Distinct Symptoms Research Articles

First Report of Bacterial Kidney Disease (BKD) Caused by Renibacterium salmoninarum in Chum Salmon (Oncorhynchus keta) Farmed in South Korea

In 2021, a prominent increase in mortality was observed in juvenile and subadult cultured chum salmon (Oncorhynchus keta) on a mariculture farm in Jeollanam-do Province, South Korea. The affected fish displayed distinct symptoms: pale gills, petechial hemorrhages in the muscles, and white nodules on the kidneys. Infectious pancreatic necrosis virus (IPNV) was cultured from some fish samples using fish cell lines. Bacteria were isolated from various fish tissues using kidney disease medium-two (KDM-2) culture medium. By detecting and sequencing the 16S rRNA gene using DNA extracted from the kidneys of the infected fish via PCR, the isolated bacteria were identified as Renibacterium salmoninarum. Histopathological examination primarily focused on hematopoietic tissues of kidneys and revealed clear evidence of severe necrosis and granulomatous changes. Additionally, nuclei with peripherally displaced chromatin were abundant in the kidneys of affected fish. These findings suggest that mass mortality of chum salmon was caused by R. salmoninarum, which induced typical bacterial kidney disease (BKD) symptoms, without IPNV infection. This represents the first outbreak of BKD attributed to R. salmoninarum infection in farmed chum salmon in South Korea.

A phenomenological approach to violence in schizophrenia spectrum disorders: examination of anomalous self-experience

ABSTRACT Persons with schizophrenia spectrum disorders (SSDs) are at higher risk of becoming violent compared to the general population. Hence, obtaining a detailed account of characteristics of individuals with SSDs at high risk for committing violent acts is of high interest. A closer look on the phenomenological perspective by exploring basic self-disorders (BSDs) may help to better understand the complex interplay between schizophrenia and violence. To explore if persons with SSDs and a history of severe violence show distinct BSDs, clinical symptoms, sociodemographic data and BSDs were obtained in a sample of 33 male patients with SSDs, including 17 with a history of severe violence. Degree of BSDs were examined with the Examination of Anomalous Self-Experience (EASE). EASE-scores were compared between the two groups. The EASE composite score (degree of BSDs) was lower in SSD patients with a history of severe violence compared to non-violent SSD patients. When controlling for confounders, insight turned out to be the key factor in the relationship. The results are inconclusive regarding an association between proneness to violent behaviour and level of basic self-disorders but suggest that such associations may exist and call for further investigations.

PRONA: An R-package for Patient Reported Outcomes Network Analysis.

Network analysis (NA) has recently emerged as a new paradigm by which to model the symptom patterns of patients with complex illnesses such as cancer. NA uses graph theory-based methods to capture the interplay between symptoms and identify which symptoms may be most impactful to patient quality of life and are therefore most critical to treat/prevent. Despite NA's increasing popularity in research settings, its clinical applicability is hindered by the lack of a unified platform that consolidates all the software tools needed to perform NA, and by the lack of methods for capturing heterogeneity across patient cohorts. Addressing these limitations, we present PRONA, an R-package for Patient Reported Outcomes Network Analysis. PRONA not only consolidates previous NA tools into a unified, easy-to-use analysis pipeline, but also augments the traditional approach with functionality for performing unsupervised discovery of patient subgroups with distinct symptom patterns. PRONA is implemented in R. Source code, installation, and use instructions are available on GitHub at https://github.com/bbergsneider/PRONA. Supplementary information is available at Bioinformatics online.

Deciphering the vectors: Unveiling the local dispersal of Litylenchus crenatae ssp. mccannii in the American beech (Fagus grandifolia) forest ecosystem.

Beech leaf disease (BLD), caused by the Litylenchus crenatae ssp. mccannii (Lcm) nematode, is an emerging threat to beech trees. This disease is characterized by distinct leaf symptoms, including leaf interveinal banding and thickened leaf texture, which leads to eventual tree mortality. Understanding Lcm dispersal mechanism(s) is crucial for managing BLD, yet these remain largely unknown, posing a major barrier to its effective management. This study represents a pioneering investigation into the abiotic and biotic vectors that potentially contribute to the local dispersal of Lcm in natural American beech (Fagus grandifolia) forest systems in the Northeastern United States. An experiment was set up in Stone Valley Forest, Pennsylvania (PA), using four funnel stands placed at variable distances from naturally BLD-infected beech trees. This approach enabled the recovery of active Lcm nematodes from each funnel, demonstrating their ability to naturally disperse at least 11.74 m from the nearest BLD-infected tree. The findings highlight the role of abiotic factors involved in the dispersal dynamics of Lcm, especially wind and humidity, as indicated by a generalized linear model. The current study also uncovered the incidental association of Lcm with other organisms beneath the canopy of BLD trees, including spiderwebs and caterpillars. To our knowledge, this is the first study to document the potential vectors involved in the local dispersal of Lcm, offering valuable information for the biology of this nematode, as well as insight into the development of effective BLD management strategies. The findings contribute to broader efforts in advancing the understanding of the local spread of BLD, highlighting the complex interplay of abiotic and biotic factors in this disease dispersal.

Psychiatric symptom profiles of inpatient refugees in Egypt: insights from a single center study

BackgroundThe global surge in refugee populations has sparked considerable apprehension regarding their mental health. This study explores the symptom profile and associated factors among refugee patients admitted to Okasha Institute of Psychiatry, Ain Shams University, hospitals between 2018 and 2021.MethodsA retrospective analysis was conducted utilizing the medical records of refugee patients admitted to Okasha Institute of Psychiatry between 2018 and 2021. Data collection encompassed socio-demographic characteristics, diagnoses, reported stressors, and specific symptom profiles.ResultsThe study involved 36 refugee patients, averaging 26.7 years, predominantly female (61.1%), and predominantly of Sudanese origin (45.9%). Prevalent diagnoses included bipolar disorder (36.8%), schizophrenia (26.3%), and major depressive disorder (13.2%). A diverse array of stressors emerged, with immigration-related issues ranking highest (21.2%). Other noteworthy stressors comprised marital conflicts (15.2%) and house-related difficulties (15.2%).ConclusionThis study highlighted the prevalence of severe mental illnesses, varied stressors, and distinct symptom profiles in refugees with psychiatric admissions and underscored the imperative for culturally sensitive and trauma-informed mental health services. Further research is imperative to devise effective interventions and enhance the mental health outcomes of refugee populations.

Dissociable Default Mode Network Connectivity Patterns Underlie Distinct Symptoms in Psychosis Risk.

The Clinical High Risk (CHR) stage of psychosis is characterized by subthreshold symptoms of schizophrenia including negative symptoms, dysphoric mood, and functional deterioration. Hyperconnectivity of the default-mode network (DMN) has been observed in early schizophrenia, but the extent to which hyperconnectivity is present in CHR, and the extent to which such hyperconnectivity may underlie transdiagnostic symptoms, is not clear. As part of the Shanghai At-Risk for Psychosis (SHARP) program, resting-state fMRI data were collected from 251 young adults (158 CHR and 93 controls, M = 18.72, SD = 4.68, 129 male). We examined functional connectivity of the DMN by performing a whole-brain seed-to-voxel analysis with the MPFC as the seed. Symptom severity across a number of dimensions, including negative symptoms, positive symptoms, and affective symptoms were assessed. Compared to controls, CHRs exhibited significantly greater functional connectivity (p < 0.001 uncorrected) between the MPFC and 1) other DMN nodes including the posterior cingulate cortex (PCC), and 2) auditory cortices (superior and middle temporal gyri, STG/MTG). Furthermore, these two patterns of hyperconnectivity were differentially associated with distinct symptom clusters. Within CHR, MPFC-PCC connectivity was significantly correlated with anxiety (r= 0.23, p=0.006), while MPFC-STG/MTG connectivity was significantly correlated with negative symptom severity (r=0.26, p=0.001). Secondary analyses using item-level symptom scores confirmed a similar dissociation. These results demonstrate that two dissociable patterns of DMN hyperconnectivity found in the CHR stage may underlie distinct dimensions of symptomatology.

Constructing a picture of fatigue in the context of cancer: assessment of construct overlap in common fatigue scales.

Individuals diagnosed with cancer experience multiple inter-related short- and long-term side effects. Chief among such symptomology is cancer-related fatigue (CRF), which, if left unmanaged, can become chronic and result in increased disability and healthcare utilization. A growing number of self-report scales have been developed to measure CRF symptoms based on various theoretical conceptualizations with the aim of promoting targeted assessment and intervention efforts. It may be, however, unwise to assume that the various measures are conceptually similar (i.e., that they assess for the same constructs). Accordingly, we aimed to characterize item content among nine self-report scales, using a Jaccard index to quantify content overlap among scales. We characterized construct assessment among nine self-report scales recommended to assess CRF by a recent clinical practice guideline, and used a Jaccard index to quantify content overlap among scales. Analysis of 208 items across nine rating scales resulted in 20 distinct symptoms of CRF assessed. The most common symptoms were energy level (captured in all nine scales), cognitive function, impaired task performance (in eight scales), sleepiness, and physical function (in seven scales). Mean overlap among all scales was low (Jaccard index = 0.455). Only one construct (duration of fatigue; 5.0%) was captured by a single scale, and one symptom (energy level; 5.0%) was common across all scales. The PFS, MFSI, and BFI each captured at least one symptom from each of the NCCN domains of CRF. CRF scales are heterogeneousin the content they measure, critically impairing integration of knowledge across studies using disparate scales. Future work is urgently needed to build more integrated theoretical and/or computational models of CRF based on relevant mechanisms.

Use of passively collected actigraphy data to detect individual depressive symptoms in a clinical subpopulation and a general population.

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data. The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data. Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58-0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56-0.60) in the general population (n = 8,378). Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Autism Spectrum Disorder Symptom Profiles in Fragile X Syndrome, Angelman Syndrome, Tuberous Sclerosis Complex and Neurofibromatosis Type 1.

Studying Autism Spectrum Disorder (ASD) heterogeneity in biologically homogeneous samples may increase our knowledge of ASD etiology. Fragile X syndrome (FXS), Angelman syndrome (AS), Tuberous Sclerosis Complex (TSC), and Neurofibromatosis type 1 (NF1) are monogenic disorders with high a prevalence of ASD symptomatology. This study aimed to identify ASD symptom profiles in a large group of children and adolescents (0;9-28 years) with FXS, AS, TSC, and NF1. Data on ASD symptomatology (Autism Diagnostic Observation Scale (ADOS-2) & Social Responsiveness Scale (SRS-2)) were collected from children and adolescents with FXS (n = 54), AS (n = 93), TSC (n = 112), and NF1 (n = 278). To identify groups of individuals with similar ASD profiles, we performed two latent profile analyses. We identified a four-profile model based on the ADOS-2, with a (1) 'Non-spectrum symptom profile', (2) 'Social Affect symptom profile', (3)'Restricted/Repetitive Behaviors symptom profile', and (4)'ASD symptom profile'. We also identified a four-profile model based on the SRS, with a (1)'Non-clinical symptom profile', (2)'Mild symptom profile', (3)'Moderate symptom profile', and (4)'Severe symptom profile'. Although each syndrome group exhibited varying degrees of severity, they also displayed heterogeneity in the profiles in which they were classified. We found distinct ASD symptom profiles in a population consisting of children and adolescents with FXS, AS, TSC, and NF1. Our study highlights the importance of a personalized approach to the identification and management of ASD symptoms in rare genetic syndromes. Future studies should aim to include more domains of functioning and investigate the stability of latent profiles over time.

Correlation of patient symptoms with SARS-CoV-2 Omicron variant viral loads in nasopharyngeal and saliva samples and their influence on the performance of rapid antigen testing.

We examined nasopharyngeal and salivary viral loads using samples collected from outpatients with SARS-CoV-2 infection during the Omicron epidemic in Japan and explored the outpatient factors correlated with viral loads. In addition, we evaluated the performance of an authorized rapid antigen testing (RAT) kit using nasopharyngeal and saliva samples with RT-PCR testing as the reference. Intriguingly, a correlation between fever and other symptoms and SARS-CoV-2 viral loads in nasopharyngeal and saliva samples was observed based on one COVID-19 outpatient visit. RAT sensitivity was influenced by viral loads. Nevertheless, nasopharyngeal RAT is considered useful for SARS-CoV-2 point-of-care diagnosis. In patients with distinct symptoms, including high-grade fever, salivary RAT could be a practical diagnostic tool because of the higher estimated viral loads. After the Omicron epidemic, outpatients with mild COVID-19 have become the main focus of diagnosis and treatment. Our study provides valuable information regarding the point-of-care diagnosis of these patients.

Biological and Molecular Characterization of a New Isolate of Tomato Mottle Mosaic Virus Causing Severe Shoestring and Fruit Deformities in Tomato Plants in India

Tomato (Solanum lycopersicum L.), the second most important vegetable crop globally, faces a significant threat from various viral diseases. A newly emerging disease, characterised by distinctive shoestring symptoms on leaves and the development of unripe, small, and hard fruit, poses a serious challenge to tomato cultivation in India. An initial survey in an experimental field revealed more than 50% of the plants displayed symptoms of the shoestring disease, resulting in substantial reductions in fruit yield and quality. Transmission electron microscopy (TEM) and molecular analyses identified an isolate of the tomato mottle mosaic virus (ToMMV) in the affected plants. When the partially purified virus was mechanically inoculated into tomato cv. Pusa Ruby plants, it reproduced the characteristic shoestring symptoms, confirming its causal relationship with the disease. Notably, the present shoestring isolate of ToMMV (ToMMV-Tss) was found to induce similar shoestring symptoms in most of the major commercial tomato varieties when inoculated under controlled experimental conditions in the glasshouse, indicating its aggressive nature. Host range studies demonstrated that the ToMMV-Tss can infect several solanaceous species, while cucurbitaceous hosts remained unaffected. Moreover, the virus was found to be seed-transmissible, with a small percentage of seedlings from infected plants displaying symptoms. These findings underscore the significant impact of ToMMV on tomato production in India and emphasise the need for reliable diagnostic tools and effective management strategies to curb the spread and mitigate the impact of this virus on commercial tomato cultivation.

Health symptoms and cosmetic silicone breast implants: A retrospective cohort study

BackgroundThere has been a growing concern about a possible causal relationship between silicone breast implants (SBIs) and health symptoms, often referred to as Breast Implant Illness. This study assessed the association between SBIs for cosmetic augmentation and health symptoms. MethodsRetrospective cohort study using data linkage with the Dutch Breast Implant Registry and Nivel Primary Care Database. A total of 688 women with cosmetic SBIs were age-matched with 1301 women without SBIs. The occurrence of thirteen health symptoms presented in general practice was assessed one year before implantation until three years after implantation. Comparisons were made regarding the number of symptoms and general practice consultations, both before and after implantation, and between the two groups. ResultsWomen with SBIs were more likely to experience three or more distinct health symptoms and a combination of multiple symptoms with multiple consultations during follow-up than women without SBIs (adjusted OR 1.44, 95% CI 1.06 to 1.96; adjusted OR 1.44, 95% CI 1.04 to 2.00, respectively). Women with SBIs also had a more than twice greater likelihood of these outcomes in the second year after implantation compared to one year before implantation (OR 2.13, 95% CI 1.27 to 3.57; OR 2.13, 95% CI 1.22 to 3.72, respectively). ConclusionsWomen with cosmetic SBIs had increased odds of health symptoms after implantation compared to before and to women without SBIs. These results suggest an association between health symptoms and cosmetic SBIs that needs further research to explore whether there is causality or residual confounding.

Symptom clusters and network analysis in patients with gynecologic cancer undergoing chemotherapy: A cross-sectional study

ObjectivesThis study aimed to explore the complex relationships among symptoms and symptom clusters in patients with gynecologic cancer receiving chemotherapy using symptom network analysis, and to identify core symptoms and core symptom clusters. MethodsA cross-sectional study was conducted at the Affiliated Hospital of Jiangnan University from December 2023 to June 2024, including 221 patients with gynecologic tumors. Participants completed demographic and clinical information questionnaires and the Chinese version of the MD Anderson Symptom Inventory (MDASI-C). Univariate analysis and multiple linear regression were used to screen covariates, exploratory factor analysis to determine symptom clusters, and network analysis to identify core symptoms and core symptom clusters. ResultsA total of 221 patients were included, with an average age of 58.73 years (SD ​= ​11.50). Fatigue (n ​= ​197, 89.1%) and lack of appetite (n ​= ​192, 86.9%) were the most common symptoms, while fatigue (mean ​= ​4.17, SD ​= ​2.07) and distress (mean ​= ​3.43, SD ​= ​2.20) were the most severe symptoms. Several distinct symptom clusters were identified: sickness behavior, gastrointestinal, psychological, and side-effect clusters. In the constructed network, fatigue emerged as the most central symptom (rs ​= ​1.28), while the sickness behavior cluster was identified as the most central symptom cluster (rs ​= ​1.11). ConclusionsPatients with gynecologic cancer undergoing chemotherapy commonly experience a range of symptoms. Our findings suggest that targeted interventions focusing on the sickness behavior symptom cluster may help reduce the overall symptom burden and assist caregivers in developing more effective symptom management strategies.

Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns.

Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden. Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression. Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership. Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.

Subsyndromes and symptom clusters: Multilevel factor analysis of behavioral and psychological symptoms of dementia with intensive longitudinal data.

Behavioral and psychological symptoms in dementia (BPSD) are dynamic phenomena with a high amount of intraindividual variability. We applied a multilevel framework to identify subsyndromes (between-person factors) that represent clinically relevant profiles of BPSD and identify symptom clusters (within-person factors) that represent contextually driven daily symptom experiences. This study used an intensive longitudinal design in which 68 co-residing family caregivers to persons living with dementia were recruited to proxy report on their care recipient's daily symptom experiences of 23 different BPSD for eight consecutive days (n=443 diaries). A multilevel exploratory/confirmatory factor analysis was used to account for nested data and separate within-person variances from between-level factor estimates. Exploratory factor analysis identified a 4-between 3-within factor structure based on fit statistics and clinical interpretability. This study offers major methodological and conceptual advancements for management of BPSD within Alzheimer's disease and related dementias by introducing two related but distinct concepts of subsyndromes and symptom clusters. Because behavioral and psychological symptoms of dementia (BPSD) are dynamic temporal phenomenon, this introduces measurement error into aggregate group-level estimates when trying to create subsyndromes. We propose a multilevel analysis to provide a more valid and reliable estimation by separating out variance due to within-person daily fluctuations. Using a multilevel exploratory factor analysis with intensive longitudinal data, we identified distinct and meaningful groups of BPSD. The four factors at the between-person level represented subsyndromes that are based on how BPSD co-occurred among persons with Alzheimer's disease (AD). These subsyndromes are clinically relevant because they share features of established clinical phenomena and may have similar neurobiological etiologies. We also found three within-person factors representing distinct symptom clusters. They are based on how BPSD clustered together on a given day for an individual with AD and related dementias. These clusters may have shared environmental triggers.

Characterization and Fungicide Sensitivity of Colletotrichum spp. from Capsicum peppers in South Korea.

Capsicum peppers, peppers from plants of the genus Capsicum (family Solanaceae), are widely cultivated in South Korea, where annual production was 92,756 tons in 2021, 54.4% higher than that of the previous year. Occurring throughout the production cycle, anthracnose is a major disease limiting commercial Capsicum pepper production worldwide, including in South Korea. This study investigates the diversity and pathogenicity of Colletotrichum species responsible for Capsicum pepper anthracnose in Gyeongbuk, South Korea, focusing on disease incidence and symptomatology in the field and the identification, morphological characteristics, pathogenicity, and fungicide sensitivity of the causative species. Disease incidence ranged from 30 to 50%, with samples categorized into three distinct symptom types, aiding accurate field diagnosis. Phylogenetic analysis classified 41 isolates into six species in the acutatum, gloeosporioides, and truncatum species complexes, revealing significant genetic diversity. Morphological characterization supported these identifications, providing a comprehensive profiling. Pathogenicity tests confirmed that all identified species induced typical anthracnose lesions, with lesion size variations suggesting differential aggressiveness. Temperature significantly influenced mycelial growth, with optimal growth between 20 and 26°C, and C. truncatum demonstrating high-temperature tolerance.In vitro fungicide sensitivity tests showed variable responses, with tebuconazole being generally effective. These findings underscore the need for species-specific fungicide recommendations and highlight the importance of continuous monitoring of Colletotrichum species. Future research should explore the molecular mechanisms of pathogenicity, host specificity, and fungicide resistance, integrating these findings with breeding programs to develop resistant pepper varieties. This study provides critical insights for effective anthracnose management in pepper cultivation and future research directions.

Artemisia pallens W. Attenuates Inflammation and Oxidative Stress in Freund's Complete Adjuvant-Induced Rheumatoid Arthritis in Wistar Rats.

Rheumatoid arthritis (RA) is an autoimmune disease that causes distinctive inflammatory symptoms and affects over 21 million people worldwide. RA is characterized by severe discomfort, swelling, and degradation of the bone and cartilage, further impairing joint function. The current study investigates the antiarthritic effect of a methanolic extract of Artemisia pallens (methanolic extract of A. pallens, MEAP), an aromatic herb. Artemisinin content (% per dry weight of the plant) was estimated using a UV Vis spectrophotometer. In the present study, animals were divided into six groups (n = 6). The control group (group I) was injected with 0.25% of carboxymethyl cellulose. The arthritic control group (group II) was treated with Freund's complete adjuvant (by injecting 0.1 mL). Prednisolone (10 mg/kg), a lower dose of MEAP (100 mg/kg), a medium dose of MEAP (200 mg/kg), and a higher dose of MEAP (400 mg/kg) were orally delivered to groups III, IV, V, and VI, respectively. Freund's complete adjuvant was administered into the sub-plantar portion of the left-hind paw in all the groups except vehicle control to induce rheumatoid arthritis. Weight variation; joint diameter; paw volume; thermal and mechanical hyperalgesia; hematological, biochemical, and oxidative stress parameters; radiology; and a histopathological assessment of the synovial joint were observed in order to evaluate the antiarthritic effect of the methanolic extract of A. pallens. In this study, the estimated content of artemisinin was found to be 0.28% (per dry weight of the plant), which was in good agreement with the reported value. MEAP (200 and 400 mg/kg) caused a significant reduction in increased paw volume and joint diameter in arthritic rats while significantly increasing body weight and the mechanical threshold of thermal algesia. Moreover, complete blood counts and serum enzyme levels improved significantly. Radiological analysis showed a reduction in soft tissue swelling and small erosions. A histopathological examination of the cells revealed reduced cell infiltration and the erosion of joint cartilage in MEAP-administered arthritic rats. The present research suggests that the antiarthritic activity of the methanolic extract of A. pallens wall is promising, as evidenced by the findings explored in our rat model.

A Rose by Any Other Name: The Long Intricate History of Localized Aggressive Periodontitis.

This review addresses the recent World Workshop Consensus Conference (WWCC) decision to eliminate Localized Aggressive Periodontitis (LAgP) in young adults as a distinct form of periodontitis. A "Consensus" implies widespread, if not unanimous, agreement among participants. However, a significant number of attendees were opposed to the elimination of the LAgP classification. The substantial evidence supporting a unique diagnosis for LAgP includes the (1) incisor/molar pattern of disease, (2) young age of onset, (3) rapid progression of attachment and bone loss, (4) familial aggregation across multiple generations, and (5) defined consortium of microbiological risk factors including Aggregatibacter actinomycetemcomitans. Distinctive clinical signs and symptoms of LAgP are presented, and the microbial subgingival consortia that precede the onset of signs and symptoms are described. Using Bradford-Hill guidelines to assess causation, well-defined longitudinal studies support the unique microbial consortia, including A. actinomycetemcomitans as causative for LAgP. To determine the effects of the WWCC elimination of LAgP on research, we searched three publication databases and discovered a clear decrease in the number of new publications addressing LAgP since the new WWCC classification. The negative effects of the WWCC guidelines on both diagnosis and treatment success are presented. For example, due to the localized nature of LAgP, the practice of averaging mean pocket depth reduction or attachment gain across all teeth masks major changes in disease recovery at high-risk tooth sites. Reinstating LAgP as a distinct disease entity is proposed, and an alternative or additional way of measuring treatment success is recommended based on an assessment of the extension of the time to relapse of subgingival re-infection. The consequences of the translocation of oral microbes to distant anatomical sites due to ignoring relapse frequency are also discussed. Additional questions and future directions are also presented.

Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.

Significant variations in immune profiles across different age groups manifest distinct clinical symptoms and prognoses in Coronavirus Disease 2019 (COVID-19) patients. Predominantly, severe COVID-19 cases that require hospitalization occur in the elderly, with the risk of severe illness escalating with age among young adults, children, and adolescents. This study aimed to delineate the unique immune characteristics of COVID-19 across various age groups and evaluate the feasibility of detecting COVID-19-induced immune alterations through peripheral blood analysis. By employing a machine learning approach, we analyzed gene expression data from nasopharyngeal and peripheral blood samples of COVID-19 patients across different age brackets. Nasopharyngeal data reflected the immune response to COVID-19 in the upper respiratory tract, while peripheral blood samples provided insights into the overall immune system status. Both datasets encompassed COVID-19 patients and healthy controls, with patients divided into children, adolescents, and adult age groups. The analysis included the expression levels of 62,703 genes per patient. Then, 9 feature-sequencing methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, random forest, ridge regression, adaptive boosting, categorical boosting, extremely randomized trees, and extreme gradient boosting) were employed to evaluate the association of the genes with COVID-19. Key genes were then utilized to develop efficient classification models. The findings identified specific markers: insulin-like growth factor binding protein 3 (downregulated in the peripheral blood of COVID-19 patients), interferon alpha-inducible protein 27 (upregulated), and SERPING1 (upregulated in nasopharyngeal tissues). In addition, fibulin-2 was downregulated in adolescent patients, but upregulated in the other groups, while epoxide hydrolase 3 was upregulated in healthy controls, but downregulated in children and adolescents. This study offers valuable insights into the local and systemic immune responses of COVID-19 patients across age groups, aiding in identifying potential therapeutic targets and formulating personalized treatment strategies.

A patient report scale research to access the symptom burden in patients with IgA nephropathy

Patients diagnosed with IgA nephropathy (IgAN) commonly experience a substantial burden of symptoms encompassing both physical and psychological aspects. Presently, there's a dearth of standardized assessment tools to effectively gauge the extent of symptom burden in IgAN patients. Therefore, this study aims to devise an IgAN Symptom Assessment Tool that enables a comprehensive evaluation of patient symptom burden and their self-perceived severity. Employing a prospective observational design, this study conducted a survey among patients diagnosed with IgAN at a hospital in China. The research team formulated an IgAN Symptom Burden Assessment Scale and administered a questionnaire to gauge patient symptom burden. Severity assessment was conducted on a 5-point Likert scale, with higher scores indicating a more pronounced burden of symptoms. The finalized scale comprised 14 distinct symptom items, and the questionnaire survey garnered responses from 200 patients, achieving a 100% response rate. Statistical analysis unveiled that nearly all patients regarded these symptoms as prevalent and significantly impactful on their daily lives, resulting in a considerable burden. Notably, mild oliguria, moderate nasal congestion, bitter taste , throat discomfort, alongside severe manifestations such as muscle weakness, fatigue, and foamy urine, were frequently reported by patients. The findings underscore that a substantial proportion of IgAN patients grapple with a significant burden of symptoms, emphasizing the imperative for healthcare providers to prioritize symptom management and implement proactive measures to alleviate these challenges. This study presents an innovative tool tailored for evaluating symptom burden specifically in IgAN patients. Subsequent research should center on validating this tool within larger patient cohorts to optimize the efficacy of symptom management in this demographic.