Immunoinformatic-guided designing and evaluating protein and mRNA-based vaccines against Cryptococcus neoformans for immunocompromised patients.

BACKGROUND: Cryptococcus neoformans is a fungal pathogen that can cause serious meningoencephalitis in individuals with compromised immune systems due to HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome), liver cirrhosis, and transplantation. Mannoproteins (MPs), glycoproteins in the C. neoformans capsule, crucially impact virulence by mediating adhesion to lung cells and modulating immune response via cytokine induction and phagocytosis influence. Therefore, creating a vaccine that can generate targeted antibodies to fight infection and prevent fungal illnesses is essential. RESULTS: This research aims to create a unique, stable, and safe vaccine through bioinformatics methodologies, aiming at epitopes of T and B cells found in the MP of C. neoformans. Based on toxicity, immunogenicity, and antigenicity, this research predicted novel T cells (GNPVGGNVT, NPVGGNVTT, QTSYARLLS, TSVGNGIAS, WVMPGDYTN, AAATGSSSSGSTGSG, GSTGSGSGSAAAGST, SGSTGSGSGSAAAGS, SSGSTGSGSGSAAAG, and SSSGSTGSGSGSAAA) and B cell (ANGSTSTFQQRYTGTYTNGDGSLGTWTQGETVTPQTAYSTPATSNCKTYTSVGNGIASLALSNAGSNSTAAATNSSSGGASAAATGSSSSGSTGSGSGSAAAGSTAAASSSGDSSSSTSAAMSNGI, HGATGLGNPVGGNVTT, TMGPTNPSEPTLGTAI, GNPVGGNVTTNATGSD, and NSTAAATNSSSGGASA) epitopes for a multiple-epitope vaccine and constructed a vaccine subunit with potential immunogenic properties. The present study used four linkers (AAY, GPGPG, KK, and EAAAK linkers) to connect the epitopes and adjuvant. After constructing the vaccine, it was confronted with receptor docking and simulation analysis. Subsequently, the vaccine was cloned into the vector of Escherichia coli pET-28a ( +) by ligation process for the expression using the SnapGene tool, which confirmed a significant immune response. To assess the constructed vaccine's properties, multiple computational tools were employed. Based on the MP sequence, the tools evaluated the antigenicity, immunogenicity, cytokine-inducing capacity, allergenicity, toxicity, population coverage, and solubility. CONCLUSION: Eventually, the results revealed a promising multi-epitope vaccine as a potential candidate for addressing global C. neoformans infection, particularly in immunocompromised patients. Yet, additional in vitro and in vivo investigations are necessary to validate its safety and effectiveness.

[SEASONALITY, HOLIDAYS AND THE DEMAND FOR URGENT PSYCHIATRIC CARE IN ISRAEL].

INTRODUCTION: The demand for urgent psychiatric services provided in emergency departments demonstrates high variability throughout the year. Seasonality and holidays may influence this demand. While these two phenomena are widely documented for mental healthcare, the unique climate and Israeli population, which is mostly Jewish, raises doubts regarding the relevance of prior results.

Hospitalisation outcomes for patients with severe mental illness treated by female vs. male psychiatry residents.

OBJECTIVE: Recent literature suggests that female physicians provide higher quality of care compared to their male counterparts across a variety of physical medical conditions. We examine whether a similar phenomenon is observed for psychiatry residents treating hospitalised psychiatric patients. METHODS: We analysed 300 hospitalised patient records from Shalvata Mental Healthcare Centre (Hod Hasharon, Israel). Resident-patient sex matchings were compared. RESULTS: No significant differences were observed in terms of residents' age and patients' age, medical condition and hospitalisation history. Male and female patients treated by female residents presented shorter hospitalisations (58 and 54 days compared to 67 and 66 days, respectively, p < .05), longer time to next hospitalisation (269 and 179 days compared to 179 and 123 days, respectively, p < .01), lower 30-day readmission rate (37% and 35% compared to 10% and 19%, respectively, p < .05), higher levels of family involvement during hospitalisation (2.6 and 2.7 points compared to 2.1 and 1.9 points, respectively, p < .01) and higher chances of obtaining rehabilitation services (39% and 34% vs. 23% and 17%, respectively, p < .05). CONCLUSIONS: Hospitalised patients treated by female psychiatry residents are associated with better hospitalisation outcomes compared to those cared for by male residents. KEY POINTSBoth male and female patients treated by female residents presented better hospitalisation outcomes.These hospitalisation outcomes include shorter hospitalisation periods, longer time to next hospitalisation, lower 30-day remission rate, significantly higher levels of family involvement and higher chances of obtaining rehabilitation services.Further work is needed in order to investigate the sources and reasons for the identified differences.

EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia.

The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.

COVID-19 restrictions and visitations to an Israeli psychiatric emergency department: A four-lockdowns retrospective study.

We examine the volume and characteristics of psychiatric ED visitations through a perspective of four COVID-19 lockdowns. All adult visitations to the ED of Shalvata Mental Healthcare center (Israel) during 2020-2021 were retrieved and statistically analysed and data from 2017 to 2019 was considered as control. Voluntary and involuntary ED visitations were considered, separately and combined. We find that the significant decrease in the volume of voluntary ED visitations during the 1st lockdown was quickly overturned, roughly returning to the pre-pandemic state following its conclusion. In parallel, the volume of involuntary ED visitations has dramatically increased, with the most striking levels observed during the second and third lockdowns. Elapsed time since the first occurrence of COVID-19 in Israel and the level of governmental restrictions is significantly associated with the increase in the volume of ED visits and admissions, the admission rate and the rate of involuntary visits. The prolonged consequences associated with the pandemic and the measures taken to control it suggest that it is unreasonable to expect a return to normal ED utilization in the near future. As such, alternatives to strict lockdowns should be favored when possible and urgent strengthening of psychiatric care is warranted.

Paroxysmal slow wave events predict epilepsy following a first seizure.

OBJECTIVE: Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG-based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure. METHODS: We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow-up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs). RESULTS: Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%. SIGNIFICANCE: The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure.

Treatment selection using prototyping in latent-space with application to depression treatment.

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline.

No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer's disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.