The Prevalence of Previously Undiagnosed Leprosy in the General Population of Northwest Bangladesh

In order to estimate the level of leprosy in an area with many leprosy patients, we determined the prevalence of previously undiagnosed leprosy in the general population and compared this with the registered (or known) number of leprosy patients. We also compared it with the known prevalence of leprosy in contacts of leprosy patients. We examined 20 randomly selected geographical clusters of 1,000 persons each in two districts of Bangladesh, with over 4 million population. Physical examination was performed on all individuals. The number of newly found leprosy cases among 17,862 people above 5 years of age from the clusters was 27, giving a rate of previously undiagnosed leprosy of 15.1 per 10,000. This rate is six times higher than the registered prevalence, but three times lower than the rate in the most distant subgroup of contacts (neighbour of neighbour and social contacts) of leprosy patients in the same area. We conclude that in areas where leprosy is common, it may be preferable to do full village or neighbourhood surveys when a new leprosy patient is found, rather than to limit contact surveys to close contacts only, such as household members

Cost-Effectiveness of a Chemoprophylactic Intervention with Single Dose Rifampicin in Contacts of New Leprosy Patients

In 2008, 249,007 new leprosy patients were detected in the world. It therefore remains necessary to develop new and effective interventions to interrupt the transmission of M. leprae. We assessed the economic benefits of single dose rifampicin (SDR) for contacts as chemoprophylactic intervention in the control of leprosy. The study is based on a large trial including 21,711 contacts of 1,037 patients with newly diagnosed leprosy. We gave a single dose of rifampicin or placebo to contacts and followed them up for four years. The main outcome measure was the development of clinical leprosy. The cost effectiveness was expressed in US dollars per prevented leprosy case. Chemoprophylaxis with SDR for preventing leprosy among contacts of leprosy patients is cost-effective at all contact levels and thereby a cost-effective prevention strategy. In total $6,009 was invested and 38 leprosy cases were prevented after 2 years, costing$158 per prevented leprosy case. Implementation studies are necessary to establish whether this intervention is acceptable and feasible in other leprosy endemic areas of the world

Serratia marcescens necrotizing fasciitis presenting as bilateral breast necrosis

Serratia marcescens is an extremely rare cause of necrotizing fasciitis. We report the first case of necrotizing fasciitis of the chest wall due to infection with S. marcescens that initially manifested as bilateral breast necrosis. The patient had a fulminant course leading to death within 72 h of presentation. Literature pertinent to S. marcescens-mediated necrotizing fasciitis is also reviewed

Preventing Nerve Function Impairment in Leprosy: Validation and Updating of a Prediction Rule

Leprosy is caused by a bacterium that attacks the peripheral nerves. This may cause nerve function impairment (NFI), resulting in handicaps and disabilities. Therefore, prediction and prevention of NFI is extremely important in the management of leprosy. In 2000, a prediction rule for NFI was published, but circumstances have changed since the study was performed in the 1990s: the leprosy detection delay has shortened and the definition of NFI has changed. The original rule used ‘leprosy classification’ and ‘NFI present at diagnosis’ to predict future NFI. In the current patient population we studied an adjusted rule based on ‘leprosy classification’ and ‘presence of antibodies’. This adjusted rule predicted NFI more often than the original rule. With the adjusted rule it is now also possible to assess NFI risk before the first nerve damage event takes place. This may help doctors and health workers to improve surveillance for people at high risk. Early detection and treatment can then prevent permanent disabilities

Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterised by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility to identify integrative patterns from clinical parameters, plasma biomarkers and extensive phenotyping of blood immune cells. Whilst no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90 day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90 day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical and clinical parameters

Leprosy among Patient Contacts: A Multilevel Study of Risk Factors

Leprosy is an infectious disease that can lead to physical disabilities, social stigma, and great hardship. Transmitted from person to person, it is still endemic in developing countries, like Brazil and India. Effective treatment has been available since 1960, but early diagnosis of the disease remains the most effective way to stop the transmission chain and avoid late diagnoses and subsequent disabilities. Knowledge of the risk factors for leprosy can facilitate early detection; therefore, our study aimed to investigate the factors presented by leprosy patients and their contacts, who are considered at highest risk of contracting the disease. We studied 6,158 contacts of 1,201 patients under surveillance from 1987 to 2007 in a Public Health Care Center in the City of Rio de Janeiro, Brazil. We evaluated the ways patient and contact demographics and epidemiological characteristics were associated with the detection of leprosy. Statistical analyses took into account both individual and group characteristics and their interrelationships. The main characteristics facilitating the contraction of leprosy among contacts were shown to be consanguinity and household association. Conversely, the bacillary load index of leprosy patients was the principle factor leading to disease among their contacts

The Critical Turning Points Database : Concept, Methodology and Dataset of an International Transformative Social Innovation Comparison (TRANSIT Working Paper # 10, July 12th 2017)

[Abstract] This working paper presents the TRANSIT open-access online database on Critical Turning Points (CTP) in Transformative Social Innovation. It specifies the contents of the database, comprising qualitative accounts of more than 450 ‘critical’ episodes in the evolution of social innovation initiatives in 27 different countries. Providing the theoretical-methodological context to these data, the paper also describes the theoretical background of the CTP concept and the methodology though which the CTP accounts have been reconstructed through interviews with members of SI initiatives. The paper concludes with reflections on the open access CTP database as a knowledge infrastructure, discussing its significance in terms of mapping, dissemination and framing of social innovation.This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 61316

Serum Metabolomics Reveals Higher Levels of Polyunsaturated Fatty Acids in Lepromatous Leprosy: Potential Markers for Susceptibility and Pathogenesis

Leprosy is an infectious disease caused by the obligate intracellular bacterium Mycobacterium leprae. M. leprae infects the skin and nerves, leading to disfigurement and nerve damage, with the severity of the disease varying widely. We believe there are multiple factors (genetic, bacterial, nutritional and environmental), which may explain the differences in clinical manifestations of the disease. We studied the metabolites in the serum of infected patients to search for specific molecules that may contribute to variations in the severity of disease seen in leprosy. We found that there were variations in levels of certain lipids in the patients with different bacterial loads. In particular, we found that three polyunsaturated fatty acids (PUFAs) involved in the inhibition of inflammation were more abundant in the serum of patients with higher bacterial loads. However, we do not know whether these PUFAs originated from the host or the bacteria. The variations in the metabolite profile that we observed provide a foundation for future research into the explanations of how leprosy causes disease