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Transport of CCL2 across an induced pluripotent stem cell-derived in vitro model of the human blood-brain barrier is heparan sulfate-dependent

Transport of immune-active substances across the blood-brain barrier (BBB) is an important mechanism of neuroimmune regulation. CCL2 is an exemplary chemokine that regulates neuroinflammation and can cross the intact BBB from blood to brain in mice. This study aimed to characterize the blood-to-brain transport mechanisms of human CCL2 using human induced pluripotent stem cell (iPSC)-derived brain endothelial-like cells (iBECs), an in vitro BBB model. Since heparan sulfate (HS) is an important component of the in vivo BBB that regulates CCL2 transport in mice, we first assessed HS deposition in iBECs and found that HS levels increased with extended culture time. We therefore evaluated transport of radiolabeled 125 I-CCL2 and 131 I-BSA after nine days in subculture, after HS had sufficient time to accumulate. To determine the predominant transport mechanism in vitro , we evaluated whether transport of 125 I-CCL2 and 131 I-BSA was altered in the presence of an inhibitor of the chemokine receptor CCR2, and following treatment with heparin, heparinase enzymes, or the heparan sulfate synthesis inhibitor GalNaz which all test HS-dependent mechanisms of transport. We also evaluated the expression of CCR2 and membrane-bound HS proteoglycans (HSPGs) in iBECs and in isolated human brain microvessels. We found that iBECs have a functional blood-to-brain transport system for CCL2. Similar to findings in mice, heparin inhibited CCL2 transport whereas the CCR2 inhibition did not. Both heparinase treatment and treatment with GalNaz inhibited CCL2 transport across the BBB, further supporting the involvement of HS in CCL2 transport. The iBECs expressed CCR2 at levels comparable to human brain microvessels, and had detectable expression of the syndecans 1–4 and glypicans 1–6, whereas human brain microvessels expressed syndecans 2–4 and glypicans 2–5 in all subjects tested. Our findings highlight that iBECs are a useful tool for studying the involvement of heparan sulfate/glycocalyx components in the transport of substances across the BBB.

Pancreatic cancer is evasive. Is the nervous system the reason why?

Implementation of a compact diplexer based on a modified T-shaped step-impedance resonator (MTSIR) for 5G networks

The effects of high-frequency repetitive transcranial magnetic stimulation on negative symptoms in schizophrenia patients: A systemic review and meta-analysis

Objective The research on high-frequency repetitive transcranial magnetic stimulation (rTMS) is very ambiguous, particularly the variability in treatment parameters and the need for standardized protocols, and its effectiveness have hardly been provided conclusive evidence. This systematic review and meta-analysis aimed to determine the effects of high-frequency rTMS on negative symptoms in schizophrenia patients. Methods Six databases (PubMed, Embase, Web of Science, SinoMed, PsycINFO and MEDLINE) were searched from inception to March 2024. Relevant data were extracted and analysed by two independent investigators using Cochrane RevMan software (version 5.3) and R software (version 4.3.1). Results A total of 17 Randomized Controlled Trials (RCTs) were included in the current meta-analysis. Compared with control treatment, active rTMS showed advantage in treating negative symptoms of schizophrenia [standardized mean difference (SMD): −0.22, 95% confidence interval (CI): −0.38, −0.05, P  = 0.009; I 2  = 9%]. Subgroup analysis revealed that rTMS with a total treatment course of more than 15 sessions [SMD: −0.31 (95% CI: −0.49, −0.13), P  = 0.0007; I 2  = 0%], active rTMS with a frequency of 20 Hz [SMD: −0.40 (95% CI: −0.67, −0.13), P  = 0.004; I 2  = 0%], and active rTMS targeting the dorsolateral prefrontal cortex (DLPFC) [SMD: −0.25 (95% CI: −0.40, −0.09), P  = 0.003; I 2  = 0%] were significantly effective in terms of reducing negative symptoms. Conclusions The effect of rTMS on negative symptoms is influenced by rTMS parameters. We suggest that researchers should focus on changing the frequency, location and length of treatment to improve negative symptoms of schizophrenia.

A time-domain Runge-Kutta dual reciprocity boundary element method for scalar wave propagation problem

Clear tracks or missed connections? A qualitative study exploring how interest-holder perceptions of purpose shape the implementation and experience of the six-month review for stroke survivors

Introduction The six-month review (6MR) for stroke survivors is recommended in clinical guidelines. However, the purpose of the review lacks clarity and has been implemented in variable ways. This study aims to better understand the purpose of the 6MR by comparing and contrasting the perspectives of different interest-holders, and to identify what impact this might have on the functioning of services. Method This paper reports a qualitative analysis drawn from a multiple case study project. Participants were recruited from three interest-holder groups: Service Providers (staff members), Service Influencers (managers, commissioners and regional leaders), and Service Users (stroke survivors and their carers). Data were collected from semi-structured interviews, clinical observations, and documentary evidence. Interest-holder groups were analysed separately using reflexive thematic analysis. Themes were then compared across interest-holder groups. Results Thirty-six participants were recruited across three interest-holder groups: Service Providers (n = 8), Service Influencers (n = 6), and Service Users (n = 22). Seven themes were identified: two each for Service Providers and Service Influencers, and three for Service Users. Service providers emphasised a desire to deliver person-centred care but were often constrained by systemic pressures. Service influencers saw the review primarily as a mechanism for population-level data collection and service planning, while also acting as a safety net to capture unmet or evolving needs. Service users typically viewed the review as a routine check-up rather than a pivotal moment in the pathway. For some, it marked the end of formal support while others valued the sense of reassurance and validation it provided. Conclusion Interest-holders have differing views on the purpose of the 6MR, with tensions emerging between system-level priorities and person-centred care. Greater clarity on the function of the 6MR may help reduce unwarranted variation in its implementation and ensure it delivers meaningful value to all involved.

Evaluation of medical education mobile apps with MARS and MARuL in a TPACK informed model

Statistical approaches for service delivery differentials as assessed through a composite indicator: Application to Ugandan local governments

This study assesses differentials in service delivery among Ugandan local governments through a composite indicator that consolidates essential performance data from the education, health, and water sectors. The composite indicator scores as an outcome variable and it is modeled against probable determinants using beta regression, generalized additive models (GAMs) and random forest regression approaches. Beta regression, is a parametric approach, which includes varying precision parameters, is effective for modelling bounded data such as composite indicators, whereas random forest regression, as a non-parametric approach, emphasizes the relative importance of predictors. On the other hand, GAMs are semi-parametric, and bring to the fore non-linear covariate effects employing splines. Employing the minimax transformation, equal weighting and multiplicative aggregation, the composite indicator of service delivery scores varied from 0.25 to below 0.60 (on a scale of 0–1), with a substantial number of local governments scoring below 0.5. The findings reveal budgetary constraints, fragmentation at sub-county level, and geographical challenges in terms of distance from the capital city as significant obstacles to service delivery at local government level. The predictive accuracy of the three approaches as determined through the mean square error (RMSE) were found to be comparable (RMSE ≈0.05; MAE ≈ 0.042), suggesting that these approaches, grounded in contextualized theoretical frameworks, are effective in assessing service delivery outcomes and may therefore be employed in similar studies after careful consideration of the analytical objective. The study recommends broadening the range of service dimensions and predictors to develop a more relatable composite indicator. Given the group structures present in the predictors, employing grouped regression within the framework of beta regression modeling is advisable to provide a more robust and efficient modeling strategy.

Six highlights from pancreatic cancer research

Knowledge, attitudes, and practices of parents of pediatric patients undergoing eye surgery under general anesthesia: a cross-sectional study

Determination of silicon dioxide in fluorite by ICP OES with closed digestion and boric acid complex reaction

Fluorite is widely utilized in the new energy, semiconductor, high-end manufacturing, and steelmaking industries. The silicon dioxide (SiO₂) content is critical for both the quality evaluation of fluorite and the design of beneficiation processes. However, the traditional fusion method suffered from several limitations, including cumbersome pretreatment, silica precipitation during acidification, and significant matrix effects caused by alkali metals introduced during fusion. To address these challenges, this study developed an innovative method employing acid dissolution for the determination of SiO₂ content in fluorite using inductively coupled plasma optical emission spectrometry (ICP OES). This method employs a mixture of nitric acid and hydrofluoric acid in a water bath to digest the sample within a closed system. Systematic optimization experiments were conducted to evaluate key parameters, including the volumes of nitric acid and hydrofluoric acid, digestion time in the water bath, and the quantity of boric acid solution, with the aim of enhancing analytical performance. Complete dissolution of silicon dioxide in fluorite was achieved by treating 0.2000 g of sample with 5.0 mL nitric acid and 2.0 mL hydrofluoric acid, followed by heating at 30 minutes. Subsequently, excess fluoride ions were complexed by adding 10 mL of a 50 mg mL -1 boric acid solution. After dilution, the samples are analyzed by ICP OES. At a dilution factor of 2500, the limit of detection (LOD) for SiO₂ was 0.974 µg g -1 , with a measurement range spanning from 0.0004% to 25%. Method validation was performed by analyzing six fluorite certified reference materials in seven replicates. The results were in good agreement with certified values, with relative errors (RE) ≤ 3.61%. Seven replicate analyses of five real fluorite samples also showed consistency with results obtained using the standard alkaline fusion colorimetric method (GB/T 5159.8–2006), with relative deviations ≤ 2.71% and relative standard deviations (RSD, n = 7) ≤ 3.04%. This method employs nitric acid and hydrofluoric acid under sealed water bath digestion conditions for the dissolution of fluorite samples, effectively preventing the volatilization loss of silicon tetrafluoride formed during the reaction between fluorine and silicon. By introducing boric acid to complex excess fluoride ions, the interference caused by residual hydrofluoric acid is eliminated, thereby enabling the use of conventional sampling systems that are otherwise incompatible with hydrofluoric acid media. This approach facilitates the rapid determination of silicon dioxide in fluorite via acid dissolution combined with ICP OES analysis. The procedure is straightforward, efficient, and cost-effective, significantly enhancing analytical throughput. It is well suited for the routine and high-throughput determination of SiO₂ in large batches of fluorite samples.

Performance and challenges of malaria rapid diagnostic tests in endemic regions of Africa

Exploring the link between osteoporosis and stroke risk: An exploratory study based on 2017–2018 NHANES clinical data and bioinformatics analysis

Objective This study aims to explore the correlation between Osteoporosis and stroke risk, and find potential common key genes and drugs for intervention through bioinformatics methods. Methods This study used clinical data to assess the relationship between Osteoporosis and stroke risk through univariate and multivariate logistic regression analyses. Additionally, blood sequencing data from patients with Osteoporosis and stroke were obtained from the GEO database, and common key genes were identified using differential analysis, LASSO regression, and ROC curve methods. Potential interventional drugs were predicted using the DSigDB database. Results In the initial model, Osteoporosis was significantly associated with stroke risk (OR=1.78, 95% CI: 1.14–2.78, p  < 0.01). This association was still significant after adjusting for factors such as age, gender, race, and BMI (OR=1.84, 95% CI: 1.18–2.89, p  = 0.007). Bioinformatics analysis identified LILRA5 , HNRNPL and AGBL3 as common key genes for Osteoporosis and stroke, and these genes were highly effective in diagnosing both diseases. The DSigDB database predicted that Cyclopenthiazide, Neostigmine bromide, and R-atenolol could potentially intervene with these three genes. Conclusion There is a significant positive correlation between Osteoporosis and stroke risk. LILRA5 , HNRNPL and AGBL3 could be key genes common to both diseases, and Cyclopenthiazide, Neostigmine bromide, and R-atenolol could be potential drugs for intervention.

A selective Kalman filtering approach to online neural network updating under system drift

The role of economic stability in boosting exports in COMESA: Threshold effects analysis

Economic stability is crucial for improving export performance, particularly within regional trade blocs such as the Common Market for Eastern and Southern Africa (COMESA). However, empirical studies examining how threshold levels of economic stability affect export dynamics among COMESA member states have been limited. This study explores the threshold effects of key economic indicators, economic growth, trade openness, and inflation, on export performance in COMESA countries using panel data from 2000 to 2022. Employing panel threshold regression analysis, this study investigates the nonlinear relationships between these variables, focusing particularly on the impact of inflation thresholds. The study found an optimal inflation threshold of 3.662%, aligning with the lower end of the global benchmark of 3–7% typically observed in emerging and developing economies. In low-inflation regimes (LnInfl ≤ 3.662), GDP significantly enhances export growth by 0.348% per unit increase, while trade openness yields a smaller contribution of 0.091%. In contrast, high-inflation scenarios (LnInfl > 3.662) show that GDP continues to support exports (0.779% increase), but trade openness negatively impacts export performance by 0.127%. Policymakers need to focus on maintaining inflation under the critical threshold of 3.662%, as this is vital for enhancing the benefits of GDP growth and trade openness on export performance. These insights underscore the necessity of keeping inflation in check to improve export competitiveness. Additionally, prioritizing monetary, fiscal, and institutional stability initiatives will support sustainable growth and strengthen regional trade dynamics within COMESA, as exceeding this inflation limit could impede export potential and regional integration efforts. This study offers valuable insights for developing regions aiming to enhance their trade performance through effective economic policies.

Spatiotemporal trends in P. falciparum malaria and identification of high-risk villages in Eastern Myanmar: an 8-year observational study

Abstract One barrier to achieving Plasmodium falciparum elimination is the persistence of villages where transmission remains high. While targeted interventions can effectively reduce transmission in these areas, identifying priority target villages is often resource-intensive. This study investigates the use of a geostatistical model to analyse routinely collected surveillance data and identify high-risk villages in Hpapun Township, Myanmar. A geostatistical model was fitted using routine surveillance data (2014–2021) collected from 507 village-based malaria posts to assess temporal changes in P. falciparum incidence and make incidence predictions while accounting for elevation, prior interventions and spatial correlation between villages. Between 2014 and 2019, P. falciparum incidence decreased by 93.9%. Villages that received targeted interventions were characterised by higher pre-intervention incidence (incidence rate ratio (IRR) = 4.72, 95% confidence interval (CI) 4.56–4.90) relative to non-intervention villages and were associated with lower incidence post-intervention (IRR = 0.26, 95% CI 0.24–0.27). In 2021, 12 high-risk villages were identified, with a reported incidence exceeding the predicted incidence for at least three months, and eight villages were identified as persistently high-risk (≥ 90th percentile difference in at least six months). Our findings suggest that geostatistical modelling can be utilised to identify persistent high-risk villages, thereby efficiently supporting malaria elimination efforts.

Safety outcomes of statin vs non-statin lipid-lowering interventions in patients with prior statin-associated muscle symptoms: A systematic review and meta-analysis

Background Statin-associated muscle symptoms (SAMS) are an obstacle in the prevention of cardiovascular events. A systematic assessment of the evidence of interventions in the setting of SAMS is lacking. Objective To assess the evidence of strategies of statin-based vs non-statin based therapies in patients with a history of SAMS. Methods MEDLINE, EMBASE, Cochrane Central Register of Controlled Clinical Trials, Scopus, Clinicaltrials.gov and Proquest databases were searched from inception up to February 2024.We included randomized controlled trials (RCTs) and non-randomized studies involving patients with history of prior SAMS, comparing statin-based therapy to a comparator. We followed the PRISMA guideline with multiple authors involved at each stage. A random-effect model was used in the meta-analysis. We defined the primary outcome as incidence of muscle symptoms. The secondary outcomes were proportion of statin discontinuation of statin-based therapy within patients with history of SAMS. The protocol was registered on PROSPERO (CRD42020202619). Results In 23 studies (13 RCTs, 2 prospective and 8 retrospective studies) there were in total 1868 participants in RCTs and 47’628 participants in non-RCTs (follow-up 12 weeks – 31 months). Our confidence in the body of evidence using GRADE was moderate for the primary outcome and low-moderate for the secondary outcome. In RCTs among patients with history of SAMS, there was high heterogeneity in the statin regimens and controls (placebo/non-daily dosing/ezetimibe/PCSK9 inhibitors). In RCTs, the meta-analysis showed no difference between statin-based and control groups in the incidence of muscle symptoms (OR 1.19, 95%CI 0.86–1.64, I 2 : 46.3%)). Therapy discontinuation due to muscle symptoms in RCTs was higher in the statin-based than the comparator groups (OR 1.48, 95%CI 1.03–2.12, I 2  = 17.6%). Conclusion Our findings suggest that patients with a history of SAMS can be re-challenged with statins. More high-quality evidence is needed to strengthen guidelines regarding the management of SAMS.

Observation of deuteron and antideuteron formation from resonance-decay nucleons

Abstract High-energy hadronic collisions generate environments characterized by temperatures above 100 MeV (refs.  1,2 ), about 100,000 times hotter than the centre of the Sun. At present, it is therefore unclear how light (anti)nuclei with mass number A of a few units, such as the deuteron, 3 He or 4 He, each bound by only a few MeV, can emerge from these collisions 3,4 . Here, the ALICE Collaboration reports that deuteron–pion momentum correlations in proton–proton (pp) collisions provide model-independent evidence that about 90% of the observed (anti)deuterons are produced in nuclear reactions 5 following the decay of short-lived resonances, such as the Δ(1232). These findings, obtained by the ALICE Collaboration at the Large Hadron Collider, resolve a gap in our understanding of nucleosynthesis in ultrarelativistic hadronic collisions. Apart from offering insights on how (anti)nuclei are formed in hadronic collisions, the results can be used in the modelling of the production of light and heavy nuclei in cosmic rays 6 and dark-matter decays 7,8 .

The gut microbiota as a potential biomarker in patients with EGFR-mutant lung cancer

Grape seed proanthocyanidin extract inhibits ferroptosis by activating Nrf2/HO-1 and protects against diabetic kidney disease

Background The main pathological characteristic of diabetic kidney disease (DKD) is renal dysfunction caused by tubular injury. Ferroptosis is a recently discovered form of cell death closely linked to renal tubular injury in DKD. Nuclear factor erythroid-2 related factor 2/heme oxygenase 1(Nrf2/HO-1) is crucial in controlling ferroptosis. Grape seed proanthocyanidin extract (GSPE) mitigates renal dysfunction in DKD by activating Nrf2/HO-1 pathway. However, the role of GSPE in protecting against DKD through ferroptosis modulation has been insufficiently explored. Methods Streptozotocin (STZ)-induced diabetic rat models and HK2 cells cultured with high glucose were used as experimental objects in this study. HE and PAS staining was used to observe the morphological changes of rat kidney. Fe 2+ and reactive oxygen species (ROS) levels as well as apoptosis were determined by fluorescent staining. Transferrin receptor protein 1(TfR1), acylcoa synthetase long chain family member 4(ACSL4), glutathione peroxidase 4 (GPX4), Nrf2 and HO-1 proteins were detected by western blot. Subsequently, Nrf2 was knocked down and oxidative stress and ferroptosis were observed in HK2 cells. Results In vivo and vitro result showed that GSPE treatment significantly lessened renal impairment, oxidative stress, and ferroptosis in DKD, while the application of Ferrostatin-1 (Fer-1) notably amplified the anti-ferroptotic effect of GSPE. Moreover, the anti-ferroptotic effect of GSPE was markedly diminished after Nrf2 expression was downregulated in HK2 cells. Conclusion GSPE treatment reduced ferroptosis in DKD by modulating the Nrf2/HO-1. In conclusion, our results underscore the significance of ferroptosis in DKD and present new perspectives on the protective effects of GSPE in this condition.