Comparison of intranasal naloxone and intranasal nalmefene in a translational model assessing the impact of synthetic opioid overdose on respiratory depression and cardiac arrest.

Introduction: Using a validated translational model that quantitatively predicts opioid-induced respiratory depression and cardiac arrest, we compared cardiac arrest events caused by synthetic opioids (fentanyl, carfentanil) following rescue by intranasal (IN) administration of the µ-opioid receptor antagonists naloxone and nalmefene. Methods: This translational model was originally developed by Mann et al. (Clin Pharmacol Ther 2022) to evaluate the effectiveness of intramuscular (IM) naloxone. We initially implemented this model using published codes, reproducing the effects reported by Mann et al. on the incidence of cardiac arrest events following intravenous doses of fentanyl and carfentanil as well as the reduction in cardiac arrest events following a standard 2 mg IM dose of naloxone. We then expanded the model in terms of pharmacokinetic and µ-opioid receptor binding parameters to simulate effects of 4 mg naloxone hydrochloride IN and 3 mg nalmefene hydrochloride IN, both FDA-approved for the treatment of opioid overdose. Model simulations were conducted to quantify the percentage of cardiac arrest in 2000 virtual patients in both the presence and absence of IN antagonist treatment. Results: Following simulated overdoses with both fentanyl and carfentanil in chronic opioid users, IN nalmefene produced a substantially greater reduction in the incidence of cardiac arrest compared to IN naloxone. For example, following a dose of fentanyl (1.63 mg) producing cardiac arrest in 52.1% (95% confidence interval, 47.3-56.8) of simulated patients, IN nalmefene reduced this rate to 2.2% (1.0-3.8) compared to 19.2% (15.5-23.3) for IN naloxone. Nalmefene also produced large and clinically meaningful reductions in the incidence of cardiac arrests in opioid naïve subjects. Across dosing scenarios, simultaneous administration of four doses of IN naloxone were needed to reduce the percentage of cardiac arrest events to levels that approached those produced by a single dose of IN nalmefene. Conclusion: Simulations using this validated translational model of opioid overdose demonstrate that a single dose of IN nalmefene produces clinically meaningful reductions in the incidence of cardiac arrest compared to IN naloxone following a synthetic opioid overdose. These findings are especially impactful in an era when >90% of all opioid overdose deaths are linked to synthetic opioids such as fentanyl.

Towards a survival risk prediction model for metastatic NSCLC patients on durvalumab using whole-lung CT radiomics.

Background: Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting 'less-than-median-survival risk' in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet. Methods: To ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features. Results: The classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively. Conclusion: Machine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Imputing Biomarker Status from RWE Datasets-A Comparative Study.

Missing data is a universal problem in analysing Real-World Evidence (RWE) datasets. In RWE datasets, there is a need to understand which features best correlate with clinical outcomes. In this context, the missing status of several biomarkers may appear as gaps in the dataset that hide meaningful values for analysis. Imputation methods are general strategies that replace missing values with plausible values. Using the Flatiron NSCLC dataset, including more than 35,000 subjects, we compare the imputation performance of six such methods on missing data: predictive mean matching, expectation-maximisation, factorial analysis, random forest, generative adversarial networks and multivariate imputations with tabular networks. We also conduct extensive synthetic data experiments with structural causal models. Statistical learning from incomplete datasets should select an appropriate imputation algorithm accounting for the nature of missingness, the impact of missing data, and the distribution shift induced by the imputation algorithm. For our synthetic data experiments, tabular networks had the best overall performance. Methods using neural networks are promising for complex datasets with non-linearities. However, conventional methods such as predictive mean matching work well for the Flatiron NSCLC biomarker dataset.

Association of circulating protein biomarkers with clinical outcomes of durvalumab in head and neck squamous cell carcinoma.

The potential for durvalumab, a programmed cell death ligand-1 (PD-L1)-blocking monoclonal antibody, to treat head and neck squamous cell carcinoma (HNSCC) is being evaluated in multiple clinical trials. We assessed circulating proteins at baseline to identify potential biomarkers and to understand pathways related to clinical outcomes for durvalumab. Prior to treatment, 66 serum proteins were measured using multiplex immunoassays for 158 durvalumab-treated HNSCC patients in the phase II HAWK and CONDOR trials as a discovery dataset and 209 durvalumab-treated HNSCC patients in the phase III EAGLE trial as a validation dataset. Multivariate Cox modeling of HAWK and CONDOR datasets established that higher baseline concentrations of interleukin-6 (IL-6), C-reactive protein, S100 calcium-binding protein A12, and angiopoietin-2 (ANGPT2) were associated with shorter overall survival (OS), while higher concentrations of osteocalcin correlated with longer OS after durvalumab treatment (p < .05). All five proteins remained significantly correlated with OS after adjusting for baseline clinical factors, with consistent results across clinical efficacy endpoints based on univariate correlation analyses. The validation dataset from the EAGLE trial confirmed the independent association of IL-6 and osteocalcin with OS, and preserved directional trends for the other biomarkers identified in the discovery dataset. Our results demonstrate the important role of immunosuppressive proteins in the resistance of HNSCC to durvalumab treatment. Osteocalcin showed a positive correlation with clinical outcomes, which remains to be further investigated.

Pharmacokinetic-Pharmacodynamic Modelling of Systemic IL13 Blockade by Monoclonal Antibody Therapy: A Free Assay Disguised as Total.

A sequential pharmacokinetic (PK) and pharmacodynamic (PD) model was built with Nonlinear Mixed Effects Modelling based on data from a first-in-human trial of a novel biologic, MEDI7836. MEDI7836 is a human immunoglobulin G1 lambda (IgG1λ-YTE) monoclonal antibody, with an Fc modification to reduce metabolic clearance. MEDI7836 specifically binds to, and functionally neutralizes interleukin-13. Thirty-two healthy male adults were enrolled into a dose-escalation clinical trial. Four active doses were tested (30, 105, 300, and 600 mg) with 6 volunteers enrolled per cohort. Eight volunteers received placebo as control. Following single subcutaneous administration (SC), individual time courses of serum MEDI7836 concentrations, and the resulting serum IL13 modulation in vivo, were quantified. A binding pharmacokinetic-pharmacodynamic (PK-PD) indirect response model was built to characterize the exposure-driven modulation of the target over time by MEDI7836. While the validated bioanalytical assay specification quantified the level of free target (i.e., a free IL13 assay), emerging clinical data suggested dose-dependent increase in systemic IL13 concentration over time, indicative of a total IL13 assay. The target time course was modelled as a linear combination of free target and a percentage of the drug-target complex to fit the clinical data. This novel PK-PD modelling approach integrates independent knowledge about the assay characteristics to successfully elucidate apparently complex observations.

Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy.

We developed and evaluated a method for making early predictions of best overall response (BOR) and overall survival at 6 months (OS6) in patients with cancer treated with immunotherapy. This method combines machine learning with modeling of longitudinal tumor size data. We applied our method to data from durvalumab-exposed patients with recurrent/metastatic head and neck cancer. A fivefold cross-validation was used for model selection. Independent trial data, with various degrees of data truncation, were used for model validation. Mean classification error rates (90% confidence intervals [CIs]) from cross-validation were 5.99% (90% CI 2.98%-7.50%) for BOR and 19.8% (90% CI 15.8%-39.3%) for OS6. During model validation, the area under the receiver operating characteristic curves was preserved for BOR (0.97, 0.97, and 0.94) and OS6 (0.85, 0.84, and 0.82) at 24, 18, and 12 weeks, respectively. These results suggest our method predicts trial outcomes accurately from early data and could be used to aid drug development.

Two-step in vitro-in vivo correlations: Deconvolution and convolution methods, which one gives the best predictability? Comparison with one-step approach.

Finding predictive dissolution tests and valid IVIVCs are essential activities in generic industry, as they can be used as substitutes of human bioequivalence studies. IVIVCs can be developed by two different strategies: a one-step approach or a two-step approach. The objectives of this work were to compare different deconvolution and convolution methods used in the development of two-step level A IVIVCs and to study if the relationship between the in vitro dissolution rate and the in vivo dissolution rate should guide the decision between using a two-step approach or a one-step approach during the development of a new IVIVC. When the in vitro and the in vivo dissolution rates had a linear relationship, valid and biopredictive two-step IVIVCs were obtained, although there was not a combination of deconvolution and convolution methods that could be named as the best one, as long as all the prediction errors for any combination were within the limits. It was not possible to obtain a valid two-step IVIVC when the relationship between dissolution rates was non-linear, but the one-step approach was able to overcome this fact and it gave valid IVIVCs regardless of whether the relationship between dissolution rates was linear or non-linear.

Safety and Clinical Activity of MEDI1873, a Novel GITR Agonist, in Advanced Solid Tumors.

PURPOSE: The safety and preliminary efficacy of MEDI1873, an agonistic IgG1 fusion protein targeting glucocorticoid-induced TNF receptor-related protein (GITR), were evaluated in an open-label, first-in-human, phase I, dose escalation study in previously treated patients with advanced solid tumors. PATIENTS AND METHODS: Two single-patient cohorts at 1.5 and 3 mg i.v. were followed by 3+3 dose escalation in six cohorts at 7.5, 25, 75, 250, 500, and 750 mg, all every 2 weeks, for up to 52 weeks. Primary endpoints were safety and tolerability, dose-limiting toxicities (DLT), and MTD. Secondary endpoints included antitumor activity, pharmacokinetics, immunogenicity, and pharmacodynamics. RESULTS: Forty patients received MEDI1873. Three experienced DLTs: grade 3 worsening tumor pain (250 mg); grade 3 nausea, vomiting, and headache (500 mg); and grade 3 non-ST segment elevation myocardial infarction (750 mg). An MTD was not reached and treatment was well tolerated up to 500 mg. Most common treatment-related adverse events were headache (25%), infusion-related reaction (17.5%), and decreased appetite (17.5%). MEDI1873 exposure was dose proportional. Antidrug-antibody incidence was low. MEDI1873 increased peripheral CD4+ effector memory T-cell proliferation as well as cytokines associated with effector T-cell activation at dose levels ≥75 mg. The best response was stable disease (SD) in 17 patients (42.5%), including 1 unconfirmed partial response. Eight patients (20.0%) had SD ≥24 weeks. CONCLUSIONS: MEDI1873 showed acceptable safety up to 500 mg i.v. every 2 weeks with pharmacodynamics activity, and prolonged SD in some patients. However, further development is not planned because of lack of demonstrated tumor response.

A systems pharmacology model for inflammatory bowel disease.

MOTIVATION: The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. RESULTS: In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.

Population pharmacokinetic model of lithium and drug compliance assessment.

Population pharmacokinetic analysis of lithium during therapeutic drug monitoring and drug compliance assessment was performed in 54 patients and 246 plasma concentrations levels were included in this study. Patients received several treatment cycles (1-9) and one plasma concentration measurement for each patient was obtained always before starting next cycle (pre-dose) at steady state. Data were analysed using the population approach with NONMEM version 7.2. Lithium measurements were described using a two-compartment model (CL/F=0.41Lh-1, V1/F=15.3L, Q/F=0.61Lh-1, and V2/F = 15.8L) and the most significant covariate on lithium CL was found to be creatinine clearance (reference model). Lithium compliance was analysed using inter-occasion variability or Markovian features (previous lithium measurement as ordered categorical covariate) on bioavailability parameter. Markov-type model predicted the lithium compliance in the next cycle with higher success rate (79.8%) compared to IOV model (65.2%) and reference model (43.2%). This model becomes an efficient tool, not only being able to adequately describe the observed outcome, but also to predict the individual drug compliance in the next cycle. Therefore, Bipolar disorder patients can be classified regarding their probability to become extensive or poor compliers in the next cycle and then, individual probabilities lower than 0.5 highlight the need of intensive monitoring, as well as other pharmaceutical care measurements that might be applied to enhance drug compliance for a better and safer lithium treatment.

Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach.

Drug development in Systemic Lupus Erythematosus (SLE) has been hindered by poor translation from successful preclinical experiments to clinical efficacy. This lack of success has been attributed to the high heterogeneity of SLE patients and to the lack of understanding of disease physiopathology. Modelling approaches could be useful for supporting the identification of targets, biomarkers and patient subpopulations with differential response to drugs. However, the use of traditional quantitative models based on differential equations is not justifiable in a sparse data situation. Boolean networks models are less demanding on the required data to be implemented and can provide insights into the dynamics of biological networks. This methodology allows the integration of all the available knowledge into a single framework to evaluate the behavior of the system under different conditions and test hypotheses about unknown aspects of the disease. In this proof-of-concept study, we explored the potential of a systems pharmacology model based on Boolean networks to support drug development in SLE. We focused the analysis on the antigen presentation by the antigen presenting cells (APC) to the T-cells to evaluate the scope of this methodology in a medium size network before full implementation of the whole SLE pathway. The heterogeneity of SLE patients was replicated using this methodology simulating subjects with distinct pathway alterations. A perturbation analysis of the network coupled with clustering analysis showed potential to identify drug targets, optimal combinatorial regimens and subpopulations of responders and non-responders to drug treatment. We propose this approach as a first step towards the development of more quantitative platforms to address the current challenges in drug development for complex diseases.

In vitro-in vivo correlations: general concepts, methodologies and regulatory applications.

The major objective of in vitro-in vivo correlations is to be able to use in vitro data to predict in vivo performance serving as a surrogate for an in vivo bioavailability test and to support biowaivers. Therefore, the aims of this review are: (i) to clarify the factors involved during bio-predictive dissolution method development; and (ii) the elements that may affect the mathematical analysis in order to exploit all information available. This article covers the basic aspects of dissolution media and apparatus used in the development of in vivo predictive dissolution methods, including the latest proposals in this field as well as the summary of the mathematical methods for establishing the in vitro-in vivo relationship and their scope and limitations. The incorporation of physiological relevant factors in the in vitro dissolution method is essential to get accurate in vivo predictions. Standard quality control dissolution methods do not necessarily reflect the in vivo behavior, so they rarely are useful for predicting in vivo performance. The combination of physiological based dissolution methods with physiological-based pharmacokinetics models incorporating gastrointestinal variables will lead to robust tools for drug and formulation development, nevertheless their regulatory use for biowaiver application still require harmonization of the mathematical methods proposed and more detailed recommendations about the procedures for setting up dissolution specifications.

MicroRNAs dysregulation in hepatocellular carcinoma: Insights in genomic medicine.

Hepatocellular carcinoma (HCC) is the leading primary liver cancer and its clinical outcome is still poor. MicroRNAs (miRNAs) have demonstrated an interesting potential to regulate gene expression at post-transcriptional level. Current findings suggest that miRNAs deregulation in cancer is caused by genetic and/or epigenetic, transcriptional and post-transcriptional modifications resulting in abnormal expression and hallmarks of malignant transformation: aberrant cell growth, cell death, differentiation, angiogenesis, invasion and metástasis. The important role of miRNAs in the development and progression of HCC has increased the efforts to understand and develop mechanisms of control overt this single-stranded RNAs. Several studies have analyzed tumoral response to the regulation and control of deregulated miRNAs with good results in vitro and in vivo, proving that targeting aberrant expression of miRNAs is a powerful anticancer therapeutic. Identification of up and/or down regulated miRNAs related to HCC has led to the discovery of new potential application for detection of their presence in the affected organism. MiRNAs represent a relevant new target for diagnosis, prognosis and treatment in a wide variety of pathologic entities, including HCC. This manuscript intends to summarize current knowledge regarding miRNAs and their role in HCC development.

Variability of permeability estimation from different protocols of subculture and transport experiments in cell monolayers.

INTRODUCTION: In vitro models with high predictive ability have been revealed as strong tools for pharmaceutical industry. However, the variability in permeability estimations complicates the comparison and combination of data from different laboratories and it makes necessary the careful validation of the model and the continuous suitability demonstration. The adequate standardization of pre-experimental, experimental and post-experimental factors might help to reduce the inter- and intra-laboratory variability in permeability values. METHODS: The objective of this paper is the evaluation of the effect of passage number, experimental protocol, time after seeding and calculation method on the permeability values and their variability in transport experiments in Caco-2, MDCK and MDCK-MDR1 cells. Metoprolol, Lucifer yellow and Rhodamine-123 were used to check the performance of the cell lines. Protocols used differ mainly in the differentiation time and the filter support coating with collagen. Data was analyzed with sink and non-sink approaches. The final purpose was to explore pre-experimental, experimental and post-experimental conditions in order to select the best experimental scenarios for permeability assays. RESULTS: Results indicated that for passive diffusion studies, coating helps cell differentiation in a more stable manner in all cell lines compared to protocol without coating which showed permeability changes with passages and more variable values. In both protocols the paracellular route became more restricted with higher passage numbers. Functionality of P-gp assessed with Rhodamine permeability did not change with passage number in Caco-2 cells with any of the protocols but increased in both protocols in MDCK and MDCK-MDR1 cells. Protocol without coating showed the less variable results in these cell lines. Rhodamine permeabilities increased with higher maturation times due to a higher expression of the transporter. Nevertheless for compounds absorbed by passive diffusion there was not a clear trend neither in permeability values nor in variability. DISCUSSION: As a conclusion, we have confirmed the influence of maturation conditions and passage number in permeability values and in their variability. Based on our results protocol with coating would be more adequate for studies of compounds absorbed by passive diffusion but the protocol without coating gave us better results for studies about P-gp interactions.

Effect of abdominoplasty in the lipid profile of patients with dyslipidemia.

Introduction. Dyslipidemia like other chronic degenerative diseases is pandemic in Latin America and around the world. A lot of patients asking for body contouring surgery can be sick without knowing it. Objective. Observe the lipid profile of patients with dyslipidemia, before and three months after an abdominoplasty. Methods. Patients candidate to an abdominoplasty without morbid obesity were followed before and three months after the surgery. We compared the lipid profile, glucose, insulin, and HOMA (cardiovascular risk marker) before and three months after the surgery. We used Student's t test to compare the results. A P value less than 0.05 was considered as significant. Results. Twenty-six patients were observed before and after the surgery. At the third month, we found only statistical differences in LDL and triglyceride values (P 0.04 and P 0.03). The rest of metabolic values did not reach statistical significance. Conclusion. In this group of patients with dyslipidemia, at the third month, only LDL and triglyceride values reached statistical significances. There is no significant change in glucose, insulin, HOMA, cholesterol, VLDL, or HDL.

Face transplant: is it feasible in developing countries?

This article is based on the case of a 28-year-old woman who was involved in a car accident, with diagnosis of polytrauma, loss of left eye, and second- and third-degree burns over the left midface, rendering an exposed area of 8 cm wide and 19 cm length, ranging from glabella to mandible, with skull exposure and loss of left eye.A latissimus dorsi musculocutaneous free flap was transferred into the defect; left eye and nose prosthetics were necessary to restore normal appearance. Excellent results were obtained; reinsertion to patient's normal life and reinstatement of facial appearance were achieved with minimal costs and no postsurgical complications.Analysis of the current situation in developing countries demonstrates that technique and infrastructure do not represent a real challenge to carry on face transplants. However, socioeconomic reality in these societies makes it difficult to establish face transplant as a feasible therapeutic opportunity for the overwhelming majority of patients who are victims of severe facial damage.Therefore, strategies such as latissimus dorsi free flap remains as an excellent therapy to face off our complex facial reconstructive challenges in developing countries such as Mexico.

Adenoviral gene therapy in hepatocellular carcinoma: a review.

BACKGROUND: Hepatocellular carcinoma is the third leading cause of cancer death. Single or multiple mutations in genes related to growth control, apoptosis, invasion and metastasis have been determined; so a better understanding of the molecular genetic basis of malignant transformation, tumor progression and host interaction has led to significant progress in the development of new therapeutic agents. The ability of adenovirus vectors to deliver and express genes at high yields in HCC treatment has been demonstrated and well documented over the last few years. OBJECTIVE: To overview and provide an update of what has been accomplished in the field of adenoviral gene therapy and its application in hepatocellular carcinoma treatment. METHODS: Original articles were searched using Pubmed and other medical databases to get the most representative and actual information to establish the current state of the investigation of Ad vectors in HCC. RESULTS: Good results have been accomplished in preclinical models using new Ad vectors and especially AAV vectors, it is important to motivate further clinical trials to corroborate all the experience obtained. CONCLUSIONS: Ad and AAV must be considered as an opportunity to improve the quality of life and survival of HCC patients.

A controlled clinical trial with pirfenidone in the treatment of pathological skin scarring caused by burns in pediatric patients.

BACKGROUND: Pathologic skin scarring reversion remains a big challenge for surgeons, as disfiguring scars have a dramatic influence on patient's quality of life. METHODS: A controlled clinical trial was conducted to evaluate 8% pirfenidone (PFD) gel administered topically 3 times a day during 6 months to 33 pediatric patients with hypertrophic scars caused by burns. A total of 30 patients with hypertrophic scars with identical Vancouver Scar Scale values were treated with pressure therapy and included as controls. Improvements were evaluated by Vancouver Scar Scale and a Visual Analog Scale. Safety parameters were determined by the presence of adverse events and monitoring laboratory and hematology parameters. RESULTS: Patients treated with PFD during 6 months presented a continuous monthly statistically significant scar regression in comparison with the initial Vancouver measurement (P = <0.001). PFD group showed a higher improvement of all scar features as compared with control group treated with pressure therapy (P = <0.001). In the PFD group, 9 of 33 patients (27%) had their scores decreased in Vancouver classification by more than 55%, 22 patients (67%) had a 30% to 45% decrease, whereas 2 patients (6%) had a 30% decrease or less. Control group treated with pressure therapy showed a slight improvement in 16% of cases on an average. Patients did not show serious adverse effects or laboratory alterations throughout the study. CONCLUSIONS: Topical administration of 8% PFD gel 3 times a day is more effective and safe in the treatment of hypertrophic scars caused by burns in children, as compared with standard pressure therapy.

Pneumomediastinum as a complication of emphysematous cholecystitis: case report.

BACKGROUND: Emphysematous cholecystitis is a variant of acute cholecystitis which is generally caused by gas-forming organisms. Emphysematous cholecystitis may cause gas spreading within the subcutaneous tissue, peritoneal cavity and retroperitoneum. CASE PRESENTATION: We present a case of emphysematous cholecystitis in a middle-aged diabetic patient who, postoperatively, presented edema in both flanks and left chest crepitation on palpation, associated with hemodynamic worsening. Computed tomography scan of the chest and abdomen revealed a large pneumomediastinum, pneumoretroperitoneum, gas in subcutaneous tissue and flank abscesses. In both blood and surgical wound exudate cultures, Escherichia coli was found. CONCLUSION: Emphysematous cholecystitis should be considered as a possible cause of pneumomediastinum.