Impact of natural killer cells on outcomes after allogeneic hematopoietic stem cell transplantation: A systematic review and meta-analysis.

Background: Natural killer (NK) cells play a vital role in early immune reconstitution following allogeneic hematopoietic stem cell transplantation (HSCT). Methods: A literature search was performed on PubMed, Cochrane, and Clinical trials.gov through April 20, 2022. We included 21 studies reporting data on the impact of NK cells on outcomes after HSCT. Data was extracted following the PRISMA guidelines. Pooled analysis was done using the meta-package (Schwarzer et al.). Proportions with 95% confidence intervals (CI) were computed. Results: We included 1785 patients from 21 studies investigating the impact of NK cell reconstitution post-HSCT (8 studies/1455 patients), stem cell graft NK cell content (4 studies/185 patients), therapeutic NK cell infusions post-HSCT (5 studies/74 patients), and pre-emptive/prophylactic NK cell infusions post-HSCT (4 studies/77 patients). Higher NK cell reconstitution was associated with a better 2-year overall survival (OS) (high: 77%, 95%CI 0.73-0.82 vs low: 55%, 95%CI 0.37-0.72; n=899), however, pooled analysis for relapse rate (RR) or graft versus host disease (GVHD) could not be performed due to insufficient data. Higher graft NK cell content demonstrated a trend towards a better pooled OS (high: 65.2%, 95%CI 0.47-0.81 vs low: 46.5%, 95%CI 0.24-0.70; n=157), lower RR (high: 16.9%, 95%CI 0.10-0.25 vs low: 33%, 95%CI 0.04-0.72; n=157), and lower acute GVHD incidence (high: 27.6%, 95%CI 0.20-0.36 vs low: 49.7%, 95%CI 0.26-0.74; n=157). Therapeutic NK or cytokine-induced killer (CIK) cell infusions for hematologic relapse post-HSCT reported an overall response rate (ORR) and complete response (CR) of 48.9% and 11% with CIK cell infusions and 82.8% and 44.8% with NK cell infusions, respectively. RR, acute GVHD, and chronic GVHD were observed in 55.6% and 51.7%, 34.5% and 20%, and 20.7% and 11.1% of patients with CIK and NK cell infusions, respectively. Pre-emptive donor-derived NK cell infusions to prevent relapse post-HSCT had promising outcomes with 1-year OS of 69%, CR rate of 42%, ORR of 77%, RR of 28%, and acute and chronic GVHD rates of 24.9% and 3.7%, respectively. Conclusion: NK cells have a favorable impact on outcomes after HSCT. The optimal use of NK cell infusions post-HSCT may be in a pre-emptive fashion to prevent disease relapse

Sustainable Irrigation Management for Higher Yield

Sustainable irrigation is sensible application of watering to plants in agriculture, landscapes that aids in meeting current survival and welfare needs. Sustainable irrigation management can help with climate change adaptation, labor, energy savings, and the production of higher-value and yield of crops to achieve zero hunger in water-scarce world. To ensure equal access to water and environmental sustainability, investments in expanded and enhanced irrigation must be matched by improvements in water governance. Sustainable irrigation must be able to cope with water scarcity, and be resilient to other resource scarcities throughout time in context of energy and finance. The themes and SDGs related to clean water, water resources sustainability, sustainable water usage, agricultural and rural development are all intertwined in the concept of “sustainable irrigation for higher yield.” Sustainable irrigation management refers to the capability of using water in optimum quantity and quality on a local, regional, national, and global scale to meet the needs of humans and agro-ecosystems at present and in the future to sustain life, protect humans and biodiversity from natural and human-caused disasters which threaten life to exist. Resultantly higher yields will ensure food security

Outcomes with chimeric antigen receptor t-cell therapy in relapsed or refractory acute myeloid leukemia: a systematic review and meta-analysis

BackgroundWe conducted a systematic review and meta-analysis to evaluate outcomes following chimeric antigen receptor T cell (CAR-T) therapy in relapsed/refractory acute myeloid leukemia (RR-AML).MethodsWe performed a literature search on PubMed, Cochrane Library, and Clinicaltrials.gov. After screening 677 manuscripts, 13 studies were included. Data was extracted following PRISMA guidelines. Pooled analysis was done using the meta-package by Schwarzer et al. Proportions with 95% confidence intervals (CI) were computed.ResultsWe analyzed 57 patients from 10 clinical trials and 3 case reports. The pooled complete and overall response rates were 49.5% (95% CI 0.18-0.81, I2 =65%) and 65.2% (95% CI 0.36-0.91, I2 =57%). The pooled incidence of cytokine release syndrome, immune-effector cell associated neurotoxicity syndrome, and graft-versus-host disease was estimated as 54.4% (95% CI 0.17-0.90, I2 =77%), 3.9% (95% CI 0.00-0.19, I2 =22%), and 1.6% (95%CI 0.00-0.21, I2 =33%), respectively.ConclusionCAR-T therapy has demonstrated modest efficacy in RR-AML. Major challenges include heterogeneous disease biology, lack of a unique targetable antigen, and immune exhaustion

Family-based factors associated with overweight and obesity among Pakistani primary school children

<p>Abstract</p> <p>Background</p> <p>Childhood obesity epidemic is now penetrating the developing countries including Pakistan, especially in the affluent urban population. There is no data on association of family-based factors with overweight and obesity among school-aged children in Pakistan. The study aimed to explore the family-based factors associated with overweight and obesity among Pakistani primary school children.</p> <p>Methods</p> <p>A population-based cross-sectional study was conducted with a representative multistage cluster sample of 1860 children aged five to twelve years in Lahore, Pakistan. Overweight (> +1SD BMI-for-age z-score) and obesity (> +2SD BMI-for-age z-score) were defined using the World Health Organization reference 2007. Chi-square test was used as the test of trend. Linear regression was used to examine the predictive power of independent variables in relation to BMI. Logistic regression was used to quantify the independent predictors of overweight and adjusted odds ratios (aOR) with 95% confidence intervals (CI) were obtained. All regression analyses were controlled for age and gender and statistical significance was considered at P < 0.05.</p> <p>Results</p> <p>Significant family-based correlates of overweight and obesity included higher parental education (P < 0.001), both parents working (P = 0.002), fewer siblings (P < 0.001), fewer persons in child's living room (P < 0.001) and residence in high-income neighborhoods (P < 0.001). Smoking in living place was not associated with overweight and obesity. Higher parental education (P < 0.001) and living in high-income neighborhoods (P < 0.001) showed a significant independent positive association with BMI while greater number of siblings (P = 0.001) and persons in child's living room (P = 0.022) showed a significant independent inverse association. College-level or higher parental education as compared to high school-level or lower parental education (aOR 2.54, 95% CI 1.76-3.67), living in high-income neighborhoods as compared to low-income neighborhoods (aOR 2.13, 95% CI 1.31-3.46) and three or less siblings as compared to more than three siblings (aOR 1.75, 95% CI 1.26-2.42) were significant independent predictors of overweight.</p> <p>Conclusion</p> <p>Family-based factors were significantly associated with overweight and obesity among school-aged children in Pakistan. Higher parental education, living in high-income neighborhoods and fewer siblings were independent predictors of overweight. These findings support the need to design evidence-based child health policy and implement targeted interventions, considering the impact of family-based factors and involving communities.</p

Sustaining Alfalfa Forage Production with Limited Water Resources

Variability in climatic conditions and uncertainty in water supply brings challenges for sustainable irrigated forage production. Alfalfa (Medicago sativa L.), one of the major forage crops worldwide, is affected due to its high water demands for successful irrigated production. To address the challenge of sustaining forage yields in a changing climate, we designed three studies to understand the yield response of alfalfa under different irrigation systems and with varying water deficits. We also examined the usefulness of aerial imagery to understand the yield and quality variability in the field as affected by drought. In study 1, the potential of sub-surface drip irrigation (SDI) combined with reduced irrigation for improving forage yield, quality, and water productivity of alfalfa under water deficit conditions in California’s Central Valley was explored. In study 2, we examined the two overhead irrigation systems: low elevation spray application (LESA) and mobile drip irrigation (MDI) for producing alfalfa and the impacts of deficit irrigation using these two technologies on alfalfa production, quality, and productivity. In study 3, we examined the ability of aerial imagery, specifically, multispectral and LiDAR imaging technologies to understand yield and quality variability on a field scale with differential drought treatments. Study 1 was conducted at Kearney Agricultural Research and Extension Center (Parlier, CA) from 2016-2019 using randomized complete block (RCB) design on sandy loam soil while study 2 and 3 were conducted during 2019 and 2020 at Davis, CA using split plot randomized complete block design on a clay-loam soil. In Study 1, we found that there were no significant differences between SDI and flood irrigation systems over two years of study. In small-plot studies, it is likely that flood irrigation techniques are advantageous due to smaller checks, an advantage that may disappear when larger fields are considered- thus advantages of these systems should be viewed at scale. In addition, the SDI plots had maintenance issues in the later part of the study, a common problem with SDI. But SDI had the advantage of applying water to more closely match crop evapotranspiration demands while flood seems slightly stressed during the growth cycle due to less frequent irrigation. Over the three years of study, deficit irrigation utilizing SDI resulted in yields that were 82%, 84%, and 87% of fully irrigated treatments for the 50%, 75% (sudden cutoffs), 75% (gradual deficits), respectively (percent of full ET requirement). There were slight improvements in forage quality using deficits, but differences were not great. Higher water productivities were found in deficit irrigation compared with full irrigation under SDI. Under water uncertainty, deficit irrigation using subsurface drip irrigation (SDI) could be beneficial to address the challenges of climate change, if such systems can be economically managed and maintained. In study 2, we found few differences in yield results between LESA and MDI systems in overhead irrigation of alfalfa over two years. Yields were sustained or were higher under MDI 60% cutoff compared with LESA 60% summer cutoffs, likely due to superior sub-soil moisture recharge with the dragging drip lines. Higher water productivity and irrigation water use efficiency was found in MDI-60% ET- Cutoff in 2020 (21.2 kg ha-1 mm-1) while there were no significant differences found in 2019. In general, LESA performed better in all other treatments than MDI over the two years of study period. Both LESA and MDI systems minimized wind losses and improve deep moisture availability. MDI systems do not have the rodent or maintenance issues as does SDI but have the disadvantage of requiring some filtration. In study 3, we utilized the drought affected research field and tried to understand the spatial-temporal variability in alfalfa yield and quality using aerial imagery. Aerial flights were conducted at harvest using multispectral and LiDAR camera in separate flights. We trained the models on the field data for plant height and dry matter yields and it was found that model performed well when an unknown dataset was provided for model testing. The model was created using a step-wise regression model and was compared with random forest (RF) and support vector machine (SVM) for multispectral imagery. It was found that step-wise regression model performed somewhat better than RF and SVM with an R2 of 0.82, 0.79 and 0.81 respectively. The model also performed well for predicting yield in a separately measured area of 11.15 m2 with an R2 0.83. LiDAR also performed well and predicted the yield with slightly lower R2 (0.67) but successfully predicted yields in 11.15 m2 area (R2 0.91). Both multispectral and LiDAR imagery were able to predict the dry matter yield for alfalfa based on the trained models, and each system has advantages and disadvantages. LiDAR is more demanding in terms of cost and analysis requirements. Some of the predicted results exhibited bias, these may have resulted from differences in sample size or sampling protocols, but such biases can be corrected mathematically. Aerial imagery should be considered a useful tool to create yield maps, to estimate the impacts of drought on yield, and to understand sources of field variability to guide management decisions. For model applicability, further work at different scales should be conducted to predict yields on a farm or regional scale.Keywords: Alfalfa, SDI, Flood, LESA, MDI, deficit irrigation, forage quality, irrigation efficiency, water productivity, UAV remote sensing, yield prediction, drought, field diagnostics, Multispectral, LiDA

Analyzing crop yield gaps and their causes using cropping systems modelling–a case study of the Punjab rice-wheat system, Pakistan

Pakistan faces significant future challenges to feed a growing population, with 47% of the population currently categorised as food insecure. The rice-wheat (RW) zone of Punjab is the major aromatic rice producing area of Pakistan. Here the rice-wheat cropping rotation is practiced over a variety of soils under a range of agronomic and irrigation practices. It is important to quantify and understand the existing crop yield gaps and associated factors in this zone to identify opportunities for gap-reduction leading to increased land and water productivity and sustainable water use in Pakistan. The APSIM model was parameterised for local soils and climate, and then calibrated for rice and wheat growth, phenology and yields using experimental data sets. The model calibration was then validated using 5-year (2009–2014) farmer grain yield records from each of the highest (Gujranwala) and lowest (Narowal) performing districts in Punjab, before being used to simulate long-term (34 years) rice-wheat yields for current farmer practices and also for potential (no water or N-stress) production, in both districts. The revealed yield gaps were further assessed as a function of sowing date. Observed and simulated farmer R–W yields for Gujranwala were greater than those for Narowal; however potential yields were similar at both sites (for both rice and wheat). Farmers currently achieve only around 36% (Narowal) to 67% (Gujranwala) of potential yield for rice, and 48–56% of potential for wheat (Narowal and Gujranwala respectively). The modelling analysis of abiotic stresses under which the farmers’ crops were grown revealed that nitrogen (N) deficiency was the primary driver for the large yield gaps at both sites, for both crops, with low topsoil carbon (0.19% Narowal; 0.49% Gujranwala). The simulated farmer crops were not significantly constrained by water limitations under the current N application rates, or by high/low temperatures. Gaining a better understanding of farmers’ reasons for low N-rates is important – it could be related to (non-modelled) soil micronutrient constraints or risk aversion associated with irrigation water supply reliability. Current sowing dates are also problematic for the existing varieties, with optimum rice sowing dates being up to one month later than the current ones, yet optimum wheat sowing dates being a month (or more) earlier than current ones. This wicked problem points towards the need for further investigations into system optimisation, with better nutrient management, increased soil carbon levels, and shorter season high-yielding rice varieties likely to be key elements