Lues maligna represents a rare form secondary syphilis and is also known as 'malignant syphilis' or 'ulceronodular syphilis'. This clinical entity is predominantly found in immunodeficient patients such as patients with HIV or AIDS. The patient presented here suffered from unspecific symptoms such as painful joints, headache, flu-like symptoms and a disseminated exanthema presenting with ulcerating nodules for 1 week. He further reported a 7 weeks history of painless ulcer, involving his external genitals and anus. Unsafe sexual contacts were strictly denied first, but as serological investigation could prove active syphilis and HIV infection, the patient finally stated promiscuous unprotected homosexual contacts in the past. He was treated with penicillin G intravenously three times daily. The unspecific flu-like symptoms disappeared quickly within several days, all skin lesions healed, partly with scars after 2 weeks.
Infants are born into a world filled with microbes and must adapt without undue immune response while exploiting the microbiota's ability to produce otherwise unavailable nutrients. The process by which humans and microbes establish this relationship has only recently begun to be studied with the aid of genomic methods. Nearly half of all pregnant women receive antibiotics during gestation to prevent maternal and neonatal infection. Though this has been largely successful in reducing early-onset sepsis, we have yet to understand the long-term consequences of antibiotic administration during gestation to developing infants. Studies involving antibiotic use in infants suggest that dysbiosis during this period is associated with increased obesity, allergy, autoimmunity, and chronic diseases in adulthood, however, research around the limited doses of intravenous antibiotics used for intrapartum prophylaxis is limited. In this mini review, we focused on the state of the science regarding the effects of intrapartum antibiotic prophylaxis on the newborn microbial colonization process. Although, the literature indicates that there is wide variety in the specific bacteria that colonize infants from birth, limited parenteral antibiotic administration prior to delivery consistently affects the microbiota of infants by decreasing bacteria in the phylum Bacteroidetes and increasing bacteria in the phylum Proteobacteria, thus altering the normal pattern of colonization that infants experience. Delivery by cesarean section and formula feeding magnify and prolong this effect. Our mini review shows that the impact of intravenous antibiotic administration during gestation has on early colonization, growth, or immune programming in the developing offspring has not been well studied in human or animal models.
PurposeThe main objective of this study was to utilize an artificial neural network in an exploratory fashion to predict self‐management behaviors based on reported symptoms in a sample of stable patients with chronic obstructive pulmonary disease (COPD).Design and MethodsPatient symptom data were collected over 21 consecutive days. Symptoms included distress due to cough, chest tightness, distress due to mucus, dyspnea with activity, dyspnea at rest, and fatigue. Self‐management abilities were measured and recorded periodically throughout the study period and were the dependent variable for these analyses. Self‐management ability scores were broken into three equal tertiles to signify low, medium, and high self‐management abilities. Data were entered into a simple artificial neural network using a three‐layer model. Accuracy of the neural network model was calculated in a series of three models that respectively used 7, 14, and 21 days of symptom data as input (independent variables). Symptom data were used to determine if the model could accurately classify participants into their respective self‐management ability tertiles (low, medium, or high scores). Through analysis of synaptic weights, or the strength or amplitude of a connection between variables and parts of the neural network, the most important variables in classifying self‐management abilities could be illuminated and served as another outcome in this study.FindingsThe artificial neural network was able to predict self‐management ability with 93.8% accuracy if 21 days of symptom data were included. The neural network performed best when predicting the low and high self‐management abilities but struggled in predicting those with medium scores. By analyzing the synaptic weights, the most important variables determining self‐management abilities were gender, followed by chest tightness, age, cough, breathlessness during activity, fatigue, breathlessness at rest, and phlegm.ConclusionsThe results of this study suggest that self‐management abilities could potentially be predicted through understanding and reporting of patient’s symptoms and use of an artificial neural network. Future research is clearly needed to expand on these findings.Clinical RelevanceSymptom presentation in chronically ill patients directly impacts self‐management behaviors. Patients with COPD experience a number of symptoms that have the potential to impact their ability to manage their chronic disease, and artificial neural networks may help clinicians identify patients at risk for poor self‐management abilities.
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