A body of evidence shows that HIV and SARS-CoV-2 are able to activate inflammatory pathways, acute in the case of SARS-CoV-2, chronic in the case of HIV, while the comorbidities seem to represent, in the first case, a contributory cause, in the second an effect of the virus-induced damage.
 A body of evidence shows that HIV and SARS-CoV-2 are able to activate inflammatory pathways, acute in the case of SARS-CoV-2, chronic in the case of HIV, while the comorbidities seem to represent, in the first case, a contributory cause, in the second an effect of the virus-induced damage.
  Neuronal ceroid lipofuscinosis type 2 (CLN2 disease) is a rare pediatric neurodegenerative condition, which is usually fatal by mid-adolescence. Seizures are one of the most common early symptoms of CLN2 disease, but patients often experience language deficits, movement disorders, and behavioral problems. Diagnosis of CLN2 disease is challenging (particularly when differentiating between early-onset developmental, metabolic, or epileptic syndromes), and diagnostic delays often overlap with rapid disease progression. An enzyme replacement therapy (cerliponase alfa) is now available, adding CLN2 disease to the list of potentially treatable disorders requiring a prompt diagnosis.
 
  Although advances in enzymatic activity testing and genetic testing have facilitated diagnoses of CLN2 disease, our review highlights the presenting symptoms that are vital in directing clinicians to perform appropriate tests or seek expert opinion. We also describe common diagnostic challenges and some potential misdiagnoses that may occur during differential diagnosis.
 
  An awareness of CLN2 disease as a potentially treatable disorder and increased understanding of the key presenting symptoms can support selection of appropriate tests and prompt diagnosis. The available enzyme replacement therapy heralds an even greater imperative for early diagnosis, and for clinicians to direct patients to appropriate diagnostic pathways.
 An awareness of CLN2 disease as a potentially treatable disorder and increased understanding of the key presenting symptoms can support selection of appropriate tests and prompt diagnosis. The available enzyme replacement therapy heralds an even greater imperative for early diagnosis, and for clinicians to direct patients to appropriate diagnostic pathways.
  This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making.
 
  Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013-2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models.
 
  In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models.
 
  A perfect prediction model may help improve clinicians' decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.
 A perfect prediction model may help improve clinicians' decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.Background Liver cancer (LC) is a serious late complication after the Fontan operation. However, the incidence, predictors, and prognosis remain unknown. The purpose of our study was to determine these clinical characteristics. Methods and Results We assessed liver function in 339 consecutive patients who had undergone the Fontan procedure from 2005 to 2019. LC was histologically diagnosed in 10 patients after a median period of 2.9 years (range 0.3-13.8; median age 29.9 years [range 14.4-41.5 years]; overall median post-Fontan procedure follow-up 25.6 years [range 13-32.1 years]), and the annual incidence was 0.89%. Over the entire post-Fontan follow-up period, the annual incidences of new-onset LC in the second, third, and fourth decades were 0.14%, 0.43%, and 8.83%, respectively. The patients with LC had longer follow-up periods, higher levels of AFP (α-fetoprotein), and higher values of liver fibrosis indices (P less then 0.01-0.0001). Moreover, all indices were predictive of new-onset LC (P less then 0.01-0.0001). The LC treatments were surgical resection (n=3), transarterial chemoembolization (n=3), radiofrequency ablation (n=2), and hospice care (n=2). During a median follow-up of 9.4 months, 4 patients died; the survival rate at 1 year was 60%, and it was better among asymptomatic patients (P less then 0.01). Conclusions The LC incidence rapidly increased ≥30 years after the Fontan procedure, and liver fibrosis indices and AFP were predictive of new-onset LC. These LC-predictive markers should be monitored closely and mandatorily for early LC detection and better prognosis.This special issue of Evaluation and The Health Professions focuses on applications and extensions of latent transition analysis (LTA), a longitudinal parameterization of the latent class (LC) model. LTA is a model of discrete or qualitative change over time among potentially complex states (e.g., patterns of recent drug use or abuse experiences), commonly referred to as latent classes, latent profiles, or latent statuses.  https://www.selleckchem.com/  Frequently, researchers will distinguish the term "classes" for cross-sectional studies and with LTA use "statuses" to indicate the concept of "dynamic change" with individuals shifting in their response patterns and associated statuses over time. It goes without saying that LTA models are underutilized, although quite flexible. This special issue showcases articles that apply LTA and extend the capabilities of this approach to modeling discrete change in new ways.