Public health nutrition in Afghanistan-policies, strategies and capacity-building: current scenario and initiatives

Background: Afghanistan is grappling with high burden of malnutrition in women and children and a rising burden of noncommunicable diseases. Aims: A narrative review was conducted with the aim of mapping current nutrition policies and capacity development initiatives to assess policy and the institutional environment and identify gaps and opportunities. Methods: A comprehensive, broad based search was conducted, including databases and websites and policy and programme documents. Results: The policy focuses on multisectoral efforts to address nutrition challenges; however; implementation of nutrition-specific and nutrition-sensitive interventions is not delivered uniformly at the community level due to continued conflic situations and geographic inaccessibility, lack of availability of trained human resources and weak institutions. There is limited evidence on the effectiveness of nutrition programmes in Afghanistan. Limited policy provisions are available to address nutrition issues due to the rising burden of noncommunicable diseases, urbanization and changing dietary patterns. The shortage of skilled nutritional professionals is a critical issue. Lack of institutional capacity, educational standards and accreditation mechanism poses major challenges. Ongoing training programmes are fragmented and fail to meet the requirements of a professional nutrition workforce. Conclusion: The findings highlight that well-structured policies and strategies focusing on maternal and child nutrition provide an enabling policy environment to scale up nutrition interventions. Evidence on the implementation of programmes is needed to aid policy recommendations. The lack of an institutional mechanism for professional nutrition education highlights the great need for action in Afghanistan for public health nutrition and education.

Using Deep Learning to Extrapolate Protein Expression Measurements.

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R2=0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.

Comparative Evaluation of Pregabalin and Clonidine as Preemptive Analgesics for the Attenuation of Postoperative Pain Following Thoracolumbar Spine Surgery

STUDY DESIGN: Prospective, randomized, double blind, placebo-controlled study.PURPOSE: To compare clonidine and pregabalin with placebo for the attenuation of postoperative pain after thoracolumbar spinal surgery and instrumentationOVERVIEW OF LITERATURE: Spine surgery is associated with moderate to severe postoperative pain that needs to be controlled to improve patient’s outcome. Alpha 2 agonists (e.g., clonidine) and gabapentenoids (e.g., pregabalin) are successfully used as part of a multimodal analgesic regimen.METHODS: Total 75 patients were enrolled and randomly allocated into three groups. Group P received pregabalin (150 mg), group C received clonidine (150 mcg), and group N received placebo 90 minutes preoperatively. A standard anesthesia protocol comprising fentanyl, thiopentone, vecuronium, nitrous oxide, and oxygen in isoflurane was used for all patients. Postoperative recovery profile, pain, time for first analgesic, 24-hour analgesic requirement, sedation, and hemodynamic parameters were noted.RESULTS: Recovery profile was similar in all three groups; however, the patients in group P and C were more sedated (p<0.05). Group N patients had a higher Visual Analog Scale (VAS) score (p<0.05) and the time for first analgesic was also lower (p=0.02). Postoperative (24-hour) analgesic requirement was maximum in group N, followed by that in group C and group P. The VAS score was highest in the control group; however, after 12 hours, it was similar in all groups.CONCLUSIONS: Postoperative pain and analgesic requirement is significantly attenuated by preoperative administration of a single dose of clonidine (150 mcg) or pregabalin (150 mg); pregabalin was more effective. Thus, their use offers a reasonable strategy for pain management in patients undergoing spine surgery.

Association between household air pollution and neonatal mortality: an analysis of Annual Health Survey results, India

Background: In India, household air pollution (HAP) is one of the leading riskfactors contributing to the national burden of disease. Estimates indicate that 7.6%of all deaths in children aged under 5 years in the country can be attributed to HAP.This analysis attempts to establish the association between HAP and neonatalmortality rate (NMR).Methods: Secondary data from the Annual Health Survey, conducted in 284districts of nine large states covering 1 404 337 live births, were analysed. Thesurvey was carried out from July 2010 to March 2011 (reference period: January2007 to December 2009). The primary outcome was NMR. The key exposurewas the use of firewood/crop residues/cow dung as fuel. The covariates were:sociodemographic factors (place of residence, literacy status of mothers,proportion of women aged less than 18 years who were married, wealth index);health-system factors (three or more antenatal care visits made during pregnancy;institutional deliveries; proportion of neonates with a stay in the institution forless than 24 h; percentage of neonates who received a check-up within 24 h ofbirth); and behavioural factors (initiation of breast feeding within 1 h). Descriptiveanalysis, with district as the unit of analysis, was performed for rural and urbanareas. Bivariate and multivariable linear regression analysis was carried out toinvestigate the association between HAP and NMR.Results: The mean rural NMR was 42.4/1000 live births (standard deviation [SD]= 11.4/1000) and urban NMR was 33.1/1000 live births (SD=12.6/1000). Theproportion of households with HAP was 92.2% in rural areas, compared to 40.8%in urban areas, and the difference was statistically significant (P < 0.001). HAPwas found to be strongly associated with NMR after adjustment (β = 0.22; 95%confidence interval [CI] = 0.09 to 0.35) for urban and rural areas combined. Forrural areas separately, the association was significant (β = 0.30; 95% CI = 0.13 to0.45) after adjustment. In univariable analysis, the analysis showed a significantassociation in urban areas (β = 0.23; 95% CI = 0.12 to 2.34) but failed to demonstratean association in multivariable analysis (β = 0.001; 95% CI = –0.15 to 0.15).Conclusion: Secondary data from district level indicate that HAP is associatedwith NMR in rural areas, but not in urban areas in India.