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This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively.

A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed.

The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities.

Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved.

To the best of the author’s knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this study is to collect and assess the evidence available on the effect of non nutritive sweeteners on appetite, weight and glycemic regulation. As a replacement for sugars, non-nutritive sweeteners (NNSs) are widely being used in different food products with the assumption that these would lower calorie intake and help to manage weight and blood sugar levels better. However, studies using animal models have reported that chronic exposure to NNSs leads to increased food consumption, weight gain and insulin resistance.

Evidence was acquired from systematic reviews or meta-analyses (2016–2021) of relevant clinical studies, especially randomized control trials using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines.

The review showed NNSs exposure did not conclusively induce increased food intake or change in subjective appetite ratings. Appetite biomarkers like ghrelin, gastric inhibitory peptide, C-peptide levels and Peptide YY remained mostly unaffected by NNSs. Meta-analyses of human randomized control studies showed a reduced energy intake and body weight. No significant change was seen in blood glucose levels, post-prandial glycemic or insulin response after consumption of NNSs. Adequate evidence is not available to conclusively say that NNSs influence gut health at doses relevant to human use.

Most studies which are prospective cohort, observational and cross-sectional studies suggest that use of NNSs may promote obesity and metabolic syndrome in adults. Such studies are plagued by confounding variables and reverse causation. Mechanistic evidence is mostly based on in-vitro and in-vivo studies. The same causal pathways may not be operative or relevant in humans.

This review of available literature concludes that to achieve specific public health and clinical goals, the safe use of NNSs for the reduction of intakes of free sugars and energy should be explored. This would be possible by educating the consumer about energy compensation and understanding the nutritional content of artificially sweetened products in terms of calories coming from fat and complex carbohydrates used in the product.

This study was, thus, designed with the objective of examining the usefulness of NNSs in human population, especially with respect to insulin regulation, glycemic control and weight management. Well-designed randomized control trials which control for confounding variables are needed to generate high quality evidence.

This research intended the utilization of Javanese turmeric (0.4% w/v) as a kombucha substrate and analysis of its hepatoprotective activity, in comparison against nonfermented Javanese turmeric beverage (JTB) and black tea kombucha.

Forty-two healthy male Balb/c mice (two- to three-week-old, 20–30 g) were divided into six groups with seven replicates each. The treatments were normal diet, normal diet + Javanese turmeric kombucha (JTK), normal diet + diethylnitrosamine (DEN), DEN + JTB, DEN + JTK, DEN + black tea kombucha. Kombuchas and JTB were given at 0.3 mL/20 g BW/d. DEN was induced intraperitoneally at a dose of 100 mg/kg. Observed biomarkers were blood serum glutamate pyruvate transaminase (SGPT) and serum glutamate oxaloacetate transaminase (SGOT) activity, serum malonaldehyde (MDA), as well as liver histology. Data were analyzed using analysis of variance.

Among DEN-induced groups, JTK significantly (p < 0.05) diminished the level of blood SGPT, SGOT and serum MDA. JTK also had lower blood SGPT (8.604 ± 2.195 U/L) and serum MDA levels (2.884 ± 0.083 nM/mL) compared to the normal group (8.604 ± 2.195 U/L and 5.050 ± 0.998 nM/mL, respectively). JTK also produced the least damaged liver-cell numbers.

JTK demonstrated better hepatoprotective activity compared to JTB.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

This systematic review aims to gain the studies regarding the effect of Nigella Sativa (N. sativa) on adipokines including leptin, adiponectin and resistin.

Search was carried out using databases Scopus, Web of Science, PubMed and Google Scholar with no restriction on language or date until February 2023 and alert services were applied to identify any paper after the primary search.

Eighteen animal and human studies were eligible for the current systematic review. Leptin and resistin levels showed a downward tendency after consuming N. sativa and its ingredients [e.g. oil, thymoquinone (TQ) and thymol] as well as its extracts (e.g. water extract). Furthermore, considering 4 of 8 animal research studies and 2 of 5 human studies that evaluated adiponectin levels, a significant increase was observed after using N. sativa and its ingredients (e.g. oil, TQ and thymol).

The present paper collates evidence from animal and human studies regarding the effect of N. sativa on adipokines including leptin, adiponectin and resistin.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

The emotion regulation research has drawn considerable attention from academicians and scholars in the contemporary world. As a result, the publications that are specifically dedicated to emotion regulation research are rapidly escalating. Therefore, this study aims to conduct a bibliometric analysis of research articles that have been published in the field of “emotion regulation.” The study primarily examines the growth and development of scholarly publications, seminal studies, influential authors, productive journals, research production and collaboration among countries, emerging research themes, research hotspots and thematic evolution of emotion regulation research.

The Web of Science Core Collection database was used to gather the study’s data, which was then analysed using VOSviewer and Bibliometrix, Biblioshiney open-source package of the R language environment.

The study’s results reveal that the research on emotion regulation has grown significantly over the last three decades. Notably, Emotion and Frontiers in Psychology are the most dominant and productive journals in the field of emotion regulation research. The most prominent author in the area of emotion regulation is identified as James Gross, followed by Gratz, Wang and Tull. The USA is at the forefront of research on emotion regulation and has collaborated with most of the developed countries like Germany, England and Canada. The keyword analysis revealed that the most potential research areas in the field of emotion regulation are functional magnetic resonance imaging, amygdala, post-traumatic stress disorder, borderline personality disorder, alexithymia, emotion dysregulation, depression, anxiety, functional connectivity, neuroimaging, mindfulness, self-regulation, resilience and coping. The thematic evolution reflects that the research on emotion regulation has recently focused on issues including Covid-19, non-suicidal self-injury, psychological distress, intimate partner violence and mental health.

The results of this study highlighted the current knowledge gaps in emotion regulation research and suggested areas for further investigation. The present study could be useful for researchers, academicians, planners, publishers and universities engaged in emotion regulation research.