Stimulated Parotid Saliva Is a Better Method for Depression Prediction

Research on the soothing Liver - Qi stagnation method in the treatment of postoperative papillary thyroid carcinoma patients' concomitant depression: A randomized controlled clinical trial

BACKGROUND

Postoperative papillary thyroid carcinoma (P-PTC) patients often grapple with depression fueled by the looming threat of recurrence. While the Liver-Qi stagnation method is frequently employed for depression management, a notable scarcity of clinical trials exists regarding its application in patients with P-PTC and concurrent depression. This study presents a randomized controlled clinical trial, aiming to establish the efficacy of the Liver-Qi stagnation method in alleviating depression in patients with P-PTC.

METHODS

In this randomized controlled clinical trial, P-PTC patients diagnosed with concomitant depression were systematically enrolled. Subjects were randomly assigned to either the control or test group, both receiving standard treatment comprising Levothyroxine sodium tablets and decoction of benefiting Qi and nourishing Yin. Additionally, the test group received supplementation with bupleuri radix-paeoniae alba radix (CH-BS) alongside the baseline therapy. The intervention spanned 12 weeks. Pre- and post-treatment evaluations were conducted using the Hamilton Depression Scale (HAMD), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and Traditional Chinese Medicine (TCM) syndrome score scale. Concurrently, blood inflammatory factors and serum 5-hydroxytryptamine (5-HT) levels were measured to comprehensively assess treatment outcomes.

RESULTS

During the 12-week intervention, the test group demonstrated a significant reduction in HAMD scores compared to the control group (P < .05). Moreover, post-treatment serum 5-HT levels were significantly elevated in the test group compared to the control group (P < .05). Findings gleaned from the EORTC QLQ - C30 revealed a noteworthy improvement in social function and overall quality of life scores within both groups post-treatment in comparison to baseline (P < .05). Concurrently, post-treatment scores for fatigue and insomnia symptoms witnessed a significant decrease compared to baseline (P < .05). Notably, the test group exhibited superior scores in the emotional domain in contrast to the control group (P < .05). Both groups exhibited a substantial decrease in TCM syndrome scores from baseline (P < .05). Noteworthy increases were found in IFN-γ < 2.44 rate (62.86%) and IL-6 < 2.44 rate (74.29%) in the test group compared to pretreatment levels (P < .05).

CONCLUSION

The soothing Liver-Qi stagnation method induces a rise in serum 5-HT levels, reducing depression-related inflammatory factors, culminating in the alleviation of depression for P-PTC.

Advances in biosensors for major depressive disorder diagnostic biomarkers

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

A Bibliometric Analysis of Clinical Trials on Salivary Biomarkers for Mental Health (2003-2024)

Mental health conditions, such as depression, anxiety, and stress-related disorders, are often difficult to diagnose and monitor using traditional methods. Salivary biomarkers offer a promising alternative due to their non-invasive nature, ease of collection, and the potential to reflect real-time physiological changes associated with mental health. This bibliometric analysis examines 95 clinical trials on stress biomarkers for mental health, published between 2003 and 2024. The field is characterized by extensive collaboration and global participation, involving 593 authors and publications across 73 journals. Despite a consistent annual publication rate, notable increases in 2011, 2014, and 2018 indicate growing research interest. The United States leads in research output, followed by Australia, Germany, and Japan, with Psychoneuroendocrinology being the most prominent journal. Co-occurrence analysis identified nine research clusters, suggesting diverse directions such as the impact of stress-related hormones, circadian rhythms, mindfulness, various therapies, aging, psychological adaptation mechanisms, exercise therapy, anxiety disorders, and the autonomic nervous system on salivary biomarkers. Key terms such as "biomarkers/metabolism," AND "hydrocortisone/metabolism," AND "saliva/metabolism" were central, with significant activity from 2012 to 2018. This analysis highlights a growing focus on the metabolic processes and therapeutic applications of salivary biomarkers in mental health. This bibliometric analysis calls attention to the promising potential of salivary biomarkers to revolutionize mental health diagnostics and treatment through non-invasive methods, fostering interdisciplinary research, technological advancements, and global health improvements.

From CGRP to PACAP, VIP, and Beyond: Unraveling the Next Chapters in Migraine Treatment

Migraine is a neurovascular disorder that can be debilitating for individuals and society. Current research focuses on finding effective analgesics and management strategies for migraines by targeting specific receptors and neuropeptides. Nonetheless, newly approved calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) have a 50% responder rate ranging from 27 to 71.0%, whereas CGRP receptor inhibitors have a 50% responder rate ranging from 56 to 71%. To address the need for novel therapeutic targets, researchers are exploring the potential of another secretin family peptide, pituitary adenylate cyclase-activating polypeptide (PACAP), as a ground-breaking treatment avenue for migraine. Preclinical models have revealed how PACAP affects the trigeminal system, which is implicated in headache disorders. Clinical studies have demonstrated the significance of PACAP in migraine pathophysiology; however, a few clinical trials remain inconclusive: the pituitary adenylate cyclase-activating peptide 1 receptor mAb, AMG 301 showed no benefit for migraine prevention, while the PACAP ligand mAb, Lu AG09222 significantly reduced the number of monthly migraine days over placebo in a phase 2 clinical trial. Meanwhile, another secretin family peptide vasoactive intestinal peptide (VIP) is gaining interest as a potential new target. In light of recent advances in PACAP research, we emphasize the potential of PACAP as a promising target for migraine treatment, highlighting the significance of exploring PACAP as a member of the antimigraine armamentarium, especially for patients who do not respond to or contraindicated to anti-CGRP therapies. By updating our knowledge of PACAP and its unique contribution to migraine pathophysiology, we can pave the way for reinforcing PACAP and other secretin peptides, including VIP, as a novel treatment option for migraines.

Athletic performance, sports experience, and exercise addiction: an association study on ANKK1 gene polymorphism rs1800497

Introduction

Exercise addiction is a phenomenon being able to affecting the athletic performance. The gene, ANKK1 and the polymorphism NM_178510.2:c.2137G > A (rs1800497) has been linked to the exercise addiction. However, further studies on diverse populations and sport branches are needed to totally explore the possible association of this polymorphism with the athletic performance. Thus, the present study aims to decipher any possible relations of the rs1800497 polymorphism with the athletic performance/personal best (PB) and sport experience of elite athletes.

Methods

Sixty volunteer elite athletes (31 sprint/power and 29 endurance) and 20 control/sedentary participated in the study. The polymorphism was genotyped using whole exome sequencing approach and PB were determined according to the International Association of Athletics Federations (IAAF) score.

Results

Our results underlined that there were not any significance differences for both allele and genotype frequencies between the groups in terms of athletic performance, although the frequency of allele G was higher (p > 0.05). Nevertheless, sport experience significantly associated with the rs1800496 polymorphism (p < 0.05).

Discussion

In conclusion, genotype G/G could be inferred to be linked to the higher sport experience and athletic performance. Still, further studies with higher number of participants are needed to conclude the association of this polymorphism with athletic parameters.

Obtaining a Reliable Diagnostic Biomarker for Diabetes Mellitus by Standardizing Salivary Glucose Measurements

Salivary glucose is frequently utilized in diabetes mellitus (DM), and it might be proposed as a potential biomarker candidate for DM, as it is non-invasive and cost-effective and achieves adequate diagnostic performance for DM patients. However, salivary glucose levels may change under specific conditions. It is consequently essential to maintain a consistent strategy for measuring saliva, taking into account the possibility of external factors influencing salivary glucose levels. In this study, we analyzed salivary glucose levels under different handling conditions and donor-dependent factors, including age, interdiurnal variations, and collection and processing methods. A structured questionnaire was used to determine the symptoms and predisposing factors of DM. The glucose oxidase peroxidase method was used to estimate glucose levels in the blood and saliva of people in a fasting state. The aim of this study is to investigate the effect of such conditions on salivary glucose levels. We found that these extraneous variables should be taken into account in the future when salivary glucose is used as a predictive biomarker for DM.

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.