CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm.

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism.

Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.

TNFipred: a classification model to predict TNF-α inhibitors.

Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of TNF-α is a significant mechanism that results in excess swelling and pain. Drugs acting on TNF-α are known to significantly reduce the disease progression and improve the quality of life in many RA patients. Hence, inhibiting TNF-α is considered one of the most effective treatments for RA. Currently, there are only a few FDA-approved TNF-α inhibitors, which are mainly monoclonal antibodies, fusion proteins, or biosimilars with disadvantages such as poor stability, difficulty in route of administration (often given as injection or infusion), cost-prohibitive large-scale production, and increased side effects. There are just a handful of small compounds known to have TNF- inhibitory capabilities. Thus, there is a dire need for new drugs, especially small molecules in the market, such as TNF-α inhibitors. The conventional method of identifying TNF-α inhibitors is expensive, labor, and time intensive. Machine learning (ML) can be used to solve existing drug discovery and development problems. In this study, four classification algorithms-naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM)-were used to train ML models for classifying TNF-α inhibitors based on three sets of features. The performance of the RF model was found to be best when using 1D, 2D, and fingerprints as features, with an accuracy of 87.96 and a sensitivity of 86.17. To our knowledge, this is the first ML model for TNF-α inhibitor prediction. The model is available at http://14.139.57.41/tnfipred/.

Barriers and facilitators to hepatitis B birth dose vaccination: Perspectives from healthcare providers and pregnant women accessing antenatal care in Nigeria.

Nigeria is estimated to have the largest number of children worldwide, living with chronic hepatitis B virus (HBV) infection, the leading cause of liver cancer. Up to 90% of children infected at birth develop chronic HBV infection. A birth dose of the hepatitis B vaccine (HepB-BD) followed by at least two additional vaccine doses is recommended for prevention. This study assessed barriers and facilitators of HepB-BD administration and uptake, using structured interviews with healthcare providers and pregnant women in Adamawa and Enugu States, Nigeria. The Consolidated Framework for Implementation Sciences Research (CFIR) guided data collection and analysis. We interviewed 87 key informants (40 healthcare providers and 47 pregnant women) and created a codebook for data analysis. Codes were developed by reviewing the literature and reading a subsample of queries line-by-line. The overarching themes identified as barriers among healthcare providers were: the lack of hepatitis B knowledge, limited availability of HepB-BD to vaccination days only, misconceptions about HepB-BD vaccination, challenges in health facility staffing capacity, costs associated with vaccine transportation, and concerns related to vaccine wastage. Facilitators of timely HepB-BD vaccination included: vaccine availability, storage, and hospital births occurring during immunization days. Overarching themes identified as barriers among pregnant women were lack of hepatitis B knowledge, limited understanding of HepB-BD importance, and limited access to vaccines for births occurring outside of a health facility. Facilitators were high vaccine acceptance and willingness for their infants to receive HepB-BD if recommended by providers. Findings indicate the need for enhanced HepB-BD vaccination training for HCWs, educating pregnant women on HBV and the importance of timely HepB-BD, updating policies to enable HepB-BD administration within 24 hours of birth, expanding HepB-BD availability in public and private hospital maternity wards for all facility births, and outreach activities to reach home births.

Improving community coverage of Japanese encephalitis vaccination: lessons learned from a mass campaign in Battambang Province, Cambodia.

A mass Japanese encephalitis (JE) immunization campaign for children aged 9 months through 12 years was conducted in 2013 in Battambang province, western Cambodia. Vaccinators working at almost 2,000 immunization posts in approximately 800 villages provided vaccinations to almost 310,000 children using one dose of Chengdu Institute of Biological Products' live, attenuated SA14-14-2 JE vaccine (CD-JEV), achieving a coverage rate of greater than 90%. Lessons learned, in general for mass vaccination campaigns and specifically for vaccination with CD-JEV, are described. These observations will be of benefit for public health officials and to help inform planning for future campaigns for JE or other vaccine-preventable diseases in Cambodia and elsewhere.

Machine learning-based modeling to predict inhibitors of acetylcholinesterase.

Acetylcholinesterase enzyme is responsible for the degradation of acetylcholine and is an important drug target for the treatment of Alzheimer's disease. When this enzyme is inhibited, more acetylcholine is available in the synaptic cleft for the use, which leads to enhanced memory and cognitive ability. The aim of the present work is to create machine learning models for distinguishing between AChE inhibitors and non-inhibitors using algorithms like support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The developed models were evaluated by 10-fold cross-validation and external dataset. Descriptor analysis was performed to identify most important features for the activity of molecules. Descriptors which were identified as important include maxssCH2, minHssNH, SaasC, minssCH2, bit 128 MACCS key, bit 104 MACCS key, bit 24 estate fingerprint and bit 18 estate fingerprints. The model developed using fingerprints based on random forest algorithm produced better results compared to other models. The overall accuracy of best model on test set was 85.38 percent. The developed model is available at http://14.139.57.41/achepredictor/ .

Applying Adult Learning Best Practices to Design Immunization Training for Health Care Workers in Ghana.

INTRODUCTION: A 2016 assessment of frontline health care workers (HCWs) in Ghana identified knowledge, skill, and attitude gaps related to immunization during the second year of life (2YL). The U.S. Centers for Disease Control and Prevention subsequently supported the Ghana Health Service Immunization Program to apply best practices of adult learning and training of trainers (TOT) for a cascade training program for 2YL. METHODS: Five districts from each of the 3 regions (Greater Accra, Northern, and Volta) were selected for the TOT based on key measles and rubella vaccination coverage indicators. The design incorporated best practices of adult learning and TOT. The curriculum integrated 3 major topical themes: technical (immunization topics), operational, and training adults. The technical and operational content was based on HCW tasks most directly affecting 2YL objectives. A cross-functional team developed all classroom, field activity, and training evaluation materials. RESULTS: Seventy-four participants attended TOT workshops in 2017. Based on a rubric defined by the course designers, 99% of the participants reported an acceptable level of confidence to apply and teach the course content. After the TOTs, participants conducted 65 workshops, 43 field visits, and 4 review meetings, reaching 1,378 HCWs within 7 months. Fifty-four percent of HCWs who received training from TOT participants reported an acceptable level of confidence in using the skills, and 92% reported they would prioritize applying the skills acquired during the training. DISCUSSION: The success factors for effective adult learning and TOT can be applied to design and implement high-quality TOT even in resource-limited settings. The factors include using a variety of approaches, spending enough class time to prepare TOT participants for their training role, setting specific expectations for cascading the training, and following up through mentorship and reporting. Strong collaboration across the administrative levels of the Ghana Health Service enabled cascade training.

Evaluation of the Impact of Immunization Second Year of Life Training Interventions on Health Care Workers in Ghana.

INTRODUCTION: As part of a suite of training interventions to improve the knowledge and practice of immunization in the second year of life (2YL), training of trainers workshops were conducted with regional and district health management teams (DHMTs) in 15 districts in 3 regions of Ghana. Using adult learning principles, DHMTs implemented several capacity-building activities at the subdistrict and health facility levels, including health facility visits, on-the-job training, and review meetings. The current evaluation investigated whether frontline health care workers (HCWs) reported or demonstrated improvements in knowledge, attitudes, and practices after training interventions. METHODS: Quantitative and qualitative methods with a utilization-focused approach guided the framework for this evaluation. A systematic random sample of 115 HCWs in 3 regions of Ghana was selected to complete a competency survey before and after training, which focused on 3 core competency areas-Expanded Programme on Immunization (EPI) policy; communication with caregivers; and immunization data management, recording, and use. Interviews and direct observations by data collectors were done to assess HCWs' knowledge, self-reported attitude, and behavior changes in practices. RESULTS: Of 115 HCWs, 102 were surveyed before and 4 months after receiving capacity-building interventions. Modest but not statistically significant improvements were found in knowledge on EPI policy, immunization data management, and communication skills with caregivers. HCWs reported that they had improved several attitudes and practices after the 2YL training. The most improved practice reported by HCWs and observed in all 3 regions was the creation of a defaulter list. DISCUSSION: Findings of this evaluation provide encouraging evidence in taking the first step toward improving HCW knowledge, attitudes, and practices for 3 core immunization competency areas. The use of learner-focused teaching methods combined with adult learning principles is helpful in solving specific performance problems (such as lack of knowledge of EPI policy).

Use of a district health information system 2 routine immunization dashboard for immunization program monitoring and decision making, Kano State, Nigeria.

Introduction: a district health information system 2 tool with a customized routine immunization (RI) module and indicator dashboard was introduced in Kano State, Nigeria, in November 2014 to improve data management and analysis of RI services. We assessed the use of the module for program monitoring and decision-making, as well as the enabling factors and barriers to data collection and use. Methods: a mixed-methods approach was used to assess user experience with the RI data module and dashboard, including 1) a semi-structured survey questionnaire administered at 60 health facilities administering vaccinations and 2) focus group discussions and 16 in-depth interviews conducted with immunization program staff members at the local government area (LGA) and state levels. Results: in health facilities, a RI monitoring chart was used to review progress toward meeting vaccination coverage targets. At the LGA, staff members used RI dashboard data to prioritize health facilities for additional support. At the State level, immunization program staff members use RI data to make policy decisions. They viewed the provision of real-time data through the RI dashboard as a "game changer". Use of immunization data is facilitated through review meetings and supportive supervision visits. Barriers to data use among LGA staff members included inadequate understanding of the data collection tools and computer illiteracy. Conclusion: the routine immunization data dashboard facilitated access to and use of data for decision-making at the LGA, State and national levels, however, use at the health facility level remains limited. Ongoing data review meetings and training on computer skills and data collection tools are recommended.

Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease.

The COVID-19 disease is caused by a new strain of the coronavirus family (SARS-CoV-2), and it has affected at present millions of people all over the world. The indispensable role of the main protease (Mpro) in viral replication and gene expression makes this enzyme an attractive drug target. Therefore, inhibition of SARS-CoV-2 Mpro as a proposition to halt virus ingression is being pursued by scientists globally. Here we carried out a study with two objectives: the first being to perform comparative protein sequence and 3D structural analysis to understand the effect of 12 point mutations on the active site. Among these, two mutations, viz., Ser46 and Phe134, were found to cause a significant change at the active sites of SARS-CoV-2. The Ser46 mutation present at the entrance of the S5 subpocket of SARS-CoV-2 increases the contribution of other two hydrophilic residues, while the Phe134 mutation, present in the catalytic cysteine loop, can cause an increase in catalytic efficiency of Mpro by facilitating fast proton transfer from the Cys145 to His41 residue. It was observed that active site remained conserved among Mpro of both SARS-CoVs, except at the entrance of the S5 subpocket, suggesting sustenance of substrate specificity. The second objective was to screen the inhibitory effects of three different data sets (natural products, coronaviruses main protease inhibitors, and FDA-approved drugs) using a structure-based virtual screening approach. A total of 73 hits had a combo score >2.0. Eight different structural scaffold classes were identified, such as one/two tetrahydropyran ring(s), dipeptide/tripeptide/oligopeptide, large (approximately 20 atoms) cyclic peptide, and miscellaneous. The screened hits showed key interactions with subpockets of the active site. Further, molecular dynamics studies of selected screened compounds confirmed their perfect fitting into the subpockets of the active site. This study suggests promising structures that can fit into the SARS-CoV-2 Mpro active site and also offers direction for further lead optimization and rational drug design.

Mechanistic studies on the drug metabolism and toxicity originating from cytochromes P450.

Cytochromes P450 are oxidizing enzymes; a few families of cytochromes P450 are implicated in drug metabolism. These enzymatic reactions involve many processes including (i) prodrug to drug conversion, (ii) easy excretion of drug, (iii) generation of reactive metabolites, many of which cause toxicity. In this review, the fundamental biochemical mechanisms associated with the conversion of drugs into the useful or toxic metabolites have been discussed. The mechanisms can be established with the help of many experimental methods like mass spectral analysis, NMR and in vitro analysis etc. Computational methods provide detailed atomic level information, which is generally not available from experimental studies. Thus, the in silico efforts in elucidating the molecular mechanisms are complementary to the known experimental methods and are often clearer (especially in providing 3D information about the metabolites and their reactions). Quantum chemical methods and molecular docking become especially very useful. This review includes five case studies, which explain how the atomic level details were obtained to explore the reaction mechanisms of drug metabolism by cytochromes P450.

Progress Toward Hepatitis B Control - South-East Asia Region, 2016-2019.

In 2015, the World Health Organization (WHO) South-East Asia Region (SEAR)* reported an estimated 40 million persons living with chronic hepatitis B virus (HBV) infection and 285,000 deaths from complications of chronic infection, cirrhosis, and hepatocellular carcinoma (1). Most chronic HBV infections, indicated by the presence of hepatitis B surface antigen (HBsAg) on serologic testing, are acquired in infancy through perinatal or early childhood transmission (2). To prevent perinatal and childhood infections, WHO recommends that all infants receive at least 3 doses of hepatitis B vaccine (HepB), including a timely birth dose (HepB-BD)† (1). In 2016, the SEAR Immunization Technical Advisory Group endorsed a regional hepatitis B control goal with a target of achieving hepatitis B surface antigen (HBsAg) seroprevalence of ≤1% among children aged ≥5 years by 2020, which is in line with the WHO Global Health Sector Strategy on Viral Hepatitis 2016-2021 (2,3). The South-East Asia Regional Vaccine Action Plan 2016-2020 (SEARVAP) (4) identified the acceleration of hepatitis B control as one of the eight regional goals for immunization. The plan outlined four main strategies for achieving hepatitis B control: 1) achieving ≥90% coverage with 3 doses of HepB (HepB3), 2) providing timely vaccination with a HepB birth dose (HepB-BD), 3) providing catch-up vaccination of older children, and 4) vaccinating adult populations at high risk and health care workers (1,4). In 2019, SEAR established a regional expert panel on hepatitis B to assess countries' HBV control status. This report describes the progress made toward hepatitis B control in SEAR during 2016-2019. By 2016, all 11 countries in the region had introduced HepB in their national immunization programs, and eight countries had introduced HepB-BD. During 2016-2019, regional HepB3 coverage increased from 89% to 91%, and HepB-BD coverage increased from 34% to 54%. In 2019, nine countries in the region achieved ≥90% HepB3 coverage, and three of the eight countries that provide HepB-BD achieved ≥90% HepB-BD coverage. By December 2019, four countries had been verified to have achieved the hepatitis B control goal. Countries in the region can make further progress toward hepatitis B control by using proven strategies to improve HepB-BD and HepB3 coverage rates. Conducting nationally representative hepatitis B serosurveys among children will be key to tracking and verifying the regional control targets.

Building health workforce capacity for planning and monitoring through the Strengthening Technical Assistance for routine immunization training (START) approach in Uganda.

INTRODUCTION: The Global Vaccine Action Plan identifies workforce capacity building as a key strategy to achieve strong immunization programs. The Strengthening Technical Assistance for Routine Immunization Training (START) approach aimed to utilize practical training methods to build capacity of district and health center staff to implement routine immunization (RI) planning and monitoring activities, as well as build supportive supervision skills of district staff. METHODS: First implemented in Uganda, the START approach was executed by trained external consultants who used existing tools, resources, and experiences to mentor district-level counterparts and, with them, conducted on-the-job training and mentorship of health center staff over several site visits. Implementation was routinely monitored using daily activity reports, pre and post surveys of resources and systems at districts and health centers and interviews with START consultants. RESULTS: From July 2013 through December 2014 three START teams of four consultants per team, worked 6 months each across 50 districts in Uganda including the five divisions of Kampala district (45% of all districts). They conducted on-the-job training in 444 selected under-performing health centers, with a median of two visits to each (range 1-7, IQR: 1-3). More than half of these visits were conducted in collaboration with the district immunization officer, providing the opportunity for mentorship of district immunization officers. Changes in staff motivation and awareness of challenges; availability and completion of RI planning and monitoring tools and systems were observed. However, the START consultants felt that potential durability of these changes may be limited by contextual factors, including external accountability, availability of resources, and individual staff attitude. CONCLUSIONS: Mentoring and on-the-job training offer promising alternatives to traditional classroom training and audit-focused supervision for building health workforce capacity. Further evidence regarding comparative effectiveness of these strategies and durability of observed positive change is needed.

Developing standardized competencies to strengthen immunization systems and workforce.

Despite global support for immunization as a core component of the human right to health and the maturity of immunization programs in low- and middle-income countries throughout the world, there is no comprehensive description of the standardized competencies needed for immunization programs at the national, multiple sub-national, and community levels. The lack of defined and standardized competencies means countries have few guidelines to help them address immunization workforce planning, program management, and performance monitoring. Potential consequences resulting from the lack of defined competencies include inadequate or inefficient distribution of resources to support the required functions and difficulties in adequately managing the health workforce. In 2015, an international multi-agency working group convened to define standardized competencies that national immunization programs could adapt for their own workforce planning needs. The working group used a stepwise approach to ensure that the competencies would align with immunization programs' objectives. The first step defined the attributes of a successful immunization program. The group then defined the work functions needed to achieve those attributes. Based on the work functions, the working group defined specific competencies. This process resulted in three products: (1) Attributes of an immunization program described within eight technical domains at four levels within a health system: National, Provincial, District/Local, and Community; (2) 229 distinct functions within those eight domains at each of the four levels; and (3) 242 competencies, representing eight technical domains and two foundational domains (Management and Leadership and Vaccine Preventable Diseases and Program). Currently available as a working draft and being tested with immunization projects in several countries, the final document will be published by WHO as normative guidelines. Vertical immunization programs as well as integrated systems can customize the framework to suit their needs. Standardized competencies can support immunization program improvements and help strengthen effective health systems.

Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach.

BACKGROUND: The efflux transporter multidrug resistance associated protein-2 belongs to ATP-binding cassette superfamily which plays an important role in multidrug resistance and drugdrug interactions. Efflux transporters are considered to be important targets for increasing the efficacy of drugs and importance of computational study of efflux transporters for predicting substrates, non-substrates, inhibitors and non-inhibitors is well documented. Previous work on predictive models for inhibitors of multidrug resistance associated Protein-2 efflux transporter showed that machine learning methods produced good results. OBJECTIVE: The aim of the present work was to develop a machine learning predictive model to classify inhibitors and non-inhibitors of multidrug resistance associated protein-2 transporter using a well refined dataset. METHOD: In this study, the various algorithms of machine learning were used to develop the predictive models i.e. support vector machine, random forest and k-nearest neighbor. The methods like variance threshold, SelectKBest, random forest, and recursive feature elimination were used to select the features generated by PyDPI. A total of 239 molecules consisting of 124 inhibitors and 115 non-inhibitors were used for model development. RESULTS: The best multidrug resistance associated protein-2 inhibitor model showed prediction accuracies of 0.76, 0.72 and 0.79 for training, 5-fold cross-validation and external sets, respectively. CONCLUSION: It was observed that support vector machine model built on features selected using recursive feature elimination method shows the best performance. The developed model can be used in the early stages of drug discovery for identifying the inhibitors of multidrug resistance associated protein-2 efflux transporter.

Using the Stop Transmission of Polio (STOP) Program to Develop a South Sudan Expanded Program on Immunization Workforce.

In 2009, the international Stop Transmission of Polio (STOP) program began supporting the Global Polio Eradication Initiative in the Republic of South Sudan to address shortages of human resources and strengthen acute flaccid paralysis surveillance. Workforce capacity support is provided to the South Sudan Expanded Program on Immunization by STOP volunteers, implementing partners, and non-governmental organizations. In 2013, the Polio Technical Advisory Group recommended that South Sudan transition key technical support from external partners to national staff as part of the Polio Eradication and Endgame Strategic Plan, 2013-2018. To assist in this transition, the South Sudan Expanded Program on Immunization human resources development project was launched in 2015. This 3-year project aims to build national workforce capacity as a legacy of the STOP program by training 56 South Sudanese at national and state levels with the intent that participants would become Ministry of Health staff on their successful completion of the project.

Experiences and Lessons From Polio Eradication Applied to Immunization in 10 Focus Countries of the Polio Endgame Strategic Plan.

Nine polio areas of expertise were applied to broader immunization and mother, newborn and child health goals in ten focus countries of the Polio Eradication Endgame Strategic Plan: policy & strategy development, planning, management and oversight (accountability framework), implementation & service delivery, monitoring, communications & community engagement, disease surveillance & data analysis, technical quality & capacity building, and partnerships. Although coverage improvements depend on multiple factors and increased coverage cannot be attributed to the use of polio assets alone, 6 out of the 10 focus countries improved coverage in three doses of diphtheria tetanus pertussis containing vaccine between 2013 and 2015. Government leadership, evidence-based programming, country-driven comprehensive operational annual plans, community partnership and strong accountability systems are critical for all programs and polio eradication has illustrated these can be leveraged to increase immunization coverage and equity and enhance global health security in the focus countries.

Expansion of syndromic vaccine preventable disease surveillance to include bacterial meningitis and Japanese encephalitis: evaluation of adapting polio and measles laboratory networks in Bangladesh, China and India, 2007-2008.

BACKGROUND: Surveillance for acute flaccid paralysis with laboratory confirmation has been a key strategy in the global polio eradication initiative, and the laboratory platform established for polio testing has been expanded in many countries to include surveillance for cases of febrile rash illness to identify measles and rubella cases. Vaccine-preventable disease surveillance is essential to detect outbreaks, define disease burden, guide vaccination strategies and assess immunization impact. Vaccines now exist to prevent Japanese encephalitis (JE) and some etiologies of bacterial meningitis. METHODS: We evaluated the feasibility of expanding polio-measles surveillance and laboratory networks to detect bacterial meningitis and JE, using surveillance for acute meningitis-encephalitis syndrome in Bangladesh and China and acute encephalitis syndrome in India. We developed nine syndromic surveillance performance indicators based on international surveillance guidelines and calculated scores using supervisory visit reports, annual reports, and case-based surveillance data. RESULTS: Scores, variable by country and targeted disease, were highest for the presence of national guidelines, sustainability, training, availability of JE laboratory resources, and effectiveness of using polio-measles networks for JE surveillance. Scores for effectiveness of building on polio-measles networks for bacterial meningitis surveillance and specimen referral were the lowest, because of differences in specimens and techniques. CONCLUSIONS: Polio-measles surveillance and laboratory networks provided useful infrastructure for establishing syndromic surveillance and building capacity for JE diagnosis, but were less applicable for bacterial meningitis. Laboratory-supported surveillance for vaccine-preventable bacterial diseases will require substantial technical and financial support to enhance local diagnostic capacity.