered technology solutions.

Introduction: Alzheimer’s Disease (AD) is a prevalent neurodegenerative disorder, anticipated to triple in cases by 2050. It constitutes 50-75% of dementia cases and currently lacks a cure. Early diagnosis is crucial, allowing for treatments that may delay its progression. Traditional diagnostic methods, though effective, are invasive and expensive. Speech signal analysis has emerged as a promising non-invasive, cost-effective alternative for early AD diagnosis.

Methods: This study investigates the application of non-linear analysis under a Discrete Wavelet Transform (DWT) of speech signals for detecting AD stages. The dataset comprises 360 audio recordings from the DementiaBank Spanish Ivanova Corpus, categorized into AD, Mild Cognitive Impairment (MCI), and healthy control groups. The 360 speech signals were cleaned by removing artifacts through a filter and moments of silence utilizing Voice Activity Detection (VAD). A 50% overlap rectangular sliding window process of a 5-second duration was used, and within each window, the signal was decomposed by DWT into six bands. From each band, 10 non-linear parameters analyze the complex dynamics of our speech signals. Each feature time series is compressed over time per band, utilizing six compression metrics, and the resulting data are divided into groups based on gender and AD stage. Classical machine learning classification was implemented, and an iterative application of various normalization, feature selection, and optimization techniques was employed. The final step tested 20 classifiers to determine the most effective model for discrimination between groups.

Results and Discussion: Our findings show a 100% accuracy between men with AD and women with AD, healthy men and women with AD, and men with AD and healthy women. Furthermore, nearly all of our 15 group comparisons have an accuracy of higher than 90.9%.

Conclusion: In conclusion, our techniques culminated in a model that achieved good model performance and could differentiate between men and women, and between the three studied stages of AD.

A strong understanding of bio-adhesion, bio-mineralization, and metabolic processes in the oral cavity is crucial for maintaining oral health. Metabolic processes in the oral cavity are influenced by the diverse environment of oral fluids with its complex composition. Real-time monitoring is challenging due to heterogeneous composition variations across sampling volumes and secretion variability within different regions of the mouth.

The development of a multi-sensor devices for continuous monitoring within the oral cavity aims to integrate flexible sensors into dental splints to monitor glucose, pH, lactate, calcium, phosphate, and fluoride concentrations in saliva.

The sensing device, embedded into a personalized splint features four electrochemical sensors and one open circuit potentiometry sensor for pH measurements. Controlled by a low-power microcontroller handling data collection and control.

For pH-sensors, a potentiometric measurement mode was chosen. Ag/AgCl reference electrode (RE) was covered with a solid-state layer of polyvinylbutyrate (PVB) and the working electrode (WE) was functionalized with polyaniline (PANI) via electrodeposition.

The pH measurements were conducted by open circuit potentiometry. A linear response with -53.8mV/pH was achieved. Unstimulated saliva samples from different volunteers were applied and the pH values determined by our sensors fitted well the pH values measured by a commercial electrode.

Continuous in vitro measurements of glucose and lactate followed amperometric sensing approaches . Electrochemical sensors were fabricated by applying oxidases and Prussian blue onto the WE while incorporating chitosan. The prepared sensors were tested in solutions with varying analytes and were conducted with the developed intraoral electronics.

The detection of additional parameters such as calcium, phosphate and fluoride are subject of current research. The collected data can be displayed in real-time on a mobile device and forwarded to cloud storage for post-processing. This approach enables real-time monitoring of important parameters for oral disease development.

Introduction: In minimally invasive surgery, the laparoscopic lens may face contamination from condensation, smoke, blood, and debris, leading to obscured visual fields during this procedure. To address this challenge, researchers have developed numerous patents, as discussed in this systematic review. This review aims to investigate the patents proposed to achieve effective cleaning of laparoscopic lenses for optimal visualization during minimally invasive surgery.

Materials and Methods: An innovative methodology was employed to identify, describe, and categorize patents related to laparoscopic lens cleaners. The ESPACENET database was utilized to search for these patents, while patent descriptions and statuses were obtained from Google Patents and USPTO. Each patent status was assigned a score using a 3-point Likert scale: 2 points for granted, 1 point for pending, and 0 points for abandoned.

Results: A detailed examination was carried out on 61 identified patents, which were then sorted into two primary categories: mechanical interactions and chemical interactions. To enable comparisons, the scores within each category were combined. The results reveal that 48% of granted patents for laparoscopic lens cleaners are related to collision methods, while 56% of abandoned patents are associated with brush/wipe methods.

Conclusion: This review demonstrates that collision methods are the best among other methods for achieving a successful lens cleaner patent. This review recommends that future laparoscopic lens cleaning patents use a hydrophilic or hydrophobic lens surface combined with collision cleaner techniques.

INTRODUCTION

Hemorrhage, a life-threatening condition marked by rapid blood loss and tachycardia, requires real-time monitoring of key physiological markers for optimal management. A similar requirement exists for allografts under bioreactor preservation conditions. In both scenarios, the Hemorrhage Intensive Severity and Survivability (HISS) score integrates metabolic indicators (glucose, lactate, pH, potassium, pO2) that are directly measured using a minimally invasive biochip array - The Physiological Status Monitoring Biochip (PSM Biochip).

METHODS

Computational models of the five microlithographically fabricated sensor elements of the PSM Biochip were designed in both 2D and 3D using COMSOL Multiphysics v6.0 run on a PC. The biosensors were Microdisc Electrode Arrays (MDEA) for mediated enzyme-amperometric measurement of glucose or lactate. Potentiometric measurement of potassium used a Microdisc Electrode (MDE). Acidosis used a pH-responsive hydrogel on an Interdigitated Microsensor Electrode (IME) and an MDE was used for the voltametric measurement of pO₂.

RESULTS AND DISCUSSION

The biosensors, employing glucose oxidase and lactate oxidase, were validated in 0.1 MFcCO₂H and modeled as PPy/PPy⁺•PSS^- mediated enzyme-amperometric reactions of linked Hill and Butler-Volmer equations. The potentiometric response of the MDE K⁺ sensor was modeled using the Nikolsky–Eisenman equation. The impedimetric response of the IME sensor was validated in 0.1M [Fe(CN)6]^3-/4- and the pH responsive AEMA-hydrogel-IME was modeled as a cationic hydrogel by coupling the Langmuir availability of ionic states of the ionogen with the electrical charge given by Poisson’s equation across physiologic pH ranges. The MDE pO₂ sensor was modeled as a voltametric sensor using the microelectrode form of the Randles-Sevcik equation that was linked to a Langmuir adsorption of O₂ to nano-enabled Pt. The effect of overlapping electric fields and minimum feature size were examined to determine the smallest possible biochip footprint. All systems showed excellent agreement (p>0.05) with previously published sensor data.

Continuous exploitation of fossil resources and increasing energy consumption have urged the scientific community to look for a new alternative feedstock for producing bio-based materials, fuels, and chemicals. In view of this, a newly developed transgenic crop, i.e., oilcane, has been genetically engineered to accumulate vegetative lipids and carbohydrates in plant tissues; thus, it can be considered as an alternative feedstock to cater to the enhanced biofuel yield by providing lipids along with cellulosic sugars for large-scale biodiesel and bioethanol production. However, these components are entrapped in a highly recalcitrant lignin--carbohydrate matrix, which limits the efficient recovery of these components for their downstream processing.

Thus, NADESs, a combination of hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA), have demonstrated exceptional solvent characteristics as an alternative to conventional organic solvents and have benefited from easy preparation, low toxicity, high biodegradability, and high fractionation efficiency. NADESs can act as adjuvants for weakening the lignin--carbohydrate recalcitrance matrix at the desired temperature of 100–160 °C. Thus, herein, we have synthesized several choline-chloride-based NADES using bio-derived precursors, i.e., lactic acid, oxalic acid, glycerol, ethylene glycol, and acetic acid by varying the molar ratio of HBD and HBA (1:1, 1:2) at 60–80 °C and they were employed for the pretreatment of oilcane bagasse for the fractionation of lipids and carbohydrates. Compositional analysis showed that oilcane bagasse is enriched with 3.3% of total lipids and 51% of carbohydrates. The initial study showed that the suitable eutectic combination of NADES, i.e., choline chloride and lactic acid in a 1:1 molar ratio at 60–80 °C, could effectively solubilize >80% lignin while enabling high biomass digestibility (>85%) and enhance lipid recovery (>80%). These research findings could further promote the design and fabrication of a low-cost, environmentally friendly, biodegradable, NADES-assisted pretreatment for enhanced lipid and sugar recovery from transgenic oilcane for renewable production in a circular biorefinery.

The biosorption and bioaccumulation of heavy metals contained in wastewater systems offer promising bioremediation opportunities for contaminated effluents. Therefore, our study aims to evaluate and optimize the bioremediation of a synthetic contaminated effluent with Cd(II) by the biomass of Rhizobium viscosum CECT 908, previously classified as Arthrobacter viscosus, both in batch and dynamic modes, under different experimental setups.

The results obtained with the inactive Rhizobium viscosum CECT 908 biomass as a biosorbent for Cd(II) showed that the maximum biosorption efficiency and uptake capacity was 93% and, respectively, 20.23 mgCd(II)/g biomass at pH 6. At higher pH values (e.g., pH 7), the biosorption yield and the uptake capacity decreased to 87% and, respectively, 14.73 mgCd(II)/g biomass. This may be due to the formation of soluble hydroxilated complexes of the metal ions and their competition with the biomass active sites. Results of this study demonstrated that, although both inactive and active Rhizobium viscosum CECT 908 cells have high sorption capacities for Cd(II) biosorption, the binding capacity of the inactive cells seems to be higher than that of active cells in optimized experimental conditions, being able to remove more than 89% of a 100 mg/L Cd(II) solution.

As a result, this process has the scalability to go from a laboratory scale to a full scale. In this context, we were able to assess the process environmental impacts through Life Cycle Assessment methodology, within the Sphera Product Sustainability Solutions Software. The most relevant impact categories considered were Climate change ecosystems (CCEs); Climate change human health (CCHh); Particulate matter formation (PMF); Acidification potential (AP); Global warming potential (GWP); Photochemical ozone formation (POF); Agricultural land occupation (ALO); Human toxicity (HTP); and Terrestrial ecotoxicity (TETP). The outputs generated from the process modelling in this software tool pointed towards detrimental environmental impacts resulting from energy consumption and reagent transport.

This paper introduces a pioneering method for dermatological reconstruction tailored to acid attack victims, merging bio-ink technology with in-vitro tissue engineering and advanced 3D modeling. The process initiates with cultivating healthy skin tissue in vitro to establish a robust cell reservoir, succeeded by digitally replicating burnt area dimensions for customized skin graft fabrication. The bio-ink, comprising hyaluronic acid (HA, C14H21NO11) at 2%, collagen (C4H6N2O3) at 3%, fibrinogen (C72H104N18O19S2) at 1%, silver nanoparticles (Ag) at 0.1%, and Transforming Growth Factor-Beta (TGF-β) at 0.05%, is meticulously applied onto the wound bed to foster hydration, structural reinforcement, hemostasis, antimicrobial shielding, and immunomodulation. HA ensures optimal hydration and moisture retention, while collagen provides structural support for tissue remodeling. Fibrinogen promotes clot formation, creating a stable scaffold, and silver nanoparticles confer antimicrobial properties. TGF-β regulates inflammation and promotes tissue regeneration. This specialized bio-ink acts as a scaffold, facilitating cellular infiltration and tissue integration, thereby augmenting wound healing and skin regeneration. Preliminary trials indicate promising success rates surpassing 75% in moderate to severe cases, underscoring the potential of this innovative approach to enhance outcomes and quality of life for acid attack survivors. Through the amalgamation of bio-ink technology, this methodology represents a paradigm shift in dermatological reconstruction, offering a ray of hope and healing to individuals grappling with traumatic skin injuries.

Continuous-flow left ventricular assist devices (CF-LVADs) are miniaturised devices implanted in end-stage heart failure patients to support the failing left ventricle. CF-LVADs alter the blood flow in the cardiovascular system, causing further complications. Additional health disorders during CF-LVAD support may increase morbidity and mortality in patients. Therefore, continuously monitoring blood flow through CF-LVADs may help to detect the complications early, allowing for timely interventions and reducing mortality in patients implanted with CF-LVADs. The lack of long-term reliable and implantable sensors in CF-LVADs does not allow for the real-time continuous monitoring of haemodynamic signals in the cardiovascular system.

In this study, intrinsic CF-LVAD electrical current signals were continuously monitored and analysed to evaluate cardiac function and detect cardiovascular disorders. CF-LVAD electrical current signal waveforms over a 600 s period in patient-specific RR intervals were analysed to detect normal sinus rhythm, Atrial Fibrillation (AF) with unimodal and bimodal RR interval distributions, and right ventricular failure during CF-LVAD support.

The average RR interval duration in sinus rhythm was 0.828±0.051 s and the coefficient of variation was 0.006. The average RR interval duration in AF with unimodal distribution was 0.512±0.106 s and the coefficient of variation was 0.207, whereas the mean of the RR intervals in AF with a bimodal distribution was 0.884±0.260 s and the coefficient of variation was 0.294. The CF-LVAD electrical current signal waveform was altered because of right ventricular failure and AF with both unimodal and bimodal cardiac RR interval distributions. The amplitude of the CF-LVAD electrical current signal was relatively small because of right ventricular failure and AF with bimodal cardiac RR interval distribution, which caused the amplitude to vary over each cardiac cycle.

Cardiovascular disorders can be detected by monitoring and analysing the features of CF-LVAD electrical current signals in patients supported with CF-LVADs.