Browse Articles

Sulfur as a proxy for identifying coast-inland human mobility in Northern Iberia during Late Prehistory

Population movements constitute a significant driver of cultural change in prehistoric societies. In recent years, sulfur isotopes have emerged as a valuable approach for distinguishing human/animal provenance. However, the scarcity of sulfur isotope studies and the lack of baseline maps predicting their variations in the landscape limit our current knowledge about mobility behaviours. Here, we first present the δ34S isotope values of 142 human and animal bone collagen samples from coastal and inland funerary sites located in northern Iberia. Second, to apply a multivariate machine-learning regression and a random forest model to predict sulfur isotope variations across Iberia, we compiled the sulfur isotope data from 554 specimens of 41 archaeological locations from Holocene contexts. Our research demonstrated that population movement between coastal and inland locations is observable through differences in the δ34S isotope values of individuals linked to their respective environments, suggesting migrations on both sides of the Cantabrian mountain range. The resulting isoscape model demonstrates that sulfur isotope patterns are highly predictable, with 82% of the sulfur isotope variation explained by only four variables: elevation, Bouguer anomaly, distance from the coast, and strontium isotope values. While the model is highly accurate for regions with large amounts of data, such as northern Iberia, Central and Eastern Iberia still require more sulfur isotope data to predict isoscapes.

How did life get multicellular? Five simple organisms could have the answer

Electric transmission value and its drivers in United States power markets

Abstract Electric transmission infrastructure plays a vital role during extreme weather and supply disruptions and can enable low-cost electricity systems. This paper contributes to a more complete understanding of the value and cost-effectiveness of transmission, as well as barriers to its development. By studying wholesale energy market prices in the United States between 2012 and 2022, we find that additional transfer capacity between regions would have been especially valuable, with a median value of $116 million per GW per year. This capacity would often have provided balanced benefits to each region. The market value of transmission was highly influenced by a small fraction of time: 5% of hours typically captured at least 45% of the total value. These peak periods were primarily driven by unforeseen changes in conditions within one day of operations. Annualized transmission infrastructure cost estimates were lower than the average market value for most locations, including all links crossing regional seams, where the value-to-cost ratio was often greater than 4. This suggests that there are barriers to developing valuable grid infrastructure. These results complement forward-looking modeling studies and support efforts to improve modeling practices.

Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare

Liver disease encompasses a range of conditions affecting the liver, including hepatitis, cirrhosis, fatty liver, and liver cancer. It can be caused by infections, alcohol abuse, obesity, or genetic factors, and it often progresses silently until advanced stages. Early detection and lifestyle adjustments are essential for effective management and to prevent severe liver damage. This study explores the application of machine learning (ML) techniques to predict liver disease, leveraging a dataset to compare the performance of several ensemble classifiers. The algorithms include the Random Forrest Classifier, Ada Boost Classifier, and Gradient Boosting Classifier. After a series of feature extraction and selection, hyperparameter tuning by Randomized Search CV and GridSearchCV, we aimed to determine the best model for liver disease prediction in terms of accuracy, precision, recall, and F1-score. The results showed that the Random Forest Classifier, optimized with GridSearchCV, achieved the highest accuracy at just over 85.17%. The considerations presented in this classifier can be considered for potential use as a precise diagnostic tool for liver disease diagnostics as these measurements indicate that this classifier works balanced with precision at 0.85 for both the presence and absence of the given disease as well as recall of 0.81 for its presence and 0.87 for its absence and F1-measure of 0.83 and 0.85 respectively. There were also relatively high performances of AdaBoost Classifier and Gradient Boosting Classifier, though none of the classifiers outperformed Random Forest Classifier significantly. The research has shown the potential of ensemble ML techniques, especially in the diagnosis of medical conditions, including liver diseases which, if diagnosed early, are critical. The results add evidence regarding the applicability of the ML models in clinical practices with the potential to improve diagnostic activities and consequently the outcomes of patients. Future studies will build on these models, testing them on larger and more diverse sets of data, including aspects of deep learning, and apply the research to other disease domains. The work presented in this research offers a starting point for carrying out innovations with ML in the sphere of healthcare to progress the methods of diagnosing diseases and treatment.

In situ synthesis of copper-based mordenite for nitrogen/methane sieving

HIV-1 controllers possess a unique CD8+ T cell activation phenotype and loss of control is associated with increased expression of exhaustion markers

Background HIV-1 controllers are a rare population of individuals that exhibit spontaneous control of HIV-1 infection without antiretroviral therapy. Understanding the mechanisms by which HIV-1 controllers maintain and eventually lose this ability would be highly valuable in HIV-1 cure or vaccine research. Previous work revealed the ability of CD8 + T cells isolated from HIV-1 controllers to suppress HIV-1 replication in matched CD4 + T cells and PBMCs ex vivo and suggested the loss of control may be tied to CD8 + T cell exhaustion. Results We explored whether CD8 + T cell exhaustion plays a role in the maintenance and loss of control by examining immune characteristics of HIV-1 persistent controllers and transient controllers who lost control within the duration of the study. Using flow cytometry, we analyzed exhaustion marker expression on CD8 + T cells from HIV-1 controllers and determined that they maintain a unique exhaustion profile as compared to people without HIV-1 and HIV-1 standard progressors. The low level of T cell exhaustion seen in HIV-1 controllers was reversed when these individuals lost control and showed increased viral loads. Combinatorial immune checkpoint blockade targeting exhaustion markers was able to restore ex vivo control in CD8 + T cells from former controllers. Conclusions These results suggest that CD8 + T cell exhaustion compromises the ability to control viral replication in HIV-1 controllers. The character of exhaustion in response to HIV-1 and therapy is distinct in HIV-1 persistent controllers, transient controllers and standard progressors.

Explaining the mental-health burden of atopic dermatitis

Gram-scale selective telomerization of isoprene and CO2 toward 100% renewable materials

Abstract Carbon dioxide (CO2) is an ideal chemical feedstock due to its abundance, low cost, low toxicity and its role as a greenhouse gas. Telomerization with butadiene give rise to functional small molecules and polymers with significant CO2 content, but the fossil origin of the olefin offsets sustainability benefits. Here, we present a palladium-catalyzed telomerization of CO2 with isoprene, two of the most prevalent organic compounds in the atmosphere, yielding “COOIL”, an ideally 100% renewable δ-lactone containing 24 wt% CO2, with high selectivity and turnover numbers above 100. A combination of a Pd catalyst, acetate, and controlled water promoted selectivity and conversion. Density functional theory calculations reveal reductive elimination as the rate-limiting and selectivity-determining step, preceded by isoprene dimerization. The head-tail pathway is the kinetic pathway while the tail-tail product is the thermodynamic product. This functionalized lactone also shows promise for polymerization under Lewis acid-promoted conditions, opening avenues for sustainable polymers from CO2 and bio-derived feedstocks.

Exosome-mediated modulation of radioresistance: The radiation-induced bystander effect in prostate cancer cells

Exosomes are involved in intracellular communication and mediate the radiation-induced bystander effect (RIBE). We assessed the ability of exosomes to modify the radiation response of PC3 and DU145 prostate cancer cells exposed to X-rays. Irradiated cells were analyzed using clonogenic survival and apoptosis assays, while exosome-stimulated cells were evaluated for γH2AX immunostaining, immunoblotting, and apoptosis. Exosomes were isolated via size exclusion chromatography (SEC), characterized by nanoparticle tracking analysis (NTA) and immunoblotting, and ranged from 130 to 137 nm, containing CD63 and CD81. We found that exposure to ionizing radiation (IR) resulted in increased apoptosis and necrosis. To assess exosomes impact on radiation response, exosomes were transferred to non-irradiated and irradiated recipient cells. Non-irradiated PC3 cells stimulated by exosomes released from irradiated PC3 and DU145 cells showed more apoptosis and necrosis than those stimulated by exosomes released from non-irradiated cells. Non-irradiated PC3 cells co-incubated with exosomes from irradiated PC3 and DU145 cells exhibited more γH2AX foci than non-irradiated PC3 cells. Our results confirmed that DU145 cells are more radioresistant than PC3 cells and exosomes isolated from these cells may contribute to radiation resistance in prostate cancer. Thus, studying exosome functions, particularly in radiation resistance, is crucial for understanding carcinogenesis and optimizing radiotherapeutic methods.

Origins of life: the molecules that could have unlocked peptide synthesis

Mechanistic origins of temperature scaling in the early embryonic cell cycle

Abstract Temperature strongly influences physiological and ecological processes, particularly in ectotherms. While complex physiological rates often follow Arrhenius-like scaling, originally formulated for single reactions, the underlying reasons remain unclear. Here, we examine temperature scaling of the early embryonic cell cycle across six ectothermic species, including Xenopus , Danio rerio ,   Caenorhabditis , and   Drosophila . We find remarkably consistent apparent activation energies (75  ± 7 kJ/mol), corresponding to a Q 10 of 2.8 at 20°C. Computational modeling shows that both biphasic scaling in key cell cycle components and mismatches in activation energies across partially rate-determining enzymes can explain the observed approximate Arrhenius behavior and its breakdown at temperature extremes. Experimental data from cycling Xenopus extracts and in vitro assays of individual regulators support both mechanisms. These findings provide mechanistic insights into the biochemical basis of temperature sensitivity and the failure of biological processes at thermal limits.

Low Th2 and high PD1+ TFh cells in blood predict remission after CTLA-4Ig treatment for 48 weeks in early rheumatoid arthritis

Objective To determine whether baseline CD4+ T helper (Th) cell subset proportions in blood may serve as predictive biomarkers for achieving remission 48 weeks after initiating CTLA-4Ig, anti-tumor necrosis factor (TNF), or anti-interleukin 6 receptor (IL6R) treatment in patients with early rheumatoid arthritis (eRA). Methods This study included 60 untreated eRA patients from the larger randomized treatment trial NORD-STAR. They were treated with methotrexate (MTX) combined with either CTLA-4Ig (n = 17), anti-TNF (n = 22), or anti-IL6R (n = 21). Disease activity was assessed by clinical disease activity index (CDAI), C-reactive protein, and erythrocyte sedimentation rate. The primary outcome was remission (CDAI ≤ 2.8) at week 48, and the secondary outcomes were time to reach remission or sustained remission during the 48-week follow-up. CD4+ T cell subset proportions were analyzed fresh by flow cytometry at baseline and at 24 and 48 weeks. Results In CTLA-4Ig + MTX-treated patients, baseline Th2 together with PD1+ T follicular helper (TFh) cell proportions predicted CDAI remission at week 48 (AUC: 0.986, 95% CI 0.94–1.0). Survival analysis revealed that patients with Th2 proportions below 16.8% or PD1+ TFh proportions above 7.6% at baseline were more likely to achieve remission (log-rank p = 0.002 and p = 0.007, respectively), and sustained remission (log-rank p = 0.01 and p = 0.001, respectively), over the 48-week follow-up. CD4+ T cell subset proportions did not predict remission in patients treated with anti-TNF + MTX or anti-IL6R + MTX. Only CTLA-4Ig treatment reduced PD1+ TFh and PD1neg TFh fractions after 48 weeks. Conclusion Circulating Th2 and PD1+ TFh cell proportions at baseline may serve as predictive biomarkers for achieving CDAI remission after 48 weeks of CTLA-4Ig treatment in eRA.

Temperature-related hospitalization burden under climate change

NINJ1 regulates plasma membrane fragility under mechanical strain

ProSPective evaluation of the dIagnostic accuracy of siNe spiN non-contrast flatdEtectoR CT (FDCT) for the detection of intracranial hemorrhage in stroke patients - Protocol of a non-inferiority comparison to multi detector CT

Rationale Whether syngo DynaCT Sine Spin non-contrast flat detector CT (FDCT) imaging is sufficient to rule out intracranial hemorrhage in suspected acute stroke patients is unknown. Aim To determine if syngo DynaCT Sine Spin non-contrast FDCT imaging is non-inferior to conventional multidetector CT (MDCT) imaging for the detection and exclusion of intracranial hemorrhages in suspected acute stroke patients. Sample size To enroll 252 participants in three buckets (126 ischemic stroke patients, 126 hemorrhagic stroke patients (including 14 patients with an isolated infratentorial hemorrhage). Methods and design A multicenter, international, prospective, cross-sectional, endpoint assessor blinded, non-inferiority trial. Outcomes The primary outcome is the occurrence of an intracranial hemorrhage (yes versus no). This will be used to calculate the sensitivity and specificity of FDCT imaging for the detection of intracranial hemorrhages. All FDCT images will be rated by six independent raters in a blinded imaging core-lab. The rating of the MDCT images will be deemed as ground-truth. FDCT imaging will be deemed non-inferior if the lower bound of the 95%-Confidence Interval of the sensitivity and specificity is above 95%. Discussion This trial will inform physicians whether syngo DynaCT Sine Spin non-contrast FDCT imaging can reliably exclude intracranial hemorrhages in patients with suspected acute stroke. Trial registration ClinicalTrials.gov NCT05458908

A psoriasis cure could be in touching distance

Perception of healthcare administrators on the impediments of optimizing adverse events following immunization e-Reporting in Nigeria

Background Adverse events following immunization (AEFI) are any negative medical event that occurs after vaccination but may or may not be causally related to the vaccine. AEFI reporting is the gateway to AEFI surveillance systems at primary healthcare facilities where immunization services are provided. Several studies have highlighted low reporting of AEFI cases, particularly in low-resource settings, yet nothing is known about stakeholders’ perspectives on the factors associated with the AEFI reporting rate in Nigeria. Objective This study explored the stakeholders’ perspectives (from a baseline assessment) on the barriers to adequately reporting (electronically) AEFI in Nigeria. Methods The study was conducted using a qualitative approach. Key informant interviews were conducted at the national and sub-national (state) levels, who were purposively selected to acquire information from stakeholders on the challenges facing the e-reporting of AEFI at the national and state levels. All the audio interview files were transcribed into English Language, coded, and presented using a thematic approach. Results A total of 32 healthcare workers at the national and sub-national levels participated in the study. The study adapted an extant pharmacovigilance thematized framework of reporting the barriers to electronic reporting of the pharmacovigilance system. Therefore, this study categorized the participants’ responses into four main themes, including healthcare workers’ knowledge deficiency and fear, technical infrastructural challenges, poor reporting systems, and inconsistency in government commitment. Conclusion This study concludes that the AEFI surveillance system in Nigeria requires immediate and thorough attention. This stems from evidence gathered from the study participants, revealing the various challenges that are extant at the national and sub-national levels. While these challenges – healthcare workers’ knowledge deficiency and fear, poor technical infrastructure, poor reporting system, and inconsistency in government commitment – may appear mundane, they are critical to optimizing the AEFI surveillance system and maintaining the drive for an improved disease management system. Recommendation This study recommends that stakeholders at all levels should take up improved ownership of AEFI reporting (especially electronic) systems in the country.

Prophages block cell surface receptors to preserve their viral progeny

VNC-Dist: A machine learning-based semi-automated pipeline for quantification of neuronal position in the C. elegans ventral nerve cord

The C. elegans ventral nerve cord (VNC) provides a genetically tractable model for investigating the developmental mechanisms involved in neuronal positioning and organization. The VNC of newly hatched larvae contains a set of 22 motoneurons organized into three distinct classes (DD, DA, and DB) that show consistent positioning and arrangement. This organization arises from the action of multiple convergent genetic pathways, which are poorly understood. To better understand these pathways, accurate and efficient methods for quantifying motoneuron cell body positions within large microscopy datasets are required. Here, we present VNC-Dist (Ventral Nerve Cord Distances), a software toolkit that replaces manual measurements with a faster and more accurate computer-assisted approach, combining machine learning and other tools, to quantify neuron cell body positions in the VNC. The VNC-Dist pipeline integrates several components: manual neuron cell body localization using Fiji’s multipoint tool, deep learning-based worm segmentation with modified Segment Anything Model (SAM), accurate spline-based measurements of neuronal distances along the VNC, and built-in tools for statistical analysis and graphing. To demonstrate the robustness and versatility of VNC-Dist, we applied it to several genetic mutants known to disrupt neuronal positioning in the VNC. This toolbox will enable batch acquisition and analysis of large datasets across genotypes, thereby advancing investigations into the cellular and molecular mechanisms that govern VNC neuronal positioning and arrangement.

Predictors of suicide attempts among adolescents with suicidal ideations and a plan: Results from the National Survey on Drug use and Health (NSDUH)

Purpose Suicide remains an ongoing public health concern, especially among adolescents. While many studies investigated the transition from ideations to attempts, they did not specifically look at the factors underlying the transition from a suicide plan to a suicide attempt, creating a vast knowledge gap. In the following study, we aim to investigate predictors of suicide attempts among adolescents with a suicide plan using data from NSDUH. Methods We used de-identified data from consecutive cross-sectional NSDUH surveys conducted between 2015 and 2018, including respondents aged 12–17 who reported suicidal ideations and a plan. We performed bivariate analyses and multivariate logistic regression analyses to identify significant predictors of suicide attempts in our population. Results Our total sample included 3003 respondents (Population Size Estimate: 1,284,704.48). Among them, 1,780 reported suicide attempts (A) and 1,223 reported no suicide attempts (NA). The majority of adolescents in both groups were aged 15–17 years. More females were present in the (A) group compared to the (NA) group (p = 0.013). We found a positive association between antisocial behaviors and suicide attempts. Specifically, engaging in three or more antisocial behaviors significantly increased the odds of suicide attempts [adjusted odds ratio (OR) = 1.81; 95% CI = (1.11–2.96). Substance use and violent behaviors were also significantly associated with an increased suicide risk. Conclusion We found significant correlations between suicide attempts and gender, substance use disorders and engaging in antisocial behaviors. These findings underscore the importance of addressing mental health issues, substance use, and antisocial behaviors in suicide prevention efforts for at-risk adolescents.