Treatment strategies and clinical outcomes.

<p>Treatment strategies and clinical outcomes.</p

Clinical syndromes of <i>V</i>. <i>vulnificus</i> infections in the First Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.

<p>Clinical syndromes of <i>V</i>. <i>vulnificus</i> infections in the First Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.</p

Correlations between Clinical Features and Mortality in Patients with <i>Vibrio vulnificus</i> Infection

<div><p><i>Vibrio vulnificus</i> is a common gram-negative bacterium, which might cause morbidity and mortality in patients following consumption of seafood or exposure to seawater in Southeast China. We retrospectively analyzed clinical data of patients with laboratory confirmed <i>V</i>. <i>vulnificus</i> infection. Twenty one patients were divided into a survival group and a non-surviving (or death) group according to their clinical outcome. Clinical data and measurements were statistically analyzed. Four patients (19.05%) died and five patients gave positive cultures from bile fluid, and 16 other patients gave positive culture from blood or blisters. Ten patients (47.62%) had an underlying liver disease and marine-related events were found in sixteen patients (76.2%). Patients with heavy drinking habits might be at increased mortality (p = 0.028). Clinical manifestations of cellulitis (47.6%), septic shock (42.9%) and multiple organ failure (28.6%) were statistically significant when comparing survivors and non-survivors (p = 0.035, p = 0.021 and p = 0.003, respectively). The laboratory results, including hemoglobin < 9.0 g/L (p = 0.012), platelets < 2.0×10⁹ /L, prothrombin time activity (PTA) <20%, decreased serum creatinine and increased urea nitrogen were statistically significant (p = 0.012, p = 0.003, p = 0.028 and p = 0.028, respectively). Patients may be at a higher risk of mortality under situations where they have a history of habitual heavy alcoholic drink consumption (p = 0.028, OR = 22.5, 95%CI 1.5–335.3), accompanied with cellulitis, shock, multiple organ failure, and laboratory examinations that are complicated by decreased platelets, hemoglobin and significantly prolonged prothrombin time (PT).</p></div

Seasonal distribution of <i>V</i>. <i>vulnificus</i> infections at the First Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.

<p>Seasonal distribution of <i>V</i>. <i>vulnificus</i> infections at the First Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.</p

<i>V</i>. <i>vulnificus</i> infection that presented as swelling, Hemorrhagic bulla and phlegmon are observed on the right hand.

<p><i>V</i>. <i>vulnificus</i> infection that presented as swelling, Hemorrhagic bulla and phlegmon are observed on the right hand.</p

Laboratory results measured between survivors group and deaths group.

<p>Note: ULN, the upper limit of normal. At our hospital, the normal range for ALT was 5–40 U/L, the normal range for AST was 5–37 U/L, the normal range for serum creatinine was 40–108 μmol/L, and the normal range for urea nitrogen was 1.7–8.3 mmol/L.</p><p>Laboratory results measured between survivors group and deaths group.</p

Cluster analysis of the DGGE profiles of the predominant fecal bacteria of 15 patients in follow-up samples.

<p>Clustering was performed using Dice’s coefficient and UPGMA. <b>(a)</b> Cluster analysis of the DGGE profiles from the different groups. The metric scale denotes the degree of similarity. <b>(b)</b> MDS analysis of the cluster shown in (a). The plot is an optimized 3D representation of the similarity matrix obtained from BioNumerics software, and the x-, y-, and z-axes separately represent three different dimensions: Dim 1, Dim 2, and Dim 3. The Euclidean distance between two points reflects similarity. <b>(c)</b> PCA of fecal microbiota based on the DGGE fingerprinting shown in (a). The plot is reoriented to maximize variation among lanes along the first three principal components (the contributions 11.5, 20.0 and 26.7, respectively) obtained from BioNumerics software.</p

Intestinal microbial diversity comparison.

<p><b>(a)</b> Shannon’s diversity index comparison. <b>(b)</b> Shannon’s evenness index comparison. <b>(c)</b> Species richness comparison. Shannon’s diversity index and Shannon’s evenness index were lower in all infected groups compared to the control group. A decreased Shannon’s diversity, evenness and species richness in group D patients compared to patients in the A and C groups. * P<0.05, ** P<0.01 <b>(d)</b> The changes in Shannon’s diversity and evenness in patient C1. X axis, time of sampling. <b>(e)</b> The changes in Shannon’s diversity and evenness in patient C3. <b>(f)</b> The changes in Shannon’s diversity and evenness in patient C5. <b>(g)</b> The changes in Shannon’s diversity and evenness in patient D3. After <i>B</i>. <i>subtilis</i> and <i>E</i>. <i>faecium</i> or <i>C</i>. <i>butyricum</i> administration, the fecal bacterial profiles of patients without antibiotics displayed a trend of increasing diversity and evenness. <b>(h)</b> The changes in Shannon’s diversity and evenness in patient D5. <b>(i)</b> The changes in Shannon’s diversity and evenness in patient D7. The results showed that a trend toward increasing diversity and evenness in D3, D5 and D7 after antibiotic cessation.</p

DGGE profiles of the predominant intestinal bacteria in H7N9-infected patients.

<p><b>(a)</b> DGGE profiles of fecal bacteria in A, B and C groups. <b>(b)</b> Differences in the DGGE profiles of fecal samples taken at different time points from the same individual were apparent, particularly for patients D1, D2, D4, D5 and D7. This result suggests temporal instability in the predominant bacterial population in H7N9-infected patients with secondary infection.</p

Phylogenetic tree analysis of DGGE profiles.

<p>Phylogenetic tree of sequences constructed using the neighbor-joining method based on the DGGE profiles. The fragment sequences were named for their positions in the gels using the band-matching tool with BioNumerics software version 6.01 (Applied Math). Twenty-one band classes, indicated by black spots, displayed little variation in intensity in the follow-up samples. Seven band classes, indicated by black triangles, exhibited a increase in intensity in the follow-up samples. Six band classes, indicated by black squares, exhibited an decrease in the follow-up samples. The plot was generated using MEGA5.1 software.</p