A cost-effectiveness analysis of a novel algorithm to sequentially diagnose leprosy based on manufactured tests under the SUS perspective.

Brazil has the second largest number of leprosy cases (a disease with a significant burden) in the world. Despite global and local efforts to eliminate this public health problem, inadequate or late diagnosis contribute to perpetuate its transmission, especially among household contacts. Tests such as the rapid IgM antibody detection (RT) and real-time polymerase chain reaction (RT-PCR) were developed to overcome the challenges of early diagnosis of leprosy. This study aimed to analyze the cost-effectiveness of a new diagnostic algorithm recommended by the Brazilian government to diagnose leprosy in household contacts of confirmed leprosy cases, which includes the RT and RT-PCR tests. A decision tree model was constructed and the perspective of the Brazilian Unified National Health System (SUS) and a 1-year time horizon were adopted. Only direct medical costs related to diagnostic tests were included. Effectiveness was measured as the number of avoided undiagnosed leprosy cases. Different scenarios were analyzed. The sequential use of RT, slit-skin smear (SSS) microscopy, and RT-PCR as recommended by the Brazilian Ministry of Health was compared to a base case (isolated SSS microscopy), yielding an incremental cost-effectiveness ratio of USD 616.46 per avoided undiagnosed leprosy case. Univariate sensitivity analysis showed that the prevalence of leprosy among household contacts was the variable that influenced the model the most. This is the first economic model to analyze a diagnostic algorithm of leprosy. Results may aid managers to define policies and strategies to eradicate leprosy in Brazil.

Automated chronic wounds medical assessment and tracking framework based on deep learning.

Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.

Exact expressions and numerical evaluation of average evolvability measures for characterizing and comparing [Formula: see text] matrices.

Theory predicts that the additive genetic covariance ([Formula: see text]) matrix determines a population's short-term (in)ability to respond to directional selection-evolvability in the Hansen-Houle sense-which is typically quantified and compared via certain scalar indices called evolvability measures. Often, interest is in obtaining the averages of these measures across all possible selection gradients, but explicit formulae for most of these average measures have not been known. Previous authors relied either on approximations by the delta method, whose accuracy is generally unknown, or Monte Carlo evaluations (including the random skewers analysis), which necessarily involve random fluctuations. This study presents new, exact expressions for the average conditional evolvability, average autonomy, average respondability, average flexibility, average response difference, and average response correlation, utilizing their mathematical structures as ratios of quadratic forms. The new expressions are infinite series involving top-order zonal and invariant polynomials of matrix arguments, and can be numerically evaluated as their partial sums with, for some measures, known error bounds. Whenever these partial sums numerically converge within reasonable computational time and memory, they will replace the previous approximate methods. In addition, new expressions are derived for the average measures under a general normal distribution for the selection gradient, extending the applicability of these measures into a substantially broader class of selection regimes.

Artificial intelligence: Its role in dermatopathology.

Artificial intelligence (AI), a major frontier in the field of medical research, can potentially lead to a paradigm shift in clinical practice. A type of artificial intelligence system known as convolutional neural network points to the possible utility of deep learning in dermatopathology. Though pathology has been traditionally restricted to microscopes and glass slides, recent advancement in digital pathological imaging has led to a transition making it a potential branch for the implementation of artificial intelligence. The current application of artificial intelligence in dermatopathology is to complement the diagnosis and requires a well-trained dermatopathologist's guidance for better designing and development of deep learning algorithms. Here we review the recent advances of artificial intelligence in dermatopathology, its applications in disease diagnosis and in research, along with its limitations and future potential.

Artificial intelligence in dermatology and healthcare: An overview.

Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.

Management of Darier disease: A review of the literature and update.

Darier disease (DD) is a rare type of inherited keratinizing disorder with no definitive therapeutic approach. The objective of this study is to provide a detailed literature review of all the available treatment modalities of Darier disease, including those that are both surgical and non surgical, to compare their efficacies and to propose a novel therapeutic approach. A complete search of the literature for all articles describing the different treatments of Darier disease, with no restrictions on patients' ages, gender or nationalities, was performed with the use of PubMed. A total of 68 articles were included in the study: 3 prospective studies, 44 case reports/case series and 21 letters/correspondences/clinical images. The treatments described were topical, oral or physical. Retinoids (isotretinoin, tazarotene and adapalene) and fluorouracil were the two most effective topical treatments. Oral retinoids were the most effective oral therapy and were prescribed in the cases of generalized Darier disease. For localized and resistant skin lesions, physical therapies including surgical excision, dermabrasion and CO2 laser ablation were the first line choices. Limitations of this article include the inability to verify the accuracy of the published data, the relatively small sample size, the absence of randomized controlled clinical trials and possible unidentified confounding factors in various studies. In every therapeutic approach to Darier disease, consideration of patient comorbidities, disease distribution, severity and treatment accessibility is essential. Large and randomized clinical trials are necessary for the comparison of the efficacy and the safety of all the treatments of Darier disease and settling a consensus for management.

Status and forecast of leprosy in the still endemic province of Formosa in northern Argentina.

BACKGROUND: The province of Formosa, Argentina, is endemic for leprosy. In the present paper, we assessed the trend (T, 2002-2016 time series) and the forecast for 2022 of new case detection rate (NCDR) and determined the spatial distribution of new cases detected (NCD) of leprosy. METHODOLOGY/PRINCIPAL FINDINGS: This is a descriptive observational study of 713 NCD of leprosy from provincial medical records between January 2002 and December 2016. The whole dataset from the provincial medical record was used to independently estimate the NCDR trends of the general population, age groups, sexes and Departments. This same database was used to estimate the NCDR forecast of the general population for 2022, applying a dynamic linear model with a local linear trend, using the MCMC algorithm. The NCDR was higher in men (p<0.05), increased with age (0.20, 8.17, 21.04, and 29.49 for the 0-14, 15-44, 45-64 and over 65-year-old age groups, respectively; p<0.05) and showed a downward trend (negative values) of estimated slopes for the whole province and each Department. Bermejo Department showed the highest (T:-1.02, 95%CI: [-1.42, -0.66]) and Patiño the lowest decreasing trend (T:-0.45, 95%CI: [-0.74, -0.11]). The NCDR trend for both sexes was similar (T:-0.55, 95%CI: [-0.64, -0.46]), and age groups showed a decreasing trend (S15-44:-103, S45-64:-81, S>65:-61, p<0.05), except for the 0-14 age group (S:-3, p>0.05), which showed no trend. Forecasts predicted that leprosy will not be eliminated by 2022 (3.64, 95%CI: [1.22, 10.25]). CONCLUSIONS/SIGNIFICANCE: Our results highlight the status of leprosy in Formosa and provide information to the provincial public health authorities on high-risk populations, stressing the importance of timely detection of new cases for further elimination of the disease in the province.

Developing and validating a clinical algorithm for the diagnosis of podoconiosis.

BACKGROUND: Difficulties in reliably diagnosing podoconiosis have severely limited the scale-up and uptake of the World Health Organization-recommended morbidity management and disability prevention interventions for affected people. We aimed to identify a set of clinical features that, combined into an algorithm, allow for diagnosis of podoconiosis. METHODS: We identified 372 people with lymphoedema and administered a structured questionnaire on signs and symptoms associated with podoconiosis and other potential causes of lymphoedema in northern Ethiopia. All individuals were tested for Wuchereria bancrofti-specific immunoglobulin G4 in the field using Wb123. RESULTS: Based on expert diagnosis, 344 (92.5%) of the 372 participants had podoconiosis. The rest had lymphoedema due to other aetiologies. The best-performing set of symptoms and signs was the presence of moss on the lower legs and a family history of leg swelling, plus the absence of current or previous leprosy, plus the absence of swelling in the groin, plus the absence of chronic illness (such as diabetes mellitus or heart or kidney diseases). The overall sensitivity of the algorithm was 91% (95% confidence interval [CI] 87.6 to 94.4) and specificity was 95% (95% CI 85.45 to 100). CONCLUSIONS: We developed a clinical algorithm of clinical history and physical examination that could be used in areas suspected or endemic for podoconiosis. Use of this algorithm should enable earlier identification of podoconiosis cases and scale-up of interventions.

Reanalysis and integration of public microarray datasets reveals novel host genes modulated in leprosy.

Due to multiple hypothesis testing with often limited sample size, microarrays and other-omics technologies can sometimes produce irreproducible findings. Complementary to better experimental design, reanalysis and integration of gene expression datasets may help overcome reproducibility issues by identifying consistent differentially expressed genes from independent studies. In this work, after a systematic search, nine microarray datasets evaluating host gene expression in leprosy were reanalyzed and the information was integrated to strengthen evidence of differential expression for several genes. Our results are relevant in prioritizing genes and pathways for further investigation, whether in functional studies or in biomarker discovery. Reanalysis of individual datasets revealed several differentially expressed genes (DEGs) in accordance with original reports. Then, five integration methods (P value and effect size based) were tested. In the end, random-effects model and ratio association were selected as the main methods to pinpoint DEGs. Overall, classic pathways were found corroborating previous findings and validating this approach. Also, we identified some novel DEG involved especially with skin development processes (AQP3, AKR1C3, CYP27B1, LTB, VDR) and keratinocyte biology (CSTA, DSG1, KRT14, KRT5, PKP1, IVL), both still poorly understood in leprosy context. In addition, here we provide aggregated evidence towards some gene candidates that should be prioritized in further leprosy research, as they are likely important in immunopathogenesis. Altogether, these data are useful in better understanding host responses to the disease and, at the same time, provide a list of potential host biomarkers that could be useful in complementing leprosy diagnosis based on transcriptional levels.

Solubility, Hansen Solubility Parameters and Thermodynamic Behavior of Emtricitabine in Various (Polyethylene Glycol-400 + Water) Mixtures: Computational Modeling and Thermodynamics.

This study was aimed to find out the solubility, thermodynamic behavior, Hansen solubility parameters and molecular interactions of an antiviral drug emtricitabine (ECT) in various "[polyethylene glycol-400 (PEG-400) + water]" mixtures. The solubility of ECT in mole fraction was determined at "T = 298.2 to 318.2 K" and "p = 0.1 MPa" using an isothermal method. The experimental solubilities of ECT in mole fraction were validated and correlated using various computational models which includes "Van't Hoff, Apelblat, Yalkowsky-Roseman, Jouyban-Acree and Jouyban-Acree-Van't Hoff models". All the models performed well in terms of model correlation. The solubility of ECT was increased with the raise in temperature in all "PEG-400 + water" mixtures studied. The highest and lowest solubility values of ECT were found in pure PEG-400 (1.45 × 10-1) at "T = 318.2 K" and pure water (7.95 × 10-3) at "T = 298.2 K", respectively. The quantitative values of activity coefficients indicated higher interactions at molecular level in ECT and PEG-400 combination compared with ECT and water combination. "Apparent thermodynamic analysis" showed an "endothermic and entropy-driven dissolution" of ECT in all "PEG-400 + water" combinations studied. The solvation nature of ECT was found an "enthalpy-driven" in each "PEG-400 + water" mixture studied.

Development and Validation of a Virtual Gelatin Model Using Molecular Modeling Computational Tools.

To successfully design and optimize the application of hydrogel matrices one has to effectively combine computational design tools with experimental methods. In this context, one of the most promising techniques is molecular modeling, which requires however accurate molecular models representing the investigated material. Although this method has been successfully used over the years for predicting the properties of polymers, its application to biopolymers, including gelatin, is limited. In this paper we provide a method for creating an atomistic representation of gelatin based on the modified FASTA codes of natural collagen. We show that the model created in this manner reproduces known experimental values of gelatin properties like density, glass-rubber transition temperature, WAXS profile and isobaric thermal expansion coefficient. We also present that molecular dynamics using the INTERFACE force field provides enough accuracy to track changes of density, fractional free volume and Hansen solubility coefficient over a narrow temperature regime (273-318 K) with 1 K accuracy. Thus we depict that using molecular dynamics one can predict properties of gelatin biopolymer as an efficient matrix for immobilization of various bioactive compounds, including enzymes.

Validity of maximal oxygen consumption prediction equations in young Saudi females.

OBJECTIVES: To determine the applicability of Jones, Hansen, and Wasserman predictive equations for maximal oxygen consumption (VO2max) in Saudi females.  Methods: This cross-sectional study was conducted at Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia, between March and May 2017. Maximal oxygen consumption was measured directly through the COSMED system for cardiopulmonary exercise testing in 102 girls with normal body mass index (19-25 years old). Maximal oxygen consumption was indirectly predicted by Jones, Hansen, and Wasserman equations. Paired t-test, Pearson correlation, and Bland-Altman plot were used for comparison, correlation, and agreement analysis. Results: The difference between the mean and standard deviation (±SD) VO2max values of the direct measurement (27.39±4.06 ml/kg-1/min-1), and the Jones (35.19±2.12 ml/kg-1/min-1), Hansen (33.64±0.24 ml/kg-1/min-1), and Wasserman (35.20±0.17 ml/kg-1/min-1) equations, was statistically significant (p less than 0.001). Bland-Altman plot analysis suggested a lack of agreement between direct and predicted VO2max. Pearson correlation failed to reveal any correlation between direct VO2max and VO2max calculated with any of the 3 equations. Conclusion: Jones, Hansen, and Wasserman equations for prediction of VO2max cannot be justified in the studied population. For the better prediction of VO2max, either these equations should be modified, or a new equation should be developed for the Saudi population.

A critical issue of using the variance of the total in the linearization method - In the context of unequal probability sampling.

Publicly available national survey data are useful for the evidence-based research to advance our understanding of important questions in the health and biomedical sciences. Appropriate variance estimation is a crucial step to evaluate the strength of evidence in the data analysis. In survey data analysis, the conventional linearization method for estimating the variance of a statistic of interest uses the variance estimator of the total based on linearized variables. We warn that this common practice may result in undesirable consequences such as susceptibility to data shift and severely inflated variance estimates, when unequal weights are incorporated into variance estimation. We propose to use the variance estimator of the mean (mean-approach) instead of the variance estimator of the total (total-approach). We show a superiority of the mean-approach through analytical investigations. A real data example (the National Comorbidity Survey Replication) and simulation-based studies strongly support our conclusion.

[Treatment of ankle fractures].

Fractures of the ankle joint are among the most frequent fractures encountered in any accident and emergency department. The traditional classification systems, i.e. the Lauge-Hansen- and the Weber classifications, have proven inadequate, when it comes to deciding, whether treatment should be surgical or conservative. About ten years ago, a stability-based classification was proposed in view of the fact, that the stabilisation of osteosynthesis depends on the stability of the ankle joint. The literature shows excellent long-term results for conservatively treated stable ankle joint fractures.

Characterization of Ofloxacin Interaction with Mutated (A91V) Quinolone Resistance Determining Region of DNA Gyrase in Mycobacterium Leprae through Computational Simulation.

Mycobacterium leprae, the causal agent of leprosy is non-cultivable in vitro. Thus, the assessment of antibiotic activity against Mycobacterium leprae depends primarily upon the time-consuming mouse footpad system. The GyrA protein of Mycobacterium leprae is the target of the antimycobacterial drug, Ofloxacin. In recent times, the GyrA mutation (A91V) has been found to be resistant to Ofloxacin. This phenomenon has necessitated the development of new, long-acting antimycobacterial compounds. The underlying mechanism of drug resistance is not completely known. Currently, experimentally crystallized GyrA-DNA-OFLX models are not available for highlighting the binding and mechanism of Ofloxacin resistance. Hence, we employed computational approaches to characterize the Ofloxacin interaction with both the native and mutant forms of GyrA complexed with DNA. Binding energy measurements obtained from molecular docking studies highlights hydrogen bond-mediated efficient binding of Ofloxacin to Asp47 in the native GyrA-DNA complex in comparison with that of the mutant GyrA-DNA complex. Further, molecular dynamics studies highlighted the stable binding of Ofloxacin with native GyrA-DNA complex than with the mutant GyrA-DNA complex. This mechanism provided a plausible reason for the reported, reduced effect of Ofloxacin to control leprosy in individuals with the A91V mutation. Our report is the first of its kind wherein the basis for the Ofloxacin drug resistance mechanism has been explored with the help of ternary Mycobacterium leprae complex, GyrA-DNA-OFLX. These structural insights will provide useful information for designing new drugs to target the Ofloxacin-resistant DNA gyrase.

Revisiting primary neural leprosy: Clinical, serological, molecular, and neurophysiological aspects.

BACKGROUND: Leprosy neuropathy is considered the most common peripheral neuropathy of infectious etiology worldwide, representing a public health problem. Clinical diagnosis of primary neural leprosy (PNL) is challenging, since no skin lesions are found and the slit skin smear bacilloscopy is negative. However, there are still controversial concepts regarding the primary-neural versus pure-neural leprosy definition, which will be explored by using multiple clinical-laboratory analyses in this study. METHODOLOGY/PRINCIPAL FINDINGS: Seventy patients diagnosed with primary neural leprosy from 2014 to 2016 underwent clinical, laboratorial and neurophysiological evaluation. All patients presented an asymmetric neural impairment, with nerve thickening in 58.6%. Electroneuromyography showed a pattern of mononeuropathy in 51.4%. Positivity for ELISA anti-PGL1 was 52.9%, while the qPCR of slit skin smear was 78.6%. The qPCR of nerve biopsies was positive in 60.8%. Patients with multiple mononeuropathy patterns showed lower levels of anti-PGL-1 (p = 0.0006), and higher frequency of neural thickening (p = 0.0008) and sensory symptoms (p = 0.01) than those with mononeuropathy. CONCLUSIONS/SIGNIFICANCE: PNL is not a synonym of pure neural leprosy, as this condition may include a generalized immune response and also a skin involvement, documented by molecular findings. Immunological, molecular, and neurophysiological tools must be implemented for diagnosing primary neural leprosy to achieve effective treatment and reduction of its resultant disabilities that still represent a public health problem in several developing nations. Finally, we propose a algorithm and recommendations for the diagnosis of primary neural leprosy based on the combination of the three clinical-laboratorial tools.

Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC.

This study investigates an efficient and accurate computational method for predicating mycobacterial membrane protein. Mycobacterium is a pathogenic bacterium which is the causative agent of tuberculosis and leprosy. The existing feature encoding algorithms for protein sequence representation such as composition and translation, and split amino acid composition cannot suitably express the mycobacterium membrane protein and their types due to biasness among different types. Therefore, in this study a novel un-biased dipeptide composition (Unb-DPC) method is proposed. The proposed encoding scheme has two advantages, first it avoid the biasness among the different mycobacterium membrane protein and their types. Secondly, the method is fast and preserves protein sequence structure information. The experimental results yield SVM based classification accurately of 97.1% for membrane protein types and 95.0% for discriminating mycobacterium membrane and non-membrane proteins by using jackknife cross validation test. The results exhibit that proposed model achieved significant predictive performance compared to the existing algorithms and will lead to develop a powerful tool for anti-mycobacterium drugs.

Evaluation of the Bifactor Nominal Response Model Analysis of a Health Efficacy Measure.

The bifactor nominal response item response theory (IRT) model, proposed by Cai, Yang and Hansen (2011), provides an extension of Bock's (1972, 1997) unidimensional nominal response model to multidimensional IRT. This model has not been utilized in any published studies since its original development. In this study, the model was applied to data from a sample of college students (N = 799) to evaluate the psychometric properties of a health efficacy measure. The nominal response model has the unique capability to estimate the functioning of each single response category, and higher response categories were found to have better functioning in this study. Poor-functioning categories were identified and combined into their adjacent categories. Items with revised response format showed improved functioning. The bifactor nominal response model is a useful tool for evaluation of bifactor scales with ordered while non-equivalently functioning categories.

Phenotypic characterization of a pair of molecules in tissues confer to classical Mendelian or non Mendelian ratios.

Studies have reported a wide range of inflammatory responses in the nerve, skin and plasma of leprosy patients. The expression levels of each biomolecule was individualistic, however could be categorized as high and low based on their statistical mean level. Here we report for the first time, expression of a set of biomolecules relating with each other in a defined proportion. The hypothesis of this paper is that the segregation of high and low combinations of a set of biomolecules follows either classical Mendelian dihybrid ratio or epistatic ratios. This hypothesis was tested for 17 molecules in three tissues; nerve, skin and plasma and were confirmed to interact in 9:7, 9:3:4, 12:3:1, 13:3, 15:1 epistatic proportions. These findings suggest that there could be a significant role of networking of molecules in defined epistatic proportions and could be important in pathophysiology of peripheral nerve.