Neuropsychiatric Syndromes in Childhood-Onset Systemic Lupus Erythematosus: A Systematic Review and Meta-analysis.

OBJECTIVE: The aim of this study was to access the prevalence of 19 neuropsychiatric syndromes in childhood-onset systemic lupus erythematosus (cSLE), as defined by the American College of Rheumatology (ACR) in 1999, by performing a systematic review and meta-analysis of relevant publications. METHODS: A literature search from April 1999 to March 2018 identified studies investigating neuropsychiatric syndromes in cSLE patients, applying 1999 ACR Case Definitions, with a sample of at least 20 patients. Case reports, small case series, reviews, articles that did not use 1999 ACR case definitions, and those with adult SLE patients were excluded. The methodological quality of the studies was determined through the Loney quality assessment. Prevalence estimates with a 95% confidence interval were combined using random-effect (DerSimonian-Laird) models. RESULTS: A total of 143 articles were identified and 9 were included. In a population of 1463 cSLE patients, 351 (29.9%) presented 869 neuropsychiatric cSLE events (2.48 events/patient). The results for each syndrome were headache (52.2%), seizure disorders (48.6%), cognitive dysfunction (32.9%), mood disorder (28.3%), psychosis (22.7%), cerebrovascular disease (19.5%), acute confusional state (15.7%), movement disorder (9.4%), anxiety disorder (7.2%), aseptic meningitis (5.1%), mononeuropathy single/multiplex (4.9%), myelopathy (4.2%), demyelinating syndrome (3.2%), cranial neuropathy (2.7%), polyneuropathy (2.6%), Guillain-Barré syndrome (2.5%), autonomic disorder (1.9%), plexopathy (1.3%), and myasthenia gravis (1.3%). CONCLUSIONS: Neuropsychiatric syndromes are prevalent among cSLE patients. The most prevalent were headaches, seizures, cognitive dysfunction, mood disorders, and psychosis. Determining the prevalence of each neuropsychiatric syndrome in cSLE may improve clinical awareness of these potentially fatal and disabling conditions.

Suicidality among pregnant women in Brazil: prevalence and risk factors.

Suicide is one of the major causes of preventable death. We evaluated suicidality among pregnant women who participated in prenatal care in Brazil. A total of 255 patients were assessed using semi-structured interviews as well as the Edinburgh Postnatal Depression Scale (EPDS), Beck Depression Inventory (BDI), and Mini-International Neuropsychiatric Interview (MINI) Plus. Thereafter, Stata 12 was used to identify the significant predictors of current suicide risk (CSR) among participants using univariate and multivariate analyses (p < 0.05). According to MINI Plus module C, the lifetime suicide attempt rate was 12.55%. The overall CSR was 23.53%, distributed across risk levels of low (12.55%), moderate (1.18%), and high (9.80%). Our rates approximate those found in another Brazilian study (18.4%). Antenatal depression (AD), lifetime bipolar disorder, and any current anxiety disorder (as measured using the MINI) as well as BDI scores ≥15 and EPDS scores ≥11 were identified as positive risk factors in a univariate analysis (p < 0.001). These factors changed after a multivariate analysis was employed, and only years of education [odds ratio (OR) = 0.45; 95% confidence intervals (CIs) = 0.21-0.99], AD (OR = 3.42; 95% CIs = 1.37-8.53), and EPDS scores ≥11 (OR = 4.44; 95% CIs = 1.97-9.97) remained independent risk factors. AD and other psychiatric disorders were the primary risk factors for suicidality, although only the former remained an independent factor after a multivariate analysis. More than 10 years of education and EPDS scores ≥11 were also independent factors; the latter can be used as a screening tool for suicide risk.

Dataset of breast thermography images for the detection of benign and malignant masses.

Thermographic image analysis is a subfield of diagnostic image processing aimed at detecting breast abnormalities in women at an early stage. It is a developing field of research and its effectiveness and scope require scientific assessment to be determined. An open-access dataset has been created for the scientific community to test and develop techniques for computational detection of normal and abnormal breast conditions from thermograms. This dataset is a valuable resource for researchers due to the scarcity of publicly available datasets of breast thermographic images. It includes thermographic images of the female chest area in three capture positions: anterior, left oblique and right oblique. The data set comes from 119 women ranging from 18 to 81 years of age. A table is attached to the dataset with the diagnosis of breast pathology, showing that 84 patients had benign pathology and 35 patients had malignant pathology. The diagnoses of women with healthy breast pathology are not included.

Significant sleep disturbances in euthymic bipolar patients.

OBJECTIVE: A growing amount of data suggests that sleep dysfunction is frequently observed in bipolar disorder (BD) patients even when they do not fulfill the criteria for major mood episodes. Thus, we performed a case-control study assessing sleep status in a group of euthymic BD patients and a group of health controls. METHODS: A total of 209 subjects (104 health controls and 105 BD patients) were enrolled in the study. The Pittsburgh Sleep Quality Index (PSQI) was used for sleep assessment. Inclusion criteria for the BD group were a diagnosis of BD, following DSM-IV-TR criteria, according to the MINI-plus structured clinical interview. Euthymia was established as a score lower than 7 both in the Hamilton Depression Rating Scale (HDRS) and in the Young Mania Rating Scale (YMRS). Health controls were also interviewed using the MINI-plus and included in this study if they were free of any current or past DSM-IV-TR axis I psychiatric disorder as well the actual use of psychopharmacological medications. RESULTS: While 21.2 % of the control group displayed poor sleep quality according to the global PSQI-BR score, 82.9 % of the euthymic BD patients had poor sleep quality (p=0.000). PSQI sleep duration subcomponent showed comparable results in the two groups (p=0.535), even though BD patients had significant disruptions in sleep latency (p=0.000) and sleep efficiency (p=0.000) subcomponents. CONCLUSION: We were able to show that BD patients, even in euthymic phase, exhibit a significantly worse sleep quality as compared with health controls as assessed by PSQI total score and five of its seven subcomponents.

Sex differences in dimensions of impulsivity in a non-clinical sample.

Impulsivity has been more closely associated with men than with women because men are more often involved in illegal behaviors. The few studies that have investigated sex differences in impulsivity have used self-report questionnaires and have obtained contradictory results. Two computerized behavioral tests were administered to 125 healthy undergraduate students (75 women, M age 23.8 yr.; 50 men, M age 25.0 yr.). Men exhibited higher scores on motor impulsivity, but there were no significant differences between men and women on attentional and non-planning impulsivity scores. These findings are discussed in terms of the relationship between impulsivity and low- and high-order control.

Challenges and potential solutions for physician suicide risk factors in the COVID-19 era: psychiatric comorbidities, judicialization of medicine, and burnout.

INTRODUCTION: Suicide among physicians constitutes a public health problem that deserves more consideration. A recently performed meta-analysis and systematic review evaluated suicide mortality in physicians by gender and investigated several related risk factors. It showed that the post-1980 suicide mortality was 46% higher in female physicians than among women in the general population, while the risk in male physicians was 33% lower than among men in general, despite an overall contraction in physician mortality rates in both genders. METHODS: This narrative review was conducted by searching and analyzing articles/databases that were relevant to addressing questions raised by a prior meta-analysis and how they might be affected by COVID-19. This process included unstructured searches on Pubmed for physician suicide, burnout, judicialization of medicine, healthcare organizations, and COVID-19, and Google searches for relevant databases and medical society, expert, and media commentaries on these topics. We focus on three factors critical to addressing physician suicides: epidemiological data limitations, psychiatric comorbidities, and professional overload. RESULTS: We found relevant articles on suicide reporting, physician mental health, the effects of healthcare judicialization, and organizational involvement on physician and patient health, and how COVID-19 may impact such factors. This review addresses information sources, underreporting/misreporting of physician suicide rates, inadequate diagnosis and management of psychiatric comorbidities and the chronic effects on physicians' work capacity, and, finally, judicialization of medicine and organizational failures increasing physician burnout. We discuss these factors in general and in relation to the COVID-19 pandemic. CONCLUSIONS: We present an overview of the above factors, discuss possible solutions, and specifically address how COVID-19 may impact such factors.

Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study.

OBJECTIVE: Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. METHODS: Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. RESULTS: Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). CONCLUSION: Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.

Cognitive dysfunction in patients with childhood-onset systemic lupus erythematosus may impact treatment.

BACKGROUND: Cognitive dysfunction (CD) is a widespread manifestation in adult systemic lupus erythematosus (SLE) patients, but this subject is rarely examined in patients with childhood-onset SLE (cSLE). This study aimed to assess the frequency of CD, its associations with lupus clinical manifestations and its impact on the health-related quality of life (HRQL) in young adult cSLE patients. METHODS: We evaluated 39 cSLE patients older than 18 years. They underwent a rheumatologic evaluation and extensive neuropsychological assessment, encompassing all cognitive domains described by the American College of Rheumatology. HRQL was assessed with the WHOOQOL-BREEF, General Activities of Daily Living Scale (GADL) and Systemic Lupus Erythematosus-specific quality-of-life instrument (SLEQOL). The activity of SLE was evaluated with the modified sle disease activity index (sledai-2k). RESULTS: Impairment in at least one cognitive domain was found in 35 (87.2%) patients. The most compromised domains were attention (64.1%), memory (46.2%), and executive functions (38.5%). Patients with cognitive impairment were older, had more accumulated damage and had worse socioeconomic status. Regarding the association between cognitive dysfunction and HRQL, memory impairment was correlated with worse environmental perception and a worse relationship with the treatment. CONCLUSION: In this study, the frequency of CD in cSLE patients was as high as that in the adult SLE population. CD can significantly impact the response of cSLE patients to treatment, justifying preventive measures in the care of this population.

Defect Detection and Depth Estimation in Composite Materials for Pulsed Thermography Images by Nonuniform Heating Correction and Oriented Gradient Information.

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy.

6D-ViCuT: Six degree-of-freedom visual cuboid tracking dataset for manual packing of cargo in warehouses.

Visual tracking of objects is a fundamental technology for industry 4.0, allowing the integration of digital content and real-world objects. The industrial operation known as manual cargo packing can benefit from the visual tracking of objects. No dataset exists to evaluate the visual tracking algorithms on manual packing scenarios. To close this gap, this article presents 6D-ViCuT, a dataset of images, and 6D pose ground truth of cuboids in a manual packing operation in intralogistics. The initial release of the dataset comprehends 28 sessions acquired in a space that rebuilds a manual packing zone: indoors, area of (6 × 4 × 2) m3, and warehouse illumination. The data acquisition experiment involves capturing images from fixed and mobile RGBD devices and a motion capture system while an operator performs a manual packing operation. Each session contains between 6 and 18 boxes from an available set of 10 types, with each type varying in height, width, depth, and texture. Each session had a duration in the range of 1 to 5 minutes. Each session exhibits operator speed and box type differences (box texture, size heterogeneity, occlusion).

Dataset of thermographic images for the detection of buried landmines.

This article presents a dataset of thermographic images of terrain with antipersonnel mines to identify the presence or absence of these artifacts using machine learning and artificial vision techniques. The dataset has 2700 thermographic images acquired at different heights, using a Zenmuse XT infrared camera (7-13 µm), embedded in the DJI Matrice 100 drone. The data acquisition experiment consists of capturing aerial infrared images of a terrain where elements with characteristics similar to antipersonnel mines type legbreaker were buried. The mines were planted in the ground between 0 cm and 10 cm deep and were spread over an area of 10 m x 10 m. The drone used a flight protocol that set the trajectory, the time of the flight, the acquisition height, and the image sampling frequency. This dataset was used in "Detection of "legbreaker" antipersonnel landmines by analysis of aerial thermographic images of the soil" [7].

The Tower of London Test: different scoring criteria for diagnosing Alzheimer's disease and mild cognitive impairment.

The Tower of London (TOL) is used for evaluating planning skills, which is a component of the executive functions. Different versions and scoring criteria were developed for this task, and some of them present with different psychometrical properties. This study aimed to evaluate two specific scoring methods of the TOL in diagnosing Mild Cognitive Impairment and probable Alzheimer's disease. The TOL total scores from 60 patients of each diagnosis were compared with the performance of 60 healthy-aged controls using receiver operating characteristics analysis and multinomial logistic regression. Krikorian method better diagnosed Alzheimer's disease, while Portellas's was better at discriminating healthy controls from Mild Cognitive Impairment, but were not efficient at comparing this last group with Alzheimer's patients. Regression analysis indicates that in addition to screening tests, TOL improves the classification of the three groups. The results suggest the two scoring methods used for this task may be useful for different diagnostic purposes.

Evaluating vision-specific quality of life and functional vision in a Brazilian population with Stargardt disease.

PURPOSE: To compare the vision-specific quality of life (QoL) of individuals with Stargardt disease (STGD) with that of healthy individuals and to investigate the association between vision-specific QoL and functional vision. METHODS: This cross-sectional study included 41 patients with STGD and 46 healthy volunteers matched by age and gender. Best corrected visual acuity (BCVA) was used to calculate the Functional Acuity Score (FAS) and Goldmann perimetry the Functional Field Score (FFS). These scores were combined to obtain the Functional Vision Score (FVS). Vision-specific QoL was assessed using the 25-item version of the National Eye Institute Visual Function Questionnaire (NEI VFQ-25). RESULTS: The groups had similar sociodemographic and clinical characteristics. There were significant differences in BCVA, vision-specific QoL assessed by the NEI VFQ-25, and FVS (p < 0.001) between individuals with STGD and controls. The final total score and all the subscales of the NEI VQF-25 questionnaire were significantly lower in the STGD group (p < 0.001), except for the subscales general health, color vision, and ocular pain. NEI VFQ-25 results in the STGD group were positively correlated with family income, FVS, and FFS. FVS was the score best correlated with the NEI VFQ-25 total score. CONCLUSIONS: Individuals with STGD had significant impairment of vision-specific QoL and functional vision compared with controls and the FVS was the objective evaluation method most correlated with vision-specific QoL in this population.

Physician suicide demographics and the COVID-19 pandemic.

OBJECTIVE: To identify suicide rates and how they relate to demographic factors (sex, race and ethnicity, age, location) among physicians compared to the general population when aggravated by the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We searched U.S. databases to report global suicide rates and proportionate mortality ratios (PMRs) among U.S. physicians (and non-physicians in health occupations) using National Occupational Mortality Surveillance (NOMS) data and using Wide-ranging Online Data for Epidemiologic Research (WONDER) in the general population. We also reviewed the effects of age, suicide methods and locations, COVID-19 considerations, and potential solutions to current challenges. RESULTS: Between NOMS1 (1985-1998) and NOMS2 (1999-2013), the PMRs for suicide increased in White male physicians (1.77 to 2.03) and Black male physicians (2.50 to 4.24) but decreased in White female physicians (2.66 to 2.42). CONCLUSIONS: The interaction of non-modifiable risk factors, such as sex, race and ethnicity, age, education level/healthcare career, and location, require further investigation. Addressing systemic and organizational problems and personal resilience training are highly recommended, particularly during the additional strain from the COVID-19 pandemic.

Association between tryptophan hydroxylase-2 gene and late-onset depression.

OBJECTIVE: The aim of this study was to examine the association between polymorphisms (SNP) in the tryptophan hydroxylase-2 (TPH2) gene and late-onset depression (LOD) in the Brazilian population. METHODS: We genotyped 8 tag SNPs in the TPH2 gene in 84 outpatients with LOD and 79 individuals belonging to the comparison group to investigate an association between the TPH2 gene and LOD. RESULTS: Our findings suggested an association between tag SNP rs4565946 heterozygous C/T (p = 0.034; χ2 = 6.7; df = 2) and decreased risk of LOD. The tag SNP rs11179000 ancestral homozygous A/A (p = 0.025; χ2 = 7.3; df = 2) and increase risk of LOD and allelic association of ancestral allele A and increase risk of LOD was demonstrated (p = 0.005; χ2 = 7.8; df = 1). CONCLUSION: We found the statistically significant association between two tag SNPs and LOD. Our results support the hypothesis that the TPH2 gene is associated with LOD.

Behavioral disorders and low quality of life in children and adolescents with chronic kidney disease.

Recent years has seen an increasing interest in the quality of life (QOL) of children with chronic kidney disease (CKD). The objective of this cross-sectional study was to investigate the prevalence of behavioral disorders and to assess the health-related QOL (HRQOL) in 136 patients with CKD. To estimate the prevalence of behavior disorders and analyze HRQOL, we used the Strengths and Difficulties Questionnaire (SDQ) and Pediatric Inventory of Quality of Life (PedsQL) Core Scales as assessment tools for both the patients and caregivers. When compared to healthy controls, the CKD group had significantly lower scores in almost all PedsQL domains. After adjustment, only absence of religion/other religions remained significantly associated with a lower global HRQOL score [odds ratio (OR) 6.2, P=0.009]. Among the parents, two factors remained significantly associated with a lower global HRQOL score: patients' age >10 years (OR 5.4, P=0.033) and absence of religion/other religions (OR 3.2, P=0.038). The CKD group demonstrated a higher proportion of behavioral and emotional disorders in all SDQ domains. There was a negative correlation between the presence of behavior and emotional disorders and HRQOL score (r= -0.552, P<0.001). Our findings suggest the importance of evaluating behavioral and social repercussions of CKD in order to improve the life quality of this pediatric population.

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart.

The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics.

Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

Dataset of microscope images of prefrontal cortex from wistar rat tissue after an induced stroke for image registration and stitching.

This article presents a dataset of raw microscopic images of the prefrontal cortex from wistar rat tissues, after an induced stroke, stained with NeuN antibody. The raw images were captured using a microscope equipped with a digital camera. The dataset is useful for testing techniques for the improvement, registration, and stitching to generate a high-resolution image with a full reconstruction of tissues. Besides, this dataset can be used to assess the neuronal brain after an ischemic event. The dataset contains 1370 microscope images with 20x magnification and 36 (Hierarchical Data Format version 5) hdf5 files with homography matrices between every pair of sequential images per tissue rows.

Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.