Annals of Neurosciences最新文献

Background: Atherosclerosis, a precursor to macrovascular complications in type 2 diabetes, has shown links to blood glucose fluctuations. Carotid intima-media thickness (CIMT) serves as a non-invasive tool for early atherosclerosis assessment.

Purpose: This study explores associations between specific glycaemic variability markers and CIMT in young type 2 diabetes patients.

Methodology: A cross-sectional study was conducted on 52 patients aged ≤50, assessing mean blood glucose (MBG), standard deviation (SD), mean amplitude of glucose excursion (MAGE), largest amplitude of glucose excursion (LAGE) and CIMT. Statistical analyses included descriptive statistics and Spearman's rank correlation tests.

Results and discussion: Participants exhibited diverse characteristics and poor glycaemic control (mean glycated hemoglobin (HbA1c): 8.7%). Nephropathy (52%), neuropathy (44.2%) and diabetic retinopathy (67.3%) were prevalent. Continuous glucose monitoring (CGM) revealed elevated glycaemic parameters. Significant correlations were found between MBG, SD, MAGE, LAGE and CIMT. Despite regional variations and elevated risk factors, findings align with prior studies, emphasising the multifactorial nature of atherosclerosis. The study highlights the importance of targeted interventions in diabetes management.

Conclusion: The study reveals suboptimal glycaemic control and a high prevalence of microvascular complications. Contrary to some studies, it establishes a significant correlation between glycaemic variability and CIMT, emphasising the importance of targeted interventions.

Background: Any risk behaviour may result in a negative outcome. This highly depends on the complex interplay of emotions and an individual's perception of risk. AI and ML can study the biological signature and speech of individuals, which can help clinicians intervene with individualised structured interventions.

Summary: This review investigates how AI and ML-based algorithms are used for detecting risk behaviours such as along with their diagnostic characteristics and treatment results. The review serves to collect all modern research about risk detection using existing ML techniques, along with their positive impact on clinical practice. The research explores how applying various DL models enhances the diagnostic accuracy and reliability of the findings.

Key message: Though many ML models show a strong potential in detecting the risk behaviours, they do face limitations like a sub-optimal level of precision and sensitivity, Limited clinical value, external validity, high false positive rates, and less interpretability. Hence, HMM is recommended as a good alternative because of its excellence in uncovering the hidden states from overt behaviours, especially using language or speech analysis. The research currently in the field of risk prediction works on text or speech analysis and uses neuroimaging data. The implementation of DL practice is needed through validation, and at the same time, ethical considerations, data privacy issues should also be considered. There is strong evidence to suggest that DL and ML models and their adaptations show promising ways to predict and prevent risky behaviours.

Background: Shoulder subluxation and shoulder pain are frequent after a stroke. Neuromuscular electrical stimulation (NMES) has been used widely in the sub-acute and chronic stages, but its use in the early stage is infrequent.

Aims and objectives: The study's objective is to see the effect of early neuromuscular electrical stimulation on hand function, shoulder subluxation, and shoulder pain after three months of stroke.

Methods: This study included 60 acute ischaemic stroke survivors. The intervention group received early NMES with standard rehabilitation, whereas the control group only received standard rehabilitation. The assessment was done at baseline and three months follow-up. The outcome measures were the presence of a sulcus sign, the Numerical Rating Scale (NRS), the Fugl-Meyer Assessment-Upper Extremity (FMA-UE, sub-scale A and A-D) and the Motor Activity Log (MAL) (sub-scale HOW WELL and AMOUNT).

Result: The intervention group had eight patients and the control group had nine patients with shoulder subluxation at follow-up. Hand functional outcomes were significantly better in the intervention group compared to the control group, with the FMA-UE-A scores of 27 (19-36) versus 18.5 (4.7-24.75), p = .007 and FMA-UE-A-D scores of 28.5 (4-48.25) versus 7.5 (4-23.75), p = .011. The HOW WELL and AMOUNT scores also showed significant differences, with 90 (30-150) versus 30 (0-90), p = .027. The intervention group did not experience any adverse events related to NMES.

Conclusion: The application of early NMES prevents shoulder subluxation and improves hand function. Randomised control trials with larger sample sizes and additional treatment sessions are needed to generalise the results.

Background: High altitude (HA) environments expose individuals to chronic hypoxia, which can affect cognitive function. While studies have explored cognitive deficits in lowlanders ascending to HAs, there is limited research on cognitive function among natives of HA.

Purpose: This study aimed to evaluate cognitive functions in the tribal population of Himachal Pradesh India using the Montreal Cognitive Assessment (MoCA) tool and assess the effect of high and very HAs along with age and gender variation in cognitive functions in these HA natives.

Methods: This study included 359 participants who lived at HAs. Cognitive function was assessed using MoCA. Participants were stratified by altitude (3,000-3,500 m and >3,500 m) and age group (20-40, 41-60 and >60 years). Appropriate statistical analyses were performed to determine the association of altitude, age, and sex with MoCA total and subscale scores.

Results: Significant associations were found between altitude and performance on the Naming, Attention, Delayed Recall, and Orientation subscales (p < .001, p < .001, p < .001 and p = .002, respectively). Specifically, a significant age-related decline was observed across the MoCA total score and most subscales (p < .001, Kendall's Tau = 0.48). Gender also had a significant association with the Visuospatial/Executive and Attention subscales (p < .001 for both), with males scoring higher than females.

Conclusion: Increasing altitude was associated with lower scores on specific cognitive domains. Age is a primary factor influencing cognitive performance in high-altitude natives and is associated with lower MoCA scores. Gender differences were also observed in specific cognitive functions. These findings suggest that altitude, age, and sex play important roles in shaping the cognitive profiles of individuals living at HAs.

Background: Anxiety is a prevalent mental health concern among undergraduate students, adversely affecting academic performance, social functioning and emotional well-being. Its multifactorial causes and coping strategies, particularly in the context of global disruptions such as the COVID-19 pandemic, warrant a comprehensive synthesis.

Purpose: This systematic review aims to analyse empirical studies on the causes of anxiety and the effectiveness of coping strategies among undergraduate students from 2010 to 2025.

Methods: A systematic review was conducted using Scispace.ai, selecting 40 empirical studies published between 2010 and 2025. Keywords used included 'anxiety', 'undergraduate students' and 'coping strategies'. Thematic analysis was performed to categorise findings into major focus areas.

Results: Seven key themes emerged: Prevalence (16%), academic stress (18%), social anxiety (16%), emotional comorbidities (16%), demographic differences (12%), coping mechanisms (14%) and contextual stressors such as COVID-19 (8%). Social judgement, academic pressure and fear of failure were primary causes of anxiety, often co-occurring with depression and emotional dysregulation. Female students and first-year undergraduates were identified as more vulnerable groups. Effective coping strategies included mindfulness practices, yoga, peer support and biofeedback; however, utilisation was limited by stigma and inadequate mental health services. The COVID-19 pandemic further intensified anxiety levels due to social isolation and academic uncertainty.

Conclusion: This review underscores the urgent need for inclusive support systems, integration of digital mental health tools and culturally sensitive interventions within educational institutions. Future research should explore longitudinal trends and develop context-specific strategies to improve mental health outcomes among undergraduate students.

Background: Infection Prevention and Control (IPC) processes are associated with inherent changes in the general operations of hospitals as they directly modify the clinical outcomes, effective functioning and the internal image of any healthcare setting. Positive IPC is essential to the safety of patients and reliability of the institution. Insight into the perceptions of IPC practice by the hospital staff is a vital element to enhance practising procedures and optimise practice-related measures.

Purpose: The purpose of the study was to evaluate the perception and practicability of the IPC between the members of staff working in the hospital in different clinical and non-clinical departments. In particular, the study aimed at defining the impact of such practices on the efficiency and internal reputability of the hospital.

Methods: A questionnaire was designed to be given to the members of staff at the hospital, a research of Linking Infection Prevention Control to Hospital Efficiency and Reputation in several departments, both clinical and non-clinical. The collected data involved staff perceptions about IPC practices, factors that impede proper implementation and infection high-risk areas. The answers were compared in a logical manner to discover a shared theme and possible missing links in the modern IPC strategies.

Results: Current IPC measures negatively influenced hospital efficiency and the internal reputation, as established by the analysis. Employees have pointed out the great variance in the practice levels of IPC compliance between the departments, and that intervention in this may be required to complement training with targeted solutions, especially where there is the greatest risk. The research reported that persistency in training and education enhanced compliance as well as awareness among the health workers.

Conclusion: Findings support the need to make hospitals focus on high-risk processes and ensure implementation of IPC practices by reinforcing it with specific training sessions. Educational programs have the prospect to significantly increase efficiency and internal image of the hospital because they may influence staff members and their perspective on it. The need to study the most appropriate sample size and the contribution of cutting-edge technologies in enhancing IPC further should also be answered in subsequent research.

Background: Climate related risks are increasingly affecting financial markets, most importantly, policy risk regarding climate regulation. Not only do such risks impact market dynamics, but they can also have an impact on investor behaviour as cognitive and emotional reactions to risk. Understanding the interplay between climate policy and market volatility is essential for both economic forecasting and behavioural finance.

Purpose: This study aims to examine the dynamic relationship between climate policy uncertainty and the volatility of major stock market indices viz. Nifty 50 and Sensex in India, and Nasdaq and Dow Jones in the USA and exploring potential neurobehavioural responses of investors to such uncertainty.

Methods: This research aims to determine the dynamic relationship between climate policy uncertainty, Indian benchmark indices Nifty 50 and Sensex, and USA stock market indices Nasdaq and Dow Jones for the monthly data from 1st April 2010 to 31st March 2024. The article has adopted Diebold and Yilmaz's connectedness framework and WC approach for data analysis. The analysis is interpreted through the lens of neuroeconomics, considering how climate policy uncertainty may influence cognitive risk processing in financial decision-making.

Results: Findings show that indices such as Sensex, Nasdaq and Dow Jones are more responsive to climate policy uncertainty compared with others. These trends suggest that the reactions of global investors are not only strategic but also subject to psychological tension and risk perception mechanisms.

Conclusion: Uncertainty in climate policy exerts a notable influence on stock market volatility with far-reaching implications extending beyond the classical economic indicators to encompass investor cognition and neurobehavioural reactions. The outcome of the current research underscores incorporating neuroscience-informed methods into financial decision-making, providing significant feedback for investors and policymakers regarding risk management, portfolio maximisation and interpreting behavioural responses under environmental uncertainty.

Background: World Health Organisation (WHO) in 2024 identified that approximately one in 100 children globally has autism spectrum disorder (ASD). ASD is a collection of neurodevelopmental disorders that impact a person's ability to socially interact and communicate, which can typically be noticed in early childhood. While 'autism' as a term was initially used for schizophrenic patients, later psychiatrists Dr. Kanner and paediatrician Dr. Asperger introduced it as a syndrome in children with behavioural differences in social interaction and communication with restrictive and repetitive interests. In today's time, the umbrella term 'ASDs' is used to describe a clinically heterogeneous group of neurodevelopmental disorders (NDDs).

Purpose: To examine the role of traditional approaches and the potential effectiveness of artificial intelligence (AI) methods in dealing with ASDs for improving the accuracy in its diagnosis and treatment.

Methodology: The study adopts a narrative review approach to understand the application of AI in ASD. For this purpose, around a hundred research articles were selected from the years 2010-2024. Inclusion and exclusion criteria were identified. The review is organised and grounded on the medical treatment, occupational remedy, vocational remedy, psychology, family remedy and recuperation engineering.

Results and conclusion: The results show the undisputed role of AI and its ability to identify early indicators of autism, in accordance with the UN Sustainable Development Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice and Strong Institutions). Further, healthcare sectors which are using a variety of AI analyses on data sources, genetics, neuroimaging, behavioural patterns and electronic medical records are able to early detect for individualised evaluation of ASD. The significance of timely interventions with the help of machine learning (ML) algorithms demonstrates high accuracy in differentiating ASD from neurotypical development and other developmental disorders.AI-driven therapeutic interventions expand social interactions and communication skills in people with ASD in the form of virtual reality-based training, augmentative communication systems and robot-assisted therapies. Thus, the future of AI in ASD holds promise for improving diagnostic accuracy, implementing telehealth platforms and customising treatment plans, despite obstacles such as data privacy and interpretability.

Background: Modern integrative frameworks such as the self-awareness, self-regulation and self-transcendence (S-ART) and LIBRE/EMC² highlight the neurocognitive-affective mechanisms underpinning mindfulness in young learners.

Purpose: This study explored how trait-level psychological dispositions and academic factors relate to electrophysiological and behavioural markers of state mindfulness in novice student meditators.

Methods: Ninety-seven university students (mean age = 24.59 years) participated in the study, with 89 consenting to electroencephalography (EEG) recording during a tristage Ānāpānasati-inspired meditation. EEG spectral powers were analysed across five neural oscillatory bands in the prefrontal, occipital, and default mode regions to assess state mindfulness, complemented by behavioural evaluation using the Amsterdam Resting-State Questionnaire. Additionally, trait-level data encompassing personality, values, mindfulness and academic background were gathered through validated self-report measures.

Results: The mindfulness trait of Acting with Awareness was associated with reduced Discontinuity of Mind and Theory of Mind, as well as heightened theta power during meditation, indicating enhanced breath-focused attention. Discontinuity of Mind also correlated with elevated posterior alpha and prefrontal beta, consistent with fragmented or effortful mentation. Planning-related thinking was consistently associated with self-reported early STEM subjects' proficiency, and corresponded with reduced delta and alpha power-suggesting unnecessary mentation during meditation. While value traits showed negligible robust behavioural correlations, EEG findings revealed that individuals scoring higher on Stimulation values exhibited lower prefrontal alpha power, possibly reflecting heightened alertness while meditating. Modest associations also emerged between Extraversion, Emotional Stability and state mindfulness dimensions across neurobehavioural indices.

Conclusion: These findings offer preliminary support for trait-to-state continuity in early meditative experience, highlighting how attentional traits and academic conditioning may shape neural engagement in novices. Although predictive value was weak, the results underscore the importance of dispositional context in shaping novice state mindfulness, warranting further replications and investigations to clarify the directionality and stability of the observed trait-state associations.

Background: Creativity involves the generation of novel ideas that are original and unique. It is a subjective process, and few studies are available in support of objective measures. Available tests of creativity are limited to questions related to an individual's trait and subjective responses. Though creativity is a divergent construct, an objective approach to computing and marking one as creative is required. This is so because creativity is an important factor for success, and a subjective approach would bring bias.

Purpose: The present study aims to develop a creativity score using the Rorschach Inkblot Test (RIBT) and then test it with eye-tracking technology.

Methods: Thirty-four university students were recruited for the study using a purposive sampling technique. RIBT cards were shown on a computer screen with an eye tracker mounted on it. Their responses were recorded and analysed to develop a novel construct of the Creativity measure. The Creativity score is then divided into high, medium and low creativity using the k-means clustering algorithm. Eye parameters of fixations, saccades and pupil diameter were explored for each group.

Results: ANOVA revealed significant differences between the three groups. In the high-creativity group, fixation count, variations in pupil diameter and total saccadic duration were higher than their counterparts. Mean fixation duration was highest for the low-creativity group.

Conclusion: The results indicated that using unstructured blots with Eye-tracking technology helps assess creativity objectively, further broadening avenues to measure creativity.