Laboratory, imaging, cystoscopic, and pathological findings of the eight patients presenting with <i>Schistosoma haematobium</i> infection.

<p>Laboratory, imaging, cystoscopic, and pathological findings of the eight patients presenting with <i>Schistosoma haematobium</i> infection.</p

Virologic response in the global population (A) and in the subgroup that has reached week 48 (B).

<p>Proportion of viremia ranks over time, and median viral decay (range) and trend in viremic subjects.</p

Representative ultrasonographic, cystoscopic, and pathological findings in a patient with chronic schistosomiasis presenting to the ED.

<p>(A) Ultrasonograph of the bladder shows a vegetation associated with bladder wall thickening; fine and diffuse calcifications are also visible. (B) Cystoscopic image obtained after transurethral resection shows an adult <i>S</i>. <i>haematobium</i> worm. (C) An area of marked, diffuse, acute inflammation with numerous eosinophils surrounds some calcified <i>S</i>. <i>haematobium</i> eggs; the parasite is also identified.</p

Absolute CD4+ T-cell count (i) and proportion of CD4+ T-cells (ii) in the global population and in the subgroup that reached week 48, median (range).

<p>Absolute CD4+ T-cell count (i) and proportion of CD4+ T-cells (ii) in the global population and in the subgroup that reached week 48, median (range).</p

Patients’ baseline disposition towards antiretrovirals was based on: a) the complexity of the former regimen; b) the frequency of single antivirals in the former regimens of the population; c) the complexity of drug resistance at baseline; d), e), f) the frequency of single baseline mutations in the population by reverse transcriptase, protease and integrase regions, respectively.

<p>ELV/c = elvitegravir/cobicistat, AZT = zidovudine, LPV = lopinavir, /r = boosted with ritonavir, ATV = atazanavir, MVC = maraviroc, NVP = nevirapine, EFV = efavirenz, ETV = etravirine, ABC = abacavir, RPV = rilpivirine, DRV = darunavir, 3TC = lamivudine, FTC = emtricitabine, TDF = tenofovir, RAL = raltegravir.</p

Relative hazard of time to FIB4 progression.

<p>Relative hazard of time to FIB4 progression.</p

Ordinal logistic regression model.

<p>Ordinal logistic regression model.</p

Characteristics of patients according to estimated tropism.

<p>Characteristics of patients according to estimated tropism.</p

Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project

Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48–72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis. In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project. Overall, 12,483 episodes of candidemia (1275; 10%) or bacteremia (11,208; 90%) were included in the analysis. A random forest classifier achieved the best diagnostic performance for candidemia, with sensitivity 0.98 and specificity 0.65 on the training set (true skill statistic [TSS] = 0.63) and sensitivity 0.74 and specificity 0.57 on the test set (TSS = 0.31). Then, the random classifier was trained in the subgroup of patients with available serum β-D-glucan (BDG) and procalcitonin (PCT) values by exploiting the feature ranking learned in the entire dataset. Although no statistically significant differences were observed from the performance measures obtained by employing BDG and PCT alone, the performance measures of the classifier that included the features selected in the entire dataset, plus BDG and PCT, were the highest in most cases. Random forest classifiers trained on large datasets of automatically extracted data have the potential to improve current diagnostic algorithms for candidemia. However, further development through implementation of automatically extracted clinical features may be necessary to achieve crucial improvements.</p

Bayesian phylogeographical tree of 229 HIV-1 A <i>pol</i> sequences with branches colored on the basis of the most probable location of the descendent nodes.

<p>The correspondences between the locations and colors are shown in the panel (left), and the posterior probabilities >0.8 are indicated on the internal nodes of the tree. The scale axis below the tree shows the number of years before the last sampling time.</p