The Impact of Chronic Diseases on Mental Health: An Overview and Recommendations for Care Programs.

PURPOSE OF REVIEW: The current achievement of medical advancement is noteworthy; however, the occurrence of chronic diseases is increasing day by day, with a significant percentage of affected people are suffering from a mental health crisis. This article aims to present a thorough yet brief review of methods that can be employed to build the emotional wellness of chronic patients. RECENT FINDINGS: The mental health care strategies include resilience-building, coping skills training, professional counseling, and lifestyle adaptations. Additionally, the article highlights the efficacy of several modern interventions, such as mindfulness-based therapies, cognitive behavioral therapy, eye movement desensitization, and recovery from stress therapy. The global burden of chronic illness emphasizes the pressing need to mitigate mental health problems among chronic patients. By providing actionable insights, our study clears the path for targeted interventions and holistic approaches for chronic disease patients. Moreover, the article suggests to policymakers and clinicians the need for collaboration and multifaceted interventions.

Serum levels of interleukin-33 and mesencephalic astrocyte derived neurotrophic factors in patients with major depressive disorder: a cross-sectional comparative design.

BACKGROUND: Major depressive disorder (MDD) is a debilitating health condition that has significant morbidity and mortality rates. Depression can be caused due to social, biological, environmental, psychological, and genetic factors. A few biological processes have been proposed as the pathophysiological pathways of depression. Neurotrophic factors and inflammatory cytokines have been linked to depression. Thus, we aimed to investigate the serum interleukin-33 (IL-33) and mesencephalic astrocyte-derived neurotrophic factor (MANF) in MDD patients and corresponding healthy controls (HCs). METHOD: This study involved the inclusion of 129 MDD patients and 125 HCs matched by sex and age. A psychiatrist evaluated the study participants following DSM-5 criteria. The severity of the illness was assessed utilizing the Hamilton Depression Rating Scale (Ham-D). The serum concentrations of IL-33 and MANF were measured using enzyme-linked immunosorbent assay (ELISA) kits. RESULTS: The mean serum levels of IL-33 were decreased (159.12 ± 6.07 pg/ml vs. 180.60 ± 8.64 pg/ml, p = 0.042), and the MANF levels were increased (5.40 ± 0.19 ng/ml vs. 4.46 ± 0.21 ng/ml, p = 0.001) in MDD patients when compared to HCs. CONCLUSIONS: The current study proposes that lower IL-33 and higher MANF serum levels are associated with MDD progression and depression severity. These biomarkers could be used as risk assessment tools for MDD. We recommend more investigation, including a significant population, to determine the precise function of IL-33 and MANF in depression.

Association of interleukin-2 and interleukin-10 with the pathophysiology and development of generalized anxiety disorder: a case-control study.

BACKGROUND: Generalized anxiety disorder (GAD) is a devastating mental health condition characterized by constant, uncontrolled worrying. Recent hypotheses indicate that pro-inflammatory cytokines and chemokines are potential contributors to the pathogenesis of GAD. Here, we aimed to assess the role of interleukin-2 (IL-2) and interleukin-10 (IL-10) in the pathophysiology and development of GAD. METHODS: This study recruited 50 GAD patients diagnosed according to the DSM-5 criteria and 38 age-sex-matched healthy controls (HCs). A qualified psychiatrist evaluated all study subjects. The socio-demographic and clinical characteristics of the study population were determined using pre-structured questionnaires or interviews, and cytokine serum levels were estimated using commercially available ELISA kits. RESULTS: We observed reduced serum IL-10 levels in GAD patients compared to HCs (33.69 ± 1.37 pg/ml vs. 44.12 ± 3.16 pg/ml). Also, we observed a significant negative correlation between altered IL-10 levels and GAD-7 scores (r=-0.315, p = 0.039). Moreover, IL-10 serum measurement exhibited good predictive value in receiver operating characteristics (ROC) analysis with an area under the curve (AUC) value of 0.793 (p < 0.001) with 80.65% sensitivity and 62.79% specificity at a cutoff value of 33.93 pg/ml. Conversely, we noticed elevated serum IL-2 levels in GAD patients than in HCs (14.81 ± 2.88 pg/ml vs. 8.08 ± 1.1 pg/ml); however, it failed to maintain any significant association with GAD-7 scores, implying that IL-2 might not be involved in GAD pathogenesis. The lower AUC value (0.640; p > 0.05) exhibited by IL-2 serum measurement in ROC analysis further supported that IL-2 might not be associated with GAD. CONCLUSION: This study provides new insights into the complex interplay between anti-inflammatory cytokines and GAD pathogenesis. Based on the present findings, we can assume that IL-10 but not IL-2 may be associated with the pathophysiology and development of GAD. However, further research with a larger population size and longitudinal design is required to confirm the potential diagnostic efficacy of IL-10.

Functional characterization and structural prediction of hypothetical proteins in monkeypox virus and identification of potential inhibitors.

The excessive activation of the monkeypox virus (MPXV-Congo_8-156) is linked to various skin and respiratory disorders such as rashes, fluid-filled blisters, swollen lymph nodes and encephalitis (inflammation of the brain), highlighting MPXV-Congo_8-156 as a promising target for drug intervention. Despite the effectiveness of Cidofovir, in inhibiting MPXV activity, its limited ability to penetrate the skin and its strong side effects restrict its application. To address this challenge, we screened 500 compounds capable of penetrating the skin and gastrointestinal tract to identify potent MPXV inhibitors. Various characterization schemes and structural models of MPXV-Congo_8-156 were explored with bioinformatics tools like PROTPARAM, SOPMA, SWISS-MODEL and PROCHECK. Using molecular docking in PyRx, we evaluated the binding affinities of these compounds with MPXV-Congo_8-156 and identified the top five candidates ranging from - 9.2 to - 8.8 kcal/mol. ADMET analysis indicated that all five compounds were safer alternatives, showing no AMES toxicity or carcinogenicity in toxicological assessments. Molecular dynamics (MD) simulations, conducted for 100 ns each, confirmed the docking interactions of the top five compounds alongside the control (Cidofovir), validating their potential as MPXV inhibitors. The compounds with PubChem CID numbers 4061636, 4422538, 3583576, 4856107 and 4800629 demonstrated strong support in terms of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA) value, hydrogen bond analysis, and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) analysis. Thus, our investigation identified these five compounds as promising inhibitors of MPXV, offering potential therapeutic avenues. However, further in vivo studies are necessary to validate our findings.

Design, Synthesis, and Biological Evaluations of Novel Thiazolo[4,5-d]pyrimidine Corticotropin Releasing Factor (CRF) Receptor Antagonists as Potential Treatments for Stress Related Disorders and Congenital Adrenal Hyperplasia (CAH).

Corticotropin-releasing factor (CRF) is a key neuropeptide hormone that is secreted from the hypothalamus. It is the master hormone of the HPA axis, which orchestrates the physiological and behavioral responses to stress. Many disorders, including anxiety, depression, addiction relapse, and others, are related to over-activation of this system. Thus, new molecules that may interfere with CRF receptor binding may be of value to treat neuropsychiatric stress-related disorders. Also, CRF1R antagonists have recently emerged as potential treatment options for congenital adrenal hyperplasia. Previously, several series of CRF1 receptor antagonists were developed by our group. In continuation of our efforts in this direction, herein we report the synthesis and biological evaluation of a new series of CRF1R antagonists. Representative compounds were evaluated for their binding affinities compared to antalarmin. Four compounds (2, 5, 20, and 21) showed log IC50 values of -8.22, -7.95, -8.04, and -7.88, respectively, compared to -7.78 for antalarmin. This result indicates that these four compounds are superior to antalarmin by 2.5, 1.4, 1.7, and 1.25 times, respectively. It is worth mentioning that compound 2, in terms of IC50, is among the best CRF1R antagonists ever developed in the last 40 years. The in silico physicochemical properties of the lead compounds showed good drug-like properties. Thus, further research in this direction may lead to better and safer CRF receptor antagonists that may have clinical applications, particularly for stress-related disorders and the treatment of congenital adrenal hyperplasia.

Observing Real-Time Adhesion of Microparticles on Glass Surfaces.

Fouling on glass surfaces reduces the solar panel efficiency and increases water consumption for cleaning. Superhydrophobic coatings on glass enable self-cleaning by allowing water droplets to carry away dirt particles. Observing the interaction between charged particles and surfaces provides insights into effective cleaning. Using a high-speed camera and a long-distance objective, we analyzed the in situ deposition of variously functionalized and charged silica dust microparticles on chemically treated glass. The ambient charges for the control, hydrophobic, and positively charged particles were approximately -0.5, -0.13, and +0.5 nC, respectively. We found that a positively charged particle of 2.3 ± 1.2 µm diameter adhered to hydroxylated glass in ∼0.054 s, compared to 0.40 and 0.45 s for quaternary ammonium- and fluorosilane-functionalized hydrophobic glass. Experiments suggest that quaternary ammonium-functionalized glass surfaces are about 77.8% more resistant to soiling than bare surfaces.

Altered IL-3 and lipocalin-2 levels are associated with the pathophysiology of major depressive disorder: a case-control study.

BACKGROUND: Major Depressive Disorder (MDD) is a common mental ailment and is the primary reason for disability. It manifests a severe impact on moods, thoughts, and physical health. At present, this disorder has become a concern in the field of public health. Alteration of neurochemicals is thought to be involved in the pathogenesis of many psychiatric disorders. Therefore, we aimed to evaluate serum IL-3 and lipocalin-2 in MDD patients and healthy controls (HCs). METHOD: We included a total of 376 participants in this study. Among them, 196 were MDD patients, and 180 were age-sex-matched HCs. MDD patients were recruited from the Psychiatry Department of Bangabandhu Sheikh Mujib Medical University (BSMMU), but the controls were from different parts of Dhaka. All study participants were evaluated by a psychiatrist using the DSM-5 criteria. To assess the severity of the depression, we used the Hamilton depression (Ham-D) rating scale. Serum IL-3 and lipocalin-2 levels were measured using commercially available enzyme-linked immune-sorbent assay kits (ELISA kits). RESULTS: According to this study, we observed elevated serum levels of IL-3 (1,024.73 ± 29.84 pg/mL) and reduced levels of serum lipocalin-2 (29.019 ± 2.073 ng/mL) in MDD patients compared to HCs (911.11 ± 20.55 pg/mL and 48.065 ± 3.583 ng/mL, respectively). No associations between serum levels of IL-3 and lipocalin-2 and depression severity were observed in patients. CONCLUSIONS: According to the present findings, alterations of serum IL-3 and lipocalin might be associated with the pathogenesis of MDD. These results support that altered serum neurochemicals can serve as early risk assessment markers for depression. Further interventional studies are recommended for a better understanding of the role of IL-3 and lipocalin-2 in the pathophysiology of depression.

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network.

In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models.

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues.

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems' progress.

Determinants of Hepatitis B Virus (HBV) Infection Among University Students in Central Bangladesh.

This study aimed to determine the seroprevalence and determinants of hepatitis B virus (HBV) infection among university students in Bangladesh. This cross-sectional study was conducted among 614 students from five universities in central Bangladesh. Data were collected on demographic information, immunization history, medical and blood transfusion history through the face-to-face interview. Blood samples were collected and screened for anti-HBsAg using ELISA, HBsAg Rapid Test-cassette, and immune chromatographic test. The overall seroprevalence of HBV infection was 5.0%, and vaccination coverage was 19.2% among the participants. Students having a history of surgery (OR 11.004, 95% CI 3.211-37.707), blood transfusion (OR 5.651, 95% CI 0.965-33.068), being married (OR 4.776, 95% CI 1.508-15.127), and not being vaccinated (OR 9.825, 95% CI 1.130-85.367) were at higher risk of being infected by HBV. This study showed the endemicity of HBV infection among the Bangladeshi population. Marriage, surgical or blood transfusion history, not being vaccinated were the determinants of HBV infection within the study population. Public health initiatives for preventing HBV infection at the university levels should be envisaged.

Attitudes of mental healthcare professionals and media professionals towards each other in reducing social stigma due to mental illness in Bangladesh.

Mental illness is a neglected issue all over the world. The development of negligence towards mental illness could be due to the stigmatized attitudes. Also, social stigma due to mental illness is prevalent in many south Asian countries. In Bangladesh, the condition is even worse than regional data in Asia. The general people consider media as a primary source of information about mental health. However, mental health professionals (MHP) have direct contact with psychiatric patients and their family members. Therefore, both MHP and media professionals (MP) have a role in reducing social stigma due to mental illness. The present study aimed to identify attitudes of MHP and MP towards each other in reducing social stigma due to mental illness. We conducted a cross-sectional online survey among 174 participants (80 MHP and 94 MP). We used two sets of self-reporting questionnaires to assess the attitudes of MHP and MP towards one another. Also, we collected and analyzed sociodemographic profiles of study participants. We observed significant differences in terms of their profession-related statements between MHP and MP. Also, we have seen significant gaps between MHP and MP in terms of their complete agreement on attitude-related aspects. However, most respondents agreed that stigma due to mental illness is a vital problem in Bangladesh. Moreover, maximum participants accepted that the current public images about psychiatry and media role in reducing social stigma need to be improved. We observed gap between MHP and MP in their roles and beliefs in reducing social stigma due to mental illness in Bangladesh. Reciprocal training and awareness programs would help them in reducing social stigma due to mental illness. The findings of the present study would help to sort out the possible areas for interventions. However, the present study has few limitations; therefore, we recommend further studies with more homogeneous and large samples to make better conclusion.

A review of common mental health problems in the general population during the COVID-19 pandemic in South Asia.

The coronavirus disease (COVID-19) pandemic has negatively impacted people's lives, neurobiological health, and general well-being throughout the world. South Asian countries have experienced high frequencies of psychiatric concerns that have worsened during the pandemic. This review aimed to determine the aggregated prevalence of common mental health problems (e.g., depression, fear, anxiety, stress, insomnia, and suicidal tendencies) and their associated factors among community populations in South Asian countries. Following the PRISMA guidelines, a comprehensive literature search was undertaken using multiple databases, including PubMed, Scopus, Global Health, Google Scholar and ResearchGate to retrieve articles. Overall, 22 articles were included. The prevalence of concerns ranged widely with respect to estimates of symptoms of depression (10.2-85.9%), fear (38.5-88.8%), anxiety (8.1-62.5%), stress (10.56-91.77%), and insomnia (13.32-53.45%). In addition, the prevalence of suicidal ideation was estimated to range between 5% and 8%. Factors associated with mental health concerns included socio-demographic, behavioral and health-related measures, and COVID-19 pandemic-related stressors. The findings suggest poor mental health among general populations in South Asia during the COVID-19 pandemic. Thus, identifying individuals with mental health concerns and implementing interventions including mental health counseling appear warranted among these populations.

First characterization of a newly emerging phytopathogen, Sclerotinia sclerotiorum causing white mold in pea.

Pea (Pisum sativum L.) is of global importance as a food crop for its edible pod and seed. A new disease causing the tan to light brown blighted stems and pods has occurred in pea (P. sativum L.) plants in Chapainawabganj district, Bangladesh. A fungus with white-appressed mycelia and large sclerotia was consistently isolated from symptomatic tissues. The fungus formed funnel-shaped apothecia with sac-like ascus and endogenously formed ascospores. Healthy pea plants inoculated with the fungus produced typical white mold symptoms. The internal transcribed spacer sequences of the fungus were 100% similar to Sclerotinia sclerotiorum, considering the fungus to be the causative agent of white mold disease in pea, which was the first record in Bangladesh. Mycelial growth and sclerotial development of S. sclerotiorum were favored at 20°C and pH 5.0. Glucose was the best carbon source to support hyphal growth and sclerotia formation. Bavistin and Amistar Top inhibited the radial growth of the fungus completely at the lowest concentration. In planta, foliar application of Amistar Top showed the considerable potential to control the disease at 1.0% concentration until 7 days after spraying, while Bavistin prevented infection significantly until 15 days after spraying. A large majority (70.93%) of genotypes, including tested released pea cultivars, were susceptible, while six genotypes (6.98%) appeared resistant to the disease. These results on identification, characterization, host resistance, and fungicidal control of white mold could be valuable to achieve improved management of a new disease problem for pea cultivation.

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus.

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

Association between the use of social networking sites and mental health of young generation in Bangladesh: A cross-sectional study.

The social networking sites (SNS) are popular among people of all levels. Here we aimed to evaluate the relationship between the use of SNS and the mental health of the young generation in Bangladesh. We conducted this cross-sectional study among 791 subjects aged between 15 and 40 years. Self-reported questionnaires were used to collect demographics and SNS usage patterns of the respondents. We have measured the levels of loneliness, depression, generalized anxiety, and sleep disturbance using the recommended mental health assessment scales. The estimations of loneliness, depression, generalized anxiety, and sleep disturbance among the respondents were 64%, 38%, 63%, and 75%, respectively. Young adults, female sex, unmarried individuals, students, and urban residents reported higher psychological issues regarding the SNS use. Reported mental health issues were associated with time spent in SNS, the number of friends and groups connected with, personal beliefs, and feeling about SNS. A notable association between the use of SNS and the mental health issues of users has been ascertained. The present study findings might have significant implications in the field of clinical psychology and psychotherapy. Therefore, we recommend more comprehensive clinical interventions to find the tangible impact of SNS on the mental health of users.

Evaluation of serum amino acids and non-enzymatic antioxidants in drug-naïve first-episode major depressive disorder.

BACKGROUND: The alterations of biological markers are thought to be effective tools to understand the pathophysiology and management of major depressive disorder (MDD). A lot of researches has implied many markers for depression, but any of them fully discovered the association between the markers and depression. The present study investigated the serum levels of amino acids and non-enzymatic antioxidants in major depression, and also explained their association with depression. METHODS: This study examined 247 MDD patients and 248 healthy controls (HCs) matched by age and sex. The Hamilton Depression Rating Scale (Ham-D) was used to all the participants to measure the severity of depression. Quantification of serum amino acids, vitamin A and E were carried out using the HPLC system whereas vitamin C levels were measured by UV-spectrophotometer. All the statistical analysis was performed by SPSS statistical software (version 23.0). The independent sample t-test, the Mann-Whitney U test, and the Fisher's exact test were applied to detect the group differences where a Bonferroni correction applied to the p value. RESULTS: It was observed that serum levels of four amino acids (methionine, phenylalanine, tryptophan, and tyrosine) along with three non-enzymatic antioxidants (vitamin A, E, and C) were significantly dropped in MDD patients compared to HCs (Cohen's d (d): - 0.45, - 0.50, - 0.68, - 0.21, - 0.27, - 0.65, and - 0.24, respectively). Furthermore, Ham-D scores of cases were negatively correlated with serum levels of methionine (r = - 0.155, p = 0.015) and tyrosine (r = - 0.172, p = 0.007). CONCLUSION: The present study suggests that lowered serum methionine, phenylalanine, tryptophan, tyrosine, and non-enzymatic antioxidants are associated with depression. The reduction of these parameters in MDD patients may be the consequence, and not the cause, of major depression.

Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography.

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted from each subband signal of an EEG frame. The features extracted from individual subbands are concatenated, yielding a high-dimensional feature vector. A discriminative subset of features is selected from the feature vector using a graph eigen decomposition (GED)-based approach. Thus, the reduced number of features obtained is effective for differentiating the underlying characteristics of EEG signals that indicate seizure events and those that indicate nonseizure events. The GED method ranks the features according to their contribution to correct classification. The selected features are used to classify seizure and nonseizure EEG signals using a feedforward neural network (FfNN). The performance of the proposed method is evaluated by conducting various experiments with a standard dataset obtained from the University of Bonn. The experimental results show that the proposed seizure-detection scheme achieves a classification accuracy of 99.55%, which is higher than that of state-of-the-art methods. The efficiency of FfNN is compared with linear discriminant analysis and support vector machine classifiers, which have classification accuracies of 98.72% and 99.39%, respectively. Hence, the proposed method is confirmed as a potential marker for EEG-based seizure detection.

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy.

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.

Population Genetics of Calotropis gigantea, a Medicinal and Fiber Resource Plant, as Inferred from Microsatellite Marker Variation in two Native Countries.

Calotropis gigantea is well known for its aesthetic, medicinal, pharmacological, fodder, fuel, and fiber production potential. Unfortunately, this plant species is still undomesticated, and the genetic information available for crop improvement is limited. For this study, we sampled 21 natural populations of C. gigantea from two key areas of its natural distribution range (Bangladesh and China) and genotyped 379 individuals using nine nuclear microsatellite markers. Population genetic diversity was higher in Bangladesh than that observed in Chinese populations. Overall, a moderate level of genetic diversity was found (Na = 3.73, HE = 0.466), with most of the genetic variation detected within populations (65.49%) and substantial genetic differentiation (FST = 0.345) between the study regions. We observed a significant correlation between genetic and geographic distances (r = 0.287, P = 0.001). The Bayesian clustering, UPGMA tree, and PCoA analyses yielded three distinct genetic pools, but the number of migrants per generation was high (NM = 0.52-2.78) among them. Our analyses also revealed that some populations may have experienced recent demographic bottlenecks. Our study provides a baseline for exploitation of the genetic resources of C. gigantea in domestication and breeding programs as well as some insights into the germplasm conservation of this valuable plant.