Browse Articles

Spatial interpolation of health and demographic variables: Predicting malaria indicators with and without covariates

Accurate mapping and disaggregation of key health and demographic risk factors have become increasingly important for disease surveillance, which can reveal geographical social inequalities for improved health interventions and for monitoring progress on relevant Sustainable Development Goals (SDGs). Household surveys like the Demographic and Health Surveys have been widely used as a proxy for mapping SDG-related household characteristics. However, there is no consensus on the workflow to be used, and different methods have been implemented with varying complexities. This study aims to compare multiple modelling frameworks to model indicators of human vulnerability to malaria (SDG Target 3.3) in Senegal. These indicators were categorised into socioeconomic (e.g., stunting prevalence, wealth index) and malaria prevention indicators (e.g., indoor residual spraying, insecticide-treated net ownership). We compared three categories of the commonly used methods: (1) spatial interpolation methods (i.e., inverse distance weighting, thin plate splines, kriging), (2) ensemble methods (i.e., random forest), and (3) Bayesian geostatistical models. Most indicators could be modelled with medium to high predictive accuracy, with R2 values ranging from 0.40 to 0.86. No method or method category emerged as the best, but performance varied widely. Overall, socioeconomic indicators were generally better predicted by covariate-based models (e.g., random forest and Bayesian models), while methods using spatial autocorrelation alone (e.g., thin plate splines) performed better for variables with heterogeneous spatial structure, such as ethnicity and malaria prevention indicators. Increasing the complexity of the models did not always improve predictive performance, e.g., thin plate splines sometimes outperformed random forest or Bayesian geostatistical models. Beyond performance, we compared the different methods using other criteria (e.g., the ability to constrain the prediction range or to quantify prediction uncertainty) and discussed their implications for selecting a modelling approach tailored to the needs of the end user.

Polydatin exerts therapeutic effects on myelodysplastic syndrome by inhibiting the protein expression of oncogenes via hypermethylation in vitro

Quasar radiation transforms the gas in a merging companion galaxy

A cross-sectional study of factors influencing sexual health among spouses of patients with cervical cancer

Background After treatment, cervical cancer patients commonly experience sexual health problems that lead to marital conflict. However, the sexual health cognition, distress and needs of patients’ spouses remain unclear. This study examined the factors influencing sexual health among spouses of patients with cervical cancer and provided a reference for targeted nursing interventions. Methods This cross-sectional study was conducted in Chongqing, China. A total of 202 spouses of patients with cervical cancer were selected via convenience sampling. All participants completed the questionnaire via WeChat from September 27 to October 5, 2022. The questionnaire assessed demographic information, clinical information, and information about sexual health cognition, sexual distress and sexual needs. Multiple linear regression analysis was used to identify the factors associated with sexual health cognition. Pearson’s correlation analysis was used to analyse the correlations among sexual cognition, distress and needs. Results The cognitive scores of the spouses of cervical cancer patients with respect to sexual health ranged from 6 to 25 (13.79 ± 6.74). The distress scores ranged from 8 to 35 (27.24 ± 7.88). The need scores ranged from 3 to 12 (8.68 ± 3.64). Age, education level, place of residence and other factors affected sexual health cognition. Age (β, -0.178; 95% CI, 0.099–1.060), education level (β, 0.152; 95% CI, 0.021–0.663), place of residence (β, 0.665; 95% CI, 0.102–5.789), occupation type (β, 0.507; 95% CI, 0.485–4.982), and monthly family income (β, 0.229; 95% CI, 0.311–1.344) were associated with higher levels of sexual health cognition. The results of Pearson’s correlation analysis revealed that there was a negative correlation between sexual health cognition and distress (r = - 0.6165, 95% CI: -0.69 to -0.52, P < 0.001), that cognition was positively correlated with needs (r = 0.6757,95% CI 95% CI: 0.59 to 0.74, P < 0.001), and that distress was positively correlated with needs (r = 0.6860, 95% CI: 0.60 to 0.75, P < 0.001). Conclusions Our study revealed that sexual health cognition among spouses of patients with cervical cancer is affected by multiple factors. The degree of sexual distress among these individuals is high. There was a negative correlation between sexual cognition and distress, a positive correlation between sexual cognition and demand, and a positive correlation between sexual distress and demand. These results suggest that nurses should also include the patient’s spouse in the care process and provide targeted sexual health guidance, thereby improving the overall quality of life of patients and spouses.

Impact of pelvic lymph node dissection on survival outcomes in non-muscle invasive bladder cancer: a multicenter retrospective study

Impact of persistence to secondary preventive medication on prognosis for patients with myocardial infarction with and without obstructive coronary arteries

Background Poor adherence to secondary preventive medication after myocardial infarction (MI) negatively affects long-term prognosis, but knowledge is lacking regarding the impact of poor adherence on prognosis for patients with myocardial infarction with non-obstructive coronary arteries (MINOCA). We therefore investigated the effect of persistence to secondary preventive medication on prognosis in patients with MINOCA compared with patients with myocardial infarction with obstructive coronary arteries (MI-CAD). Methods In this nationwide observational study of 116,143 patients with MI recorded in the SWEDEHEART registry between 2006─2017, MINOCA were identified in 9,124 patients and MI-CAD in 107,019 patients. Persistence to treatment with aspirin, statins, beta blockers and angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin-receptor blockers (ARBs) was investigated for 5 years post discharge and patients were followed for a composite endpoint of major adverse cardiovascular events (MACE), including all-cause death, MI, ischemic stroke and heart failure. Results Persistent use of secondary preventive medications was associated with a decrease in the risk of MACE during follow-up in both MINOCA and MI-CAD patients; aspirin HR 0.70 (CI 0.60–0.82) vs. HR 0.60 (CI 0.57–0.64), statins HR 0.80 (CI 0.68–0.95) vs. HR 0.66 (CI 0.63–0.69), beta blockers HR 0.77 (CI 0.65–0.92) vs. HR 0.76 (CI 0.73–0.80) and ACEIs/ARBs HR 0.62 (CI 0.50–0.77) vs. 0.67 (CI 0.63–0.71). Conclusion Persistence to secondary preventive medications after MI is associated with a reduction in the risk for MACE in both patients with MINOCA and MI-CAD. Continuous efforts to improve adherence to evidence-based medications in general to all patients with MI should be a priority.

Insulin resistance mediates the association between physical activity and mortality in US adults with metabolic syndrome

Telescope team reads the fine print — from more than a kilometre away

Changes in individuals’ glaucoma progression velocity after IOP-lowering therapy: A systematic review

Primary open-angle glaucoma (POAG) is a chronic progressive optic neuropathy often associated with increased intraocular pressure (IOP). Monitoring glaucoma progression velocity, for example, the rate of change in global indices such as mean deviation (MD), is a common way to determine whether functional deterioration has occurred. This systematic review aims to assess changes in glaucoma progression velocity in response to IOP-lowering therapy at an individual level. A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A comprehensive search of PubMed, Cochrane CENTRAL, and ClinicalTrials.gov from database inception through November 12, 2023, was conducted for randomized clinical trials involving patients with POAG, normal tension glaucoma, or progressive ocular hypertension who received IOP-lowering therapy with a target IOP reduction ≥20% from baseline. Included trials were required to report visual field progression velocity information for individual patients before and after intervention. One study was found to meet inclusion criteria and, therefore, synthesis of data and meta-analysis were unable to be performed. The study reports on 139 eyes of 109 patients from the Ocular Hypertension Treatment Study who reached a POAG endpoint. In these patients, the post-treatment rate of MD change (−0.27 ± 0.7 dB/year) was significantly slower than the pre-treatment rate (−0.51 ± 0.8), P < 0.01. In addition, the rate of MD change significantly correlated with mean IOP reduction (p < 0.001). The singular study demonstrated that IOP-lowering therapy did variably slow glaucoma progression rate in that patient cohort. There is a need for more studies that focus on individual patients’ responses to glaucoma treatment. Furthermore, this information should be used to classify the magnitude of patients’ responsiveness to IOP reduction. Future studies should report pre- and post-intervention progression velocities.

Framework development of continuous non-linear Diophantine fuzzy sets and its application to renewable energy source selection

Navigating Nepal’s health financing system: A road to universal health coverage amid epidemiological and demographic transitions

Background Nepal has been undergoing demographic and epidemiological transitions, marked by an increasing burden of non-communicable diseases (NCDs) and injuries. These transitions have led to financial implications, including rising out-of-pocket (OOP) expenses. This study reviews and synthesizes evidence on the status, issues and challenges in health financing system, policies, and programs to achieve universal health coverage (UHC) in Nepal. Methods We conducted a scoping review of literature on Nepal’s health financing system, policies, and programs. A search strategy was developed using keywords related to two core concepts: health financing and universal health coverage. Grey literature was identified from the web pages of relevant ministries and organizations. A total of 148 studies/policy documents published in Nepali and English up to 31 December 2024 were included. Policies and content related to the health financing system were reviewed to understand the status, issues and challenges of health financing functions, and UHC . A framework-guided deductive content analysis approach was employed, and findings were interpreted using the three UHC components: service coverage, population coverage, and financial coverage. Results Nepal’s health policy documents prioritize financial protection for low-income people and target groups through social health protection programs/schemes. However, multiple social health protection schemes coexist with fragmented risk pooling and low efficiency in health financing. OOP expenditure is high at 54.2%, with 10% of the population facing catastrophic health expenditures. Injuries and chronic morbidities contribute significantly to this burden, with 70% of injury-related and 62% of NCD-related expenses borne through OOP payments. Despite efforts to improve financial risk protection, the National Health Insurance Program (NHIP) suffers from low population coverage (28%), low renewal rate (54%), and financial sustainability issues (as provider payments exceed revenue collection). The UHC service coverage index, though improving, was only 54 out of 100 in 2021 reflecting limited health system capacity and insufficient readiness to address health challenges, including those posed by shifting demographics and the growing burden of NCDs. Nepal’s total health expenditure remains around 2% of GDP, with persistent inefficiencies in resource allocation, fiscal decentralization, and budget absorption. Conclusions Nepal’s health financing policies align with UHC goals, yet critical gaps remain in multiple dimensions . Issues such as inefficiencies, underfunding, and fragmented social health protection schemes limit equitable access to quality health care. Therefore, comprehensive structural reforms-spanning legal, institutional, and policy frameworks-are urgently needed. Key reforms  include: (1) merging or harmonizing existing social health protection schemes for efficient pooling and purchasing; (2) enhancing domestic health financing through increased health funding (≥5% of GDP) via payroll contributions, progressive taxation, and earmarked sin taxes; (3) reforming NHIP to mandatory enrollment starting from formal sector, subsidizing premium for informal sector and free coverage for disadvantaged groups, alongside strengthening policy implementation including accrediting of health facilities, ensuring service quality, prioritising and expanding coverage packages with strategic purchasing from all public and private health facilities; and (4) equitable public financing to ensure needs-based allocation across government levels that respond to demographic and epidemiological patterns. Further research is needed to assess hybrid tax and premium based insurance models, strategic purchasing optimization, and digital health innovations for financial sustainability.

Improving the drug delivery performance of ZIF-8 with amine functionalization as a 5-fluorouracil nanocarrier

Richard Garwin obituary: designer of the hydrogen bomb who advocated for arms control

DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation

Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe anatomical structures more clearly, thereby achieving more accurate disease diagnoses. However, existing medical image segmentation networks have issues such as insufficient capability to extract features from target areas, as well as high number of parameters and increased computational complexity. To address these issues, a lightweight Dual-Encoding Feature Interaction Fusion network (DEFIF-Net) is proposed in this paper for medical image segmentation. Firstly, in the encoding stage of DEFIF-Net, a global dependency fusion branch is introduced as an additional encoder to capture distant feature dependencies, whereby the neighboring and distant feature dependencies are effectively integrated by the newly designed feature interaction fusion convolution. Secondly, between the encoder and decoder, channel feature reconstruction modules (CFRMs) are used to enhance the feature representation of important channels. Additionally, a novel multi-branch ghost module (MBGM) is used in the bottleneck layer of the network to enhance its efficiency in capturing and retaining different types of feature information. Lastly, a novel residual feature enhancement (RFE) decoder is utilized to emphasize boundary features, thereby increasing the network’s sensitivity to lesion boundaries. The segmentation performance of the proposed DEFIF-Net network is evaluated in two different medical image segmentation tasks. The obtained experimental results demonstrate that, compared to state-of-the-art networks, DEFIF-Net exhibits superior segmentation performance on all three datasets used, while also having a lower parameter count and computational complexity.

Spatio-temporal evolution and coupling relationship between biodiversity and urbanization in the areas along the Yellow River of Shandong province

DNA-guided transcription factor interactions extend human gene regulatory code

Abstract In the same way that the mRNA-binding specificities of transfer RNAs define the genetic code, the DNA-binding specificities of transcription factors (TFs) form the molecular basis of the gene regulatory code1,2. The human gene regulatory code is much more complex than the genetic code, in particular because there are more than 1,600 TFs that commonly interact with each other. TF–TF interactions are required for specifying cell fate and executing cell-type-specific transcriptional programs. Despite this, the landscape of interactions between DNA-bound TFs is poorly defined. Here we map the biochemical interactions between DNA-bound TFs using CAP-SELEX, a method that can simultaneously identify individual TF binding preferences, TF–TF interactions and the DNA sequences that are bound by the interacting complexes. A screen of more than 58,000 TF–TF pairs identified 2,198 interacting TF pairs, 1,329 of which preferentially bound to their motifs arranged in a distinct spacing and/or orientation. We also discovered 1,131 TF–TF composite motifs that were markedly different from the motifs of the individual TFs. In total, we estimate that the screen identified between 18% and 47% of all human TF–TF motifs. The novel composite motifs we found were enriched in cell-type-specific elements, active in vivo and more likely to be formed between developmentally co-expressed TFs. Furthermore, TFs that define embryonic axes commonly interacted with different TFs and bound to distinct motifs, explaining how TFs with a similar specificity can define distinct cell types along developmental axes.

A tool for assessing sex/gender bias in epidemiological studies of occupational health: Pilot testing on studies of sedentary behaviour at the workplace and cardiometabolic health

Background The perspective of sex/gender bias is often missing in tools used to assess study risk of bias in systematic reviews. The aim was to pilot a checklist using an aetiological occupational health research question regarding the impact of sedentary behaviour at the workplace and cardiometabolic health. The checklist examined whether the consideration of sex/gender was associated with different study characteristics. Methods A sex/gender checklist developed based on a synopsis of existing instruments with input from the Cochrane Sex/Gender Methods Group was adapted for the present study. This checklist comprises four categories: 1. “Background and conceptual considerations” (3 items), 2. “Study design” (2 items), 3. “Study procedures of investigation or intervention and statistical analysis” (2 items), and 4. “Presentation and interpretation of findings” (3 items). Two independent reviewers evaluated all included studies. Bivariate and multivariable logistic regression analyses were used to examine the consideration of sex/gender across study designs, years of publication, and risk of bias levels (based on the Scottish Intercollegiate Guidelines Network [SIGN]). Results Of the 49 studies evaluated with the checklist, none provided detailed information, 69% (n = 34) provided basic information and 31% (n = 15) no information for the consideration of sex/gender. No intervention study provided information for the first two categories. In the third category, all intervention studies (n = 17) and case-control studies (n = 5) provided basic information on sex/gender, while two of the 23 cohort studies did not. In the fourth category, detailed information was found for all study designs (n = 8). Bivariate analyses revealed no association between the consideration of sex/gender and the year of publication (OR per year = 0.89; 95% CI: 0.65, 1.23). A low risk of bias level was not associated with consideration of sex/gender (OR = 0.60; 95% CI: 0.14; 2.50). Compared to intervention studies the odds of considering sex/gender was increased by a factor of 3.6 (95% CI: 1.0, 12.8) in observational studies. Conclusion The adapted checklist was applicable to assess the consideration of sex/gender in all studies. None of the primary studies considered sex/gender perspectives in all of the four categories. Further optimisation of the sex/gender tool seems warranted, based on further research on weighting individual categories or items and application of the checklist for occupational epidemiology in general.

Synergistic effects of cardiovascular health and social isolation on adverse pregnancy outcomes

Abstract Adverse pregnancy outcomes (APOs) affect approximately 20% pregnant women, and their incidence is increasing. The aim of this study was to investigate the effect of cardiovascular health (CVH) during pregnancy on APOs. We analyzed data from 14,930 pregnant women in the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. CVH status during pregnancy was assessed using the eight components of Life’s Essential 8. APOs were defined as composite outcomes encompassing preeclampsia, gestational diabetes mellitus, preterm birth, and small for gestational age. The numbers of participants with high, moderate, and low CVH status were 2891 (19.4%), 11,498 (77.0%), and 541 (3.6%), respectively. Poisson regression analyses with robust error variance, which adjusted for maternal age at conception, alcohol consumption, conception via in vitro fertilization, parity, psychological distress, social isolation, and household income, showed a positive association between moderate and low CVH levels and APOs (risk ratio and 95% confidence interval 1.15 [1.03–1.28] and 2.14 [1.78–2.58], respectively). Among pregnant women with low CVH, those who reported social isolation had a higher prevalence of APOs than did those without social isolation (36.4% vs. 27.4%). This difference was attenuated for pregnant women with high CVH status (13.6% vs. 13.1%). In conclusion, CVH status may be useful for assessing the risk of APOs. Socially isolated pregnant women are more vulnerable to the effects of low CVH status.

End-to-end data-driven weather prediction

Abstract Weather prediction is critical for a range of human activities, including transportation, agriculture and industry, as well as for the safety of the general public. Machine learning transforms numerical weather prediction (NWP) by replacing the numerical solver with neural networks, improving the speed and accuracy of the forecasting component of the prediction pipeline1–6. However, current models rely on numerical systems at initialization and to produce local forecasts, thereby limiting their achievable gains. Here we show that a single machine learning model can replace the entire NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests observations and produces global gridded forecasts and local station forecasts. The global forecasts outperform an operational NWP baseline for several variables and lead times. The local station forecasts are skilful for up to ten days of lead time, competing with a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters. End-to-end tuning further improves the accuracy of local forecasts. Our results show that skilful forecasting is possible without relying on NWP at deployment time, which will enable the realization of the full speed and accuracy benefits of data-driven models. We believe that Aardvark Weather will be the starting point for a new generation of end-to-end models that will reduce computational costs by orders of magnitude and enable the rapid, affordable creation of customized models for a range of end users.

Innovation of eco-friendly TiO2 nano catalyst for new pyrimidine carbonitiriles candidates, assessed for significant antioxidant activity, anti-inflammatory effects, and by insilico studies

Production of titanium dioxide nanoparticles (TiO2- NPs) is carried out in high quantities for an extensive range of applications. These nanoparticles have various physicochemical properties, which can affect their bioactivity. The Biginelli synthesis of 4-cyanobenzaldehyde, ethyl cyanoacetate, and thiourea in the presence of TiO2 nanoparticle yields 4-(4-cyanophenyl)-6-oxo-2-thioxohexahydropyrimidine-5-carbonitrile (1). This multi-component system is safe, eco-friendly, and non-toxic. The introduction of the new system saves time and reduces chemical usage. Compound (1) underwent reactions with various additional compounds such as (methyl iodide, chloroacetonitrile, chloroacetone, acrylonitrile, ethyl chloroacetate, and chloroacetic acid/benzaldehyde). The newly synthesized compounds possess remarkable anti-inflammatory and antioxidant activities, making them an excellent choice for therapeutic use.. The in-silico studies include molecular docking using MOE, Pharmacokinetics using Toxoradar, SAR, and DFT studies.