Prolonged viral suppression with anti-HIV-1 antibody therapy.

HIV-1 infection remains a public health problem with no cure. Anti-retroviral therapy (ART) is effective but requires lifelong drug administration owing to a stable reservoir of latent proviruses integrated into the genome of CD4+ T cells1. Immunotherapy with anti-HIV-1 antibodies has the potential to suppress infection and increase the rate of clearance of infected cells2,3. Here we report on a clinical study in which people living with HIV received seven doses of a combination of two broadly neutralizing antibodies over 20 weeks in the presence or absence of ART. Without pre-screening for antibody sensitivity, 76% (13 out of 17) of the volunteers maintained virologic suppression for at least 20 weeks off ART. Post hoc sensitivity analyses were not predictive of the time to viral rebound. Individuals in whom virus remained suppressed for more than 20 weeks showed rebound viraemia after one of the antibodies reached serum concentrations below 10 µg ml-1. Two of the individuals who received all seven antibody doses maintained suppression after one year. Reservoir analysis performed after six months of antibody therapy revealed changes in the size and composition of the intact proviral reservoir. By contrast, there was no measurable decrease in the defective reservoir in the same individuals. These data suggest that antibody administration affects the HIV-1 reservoir, but additional larger and longer studies will be required to define the precise effect of antibody immunotherapy on the reservoir.

Sequence Evaluation and Comparative Analysis of Novel Assays for Intact Proviral HIV-1 DNA.

The HIV proviral reservoir is the major barrier to cure. The predominantly replication-defective proviral landscape makes the measurement of virus that is likely to cause rebound upon antiretroviral therapy (ART)-cessation challenging. To address this issue, novel assays to measure intact HIV proviruses have been developed. The intact proviral DNA assay (IPDA) is a high-throughput assay that uses two probes to exclude the majority of defective proviruses and determine the frequency of intact proviruses, albeit without sequence confirmation. Quadruplex PCR with four probes (Q4PCR) is a lower-throughput assay that uses limiting dilution long-distance PCR amplification followed by quantitative PCR (qPCR) and near-full-length genome sequencing (nFGS) to estimate the frequency of sequence-confirmed intact proviruses and provide insight into their clonal composition. To explore the advantages and limitations of these assays, we compared IPDA and Q4PCR measurements from 39 ART-suppressed people living with HIV. We found that IPDA and Q4PCR measurements correlated with one another, but frequencies of intact proviral DNA differed by approximately 19-fold. This difference may be in part due to inefficiencies in long-distance PCR amplification of proviruses in Q4PCR, leading to underestimates of intact proviral frequencies. In addition, nFGS analysis within Q4PCR explained that some of this difference is explained by proviruses that are classified as intact by IPDA but carry defects elsewhere in the genome. Taken together, this head-to-head comparison of novel intact proviral DNA assays provides important context for their interpretation in studies to deplete the HIV reservoir and shows that together the assays bracket true reservoir size.IMPORTANCE The intact proviral DNA assay (IPDA) and quadruplex PCR (Q4PCR) represent major advances in accurately quantifying and characterizing the replication-competent HIV reservoir. This study compares the two novel approaches for measuring intact HIV proviral DNA in samples from 39 antiretroviral therapy (ART)-suppressed people living with HIV, thereby informing ongoing efforts to deplete the HIV reservoir in cure-related trials.

Control of non-homeostatic feeding in sated mice using associative learning of contextual food cues.

Feeding is a complex motivated behavior controlled by a distributed neural network that processes sensory information to generate adaptive behavioral responses. Accordingly, studies using appetitive Pavlovian conditioning confirm that environmental cues that are associated with food availability can induce feeding even in satiated subjects. However, in mice, appetitive conditioning generally requires intensive training and thus can impede molecular studies that often require large numbers of animals. To address this, we developed and validated a simple and rapid context-induced feeding (Ctx-IF) task in which cues associated with food availability can later lead to increased food consumption in sated mice. We show that the associated increase in food consumption is driven by both positive and negative reinforcement and that spaced training is more effective than massed training. Ctx-IF can be completed in ~1 week and provides an opportunity to study the molecular mechanisms and circuitry underlying non-homeostatic eating. We have used this paradigm to map brain regions that are activated during Ctx-IF with cFos immunohistochemistry and found that the insular cortex, and other regions, are activated following exposure to cues denoting the availability of food. Finally, we show that inhibition of the insular cortex using GABA agonists impairs performance of the task. Our findings provide a novel assay in mice for defining the functional neuroanatomy of appetitive conditioning and identify specific brain regions that are activated during the development of learned behaviors that impact food consumption.

Sarcopenia is an independent risk factor for short-term mortality in patients undergoing transjugular intrahepatic portosystemic shunt.

BACKGROUND: Sarcopenia is common in patients with cirrhosis and is a risk factor for increased mortality. Transjugular intrahepatic portosystemic shunt (TIPS) placement has been utilized in cirrhosis patients with decompensation . We investigated the role of sarcopenia in predicting mortality in patients undergoing TIPS. METHODS: We conducted a single-center retrospective study of 232 patients with cirrhosis who underwent TIPS between January 2010 and December 2015. Sarcopenia was defined by the psoas muscle index (PMI) cutoff value, calculated based on dynamic time-dependent outcomes using X-tile software. Kaplan-Meier analysis demonstrated the difference in survival in the sarcopenia group versus the non-sarcopenia group. . Univariate and multivariate analyses were used to identify the relationship between sarcopenia and post-TIPS mortality during a follow-up period of 1 year. RESULTS: For TIPS indications, 111 (47.84%) patients had refractory ascites, 69 (29.74%) patients had variceal bleeding, 12 (5.17%) patients had ascites, and 40 (17.24%) for other indications. The mean PMI was 4.40 ±â€…1.55. Sarcopenia was defined as a PMI value of <4.36 in males, and <3.23 in females. Sarcopenia was present in 96 (41.38%) of patients. . Kaplan-Meier analysis showed thatsarcopenia is associated with worse survival (log-rank P  < 0.01). Multivariate Cox regression analysis showed that sarcopenia is independently associated with worse survival during the 1-year follow-up period with an hazard ratio of 2.435 (95% CI 1.346-4.403) ( P  < 0.01), after adjusting for age, BMI, indications for TIPS, etiology for cirrhosis, and MELD score and stratified by sex. CONCLUSION: Sarcopenia is an independent risk factor for 1-year mortality in patients undergoing TIPS and should be considered when patients are evaluated as a candidate for TIPS.

Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study.

OBJECTIVE: To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience. METHODS: In this retrospective study, all breast ultrasound examinations from January 1, 2014 to December 31, 2014 at our institution were reviewed. Patients with post-surgical scars, initially indeterminate, or malignant lesions with histological diagnoses or 2-year follow-up were included. The DLS was trained with 70% of the images, and the remaining 30% were used to validate the performance. Three readers with variable expertise also evaluated the validation set (radiologist, resident, medical student). Diagnostic accuracy was assessed with a receiver operating characteristic analysis. RESULTS: 82 patients with malignant and 550 with benign lesions were included. Time needed for training was 7 min (DLS). Evaluation time for the test data set were 3.7 s (DLS) and 28, 22 and 25 min for human readers (decreasing experience). Receiver operating characteristic analysis revealed non-significant differences (p-values 0.45-0.47) in the area under the curve of 0.84 (DLS), 0.88 (experienced and intermediate readers) and 0.79 (inexperienced reader). CONCLUSION: DLS may aid diagnosing cancer on breast ultrasound images with an accuracy comparable to radiologists, and learns better and faster than a human reader with no prior experience. Further clinical trials with dedicated algorithms are warranted. Advances in knowledge: DLS can be trained classify cancer on breast ultrasound images high accuracy even with comparably few training cases. The fast evaluation speed makes real-time image analysis feasible.

Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis.

PURPOSE: To evaluate the accuracy of a deep learning software (DLS) in the discrimination between phyllodes tumors (PT) and fibroadenomas (FA). METHODS: In this IRB-approved, retrospective, single-center study, we collected all ultrasound images of histologically secured PT (n = 11, 36 images) and a random control group with FA (n = 15, 50 images). The images were analyzed with a DLS designed for industrial grade image analysis, with 33 images withheld from training for validation purposes. The lesions were also interpreted by four radiologists. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, negative and positive predictive values were calculated at the optimal cut-off (Youden Index). RESULTS: The DLS was able to differentiate between PT and FA with good diagnostic accuracy (AUC = 0.73) and high negative predictive value (NPV = 100%). Radiologists showed comparable accuracy (AUC 0.60-0.77) at lower NPV (64-80%). When performing the readout together with the DLS recommendation, the radiologist's accuracy showed a non-significant tendency to improve (AUC 0.75-0.87, p = 0.07). CONCLUSION: Deep learning based image analysis may be able to exclude PT with a high negative predictive value. Integration into the clinical workflow may enable radiologists to more confidently exclude PT, thereby reducing the number of unnecessary biopsies.