Identification of eight new HLA class I alleles by next-generation sequencing.

Eight novel HLA class I alleles detected by next-generation sequencing.

Characterisation of two new HLA-A alleles: HLA-A*01:454 and HLA-A*31:229.

HLA-A*01:454 and HLA-A*31:229, two novel HLA-A alleles detected during routine typing by next-generation sequencing.

Identification of a new HLA-B allele, HLA-B*35:594.

A novel HLA-B*35 allele, officially designated HLA-B*35:594, was identified by next-generation sequencing.

Pseudo-spin switches and Aharonov-Bohm effect for topological boundary modes.

Topological boundary modes in electronic and classical-wave systems exhibit fascinating properties. In photonics, topological nature of boundary modes can make them robust and endows them with an additional internal structure-pseudo-spins. Here, we introduce heterogeneous boundary modes, which are based on mixing two of the most widely used topological photonics platforms-the pseudo-spin-Hall-like and valley-Hall photonic topological insulators. We predict and confirm experimentally that transformation between the two, realized by altering the lattice geometry, enables a continuum of boundary states carrying both pseudo-spin and valley degrees of freedom (DoFs). When applied adiabatically, this leads to conversion between pseudo-spin and valley polarization. We show that such evolution gives rise to a geometrical phase associated with the synthetic gauge fields, which is confirmed via an Aharonov-Bohm type experiment on a silicon chip. Our results unveil a versatile approach to manipulating properties of topological photonic states and envision topological photonics as a powerful platform for devices based on synthetic DoFs.

Water-Wave Vortices and Skyrmions.

Topological wave structures-phase vortices, skyrmions, merons, etc.-are attracting enormous attention in a variety of quantum and classical wave fields. Surprisingly, these structures have never been properly explored in the most obvious example of classical waves: water-surface (gravity-capillary) waves. Here, we fill this gap and describe (i) water-wave vortices of different orders carrying quantized angular momentum with orbital and spin contributions, (ii) skyrmion lattices formed by the instantaneous displacements of the water-surface particles in wave interference, and (iii) meron (half-skyrmion) lattices formed by the spin-density vectors, as well as (iv) spatiotemporal water-wave vortices and skyrmions. We show that all these topological entities can be readily generated in linear water-wave interference experiments. Our findings can find applications in microfluidics and show that water waves can be employed as an attainable playground for emulating universal topological wave phenomena.

Comparing the Anti-Depressive Effect of Electroconvulsive Therapy (ECT) Versus Transcranial Magnetic Stimulation (TMS) in the Treatment of Patients with Depression.

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for depressive disorders. However, ECT has a number of limitations, such as significant side effects in the neurocognitive domain and the requirement for general anesthesia. Transcranial magnetic stimulation (TMS) is an intervention that applies electric stimulation to the brain without causing convulsions, thus representing an attractive alternative to ECT. The aim of our study is to review systematic reports of the effectiveness of ECT and TMS in the treatment of depressive spectrum disorders. SUBJECTS AND METHODS: We performed search queries in PubMed and eLibrary databases, which retrieved 391 articles, of which 14 met our inclusion criteria for the analysis. The articles comprised three comparisons: TMS vs SHAM, ECT vs sham ECT (SECT), and ECT vs PHARM. The protocol parameters analyzed for TMS were coil type, targeted brain area, amplitude of resting motor threshold, duration of session, number of sessions in total and per week, number and pulses per session and inter-train pause. For ECT, we evaluated the type of ECT device, targeted brain area, type of stimuli, and for ECT vs PHARM we recorded types of anesthesia and antidepressant medication. RESULTS: Three of 6 studies showed a therapeutic effect of TMS compared to placebo; efficacy was greater for TMS frequency exceeding 10 Hz, and with stimulation of two areas of cerebral cortex rather than a single area. There was insufficient data to identify a relationship between the success of TMS and intertrain pause (IP). Three of four studies showed a therapeutic effect of ECT compared to placebo. Three studies of bilateral ECT showed a significant reduction in depression scores compared to the SECT groups. ECT protocols with brief pulses were generally of lesser efficacy. Four of 5 ECT vs PHARM studies showed superior efficacy of ECT compared to PHARM. Among several antidepressants, only the ketamine study showed greater efficacy compared to ECT. CONCLUSIONS: There of six TMS studies and 7 of 9 ECT studies showed efficacy in reducing depressive symptoms. A prospective study of crossover design might reveal the relative efficacies of ECT and TMS.

Neuronetwork Approach in the Early Diagnosis of Depression.

BACKGROUND: Depression is a common mental illness, with around 280 million people suffering from depression worldwide. At present, the main way to quantify the severity of depression is through psychometric scales, which entail subjectivity on the part of both patient and clinician. In the last few years, deep (machine) learning is emerging as a more objective approach for measuring depression severity. We now investigate how neural networks might serve for the early diagnosis of depression. SUBJECTS AND METHODS: We searched Medline (Pubmed) for articles published up to June 1, 2023. The search term included Depression AND Diagnostics AND Artificial Intelligence. We did not search for depression studies of machine learning other than neural networks, and selected only those papers attesting to diagnosis or screening for depression. RESULTS: Fifty-four papers met our criteria, among which 14 using facial expression recordings, 14 using EEG, 5 using fMRI, and 5 using audio speech recording analysis, whereas 6 used multimodality approach, two were the text analysis studies, and 8 used other methods. CONCLUSIONS: Research methodologies include both audio and video recordings of clinical interviews, task performance, including their subsequent conversion into text, and resting state studies (EEG, MRI, fMRI). Convolutional neural networks (CNN), including 3D-CNN and 2D-CNN, can obtain diagnostic data from the videos of the facial area. Deep learning in relation to EEG signals is the most commonly used CNN. fMRI approaches use graph convolutional networks and 3D-CNN with voxel connectivity, whereas the text analyses use CNNs, including LSTM (long/short-term memory). Audio recordings are analyzed by a hybrid CNN and support vector machine model. Neural networks are used to analyze biomaterials, gait, polysomnography, ECG, data from wrist wearable devices, and present illness history records. Multimodality studies analyze the fusion of audio features with visual and textual features using LSTM and CNN architectures, a temporal convolutional network, or a recurrent neural network. The accuracy of different hybrid and multimodality models is 78-99%, relative to the standard clinical diagnoses.

G.T. MSRS On-Line Calculator Targeting the Early Diagnosis of Mixed Affective States: the Multicenter Study Project Design on the Approbation and Validity of the Three Language Versions of the Rating Scale.

BACKGROUND: Mixed affective states (MS) are often misdiagnosed by the clinicians and lead to the inappropriate treatment strategies contributing to the severe forms, poor outcome with higher rates of comorbid conditions and worse remission quality in affective and bipolar disorders, as well as to a decreased psychosocial functioning of these patients. The development of reliable tools for the MS assessments or so called the phenomenon of mixity in affective disorders, is still an unmet need of psychiatric research and clinical practice. In this paper, we present PC program "Multilingual IBM-PC on-line calculator for early diagnosis of the mixed affective states" based on the Giuseppe Tavormina Mixed States Rating Scale (G.T. MSRS) (Russian, English, Italian language versions) created by the ICERN group using programming language JavaScript, working for such operation systems (OS) as Windows, Linux, MacOs, Android, iOS, and aims to contribute to the accurate assessment of the presence and severity of the mixed states among affective disorders. SUBJECTS AND METHODS: The G.T. Mixed States Rating Scale on-line calculator consists of 11 items which evaluate various aspects of MS in patients with major depression, bipolar or recurrent depressive disorder. A total score is automatically calculated, considering the type of affective temperament according to the Akiskal's and Tavormina's schemas, thus, the specific sub-groups of MS are differentiated based on the ranges: Medium-light (2-6), Medium (7-12), and High (13-19) Mixity level. The study will enroll 330 participants during two (three months follow-up) visits (110 native speakers in each of the three languages) of both genders aged from 18 till 55 y.o. with the diagnoses of depressive episodes, major depression, bipolar or recurrent depressive disorder according to the DSM-5 criteria. Assessments will be provided by the two investigators (second one will be blinded to the G.T. MSRS results obtained by the first researcher), based on the use of DSM-5 clinical interview, Young Mania Rating Scale, Montgomery-Åsberg Depression Rating Scale, Clinical Global Impression - Severity scale (CGI-S), Clinical Global Impression - Improvement scale (CGI-I), Patient Global Impression of Change (PGIC). RESULTS: The study will assess the agreement between the diagnostic category/clinical impression and the on-line G.T. MSRS calculator use results, its test-retest reliability, and diagnosis stability for further assessment of the on-line G.T. MSRS calculator validity and clinical utility. Secondary variables will evaluate internal reliability and such statistical analyses as Cronbach's alpha, Cohen's Kappa, biserial correction, and agreement between initial and follow-up diagnoses. CONCLUSIONS: Current multicenter validation study of the on-line G.T. MSRS calculator will enhance the understanding of the mixity phenomenon and its clinical implication, aiming to improve the treatment strategies to manage the course of mental disorder, quality of life and psychosocial functioning in patients with affective (bipolar) disorders, accompanied by MS, and a clinical recommendation for the routine administration of the user-friendly on-line G.T. MSRS version in clinical practice.

Peripheral Blood Mononuclear Cell-Related Biomarkers in Schizophrenia.

INTRODUCTION: Schizophrenia is a severe mental illness causing significant impairment in personal, family, social, educational, occupational, and other important areas of life. While there is no widely accepted endophenotype, peripheral blood cells may serve as an accessible model of intracellular changes in schizophrenia. METHODS: We reviewed the literature on the query "peripheral blood mononuclear cells AND schizophrenia" in Medline (Pubmed), selecting studies that searched for specific biomarkers of schizophrenia. We considered both diagnostic biomarkers and biomarkers of therapeutic response, specific schizophrenia disorders or differential diagnostic biomarkers. RESULTS: We retrieved 41 articles matching the search criteria, among which were studies that considered changes in the production of pro-inflammatory and anti-inflammatory markers, proteins, receptors, enzyme activity, and gene expression as potential biomarkers. CONCLUSION: Approaches analysing a biological axis or a group of related biomarkers may hold the greatest promise for identifying schizophrenia. In addition, pharmacological status, smoking status, inflammatory markers and glucose metabolites, the presence of comorbidities should be considered. Certain biomarkers, while not specific for the diagnosis of schizophrenia, may indicate the prognosis and effectiveness of treatment in the established diagnosis.

Aging Brain, Dementia and Impaired Glymphatic Pathway: Causal Relationships.

Cellular and molecular processes that are of key importance in the development of neuroinflammation and increased cytokine response, activation of microglia and astrogliosis, contributing to the accumulation of metabolites and aberrant proteins in the brain tissue due to their overproduction and insufficient clearance, concomitant disturbance of architecture and sleep patterns are interconnected and induce brain aging with the formation its complex neurobiological mechanism. The study of these processes brings us closer to understanding the main determinants of healthy and unhealthy aging, primary prevention and preclinical diagnosis of age-related diseases, as well as to solving problems of longevity and increasing quality of life. The imbalance of homeostatic functions that support the exchange of fluids and solutes in the brain tissue is observed both in physiological aging and in the development of pathology of the nervous system with long-term consequences - from impaired synaptic signaling to the development of neurodegenerative diseases. Dementia is one of the major health problems worldwide and is very complex in terms of pathophysiology. Therefore, one of the priorities of fundamental neurobiology is to elucidate the main morbid mechanisms of Alzheimer's disease as the most common form of dementia. The hypotheses of ß-amyloid and tau protein largely explain the main pathological features of Alzheimer's disease, however, there remains a need for further research on biomarkers with high validity and predictive applicability in people without cognitive impairment and clinical symptoms and in the early stages of the disease. There is a need to intensify the search for effective solutions to slow or stop the progression of the disease, especially therapeutic approaches that modify the disease at the preclinical stage, when it is most beneficial to change its course. At the same time, the discovery of aquaporin-dependent clearance pathways in the brain made it possible to identify new mechanisms underlying the etiology and progression of neurodegeneration and neuroinflammation. Glial-mediated clearance plays a fundamental role in the process of physiological aging, the development of age-related changes in the brain, and neurodegenerative processes. We analyzed 273 articles posted in PubMed database selected by keywords "glymphatic system, Alzheimer's disease, dementia, amyloid Aß, aquaporin, aging, brain clearance". A total of 102 full-text articles were included in this review. This article presents up-to-date evidence on the causes and consequences in the study of the relationship between dysfunction of the glymphatic pathway and the accumulation of pathological proteins with insufficient excretion of toxic metabolites from the brain parenchyma, which is considered a key factor in the development of Alzheimer's dementia.

Post-COVID Neuropsychiatric Complications in Children and Adolescents: a Study Design for Early Diagnosis and Treatment Using Innovative Technologies.

BACKGROUND: The COVID-19 pandemic has had significant impacts on the child and adolescent population, with long-term consequences for physical health, socio-psychological well-being, and cognitive development, which require further investigation. We herein describe a study design protocol for recognizing neuropsychiatric complications associated with pediatric COVID-19, and for developing effective prevention and treatment strategies grounded on the evidence-based findings. METHODS: The study includes two cohorts, each with 163 participants, aged from 7 to 18 years old, and matched by gender. One cohort consisted of individuals with a history of COVID-19, while the other group presents those without such a history. We undertake comprehensive assessments, including neuropsychiatric evaluations, blood tests, and validated questionnaires completed by parents/guardians and by the children themselves. The data analysis is based on machine learning techniques to develop predictive models for COVID-19-associated neuropsychiatric complications in children and adolescents. RESULTS: The first model is focused on a binary classification to distinguish participants with and without a history of COVID-19. The second model clusters significant indicators of clinical dynamics during the follow-up observation period, including the persistence of COVID-19 related somatic and neuropsychiatric symptoms over time. The third model manages the predictors of discrete trajectories in the dynamics of post-COVID-19 states, tailored for personalized prediction modeling of affective, behavioral, cognitive, disturbances (academic/school performance), and somatic symptoms of the long COVID. CONCLUSIONS: The current protocol outlines a comprehensive study design aiming to bring a better understanding of COVID-19-associated neuropsychiatric complications in a population of children and adolescents, and to create a mobile phone-based applications for the diagnosis and treatment of affective, cognitive, and behavioral conditions. The study will inform about the improved management of preventive and personalized care strategies for pediatric COVID-19 patients. Study results support the development of engaging and age-appropriate mobile technologies addressing the needs of this vulnerable population group.

Beliefs in Conspiracy Theories and Mental Health in the Student Community of Latvia During the COVID-19 Outbreak.

BACKGROUND: The objective of this study was to investigate self-reported changes in mental health and their association with various sociodemographic factors and beliefs in conspiracy theories among university and college students in Latvia during the second state of emergency caused by COVID-19. SUBJECTS AND METHODS: This cross-sectional study was conducted as part of an international research project, where university and college students were anonymously asked to complete an online self-report questionnaire. Changes in anxiety and depression were assessed using self-rated questions. Statistical analysis involved Pearson's chi-square test and univariate binary logistic regression.ression. RESULTS: The study comprised 1047 students, with 828 females (79.08% aged 21.71±0.09). Worsening in self-reported anxiety was more prevalent among females (69.3%, p<0.001), unemployed respondents (70.0%, p=0.003), individuals who were were not working during the lockdown (70.3%, p<0.001), those experiencing deterioration in general health condition (93.0%, p<0.001), and those belonging to or having knowledge of someone in a vulnerable group (69.5%, p=0.004). Worsening self-reported depression was more prevalent in respondents who did not work during the lockdown (63.9%, p=0.014) and those with deteriorating general health conditions (93.0%, p<0.001). Increased odds ratios (OR) for experiencing changes in anxiety and depression were associated with beliefs in the following conspiracy theories: 'Recommended measures are an attempt to restrict human rights' (OR=1.49, p=0.019 and OR=2.40, p<0.001, respectively). Furthermore, increased OR for experiencing changes in depression were associated with beliefs in the following conspiracy theories: 'The COVID-19 vaccine was ready before the virus spread' (OR=3.11, p=0.007), 'COVID-19 has a lower mortality rate" (OR=1.85, p<0.001)', 'Recommended measures are an attempt to restrict human rights' (OR=2.40, p<0.001), and 'The COVID-19 outbreak is the creation of world leaders' (OR=2.17, p=0.003). CONCLUSIONS: Self-reported changes in depression and anxiety were associated with certain beliefs in specific conspiracy theories.

The Use of eHealth for Pharmacotherapy Management With Patients With Respiratory Disease, Cardiovascular Disease, or Diabetes: Scoping Review.

BACKGROUND: eHealth is increasingly considered an important tool for supporting pharmacotherapy management. OBJECTIVE: We aimed to assess the (1) use of eHealth in pharmacotherapy management with patients with asthma or chronic obstructive pulmonary disease (COPD), diabetes, or cardiovascular disease (CVD); (2) effectiveness of these interventions on pharmacotherapy management and clinical outcomes; and (3) key factors contributing to the success of eHealth interventions for pharmacotherapy management. METHODS: We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review) statement. Databases searched included Embase, MEDLINE (PubMed), and Cochrane Library. Screening was conducted by 2 independent researchers. Eligible articles were randomized controlled trials and cohort studies assessing the effect of an eHealth intervention for pharmacotherapy management compared with usual care on pharmacotherapy management or clinical outcomes in patients with asthma or COPD, CVD, or diabetes. The interventions were categorized by the type of device, pharmacotherapy management, mode of delivery, features, and domains described in the conceptual model for eHealth by Shaw at al (Health in our Hands, Interacting for Health, Data Enabling Health). The effectiveness on pharmacotherapy management outcomes and patient- and clinician-reported clinical outcomes was analyzed per type of intervention categorized by number of domains and features to identify trends. RESULTS: Of 63 studies, 16 (25%), 31 (49%), 13 (21%), and 3 (5%) included patients with asthma or COPD, CVD, diabetes, or CVD and diabetes, respectively. Most (38/63, 60%) interventions targeted improving medication adherence, often combined for treatment plan optimization. Of the 16 asthma or COPD interventions, 6 aimed to improve inhaled medication use. The majority (48/63, 76%) of the studies provided an option for patient feedback. Most (20/63, 32%) eHealth interventions combined all 3 domains by Shaw et al, while 25% (16/63) combined Interacting for Health with Data Enabling Health. Two-thirds (42/63, 67%) of the studies showed a positive overall effect. Respectively, 48% (23/48), 57% (28/49), and 39% (12/31) reported a positive effect on pharmacotherapy management and clinician- and patient-reported clinical outcomes. Pharmacotherapy management and patient-reported clinical outcomes, but not clinician-reported clinical outcomes, were more often positive in interventions with ≥3 features. There was a trend toward more studies reporting a positive effect on all 3 outcomes with more domains by Shaw et al. Of the studies with interventions providing patient feedback, more showed a positive clinical outcome, compared with studies with interventions without feedback. This effect was not seen for pharmacotherapy management outcomes. CONCLUSIONS: There is a wide variety of eHealth interventions combining various domains and features to target pharmacotherapy management in asthma or COPD, CVD, and diabetes. Results suggest feedback is key for a positive effect on clinician-reported clinical outcomes. eHealth interventions become more impactful when combining domains.

Adiabatic topological photonic interfaces.

Topological phases of matter have been attracting significant attention across diverse fields, from inherently quantum systems to classical photonic and acoustic metamaterials. In photonics, topological phases offer resilience and bring novel opportunities to control light with pseudo-spins. However, topological photonic systems can suffer from limitations, such as breakdown of topological properties due to their symmetry-protected origin and radiative leakage. Here we introduce adiabatic topological photonic interfaces, which help to overcome these issues. We predict and experimentally confirm that topological metasurfaces with slowly varying synthetic gauge fields significantly improve the guiding features of spin-Hall and valley-Hall topological structures commonly used in the design of topological photonic devices. Adiabatic variation in the domain wall profiles leads to the delocalization of topological boundary modes, making them less sensitive to details of the lattice, perceiving the structure as an effectively homogeneous Dirac metasurface. As a result, the modes showcase improved bandgap crossing, longer radiative lifetimes and propagation distances.

Genomic sequence of the HLA-B*08:01:71, HLA-B*51:382, and HLA-B*55:01:30 alleles.

The full length sequence of the HLA-B*08:01:71, HLA-B*51:382 and HLA-B*55:01:30 alleles are reported.

The HLA-B*35:02:21 allele identified in a volunteer bone marrow donor.

The HLA-B*35:02:21 allele differs from HLA-B*35:02:01:01 in codon 183 in exon 4.

Genomic sequence of the HLA-A*01:432, HLA-C*07:1068 and HLA-DQB1*02:213 alleles.

The sequences of HLA-A*01:432, HLA-C*07:1068 and HLA-DQB1*02:213 alleles are reported.

SpineTool is an open-source software for analysis of morphology of dendritic spines.

Dendritic spines form most excitatory synaptic inputs in neurons and these spines are altered in many neurodevelopmental and neurodegenerative disorders. Reliable methods to assess and quantify dendritic spines morphology are needed, but most existing methods are subjective and labor intensive. To solve this problem, we developed an open-source software that allows segmentation of dendritic spines from 3D images, extraction of their key morphological features, and their classification and clustering. Instead of commonly used spine descriptors based on numerical metrics we used chord length distribution histogram (CLDH) approach. CLDH method depends on distribution of lengths of chords randomly generated within dendritic spines volume. To achieve less biased analysis, we developed a classification procedure that uses machine-learning algorithm based on experts' consensus and machine-guided clustering tool. These approaches to unbiased and automated measurements, classification and clustering of synaptic spines that we developed should provide a useful resource for a variety of neuroscience and neurodegenerative research applications.

Spin-dependent properties of optical modes guided by adiabatic trapping potentials in photonic Dirac metasurfaces.

The Dirac-like dispersion in photonic systems makes it possible to mimic the dispersion of relativistic spin-1/2 particles, which led to the development of the concept of photonic topological insulators. Despite recent demonstrations of various topological photonic phases, the full potential offered by Dirac photonic systems, specifically their ability to emulate the spin degree of freedom-referred to as pseudo-spin-beyond topological boundary modes has remained underexplored. Here we demonstrate that photonic Dirac metasurfaces with smooth one-dimensional trapping gauge potentials serve as effective waveguides with modes carrying pseudo-spin. We show that spatially varying gauge potentials act unevenly on the two pseudo-spins due to their different field distributions, which enables control of guided modes by their spin, a property that is unattainable with conventional optical waveguides. Silicon nanophotonic metasurfaces are used to experimentally confirm the properties of these guided modes and reveal their distinct spin-dependent radiative character; modes of opposite pseudo-spin exhibit disparate radiative lifetimes and couple differently to incident light. The spin-dependent field distributions and radiative lifetimes of their guided modes indicate that photonic Dirac metasurfaces could be used for spin-multiplexing, controlling the characteristics of optical guided modes, and tuning light-matter interactions with photonic pseudo-spins.