Classification Of Disorders Research Articles

Comparison of migraine with left- versus right-sided headache: A cross-sectional study.

The goal of this study was to clarify whether clinical differences exist between patients with migraine who experience headache that is typically left-sided ("left-migraine") versus right-sided ("right-migraine") during attacks. Migraine has been associated with unilateral headache for millennia and remains a supportive trait for the clinical diagnosis of migraine of the International Classification of Headache Disorders. It is currently unknown why headache in migraine is commonly unilateral, and whether headache-sidedness is associated with other clinical features. This is a cross-sectional study comparing left- versus right-migraine using all available intake questionnaires of new patients evaluated at an academic tertiary headache center over a 20-year period. Eligibility was based on patient written responses indicating the typical location of headache during attacks. In our analyses, the side of headache (left or right) was the predictor variable. The outcomes included various migraine characteristics and psychiatric comorbidities. We identified 6527 patients with migraine, of which 340 met study eligibility criteria. Of these, 48.8% (166/340) had left migraine, and 51.2% (174/340) had right migraine. When comparing patients with left- versus right-migraine, patients with left migraine experienced 3.6 fewer headache-free days (95% confidence interval [CI] 1.3-5.9; p = 0.002) and 2.4 more severe headache days (95% CI 0.8-4.1; p = 0.004) in the previous 4 weeks. No significant differences in age, sex, handedness, migraine characteristics, or psychiatric comorbidities were identified between the two groups. Patients with migraine with typically left-sided headache during attacks reported a higher burden of headache frequency and severity than those with typically right-sided headache during attacks. These findings may have implications for our understanding of migraine pathophysiology, treatment, and clinical trial design.

Enhanced Whale Optimization and Ensemble Learning Algorithm for Autism Spectrum Disorder Classification

Enhanced Whale Optimization and Ensemble Learning Algorithm for Autism Spectrum Disorder Classification

THE USE PICCO TECHNOLOGY FOR ASSESSMENT THE FUNCTIONAL STATE OF THE HEART AND SYSTEMIC GEODYNAMICS DURING ORTHOTOPIC HEART TRANSPLANTATION

Summary. Approximately 5000 heart transplants are performed annually in the world. According to the American College of Cardiology/American Heart Association/ Heart Failure Society of America (ACC/AHA/HFSA) and the European Society of Cardiology (ESC), there are currently more people on the waiting list for heart transplants than there are donor organs available. The Ukrainian National Waiting List of the Unified State Transplantation Information System contains more than 2,000 patients, of whom about 500 need a heart transplant. Objective. The aim was to evaluate the effectiveness of haemodynamic monitoring using a continuous analysis system for measuring pulse waveform and transpulmonary thermodilution and its impact on orthotopic heart transplantation. Materials and methods. The work is based on the results of 30 recipients diagnosed with dilated cardiomyopathy who underwent orthotopic heart transplantation. The average age of the recipients was 41.4±3.2 years. There were 90 % of male recipients and 10 % of female recipients. All recipients were diagnosed with dilated cardiomyopathy with reduced ejection fraction with an average of 18 ± 4.4 %. All patients underwent heart transplantation using PiCCO hemodynamic monitoring, which assessed the optimality of fluid, inotropic and vasopressor therapy with analysis of changes in the functional state of systemic hemodynamic (blood pressure, heart rate, central venous pressure, cardiac index, cardiac output, global end-diastolic volume index, extravascular lung water index, systemic vascular resistance and global ejection fraction) at the time of donor heart inclusion in the recipient’s circulation Results. It was found that all patients had a reduced ejection fraction. In 100% of patients undergoing orthotopic heart transplantation, PiCCO monitoring was used, which showed cardiac dysfunction with a reduced cardiac index of 1.90 ± 0.24 l/min/m on average, global end-diastolic blood volume index was 571 ± 163.7 ml/m2 and was reduced, reflecting a decrease in preload. While the extravascular water index in the lungs averaged 9.2 ± 1.41 ml/kg of body weight and was increased, indicating possible pulmonary oedema at the beginning of surgery. Conclusions. PiCCO monitoring allows real-time diagnosis of functional changes in the myocardium, differentiation of major circulatory disorders in patients with involvement of vital organs and systems, including breathing (gas exchange), classification of haemodynamic disorders and timely correction. During the study, PiCCO monitoring revealed an improvement in extravascular lung water in 95 % of patients by the end of CPR, which made it possible to adequately treat these disorders and prevent pulmonary oedema, provide adequate correction with inotropic support and balance postload with the systemic vascular resistance index and improve the cardiac index by 99.1 %.

Psychometric evaluation and medication usage pattern in Covid-19 patients: A retrospective study

Aim and objective: Assessing and evaluating Psychometric and medication usage patterns in COVID-19 patients retrospective study. assessing the post-COVID-19 subjects, evaluating the drug report in a post-COVID-19 subject, and measuring psychological status. Evaluation of the serious adverse reaction on the south-Indian population of the post-COVID-19 subject. Methodology: In which 150 subjects were taken and 127 participated. Inclusion criteria COVID-19 subject with adult age group, Patient was admitted and discharged before 3 months and above for COVID-19 with positive RT-PCR Report exclusion criteria patient who is recently tested for COVID-19 and pregnant women and lactating women and who are below the adult age group. Where follow-up is done to assess the psychometric evaluation it is a retrospective study with follow-up. Result: 127 patients were analyzed, in that there are 49 female subjects and 78 male subjects, therefore a total of 1 of 27 patients. The vaccination status of the subjects was analyzed with the data depleted. In that, 118 subjects were affected by Covid-19 before vaccination and only 9 subjects were affected after vaccination. In this pandemic, many subjects were Mentally stressed by their Neighborhood and 32 subjects were not stressed. The generalized anxiety disorder score shows that 15 subjects were currently experiencing low risk, 58 subjects were in the mild range, 32 subjects were in the moderate range and 22 subjects were in the mild range. The discharge medication data shows Vitamin C and Multivitamin tablets were prescribed nearly for 114-116 subjects, secondly, Pantoprazole was prescribed for 96 subjects, thirdly Prednisolone was prescribed for 96 subjects and Paracetamol was prescribed for 68 subjects. At last, other drugs are prescribed according to the Co-Morbidities of the subject. Conclusion: 127 patients were analyzed, in that there are 49 female subjects and 78 male subjects. 85 subjects were affected between ages 18-60 years whereas 42 subjects were affected above the age of 60 years. The vaccination and the non-vaccination status report shows that non-vaccinated people are affected more than vaccinated people. The Generalized Anxiety Disorder (GAD) category depletes that the patients experienced mild to moderate levels of anxiety. The Pearson Chi-square value is 23.271 and the P value is <0.001. This shows the association between anxiety with COVID-19 patients. The Major Depression Inventory (MDI) score depletes severe depression which might be due to the pandemic situation, economic situation, and local restrictions. The Pearson Chi-square value is 70.572 and the P-value is <0.001 showing that there is an association between depression and COVID in patients. This shows that post-COVID-19 affected subjects are experiencing Psychological effects in many terms. These Psychological effects are due to COVID-19 and the intake of Steroids during the treatment and after discharge.

A comprehensive review of the new FIGO classification of ovulatory disorders.

The World Health Organization (WHO) system for the classification of disorders of ovulation was produced 50 years ago and, by international consensus, has been updated by the International Federation of Gynecology and Obstetrics (FIGO). This review outlines in detail each component of the FIGO HyPO-P (hypothalamic, pituitary, ovarian, PCOS) classification with a concise description of each cause, and thereby provides a systematic method for diagnosis and management. We searched the published articles in the PubMed database in the English-language literature until October 2022, containing the keywords ovulatory disorders; ovulatory dysfunction; anovulation, and each subheading in the FIGO HyPO-P classification. We did not include abstracts or conference proceedings because the data are usually difficult to assess. We present the most comprehensive review of all disorders of ovulation, published systematically according to the logical FIGO classification. Improving the diagnosis of an individual's ovulatory dysfunction will significantly impact clinical practice by enabling healthcare practitioners to make a precise diagnosis and plan appropriate management.

Association of antiseizure medications and adverse cardiovascular events: A global health federated network analysis.

A diagnosis of epilepsy has been associated with adverse cardiovascular events (CEs), but the extent to which antiseizure medications (ASMs) may contribute to this is not well understood. The aim of this study was to compare the risk of adverse CEs associated with ASM in patients with epilepsy (PWE). A retrospective case-control cohort study was conducted using TriNetX, a global health federated network of anonymized patient records. Patients older than 18 years, with a diagnosis of epilepsy (International Classification of Diseases, 10th Revision code G40) and a medication code of carbamazepine, lamotrigine, or valproate were compared. Patients with cardiovascular disease prior to the diagnosis of epilepsy were excluded. Cohorts were 1:1 propensity score matched (PSM) according to age, sex, ethnicity, hypertension, heart failure, atherosclerotic heart disease, atrial and cardiac arrythmias, diabetes, disorders of lipoprotein metabolism, obesity, schizophrenia and bipolar disorder, medications, and epilepsy classification. The primary outcome was a composite of adverse CEs (ischemic stroke, acute ischemic heart disease, and heart failure) at 10 years. Cox regression analyses were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) following 1:1 PSM. Of 374 950 PWE included; three cohorts were established after PSM: (1) carbamazepine compared to lamotrigine, n = 4722, mean age 37.4 years; (2) valproate compared to lamotrigine, n = 5478, mean age 33.9 years; and (3) valproate compared to carbamazepine, n = 4544, mean age 37.0 years. Carbamazepine and valproate use were associated with significantly higher risk of composite cardiovascular outcome compared to lamotrigine (HR = 1.390, 95% CI = 1.160-1.665 and HR = 1.264, 95% CI = 1.050-1.521, respectively). Valproate was associated with a 10-year higher risk of all-cause death than carbamazepine (HR = 1.226, 95% CI = 1.017-1.478), but risk of other events was not significantly different. Carbamazepine and valproate were associated with increased CE risks compared to lamotrigine. Cardiovascular risk factor monitoring and careful follow-up should be considered for these patients.

Sex Differences in Psychopathology Following Potentially Traumatic Experiences

Various psychopathology may follow trauma; however, sex differences in these ranging manifestations of posttraumatic psychopathology remain understudied. To investigate sex-specific incidence of posttraumatic psychopathology. This population-based cohort study of Danish national health registries included a cohort of individuals who experienced a potentially traumatic event (PTE) from 1994 to 2016. Individuals were further categorized by presence of any pretrauma psychopathology. A comparison group of individuals who experienced a nontraumatic stressor (nonsuicide death of a first-degree relative) was examined as a reference cohort. At least 1 of 8 PTEs (eg, physical assault, transportation accident) derived through health registry International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes, with additional qualifiers to improve classification accuracy. Incidence of 9 categories of ICD-10 psychiatric disorders recorded in registries within 5 years of PTEs. The standardized morbidity ratios (SMRs) for psychopathology outcomes were also calculated to compare individuals experiencing PTEs with those experiencing a nontraumatic stressor. This study included 1 398 026 individuals who had been exposed to trauma (475 280 males [34.0%]; 922 750 females [66.0%]). The group of males who had been exposed to trauma were evenly distributed across age, while most females in the trauma-exposed group were aged 16 to 39 years (592 385 [64.2%]). Males and females were equally distributed across income quartiles and predominantly single. Following PTEs, the most common diagnosis was substance use disorders for males (35 160 [7.4%]) and depressive disorders for females (29 255 [3.2%]); incidence proportions for these and other disorders were higher among males and females with any pretrauma psychopathology. Certain PTEs had elevated onset of various psychiatric disorders and some sex differences emerged. Following physical assault, associations were found with schizophrenia or psychotic disorders for males (SMR, 17.5; 95% CI, 15.9-19.3) and adult personality disorders for females (SMR, 16.3; 95% CI, 14.6-18.3). For noninterpersonal PTEs, males had larger SMRs for substance use, schizophrenia or psychotic disorders, and adult personality disorders (SMR, 43.4; 95% CI, 41.9-45.0), and females had larger SMRs for depressive disorders (SMR, 19.0; 95% CI, 18.6-19.4). Sex differences were also observed, particularly when considering pretrauma psychopathology. For example, among interpersonal PTEs, males were most likely to develop substance use disorders after physical assault, whereas females were more likely to develop various disorders, with stronger associations seen for females without pretrauma psychiatric diagnoses. Among noninterpersonal PTEs, exposure to toxic substance showed robust associations with psychopathology, particularly in those without pretrauma psychopathology, with sex-specific differences across psychiatric categories. Mental disorders after trauma were wide-ranging for males and females, and sex differences in patterns of posttraumatic psychopathology were more pronounced when accounting for pretrauma psychopathology. Findings provide new insights for sex-relevant PTEs and their mental health consequences. It also outlines future directions for advancing understanding of a constellation of posttraumatic psychopathology in males and females.

Prevalence of mental disorders among Australian females: Comparison according to motherhood status using Australian birth cohort data

PurposeStudies examining mental disorders among women have primarily focused on either depression, anxiety, or substance use disorders and not included the broader spectrum of mental disorders. Mixed evidence exists on the prevalence rates of mental disorders among mothers. This study compares the prevalence of different mental disorders and mental comorbidities between mothers and non-mothers and assesses correlates of mental disorders among mothers.MethodsA population-based birth cohort design was adopted, consisting of 40,416 females born in Queensland, Australia, in 1983/84. Linked administrative data from hospital admissions were used to identify mental disorders. Cumulative incidence curves of different mental disorders were created separately for mothers and non-mothers.ResultsMental disorder prevalence among females by age 29–31 years was 7.8% (11.0% for mothers and 5.2% for non-mothers). Mothers were overrepresented in almost all categories of mental disorders, with overrepresentation becoming more pronounced with age. Mothers with a mental disorder were more likely to be unmarried, Indigenous, young at birth of first child, have greater disadvantage, and have a single child, compared to mothers without a mental disorder. Nearly half of the mothers (46.9%) had received a mental disorder diagnosis before having their first child.ConclusionsMothers, particularly unmarried, Indigenous, having greater disadvantage, and younger at birth of first child, represent a unique group with high vulnerability to mental disorders, that begins in childhood and is amplified with age. Presence of significant mental disorder comorbidities among females highlights the critical importance of a comprehensive, integrated approach to prevent and address multiple comorbidities.

Polish version of the questionnaire for measuring post-traumatic stress disorders PTSD-8 in studies of four groups of medical professions

There are several techniques to diagnose post-traumatic stress disorder (PTSD) symptoms that correspond to the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification of mental disorders. This study aimed at testing the reliability and construct validity of the Polish version of the PTSD-8. The results obtained in four professional groups of healthcare workers (physicians, nurses, paramedics, other occupations) were compared. The PTSD-8 questionnaire includes questions on stress reactions manifested by intrusion (4 items), avoidance (2 items), or hypervigilance (2 items). Respondents were asked about their reactions to the traumatic work-related experiences occurring during the COVID-19 pandemic. The nationwide survey conducted in 2022 included 2303 employees, among whom 746 had such an experience and completed the PTSD-8 questionnaire. Based on confirmatory factor analysis (CFA) results, it was shown that a single-factor model is acceptable, but under the condition of modification by correlation of random errors at selected observable variables. The reliability of this scale is very good (McDonald's Ω = 0.890), and the goodness-of-fit of the overall CFA model is confirmed by: CMIN/DF = 3.969, NFI = 0.982, RFI = 0.964, IFI = 0.986, TLI = 0.983, RMSEA = 0.063. The model estimated for female nurses has the best psychometric properties. In the study group of 2303 respondents, 16.76% met the criteria for PTSD, accounting for 52.74% of those who were traumatized by a work-related event. The PTSD-8 index is M±SD 20.01±5.55 pts (range: 8-32 pts). The PTSD-8 index is significantly higher in women than in men, and higher in nurses compared to other professions. However, the differences between professional groups are significant only for women, and the largest when comparing female physicians and nurses. The analyses confirmed the rationale of further implementation of the PTSD-8 questionnaire in Poland, both as a tool for identifying PTSD and for studying the severity of the syndrome, its determinants and health effects. Med Pr Work Health Saf. 2024;75(1):45-56.

A case report: new-onset nummular headache after stenting of the middle cerebral artery

BackgroundNummular headache (NH) is categorized as a primary headache in the International Classification of Headache Disorders, Third edition (ICHD-3) diagnostic criteria, but there are secondary etiologies as well. We present a case of secondary NH that associated with vascular lesion.Case presentationWe report on a 40-year-old man with a medical history of symptomatic intracranial arterial stenosis who developed a headache after percutaneous transluminal angioplasty and stenting because of Intracranial atherosclerotic stenosis(ICAS). This new-onset headache was a pinprick headache confined to the parietal part of the head and 5 cm in size. This headache most closely resembled the phenotype of a NH. And other causes of secondary headache were excluded. Thus, the diagnosis of NH was highly speculated. This patient represents a rare headache phenomenon after intracranial arterial stent placement.ConclusionThis is the first report of NH after stent placement treatment in a patient with ICAS.

Mortalité maternelle par complications hypertensives en France, 2016–2018

Between 2016 and 2018, 13 maternal deaths were due to hypertensive disorders. During this period, the maternal mortality ratio was 0.6/100 000 live births. Hypertensive disorders were responsible for 4.8% of maternal deaths during the first year, 5.1% up to 42 days postpartum and for 13.5% of direct maternal mortality. Maternal deaths due to hypertensive disorders increased close to signification (p=0.09) compared to the last triennium (MMR=0.2/100.000). Classification of the hypertensive disorders was: 5 severe preeclampsia, 3 eclampsia, 4 HELLP syndromes et 1 undefined hypertension. In five cases, a stroke was associated. Mode of delivery was a cesarean section when the hypertensive disorder started before the labour (8/13, 62%). Six women were older than 35years old and 5/12 were nulliparous. Among the 12 cases where place of birth was known, 5 were born foreigners. BMI was over 30 for 46%. Medical care were estimated non optimal in 11/13 of the cases. Among these deaths, 66% (8/12) seemed to be preventable versus 82% for the last period 2013–2015. The main causal factor of suboptimal management was inappropriate management by the obstetrical or anesthetist/intensive care squads, respectively: 3 lack of diagnosis, 8 delays for diagnosis and 5 underestimated severity. Four cases corresponded to inappropriate health care organization. This study offers the opportunity to stress major points to optimize medical management and health care organization facing hypertensive disorders during pregnancy.

Regulatory problems and developmental psychopathology within the first 2 years of living—a nested in cohort population-based study

BackgroundInfancy regulatory problems (RP) of sleep, feeding and eating, and excessive crying are thought to play a key role in the development of psychopathology in childhood, but knowledge of the early trajectories is limited.ObjectiveTo explore RP at ages 8–11 months and the associations with mental health problems at 1½ years, and assess the influences of maternal mental health problems and relationship problems.MethodsRP was explored in a nested in-cohort sample (N = 416) drawn from a community-based cohort (N = 2,973). Cohort children were examined by community health nurses, using a mental health screening, which included seven items of RP. Follow-up at 1½ years included diagnostic assessment according to the International Classification of Diseases, ICD-10, and the Diagnostic Classification of Mental Health and Developmental Disorders in Infancy and Early Childhood: Revised edition, DC:0-3R. Data analyses included logistic regression models and analyses of the mediation effect of maternal mental health and relationship problems.ResultsRP of sleep were associated with a 2-fold increased risk of child mental disorder specifically sleep disorders, adjusted odds ratio (OR) 9.3 [95% confidence interval (CI): 2.0–42.9], disorders of behavior and emotions, adjusted OR 2.9 (95% CI: 1.0–8.4), and DC:0-3R regulatory disorders, adjusted OR 2.7 (95% CI: 1.0–7.5). Children with RP of feeding and eating showed an increased risk of overall mental disorder, adjusted OR 1.4 (95% CI: 0.7–2.4), and specifically, feeding and eating disorders, adjusted OR 6.0 (95% CI: 1.6–21.7), disorders of behavior and emotions, adjusted OR 2.2 (95% CI: 0.9–5.8), as well as DC:0-3R regulatory disorders, adjusted OR 1.6 (1.0–7.5). RP of emotional regulation were associated with increased risk of any mental disorder, adjusted OR 1.5 (1.0–2.4), and specifically behavioral and emotional disorders, adjusted OR 2.2 (95% CI: 0.9–5.9) and DC:0-3R relationship disorders, adjusted OR 1.8 (95% CI: 0.9–3.8). The mediation effect of maternal mental health problems and relationship problems ranged between 0% and 48%.ConclusionRP at ages 8–11 months is associated with increased risk of ICD-10 and DC:0-3R disorders at 1½ years. Study findings highlight a group of vulnerable infants in need of preventive intervention to break the early trajectories of psychopathology.

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

Next-generation therapeutics for rare genetic disorders.

The therapeutic potential of the human genome has been explored through the development of next-generation therapeutics, which have had a high impact on treating genetic disorders. Classical treatments have traditionally focused on common diseases that require repeated treatments. However, with the recent advancements in the development of nucleic acids, utilizing DNA and RNA to modify or correct gene expression in genetic disorders, there has been a paradigm shift in the treatment of rare diseases, offering more potential one-time cure options. Advanced technologies that use CRISPR-Cas 9, antisense oligonucleotides, siRNA, miRNA, and aptamers are promising tools that have achieved successful breakthroughs in the treatment of various genetic disorders. The advancement in the chemistry of these molecules has improved their efficacy, reduced toxicity, and expanded their clinical use across a wide range of tissues in various categories of human disorders. However, challenges persist regarding the safety and efficacy of these advanced technologies in translating into clinical practice. This review mainly focuses on the potential therapies for rare genetic diseases and considers how next-generation techniques enable drug development to achieve long-lasting curative effects through gene inhibition, replacement, and editing.

Sleep health in Swiss elite athletes. An online survey

Introduction There is no doubt that high-quality sleep is essential for athletes’ health, regeneration, and performance (Vorster et al., 2022). Unfortunately, sleep problems are as widespread in professional sports as they are in the rest of the population: about 11% of athletes have problems falling asleep and staying asleep, 20% have serious problems with sleep during competition periods, 30% are taking sleeping pills during the season, 10% show symptoms of obstructive sleep apnoea, 5% show symptoms of restless leg syndrome, and 15% report anxiety dreams before competitions (Gupta et al., 2017; Tuomilehto et al., 2017; Walsh et al., 2021). Comparative work that systematically analyses and compares sleep problems across sports remains scarce, so these figures should be viewed with caution. Especially for Swiss elite athletes’ representative numbers about the main sleep disorders are missing. The aim of the study is therefore to examine the status of sleep health among elite Swiss athletes. Methods An online questionnaire was sent to all Swiss elite athletes. Over 1,000 athletes (data collection is still running) filled out the questionnaire on various symptoms of sleep problems. The questionnaire included at least one item to screen for the six main categories of sleep disorders. These items were taken from different validated sleep questionnaires (e.g., PSQI, ASBQ, SDS). Results The prevalence of symptoms associated with sleep disorders of the six main categories: ‘insomnias’ ‘sleep-related breathing disorders’, ‘central disorders of hypersomnolence’, ‘circadian rhythm sleep-wake disorders’, ‘parasomnias’, ‘sleep-related movement disorders’ will be presented. Discussion/Conclusion This online survey will provide representative data on the prevalence of sleep problems in Swiss elite athletes. The dataset will provide novel insides into the distribution of certain sleep problems over the various sports in Switzerland. Sleep problems in athletes certainly need to be taken seriously and treated. They are often not just concomitants of other illnesses and problems. Therefore, important practical implications will be discussed.

Current Insights in Prolactin Signaling and Ovulatory Function.

Prolactin (PRL) is a pleiotropic hormone released from lactotrophic cells of the anterior pituitary gland that also originates from extrapituitary sources and plays an important role in regulating lactation in mammals, as well as other actions. Acting in an endocrine and paracrine/autocrine manner, PRL regulates the hypothalamic-pituitary-ovarian axis, thus influencing the maturation of ovarian follicles and ovulation. This review provides a detailed discussion of the current knowledge on the role of PRL in the context of ovulation and ovulatory disorders, particularly with regard to hyperprolactinemia, which is one of the most common causes of infertility in women. Much attention has been given to the PRL structure and the PRL receptor (PRLR), as well as the diverse functions of PRLR signaling under normal and pathological conditions. The hormonal regulation of the menstrual cycle in connection with folliculogenesis and ovulation, as well as the current classifications of ovulation disorders, are also described. Finally, the state of knowledge regarding the importance of TIDA (tuberoinfundibular dopamine), KNDγ (kisspeptin/neurokinin B/dynorphin), and GnRH (gonadotropin-releasing hormone) neurons in PRL- and kisspeptin (KP)-dependent regulation of the hypothalamic-pituitary-gonadal (HPG) axis in women is reviewed. Based on this review, a rationale for influencing PRL signaling pathways in therapeutic activities accompanying ovulation disorders is presented.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n=12), attention deficit and hyperactivity disorder (n=7), and autism spectrum disorder (n=6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM=20) (DL=10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n=11) and ΔHbO-based functional connections (n=11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

Machine learning classification of autism spectrum disorder based on reciprocity in naturalistic social interactions

Autism spectrum disorder is characterized by impaired social communication and interaction. As a neurodevelopmental disorder typically diagnosed during childhood, diagnosis in adulthood is preceded by a resource-heavy clinical assessment period. The ongoing developments in digital phenotyping give rise to novel opportunities within the screening and diagnostic process. Our aim was to quantify multiple non-verbal social interaction characteristics in autism and build diagnostic classification models independent of clinical ratings. We analyzed videos of naturalistic social interactions in a sample including 28 autistic and 60 non-autistic adults paired in dyads and engaging in two conversational tasks. We used existing open-source computer vision algorithms for objective annotation to extract information based on the synchrony of movement and facial expression. These were subsequently used as features in a support vector machine learning model to predict whether an individual was part of an autistic or non-autistic interaction dyad. The two prediction models based on reciprocal adaptation in facial movements, as well as individual amounts of head and body motion and facial expressiveness showed the highest precision (balanced accuracies: 79.5% and 68.8%, respectively), followed by models based on reciprocal coordination of head (balanced accuracy: 62.1%) and body (balanced accuracy: 56.7%) motion, as well as intrapersonal coordination processes (balanced accuracy: 44.2%). Combinations of these models did not increase overall predictive performance. Our work highlights the distinctive nature of non-verbal behavior in autism and its utility for digital phenotyping-based classification. Future research needs to both explore the performance of different prediction algorithms to reveal underlying mechanisms and interactions, as well as investigate the prospective generalizability and robustness of these algorithms in routine clinical care.