Overlap Of Symptoms Research Articles

An Umbrella Review of Self-Management Interventions for Health Conditions With Symptom Overlap With Traumatic Brain Injury.

To synthesize evidence for the effectiveness of self-management interventions for chronic health conditions that have symptom overlap with traumatic brain injury (TBI) in order to extract recommendations for self-management intervention in persons with TBI. An umbrella review of existing systematic reviews and/or meta-analyses of randomized controlled trials or nonrandomized studies targeting self-management of chronic conditions and specific outcomes relevant to persons with TBI. A comprehensive literature search of 5 databases was conducted using PRISMA guidelines. Two independent reviewers conducted screening and data extraction using the Covidence web-based review platform. Quality assessment was conducted using criteria adapted from the Assessing the Methodological Quality of Systematic Reviews-2 (AMSTAR-2). A total of 26 reviews met the inclusion criteria, covering a range of chronic conditions and a range of outcomes. Seven reviews were of moderate or high quality and focused on self-management in persons with stroke, chronic pain, and psychiatric disorders with psychotic features. Self-management interventions were found to have positive effects on quality of life, self-efficacy, hope, reduction of disability, pain, relapse and rehospitalization rates, psychiatric symptoms, and occupational and social functioning. Findings are encouraging with regard to the effectiveness of self-management interventions in patients with symptoms similar to those of TBI. However, reviews did not address adaptation of self-management interventions for those with cognitive deficits or for populations with greater vulnerabilities, such as low education and older adults. Adaptations for TBI and its intersection with these special groups may be needed.

Diagnostic value of D-dimer in differentiating multisystem inflammatory syndrome in Children (MIS-C) from Kawasaki disease: systematic literature review and meta-analysis.

Coronavirus disease 2019 (COVID-19) is frequently associated with thrombo inflammation, which can predispose to developing of life-threatening conditions in children such as the multisystem inflammatory syndrome (MIS-C) and Kawasaki disease. Because of the consistent overlap in pathogenesis and symptoms, identifying laboratory tests that may aid in the differential diagnosis of these pathologies becomes crucial. We performed an electronic search in PubMed, Web of Science and Scopus, without date or language restrictions, to identify all possible studies reporting D-dimer values in separate cohorts of children with MIS-C or Kawasaki disease. Three multicenter cohort studies were included in our analysis, totaling 487 patients (270 with MIS-C and 217 with Kawasaki disease). In this meta-analysis, significantly higher D-dimer values were found in MIS-C compared to Kawasaki disease in all three studies, yielding an SMD of 1.5 (95 % CI, 1.3-1.7) mg/L. Thus, very high D-dimer values early in the course of disease should raise the clinical suspicion of MIS-C rather than Kawasaki disease. Further studies should be planned to identify harmonized D-dimer diagnostic thresholds that may help discriminate these conditions.

PCOS stratification for precision diagnostics and treatment.

Globally, polycystic ovarian syndrome (PCOS) affects approximately 10% of fertile women, leading to great health and economic burden. PCOS is a heterogenous illness that can cause infertility, irregular menstrual cycles, acne, and hirsutism, among other symptoms. The clinical diagnosis is primarily a diagnosis of exclusion if one or more of the three primary symptoms, namely, oligo- or anovulation, hyperandrogenism, and polycystic ovarian morphology, are present. Obesity and PCOS are often coexisting disorders that may be bidirectionally causally related. Phenotypic heterogeneity throughout the reproductive lifespan, such as the overlap of PCOS symptoms with regular fluctuations in a woman's menstrual cycle and metabolism during the menarche and menopausal transition, further complicates diagnosis. PCOS etiology is mostly unknown and complex, likely due to the fact that it is a group of disorders with overlapping metabolic and reproductive problems. Evidence-based, common, standardized guidelines for PCOS diagnosis and treatment are urgently needed. Genomics and clinical data from populations across diverse ages and ethnicities are urgently needed to build efficient machine learning models for the stratification of PCOS. PCOS subtype-specific strategies for early screening, an accurate diagnosis, and management throughout life will optimize healthcare resources and reduce unnecessary testing. This will pave the way for women to be able to take the best possible care of their own health using the latest clinical expertise combined with their unique needs and preferences.

Translation of the nine item avoidant/restrictive food intake disorder screen (NIAS) questionnaire in French (NIAS-Fr).

The Nine Item Avoidant/Restrictive Food Intake Disorder (ARFID) Screen (NIAS) questionnaire is originally available in English. Given the significant overlap of ARFID-like symptoms in gastrointestinal (GI) diseases, ARFID screening becomes crucial in these patient populations. Consequently, the translation of the NIAS questionnaire into French is necessary for its utilization in French-speaking countries. Clinical experts in neuro-gastroenterology and dietetics from four medical centres in two French-speaking countries (France and Belgium) took part in a well-structured questionnaire translation procedure. This process involved six steps before final approval: translation from English to French, backward translation, comparison between the original and retranslated versions, testing the translated version on patients, making corrections based on patient feedback, and testing the corrected version on an additional sample of patients. The NIAS questionnaire in French (NIAS-Fr) was tested on 18 outpatients across the involved centres. For the majority of native French-speaking patients, the translated questionnaire was well understood and clear. After incorporating two relevant modifications suggested by the patients, the translated questionnaire was approved through testing on an additional sample of patients. The involvement of two French-speaking countries was crucial for the harmonization and cultural adaptation of the questionnaire. As a result, the NIAS-Fr is now available for use in 54 French-speaking countries, serving approximately 321 million French speakers across five continents for screening ARFID, for both clinical and research purposes.

Cholangiocarcinoma Insights: Established Foundations and Cutting-Edge Innovations from Dr. James Cleary’s Pioneering Research

This paper provides insights into the conventional understanding of biliary tract malignancies, with a specific focus on cholangiocarcinoma (CCA). We then delve into the groundbreaking ideas presented by Dr. James Cleary. CCA, originating from biliary tree cells, manifests diverse subtypes contingent upon anatomical localization and differentiation status. These variants exhibit discrete genetic aberrations, yielding disparate clinical phenotypes and therapeutic modalities. Intrahepatic, perihilar, and distal CCAs intricately involve distinct segments of the biliary tree, further categorized as well-differentiated, moderately differentiated, or poorly differentiated adenocarcinomas based on their histological differentiation. Understanding the etiological factors contributing to CCA development assumes paramount importance. Stratifying these factors into two groups, those unrelated to fluke infestations (e.g., viral hepatitis and fatty liver conditions) and those associated with fluke infestations (e.g., chronic liver inflammation), facilitates predictive modeling. The epidemiology of CCA exhibits global variability, with Southeast Asia notably displaying higher incidences attributed primarily to liver fluke infestations. Jaundice resulting from bile duct obstruction constitutes a prevalent clinical manifestation of CCA, alongside symptoms like malaise, weight loss, and abdominal pain. Diagnostic challenges arise due to the symptomatic overlap with other biliary disorders. Employing comprehensive liver function tests and imaging modalities such as computed tomography assumes a pivotal role in ensuring accurate diagnosis and staging. However, the definitive confirmation of CCA necessitates a biopsy. Treatment modalities, predominantly encompassing surgical resection and radiation therapy, hold curative potential, although a considerable subset of patients is deemed unresectable upon exploration. Challenges intensify, particularly in cases classified as cancer of unknown origin, underscoring the imperative for early intervention. Advancements in genomic sequencing have revolutionized precision medicine in CCA. Distinct genomic markers, including fibroblast growth factor receptor 2 (FGFR2) alterations and isocitrate dehydrogenase 1 (IDH1) mutations, have emerged as promising therapeutic targets. FGFR2 alterations, encompassing mutations and rearrangements, play pivotal roles in oncogenesis, with FGFR inhibitors demonstrating promise despite identified resistance mechanisms. Similarly, IDH1 inhibitors face challenges with resistance, despite encouraging early clinical trial results, prompting exploration of novel irreversible inhibitors. Dr. James Cleary’s illuminating discourse underscores the significance of diverse FGFR2 alterations and the potential of IDH1 inhibition in reshaping the treatment landscape for CCA. These findings unveil critical avenues for targeted therapeutic interventions, emphasizing the critical need for ongoing research to optimize outcomes in this challenging cancer subtype, incorporating innovative insights from Dr. Cleary.

Isolated reversible involvement of bilateral internal capsule on magnetic resonance imaging in case of acute hypoglycemic encephalopathy: A case report

Hypoglycemic encephalopathy (HE) is a metabolic disorder that can lead to morbid manifestations including motor deficits, transient memory loss, persistent vegetative state, and even deep coma. Due to potential clinical symptom overlap with acute stroke, accurate diagnosis is very crucial. Besides the patient’s medical history and routine blood investigations, magnetic resonance imaging (MRI), particularly diffusion-weighted imaging (DWI), plays a valuable role in diagnosing HE. We present a case of acute hypoglycemia with reversible changes in the bilateral internal capsule seen on DWI-MRI.

Are We Measuring ADHD or Anxiety? Examining the Factor Structure and Discriminant Validity of the Adult ADHD Self-Report Scale in an Adult Anxiety Disorder Population.

Adults with clinical anxiety have significant symptom overlap and above average rates of attention-deficit/hyperactivity disorder (ADHD). Despite this, ADHD remains a vastly under-detected disorder among this population, indicating the need for a screener with well-understood symptom dimensions and good discriminant validity. The current study compared competing models of ADHD as well as discriminant properties of self-reported ADHD symptoms as measured by the Adult ADHD Self-Report Scale (ASRS-v1.1) in 618 adults with clinical anxiety. A three-factor correlated model of Inattention, Impulsivity, and Hyperactivity, with the movement of one item, talks excessively, to a factor of Impulsivity from Hyperactivity fit better than the one-factor, two-factor, and traditional three-factor models of ADHD. Discriminant properties of the screener were fair to good against measures of clinical anxiety and distress; however, some items within the Hyperactivity factor (e.g., difficulty relaxing; feeling driven by a motor) loaded more strongly onto factors of clinical anxiety than ADHD when measures were pooled together. These results suggest that clinicians making differential diagnoses between adult ADHD and anxiety or related disorders should look for evidence of ADHD beyond the overlapping symptoms, particularly for those within the Hyperactivity factor.

Investigating the Neural Correlates of Partial PTSD: A Systematic Literature Review

Background: Partial PTSD (PPTSD) describes the presence of clinically significant trauma symptoms that do not meet the diagnostic criteria of posttraumatic stress disorder (PTSD). This study examines the neural correlates of PPTSD by drawing a symptomatic overlap between Generalised Anxiety Disorder (GAD) and Major Depressive Disorder (MDD). Methods: Literature databases included Google Scholar, PubMed and Mendeley. Following a preliminary screening, six exclusion criteria (Inadequate data, No neural correlates, Comorbidities, Lifetime prevalence of PPTSD, Inconsistent results, Age criteria) were applied. The reference lists of all selected papers were also screened. Results: A total of sixty-three papers were reviewed and critically appraised using the Joanna Briggs Inventory. This study examined the neural networks and dysregulations associated with Partial Post-Traumatic Stress Disorder (PPTSD), shedding light on the intricate interplay of brain regions contributing to the spectrum of PPTSD symptoms. Five key neural networks and regions that play a central role in PPTSD were identified. The Default Mode Network (DMN), Salience Network (SN), Executive Control Network (ECN), Emotion Regulation Network, and the Hypothalamic-Pituitary-Adrenal (HPA) Axis. Along with which the role of the hippocampus in conjunction with these networks was also highlighted. Conclusion: These findings emphasise the complex neural landscape underlying PPTSD, with the interplay between these networks and regions contributing to a wide range of neural dynamics. Further research, including neuroimaging and neuromodulatory studies, is needed to explore these interactions in greater depth. Understanding these neural mechanisms is crucial for developing effective interventions and treatments for PPTSD and related disorders.

The Role of Emotion Dysregulation in Heightened Alcohol Craving Related to Posttraumatic Stress Disorder and Depression Symptoms

Objective: Compared to their male counterparts, women with alcohol use disorders (AUD) alone and those with symptoms of co-occurring emotional disorders (posttraumatic stress disorder, PTSD, and depression) are particularly likely to have increased alcohol craving in response to negative affect and daily stressors. Emotion dysregulation is one transdiagnostic construct that may underlie heightened craving in response to stress within this population. In a secondary data analysis, the current study examined emotion dysregulation as a mediator of the associations of posttraumatic stress symptoms (PTSS) and depression symptoms with heightened stress-induced alcohol craving, as measured in the lab. Given the overlap in symptoms, the relative associations of PTSD and depression symptom clusters with stress-induced craving were explored. Method: 50 women Veterans (84% White, 88% Non-Hispanic, Mage=45.68) attended two in-lab sessions. Self-report measures of emotion dysregulation, PTSD, and depression symptoms were administered at baseline. During session two, participants reported on alcohol craving and negative affect at baseline and again after a personalized stress induction procedure. Results: Emotion dysregulation mediated the association of greater PTSS with heightened stress-induced craving, although emotion dysregulation was not a mediator of the association between depression and stress-induced craving. Greater alcohol craving after the stress induction was positively associated with cognitive-affective symptoms in PTSD and depression (and not with other symptom clusters of these diagnoses, e.g., avoidance, somatic-vegetative symptoms). Conclusions: Emotion dysregulation may be a transdiagnostic factor that helps to explain greater alcohol cravings and drinking in stressful contexts among women Veterans with heightened symptoms of co-occurring emotional disorders.

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach.

Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders. In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking. Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals. The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages. This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.

Extensive Expertise in Endocrinology: Adrenal crisis in assisted reproduction and pregnancy.

Appropriate management of adrenal insufficiency in pregnancy is challenging due to the rarity of both primary, secondary and tertiary forms of the disease as well as the lack of evidence-based recommendations to guide clinicians to glucocorticoid and sometimes also mineralocorticoid dosage adjustments. Debut of adrenal insufficiency during pregnancy requires immediate diagnosis as it can lead to adrenal crisis, intrauterine growth restriction and foetal demise. Diagnosis is difficult due to overlap of symptoms of adrenal insufficiency and its crisis with those of pregnancy. Adrenal insufficiency in stable replacement treatment needs careful monitoring during pregnancy to adapt to the physiological changes in the requirement of the adrenal hormones. This is hampered because the diagnostic threshold of most adrenocortical hormones is not applicable during pregnancy. The frequent use of assisted reproduction technology with controlled ovarian hyperstimulation in these patient groups with disease induced low fertility has created an unrecognised risk of adrenal crises due to accelerated oestrogen stimulation with increased risk of even be life-threatening complications for both the woman and foetus. The area needs consensus recommendations between gynaecologists and endocrinologists in tertiary referral centres to alleviate such increased gestational risk. Patient and partner education, use of the EU emergency card for management of adrenal crises can also contribute to better pregnancy outcomes. There is a strong need of more research on e.g. improvement of glucocorticoid replacement as well as crisis management treatment, and biomarkers for treatment optimisation in this field, which suffers from the rare nature of the diseases and poor funding.

Reverse causation bias: A simulation study comparing first- and second-line treatments with an overlap of symptoms between treatment indication and studied outcome.

Reverse causation is a challenge in many drug-cancer associations, where the cancer symptoms are potentially mistaken for drug indication symptoms. However, tools to assess the magnitude of this type of bias are currently lacking. We used a simulation-based approach to investigate the impact of reverse causation on the association between the use of topical tacrolimus and cutaneous T-cell lymphoma (CTCL) in a multinational, population-based study using topical corticosteroids (TCS) as comparator. We used a multistate model to simulate patients' use over time of a first- (TCS) and second-line treatment (topical tacrolimus), onset of atopic dermatitis (indication for drugs) and CTCL (the studied outcome). We simulated different scenarios to mimic real-life use of the two treatments. In all scenarios, it was assumed that there was no causal effect of the first- or second-line treatment on the occurrence of CTCL. Simulated data were analysed using Cox proportional hazards models. The simulated hazard ratios (HRs) of CTCL for patients treated with tacrolimus vs. TCS were consistently above 1 in all 9 settings in the main scenario. In our main analysis, we observed a median HR of 3.09 with 95% of the observed values between 2.11 and 4.69. We found substantial reverse causation bias in the simulated CTCL risk estimates for patients treated with tacrolimus vs. TCS. Reverse causation bias may result in a false positive association between the second-line treatment and the studied outcome, and this simulation-based framework can be adapted to quantify the potential reverse causation bias.

Unveiling Transitions in Disease States: Study of Depressive and Anxiety Symptom Networks over Time

Background. Major depressive disorder (MDD) and anxiety disorders (AD) have high degrees of comorbidity and show great overlap in symptoms. The analysis of covarying depressive‐ and anxiety symptoms in longitudinal, sparse data panels has received limited attention. Dynamic time warping (DTW) analysis may help to provide new insights into symptom network properties based on diagnostic‐ and disease‐state stability criteria. Materials and Methods. In the Netherlands Study of Depression and Anxiety depressive‐, anxiety‐, and worry symptoms were assessed four or five times over the course of 9 years using self‐report questionnaires. The sample included 1,649 participants at baseline, comprising controls (n = 360), AD patients (n = 158), MDD patients (n = 265), and comorbid AD–MDD patients (n = 866). With DTW, 1,649 distance matrices were calculated, which yielded symptom networks and enabling comparison of network densities among subgroups. Results. The mean age of the sample was 41.5 years (standard deviations, 13.2), of whom 66.4% were female. The largest distance was between worry symptoms and physiological arousal symptoms, implicating the most dissimilar dynamics over time. The network density in the groups, from lowest to highest, followed the order: controls, AD, MDD, and comorbid AD–MDD. The comorbid group showed strongly connected mood and cognitive symptoms, which contrasted with the more strongly connected somatic and arousal symptoms in the AD and MDD groups. Groups that showed more transitions in disease states over follow‐up, regardless of the diagnoses, had the highest network density compared to more stable states of health or disease (beta for quadratic term = −0.095; P < 0.001). Conclusions. Symptom networks over time can be visualized by applying DTW methods on sparse panel data. Network density was highest in patients with comorbid anxiety and depressive disorders and those with more instability in disease states, suggesting that a stronger internal connectivity may facilitate “critical transitions” within the complex systems framework.

Sputum proteomics in lung disorders.

Lung diseases affect pulmonary and respiratory function and are caused by bacterial viral and fungal infection as well as environmental factors. Unfortunately, symptom overlap between various pulmonary diseases often prevents clear differentiation and uncertain diagnosis. Accordingly, identification of specific markers of inflammatory activity in early disease stage could potential unveil the intrinsic molecular mechanisms of the underlying pathology. Proteomic studies aimed at understanding the genetic/environmental contributions to the development and progression of lung diseases represent a promising approach for diagnosis and treatment. The fluid phase of sputum represents a rich protein source and is frequently used in these studies. This chapter addresses causes of lung disorders, sputum composition, collection and processing as well as the clinical significance and challenges associated with the presence of interfering factors. Basics of proteomics and mass spectrometry are also described, together with the analytical approaches to investigate the sputum proteome. Finally, we explore the application of sputum proteomics in severe lung disorders including COVID-19 infection, chronic obstructive pulmonary disease, asthma, cystic fibrosis, lung cancer and tuberculosis.

Biomarkers of heart failure: advances in omics studies.

Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.

Panic disorder in epilepsy

A 51-year-old woman showed structural epilepsy following an atypical, nontraumatic intracranial hemorrhage in the right frontal area. Despite successful seizure control with lamotrigine, she developed severe morning anxiety and panic attacks, leading to agoraphobia, social withdrawal, and psychogenic nonepileptic seizures. Neuropsychiatric and psychological assessments confirmed an anxiety disorder with no significant symptoms of depression. The patient received various psychopharmacological treatments with limited success. This case report illustrates that managing panic disorder in patients with structural epilepsy requires a comprehensive treatment approach that includes pharmacotherapy and psychotherapy. Differential diagnosis and accurate treatment are crucial because of the symptom overlap between panic attacks and peri-ictal fear. Screenings instruments such as the Panic and Agoraphobia Scale (PAS) can aid in assessing anxiety-related symptoms. First-line pharmacotherapy with selective serotonin reuptake inhibitors, especially sertraline, or venlafaxine can effectively reduce panic attacks and can be recommended in patients with epilepsy. Psychotherapy, particularly cognitive-behavioral therapy, is the treatment of choice. Referral to a psychiatrist is indicated when symptoms are severe or refractory to treatment.

Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

The Patient Health Questionnaire 9 (PHQ-9) is the current standard outpatient screening tool for measuring and tracking the nine symptoms of major depressive disorder (MDD). While the PHQ-9 was originally conceptualized as a unidimensional measure, it has become clear that MDD is not a monolithic construct, as evidenced by high comorbidities with other theoretically distinct diagnoses and common symptom overlap between depression and other diagnoses. Therefore, identifying reliable and temporally stable subfactors of depressive symptoms could allow research and care to be tailored to different depression phenotypes. This study improved on previous factor analysis studies of the PHQ-9 by leveraging samples that were clinical (participants with depression only), large (N = 1483 depressed individuals in total), longitudinal (up to 5 years), and from three diverse (matching racial distribution of the United States) datasets. By refraining from assuming the number of factors or item loadings a priori, and thus utilizing a solely data-driven approach, we identified a ranked list of best-fitting models, with the parsimonious one achieving good model fit across studies at most timepoints (average TLI >= 0.90). This model categorizes the PHQ-9 items into four factors: (1) Affective (Anhedonia + Depressed Mood), (2) Somatic (Sleep + Fatigue + Appetite), (3) Internalizing (Worth/Guilt + Suicidality), (4) Sensorimotor (Concentration + Psychomotor), which may be used to further precision psychiatry by testing factor-specific interventions in research and clinical settings.

Pre-analytical properties of different respiratory viruses for PCR-based detection: Comparative analysis of sampling devices and sample stabilization solutions

After the decline of the COVID-19 pandemic, health systems were challenged by the simultaneous prevalence of different respiratory viruses causing a wide overlap in symptoms. This increased the demand for multi-virus diagnostic tests which require suitable pre-analytical workflow solutions in order to receive valid diagnostic results. In this context, the effects of specimen storage duration and temperature on the RNA/DNA copy number stability of influenza A/B, RSV A/B, SARS-CoV-2 and adenovirus were examined for four commercially available transport swab systems and saliva collection devices. The respiratory viruses were more stable in the saliva collection devices than in the transport swab systems when stored at RT or 37 °C for up to 96 h. Moreover, no differences between viral nucleic acid stability of enveloped and non-enveloped viruses were observed. The infectivity of all enveloped viruses could be inactivated by the saliva collection device from PreAnalytiX. The Norgen saliva device completely inactivated influenza A/B, while RSV A/B were partially inactivated. The non-enveloped adenovirus was inactivated by a reduction factor of 10E+ 4 in both saliva collection devices. All respiratory viruses remained infectious in the transport swab systems. Two possible transport medium additives were tested which inactivated or strongly reduced viral replication of tested enveloped viruses but had no effect on the non-enveloped adenovirus. Finally the implementation of multi-target detection procedures involving a direct amplification approach was successfully tested by spike-in of all enveloped viruses simultaneously into transport swab systems. This fast and reproducible setup presents a valuable solution for future implementations in multi-virus testing strategies.

Imaging approach to prosthetic joint infection.

The diagnosis of prosthetic joint infection (PJI) remains challenging, despite multiple available laboratory tests for both serum and synovial fluid analysis. The clinical symptoms of PJI are not always characteristic, particularly in the chronic phase, and there is often significant overlap in symptoms with non-infectious forms of arthroplasty failure. Further exacerbating this challenge is lack of a universally accepted definition for PJI, with publications from multiple professional societies citing different diagnostic criteria. While not included in many of the major societies' guidelines for diagnosis of PJI, diagnostic imaging can play an important role in the workup of suspected PJI. In this article, we will review an approach to diagnostic imaging modalities (radiography, ultrasound, CT, MRI) in the workup of suspected PJI, with special attention to the limitations and benefits of each modality. We will also discuss the role that image-guided interventions play in the workup of these patients, through ultrasound and fluoroscopically guided joint aspirations. While there is no standard imaging algorithm that can universally applied to all patients with suspected PJI, we will discuss a general approach to diagnostic imaging and image-guided intervention in this clinical scenario.

Targeted plasma eQTL and pQTL to identify potential novel biomarkers for Alzheimer’s Disease in African Americans

AbstractBackgroundGiven the overlap of symptoms between Alzheimer’s disease (AD) and other dementias, it is essential to develop widely accessible biomarkers that will ensure an accurate diagnosis of AD to enable its proper treatment. African Americans (AA) are twice more likely to develop AD than non‐Hispanic whites (NHW) and yet they remain underrepresented in AD research. The few studies conducted in AA suggest that there are different factors that influence AD in this population; therefore, it is possible that different biomarkers and/or therapies may be needed to effectively treat AD in AA. In this study we are testing the hypothesis that genetic variants that show association with AD‐risk in AA and with plasma levels of transcript and proteins may serve as AD biomarkers which are more accessible and less costly than cerebrospinal fluid or neuroimaging biomarkers.MethodsIn 189 AD cases and 183 cognitively unimpaired (CU) AA participants of the Florida Consortium of African American Alzheimer’s Disease Studies (FCA3DS), we performed targeted DNA sequencing of 10 AD‐associated loci. Genetic variants were tested for association with plasma transcripts and plasma total tau protein levels previously measured in this cohort using a nanoString® custom panel (Reddy J, et al. 2022) and Simoa assays (Deniz K, et al. 2021), respectively. In receiver operating characteristic (ROC) analyses, the most significant expression/protein quantitative trait loci (eQTL/pQTL) were added sequentially to a base model that included only age and sex to identify eQTL/pQTL that improved the accuracy to correctly classify AD cases vs. CU.ResultsOf the 5112 variants identified in the targeted sequencing, 71 were nominally associated (p<0.05) with plasma transcript/protein levels and with AD‐risk. ROC analysis of age, sex, eQTL/pQTL in APOE, ABCA7, AKAP9, EPHA1, SORL1, and TLR4, and plasma levels of APP, ABCA7, AKAP9, CD14, CLU, and APOE‐ɛ4 dosage achieved 77.6% area under the curve (AUC) to discriminate AD vs. CU, a 19.9% improvement over a model that only included age and sex.ConclusionsPlasma transcript levels and eQTL/pQTL are promising diagnostic biomarkers for AD that may improve accessibility and reduce costs for a more accurate diagnosis of AD.