Low-distortion information propagation with noise suppression in swarm networks.

A method for low-distortion (low-dissipation, low-dispersion) information propagation in swarm-type networks with suppression of high-frequency noise is presented. Information propagation in current neighbor-based networks, where each agent seeks to achieve a consensus with its neighbors, is diffusion-like, dissipative, and dispersive and does not reflect the wave-like (superfluidic) behavior seen in nature. However, pure wave-like neighbor-based networks have two challenges: i) It requires additional communication for sharing information about time derivatives and ii) it can lead to information decoherence through noise at high frequencies. The main contribution of this work is to show that delayed self-reinforcement (DSR) by the agents using prior information (e.g., using short-term memory) can lead to the wave-like information propagation at low-frequencies as seen in nature without the need for additional information sharing between the agents. Moreover, it is shown that the DSR can be designed to enable suppression of high-frequency noise transmission while limiting the dissipation and dispersion of (lower-frequency) information content leading to similar (cohesive) behavior of agents. In addition to explaining noise-suppressed wave-like information transfer in natural systems, the result impacts the design of noise-suppressing cohesive algorithms for engineered networks.

Interplay of lipid head group and packing defects in driving amyloid-beta-mediated myelin-like model membrane deformation.

Accumulating evidence suggests that amyloid plaque-associated myelin lipid loss as a result of elevated amyloid burden might also contribute to Alzheimer's disease. The amyloid fibrils are closely associated with lipids under physiological conditions; however, the progression of membrane remodeling events leading to lipid-fibril assembly remains unknown. Here we first reconstitute the interaction of amyloid Beta 40 (Aß-40) with myelin-like model membrane and show that the binding of Aß-40 induces extensive tubulation. To look into the mechanism of membrane tubulation, we chose a set of membrane conditions varying in lipid packing density and net charge that allows us to dissect the contribution of lipid specificity of Aß-40 binding, aggregation kinetics, and subsequent changes in membrane parameters such as fluidity, diffusion, and compressibility modulus. We show that the binding of Aß-40 depends predominantly on the lipid packing defect densities and electrostatic interactions and results in rigidification of the myelin-like model membrane during the early phase of amyloid aggregation. Furthermore, elongation of Aß-40 into higher oligomeric and fibrillar species leads to eventual fluidization of the model membrane followed by extensive lipid membrane tubulation observed in the late phase. Taken together, our results capture mechanistic insights into snapshots of temporal dynamics of Aß-40-myelin-like model membrane interaction and demonstrate how short timescale, local phenomena of binding, and fibril-mediated load generation results in the consequent association of lipids with growing amyloid fibrils.

Amyloids on Membrane Interfaces: Implications for Neurodegeneration.

Membrane interfaces are vital for various cellular processes, and their involvement in neurodegenerative disorders such as Alzheimer's and Parkinson's disease has taken precedence in recent years. The amyloidogenic proteins associated with neurodegenerative diseases interact with the neuronal membrane through various means, which has implications for both the onset and progression of the disease. The parameters that regulate the interaction between the membrane and the amyloids remain poorly understood. The review focuses on the various aspects of membrane interactions of amyloids, particularly amyloid-ß (Aß) peptides and Tau involved in Alzheimer's and α-synuclein involved in Parkinson's disease. The genetic, cell biological, biochemical, and biophysical studies that form the basis for our current understanding of the membrane interactions of Aß peptides, Tau, and α-synuclein are discussed.

Leprosy services in primary health care in India: comparative economic cost analysis of two public-health settings.

OBJECTIVES: The WHO recommends inclusion of post-exposure chemoprophylaxis with single-dose rifampicin in national leprosy control programmes. The objective was to estimate the cost of leprosy services at primary care level in two different public-health settings. METHODS: Ingredient-based costing was performed in eight primary health centres (PHCs) purposively selected in the Union Territory of Dadra and Nagar Haveli (DNH) and the Umbergaon block of Valsad district, Gujarat, India. All costs were bootstrapped, and to estimate the variation in total cost under uncertainty, a univariate sensitivity analysis was performed. RESULTS: The mean annual cost of providing leprosy services was USD 29 072 in the DNH PHC (95% CI: 22 125-36 020) and USD 11 082 in Umbergaon (95% CI: 8334-13 830). The single largest cost component was human resources: 79% in DNH and 83% in Umbergaon. The unit cost for screening the contact of a leprosy patient was USD 1 in DNH (95% CI: 0.8-1.2) and USD 0.3 in Umbergaon (95% CI: 0.2-0.4). In DNH, the unit cost of delivering single-dose of rifampicin (SDR) as chemoprophylaxis for contacts was USD 2.9 (95% CI: 2.5-3.7). CONCLUSIONS: The setting with an enhanced public-health financing system invests more in leprosy services than a setting with fewer financial resources. In terms of leprosy visits, the enhanced public-health system is hardly more expensive than the non-enhanced public-health system. The unit cost of contact screening is not high, favouring its sustainability in the programme.

Attenuation of neuroblastoma cell growth by nisin is mediated by modulation of phase behavior and enhanced cell membrane fluidity.

Antimicrobial peptides have been attracting significant attention as potential anti-cancer therapeutic agents in recent times. Yet most antimicrobial peptides seem to possess cytotoxic effects on non-cancerous cells. Nisin, an antimicrobial peptide and FDA approved food preservative, has recently been found to induce selective apoptotic cell death and reduced cell proliferation in different cancer cell lines. However, the mechanism of nisin interaction with cancer cell membranes remains unexplored. Using potentiometric dye-based fluorescence and monolayer surface pressure-area isotherms we find that nisin interaction enhances the fluidity and reduces the dipole potential of a neuroblastoma cell membrane model. The quantified compressibility modulus suggests that the changes in fluidity are predominantly driven by the nisin interaction with the non-raft like regions. However, the measured positive Gibbs free energy of mixing and enthalpy hints that nisin, owing to its unfavorable mixing with cholesterol, might significantly disrupt the raft-like domains.

Population-wide administration of single dose rifampicin for leprosy prevention in isolated communities: a three year follow-up feasibility study in Indonesia.

BACKGROUND: Indonesia ranking third in the world, regarding leprosy burden. Chemoprophylaxis is effective in reducing risk of developing leprosy among contacts. 'Blanket approach' is an operational strategy for leprosy post-exposure prophylaxis in which all members of an isolated community, high endemic for leprosy are screened and given a single dose of rifampicin (SDR) in the absence of signs and symptoms of leprosy. The objective is to assess the operational feasibility of a population-wide 'blanket' administration of SDR for leprosy prevention in isolated communities in a remote island. METHODS: A prospective follow-up study was conducted in the year 2014, 2015 and 2016 in Lingat village of Selaru Island, Indonesia. During the first two visits, screening and SDR were provided, whereas only screening was conducted during the third visit. The demographic and clinical data were used for a descriptive analysis of the project coverage and leprosy epidemiology. RESULTS: During the first two visits, 1671 persons (88%) were screened, 1499 (79%) received SDR, and 213 (11%) were excluded based on the exclusion criteria. During the first two visits, 43 (2.6%) cases were diagnosed with leprosy with a rate of 2263 per 100,000 population. The prevalence was highest in the age groups 15-24 and 25-49 years. Total, 14 (33%) cases had MB and 29 (67%) PB leprosy. Two cases (5%) had grade 2 disability. During the third visit, 10 new leprosy cases, with no grade 2 disability, were detected out of 1481 screened persons at the rate of 484 cases per 100,000 population (n = 2065 population in 2016). Among those screened during the third visit, there was a 50% reduction of leprosy among those who had previously received SDR compared to those who had not. CONCLUSION: With adequate planning and some additional investment, it is feasible to implement a blanket approach of chemoprophylaxis in a remote island of Indonesia, although effort needs to be made to cover as many people as possible in the first visit. Contingency plans need to be made to actively follow this village closely in the coming years and continue leprosy elimination efforts until no new cases are found any more.

The Leprosy Post-Exposure Prophylaxis (LPEP) programme: update and interim analysis.

Innovative approaches are required to further enhance leprosy control, reduce the number of people developing leprosy, and curb transmission. Early case detection, contact screening, and chemoprophylaxis currently is the most promising approach to achieve this goal. The Leprosy Post-Exposure Prophylaxis (LPEP) programme generates evidence on the feasibility of integrating contact tracing and single-dose rifampicin (SDR) administration into routine leprosy control activities in different settings. The LPEP programme is implemented within the leprosy control programmes of Brazil, Cambodia, India, Indonesia, Myanmar, Nepal, Sri Lanka and Tanzania. Focus is on three key interventions: tracing the contacts of newly diagnosed leprosy patients; screening the contacts for leprosy; and administering SDR to eligible contacts. Country-specific protocol adaptations refer to contact definition, minimal age for SDR, and staff involved. Central coordination, detailed documentation and rigorous supervision ensure quality evidence. Around 2 years of field work had been completed in seven countries by July 2017. The 5,941 enrolled index patients (89·4% of the registered) identified a total of 123,311 contacts, of which 99·1% were traced and screened. Among them, 406 new leprosy patients were identified (329/100,000), and 10,883 (8·9%) were excluded from SDR for various reasons. Also, 785 contacts (0·7%) refused the prophylactic treatment with SDR. Overall, SDR was administered to 89·0% of the listed contacts. Post-exposure prophylaxis with SDR is safe; can be integrated into the routines of different leprosy control programmes; and is generally well accepted by index patients, their contacts and the health workforce. The programme has also invigorated local leprosy control.

Investment case concepts in leprosy elimination: A systematic review.

INTRODUCTION: Leprosy continues to be a global public health problem, but draws less attention because 'prevalence based elimination' has been misinterpreted as eradication. The ongoing transmission of M. leprae has renewed interest in complete elimination. The aim of our study is to review systematically the literature regarding the elimination of leprosy, and to assess this information on its applicability for defining a Leprosy Elimination Investment Case (LEIC) based on Eradication Investment Case guidelines. METHODOLOGY: A literature search was conducted using the MeSH subheadings and synonyms of leprosy. A total of 1007 articles were considered and 112 were included in the final selection. The search focused on the literature covering leprosy elimination and its public health aspects. The LEIC framework was adapted from an existing "Guide to Preparing an Eradication Investment Case". RESULTS: The LEIC framework provided 11 topics under which information was synthesized from the literature. The fields were categorised under sections: 1) Proposed investment; 2) Rationale for investing; 3) Issues to consider when moving from control to eradication; 4) Management and governance. Scanty quantitative data are available for developing a LEIC, particularly regarding disease burden, and new interventions that could contribute to elimination are not yet applied routinely. DISCUSSION: For monitoring global elimination, it is necessary to measure disease burden comprehensively, and contact centered preventive interventions should be part of a global elimination strategy. The biological and technical feasibility of elimination is not certain and advanced microbiological and operational research is necessary to understand transmission better. The current WHO road map for leprosy elimination is too vague and needs further structuring through a thoroughly prepared LEIC.

Testicular embryonal cell carcinoma presenting as haemoptysis and skin nodules.

Embryonal cell carcinoma affects young males in the prime of their life with majority of tumours already having metastasised at the time of diagnosis. Subcutaneous metastasis from embryonal carcinoma is rare and is associated with widespread disease and poor prognosis. We report a case of 22-year-old male who presented with haemoptysis and skin nodules. Fine needle aspiration cytology of skin nodules and the lung lesion led to the diognosis of testicular embryonal cell carcinoma.

Deep learning-assisted concentration gradient generation for the study of 3D cell cultures in hydrogel beads of varying stiffness.

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50 Pa to close to 1 kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

Lipid Specificity of the Fusion of Bacterial Extracellular Vesicles with the Host Membrane.

Bacterial membrane vesicles (MVs) facilitate the long-distance delivery of virulence factors crucial for pathogenicity. The entry and trafficking mechanisms of virulence factors inside host cells are recently emerging; however, whether bacterial MVs can fuse and modulate the physicochemical properties of the host lipid membrane and membrane lipid parameter for fusion remains unknown. In this study, we reconstituted the interaction of bacterial MVs with host cell lipid membranes and quantitatively showed that bacterial MV interaction increases the fluidity, dipole potential, and compressibility of a biologically relevant multicomponent host membrane upon fusion. The presence of cylindrical lipids, such as phosphatidylcholine, and a moderate acyl chain length of C16 help the MV interaction. While significant binding of bacterial MVs to the raft-like lipid membranes with phase-separated regions of the membrane was observed, however, MVs prefer binding to the liquid-disordered regions of the membrane. Furthermore, the elevated levels of cholesterol tend to hinder the interaction of bacterial MVs, as evident from the favorable excess Gibbs free energy of mixing bacterial MVs with host lipid membranes. The findings provide new insights that might have implications for the regulation of host machinery by bacterial pathogens through manipulation of the host membrane properties.

Congenital brain tumour in a neonate: a therapeutic challenge.

A term neonate with history of ventriculomegaly in the fetal period was diagnosed with a central nervous system tumour after radiological investigations. It was confirmed as an immature teratoma after histopathological examination. He underwent left frontal craniotomy with tumour excision. Intraoperatively, massive haemorrhage (venous bleed) occurred due to the high vascularity of the tumour and led to haemodynamic instability. A massive transfusion protocol was initiated. Despite multiple transfusions and shock management, he succumbed at 2 weeks of life. This case report highlights the importance of antenatal diagnosis and fetal MRI in prognostication and also the possible role of neoadjuvant chemotherapy in reducing tumour vascularity and, hence, bleeding.

A modeling pipeline to relate municipal wastewater surveillance and regional public health data.

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Artificial intelligence and remote patient monitoring in US healthcare market: a literature review.

BACKGROUND: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions. OBJECTIVES: Identify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets' needs. METHODS: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions. RESULTS: A total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3). CONCLUSION: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.

Droplet-based methodology for investigating bacterial population dynamics in response to phage exposure.

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative methodologies for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations and to modulate growth of both commensal and pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 (Escherichia virus T7) and two phage species with RNA genomes, MS2 (Emesvirus zinderi) and Qß (Qubevirus durum). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25 h, allowing us to determine growth rates of bacterial populations and helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic single-cell and population-level approaches will expedite research towards fundamental understanding of the genetic and molecular basis of rapid phage-induced lysis and eco-evolutionary aspects of bacteria-phage dynamics, and ultimately help identify key factors influencing the success of phage therapy.

Assessing the Patient Outcomes and Performance of a Cardiothoracic and Vascular Surgery (CTVS) Unit During Its First Two Years in a Tier-2 City in India: A Comprehensive Audit and Analysis.

This detailed article presents a comprehensive overview of the initial two-year experience in establishing a new cardiothoracic vascular surgery (CTVS) facility in a tier-2 city in India. The article discusses various aspects of setting up and operating a specialized healthcare facility. The first two years of developing the CTVS facility were included in the study period. The manpower included one cardiothoracic vascular surgeon, one cardiac anesthesiologist, two perfusionists, and two physician assistants, along with four other ancillary staff to assist in the smooth functioning of the operation theater. The CTVS recovery staff included 15 nursing officers. There was only one modular operation theater reserved for cardiothoracic vascular surgeries, along with a five-bed recovery room (CTVS intensive care unit). One-hundred-seventy-two procedures were done, including 122 open heart surgeries, 36 vascular procedures, and 14 thoracic procedures. The majority of patients were discharged by the seventh day postoperatively. Overall complication and mortality rates were 8% and 4.6%, respectively. This article also discusses relevant hospital policy, challenges faced, and future recommendations for similar endeavors. The findings highlight the successful implementation of the facility and its impact on providing specialized cardiac care to the local population.

Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India.

Due to ongoing climate change, water mass redistribution and related hazards are getting stronger and frequent. Therefore, predicting extreme hydrological events and related hazards is one of the highest priorities in geosciences. Machine Learning (ML) methods have shown promising prospects in this venture. Every ML method requires training where we know both the output (extreme event) and input (relevant physical parameters and variables). This step is critical to the efficacy of the ML method. The usual approach is to include a wide variety of hydro-meteorological observations and physical parameters, but recent advances in ML indicate that the efficacy of ML may not improve by increasing the number of input parameters. In fact, including unimportant parameters decreases the efficacy of ML algorithms. Therefore, it is imperative that the most relevant parameters are identified prior to training. In this study, we demonstrate this concept by predicting avalanche susceptibility in Leh-Manali highway (one of the most severely affected regions in India) with and without Parameter Importance Assessment (PIA). The avalanche locations were randomly divided into two groups: 70% for training and 30% for testing. Then, based on temporal and spatial sensor data, eleven avalanche influencing parameters were considered. The Boruta algorithm, an extension of Random Forest (RF) ML method that utilizes the importance measure to rank predictors, was used and it found nine out of eleven parameters to be important. Support Vector Machine (SVM) based ML technique is used for avalanche prediction, and to be comprehensive, four different kernel functions were employed (linear, polynomial, sigmoid, and radial basis function (RBF)). The prediction accuracy for linear, polynomial, sigmoid, and RBF kernels, with all the eleven parameters were found to be 80.4%, 81.7%, 39.2%, and 85.7%, respectively. While, when using selected parameters, the prediction accuracy for linear, polynomial, sigmoid, and RBF kernels were 84.1%, 86.6%, 43.0%, and 87.8%, respectively. We also identified locations where occurrences of avalanches are most likely. We conclude that parameter selection should be considered when applying ML methods in geosciences.

Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.

Leprosy and cutaneous leishmaniasis affecting the same individuals: A retrospective cohort analysis in a hyperendemic area in Brazil.

BACKGROUND: Leprosy and cutaneous leishmaniasis (CL) are neglected tropical diseases (NTDs) affecting the skin. Their control is challenging but the integration of skin NTDs control programs is recommended to improve timely detection and treatment. However, little is known about the occurrence of leprosy and CL in the same individuals, and what are the characteristics of such patients. This study aimed to identify and characterize patients diagnosed with both leprosy and CL (i.e., outcome) in the hyperendemic state of Mato Grosso, Brazil. Also, we investigated the demographic risk factors associated with the period between the diagnosis of both diseases. METHODOLOGY/PRINCIPAL FINDINGS: A retrospective cohort study was conducted with patients diagnosed between 2008 and 2017. From the leprosy (n = 28,204) and CL (n = 24,771) databases of the national reporting system, 414 (0.8%; 414/52,561) patients presenting both diseases were identified through a probabilistic linkage procedure. This observed number was much higher than the number of patients that would be expected by chance alone (n = 22). The spatial distribution of patients presenting the outcome was concentrated in the North and Northeast mesoregions of the state. Through survival analysis, we detected that the probability of a patient developing both diseases increased over time from 0.2% in the first year to 1.0% within seven years. Further, using a Cox model we identified male sex (HR: 2.3; 95% CI: 1.7-2.9) and low schooling level (HR: 1.5; 95% CI: 1.2-1.9) as positively associated with the outcome. Furthermore, the hazard of developing the outcome was higher among individuals aged 40-55 years. CONCLUSIONS/SIGNIFICANCE: Leprosy and CL are affecting the same individuals in the area. Integration of control policies for both diseases will help to efficiently cover such patients. Measures should be focused on timely diagnosis by following-up patients diagnosed with CL, active case detection, and training of health professionals.

Revised estimates of leprosy disability weights for assessing the global burden of disease: A systematic review and individual patient data meta-analysis.

BACKGROUND: Leprosy is a chronic bacterial infection caused by Mycobacterium leprae, which may lead to physical disability, stigma, and discrimination. The chronicity of the disease and disabilities are the prime contributors to the disease burden of leprosy. The current figures of the disease burden in the 2017 global burden of disease study, however, are considered to be under-estimated. In this study, we aimed to systematically review the literature and perform individual patient data meta-analysis to estimate new disability weights for leprosy, using Health-Related Quality of Life (HRQOL) data. METHODOLOGY/PRINCIPAL FINDINGS: The search strategy included all major databases with no restriction on language, setting, study design, or year of publication. Studies on human populations that have been affected by leprosy and recorded the HRQOL with the Short form tool, were included. A consortium was formed with authors who could share the anonymous individual-level data of their study. Mean disability weight estimates, sorted by the grade of leprosy disability as defined by WHO, were estimated for individual participant data and pooled using multivariate random-effects meta-analysis. Eight out of 14 studies from the review were included in the meta-analysis due to the availability of individual-level data (667 individuals). The overall estimated disability weight for grade 2 disability was 0.26 (95%CI: 0.18-0.34). For grade 1 disability the estimated weight was 0.19 (95%CI: 0.13-0.26) and for grade 0 disability it was 0.13 (95%CI: 0.06-0.19). The revised disability weight for grade 2 leprosy disability is four times higher than the published GBD 2017 weights for leprosy and the grade 1 disability weight is nearly twenty times higher. CONCLUSIONS/SIGNIFICANCE: The global burden of leprosy is grossly underestimated. Revision of the current disability weights and inclusion of disability caused in individuals with grade 0 leprosy disability will contribute towards a more precise estimation of the global burden of leprosy.