Variety Of Symptoms Research Articles

A Narrative Review of Biomarkers and Imaging in the Diagnosis of Acute Aortic Syndrome

Acute aortic syndrome (AAS) encompasses a range of life-threatening conditions, including classical dissection, intramural hematoma, and penetrating aortic ulcer. Each of these conditions presents distinct clinical characteristics and carries the potential to progress to rupture. Because AAS can be asymptomatic or present with diverse symptoms, its diagnosis requires clinical evaluation, risk scoring, and biomarkers such as D-dimer (DD), C-reactive protein (CRP), homocysteine, natriuretic peptides (BNP), and imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and echocardiography. While this review primarily focuses on widely used and clinically accessible biomarkers and imaging techniques, it also discusses alternative biomarkers proposed for diagnostic use. Although CT remains the gold standard for diagnosis, biomarkers facilitate rapid risk stratification, complementing imaging techniques. Emerging technologies, such as metabolomics, are reshaping diagnostic algorithms. Despite advances in diagnostic methods, challenges such as misdiagnosis and missed diagnoses persist. Ongoing research into novel biomarkers and innovative imaging techniques holds promise for improving diagnostic accuracy and patient outcomes.

The Evaluation of Biochemical Parameters in Positive COVID-19 RealTime PCR Patients: A Retrospective Study

Coronavirus, a member of the beta coronavirus family, encompasses a range of illnesses from common colds to severe disorders. SARS-CoV-2, transmitted mainly through respiratory droplets that are generated by a patient who is sneezing, coughing and talking, caused the global pandemic COVID-19 in 2020. The virus primarily targets the respiratory system especially in the lung cell, leading to diverse symptoms and severe consequences. This study was carried out in the COVID-19 Isolation Centre in Mukalla City, Yemen, to evaluate biochemical tests in COVID-19 patients. In the retrospective single-centre study on 116 COVID-19 patients, some of biochemical Analysed Parameters were glucose, Liver function tests, renal function tests, lipid profile, and proinflammatory markers; convenience sampling based on existing records for socio-demographics, medical history, and biochemistry were analysed by using a spectrophotometry and Chemiluminescent immunoassay analyser. used SPSS version 25 to analyse data. Most COVID-19 patients in Mukalla City (91.4 % of those over 46 years old) have common symptoms such as fever, cough, and respiratory problems. The study found links between chronic health disorders (hypertension 96.6 %, diabetes mellitus 94.8 %) and greater vulnerability to COVID-19. Blood glucose levels substantially impact COVID-19 occurrences (p 0.0001). Some liver function tests are associated with COVID-19 (total protein, albumin, LDH, and GGT) (p = 0.044, 0.010, 0.0001, and 0.0001, respectively). Urea and creatinine levels imply renal function and their significant association (p=0.001 for both), whereas uric acid had no significant association with COVID-19 (p = 0.134). However, lipid profile tests lack statistical significance; they do identify trends. Proinflammatory markers CRP and ferritin strongly correlate with positive COVID-19 patients (p=0.0001 for both). In conclusion, COVID-19 significantly affects glucose metabolism, liver function, renal dynamics, and inflammatory markers, necessitating vigilant monitoring for clinical care and informing future research.

Symptom Reporting in Patients with Primary Biliary Cholangitis: Higher Burden of Symptoms detection using an interactive App.

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease causing bile duct destruction and inflammation, impacting patient quality of life (QoL) due to variable symptoms. Digital symptom-tracker apps may improve patient care through enhanced monitoring. This feasibility study reassessed symptom burden in PBC patients using a tailored symptom-tracker app, focusing on its usability, effectiveness, and impact on management and QoL. Methods: Based on Kautz5 gUG "Symptomtracker", our app in REDCap allowed users to log PBC symptoms over four weeks, alongside medication use. Ethics approval and data security complied with German regulations. User feedback was incorporated for better usability. Symptom data were standardized, and R software was used for descriptive statistics and Chi-square tests. From March 2023 to October 2024, 207 patients (184 female, 20 male) were enrolled, median age 51 years. Among 90 patients who completed the questionnaire, fatigue was most prevalent (87.8%), followed by joint pain (80%), concentration difficulties (74.4%), abdominal discomfort (70%), and sicca symptoms. Other common symptoms were leg cramps (50%) and swollen feet (40%); jaundice was rare (7.8%). Older patients, especially those aged 50-60, reported higher symptom burden, but Chi-square tests showed no significant differences across age or gender. Our study highlights a significant symptom burden in PBC, particularly fatigue and joint pain. While older patients reported more symptoms, no significant differences were observed by age or gender. The symptom-tracker app enhanced monitoring and patient engagement, showing the potential of digital tools in PBC management. Further research is needed to evaluate long-term impacts.

Automated electronic health record-based screening for Fabry disease in unexplained left ventricular hypertrophy (FAPREV-HCM)

Background and aimsHypertrophic cardiomyopathy (HCM) has various aetiologies, including genetic conditions like Fabry disease (FD), a lysosomal storage disorder. FD prevalence in high-risk HCM populations ranges from 0.3% to 11.8%....

Identification of molecular and cellular infection response biomarkers associated with anthrax infection through comparative analysis of gene expression data.

Bacillus anthracis, a gram-positive bacillus capable of forming spores, causes anthrax in mammals, including humans, and is recognized as a potential biological weapon agent. The diagnosis of anthrax is challenging due to variable symptoms resulting from exposure and infection severity. Despite the availability of a licensed vaccines, their limited long-term efficacy underscores the inadequacy of current human anthrax vaccines, highlighting the urgent need for next-generation alternatives. Our study aimed to identify molecular biomarkers and essential biological pathways for the early detection and accurate diagnosis of human anthrax infection. Using a comparative analysis of Bacillus anthracis gene expression data from the Gene Expression Omnibus (GEO) database, this cost-effective approach enables the identification of shared differentially expressed genes (DEGs) across separate microarray datasets without additional hybridization. Three microarray datasets (GSE34407, GSE14390, and GSE12131) of B. anthracis-infected human cell lines were analyzed via the GEO2R tool to identify shared DEGs. We identified 241 common DEGs (70 upregulated and 171 downregulated) from cell lines treated similarly to lethal toxins. Additionally, 10 common DEGs (5 upregulated and 5 downregulated) were identified across different treatments (lethal toxins and spores) and cell lines. Network meta-analysis identified JUN and GATAD2A as the top hub genes for overexpression, and NEDD4L and GULP1 for underexpression. Furthermore, prognostic analysis and SNP detection of the two identified upregulated hub genes were carried out in conjunction with machine learning classification models, with SVM yielding the best classification accuracy of 87.5%. Our comparative analysis of Bacillus anthracis infection revealed striking similarities in gene expression 241 profiles across diverse datasets, despite variations in treatments and cell lines. These findings underscore how anthrax infection activates shared genes across different cell types, emphasizing this approach in the discovery of novel gene markers. These markers offer insights into pathogenesis and may lead to more effective therapeutic strategies. By identifying these genetic indicators, we can advance the development of precise immunotherapies, potentially enhancing vaccine efficacy and treatment outcomes.

KLHL24 associated cardiomyopathy: Gene function to clinical management.

KLHL24 (Kelch-like protein 24) is a significant component of the ubiquitin-proteasome system (UPS), involved in regulating protein turnover through targeted ubiquitination and degradation. Germline mutations in KLHL24 gene have been known to cause Epidermolysis Bullosa Simplex characterized by skin fragility but has recently been found to cause Cardiomyopathy. Various cardiomyopathies, including hypertrophic cardiomyopathy and dilated cardiomyopathy, leading to abnormal protein degradation and affecting the stability and function of essential cardiac proteins which finally results into structural and functional abnormalities in cardiac muscle. In this review, in order to understand the disease association of germline mutations of KLHL24, we summarize all the studies performed with KLHL24 gene including studies from 2016 when KLHL24 was first identified to be associated with epidermolysis bullosa simplex till the recent studies in 2024 by using keywords such as KLHL24 gene, hypertrophic cardiomyopathy, dilated cardiomyopathy and epidermolysis bullosa simplex. Furthermore, we explored the proposed molecular mechanisms and pathophysiologies of KLHL24 associated diseases. Patients with KLHL24 mutations were usually presented with variable clinical symptoms. The main clinical presentations have been cutaneous lesions, cardiac symptoms associated with cardiomyopathies and there have been reports of skeletal muscle weakness and neurological symptoms as well. Current treatments focus on managing clinical symptoms and preventing complications through medications, lifestyle changes, and surgical interventions. In addition, researches have also been conducted cell culture based in vitro studies for reducing the clinical symptoms of KLHL24 associated diseases. However, currently there are no specific clinical trials going on regarding the therapeutic strategies among patients with KLHL24 mutations. Understanding the role of KLHL24 in cardiomyopathies is very important for developing targeted diagnostic approach with therapeutic strategies. This review emphasizes the importance of KLHL24 mutations as a newly recognized cause of cardiomyopathy, paving the way for improved clinical diagnosis, targeted therapies, and ultimately, for better patient outcomes.

Artificial Intelligence as a Tool to Prevent Autoaggressive Destructive Behavior Among Children and Adolescents: a Brief Overview

Suicides and suicidal behaviors are complex disorders with diverse symptoms, often lacking clear etiology, especially in spontaneous or childhood cases. This complicates timely diagnosis, therapy, and treatment. As a result, research into markers for depression and suicidal behavior continues. The use of artificial intelligence represents a significant advancement in suicide prevention, offering new tools for early detection and intervention to improve outcomes for at-risk individuals. According to the World Health Organization (WHO), 726,000 people commit suicide, not counting the much larger number of people who attempt suicide each year. Suicides occur throughout life, but in 2021 they became one of the leading causes of death among 15-29 year-olds worldwide. This problem is also relevant in Kazakhstan, and this article is the first to reflect an interdisciplinary approach to suicide prevention among minors using AI methods in application to scientific data obtained in the study of respondents with suicidal behavior.  Suicide is a significant public health issue with profound societal impacts. Its effects extend beyond the loss of life, leading to emotional suffering for families and loved ones, and economic losses from reduced productivity and increased healthcare costs. For each suicide, there are over 30 attempted suicides, compounding the social and economic burden. The repercussions affect countless individuals, both directly and indirectly, leaving long-lasting emotional and financial strain. Additionally, the economic impact includes treatment costs for psychosomatic and mental disorders in those left behind, highlighting the extensive and multifaceted consequences of suicidal behavior.

Enhancing Depression Detection through Multimodal Emotion Recognition and Wearable Technologies

Traditional methods for diagnosing and treating depression often lack the precision to accurately identify the diverse symptoms and parameters associated with the disorder. With advancements in artificial intelligence and key technologies, multimodal emotion recognition has emerged as a promising approach to enhance depression detection. This paper explores the integration of physiological data such as EEG (electroencephalogram), ECG (electrocardiogram), and GSR (galvanic skin response), along with data from wearable devices, to develop a highquality feature extraction method for emotion analysis. Multiple datasets, including the SEED dataset involving EEG recordings from 15 participants during emotional video viewing, were utilized to collect comprehensive physiological and psychological data. Feature extraction involved preprocessing steps like noise reduction and normalization, followed by the application of decisionlevel and representationlevel fusion algorithms. Heart rate variability and skin conductance response features were analyzed to capture emotional indicators. An attentionbased Convolutional Neural Network (CNN) was employed to model both local and global facial features, and the Weighted Sum Pooling and Projection (WSPP) method was used for depression representation extraction. Multimodal fusion techniques, particularly modellevel fusion, significantly enhanced the learning of internal multimodal interactions, leading to improved recognition rates in Automatic Depression Estimation tasks compared to previous algorithms. The applications of this approach extend to smart home integration, daily emotion monitoring, health warning systems, and interactive wearable technologies, highlighting its potential for widespread use in enhancing user experience and mental health interventions.

Interrogating the cognitive‐behavioural impairment in a motor‐dominant neurodegeneration (ALS), based on brain state dynamics of resting‐state EEG

AbstractBackgroundAmyotrophic lateral sclerosis (ALS) shares pathological and genetic underpinnings with frontotemporal dementia (FTD). ALS manifests with diverse symptoms, including progressive neuro‐motor degeneration, muscle weakness, but also cognitive‐behavioural changes in up to half of the cases. Resting‐state EEG measures, particularly spectral power and functional connectivity, have been instrumental for discerning abnormal motor and cognitive network function in ALS [1]–[3]. Based on our recent findings using EEG microstates [4], we hypothesized that dynamic analysis of time‐varying spectral EEG measures at source‐level can further interrogate the altered functional network that underpin the cognition and behaviour in ALS as a motor‐dominant neurodegeneration.MethodFor this purpose, we aimed to identify transient brain states associated with specific functional networks and to characterise the spatio‐temporal and spectral alterations in these brain states using high‐density resting‐state EEG, recorded from 99 individuals with ALS and 78 healthy controls (HC). Using a time‐delay embedded Hidden‐Markov‐Model on source‐reconstructed EEG data [5], we identified transient and recurrent brain states characterised by spectral power and coherence. The model was trained to convert source‐reconstructed time courses into sequences of functional networks (brain states).ResultTwelve brain states were identified with distinct patterns of spectral power and coherence for individuals with ALS and HC. The Brain States 1, 3, 7 and 9 showed significant association between brain states fractional occupancy and behavioural decline (Beaumont Behavioural Inventory [7]; >0.25, q<0.03, >0.65), while state 5 showed association with fluency decline (Edinburgh Cognitive and Behavioural ALS scale [8]; = ‐0.3, q = 0.004, = 0.83). States 1, 7 and 10 were characterised by frontal lobe activation (spectral power higher than the average within the state), while state 3 exhibited activation in the sensorimotor network. Highest spectral power during state 5 was in the supplementary motor area, a region primarily associated with motor planning but also possibly associated with language [9].ConclusionThis study demonstrated the utility of resting‐state EEG and its temporal decomposition based on spatio‐spectral dynamics for investigating and interrogating the less‐dominant cognitive‐behavioural decline in ALS. It shows clear potentials for applications in cognate conditions such as FTD and Alzheimer’s disease with a dominant cognitive‐behavioural decline.

Association of age at menopause with type 2 diabetes mellitus in postmenopausal women: a systematic review and meta-analysis.

The onset of menopause usually occurs between the ages of 48 and 52, leading to diverse symptoms in various body systems due to a decrease in estrogen level. Visceral obesity and diminished estrogen level during the menopausal phase are associated with unfavorable metabolic changes, resulting in insulin resistance and increased risk of type 2 diabetes mellitus (T2DM). Owing to the increase in the incidence and prevalence of T2DM in recent decades, it is important to identify predisposing factor such as menopausal age to improve T2DM prevention and management. We searched PubMed, Scopus, and Google Scholar databases to find all studies related to menopause and type 2 diabetes up to August 4, 2023. Studies were reviewed for compliance with the inclusion criteria. Sample size and number of post-menopausal T2DM cases were extracted from studies. Effect sizes and related confidence intervals were calculated for them. The combined analysis of 17 studies demonstrated a significant association between menopausal age and risk of T2DM. The calculated odds ratio (OR) demonstrated that women with menopausal age > 45 had significantly decreased risk of developing T2DM with OR of 0.13 (95% CI: 0.04, 0.22). This effect changed to OR = 0.44 (95% CI: -1.12, 2.00) and 0.21 (95% CI: -1.39, 1.82) for menopausal age > 50 and > 55, respectively. In conclusion, this present meta-analysis emphasizes the importance of the complex relationship between earlier menopausal age and risk of T2DM development.

Herbal Medicine in Three Different Mediterranean Living Areas During the COVID-19 Pandemic: The Role of Polyphenolic-Rich Thyme-like Plants.

Despite herbal medicine being popular across the Mediterranean basin, there is no evidence in favor of COVID-19 infection. This study investigates the utilization and effects of medicinal plants in Italy, Lebanon, and Tunisia during COVID-19 and its effects on post-COVID-19 pandemics. We used a tailored, web-based "Google Form" questionnaire with the random sampling method. We gathered 812 complete responses (Italy: 116, Lebanon: 557, and Tunisia: 139), revealing diverse demographics and symptom experiences. Fatigue prevailed across all groups (89.0-94.2%), while psychological impacts ranged from 20.1% to 30.9%, with higher rates in Lebanon. Post-COVID-19 symptoms affected 22.4% (Italy), 48.8% (Lebanon), and 31.7% (Tunisia). General use of herbs was consistent (41.4-50.4%), with 23.3% (Italy), 50.2% (Lebanon), and 65.5% (Tunisia) employing herbs for COVID-19 therapy. Notably, in Lebanon, Za'atar, a thyme-like plant, correlated with reduced symptoms, suggesting potential protective effects that are likely due to its polyphenol richness. This study underscores the persistent reliance on traditional medicinal plants remedies in the Mediterranean area, with regional variations. Further exploration of herbal compounds for COVID-19-like symptoms is warranted.

Clinical and genetic diversity in Iranian individuals with RAPSN-related congenital myasthenic syndrome.

Congenital myasthenic syndromes (CMSs) are genetic disorders affecting motor function with variable symptoms. RAPSN-related CMS, caused by mutations in the RAPSN gene, leads to muscle weakness. Accurate diagnosis is essential for proper management. This study aims to analyze six Iranian families affected by RAPSN-CMS, focusing on clinical manifestations, genetic variants, treatment response, and outcomes. Clinical assessments, genetic analysis, and whole-exome sequencing were performed on the six families to identify RAPSN gene mutations. The study examined symptoms, disease severity, age of onset, treatment response, and outcomes. Treatment with pyridostigmine and salbutamol was given to assess its effectiveness. Three homozygous known variants in RAPSN gene were identified: c.491G > A in three families, c.264C > A in two families, and c.-210A > G in one family. Clinical assessments showed diversity in symptoms and treatment responses. Pyridostigmine and salbutamol treatment improved symptoms and quality of life. This study highlights the significance of molecular diagnosis for RAPSN-related congenital myasthenic syndromes (CMS) in Iran, marking the first comprehensive genetic analysis in the region. The identification of specific pathogenic variants underscores the unique genetic landscape of local patients. Furthermore, our long-term follow-up revealed variable treatment responses, emphasizing the need for personalized care strategies. The clinical variability among patients with identical mutations necessitates a multidisciplinary approach for effective management. By enhancing genetic awareness and refining follow-up methods, we aim to improve diagnosis accuracy and interventions, fostering better outcomes for affected families in the Iranian population.

Minimal Variation in Functional Connectivity in Relation to Daily Affect.

Reported associations between functional connectivity and affective disorder symptoms are minimally reproducible, which can partially be attributed to difficulty capturing highly variable clinical symptoms in cross-sectional study designs. "Dense sampling" protocols, where participants are sampled across multiple sessions, can overcome this limitation by studying associations between functional connectivity and variable clinical states. Here, we characterized effect sizes for the association between functional connectivity and time-varying positive and negative daily affect in a nonclinical cohort. Data were analyzed from 24 adults who attended four research visits, where participants self-reported daily affect using the PANAS-X questionnaire and completed 39 min of functional magnetic resonance imaging across three passive viewing conditions. We modeled positive and negative daily affect in relation to network-level functional connectivity, with hypotheses regarding within-network connectivity of the default mode, salience/cingulo-opercular, frontoparietal, dorsal attention, and visual networks and between-network connectivity of affective subcortical regions (amygdala and nucleus accumbens) with both default mode and salience/cingulo-opercular networks. Effect sizes for associations between affect and network-level functional connectivity were small and nonsignificant across analyses. We additionally report that functional connectivity variance is largely attributable to individual identity with small relative variance (<3%) accounted for by within-subject daily affect variation. These results support previous reports that functional connectivity is dominated by stable subject-specific connectivity patterns, while additionally suggesting relatively minimal influence of day-to-day affect. Researchers planning studies examining functional connectivity in relation to daily affect, or other varying stable states, should therefore anticipate small effect sizes and carefully consider power in study planning.

A multi-region single nucleus transcriptomic atlas of Parkinson’s disease

Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder, characterized by motor and cognitive impairments, that affects >1% of the population over the age of 60. The pathogenesis of PD is complex and remains largely unknown. Due to the cellular heterogeneity of the human brain and changes in cell type composition with disease progression, this complexity cannot be fully captured with bulk tissue studies. To address this, we generated single-nucleus RNA sequencing and whole-genome sequencing data from 100 postmortem cases and controls, carefully selected to represent the entire spectrum of PD neuropathological severity and diverse clinical symptoms. The single nucleus data were generated from five brain regions, capturing the subcortical and cortical spread of PD pathology. Rigorous preprocessing and quality control were applied to ensure data reliability. Committed to collaborative research and open science, this dataset is available on the AMP PD Knowledge Platform, offering researchers a valuable tool to explore the molecular bases of PD and accelerate advances in understanding and treating the disease.

Symptom burden and clusters during chemotherapy in patients with lung cancer

ObjectiveThis study aims to investigate the distribution characteristics of symptoms in patients with lung cancer during chemotherapy, identify the symptom clusters (SCs) and explore the underlying mechanisms. The findings will provide evidence to assist clinical staff in effectively managing symptoms and SCs.MethodsParticipants were recruited from the Oncology Department of Guang’anmen Hospital, China Academy of Chinese Medical Sciences, between July 2022 and December 2022. The incidence and severity of symptoms were assessed and SCs were identified. Spearman’s correlation analysis was used to examine the correlation between lung cancer-specific SC and routine blood indices.ResultsA total of 169 patients participated in the study. The most prevalent and severe symptom was loss of appetite. Based on the occurrence rate and severity of symptoms, SC extraction was performed for mild, moderate, and severe symptoms, resulting in the identification of five SCs: psycho-emotional SC, chemotherapy-related SC, lung cancer-specific SC, urinary-related SC, and gastrointestinal SC. When only the moderate and severe symptoms were considered, two SCs were identified: chemotherapy-related SC and lung cancer-specific SC. Additionally, the lung cancer-specific SC showed a negative correlation with eosinophils.ConclusionPatients with lung cancer undergoing chemotherapy experience complex and diverse symptoms. A total of five SCs were extracted based on mild, moderate and severe symptoms and two SCs were extracted based on moderate and severe symptoms. The results of the study showed that lung cancer-specific SC was negatively correlated with eosinophils. Future research should focus on developing and refining research tools, methodologies, understanding the pathogenesis, and exploring intervention measures for SCs.

A neuropsychiatric systemic lupus erythematosus presenting with acute confusional state: a case report

Systemic lupus erythematosus (SLE) is a chronic multisystem autoimmune disease characterized by variable clinical symptoms. It disproportionately affects African-American women and women from other ethnic groups. The complexity of SLE, stemming from its genetic and phenotypic diversity, poses challenges in diagnosis, treatment, and development of novel therapies. Neuropsychiatric systemic lupus erythematosus (NPSLE) encompasses a spectrum of neurological syndromes involving the central, peripheral, and autonomic nervous systems, as well as psychiatric syndromes diagnosed in SLE patients after ruling out other causes. Neuropsychiatric manifestations typically emerge in the later stages of SLE (10–75%). The acute onset of neuropsychiatric symptoms as the primary presentation of NPSLE is exceptionally rare and can be diagnostically elusive when other organs are unaffected. In this article, we present the case of a 53-year-old woman who was admitted to the hospital due to disorientation and an acute confusional state. Following the diagnosis of NPSLE, she received treatment consisting of three doses of pulse corticosteroids and one dose of cyclophosphamide. She exhibited a favorable clinical response, leading to partial resolution of her symptoms and subsequent discharge from the hospital. Practical Implications. Due to the rarity and diverse clinical presentations of NPSLE, particularly with the sudden onset of neuropsychiatric symptoms, there is a significant risk of delayed or incorrect diagnosis, which can lead to ineffective treatment. Therefore, maintaining a high index of clinical suspicion is paramount for achieving early and accurate diagnosis, which is crucial for initiating timely and effective treatment strategies.

Understanding neuropathic pain: the role of neurophysiological tests in unveiling underlying mechanisms

AbstractNeuropathic pain, arising from lesions of the somatosensory nervous system, presents with diverse symptoms including ongoing pain, paroxysmal pain, and provoked pain, usually accompanied by sensory deficits. Understanding the pathophysiological mechanisms behind these symptoms is crucial for targeted treatment strategies. Neurophysiological techniques such as nerve conduction studies, reflexes, and evoked potentials help elucidate these mechanisms by assessing large myelinated non-nociceptive fibres and small nociceptive fibres. This argumentative review highlights the importance of tailored neurophysiological assessments for improving our understanding of the pathophysiological mechanisms behind neuropathic pain symptoms.

Haemophagocytic Lymphohistiocytosis Triggered by Cytomegalovirus Reactivation in An Immunosuppressed Patient with Pauci-Immune Glomerulonephritis.

Haemophagocytic lymphohistiocytosis (HLH) is a rare disease yet important to diagnose, as it is quickly life-threatening.The diverse symptoms of HLH can make diagnosis tricky, with many potential causes. As clinical presentation is not very specific, it is often mistaken for infection with severe sepsis and its diagnosis is often delayed.Limited understanding of this condition could lead to worse outcomes for patients. Recognising it early is crucial for starting the right treatment and enhancing both the well-being and survival chances of those affected by this complex disorder.In practice, HLH must be suspected when bi/pancytopaenia occurs in a patient presenting a high fever of unknown cause, especially when they have a history of immunosuppression. Managing it often requires a range of approaches, such as intensive care, immune system suppression, specialised medications or even stem cell transplants.

Investigation of Association Between Expression of DYX1C1, KIAA0319, and ROBO1 Genes and Specific Learning Disorder in Children and Adolescents.

Specific learning disorder (SLD) is prevalent worldwide and is a complex disorder with variable symptoms and significant differences among individuals. Epigenetic markers may alter susceptibility to neurodevelopmental disorders (NDDs). Aberrant expression of protein-coding (mRNA) genes in this pathology shows that the detection of epigenetic molecular biomarkers is of increasing importance in the diagnosis and treatment of individuals with SLD. We compared gene expression level of dyslexia susceptibility 1 candidate gene 1 (DYX1C1), dyslexia-associated protein KIAA0319 (KIAA0319), and roundabout guidance receptor 1 (ROBO1) between children with SLD and healthy children by performing quantitative polymerase chain reaction (qPCR). In addition, we evaluated these gene expressions of severe children with SLD compared to non-severe and male SLD children compared to females. The expression of the DYX1C1, KIAA0319, and ROBO1 genes was statistically significantly upregulated in children with SLD (P < 0.05*). DYX1C1 was also upregulated in severe SLD children (P = 0.03*). In addition, KIAA0319 and ROBO1 genes were differentially expressed in male SLD children compared to females (P < 0.05*). Furthermore, we found that DYX1C1 and ROBO1 genes significantly affect the likelihood of the SLD (respectively, P < 0.001** and P = 0.007*). We expect that the findings provided from this study may contribute to the determination expression level of the relevant genes in the diagnosis, prognosis, and treatment of SLD. In addition, our findings could be a guide for future epigenetics studies on the use of the DYX1C1, KIAA0319, and ROBO1 in therapeutic applications in the SLD.

Biological, psychological, and socio-structural underpinnings of dissociative identity disorder

Abstract. Epidemiological studies revealed the prevalence and diverse symptoms of dissociative identity disorder (DID), emphasizing diagnostic complexities. Debates over its etiology, ranging from trauma to fantasy predisposition, highlight the need for ongoing research and varied therapeutic approaches. Neuroimaging has uncovered distinct structural and functional brain differences in DID patients, contributing to our understanding of its neurological basis. Despite therapeutic progress, including behavioral and pharmacological interventions, effectively DID treatments remain challenging. Integrating biological, psychological, and sociocultural perspectives is crucial for advancing DID diagnosis and treatment.