Evaluation of Met and Unmet Needs in Patients with Severe Psychiatric Disorders and its Relation to the Quality of Life: A Cross-Sectional Study.

Background: Paying attention to the needs of patients with psychiatric disorders has recently come into focus. Failure to meet the needs of patients can affect their quality of life. This study aimed to determine the main areas of the needs of patients with severe psychiatric disorders and evaluate their relationship with the quality of life. Methods: In this cross-sectional study, 174 patients with severe mental illness who were referred to Iran Psychiatric Hospital for hospitalization or outpatient treatment were enrolled in this study (68 with schizophrenia and schizoaffective disorder, 106 with bipolar disorder type 1). A qualified psychiatry resident conducted interviews with each patient to determine their needs using the Camberwell Assessment of Need Short Appraisal Schedule (CANSAS) and the severity of their illness using the Hamilton Depression Rating Scale (HAM-D), Positive and Negative Syndrome Scale (PANSS), and Young Mania Rating Scale. A checklist for demographic data and the World Health Organization Quality of Life Brief Version (WHOQOL-BREF) questionnaire was completed by patients. Data were analyzed using descriptive statistics. Since the number of needs distribution was not normal, we used the Mann-Whitney, Kruskal-Wallis, and chi-square tests for qualitative variables. Results: The total number of patient needs was 9 (mean = 9.1, SD = 3.7). The most unmet needs were intimate relationships (69.5%), sexual expression (65.5%), and information on condition and treatment (51.1%). Unmet needs showed a negative correlation with the quality of life (P < 0.001) and a positive correlation with the severity of depression (P = 0.045), negative symptoms (P = 0.001), and general psychopathology (P < 0.001). Conclusion: A higher number of unmet needs of severe psychiatric patients is associated with lower quality of life and more severe disorders.

Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks.

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.

From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering.

The availability of millions of SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) sequences in public databases such as GISAID (Global Initiative on Sharing All Influenza Data) and EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) (the United Kingdom) allows a detailed study of the evolution, genomic diversity, and dynamics of a virus such as never before. Here, we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intrahost viral populations. We asses our results using clustering entropy-the first time it has been used in this context. Our clustering approach reaches lower entropies compared with other methods, and we are able to boost this even further through gap filling and Monte Carlo-based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the U.K. and GISAID data sets, and is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), and Gamma and Zeta (Brazil) variants in the GISAID data set. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large data sets.

New Zealand White Rabbits Effectively Clear Borrelia burgdorferi B31 despite the Bacterium's Functional vlsE Antigenic Variation System.

Borrelia burgdorferi is a tick-borne bacterium responsible for approximately 300,000 annual cases of Lyme disease (LD) in the United States, with increasing incidences in other parts of the world. The debilitating nature of LD is mainly attributed to the ability of B. burgdorferi to persist in patients for many years despite strong anti-Borrelia antibody responses. Antimicrobial treatment of persistent infection is challenging. Similar to infection of humans, B. burgdorferi establishes long-term infection in various experimental animal models except for New Zealand White (NZW) rabbits, which clear the spirochete within 4 to 12 weeks. LD spirochetes have a highly evolved antigenic variation vls system, on the lp28-1 plasmid, where gene conversion results in surface expression of the antigenically variable VlsE protein. VlsE is required for B. burgdorferi to establish persistent infection by continually evading otherwise potent antibodies. Since the clearance of B. burgdorferi is mediated by humoral immunity in NZW rabbits, the previously reported results that LD spirochetes lose lp28-1 during rabbit infection could potentially explain the failure of B. burgdorferi to persist. However, the present study unequivocally disproves that previous finding by demonstrating that LD spirochetes retain the vls system. However, despite the vls system being fully functional, the spirochete fails to evade anti-Borrelia antibodies of NZW rabbits. In addition to being protective against homologous and heterologous challenges, the rabbit antibodies significantly ameliorate LD-induced arthritis in persistently infected mice. Overall, the current data indicate that NZW rabbits develop a protective antibody repertoire, whose specificities, once defined, will identify potential candidates for a much-anticipated LD vaccine.

Diagnostic values of ultrasound and the Modified Alvarado Scoring System in acute appendicitis.

BACKGROUND: Making the diagnosis of acute appendicitis is difficult, and is important for preventing perforation of the appendix and negative appendectomy results. Ultrasound and clinical scoring systems are very helpful in making the diagnosis. Ultrasound is non-invasive, available and cost-effective, and can accomplish more than CT scans. However, there is no certainty about its effect on the clinical outcomes of patients, and it is operator dependent. Counting the neutrophils as a parameter of the Alvarado Scale is not routine in many laboratories, so we decided to evaluate the diagnostic value of the Modified Alvarado Scaling System (MASS) by omitting the neutrophil count and ultrasonography. METHODS: After ethical approval of methodology in Tehran University of Medical Sciences ethical committee, we collected the data. During 9 months, 75 patients with right lower quadrant pain were enrolled in the study, and underwent abdominal ultrasonography and appendectomy, with pathological evaluation of the appendix. The MASS score was calculated for these patients and compared with pathology results. RESULTS: Fifty-five male and 20 female patients were assessed. Of these patients 89.3% had acute appendicitis. The sensitivity, specificity, PPV, NPV and accuracy rate of ultrasonography was 71.2%, 83.3%, 97.4%, 25% and 72.4%, respectively. By taking a cutoff point of 7 for the MASS score, a sensitivity of 65.7%, specificity of 37.5%, PPV of 89.8%, NPV of 11.5% and accuracy of 62.7% were calculated. Using the cutoff point of 6, a sensitivity of 85.1%, specificity of 25%, PPV of 90.5%, NPV of 16.7% and accuracy of 78.7% were obtained. CONCLUSION: Ultrasound provides reliable findings for helping to diagnose acute appendicitis in our hospital. A cutoff point of 6 for the MASS score will yield more sensitivity and a better diagnosis of appendicitis, though with an increase in negative appendectomy.