Specific Disorders Research Articles

Vegan versus meat-based dog food: Guardian-reported health outcomes in 2,536 dogs, after controlling for canine demographic factors

To compare health outcomes between dogs fed meat and vegan diets, we surveyed 2,536 dog guardians who provided data and opinions about dogs fed conventional meat (1,370 = 54 %), raw meat (830 = 33 %) or vegan (336 = 13 %) diets for at least one year. We examined seven general indicators of illness: increased numbers of veterinary visits, medication use, progression onto a therapeutic diet after initial maintenance on a vegan or meat-based diet, reported veterinary assessment of being unwell, reported veterinary assessment and guardian opinion of increased illness severity, and number of health disorders per unwell dog. We also considered the prevalence of 22 specific health disorders, based on reported veterinary assessments. In each dietary group the proportions of dogs considered to have suffered from health disorders were: conventional meat – 49 %, raw meat – 43 % and vegan – 36 %. Probabilities of suffering from a disorder respectively appeared highest in dogs fed conventional meat for 11 disorders, raw meat for eight disorders, and vegan diets for three disorders. We conducted regression analyses to control for differences in medically-relevant canine demographic variables, including age, sex, neutering status, breed size and unusually high exercise levels. Dogs fed vegan diets had the best health outcomes. This trend was clear and consistent, with dogs fed vegan diets usually having substantial and statistically significant decreases in risks of these seven general indicators of illness. These ranged from 14.4 % to 51.3 % compared to dogs fed conventional meat-based diets. For six specific disorders, vegan diets were associated with statistically significant risk reductions of 50 %–61 % compared to dogs fed conventional meat. After pooling our results with related studies published to date, vegan dog food was consistently associated with lowered risks of multiple specific health disorders. No health disorder was consistently more prevalent in dogs fed vegan diets.

Development and Validation of the Tablet-Based ERAL Nonverbal Intelligence Test 5-17 (ERAL-NIT)

Intelligence has been extensively explored across various disciplines such as psychology, cognitive science, and neurology. Countless scholars have delved into understanding why certain individuals exhibit higher mental acuity and knowledge. Consequently, numerous studies aim to unveil the essence of intelligence and gauge human cognitive capacity. This study introduces and validates the ERAL Nonverbal Intelligence Test (ERAL-NIT) for children aged 5 to 17. Unlike many existing tests, ERAL-NIT is nonverbal, accommodating diverse linguistic backgrounds. Moreover, it incorporates parental assessments, enhancing its holistic approach. Administered via tablet with specialized software, ERAL-NIT provides comprehensive reports to practitioners and families. Its development integrates analyses of hemispheric specialization and executive functions of different brain areas. Content validity is ensured through expert input, while criterion validity is established by comparing results with established assessments and medical diagnoses. ERAL-NIT yields insights into pervasive developmental disorders, specific learning disabilities, attention deficits, and language disorders, thereby enriching diagnostic practices.

Concurrent Validity of the Anxiety Disorders Section of the Anxiety Disorder Interview Schedule- Autism Spectrum Addendum (ADIS-ASA) in Autistic Youth.

Examine the concurrent validity of specific Anxiety Disorders Section of the Anxiety Disorder Interview Schedule for DSM-IV-Autism Spectrum Addendum (ADIS-ASA)-Parent Interview in a sample of 167 autistic youth who met diagnostic criteria for an anxiety-related disorder (Mage = 9.91; 78.4% male; 82% non-Hispanic; 77.67% White). Concurrent validity of Diagnostic and Statistical Manual (DSM)-defined ADIS-ASA anxiety disorder diagnostic caseness was examined via relations with (a) parent-reported dimensions of youth anxiety symptomology and (b) dimensional measures of youth anxiety-related functional impairment, respectively, using logistic regression models and point-biserial correlations. Significant relations were found between separation anxiety disorder and social anxiety disorder (but not generalized anxiety disorder nor obsessive-compulsive disorder) caseness, respectively, and theoretically consistent facets of dimensional youth anxiety symptomology. Relations between ADIS-ASA diagnostic caseness and youth functional impairment-related variables revealed that only separation anxiety disorder demonstrated robust evidence of convergent validity. Despite mixed findings concerning relations between ADIS-ASA anxiety disorder diagnostic caseness and dimensional measures of anxiety severity and anxiety-related impairment, the present findings provide further support for the status of the ADIS-ASA as a gold standard for assessment of anxiety in autistic youth. This work also highlights the importance of continuing to improve precision in measurement of anxiety symptomology in autistic youth, with implications for clinical assessment.

Pharmacological Treatment of Binge Eating Disorder and Frequent Comorbid Diseases.

Binge eating disorder (BED) is the most common specific eating disorder (ED). It is frequently associated with attention deficit hyperactivity disorder (ADHD), depression, bipolar disorder (BD), anxiety disorders, alcohol and nicotine use disorder, and obesity. The aim of this narrative review was to summarize the evidence for the pharmacological treatment of BED and its comorbid disorders. We recommend the ADHD medication lisdexamfetamine (LDX) and the antiepileptic and antimigraine drug topiramate for the pharmacological treatment of BED. However, only LDX is approved for the treatment of BED in some countries. Medications to treat diseases frequently comorbid with BED include atomoxetine and LDX for ADHD; citalopram, fluoxetine, sertraline, duloxetine, and venlafaxine for anxiety disorders and depression; aripiprazole for manic episodes of BD; lamotrigine, lirasidone and lumateperone for depressive episodes of BD; naltrexone for alcohol use disorder; bupropion for nicotine use disorder; and liraglutide, semaglutide, and the combination of bupropion and naltrexone for obesity. As obesity is a frequent health consequence of BED, weight gain-inducing medications, such as the atypical antipsychotics olanzapine or clozapine, the novel antidepressant mirtazapine and tricyclic antidepressants, and the mood stabilizer valproate should be avoided where possible. It is currently unclear whether the novel and promising glucagon, glucose-dependent insulinotropic polypeptide (GIP), and glucagon-like peptide 1 (GLP-1) receptor agonists like tirzepatide and retatrutide help with BED and its comorbidities. However, these compounds have been reported to reduce binge eating in individuals with obesity or overweight.

Recent Advances in the Synthesis of Antioxidant Derivatives: Pharmacological Insights for Neurological Disorders.

Neurological disorders, characterized by oxidative stress (OS) and inflammation, have become a major global health concern. Redox reactions play a vital role in regulating the balance of the neuronal microenvironment. Specifically, the imbalance leads to a significant weakening of the organism's natural defensive mechanisms. This, in turn, causes the development of harmful oxidative stress, which plays a crucial role in the onset and progression of neurodegenerative diseases. The quest for effective therapeutic agents has led to significant advancements in the synthesis of antioxidant derivatives. This review provides a comprehensive overview of the recent developments in the use of novel antioxidant compounds with potential pharmacological applications in the management of neurological disorders. The discussed compounds encompass a diverse range of chemical structures, including polyphenols, vitamins, flavonoids, and hybrid molecules, highlighting their varied mechanisms of action. This review also focuses on the mechanism of oxidative stress in developing neurodegenerative disease. The neuroprotective effects of these antioxidant derivatives are explored in the context of specific neurological disorders, including Alzheimer's disease, Parkinson's disease, and Huntington's disease. The ultimate goal is to provide effective treatments for these debilitating conditions and improve the quality of life for patients.

ENHANCING RECEPTIVE SKILLS IN FOREIGN LANGUAGE LEARNING: THE INTERPLAY OF READING AND LISTENING COMPREHENSION

Receptive skills, particularly reading and listening comprehension, are crucial components of foreign language acquisition. This study explores the interplay between these skills and their significance in language learning by utilizing the scientific literary and analytical–synthetic method. The literature suggests that reading proficiency can enhance other language skills, whereas listening comprehension facilitates foreign language learning through various activities and the use of Information and Communication Technology (ICT). Specific learning disorders, auditory functioning, and instructional materials can influence the development of receptive skills. The relationship between short-term memory (STM) and listening comprehension was also examined, highlighting the importance of enhancing STM capacity in improving listening skills. Additionally, vocabulary knowledge is central to both listening and reading comprehension. The research collectively underscores the importance of targeted strategies and resources to enhance the receptive skills of learners in foreign language contexts. However, individual learner characteristics and prior language experiences may require tailored approaches for effective improvement. While receptive skills are crucial for understanding, some argue that a balanced approach that incorporates both receptive and productive skills may lead to better language outcomes. Although receptive skills are certainly important for comprehension, it can be argued that a more holistic approach to language learning, which includes both receptive and productive skills, may ultimately result in more effective language acquisition. The basic aspects of teaching listening skills involve understanding the cognitive and pedagogical underpinnings of listening comprehension and applying effective strategies, such as top-down and bottom-up processing and a metacognitive approach, to enhance this skill.

Cerebral palsy in children: A clinical practice review

Cerebral palsy is a disorder characterized by abnormal tone, posture, and movement. In clinical practice, it is often useful to approach cerebral palsy based on the predominant motor system findings – spastic hemiplegia, spastic diplegia, spastic quadriplegia, extrapyramidal or dyskinetic, and ataxic. The prevalence of cerebral palsy is between 1.5 and 3 per 1,000 live births with higher percentage of cases in low to middle income countries and geographic regions. Pre-term birth and low birthweight are recognized as the most frequent risk factors for cerebral palsy; other risk factors include hypoxic-ischemic encephalopathy, maternal infections, and multiple gestation. In most cases of cerebral palsy, the initial injury to the brain occurs during early fetal brain development. Intracerebral hemorrhage and periventricular leukomalacia are the main pathologic findings found in preterm infants who develop spastic cerebral palsy. The diagnosis of cerebral palsy is primarily based on clinical findings. Early recognition of infants at risk for cerebral palsy as well as those with cerebral palsy is possible based on a combination of clinical history, use of standardized neuromotor assessment and findings on magnetic resonance imaging; however, in clinical practice, cerebral palsy is more reliably diagnosed by 2 years of age. Magnetic resonance imaging scan is indicated to delineate the extent of brain lesions and to identify congenital brain malformations. Genetic testing and tests for inborn errors of metabolism are indicated to identify specific disorders, especially treatable disorders. Because cerebral palsy is associated with multiple associated and secondary medical conditions, its management requires a sustained and consistent collaboration among multiple disciplines and specialties. With appropriate support, most children with cerebral palsy grow up to be adults with good functional abilities.

Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography

BackgroundTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.MethodsThis study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.ResultsThe study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.ConclusionThe AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.Graphical

Prevalence and risk factors of self-reported hearing loss, tinnitus, and dizziness in a population-based sample from rural northeastern Germany

A close anatomical and physiological relationship is known between the senses of hearing and balance, while an additional pathophysiological interaction is supposed. The mechanisms underlying this association are not yet fully understood, especially in individuals without a known specific otologic disorder. In particular, only scarce information on the combined occurrence of audiovestibular sensory impairment is available so far. Therefore, this study aims to provide further insight into the prevalence and co-prevalence of the audiovestibular symptoms hearing loss, tinnitus and dizziness. Additionally, the influence of potential risk factors from lifestyle habits as well as cardiovascular and metabolic conditions on the development of those symptoms is studied. Data was analyzed from 8134 individuals from the population-based Study of Health in Pomerania (SHIP). SHIP pursues a broad and comprehensive examination program in chronologically separated cohorts with longitudinal follow-up. Cohorts are sampled from Western Pomerania, a rural region of north-eastern Germany. The study population represents a cross-sectional analysis from the cohorts SHIP-START (recruited 1997–2001) and SHIP-TREND (recruited 2008–2012), sampled for baseline investigations (SHIP-START-0 and SHIP-TREND-0) at the age of 20–79 years. Audiovestibular symptoms as outcome variables were assessed by structured questionnaires. Additionally, individuals were comprehensively characterized regarding modifiable lifestyle factors as well as cardiovascular and metabolic disorders, allowing the assessment of their role as exposure variables. We calculated a weighted prevalence of 14.2% for hearing loss, 9.7% for tinnitus, and 13.5% for dizziness in the population. Prevalence increased with age and differed among the sexes. A considerable share of 28.0% of the investigated individuals reported more than one symptom at once. The prevalence of hearing loss as well as tinnitus increased between the two cohorts. A moderate positive correlation was found between the occurrence of hearing loss and tinnitus (phi-coefficient 0.318). In multivariable regression analyses, education was identified as a significant protective factor while only smoking was significantly associated with all three symptoms. Furthermore, several cardiovascular risk factors contributed to both hearing loss and dizziness. In conclusion, audiovestibular symptoms are highly prevalent in the investigated population. A considerable but complex influence of risk factors points towards a relation with neuronal as well as cardiovascular disease processes. To clarify the underlying mechanisms, the interaction between the senses of hearing and balance as well as the mode of action of the risk factors should be evaluated in more detail in the future.

Gut microbiome and metabolic pathways linked to sleep quality.

Sleep quality is a vital determinant of human health as sleep disorders are associated with cognitive deficits, and chronic sleep deprivation is associated with a broad range of health complications. Previous studies on the association between the gut microbiome and sleep quality have been constrained by small sample sizes or have focused on specific sleep disorders, thus yielding inconsistent results. Herein, we investigated the relationship between microbial composition and sleep quality in a cohort of 159 Koreans. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), determined through a self-administered questionnaire. Gut microbiome analyses were performed using 16S rRNA amplicons. We found no direct correlation between microbial alpha diversity metrics and sleep; however, we identified differences in beta diversity among sleep quality groups (with a PSQI score > 5 indicating poor sleep quality and PSQI ≤5 indicating good sleep quality). We also found differential microbial signatures (Bacteroides, Prevotella 9, and Faecalibacterium) among the groups. Furthermore, functional metabolic pathway profiles revealed significant linear correlations of the L-arginine and L-tryptophan biosynthetic pathways as well as 4-aminobutanoate degradation with sleep status. In particular, Faecalibacterium prausnitzii, which harbors these metabolic pathways, showed differences between sleep quality groups and a linear association with sleep quality scores and was thus identified as the species most strongly associated with sleep status. This study provides a significant advance in our understanding of the relationship between gut microbiota and sleep regulation. The current findings provide a basis for further research into potential therapeutic strategies for sleep disorders targeting the gut microbiome.

Etiology of combined pituitary hormone deficiency: GNAO1 as a novel candidate gene.

Because the causes of combined pituitary hormone deficiency (CPHD) are complex, the etiology of congenital CPHD remains unknown in most cases. The aim of the study was to identify the genetic etiology of CPHD in a well-defined single-center cohort. In total, 34 children (12 girls) with congenital CPHD (growth hormone (GH) deficiency and impaired secretion of at least one other pituitary hormone) treated with GH in our center were enrolled in the study. Their median age was 11.2 years, pre-treatment height was -3.2 s.d., and maximal stimulated GH was 1.4 ug/L. Of them, 30 had central adrenal insufficiency, 27 had central hypothyroidism, ten had hypogonadotropic hypogonadism, and three had central diabetes insipidus. Twenty-six children had a midline defect on MRI. Children with clinical suspicion of a specific genetic disorder underwent genetic examination of the gene(s) of interest via Sanger sequencing or array comparative genomic hybridization. Children without a detected causal variant after the first-tier testing or with no suspicion of a specific genetic disorder were subsequently examined using next-generation sequencing growth panel. Variants were evaluated by the American College of Medical Genetics standards. Genetic etiology was confirmed in 7/34 (21%) children. Chromosomal aberrations were found in one child (14q microdeletion involving the OTX2 gene). The remaining 6 children had causative genetic variants in the GLI2, PROP1, POU1F1, TBX3, PMM2, and GNAO1 genes, respectively. We elucidated the cause of CPHD in a fifth of the patients. Moreover, our study supports the PMM2 gene as a candidate gene for CPHD and suggests pathogenic variants in the GNAO1 gene as a potential novel genetic cause of CPHD.

"When one has no REAL illness"-analysis of the knowledge component of mental health literacy in children and adolescents of parents with a mental illness.

Mental Health Literacy (MHL) is important in promoting youth mental health. One key aspect of MHL is knowledge about mental disorders, which is particularly relevant for populations at risk for developing mental disorders, such as children of parents with a mental illness (COPMI), representing a mechanism within the transgenerational transmission. Currently, COPMI's level of disorder knowledge in general, and about the specific parental disorder has not been comprehensively researched. We, therefore, aimed to assess COPMI's disorder knowledge and clarify its association with COPMI's age and sex exploratively. To assess both general and disorder-specific knowledge, we took a novel approach that makes disorder knowledge comparable across samples and over time. A mixed method analysis of N = 181 semi-structured MHL interviews with COPMI (aged 5 to 17 years) was carried out in the COMPARE-family study in Germany. We conducted a DSM-oriented deductive qualitative content analysis to assess COPMI's general and specific disorder knowledge. Chi-square tests served to identify age and sex differences. Children revealed limited knowledge of mental disorders in general, whereas adolescents displayed more knowledge that was also partly consistent with descriptions of classification systems like the DSM-5. The level of specific knowledge about the parent's disorder depended on the disorder group. More children displayed adequate knowledge of somatic and anxiety disorders compared to trauma and depressive disorders, and more adolescents displayed adequate knowledge of depressive and anxiety disorders. COPMI's age and sex were found to be significantly associated with disorder knowledge: adolescents exhibited higher levels of adequate general and specific disorder knowledge, and males exhibited higher levels of adequate general disorder knowledge. Assessing COPMI's disorder knowledge and identifying associated age and sex differences yield valuable insights into the knowledge component of the MHL theory. Our findings can help to improve psychoeducational interventions for COPMI by orienting them to their prevailing levels of disorder knowledge. We recommend employing and extending the DSM-oriented deductive approach to assess knowledge within MHL. Analyses involving additional assessments within the COMPARE-family study are in preparation to identify potential knowledge gains over time, and associations to COPMI's own well-being and mental health symptoms.

Advances in Brain Stimulation, Nanomedicine and the Use of Magnetoelectric Nanoparticles: Dopaminergic Alterations and Their Role in Neurodegeneration and Drug Addiction.

Recent advancements in brain stimulation and nanomedicine have ushered in a new era of therapeutic interventions for psychiatric and neurodegenerative disorders. This review explores the cutting-edge innovations in brain stimulation techniques, including their applications in alleviating symptoms of main neurodegenerative disorders and addiction. Deep Brain Stimulation (DBS) is an FDA-approved treatment for specific neurodegenerative disorders, including Parkinson's Disease (PD), and is currently under evaluation for other conditions, such as Alzheimer's Disease. This technique has facilitated significant advancements in understanding brain electrical circuitry by enabling targeted brain stimulation and providing insights into neural network function and dysfunction. In reviewing DBS studies, this review places particular emphasis on the underlying main neurotransmitter modifications and their specific brain area location, particularly focusing on the dopaminergic system, which plays a critical role in these conditions. Furthermore, this review delves into the groundbreaking developments in nanomedicine, highlighting how nanotechnology can be utilized to target aberrant signaling in neurodegenerative diseases, with a specific focus on the dopaminergic system. The discussion extends to emerging technologies such as magnetoelectric nanoparticles (MENPs), which represent a novel intersection between nanoformulation and brain stimulation approaches. These innovative technologies offer promising avenues for enhancing the precision and effectiveness of treatments by enabling the non-invasive, targeted delivery of therapeutic agents as well as on-site, on-demand stimulation. By integrating insights from recent research and technological advances, this review aims to provide a comprehensive understanding of how brain stimulation and nanomedicine can be synergistically applied to address complex neuropsychiatric and neurodegenerative disorders, paving the way for future therapeutic strategies.

Oculomotor Patterns in Children with Poor Reading Abilities Measured Using the Development Eye Movement Test.

Objectives: The main purpose of this work was to clinically assess the oculomotricity of one hundred Mexican children with poor reading skills but without any specific learning disorder. Methods: The D.E.M. psychometric test was used. Sex and age analyses of the ratio, type, horizontal and vertical performance, and errors were carried out. Results: Our data suggest that 84% of poor readers exhibit oculomotor difficulties. Sex did not significantly influence the results (p > 0.05), whereas age was associated with the horizontal (p = 0.04) and vertical (p = 0.29) performance, as well as the number of errors (p = 0.001). Omissions were the most prevalent error type. Conclusions: This research gives insights into the role of oculomotricity in children with poor reading skills. Our results suggest that oculomotor performance should be included in the evaluation protocol to assess poor readers to identify any influence of the visual system.

The relationship between digit ratio (2D:4D) and intelligence levels in specific learning disorders

BackgroundHigher prenatal testosterone exposure regulates brain development and affects learning and intelligence directly. The digit ratio (2D:4D) is regarded as an indicator of prenatal testosterone exposure. This study aims to investigate the 2D:4D ratios and intelligence levels in individuals with specific learning disorders (SLD) and compare the ratios with healthy subjects. MethodsThe study included a total of 117 patients diagnosed with SLD and 67 healthy controls. We measured the 2D:4D ratios and administered the Wechsler-Intelligence Scale for Children-Revised to assess intelligence quotient (IQ) scores in the SLD group. Sociodemographic data was obtained for both patients and healthy subjects and compared in both groups, as well as 2D:4D ratios. ResultsCompared to healthy controls, both-hand 2D:4D ratios were found to be lower in the SLD group. In addition, male and female participants with SLD showed lower 2D:4D ratios in both hands than controls. The total scores on the WISC-R were found to decrease as the right-hand 2D:4D ratios and the age increased in the SLD group. ConclusionOur findings add to the literature examining the influence of prenatal testosterone exposure on learning and intelligence in the SLD sample. Further research in this domain may yield valuable insights into the underlying mechanisms and potential clinical implications for the management of SLDs examining additional variables that could potentially impact alongside the impact of sex hormones on brain development.

Specific learning disabilities and associated emotional-motivational profiles: a study in Italian university students.

In this study, we analyzed the emotional and motivational aspects characterizing the profile of university students with Specific Learning Disorders (SLD). We assessed 61 university students, 32 with SLD (age = 23.6) and 29 in the control group (age = 23.00). The results highlighted that individuals with SLD exhibit higher levels of anxiety and depression and lower resilience compared to the control group. The Multidimensional Perfectionism Scale - Short Form, which explores perfectionism, did not reveal differences between the groups. Conversely, lower scores emerged in SLD students for the intrinsic motivation sub-scales of the Academic Motivation Scale. This indicates less engagement in studying out of personal cognitive curiosity. The Self-Regulated Knowledge Scale - University, which measures various cognitive strategies, showed significantly lower scores in the SLD group for knowledge linking, knowledge training, and knowledge critique. This suggests a lower frequency with which SLD students attempt to connect new knowledge with what they already possess, apply their knowledge, ask questions, and critically analyze what they have learned. Therefore, psychological and motivational consequences are evident in this population and can impact well-being and quality of life.

A multi-AS-PCR-coupled CRISPR/Cas12a assay for the detection of ten single-base mutations

Single-nucleotide polymorphism (SNP) detection is critical for diagnosing diseases, and the development of rapid and accurate diagnostic tools is essential for treatment and prevention. Allele-specific polymerase chain reaction (AS-PCR) is widely used for detecting SNPs with multiplexing capabilities, while CRISPR-based technologies provide high sensitivity and specificity in targeting mutation sites through specific guide RNAs (gRNAs).In this study, we have integrated the high sensitivity and specificity of CRISPR technology with the multiplexing capabilities of AS-PCR, achieving the simultaneous detection of ten single-base mutations. As for Multi-AS-PCR, our research identified that competitive inhibition of primers targeting the same loci, coupled with divergent amplification efficiencies of these primers, could result in diminished amplification efficiency. Consequently, we adjusted and optimized primer combinations and ratios to enhance the amplification efficacy of Multi-AS-PCR. Finally, we successfully developed a novel nested Multi-AS-PCR-Cas12a method for multiplex SNPs detection. To evaluate the clinical utility of this method in a real-world setting, we applied it to diagnose rifampicin-resistant tuberculosis (TB). The limit of detection (LoD) for the nested Multi-AS-PCR-Cas12a was 102 aM, achieving sensitivity, specificity, positive predictive value, and negative predictive value of 100 %, 93.33 %, 90.00 %, and 100 %, respectively, compared to sequencing.In summary, by employing an innovative design that incorporates a universal reverse primer alongside ten distinct forward allele-specific primers, the nested Multi-AS-PCR-Cas12a technique facilitates the parallel detection of ten rpoB gene SNPs. This method also holds broad potential for the detection of drug-resistant gene mutations in infectious diseases and tumors, as well as for the screening of specific genetic disorders.

Kynurenines as a Novel Target for the Treatment of Inflammatory Disorders.

This review discusses the potential of targeting the kynurenine pathway (KP) in the treatment of inflammatory diseases. The KP, responsible for the catabolism of the amino acid tryptophan (TRP), produces metabolites that regulate various physiological processes, including inflammation, cell cycle, and neurotransmission. These metabolites, although necessary to maintain immune balance, may accumulate excessively during inflammation, leading to systemic disorders. Key KP enzymes such as indoleamine 2,3-dioxygenase 1 (IDO1), indoleamine 2,3-dioxygenase 2 (IDO2), tryptophan 2,3-dioxygenase (TDO), and kynurenine 3-monooxygenase (KMO) have been considered promising therapeutic targets. It was highlighted that both inhibition and activation of these enzymes may be beneficial, depending on the specific inflammatory disorder. Several inflammatory conditions, including autoimmune diseases, for which modulation of KP activity holds therapeutic promise, have been described in detail. Preclinical studies suggest that this modulation may be an effective treatment strategy for diseases for which treatment options are currently limited. Taken together, this review highlights the importance of further research on the clinical application of KP enzyme modulation in the development of new therapeutic strategies for inflammatory diseases.

Diagnostic accuracy of large language models in psychiatry

IntroductionMedical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5. MethodsWe curated 20 clinical cases from the DSM-5 Clinical Cases book, covering a wide range of psychiatric diagnoses. Four advanced AI models (GPT-3.5 Turbo, GPT-4, Aya, Nemotron) were tested using prompts to elicit detailed diagnoses and reasoning. The models' performances were evaluated based on accuracy and quality of reasoning, with additional analysis using the Retrieval Augmented Generation (RAG) methodology for models accessing the DSM-5 text. ResultsThe AI models showed varied diagnostic accuracy, with GPT-3.5 and GPT-4 performing notably better than Aya and Nemotron in terms of both accuracy and reasoning quality. While models struggled with specific disorders such as cyclothymic and disruptive mood dysregulation disorders, others excelled, particularly in diagnosing psychotic and bipolar disorders. Statistical analysis highlighted significant differences in accuracy and reasoning, emphasizing the superiority of the GPT models. DiscussionThe application of AI in psychiatry offers potential improvements in diagnostic accuracy. The superior performance of the GPT models can be attributed to their advanced natural language processing capabilities and extensive training on diverse text data, enabling more effective interpretation of psychiatric language. However, models like Aya and Nemotron showed limitations in reasoning, indicating a need for further refinement in their training and application. ConclusionAI holds significant promise for enhancing psychiatric diagnostics, with certain models demonstrating high potential in interpreting complex clinical descriptions accurately. Future research should focus on expanding the dataset and integrating multimodal data to further enhance the diagnostic capabilities of AI in psychiatry.

Utilizing Multi-Class Classification Methods for Automated Sleep Disorder Prediction

Even from infancy, a human’s day-life alternates from a period of wakefulness to a period of sleep at night, during the 24-hour cycle. Sleep is a normal process necessary for human physical and mental health. A lack of sleep makes it difficult to control emotions and behaviour, reduces productivity at work, and can even increase stress or depression. In addition, poor sleep affects health; when sleep is insufficient, the chances of developing serious diseases greatly increase. Researchers in sleep medicine have identified an extensive list of sleep disorders, and thus leveraged Artificial Intelligence (AI) to automate their analysis and gain a deeper understanding of sleep patterns and related disorders. In this research, we seek a Machine Learning (ML) solution that will allow for efficient classification of unlabeled instances as being Sleep Apnea, Insomnia or Normal (subjects without a specific sleep disorder) by assessing the performance of two well-established strategies for multi-class classification tasks: the One-Vs-All (OVA) and One-Vs-One (OVO). In the context of the specific strategies, two well-known binary classification models were assumed, Logistic Regression (LR) and Support Vector Machines (SVMs). Both strategies’ validity was verified upon a dataset of diverse information related to the profiles (anthropometric data, sleep metrics, lifestyle and cardiovascular health factors) of potential patients or individuals not exhibiting any specific sleep disorder. Performance evaluation was carried out by comparing the weighted average results in all involved classes that represent these two specific sleep disorders and no-disorder occurrence; accuracy, kappa score, precision, recall, f-measure, and Area Under the ROC curve (AUC) were recorded and compared to identify an effective and robust model and strategy, both class-wise and on average. The experimental evaluation unveiled that after feature selection, 2-degree polynomial SVM under both strategies was the least complex and most efficient, recording an accuracy of 91.44%, a kappa score of 84.97%, precision, recall and f-measure equal to 0.914, and an AUC of 0.927.