Urethral Discharge as A Novel Manifestation of Urinary Tract Infection in Children ≤24 Months Old

Background: Children with urinary tract infections (UTIs) are prone to kidney scarring if they are not treated promptly; however, ambiguous symptoms before fever onset makes the early detection of UTIs difficult. Our study aimed to identify urethral discharge as an early manifestation in children with UTI. Methods: This study enrolled 678 children younger than 24 months with paired urinalysis and culture performed between 2015 and 2021; 544 children were diagnosed with UTIs. Clinical symptoms, urinalysis, and paired urine culture results were compared. Results: Urethral discharge was observed in 5.1% of children with UTI and yielded a specificity of 92.5% for diagnosing UTI. Children with urethral discharge had a less severe UTI course, furthermore, nine of them received antibiotics before fever occurred and seven of them were free of fever during UTI course. Urethral discharge was associated with alkalotic urine and Klebsiella pneumonia infection.Conclusions: Urethral discharge is an early symptom in children with UTI; it may present before fever onset and help ensure prompt antibiotic intervention.Trial registration: Not applicable.

Opóźnienia w funkcjonowaniu językowym jako wczesny symptom zaburzeń w rozwoju dziecka1

Human linguistic development is constitutionally conditioned and is achieved through contact with adult language users. All children follow the same rules and stages in the development of speech. The knowledge of their course and consequences enables early recognition of deviations from the norm, which may be delayed speech development or a symptom of other, often serious developmental disorders. The analysis of the research results shows that the most common reason for parents’ seeking diagnosis and therapeutic support is an incorrect linguistic functioning of the child. Diagnostic procedures often end with the diagnosis of other developmental disorders in which linguistic retardation was an early symptom. Therefore, it is legitimate to increase social knowledge and sensitivity of parents and specialists in monitoring the linguistic development of children under 3 years of age.

A New Leaf Blight Disease Caused by Alternaria jacinthicola on Banana in China

A leaf blight disease with an incidence level of about 50% was found on Robusta banana in Guangdong province of China in September 2020. The early symptom appeared as pale gray to black brown, irregular, small, necrotic lesions mainly on the top 3–5 leaves. Severely infected leaves were withered and necrotic. Two representative fungus strains, strain L1 and strain L2, were isolated from affected banana leaves, and morphological and molecular identification analysis confirmed that the two fungi were both Alternaria jacinthicola. Many Alternaria species have been reported to cause wilting, decay, leaf blight and leaf spots on plants and lead to serious economic losses in their production, including A. alternata, causing leaf blight and leaf sport diseases on banana. The Koch’s postulates of A. jacinthicola causing the leaf blight disease was further fulfilled, which confirmed that it is the causal agent of this disease. To our knowledge, this is the first report of A. jacinthicola causing leaf blight on banana in China.

Investigating Sequential and Simultaneous Changes in Trajectories of Cognitive Decline and Depressive Symptoms

Background. The role of depression as risk factor or early symptom of cognitive decline and dementia is still debated. Exploiting longitudinal trajectories of memory recall in a large European sample, we sought to better understand the nature of simultaneous versus sequential changes in depressive symptoms alongside memory recall at older ages. Method. A total of 4,865 respondents to the SHARE survey, mean age at t1 61.5 years (SD = 7.53), completed the EURO-D depression scale and a delayed recall task across six waves spanning ~13 years. We applied k-means clustering to distinguish trajectories of depressive symptoms and delayed recall. Clusters indicating depressive and recall trajectories were included in logistic regressions to assess likelihood of parallel versus sequential change, controlling for age, gender, employment status and education. Results. Analyses revealed six distinct trajectories each for depressive symptoms and delayed recall. Visual inspections indicated that only declining recall trajectories showed increases in depressive symptoms, occurring simultaneously rather than sequentially. Using grouped declining recall trajectories as outcome, the low-increasing depressive symptoms trajectory was associated with cognitive decline (OR = 1.52 [1.11, 2.06]), whereas the stable-high depressive symptoms trajectory was associated with cognitive decline in respondents aged 60-69 years (OR = 1.78 [1.01, 3.16]). Discussion. Distinguishing trajectories in depression and recall incorporates longitudinal information able to further elucidate relationships between depression and cognition. While the findings suggest depression as a co-morbidity, attention needs to be given to a comparatively small high-stable depressive symptoms trajectory group with elevated risk of cognitive decline in their 60s.

458. A Machine Learning Approach Identifies Distinct Early-Symptom Cluster Phenotypes Which Correlate with Severe SARS-CoV-2 Outcomes

Background The novel coronavirus disease 2019 (COVID-19) pandemic remains a global challenge. Accurate COVID-19 prognosis remains an important aspect of clinical management. While many prognostic systems have been proposed, most are derived from analyses of individual symptoms or biomarkers. Here, we take a machine learning approach to first identify discrete clusters of early stage-symptoms which may delineate groups with distinct symptom phenotypes. We then sought to identify whether these groups correlate with subsequent disease severity. Methods The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study with data and biospecimens collected from nine military treatment facilities over 1 year of follow-up. Demographic and clinical characteristics were measured with interviews and electronic medical record review. Early symptoms by organ-domain were measured by FLU-PRO-plus surveys collected for 14 days post-enrollment, with surveys completed a median 14.5 (Interquartile Range, IQR = 13) days post-symptom onset. Using these FLU-PRO-plus responses, we applied principal component analysis followed by unsupervised machine learning algorithm k-means to identify groups with distinct clusters of symptoms. We then fit multivariate logistic regression models to determine how these early-symptom clusters correlated with hospitalization risk after controlling for age, sex, race, and obesity. Results Using SARS-CoV-2 positive participants (n = 1137) from the EPICC cohort (Figure 1), we transformed reported symptoms into domains and identified three groups of participants with distinct clusters of symptoms. Logistic regression demonstrated that cluster-2 was associated with an approximately three-fold increased odds [3.01 (95% CI: 2-4.52); P < 0.001] of hospitalization which remained significant after controlling for other factors [2.97 (95% CI: 1.88-4.69); P < 0.001]. (A) Baseline characteristics of SARS-CoV-2 positive participants. (B) Heatmap comparing FLU-PRO response in each participant. (C) Principal component analysis followed by k-means clustering identified three groups of participants. (D) Crude and adjusted association of identified cluster with hospitalization. Conclusion Our findings have identified three distinct groups with early-symptom phenotypes. With further validation of the clusters’ significance, this tool could be used to improve COVID-19 prognosis in a precision medicine framework and may assist in patient triaging and clinical decision-making. Disclaimer Disclosures David A. Lindholm, MD, American Board of Internal Medicine (Individual(s) Involved: Self): Member of Auxiliary R&D Infectious Disease Item-Writer Task Force. No financial support received. No exam questions will be disclosed ., Other Financial or Material Support Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work))

Acute Ischemic Stroke Evaluation and Treatment

Acute ischemic stroke is a neurologic emergency where an estimated 2 million neurons a minute are lost secondary to ischemia. Treatments of acute stroke are directed at early revascularization of the occluded vessel and to preserve neuronal death and improve collateral flow. Treatments are time sensitive, an aspect that places great importance on early symptom recognition, correct diagnosis, and clinical management. In acute ischemic stroke, in short, “time is brain.”

NOTCH2NLC-related disorders: the widening spectrum and genotype–phenotype correlation

GGC repeat expansion in the 5′ untranslated region of NOTCH2NLC is the most common causative factor in neuronal intranuclear inclusion disease (NIID) in Asians. Such expanded GGC repeats have been identified in patients with leukoencephalopathy, essential tremor (ET), multiple system atrophy, Parkinson’s disease (PD), amyotrophic lateral sclerosis and oculopharyngodistal myopathy (OPDM). Herein, we review the recently reported NOTCH2NLC-related disorders and potential disease-causing mechanisms. We found that visual abnormalities may be NOTCH2NLC-specific and should be investigated in other patients with NOTCH2NLC mutations. NOTCH2NLC GGC repeat expansion was rarely identified in patients of European ancestry, whereas the actual prevalence of the expansion in European patients may be potentially higher than reported, and the CGG repeats in LRP12/GIPC1 are suggested to be screened in European patients with NIID. The repeat size and interruptions in NOTCH2NLC GGC expansion confer pleiotropic effects on clinical phenotype, a pure and stable ET phenotype may be an early symptom of NIID, and GGC repeats in NOTCH2NLC possibly give rise to ET. An association may also exist between intermediate-length NOTCH2NLC GGC repeat expansion and patients affected by PD and ET. NOTCH2NLC-OPDM highly resembles NOTCH2NLC-NIID, the two disorders may be the variations of a single neurodegenerative disease, and there may be a disease-causing upper limit in size of GGC repeats in NOTCH2NLC, repeats over which may be non-pathogenic. The haploinsufficiency of NOTCH2NLC may not be primarily involved in NOTCH2NLC-related disorders and a toxic gain-of-function mechanism possibly drives the pathogenesis of neurodegeneration in patients with NOTCH2NLC-associated disorders.