CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks.

MOTIVATION: Single-cell RNA sequencing allows high-resolution views of individual cells for libraries of up to millions of samples, thus motivating the use of deep learning for analysis. In this study, we introduce the use of graph neural networks for the unsupervised exploration of scRNA-seq data by developing a variational graph autoencoder architecture with graph attention layers that operates directly on the connectivity between cells, focusing on dimensionality reduction and clustering. With the help of several case studies, we show that our model, named CellVGAE, can be effectively used for exploratory analysis even on challenging datasets, by extracting meaningful features from the data and providing the means to visualize and interpret different aspects of the model. RESULTS: We show that CellVGAE is more interpretable than existing scRNA-seq variational architectures by analysing the graph attention coefficients. By drawing parallels with other scRNA-seq studies on interpretability, we assess the validity of the relationships modelled by attention, and furthermore, we show that CellVGAE can intrinsically capture information such as pseudotime and NF-ĸB activation dynamics, the latter being a property that is not generally shared by existing neural alternatives. We then evaluate the dimensionality reduction and clustering performance on 9 difficult and well-annotated datasets by comparing with three leading neural and non-neural techniques, concluding that CellVGAE outperforms competing methods. Finally, we report a decrease in training times of up to × 20 on a dataset of 1.3 million cells compared to existing deep learning architectures. AVAILABILITYAND IMPLEMENTATION: The CellVGAE code is available at https://github.com/davidbuterez/CellVGAE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Effects of Cardioprotective Antidiabetic Therapy on Microbiota in Patients with Type 2 Diabetes Mellitus-A Systematic Review.

As the pathophysiologic mechanisms of type 2 diabetes mellitus (T2DM) are discovered, there is a switch from glucocentric to a more comprehensive, patient-centered management. The holistic approach considers the interlink between T2DM and its complications, finding the best therapies for minimizing the cardiovascular (CV) or renal risk and benefitting from the treatment's pleiotropic effects. Sodium-glucose cotransporter 2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) fit best in the holistic approach because of their effects in reducing the risk of CV events and obtaining better metabolic control. Additionally, research on the SGLT-2i and GLP-1 RA modification of gut microbiota is accumulating. The microbiota plays a significant role in the relation between diet and CV disease because some intestinal bacteria lead to an increase in short-chain fatty acids (SCFA) and consequent positive effects. Thus, our review aims to describe the relation between antidiabetic non-insulin therapy (SGLT-2i and GLP-1 RA) with CV-proven benefits and the gut microbiota in patients with T2DM. We identified five randomized clinical trials including dapagliflozin, empagliflozin, liraglutide, and loxenatide, with different results. There were differences between empagliflozin and metformin regarding the effects on microbiota despite similar glucose control in both study groups. One study demonstrated that liraglutide induced gut microbiota alterations in patients with T2DM treated initially with metformin, but another failed to detect any differences when the same molecule was compared with sitagliptin. The established CV and renal protection that the SGLT-2i and GLP-1 RA exert could be partly due to their action on gut microbiota. The individual and cumulative effects of antidiabetic drugs on gut microbiota need further research.

A Critical View over the Newest Antidiabetic Molecules in Light of Efficacy-A Systematic Review and Meta-Analysis.

The increase in life expectancy without a decrease in the years lived without disability leads to the rise of the population aged over 65 years prone to polypharmacy. The novel antidiabetic drugs can improve this global therapeutic and health problem in patients with diabetes mellitus (DM). We aimed to establish the efficacy (A1c hemoglobin reduction) and safety of the newest antidiabetic drugs (considered so due to their novelty in medical practice use), specifically DPP-4i, SGLT-2i, GLP-1 Ra, and tirzepatide. The present meta-analysis followed the protocol registered at Prospero with the CRD42022330442 registration number. The reduction in HbA1c in the DPP4-i class for tenegliptin was 95% CI -0.54 [-1.1, 0.01], p = 0.06; in the SGLT2-iclass for ipragliflozin 95% CI -0.2 [-0.87, 0.47], p = 0.55; and for tofogliflozin 95% CI 3.13 [-12.02, 18.28], p = 0.69, while for tirzepatide it was 0.15, 95% CI [-0.50, 0.80] (p = 0.65). The guidelines for treatment in type 2 DM are provided from cardiovascular outcome trials that report mainly major adverse cardiovascular events and data about efficacy. The newest antidiabetic non-insulinic drugs are reported to be efficient in lowering HbA1c, but this effect depends between classes, molecules, or patients' age. The newest antidiabetic drugs are proven to be efficient molecules in terms of HbA1c decrease, weight reduction, and safety, but more studies are needed in order to characterize exactly their efficacy and safety profiles.

The Effects of Sodium-Glucose Cotransporter 2-Inhibitors on Steatosis and Fibrosis in Patients with Non-Alcoholic Fatty Liver Disease or Steatohepatitis and Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials.

Type 2 Diabetes Mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD) are part of metabolic syndrome and share multiple causal associations. Both conditions have an alarmingly increasing incidence and lead to multiple complications, which have an impact on a variety of organs and systems, such as the kidneys, eyes, and nervous and cardiovascular systems, or may cause metabolic disruptions. Sodium-glucose cotransporter 2-inhibitors (SGLT2-i), as an antidiabetic class with well-established cardiovascular benefits, and its class members have also been studied for their presumed effects on steatosis and fibrosis improvement in patients with NAFLD or non-alcoholic steatohepatitis (NASH). The MEDLINE and Cochrane databases were searched for randomized controlled trials examining the efficacy of SGLT2-i on the treatment of NAFLD/NASH in patients with T2DM. Of the originally identified 179 articles, 21 articles were included for final data analysis. Dapagliflozin, empagliflozin, and canagliflozin are some of the most used and studied SGLT2-i agents which have proven efficacy in treating patients with NAFLD/NASH by addressing/targeting different pathophysiological targets/mechanisms: insulin sensitivity improvement, weight loss, especially visceral fat loss, glucotoxicity, and lipotoxicity improvement or even improvement of chronic inflammation. Despite the considerable variability in study duration, sample size, and diagnostic method, the SGLT2-i agents used resulted in improvements in non-invasive markers of steatosis or even fibrosis in patients with T2DM. This systematic review offers encouraging results that place the SGLT2-i class at the top of the therapeutic arsenal for patients diagnosed with T2DM and NAFLD/NASH.

New-Onset Diabetes Mellitus in COVID-19: A Scoping Review.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic became superimposed on the pre-existing obesity and diabetes mellitus (DM) pandemics. Since COVID-19 infection alters the metabolic equilibrium, it may induce pathophysiologic mechanisms that potentiate new-onset DM, and we evaluated this issue. METHOD: A systematic review of the literature published from the 1 January 2020 until the 20 July 2023 was performed (PROSPERO registration number CRD42022341638). We included only full-text articles of both human clinical and randomized controlled trials published in English and enrolling adults (age > 18 years old) with ongoing or preceding COVID-19 in whom hyperglycemia was detected. The search was based on the following criteria: "(new-onset diabetes mellitus OR new-onset DM) AND (COVID-19) AND adults". RESULTS: Articles on MEDLINE (n = 70) and the Web of Science database (n = 16) were included and analyzed by two researchers who selected 20 relevant articles. We found evidence of a bidirectional relationship between COVID-19 and DM. CONCLUSIONS: This link operates as a pathophysiological mechanism supported by epidemiological data and also by the clinical and biological findings obtained from the affected individuals. The COVID-19 pandemic raised the incidence of DM through different pathophysiological and psychosocial factors.

The Safety Profile of Sodium-Glucose Cotransporter-2 Inhibitors and Glucagon-like Peptide 1 Receptor Agonists in the Standard of Care Treatment of Type 2 Diabetes Mellitus.

AIM: We evaluated the safety of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1 RAs) for their use with other glucose-lowering drugs and drugs for the treatment of type 2 diabetes mellitus (T2DM), in a standard-of-care regimen with maximum tolerated doses, and, respectively, when compared with metformin. METHODS: We conducted a retrospective, observational study on 405 patients that were seen in the outpatient clinic of the N Paulescu National Institute for Diabetes Mellitus, Bucharest, Romania, in 2019. Their demographics, metabolic parameters, and medication safety were evaluated at three follow-up visits, from baseline, six months, and twelve months. RESULTS: Both SGLT-2is and GLP-1 RAs are safe regarding creatinine, eGFR, urea, GOT, and GPT upon the comparison of the data from the six- and twelve-month visits with the initial visit, and also the twelve-month visit with the six-month visit. Moreover, when comparing SGLT-2is and GLP-1 RAs with metformin, there are safety data only for urea. CONCLUSIONS: In this retrospective analysis, both SGLT-2is and GLP-1 RAs, when used in conjunction with other glucose-lowering, blood-pressure-lowering, and lipid-lowering medications, appeared to be safe for the management of T2DM.

Non-alcoholic fatty liver disease in diabetes mellitus patients on chronic hemodialysis - A case series addressing cardiovascular and mortality risks.

Non-alcoholic fatty liver disease (NAFLD) has an important role in the pathogenesis of cardiovascular diseases in the population with diabetes and it is highly prevalent in end-stage renal disease (ESRD) patients. This case series describes NAFLD associated factors and survival in type 2 diabetes patients (T2DM) who have ESRD treated with hemodialysis. NAFLD prevalence in patients with T2DM and ESRD is 69.2%. A high number of patients (15 out of 18) have obesity evaluated by calculating body mass index (BMI) and bioimpedance measurements. Patients with NAFLD have higher cardiovascular mortality risk, 13 of 18 patients were already diagnosed with coronary heart disease, 6 of 18 had cerebrovascular disease, and 6 of 18 had peripheral artery disease. Fourteen patients were treated with insulin, two patients with sitagliptin (renal adjusted dose of 25mg/day) and two patients with medical nutrition therapy, with an HbA1c ranging from 4.4 to 9.0%. After one-year follow-up 7 of 18 patients died, the causes having roughly equal proportions: myocardial infarction, SARS-CoV2 infection, and pulmonary edema. In conclusion, our population of type 2 diabetic patients with ESRD in hemodialysis had a prevalence of ultrasound-diagnosed NAFLD of 69.2%. Also, this population had a high death rate at one-year follow-up, cardiovascular causes being among the most common.

Evaluating the Adipose Tissue Depth as a Predictor Factor for Gestational Diabetes in Later Pregnancy-A Systematic Review.

The increasing prevalence of gestational diabetes mellitus (GDM) requires non-invasive and precise techniques for evaluating the predisposing risk factors such as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). According to PRISMA, we developed a systematic review and searched after "visceral adipose tissue AND gestational diabetes" and identified 221 articles on the MEDLINE and Word of Science databases. After assessing them for inclusion criteria and two researchers screened them, 11 relevant articles were included. Although evidence is conflicting, more studies favor using US-determined VAT in GDM prediction. VAT may be more valuable than body mass index or SAT in predicting GDM. VAT can represent an additive factor to the prediction tool of the risk of developing GDM when used in conjunction with other anthropometric or biological parameters or maternal risk factors. US measurements are heterogeneous given different evaluation techniques, cut-off values and inter-operator variation. A significant limitation is the lack of a gold standard to identify GDM confidently. Pregnant women may benefit from early monitoring and preventive care if classified as high risk for GDM early in the gestational period. US-measured VAT during the first trimester of pregnancy seems a valuable and inexpensive screening approach to predict GDM development later in pregnancy, either by itself or if used in conjunction with other clinical and biological parameters.

Antidiabetic Molecule Efficacy in Patients with Type 2 Diabetes Mellitus-A Real-Life Clinical Practice Study.

In this paper, we aim to evaluate the efficacy of antidiabetic cardioprotective molecules such as Sodium-Glucose Cotransporter-2 Inhibitors (SGLT-2i) and Glucagon-like Peptide 1 Receptor Agonists (GLP-1 RAs) when used with other glucose-lowering drugs, lipid-lowering, and blood pressure (BP)-lowering drugs in a real-life setting. A retrospective, observational study on 477 patients admitted consecutively in 2019 to the outpatient clinic of a tertiary care unit for Diabetes Mellitus was conducted. Body mass index (BMI), blood pressure (BP) (both systolic and diastolic), and metabolic parameters, as well as A1c hemoglobin, fasting glycaemia and lipid profile, including total cholesterol (C), HDL-C, LDL-C and triglycerides), were evaluated at baseline and two follow-up visits were scheduled (6 months and 12 months) in order to assess the antidiabetic medication efficacy. Both SGLT-2i and GLP-1 RAs were efficient in terms of weight control reflected by BMI; metabolic control suggested by fasting glycaemia and A1c; and the diastolic component of BP control when comparing the data from the 6 and 12-month visits to the baseline, and when comparing the 12-month visit to the 6-month visit. Moreover, when comparing SGLT-2i and GLP-1 RAs with metformin, there are efficacy data for SGLT-2i at baseline in terms of BMI, fasting glycaemia, and HbA1c. In this retrospective study, both classes of cardioprotective molecules, when used in conjunction with other glucose-lowering, antihypertensive, and lipid-lowering medications, appeared to be efficient in a real-life setting for the management of T2DM.

Non-Insulin Novel Antidiabetic Drugs Mechanisms in the Pathogenesis of COVID-19.

The present study aimed to analyse the published data and to realize an update about the use and pathogenesis of the novel antidiabetic drugs, respectively, dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1 Ra), and sodium-glucose co-transporter-2 inhibitors (SGLT-2i), in patients with type 2 diabetes mellitus (T2DM) and coronavirus disease (COVID-19). Literature research in the PubMed and Web of Science database was performed in order to identify relevant published clinical trials and meta-analyses that include information about the treatment with novel antidiabetic agents in patients with T2DM and COVID-19. A total of seven articles were included, and their primary and secondary outcomes were reported and analysed. DPP-4i has mixed results on mortality in T2DM patients with COVID-19 but with an overall slightly favourable or neutral effect, whereas GLP-1 Ra seems to have a rather beneficial impact, while SGLT-2i may be useful in acute illness. Even if there are limited data, they seem to have favourable efficacy and safety profiles. The available evidence is heterogenous and insufficient to evaluate if the benefits of non-insulin novel antidiabetic drugs in COVID-19 treatment are due to the improvement of glycaemic control or to their intrinsic anti-inflammatory effects but highlights their beneficial effects in the pathogenesis and evolution of the disease.

From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges.

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.

Unsupervised generative and graph representation learning for modelling cell differentiation.

Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological factors of variation in the gene expression data. In addition, we use a graph autoencoder consisting of graph convolutional layers to predict relationships between single-cells. Based on these models, we develop a computational framework that consists of methods for identifying the cell types in the dataset, finding driver genes for the differentiation process and obtaining a better understanding of relationships between cells. We illustrate our methods on datasets from multiple species and also from different sequencing technologies.

Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study.

BACKGROUND: Brazil ranks second worldwide in total number of COVID-19 cases and deaths. Understanding the possible socioeconomic and ethnic health inequities is particularly important given the diverse population and fragile political and economic situation. We aimed to characterise the COVID-19 pandemic in Brazil and assess variations in mortality according to region, ethnicity, comorbidities, and symptoms. METHODS: We conducted a cross-sectional observational study of COVID-19 hospital mortality using data from the SIVEP-Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe) dataset to characterise the COVID-19 pandemic in Brazil. In the study, we included hospitalised patients who had a positive RT-PCR test for severe acute respiratory syndrome coronavirus 2 and who had ethnicity information in the dataset. Ethnicity of participants was classified according to the five categories used by the Brazilian Institute of Geography and Statistics: Branco (White), Preto (Black), Amarelo (East Asian), Indígeno (Indigenous), or Pardo (mixed ethnicity). We assessed regional variations in patients with COVID-19 admitted to hospital by state and by two socioeconomically grouped regions (north and central-south). We used mixed-effects Cox regression survival analysis to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation. FINDINGS: Of 99 557 patients in the SIVEP-Gripe dataset, we included 11 321 patients in our study. 9278 (82·0%) of these patients were from the central-south region, and 2043 (18·0%) were from the north region. Compared with White Brazilians, Pardo and Black Brazilians with COVID-19 who were admitted to hospital had significantly higher risk of mortality (hazard ratio [HR] 1·45, 95% CI 1·33-1·58 for Pardo Brazilians; 1·32, 1·15-1·52 for Black Brazilians). Pardo ethnicity was the second most important risk factor (after age) for death. Comorbidities were more common in Brazilians admitted to hospital in the north region than in the central-south, with similar proportions between the various ethnic groups. States in the north had higher HRs compared with those of the central-south, except for Rio de Janeiro, which had a much higher HR than that of the other central-south states. INTERPRETATION: We found evidence of two distinct but associated effects: increased mortality in the north region (regional effect) and in the Pardo and Black populations (ethnicity effect). We speculate that the regional effect is driven by increasing comorbidity burden in regions with lower levels of socioeconomic development. The ethnicity effect might be related to differences in susceptibility to COVID-19 and access to health care (including intensive care) across ethnicities. Our analysis supports an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect Pardo and Black Brazilians, as well as the population of poorer states, from their higher risk of dying of COVID-19. FUNDING: None.

Machine learning for clinical trials in the era of COVID-19.

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.

Managing symptomatic drug-induced liver injury in HIV-hepatitis C virus-coinfected patients: a role for interferon.

BACKGROUND: Human immunodeficiency virus (HIV)-infected patients with hepatitis C virus (HCV) coinfection are at increased risk for drug-induced liver injury (DILI) compared with patients with HIV infection alone. The mechanism underlying this observation is unknown. We hypothesized that interferon (IFN) would induce biochemical improvement through its anti-inflammatory properties and thereby facilitate the reintroduction of antiretroviral therapy (ART) in patients with DILI. METHODS: Patients with symptomatic DILI were referred for evaluation; biopsy of a liver sample was performed for all patients, except 1 with clinical cirrhosis. RESULTS: Twelve patients with acquired immunodeficiency syndrome and symptomatic grade 3/4 hepatotoxicity received treatment with IFN and ribavirin (RBV). Seven of these patients had a history of recurrent DILI. The mean baseline CD4(+) T cell counts and HIV RNA levels were 124 cells/mm(3) and 115,369 copies/mL, respectively. Biopsies of liver samples demonstrated significant necroinflammation (mean grade, 10.3) and fibrosis (mean stage, 2.9). Three patients continued to receive ART when they began treatment with IFN-RBV; 9 reinitiated ART within an average of 12 weeks (range, 4-20 weeks) of HCV treatment initiation. All patients attained marked improvement in aminotransferases and continued to receive ART treatment during a mean follow-up regimen of 26.5 months, with subsequent virologic suppression and immunologic reconstitution (mean CD4(+) cell count increase, 251/mm(3)). However, only 1 patient maintained HCV suppression after completion of treatment with IFN-RBV. CONCLUSIONS: In patients with symptomatic DILI, treatment with IFN-ribavirin (RBV) led to decreases in aminotransferase levels, which enabled the reinitiation of ART. The beneficial effects of IFN-based therapy may be modulated through the suppression of proinflammatory cytokines, even in virologic nonresponders. Herein, we propose a novel mechanism for DILI, whereby HCV- and HIV-associated inflammatory mediators induce liver injury synergistically.

Acute hepatitis C virus infection in incarcerated injection drug users.

BACKGROUND: The Centers for Disease Control and Prevention has emphasized the need for interventional programs regarding hepatitis C virus (HCV) infection for injection drug users, the group of persons who are at highest risk of acquiring acute infection. METHODS: We designed a pilot study to assess the feasibility of identifying injection drug users with acute HCV infection in correctional and detoxification facilities. On-site medical providers were educated regarding risk factors and signs and symptoms of infection and were instructed to refer all patients with hepatitis to our specialty clinic. RESULTS: Over a 30-month period, 21 patients received a diagnosis of acute hepatitis C, 3 received a diagnosis of hepatitis B, and 1 received a diagnosis of hepatitis A. Of the 21 patients with acute hepatitis C, 19 were identified in the prison setting shortly after incarceration. Of the 17 patients who were observed serially (mean duration of observation, 6.3 months), 8 had spontaneous virologic clearance. Early therapy with pegylated interferon was initiated for 5 patients with persistent viremia and led to a sustained virologic response in 2 individuals. All patients agreed to undergo human immunodeficiency virus counseling and testing, as well as to receive immunization for hepatitis A and B. CONCLUSIONS: Incarceration presents a unique opportunity to identify injection drug users with acute HCV infection, to initiate counseling regarding other bloodborne pathogens, and to facilitate immunizations and HCV treatment.

Delivering therapy for hepatitis C virus infection to incarcerated HIV-seropositive patients.

The increase in morbidity and mortality due to end-stage liver disease has fueled recent guidelines that recommend consideration of treatment for hepatitis C in human immunodeficiency virus (HIV)-infected patients. Unfortunately, studies indicate that few patients coinfected with HIV and hepatitis C virus (HCV) are treated for their underlying hepatitis because of ongoing substance abuse, depression, chaotic lifestyles, homelessness, and perceived nonadherence. The structured environment of the prison system enables clinicians to provide complicated therapies for HCV to HIV-infected patients in combination with substance abuse programs. Furthermore, adherence to and adverse effects of therapy can be closely monitored. Offering treatment for HCV infection during incarceration to HIV-seropositive persons is highly efficient and targets underserved minority patients who have limited access to care in the community.

Difficulty swallowing and lack of receipt of highly active antiretroviral therapy predict acute weight loss in human immunodeficiency virus disease.

In human immunodeficiency virus (HIV) disease, symptoms of underlying illness may promote weight loss through decreased caloric intake, increased metabolic needs, or nutrient malabsorption. We evaluated disease symptoms as predictors of acute weight loss (i.e., loss of > or =5% of weight). HIV-infected men and women (n=415) were telephoned every 5 weeks to obtain information about weight and recent symptoms. Weight change between each pair of consecutive calls (telephone intervals, 2814) was calculated. Acute weight loss occurred across 4.5% of intervals and among 24% of individuals. Patients reported > or =1 symptom before 58% of telephone intervals. The most common symptoms or symptom complexes before intervals were diarrhea (21% of patients), anorexia (17%), upper respiratory symptoms (16%), skin symptoms (12%), and abdominal pain (12%). Trouble swallowing (6%) and oral symptoms (7%) were less common. Risk of acute weight loss was significantly increased when oral symptoms or trouble swallowing were present, and it was decreased when highly active antiretroviral therapy (HAART) was used or when diarrhea was not present. Even when HAART is being administered, clinicians should remain vigilant regarding weight loss, oral symptoms, and trouble swallowing.