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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.

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.

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.

The goal of this narrative review was to look at the link between the Mediterranean diet (MD) and the telomere length. Furthermore, this study aims to understand the impact of the MD on obesity-related telomere length.

Relevant literature was reviewed to explore the potential influence of the MD on telomere length and its association with obesity.

The MD is one of the healthiest diets of all known dietary patterns, and it is also linked to the telomere length. Except for fruits and vegetables, the main findings for other MD components are inconsistent. In terms of antioxidant and antiinflammatory properties, using the MD as a weight loss approach is a good method. For predicting changes in obesity characteristics, the initial telomere length is critical. However, there are not many studies in the field that have looked at the MD as a weight loss approach and its link to the telomere length. As a result, more research is needed to understand these connections in various groups.

This study is unique since it examines the MD, telomere length and obesity-related consequences. This study examines the MD, telomere length and obesity to determine if the MD can help lose weight while maintaining telomere length. As there are few studies on MD weight loss and telomere length, the work emphasizes the need for greater research in this area. This study fills a research gap and improves the understanding of nutrition, telomere biology and obesity-related outcomes.

The inflammatory potential of diet may play a role in the regulation of low-grade chronic inflammation derived from obesity. This study aims to investigate the relationship between the dietary inflammatory index (DII) and anthropometric indicators of adiposity among Turkish adults.

The study sample consisted of 833 young adults (mean age = 23 ± 5.2 years). The inflammatory potential of the diet was evaluated with a DII. Data on dietary intake were collected by a single 24-h dietary recall, and DII was estimated from 25 dietary components. Also, the Mediterranean diet (Med-Diet) adherence screener was applied using the 14-item assessment tool.

The proinflammatory diet was statistically associated with young (p < 0.001), singles (p < 0.001), high school degree (p = 0.011), students (p < 0.001), not working or unemployed (p < 0.001), eating outside (p = 0.016) and lower adherence to a Med-Diet (p = 0.027). There was a statistically significant positive relationship between the DII score and body weight (BW), waist circumference and waist-to-height ratio (WHtR) (r = 0.107; p = 0.002, r = 0.092; p = 0.008, r = 0.115; p = 0.001, respectively). Besides, there was a statistically significant negative relationship between MED score and BW, WHtR and neck circumference (r = −0.101; p = 0.004, r = −0.084; p = 0.016, r = −0.175; p = 0.000, respectively).

To the best of the authors’ knowledge, this was the first Turkish study to assess the association between the anthropometric indicators of obesity and DII or adherence to Med-Diet in young adults. Lower DII scores and higher adherence to Med-Diet are related to the better status of anthropometric indicators of adiposity.