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Programmable Coacervate Droplets via Reaction-Coupled Liquid–Liquid Phase Separation (LLPS) and Competitive Inhibition

Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators

Objectives Malnutrition is a leading cause of morbidity and mortality for children under-5 globally. Low- and middle-income countries, such as Kenya, bear the greatest burden of malnutrition. The Kenyan government has been collecting clinical indicators, including on malnutrition, using District Health Information Software-2 (DHIS2) for over a decade. We aim to address the existing gap in decision-makers’ ability to develop and utilize malnutrition forecasting capabilities for timely interventions. Specifically, our objectives include: develop a spatio-temporal machine learning model to forecast acute malnutrition among children in Kenya using DHIS2 data, enhance forecasting capability by integrating external complementary indicators, such as publicly available satellite imagery-driven signals, and forecast acute malnutrition at various stages and time horizons, including moderate, severe, and aggregated cases. Methods We propose a framework to forecast malnutrition risk for each sub-county in Kenya based on clinical indicators and remote sensory data. To achieve this, we first aggregate clinical indicators and remotely sensed satellite data, specifically gross primary productivity measurements, to the sub-county level. We then label the rate of children diagnosed with acute malnutrition at the sub-county level using the standard Integrated Food Security Phase Classification for Acute Malnutrition. We then apply and compare several methods for forecasting malnutrition risk in Kenya using data collected from January 2019 to February 2024. As a baseline, we used a Window Average model, which captures the current practice at the Kenyan Ministry of Health. We also trained machine learning models, such as Logistic Regression and Gradient Boosting, to forecast acute malnutrition risk based on observed indicators from prior months. Different metrics, mainly Area Under Receiver Operating Characteristic Curve (AUC), were used to evaluate the forecasting performance by comparing their forecast values to known values on a hold-out test set. Results We found that machine learning based models consistently outperform the Window Average baselines on forecasting sub-county malnutrition rates in Kenya. For example, the Gradient Boosting model achieves a mean AUC of 0.86 when forecasting with a 6-month time horizon, compared to an AUC of 0.73 achieved by the Window Average model. The Window Average method particularly fails to correctly forecast malnutrition in parts of West and Central Kenya where the acute malnutrition rate is variable over time and typically less than 15%. We further found that machine learning models with satellite-based features alone also outperform Window Averaging baselines, while not needing clinical data at inference time. Finally, we found that recently observed outcomes and the remotely sensed data are key indicators. Our results demonstrate the ability of machine learning models to accurately forecast malnutrition in Kenya at a sub-county level from a variety of indicators. Conclusions To the best of the authors’ knowledge, this work is the first to use clinical indicators collected via DHIS2 to forecast acute malnutrition in childhood at the sub-county level in Kenya. This work represents a foundational step in developing a broader childhood malnutrition forecasting framework, capable of monitoring malnutrition trends and identifying impending malnutrition peaks across more than 80 low- and middle-income countries collecting similar DHIS2 datasets.

Click Chemistry-Assisted Rejuvenation of Aging T Cells Sensitizes Aged Mice to Tumor Immunotherapy

A systematic review and meta-analysis of yoga for arterial hypertension

Background This systematic review and meta-analysis is an update to prior research to evaluate the effects of yoga for managing prehypertension and hypertension. Methods Medline/PubMed, Scopus and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched from their inception until April 5th 2024. Randomized-controlled trials (RCTs) that compared yoga to any control intervention in participants with diagnosed prehypertension (120–139/80–89 mmHg) or hypertension (≥140/ ≥ 90mmHg) were included. Mean differences (MD) and 95% confidence intervals (CI) were calculated. Risk of Bias was assessed using the Cochrane tool. Results 30 RCTs with 2283 participants were included. Very low quality of evidence was found for positive effects of yoga on systolic blood pressure (SBP, 26 RCTs, n = 2007; MD = -7.95 mmHg, 95% CI = -10.24 to -5.66, p < 0.01), diastolic blood pressure (DBP, 23 RCTs, n = 1836; MD = -4.93 mmHg, 95% CI = -6.25 to -3.60, p < 0.01) and heart rate (HR, 14 RCTs, n = 1118; MD = -4.43 mmHg, 95% CI = -7.36 to -1.50, p < 0.01) compared to waitlist control. Compared to active control, very low quality of evidence was found for positive effects yoga on SBP (5 RCTs, n = 306; MD = -4.16 mmHg, 95%CI = -10.76 to 2.44, p = 0.22), DBP (5 RCTs, n = 306; MD = -1.88 mmHg, 95%CI = -3.41 to -0.36, p = 0.02) and HR (2 RCT, n = 128; MD = -5.16 mmHg, 95% CI = -8.39 to -1.92, p < 0.01). Overall, the studies showed a high degree of heterogeneity. The effects found were robust against selection, detection and attrition bias. Conclusion Yoga may be an option for lowering blood pressure in people with prehypertension to hypertension. More and larger high-quality studies are needed to substantiate our findings.

Solvent-Controlled Enantioselective Allylic C–H Alkylation of 2,5-Dihydrofuran via Synergistic Palladium/Nickel Catalysis

Comprehensive analysis and experiment validation of five cuproptosis-related genes in prognosis, immune infiltration and metabolic characterization of pancreatic cancer

Background Cuproposis is a new-found mechanism of cell death, and the role of cuproposis-related genes (CRGs) in pancreatic cancer prognosis remains uncertain. Methods DECRGs were identified from TCGA and GTEx databases. Five OS-associated hub genes were screened using Cox regression and LASSO analyses. A prognostic model was constructed and validated by survival analysis. GSEA, gene mutation, small-molecule drugs, immune-infiltrating and TF/miRNA/mRNA network were investigated to determine the underlying mechanism of 5-CRGs. In addition, RT-qPCR, and WB were applied to validate the expression of 5-CRGs. CCK8, colony formation and transwell assays were used to prove the function of LIPT1 in PC. Results PDP1, DLAT, DBT, LIAS, and LIPT1 were screened as hub genes. 5-CRGs prognostic model established the low-risk population has a longer OS. There was a high the risk score value for the prediction in clinicopathological features. The forest plots showed that age, N stage and the RiskScore were the significant independent risk indicators. T cells CD4 memory resting and Mast cells are the amplest immune cell subpopulations in the high-score individuals. The expression of 5 CRGs exhibited significant differences in PC cell lines and tissues, LIPT1-knockdowning inhibited proliferation and invasion of pancreatic cancer cell lines. Conclusion Five CRGs relevant to pancreatic cancer prognosis were identified. Meanwhile, a new and accurate five CRGs prognostic model of pancreatic cancer was constructed. In addition, LIPT1 may promote proliferation, invasion and migration of pancreatic cancer cell lines. This may have a specific guiding value for future development of precise anti-cancer treatment strategies.

Atomically Dispersed High-Valent d⁰-Metal Breaks the Activity–Stability Trade-Off in Proton Exchange Membrane Water Electrolysis

Pot-pollen DNA barcoding as a tool to determine the diversity of plant species visited by Ecuadorian stingless bees

Identifying the main species of plants from where Ecuadorian stingless bees collect pollen is one of the key objectives of management and conservation improvement for these insects. This study aims to determine the botanical origin of pot-pollen using two barcodes, comparing two methodologies (DNA barcoding versus electron microscopy and morphometric tools) and determine the genus and species of pollen source plants of the main honey-producing stingless bees in Ecuador. As main results, Prockia crucis, Coffea canephora, Miconia nervosa, Miconia notabilis, Laurus nobilis, Cecropia ficifolia, Theobroma sp., Artocarpus sp., Croton sp., Euphorbia sp., Mikania sp., and Ophryosporus sp., were the genera and species with the highest presence in the nests (n = 35) of three genera of stingless bees of two provinces located in different climatic regions inside the continental Ecuador. Plant species richness in both areas was statistically similar (p-value = 0.21). We concluded that floral sources’ molecular identification with the ITS2 region had a higher number of genera and species detected, than the rbcL gene and microscopy tools, for the Ecuadorian landscapes. We confirmed that the foraging behavior of Melipona sp., Scaptotrigona sp., and Tetragonisca sp., could include non-native flora (27%, 12/44 identifications) that provide a rich source of pollen. Stingless beekeepers could use this information to create flower calendars and establish a schedule for better management of stingless bees in secondary and modified environments.

Trilocked Photodynamic Senolytic Inducer Potentiating Immunogenic Senescent Cell Removal for Liver Fibrosis Resolution

A pilot pragmatic randomized controlled trial of a 12-month Healthy Lifestyles Program: A collaborative care model for chronic conditions addressing behavioural change

Background Lifestyle or behavioural changes can help to address the burden associated with chronic diseases. However, they take time and use of multiple techniques or strategies tailored to a person’s needs. The primary objective of this study was to assess the feasibility of the Healthy Lifestyles Program (HLP), a novel 12-month complex intervention based in cognitive behavioural therapy and theories of behaviour change, delivered in a community-based setting in Hamilton, Canada. The secondary objective of the study was to explore implementation factors of the HLP. Methods This pilot pragmatic randomised controlled trial used quantitative and qualitative evaluation methods. Participants were randomly allocated to either intervention group (n = 15) or comparator group (n = 15). The intervention group attended weekly group education sessions and met with the program intervention team monthly to create and review personalized health goals and action plans. The comparator group met with a trained research assistant every three months to develop health goals and action plans. We assessed program feasibility by measuring recruitment, participation and retention rates, missing data, and attendance. Implementation was assessed in accordance with the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. Participant-directed and clinical outcome measures were analyzed for between and within group changes using Generalized Estimating Equations (GEE). Thematic analysis was conducted for qualitative data. Results Retention rate was 60% (9/15) for the intervention group and 47% (7/15) for the comparator group. Less than 1% of participant-directed and clinical outcomes were missing for those that completed the study. Participants attended an average of 29 of 43 educational sessions and 100% of one-to-one sessions. The program intervention team valued the holistic approach to care, increased time and interaction with participants, professional collaboration, and the ability to provide counselling and health support. Location accessibility was an important factor facilitating implementation. Reducing the number of psycho-social education sessions and having access to a gym could improve retention and program delivery for a larger trial. Conclusion This study demonstrated the feasibility of the HLP with minor modifications recommended for a larger trial and for the intervention.

Rhodium-Catalyzed [5 + 1 + 2] Reaction of Yne-Vinylcyclopropenes and CO: The Application of Vinylcyclopropenes for Higher-Order Cycloaddition

Fragment-based drug discovery for transthyretin kinetic stabilisers using a novel capillary zone electrophoresis method

A Capillary Zone Electrophoresis (CZE) fragment screening methodology was developed and applied to the human plasma protein Transthyretin (TTR), normally soluble, but could misfold and aggregate, causing amyloidosis. Termed Free Probe Peak Height Restoration (FPPHR), it monitors changes in the level of free ligand known to bind TTR (the Probe Ligand) in the presence of competing fragments. 129 fragments were screened, 12 of the 16 initial hits (12.4% hit rate) were co-crystallised with TTR, 11 were found at the binding site (92% confirmation rate). Subsequent analogue screens have identified a novel TTR-binding scaffold 4-(3H-pyrazol-4-yl)quinoline and its derived compounds were further studied by crystallography, circular dichroism (CD), isothermal titration calorimetry (ITC) and radiolabelled 125 I-Thyroxine displacement assay in neat plasma. Two lead molecules had similar ITC K d and 125 I-Thyroxine displacement IC 50 values to that of Tafamidis, adding another potential pipeline for transthyretin amyloidosis. The methodology is reproducible, procedurally simple, automatable, label-free without target immobilisation, non-fluorescence based and site-specific with low false positive rate, which could be applicable to fragment screening of many drug targets.

Shell Thickness and Heterogeneity Dependence of Triplet Energy Transfer between Core–Shell Quantum Dots and Adsorbed Molecules

Correction: Efficacy and safety of prazequantel for the treatment of Schistosoma mansoni infection across different transmission settings in Amhara Regional State, northwest Ethiopia

Facet-Engineered Copper Electrocatalysts Enable Sustainable NADH Regeneration with High Efficiency

Comparison of three algorithms for estimating crop model parameters based on multi-source data: A case study using the CROPGRO-Soybean phenological model

Accurate prediction of crop phenological stage is essential for evaluating management strategies and assessing crop responses to environmental changes. In this work, we modified Non-dominated Sorting Genetic Algorithm with the core algorithm of PEST (MNSGA-II) and compared it to two other algorithms of Generalized Likelihood Uncertainty Estimation (GLUE) and Differential Evolution (DE) to calibrate the cultivar-specific parameters (CSPs) of CROPGRO-Soybean phenological model (CSPM) so as to exactly simulate the soybean phenology using the multi-source datasets of multi-site, multi-year, and multi-cultivar. Independent experimental data are used to validate the CSPM with the optimized parameters. The root means square error (RMSE), the mean absolute error (MAE), and coefficient of determination (R2) are used to evaluate the effects of different algorithms on calibrating the CSPs. The RMSEs (MAEs, R2) between all observed data and simulated data based on MNSGA-II, GLUE and DE are 4.28 (3.53, 0.9445) days, 4.76 (4.05, 0.9438) days and 5.17 (4.85, 0.9336) days, respectively, with little difference among the three algorithms. MNSGA-II has a certain advantage in calibration effect, and GLUE is the most stable during the repetition of each calibration. The MNSGA-II can be considered as a relatively ideal algorithm for estimating the crop model parameter. Which algorithm should be selected to calibrate the parameters of crop model according to the actual requirements. These results provide a reference to choose the suitable algorithm for estimating crop model parameter.

A Novel approach to ship valuation prediction: An application to the supramax and ultramax secondhand markets

Accurate ship valuations are very important in ship sales and purchase (S&P) transactions and for marine insurance purposes. It is equally important to select an appropriate valuation methodology. Today, one of the methods is Machine Learning (ML) algorithms stand out in generating better results than traditional methods. The aim of this study is to propose a highly accurate ship valuation model for the supramax/ultramax segment to interested parties using ML methods, with models established on the basis of linear regression. For this purpose, a four-stage path was followed. (i) The first data set, in which the significance of independent variables for supramax/ultramax ships was tested and linear regression models were created with statistically significant variables, covers the period from August 2005 to December 2022. At this stage, a model was first created that takes into account the values of the Baltic Exchange indices in the month of sale as an independent variable, and then another model that takes into account the values in the month of sale and the values in the months before the month of sale as independent variables. (ii) For the two linear regression models created; Price predictions were made with Linear Regression, Decision Tree, Random Forest and XGBoost ML algorithms. (iii) In the next stage; the two models created were simplified to include independent variables that can be easily obtained from the market in the model; and the obtained simplified models were re-predicted with ML algorithms. The fact that the first model and the simplified model are close in terms of prediction performance shows that the simplified model can be used in prediction. (iv) In order to show that the simplified model can produce reliable results when the data set is expanded, 2023 data was added and price predictions were made again with ML algorithms. As a result, the simplified model’s predicting performance was further improved with the addition of new data; the model established with the Baltic Exchange indices in the months before the sales month provided a significant superiority over the other model. XGBoost stood out as the best method according to performance criteria.

Deciphering the collisional dynamics of the Bangong-Nujiang Tethyan Ocean: multi-regional anisotropy of magnetic susceptibility (AMS) constraints from the Central Tibetan Plateau

Catalytic Enantioselective Chlorofunctionalizations of <i>N</i>-Substituted Amides Using <i>In Situ</i> Generated HOCl as Hydrogen Bond Source

Anxiety, depression, and post-traumatic stress and associated risk factors among out-of-school girls in western Kenya

Background Many adolescent girls drop out of school in sub-Saharan Africa. Mental health problems in this population and their risk factors are a neglected research area. Methods This community-based cross-sectional survey studied 904 out-of-school girls in rural western Kenya. Outcome variables were a positive screen for anxiety (GAD-7), depression (PHQ-A), post-traumatic stress disorder (PTSD; PCL-C), and a composite measure for overall symptom severity. Survey data were analysed with univariable and multivariable binary logistic and multiple linear regression analyses using SPSS 29.0. Findings The prevalence of probable anxiety was 10.6%, of probable depression 15.9%, and of probable PTSD 18.0%. One of the three items on suicidal ideation or past suicide attempt was reported by 40.2% of girls. In multivariable analyses controlling for age, fear of sexual assault and functional limitations due to menstruation were uniquely associated with each of the outcome variables, and exposure to physical violence to each outcome except anxiety. Other risk factors showed a less consistent relationship with outcome. The cross-sectional study design precludes any temporal and causal inference for the reported significant associations. Conclusion Out-of-school girls constitute a vulnerable group with high levels of PTSD and suicide risk. Multi-level and multi-sector interventions are needed to help these girls cope with their mental health problems and to address mutable risk factors such as gender-based partner and non-partner sexual and physical violence, poor menstrual hygiene, and poverty.