Evolution of Viral Load and CD4+ T Cell Counts during STI

<div><p>(A) Survival curves of time to virologic failure during the first three supervised treatment interruptions. Virologic failure was defined as having a viral load of greater than 5,000 copies RNA/ml plasma for 3 wk or greater than 50,000 copies once. Patients still achieving viral control at the last visit and individuals restarting therapy without meeting criteria or lost in follow-up are censored at the last evaluable time point. The horizontal axis represents the time off therapy since the beginning of the treatment interruption, the vertical axis corresponds to the number of patients maintaining control of viremia. The curves for first, second, and third STIs do not differ significantly from each other (log-rank test, <i>p ></i> 0.05).</p> <p>(B) Evolution of CD4+ T cell counts during the longest treatment interruption. Slopes of CD4+ T cell counts during the first year of the longest treatment interruption are shown for patients who experienced a cessation of therapy of at least 12 mo (all except AC13, AC25, and AC45), compared to the natural decline of CD4+ T cell counts in untreated patients of the MACS cohort with early chronic HIV-1 infection (CD4+ counts of >350 cells/mm³). CD4+ T cell losses were calculated on a regression line based on least squares fit. The two groups differed significantly from each other (Mann-Whitney <i>U</i> test, <i>p =</i> 0.02).</p> <p>(C) CD4+ T cell count at intercept and CD4+ T cell slopes during the longest treatment interruption. The CD4+ T cell slopes of the same 11 patients shown in (B) and of untreated patients of the MACS cohort are represented according to the CD4+ T cell count at the intercept of the regression line based on least squares fit with the vertical axis (day 0 of treatment interruption).</p></div

Data Collection and Processing for Digital Images Obtained from a Single Diluted Whole Blood Specimen from an HIV-Infected Participant

<div><p>A total of 16.5 μl of whole blood stained with antibodies specific for CD4 and CD3 markers is delivered to the flow cell after 8 min, and an image of the same region of the membrane is obtained with two different emission filters.</p> <p>(A) AlexaFluor-488-conjugated anti-CD4 antibody stains CD4⁺ cells (T lymphocytes and monocytes) green.</p> <p>(B) AlexaFluor-647-conjugated anti-CD3 antibody stains CD3⁺ T lymphocytes red.</p> <p>(C) By digitally merging the two images, CD3⁺CD4⁺ T lymphocytes (i.e., “CD4 cells”) appear yellow and are distinguished from CD4⁺CD3⁻ monocytes (green) and CD3⁺CD4⁻ T lymphocytes (red).</p> <p>(D) A lymphocyte selection algorithm is applied to the merged image, based on a lymphocyte profile as defined by size, shape, and uniformity. Objects not fitting the lymphocyte profile are deleted while remaining objects are selected and ultimately counted. A similar protocol to count CD8 cells is used in each participant.</p> <p>Boxed region indicates two CD4⁺ cells (yellow in [C]) in the original (A and B), merged (C), and processed (D) images. Large green and red objects seen in some images represent aggregates of fluorescent antibody.</p></div

HIV-1 Viral Loads and CD4+ T Cell Counts in the 14 Study Participants

<p>Time zero corresponds to first institution of highly active antiretroviral therapy (HAART). Closed squares, HIV-1 plasma viral loads; open circles, CD4+ T cell counts; shaded areas, treatment with HAART according to protocol; diagonally shaded areas, patient restarted therapy without meeting criteria of virological failure; vertical dotted lines, virological failure without reinstitution of HAART. Patients are ordered by increasing number of supervised treatment interruptions.</p

Evolution of HIV-1-Specific CD4+ and CD8+ T Cell Responses during STI

<div><p>(A–D) Magnitude and breadth of increase of HIV-specific CD8+ T cells during supervised treatment interruptions.</p> <p>(A and B) Magnitude (A) and breadth (B) of HIV-specific CD8+ responses at the first day of treatment interruption (black bars) and at the last day off therapy (white bars). Data represent the mean and standard deviation.</p> <p>(C and D) Correlation between the increase of the magnitude (C) or breadth (D) of CD8+ T cell responses and the time off therapy during the first treatment interruption.</p> <p>(E and F) Evolution of CD4+ T helper cell responses during supervised treatment interruptions.</p> <p>(E) Magnitude of CD4 T helper cell responses at baseline and at the first day of treatment interruption (closed circles) and last day off therapy (open circles). Horizontal bars correspond to median values. An stimulation index greater than five was considered significant.</p> <p>(F) Correlation between the magnitude of p24-specific lymphocyte proliferative responses at the beginning of the first treatment interruption and the time patients were able to remain off therapy during the subsequent STI.</p></div

The Membrane Flow Cell Selectively Captures Lymphocytes and Provides for the Removal of Red Blood Cells without Sample Processing

<div><p>(A) A whole blood sample collected atop a glass slide and imaged by a scanning electron microscope reveals the overabundance of red blood cells in the sample.</p> <p>(B) A whole blood sample processed through the flow cell reveals that lymphocytes are captured on the membrane support while red blood cells are largely excluded from within the cell. Arrows indicate red blood cells passing through the membrane.</p> <p>(C) Fluorescent antibody stain specific for a lymphocyte marker is used to visualize captured lymphocytes within the flow cell in a representative single-color data image.</p></div

Methods Comparison and Correlation Studies for CD4 Percentages of Total T Cells and CD4:CD8 Ratios in 67 Human Subjects

<p>(A and B) CD4 percentages of total T cells and (C and D) CD4:CD8 ratios in 67 human participants, including 61 adults and six children. In Passing–Bablok correlation plots (A and C), solid black lines indicate identity, blue lines indicate the observed correlations, and dashed black lines indicate 95% confidence limits. Correlations for CD4 percentages of total T cells (<i>r</i> = 0.98, <i>p</i> < 0.0001) and CD4:CD8 ratios (<i>r</i> = 0.98, <i>p</i> < 0.0001) are shown. For Bland–Altman methods comparison plots (B and D), notations are as described in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020182#pmed-0020182-g006" target="_blank">Figure 6</a> caption.</p

Components of the ETC System

<p>A fluid delivery system is used to introduce sample containing fluorescently stained lymphocytes in whole blood and wash buffer to a capture flow cell. Lymphocytes captured within the flow cell are visualized with a fluorescence imaging station using a mercury pressure lamp as a light source, and a CCD for image collection. Raw data images are then processed and analyzed using an automated algorithm run by an attached computer. The flow cell includes a polymer membrane supported on a chip and two transparent polymethylmethacrylate inserts that allow for the optical evaluation of captured lymphocytes.</p

CD4 Lymphocyte Dose Response

<p>Purified CD4 cells were labeled with AlexaFluor-488-conjugated anti-CD4 antibodies, introduced to the flow cell in amounts ranging from zero to 200,000 cells and imaged. There is a linear correlation between the number of cells in the sample and the intensity of light emitted from the membrane filter (<i>R</i>² = 0.999).</p

Representative Processed Data Images from Three Participants in Botswana

<p>The participants included (A) a 31-y-old woman with a CD4 count of 83 cells/μl by flow cytometry; (B) a 33-y-old woman with a CD4 count of 271 cells/μl by flow cytometry; and (C) a 5-mo-old infant with an absolute CD4 count of 2,098 cells/μl and a CD4:CD8 ratio of 1.80 by flow cytometry. In these images, CD3⁺CD8⁺ T cells appear red, monocytes appear green, and CD3⁺CD4⁺ T cells appear yellow. Each image reflects 0.18 μl of whole blood.</p

Multifunksjonelle intervensjoner og kapasitetsbygging hos konfliktaktørene : en studie av den internasjonale intervensjonen i Somalia (1992 – 1995)

Oppgaven tar for seg den internasjonale intervensjonen i Somalia i perioden 1992-1995, med særlig fokus på effekten av de intervensjonsvirkemidlene man tok i bruk. For å avgrense det empiriske nedslagsfeltet er hovedfokus for arbeidet forholdet mellom intervensjonsaktørene og General Aidid og hans klangruppe the Somali National Movement (SNM). Formålet med oppgaven er å se nærmere på utfordringer knyttet til internasjonale intervensjoner i komplekse interne konflikter. Oppgaven vil ta utgangspunkt i en antagelse om at multifunksjonelle intervensjoner vil kunne skape rom for konfliktantagonistene å bygge opp sine kapasiteter, spesielt i forhold til styring, ekstraksjon, beskyttelse og krigføring. Implisitt i dette ligger det også en antagelse om at en slik utvikling kan bidra til å skape større utfordringer for intervensjonsaktørene. Sentralt i forhold til diskusjonen rundt kapasitetsbygging vil konseptet parallellisering være, og diskusjonen rundt hvorvidt en slik utvikling er av et slikt omfang at man vil kunne betegne den som politisk. Oppgaven tar for seg parallelliseringskonseptet gjennom å benytte Tillys teoretisering knyttet til krigens funksjon i de europeiske statsdannelsesprosessene. Relasjonene mellom Aidid, hans gruppe og intervensjonsaktørene studeres gjennom bruk av aktørdeltakelsesmodellen og en analyse av hvorvidt det har skjedd en radikalisering i forholdet dem imellom. Oppgaven går gjennom de tre internasjonale operasjonene i Somalia: UNOSOM I, UNITAF og UNOSOM II. Formålet med gjennomgangen er å forsøke å vise hvordan intervensjonsaktørenes tiltak var med på å styrke General Aidid og hans gruppe