Epidemiology of community-acquired pneumonia among hospitalised children in Indonesia: a multicentre, prospective study.

OBJECTIVE: To identify aetiologies of childhood community-acquired pneumonia (CAP) based on a comprehensive diagnostic approach. DESIGN: 'Partnerships for Enhanced Engagement in Research-Pneumonia in Paediatrics (PEER-PePPeS)' study was an observational prospective cohort study conducted from July 2017 to September 2019. SETTING: Government referral teaching hospitals and satellite sites in three cities in Indonesia: Semarang, Yogyakarta and Tangerang. PARTICIPANTS: Hospitalised children aged 2-59 months who met the criteria for pneumonia were eligible. Children were excluded if they had been hospitalised for >24 hours; had malignancy or history of malignancy; a history of long-term (>2 months) steroid therapy, or conditions that might interfere with compliance with study procedures. MAIN OUTCOMES MEASURES: Causative bacterial, viral or mixed pathogen(s) for pneumonia were determined using microbiological, molecular and serological tests from routinely collected specimens (blood, sputum and nasopharyngeal swabs). We applied a previously published algorithm (PEER-PePPeS rules) to determine the causative pathogen(s). RESULTS: 188 subjects were enrolled. Based on our algorithm, 48 (25.5%) had a bacterial infection, 31 (16.5%) had a viral infection, 76 (40.4%) had mixed bacterial and viral infections, and 33 (17.6%) were unable to be classified. The five most common causative pathogens identified were Haemophilus influenzae non-type B (N=73, 38.8%), respiratory syncytial virus (RSV) (N=51, 27.1%), Klebsiella pneumoniae (N=43, 22.9%), Streptococcus pneumoniae (N=29, 15.4%) and Influenza virus (N=25, 13.3%). RSV and influenza virus diagnoses were highly associated with Indonesia's rainy season (November-March). The PCR assays on induced sputum (IS) specimens captured most of the pathogens identified in this study. CONCLUSIONS: Our study found that H. influenzae non-type B and RSV were the most frequently identified pathogens causing hospitalised CAP among Indonesian children aged 2-59 months old. Our study also highlights the importance of PCR for diagnosis and by extension, appropriate use of antimicrobials. TRAIL REGISTRATION NUMBER: NCT03366454.

Comparison of the Impact of Zika and Dengue Virus Infection, and Other Acute Illnesses of Unidentified Origin on Cognitive Functions in a Prospective Cohort in Chiapas Mexico.

Zika has been associated with a variety of severe neurologic manifestations including meningitis and encephalitis. We hypothesized that it may also cause mild to subclinical neurocognitive alterations during acute infection or over the long term. In this observational cohort study, we explored whether Zika cause subclinical or mild neurocognitive alterations, estimate its frequency and duration, and compare it to other acute illnesses in a cohort of people with suspected Zika infection, in the region of Tapachula in Chiapas, Mexico during 2016-2018. We enrolled patients who were at least 12 years old with suspected Zika virus infection and followed them up for 6 months. During each visit participants underwent a complete clinical exam, including a screening test for neurocognitive dysfunction (Montreal Cognitive Assessment score). We enrolled 406 patients [37 with Zika, 73 with dengue and 296 with other acute illnesses of unidentified origin (AIUO)]. We observed a mild and transient impact over cognitive functions in patients with Zika, dengue and with other AIUO. The probability of having an abnormal MoCA score (<26 points) was significantly higher in patients with Zika and AIUO than in those with dengue. Patients with Zika and AIUO had lower memory scores than patients with dengue (Zika vs. Dengue: -0.378, 95% CI-0.678 to -0.078; p = 0.014: Zika vs. AIUO 0.264, 95% CI 0.059, 0.469; p = 0.012). The low memory performance in patients with Zika and AIUO accounts for most of the differences in the overall MoCA score when compared with patients with dengue. Our results show a decrease in cognitive function during acute illness and provides no evidence to support the hypothesis that Zika might cause neurocognitive alterations longer than the period of acute infection or different to other infectious diseases. While effects on memory or perhaps other cognitive functions over the long term are possible, larger studies using more refined tools for neurocognitive functioning assessment are needed to identify these. Trial Registration: NCT02831699.

Persistent COVID-19 in an Immunocompromised Patient Temporarily Responsive to Two Courses of Remdesivir Therapy.

The antiviral drug remdesivir has been shown clinically effective for treatment of COVID-19. We here demonstrate suppressive but not curative effect of remdesivir in an immunocompromised patient. A man in his fifties treated with chemoimmunotherapy for chronic lymphocytic leukemia experienced a 9-week course of COVID-19 with high fever and severe viral pneumonia. During two 10-day courses of remdesivir starting 24 and 45 days after fever onset, pneumonia and spiking fevers remitted, but relapsed after discontinuation. Kinetics of temperature, C-reactive protein, and lymphocyte counts mirrored the remitting/relapsing SARS-CoV-2 infection. Combination therapy or longer treatment duration may be needed in immunocompromised patients.

INA-RESPOND: a multi-centre clinical research network in Indonesia.

Nationally representative observational and translational research is needed to address the public health challenges in Indonesia due to the geographic disparity, recently decentralized health system, and diverse infectious disease priorities. To accomplish this, the Indonesian Ministry of Health in collaboration with the US National Institute of Health has established INA-RESPOND (Indonesia Research Partnership on Infectious Disease) - a clinical research network comprising 9 referral hospitals, 7 medical faculties, and 2 research centres across Indonesia. The network provides a forum to conduct research at a national scale and to address scientific questions that would be difficult to address in smaller research settings. Further, it is currently conducting multi-centre research on the etiologies of fever, sepsis, and tuberculosis. There are opportunities to leverage existing network resources for other public health research needs. INA-RESPOND is an Indonesian-led network in a country with diverse population groups and public health needs which is poised to collaborate with researchers, universities, donors, and industry worldwide. This paper describes the network and its goals and values, as well as the management structure, process for collaboration, and future vision.

Progress toward curing HIV infections with hematopoietic stem cell transplantation.

Combination antiretroviral therapy can suppress human immunodeficiency virus (HIV) infection but cannot completely eradicate the virus. A major obstacle in the quest for a cure is the difficulty in targeting and measuring latently infected cells. To date, a single person seems to have been cured of HIV. Hematopoietic stem cell transplantation (HSCT) preceded this cancer patient's long-term sustained HIV remission, but researchers have been unable to replicate this cure, and the mechanisms that led to HIV remission remain to be established. In February 2014, the National Institute of Allergy and Infectious Diseases sponsored a workshop that provided a venue for in-depth discussion of whether HSCT could be exploited to cure HIV in cancer patients requiring such procedures. Participants also discussed how HSCT might be applied to a broader community of HIV-infected persons in whom the risks of HSCT currently outweigh the likelihood and benefits of HIV cure.

Mechanisms underlying abnormalities of immune activation/coagulation in HIV infection.

Immune activation has been recognized as an important component of the pathogenesis of HIV infection since the first recognition of cases of AIDS in the early 1980s. Early in the AIDS epidemic, patients with HIV infection were noted to have elevated levels of serum immunoglobulins. CD38 expression on CD4+ T cells was shown to be an independent predictor of survival in 1999. The characterization of HIV-associated immune activation has become progressively sophisticated over the past several years. A consistent finding has been an association of poor clinical outcomes with markers of monocyte activation (IL-6 and sCD14) and/or coagulation (D-dimer). These relationships have been shown to exist even in patients with plasma levels of HIV-1<50 copies/ml. While it is generally accepted that immune activation is related to HIV infection, there is less clarity regarding the pathways that lead to its expression. Among the forces reported to drive HIV-associated immune activation are innate and adaptive immune responses to HIV and related co-infections, homeostatic responses to CD4+ T cell depletion and translocation of microbial products across the intestinal wall. Recent work has identified a potential role for "defective" HIV-1 transcripts in driving immune activation. Studies examining the connections between the adaptive immune system and the coagulation cascade have led to the identification of PAR-1 as a potential target for therapeutic intervention. Despite the successes experienced with cART, persistent immune activation in association with HIV infection remains a scientific and clinical problem that is yet to be solved.

The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool.

OBJECTIVE: The optimum selection and sequencing of combination antiretroviral therapy to maintain viral suppression can be challenging. The HIV Resistance Response Database Initiative has pioneered the development of computational models that predict the virological response to drug combinations. Here we describe the development and testing of random forest models to power an online treatment selection tool. METHODS: Five thousand, seven hundred and fifty-two treatment change episodes were selected to train a committee of 10 models to predict the probability of virological response to a new regimen. The input variables were antiretroviral treatment history, baseline CD4 cell count, viral load and genotype, drugs in the new regimen, time from treatment change to follow-up and follow-up viral load values. The models were assessed during cross-validation and with an independent set of 50 treatment change episodes by plotting receiver-operator characteristic curves and their performance compared with genotypic sensitivity scores from rules-based genotype interpretation systems. RESULTS: The models achieved an area under the curve during cross-validation of 0.77-0.87 (mean = 0.82), accuracy of 72-81% (mean = 77%), sensitivity of 62-80% (mean = 67%) and specificity of 75-89% (mean = 81%). When tested with the 50 test cases, the area under the curve was 0.70-0.88, accuracy 64-82%, sensitivity 62-80% and specificity 68-95%. The genotypic sensitivity scores achieved an area under the curve of 0.51-0.52, overall accuracy of 54-56%, sensitivity of 43-64% and specificity of 41-73%. CONCLUSION: The models achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores. The models are being used to power an experimental system now available via the Internet.

CD4 and CD8 T cell immune activation during chronic HIV infection: roles of homeostasis, HIV, type I IFN, and IL-7.

Immune activation plays an important role in the pathogenesis of HIV disease. Although the causes are not fully understood, the forces that lead to immune dysfunction differ for CD4 and CD8 T cells. In this study, we report that the molecular pathways that drive immune activation during chronic HIV infection are influenced by differences in the homeostatic regulation of the CD4 and CD8 T cell pools. Proliferation of CD4 T cells is controlled more tightly by CD4 T cell numbers than is CD8 T cell proliferation. This difference reflects the importance of maintaining a polyclonal CD4 T cell pool in host surveillance. Both pools of T cells were found to be driven by viral load and its associated state of inflammation. In the setting of HIV-induced lymphopenia, naive CD4 T cells were recruited mainly into the proliferating pool in response to CD4 T cell depletion, whereas naive CD8 T cell proliferation was driven mainly by levels of HIV RNA. RNA analysis revealed increased expression of genes associated with type I IFN and common γ chain cytokine signaling in CD4 T cell subsets and only type I IFN-associated genes in CD8 T cell subsets. In vitro studies demonstrated enhanced STAT1 phosphorylation in response to IFN-α and increased expression of the IFNAR1 transcripts in naive and memory CD4 T cells compared with that observed in CD8 T cells. CD4 T cell subsets also showed enhanced STAT1 phosphorylation in response to exogenous IL-7.

A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy.

OBJECTIVE: HIV treatment failure is commonly associated with drug resistance and the selection of a new regimen is often guided by genotypic resistance testing. The interpretation of complex genotypic data poses a major challenge. We have developed artificial neural network (ANN) models that predict virological response to therapy from HIV genotype and other clinical information. Here we compare the accuracy of ANN with alternative modelling methodologies, random forests (RF) and support vector machines (SVM). METHODS: Data from 1204 treatment change episodes (TCEs) were identified from the HIV Resistance Response Database Initiative (RDI) database and partitioned at random into a training set of 1154 and a test set of 50. The training set was then partitioned using an L-cross (L=10 in this study) validation scheme for training individual computational models. Seventy six input variables were used for training the models: 55 baseline genotype mutations; the 14 potential drugs in the new treatment regimen; four treatment history variables; baseline viral load; CD4 count and time to follow-up viral load. The output variable was follow-up viral load. Performance was evaluated in terms of the correlations and absolute differences between the individual models' predictions and the actual DeltaVL values. RESULTS: The correlations (r(2)) between predicted and actual DeltaVL varied from 0.318 to 0.546 for ANN, 0.590 to 0.751 for RF and 0.300 to 0.720 for SVM. The mean absolute differences varied from 0.677 to 0.903 for ANN, 0.494 to 0.644 for RF and 0.500 to 0.790 for SVM. ANN models were significantly inferior to RF and SVM models. The predictions of the ANN, RF and SVM committees all correlated highly significantly with the actual DeltaVL of the independent test TCEs, producing r(2) values of 0.689, 0.707 and 0.620, respectively. The mean absolute differences were 0.543, 0.600 and 0.607log(10)copies/ml for ANN, RF and SVM, respectively. There were no statistically significant differences between the three committees. Combining the committees' outputs improved correlations between predicted and actual virological responses. The combination of all three committees gave a correlation of r(2)=0.728. The mean absolute differences followed a similar pattern. CONCLUSIONS: RF and SVM models can produce predictions of virological response to HIV treatment that are comparable in accuracy to a committee of ANN models. Combining the predictions of different models improves their accuracy somewhat. This approach has potential as a future clinical tool and a combination of ANN and RF models is being taken forward for clinical evaluation.

The development of artificial neural networks to predict virological response to combination HIV therapy.

INTRODUCTION: When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However, interpretation of resistance from genotypic data poses a major challenge. METHODS: As an alternative to current interpretation systems, we have developed artificial neural network (ANN) models to predict virological response to combination therapy from HIV genotype and other clinical information. RESULTS: ANN models trained with genotype, baseline viral load and time to follow-up viral load (1154 treatment change episodes from multiple clinics), produced predictions of virological response that were highly significantly correlated with actual responses (r2 = 0.53; P < 0.00001) using independent test data from clinics that contributed training data. Augmented models, trained with the additional variables of baseline CD4+ T-cell count and four treatment history variables, were more accurate, explaining 69% of the variance in virological response. Models trained with the full input dataset, but only those data involving highly active antiretroviral therapy (three or more full-dose antiretroviral drugs in combination), performed at an intermediate level, explaining 61% of the variance. The augmented models performed less well when tested with data from unfamiliar clinics that had not contributed data to the training dataset, explaining 46% of the variance in response. CONCLUSION: These data indicate that ANN models can be quite accurate predictors of virological response to HIV therapy even for patients from unfamiliar clinics. ANN models therefore warrant further development as a potential tool to aid treatment selection.