Rice residue promotes mobilisation and plant acquisition of soil phosphorus under wheat (Triticum aestivum)-rice (Oryza sativa) cropping sequence in a semi-arid Inceptisol.

Disposal of significant tonnages of rice straw is expensive, but using it to mobilise phosphorus (P) from inorganically fixed pools in the soil may add value. This study was carried out to determine whether the use of rice straw mixed with phosphorus-solubilizing microbes could solubilize a sizable portion fixed soil P and affect P transformation, silicon (Si) concentration, organic acid concentrations, and enzyme activity to increase plant growth. Depending on the soil temperature, the application of rice straw at 12 Mg ha-1 with phosphorus-solubilizing microbes could solubilize 3.4-3.6% of inorganic P, and minimised the hysteresis impact by 6-8%. At plant maturity, application of rice straw at 12 Mg ha-1 with phosphorus-solubilizing microbes and 75% of recommended P application raised the activity of dehydrogenase, alkaline phosphatase activity, cellulase, and peroxidase by 77, 65, 87, and 82% in soil, respectively. It also boosted Si concentration in the soil by 58%. Wheat grain yield was 40% and 18% higher under rice straw at 12 Mg ha-1 with phosphorus-solubilizing microbes with 75% of recommended P application than under no and 100% P application, respectively. Rice grain yield also increased significantly with the same treatment. Additionally, it increased root volume, length, and P uptake by 2.38, 1.74 and 1.62-times above control for wheat and 1.98, 1.67, and 2.06-times above control for rice, respectively. According to path analysis, P solubilisation by Si and organic acids considerably increased (18-32%) P availability in the rhizosphere. Therefore, cultivators could be advised to use rice straw at 12 Mg ha-1 with phosphorus-solubilizing microbes with 75% P of mineral P fertiliser to save 25% P fertiliser without reducing wheat and rice yield.

Passive flow control via tip grooving and stall fencing mechanisms of a marine energy harvesting turbine.

Remarkable advancement in wave energy conversion technology has taken place in recent years. Due to its simplicity, the Wells turbine has been one of the most widely used power take-off mechanisms in an oscillating water column type wave-energy conversion device. However, the turbine suffers from several challenges due to its narrow operating range, which hinders the commercial feasibility of the system. Several aerodynamic applications have successfully used passive control methods to modify the flow conditions. This work applied a combination of stall fences and casing grooves for passive flow control of a Wells turbine. The computational fluid dynamics (CFD) technique is used to analyze the modified turbine numerically. The casing groove modified the tip-leakage vortices, interacted with local vortices created by the stall fences, and helped reattach the flow at higher flow coefficients. As a result, the modified turbine increases the operating range up to 33.3%. In addition, the peak-to-average (PTA) power ratio decreased by up to 27.7%.

Concerted effect of Ni-in and S-out on ReS2 nanostructures towards high-efficiency oxygen evolution reaction.

Herein, a one-step hydrothermal reaction is developed to synthesize a Ni-doped ReS2 nanostructure with sulphur defects. The material exhibited excellent OER activity with a current density of 10 mA cm-2 at an overpotential of 270 mV, a low Tafel slope of 31 mV dec-1, and good long-term durability of 10 h in 1 M KOH. It shows high faradaic efficiency of 96%, benefiting from the rapid charge transfer caused by the concerted effect of Ni-in and S-out on the ReS2 nanostructure.

Impact of vaccine prioritization strategies on mitigating COVID-19: an agent-based simulation study using an urban region in the United States.

BACKGROUND: Approval of novel vaccines for COVID-19 had brought hope and expectations, but not without additional challenges. One central challenge was understanding how to appropriately prioritize the use of limited supply of vaccines. This study examined the efficacy of the various vaccine prioritization strategies using the vaccination campaign underway in the U.S. METHODS: The study developed a granular agent-based simulation model for mimicking community spread of COVID-19 under various social interventions including full and partial closures, isolation and quarantine, use of face mask and contact tracing, and vaccination. The model was populated with parameters of disease natural history, as well as demographic and societal data for an urban community in the U.S. with 2.8 million residents. The model tracks daily numbers of infected, hospitalized, and deaths for all census age-groups. The model was calibrated using parameters for viral transmission and level of community circulation of individuals. Published data from the Florida COVID-19 dashboard was used to validate the model. Vaccination strategies were compared using a hypothesis test for pairwise comparisons. RESULTS: Three prioritization strategies were examined: a minor variant of CDC's recommendation, an age-stratified strategy, and a random strategy. The impact of vaccination was also contrasted with a no vaccination scenario. The study showed that the campaign against COVID-19 in the U.S. using vaccines developed by Pfizer/BioNTech and Moderna 1) reduced the cumulative number of infections by 10% and 2) helped the pandemic to subside below a small threshold of 100 daily new reported cases sooner by approximately a month when compared to no vaccination. A comparison of the prioritization strategies showed no significant difference in their impacts on pandemic mitigation. CONCLUSIONS: The vaccines for COVID-19 were developed and approved much quicker than ever before. However, as per our model, the impact of vaccination on reducing cumulative infections was found to be limited (10%, as noted above). This limited impact is due to the explosive growth of infections that occurred prior to the start of vaccination, which significantly reduced the susceptible pool of the population for whom infection could be prevented. Hence, vaccination had a limited opportunity to reduce the cumulative number of infections. Another notable observation from our study is that instead of adhering strictly to a sequential prioritizing strategy, focus should perhaps be on distributing the vaccines among all eligible as quickly as possible, after providing for the most vulnerable. As much of the population worldwide is yet to be vaccinated, results from this study should aid public health decision makers in effectively allocating their limited vaccine supplies.

Impact of school reopening on pandemic spread: A case study using an agent-based model for COVID-19.

This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study. The study uses an agent-based simulation model that replicates community spread in an urban region of U.S.A. via daily social mixing of susceptible and infected individuals. Data representing population demographics, SARS-CoV-2 epidemiology, and social interventions guides the model's behavior, which is calibrated and validated using data reported by the government. The model indicates a modest but significant increase (8.15%) in the total number of reported cases in the region for a complete (100%) reopening compared to keeping schools and colleges fully virtual. For partial returns of 75% and 50%, the percent increases in the number of reported cases are shown to be small (2.87% and 1.26%, respectively) and statistically insignificant. The AB model also predicts that relaxing the stringency of the school safety protocol for sanitizing, use of mask, social distancing, testing, and quarantining and thus allowing the school transmission coefficient to double may result in a small increase in the number of reported infected cases (2.14%). Hence for pandemic outbreaks from viruses with similar characteristics as for SARS-CoV-2, keeping the schools and colleges open with a modest campus safety protocol and in-person attendance below a certain threshold may be advisable.

Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region.

PURPOSE: Social intervention strategies to mitigate COVID-19 are examined using an agent-based simulation model. Outbreak in a large urban region, Miami-Dade County, Florida, USA is used as a case study. Results are intended to serve as a planning guide for decision makers. METHODS: The simulation model mimics daily social mixing behavior of the susceptible and infected generating the spread. Data representing demographics of the region, virus epidemiology, and social interventions shapes model behavior. Results include daily values of infected, reported, hospitalized, and dead. RESULTS: Results show that early implementation of complete stay-at-home order is effective in flattening and reversing the infection growth curve in a short period of time. Whereas, using Florida's Phase II plan alone could result in 75% infected and end of pandemic via herd immunity. Universal use of face masks reduced infected by 20%. A further reduction of 66% was achieved by adding contact tracing with a target of identifying 50% of the asymptomatic and pre-symptomatic. CONCLUSIONS: In the absence of a vaccine, the strict stay-at-home order, though effective in curbing a pandemic outbreak, leaves a large proportion of the population susceptible. Hence, there should be a strong follow up plan of social distancing, use of face mask, contact tracing, testing, and isolation of infected to minimize the chances of large-scale resurgence of the disease. However, as the economic cost of the complete stay-at-home-order is very high, it can perhaps be used only as an emergency first response, and the authorities should be prepared to activate a strong follow up plan as soon as possible. The target level for contact tracing was shown to have a nonlinear impact on the reduction of the percentage of population infected. Increase in contact tracing target from 20% to 30% appeared to provide the largest incremental benefit.

A strategic gaming model for health information exchange markets.

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to $2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals' willingness to adopt. Hospitals' apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

Estimating disease burden of a potential A(H7N9) pandemic influenza outbreak in the United States.

BACKGROUND: Since spring 2013, periodic emergence of avian influenza A(H7N9) virus in China has heightened the concern for a possible pandemic outbreak among humans, though it is believed that the virus is not yet human-to-human transmittable. Till June 2017, A(H7N9) has resulted in 1533 laboratory-confirmed cases of human infections causing 592 deaths. The aim of this paper is to present disease burden estimates (measured by infection attack rates (IAR) and number of deaths) in the event of a possible pandemic outbreak caused by human-to-human transmission capability acquired by A(H7N9) virus. Even though such a pandemic will likely spread worldwide, our focus in this paper is to estimate the impact on the United States alone. METHOD: The method first uses a data clustering technique to divide 50 states in the U.S. into a small number of clusters. Thereafter, for a few selected states in each cluster, the method employs an agent-based (AB) model to simulate human A(H7N9) influenza pandemic outbreaks. The model uses demographic and epidemiological data. A few selected non-pharmaceutical intervention (NPI) measures are applied to mitigate the outbreaks. Disease burden for the U.S. is estimated by combining results from the clusters applying a method used in stratified sampling. RESULTS: Two possible pandemic scenarios with R 0 = 1.5 and 1.8 are examined. Infection attack rates with 95% C.I. (Confidence Interval) for R 0 = 1.5 and 1.8 are estimated to be 18.78% (17.3-20.27) and 25.05% (23.11-26.99), respectively. The corresponding number of deaths (95% C.I.), per 100,000, are 7252.3 (6598.45-7907.33) and 9670.99 (8953.66-10,389.95). CONCLUSIONS: The results reflect a possible worst-case scenario where the outbreak extends over all states of the U.S. and antivirals and vaccines are not administered. Our disease burden estimations are also likely to be somewhat high due to the fact that only dense urban regions covering approximately 3% of the geographic area and 81% of the population are used for simulating sample outbreaks. Outcomes from these simulations are extrapolated over the remaining 19% of the population spread sparsely over 97% of the area. Furthermore, the full extent of possible NPIs, if deployed, could also have lowered the disease burden estimates.

A MDP model for breast and ovarian cancer intervention strategies for BRCA1/2 mutation carriers.

PURPOSE: Women with BRCA1/2 mutations have higher risk for breast and ovarian cancers. Available intervention actions include prophylactic surgeries and breast screening, which vary significantly in cost, cancer prevention, and in resulting death from other causes. We present a model designed to yield optimal intervention strategies for mutation carriers between the ages of 30 and 65 and any prior intervention history. METHODS: A Markov decision process (MDP) model is developed that considers yearly state transitions for the mutation carriers and state dependent intervention actions. State is defined as a vector comprising mutation type, health states, prior intervention actions, and age. A discounted value iteration algorithm is used to obtain optimal strategies from the MDP model using both cost and quality-adjusted life years (QALYs) as rewards. RESULTS: The results from MDP model show that for 30-year-old women with BRCA1 mutation and no prior intervention history, the cost-optimal strategy is a combination of prophylactic mastectomy (PM) and prophylactic oophorectomy (PO) at age 30 with no screening afterwards. Whereas, the QALYs-optimal strategy suggests PO at age 30 and PM at age 50 with screening afterwards. For BRCA2 mutation carriers at age 30, the cost-optimal strategy is PO at age 30, PM at age 40, and yearly screening only after age 56. Corresponding QALYs-optimal strategy is PM at age 40 with screening. Strategies for all other ages (31 to 65) are obtained and presented. It is also demonstrated that the cost-optimal strategies offer near maximum survival rate and near minimum cancer incidence rates by age 70, when compared to other ad hoc strategies.

Design of non-pharmaceutical intervention strategies for pandemic influenza outbreaks.

BACKGROUND: As seen during past pandemic influenza outbreaks, pharmaceutical interventions (PHIs) with vaccines and antivirals are the most effective methods of mitigation. However, availability of PHIs is unlikely to be adequate during the early stages of a pandemic. Hence, for early mitigation and possible containment, non-pharmaceutical interventions (NPIs) offer a viable alternative. Also, NPIs may be the only available interventions for most underdeveloped countries. In this paper we present a comprehensive methodology for design of effective NPI strategies. METHODS: We develop a statistical ANOVA-based design approach that uses a detailed agent-based simulation as an underlying model. The design approach obtains the marginal effect of the characteristic parameters of NPIs, social behavior, and their interactions on various pandemic outcome measures including total number of contacts, infections, and deaths. We use the marginal effects to establish regression equations for the outcome measures, which are optimized to obtain NPI strategies. Efficacy of the NPI strategies designed using our methodology is demonstrated using simulated pandemic influenza outbreaks with different levels of virus transmissibility. RESULTS: Our methodology was able to design effective NPI strategies, which were able to contain outbreaks by reducing infection attack rates (IAR) to below 10% in low and medium virus transmissibility scenarios with 33% and 50% IAR, respectively. The level of reduction in the high transmissibility scenario (with 65% IAR) was also significant. As noted in the published literature, we also found school closure to be the single most effective intervention among all NPIs. CONCLUSIONS: If harnessed effectively, NPIs offer a significant potential for mitigation of pandemic influenza outbreaks. The methodology presented here fills a gap in the literature, which, though replete with models on NPI strategy evaluation, lacks a treatise on optimal strategy design.

Persistence of herbicide fenoxaprop ethyl and its acid metabolite in soil and wheat crop under Indian tropical conditions.

The persistence of fenoxaprop ethyl {Ethyl (RS)-2-[4-(6-chloro-1,3-benzoxazol-2-yloxy) phenoxy] propionate} herbicide and its active metabolite fenoxaprop acid was investigated in soil and wheat crop. Fenoxaprop acid was prepared by alkaline hydrolysis of fenoxaprop ethyl. A HPLC method was developed in which fenoxaprop ethyl herbicide and its acid metabolite showed sharp single peak at 6.44 and 2.61 min respectively. The sensitivity of the method for ester and acid was 2 and 1 ng respectively with limit of detection of 0.1 and 0.05 µg mL(-1). The recovery of fenoxaprop ethyl and fenoxaprop acid from soil, wheat straw and grain ranged between 73.8-80.2%. In a field experiment fenoxaprop ethyl (Puma super® 10 EC) when applied to wheat crop at the rate of 120 g and 240 g a.i. ha(-1) as post emergence spray, fenoxaprop ethyl converted to fenoxaprop acid. Residues of fenoxaprop ethyl and acid dissipated in soil with a half-life of 0.5 and 7.3 days, respectively. At harvest no detectable residues of fenoxaprop ethyl or acid were observed in soil, wheat grain and straw samples.

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels.

BACKGROUND: In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use. METHODS: We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers. RESULTS: While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values. CONCLUSIONS: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

Probability model for estimating colorectal polyp progression rates.

According to the American Cancer Society, colorectal cancer (CRC) is the third most common cause of cancer related deaths in the United States. Experts estimate that about 85% of CRCs begin as precancerous polyps, early detection and treatment of which can significantly reduce the risk of CRC. Hence, it is imperative to develop population-wide intervention strategies for early detection of polyps. Development of such strategies requires precise values of population-specific rates of incidence of polyp and its progression to cancerous stage. There has been a considerable amount of research in recent years on developing screening based CRC intervention strategies. However, these are not supported by population-specific mathematical estimates of progression rates. This paper addresses this need by developing a probability model that estimates polyp progression rates considering race and family history of CRC; note that, it is ethically infeasible to obtain polyp progression rates through clinical trials. We use the estimated rates to simulate the progression of polyps in the population of the State of Indiana, and also the population of a clinical trial conducted in the State of Minnesota, which was obtained from literature. The results from the simulations are used to validate the probability model.

A predictive decision-aid methodology for dynamic mitigation of influenza pandemics.

In a recent report, the Institute of Medicine has stressed the need for dynamic mitigation strategies for pandemic influenza. In response to the need, we have developed a simulation-based optimization methodology for generating dynamic predictive mitigation strategies for pandemic outbreaks affecting several regions. Our methodology can accommodate varying virus and population dynamics. It progressively allocates a limited budget to procure vaccines and antivirals, capacities for their administration, and resources required to enforce social distancing. The methodology uses measures of morbidity, mortality, and social distancing, which are translated into the costs of lost productivity and medical services. The simulation model was calibrated using historic pandemic data. We illustrate the use of our methodology on a mock outbreak involving over four million people residing in four major population centers in Florida, USA. A sensitivity analysis is presented to estimate the impact of changes in the budget availability and variability of some of the critical parameters of mitigation strategies. The methodology is intended to assist public health policy makers.

Removal of hybridization and scanning noise from microarrays.

Microarray technology for measuring gene expression values has created significant opportunities for advances in disease diagnosis and individualized treatment planning. However, the random noise introduced by the sample preparation, hybridization, and scanning stages of microarray processing creates significant inaccuracies in the gene expression levels, and hence presents a major barrier in realizing the anticipated advances. Literature presents several methodologies for noise reduction, which can be broadly categorized as: 1) model based approaches for estimation and removal of hybridization noise; 2) approaches using commonly available image denoising tools; and 3) approaches involving the need for control sample(s). In this paper, we present a novel methodology for identifying and removing hybridization and scanning noise from microarray images, using a dual-tree-complex-wavelet-transform-based multiresolution analysis coupled with bivariate shrinkage thresholding. The key features of our methodology include consideration of inherent features and type of noise specific to microarray images, and the ability to work with a single microarray without needing a control. Our methodology is first benchmarked on a fabricated dataset that mimics a real microarray probe dataset. Thereafter, our methodology is tested on datasets obtained from a number of Affymetrix GeneChip human genome HG-U133 Plus 2.0 arrays, processed on HCT-116 cell line at the Microarray Core Facility of Moffitt Cancer Center and Research Institute. The results indicate an appreciable improvement in the quality of the microarray data.

LEAD: a methodology for learning efficient approaches to medical diagnosis.

Determining the most efficient use of diagnostic tests is one of the complex issues facing medical practitioners. With the soaring cost of healthcare, particularly in the US, there is a critical need for cutting costs of diagnostic tests, while achieving a higher level of diagnostic accuracy. This paper develops a learning based methodology that, based on patient information, recommends test(s) that optimize a suitable measure of diagnostic performance. A comprehensive performance measure is developed that accounts for the costs of testing, morbidity, and mortality associated with the tests, and time taken to reach diagnosis. The performance measure also accounts for the diagnostic ability of the tests. The methodology combines tools from the fields of data mining (rough set theory, in particular), utility theory, Markov decision processes (MDP), and reinforcement learning (RL). The rough set theory is used in extracting diagnostic information in the form of rules from the medical databases. Utility theory is used in bringing various nonhomogenous performance measures into one cost based measure. An MDP model together with an RL algorithm facilitates obtaining efficient testing strategies. The methodology is implemented on a sample problem of diagnosing solitary pulmonary nodule (SPN). The results obtained are compared with those from four alternative testing strategies. Our methodology holds significant promise to improve the process of medical diagnosis.

Cardiac dysfunction in portal hypertension among patients with cirrhosis and non-cirrhotic portal fibrosis.

BACKGROUND/AIMS: In cirrhosis, diastolic dysfunction of heart is well documented. Contribution of portal hypertension towards cardiac changes in cirrhosis is difficult to assess. We examined the patients of non-cirrhotic portal fibrosis who have portal hypertension without liver insufficiency to understand the contribution of portal hypertension in causing cardiac changes. METHODS: Cardiac function was studied in four groups of patients: normal controls, patients with non-cirrhotic portal fibrosis (having portal hypertension without liver dysfunction) and cirrhotics with and without ascites. Cardiac function was evaluated by echocardiography. Additional measurements of plasma renin activity and aldosterone levels were performed. RESULTS: Diastolic function as assessed by the ratio between E wave and A wave (E/A ratio), was significantly lower in patients with non-cirrhotic portal fibrosis (median 1.3) compared to normal controls (median 1.52). However, even lower values were observed in cirrhotics without ascites (median 1.05) and with ascites (median 0.94). There was a significant correlation (r=-0.75) between plasma aldosterone levels and the E/A ratio in cirrhotics. CONCLUSIONS: Diastolic dysfunction is not only present in cirrhosis but also in non-cirrhotic portal fibrosis. It indicates that portal hypertension is an important factor in the genesis of cardiac dysfunction.

Portal pressure response to losartan compared with propranolol in patients with cirrhosis.

OBJECTIVES: Losartan, an angiotensin II receptor blocker, has portal hypotensive effects. This study evaluates the effect of losartan on portal pressure after 14 days and compares it with that of propranolol. METHODS: A total of 39 individuals with cirrhosis were randomized into two groups of 19 and 20 patients each and were treated with losartan and propranolol, respectively. Hepatic venous pressure gradient was measured at baseline and on day 14 of therapy. Responders to therapy had hepatic venous pressure gradient reduction of >/=20% of baseline value. RESULTS: With losartan, 15 of 19 (78.94%) patients were responders and with propranolol, nine of 20 (45%) patients were responders (p < 0.05). Although the hepatic venous pressure gradient reduction (i.e., percentage from baseline) with losartan (26.74 +/- 21.7%) was higher than with propranolol (14.52 +/- 32%), the difference was not significant. The reduction in hepatic venous pressure gradient with losartan was contributed mainly by a significant drop of wedge hepatic venous pressure from 32.42 +/- 6.61 mm of Hg to 28.31 +/- 5.09 mm of Hg (p < 0.05) compared to that with propranolol, which was from 34.55 +/- 5.41 mm of Hg to 32.75 +/- 8.13 mm of Hg (p > 0.05). Responders among alcohol-abusing patients were significantly higher with losartan (81.8%) compared to those on propranolol (27.2%; p < 0.05). In the losartan group, all seven nonascitic cirrhotic individuals, as compared with two of five in the propranolol group, responded to the drugs. During the study, no significant side effects were observed in either group (who were not receiving diuretics) or in follow-up with diuretics. CONCLUSIONS: Losartan is as effective as propranolol in reducing portal pressure in cirrhotic patients who are not receiving diuretics. Losartan is also superior to propranolol for achieving target level hepatic venous gradient for prevention of variceal bleeding in nonascitic and alcohol-abusing cirrhotic patients.