bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-restrained sex chromosome drive through sequential asymmetric meiosis
Authors: Meng Xuefeng , Yamashita Yukiko M. 
Published: November 2024

AbstractMeiotic drivers are selfish genetic elements that bias their own transmission, violating Mendel’s Law of Equal Segregation. It has long been recognized that sex chromosome-linked drivers present a paradox: their success in transmission can severely distort populations’ sex ratio and lead to extinction. How sex chromosome drivers may resolve this paradox remains unknown. Here, we show thatD. melanogaster’sStellate (Ste)is an X chromosome-linked driver with a self-restraining mechanism that weakens its drive and prevents extinction. Ste protein asymmetrically segregates into Y-bearing cells during meiosis I, subsequently causing their death. Surprisingly, Ste segregates asymmetrically again during meiosis II, sparing half of the Y-bearing spermatids from Ste-induced defects. Our findings reveal a novel class of sex chromosome drivers that resolve the paradox of suicidal success.
An ER-resident lipid scramblase is crucial for biogenesis and function of apicomplexan parasite secretory organelles
Authors: Reddy G. Srinivas , Gorde Somesh M. , Saxena Kanika , Behera Manash Kumar , Deshmukh Abhijit S , Sijwali Puran Singh 
Published: November 2024

AbstractApicomplexan parasites, includingPlasmodium falciparumandToxoplasma gondii, contain specialized secretory organelles such as micronemes, rhoptries, and dense granules, which are essential for parasite motility, host cell invasion, development, and egress. DedA superfamily proteins are implicated in lipid mobilization, which is a key requirement for organelle biogenesis. Herein, we identified and investigated the vacuole membrane protein 1 (VMP1), a DedA superfamily member, ofP. falciparum(PfVMP1) andT. gondii(TgVMP1). PfVMP1 and TgVMP1 are ER-localized lipid scramblases. TgVMP1 depletion adversely affected parasite development, motility, host cell invasion, and egress. These phenotypes were consistent with impaired rhoptry and dense granule biogenesis, and decreased secretion of micronemes, rhoptries, and dense granules in TgVMP1-depleted parasites, indicating a crucial role for TgVMP1 in the biogenesis and function of these organelles. TgVMP1 depletion impaired lipid droplet homeostasis and ER organization. Restoration of the ER-localized lipid scramblase by complementing TgVMP1-depleted parasites with PfVMP1 or a homolog as distant as human VMP1 rescued the depleted parasites, indicating their functional conservation and a crucial role for ER-resident lipid scramblase activity in the biogenesis and function of secretory organelles. The essentiality of TgVMP1 for parasite development and likely functional conservation of apicomplexan VMP1 proteins highlight their drug-target potential.
Results of a survey of the use of perfusion parameters as a selection tool for pumped deceased donor kidneys in the Organ Procurement Organizations of North America
Authors: Heedfeld Veerle , Jochmans Ina 
Published: November 2024

AbstractBackgroundHypothermic machine perfusion (HMP) has become a standard method for preserving deceased donor kidneys, offering advantages over static cold storage. Perfusion parameters like renal vascular resistance (RR) have been explored as potential decision-making tools for kidney transplantability, but their clinical use remains unclear.AimWe aimed to investigate the use of perfusion parameters in decision-making regarding the acceptance of pumped deceased donor kidneys among Organ Procurement Organizations (OPOs) in the USA and Canada.MethodsAn anonymous, internet-based survey was sent to 69 OPOs in the USA and Canada, collecting data on the use of HMP, perfusion parameters, and thresholds for transplantability decisions. Descriptive statistics were used for analysis.ResultsOf the 67 OPOs contacted, 15 (22%) responded, with 13 complete responses (87%). All OPOs used HMP, with 93% perfusing both donation after brain death and circulatory death kidneys. While 97% of OPOs used perfusion parameters in decision-making, none relied solely on these parameters. Two OPOs (15%) did not use them at all, while six OPOs (46%) considered them with other data or on a case-by-case basis. Only one OPO (9%) reported using specific thresholds for perfusion parameters, applying flow ≥100 mL/min, resistance <0.3 mmHg/mL/min, and pressure between 15–35 mmHg.ConclusionHMP is widely used, but substantial variability exists in the use of perfusion parameters for transplant decisions. Most OPOs do not rely on these parameters alone and lack standardized thresholds, though specific thresholds are still used.
Willingness of urban formal sector workers to support a community-based health insurance scheme in Ethiopia
Authors: Mebratie Anagaw Derseh , Shamebo Dessalegn , Alemu Getnet , Shigute Zemzem , Bedi Arjun S. 
Published: November 2024

AbstractIntroductionThe Ethiopian health system is primarily financed through household out-of-pocket expenditure and financial support from donors. High user fees lead to catastrophic health spending and limited use of services. To promote healthcare-seeking behavior and provide financial protection through enhanced domestic financing, the Ethiopian government has designed two types of health insurance schemes. These are a Community-Based Health Insurance (CBHI) scheme for the informal sector and the yet to be implemented Social Health Insurance (SHI) scheme for the formal sector. In the short run, these schemes are expected to function independently. However, in the long run it is anticipated that they will be combined, thereby pooling risk. Combining the two schemes requires solidarity across the two groups targeted by each of the schemes. Since it is likely that formal sector employees may have to bear the cost of subsidizing the CBHI scheme, this study aims to assess the extent to which formal sector employees are willing to support the CBHI scheme.MethodsThe paper is based on a survey of 1,919 formal sector employees and pensioners residing in the major administrative regions of the country. A survey experiment was used to elicit support for the CBHI scheme. Respondents were randomly allocated to one of five cases. These cases differed in terms of the information provided regarding the source of the CBHI subsidy and the benefits associated with the CBHI. Support for CBHI was assessed using descriptive statistics, binary and ordered logit models.ResultsThere is strong support from urban formal sector employees for the CBHI scheme. Regardless of the scenario presented, and despite some regional variation, the key result is that at least 66% of the surveyed participants, adjusting for non-response, supported the CBHI scheme. Good knowledge of insurance increased support while existing access to health insurance lowered it.ConclusionThe study provides strong evidence of solidarity and the willingness of formal sector employees to support the CBHI scheme. While this bodes well for the sustained expansion of the CBHI, it is ironic, as formal sector employees are resisting the introduction of the SHI. This reluctance stems from concerns about the costs and skepticism of the benefits of the proposed SHI, whereas the positive outcomes associated with the CBHI are widely known.
Time-to-event prediction in ALS using a landmark modeling approach, using the ALS Natural History Consortium Dataset
Authors: Schneck David , Arguedas Andres , Xenopoulos-Oddsson Annette , Arcila-Londono Ximena , Lunetta Christian , Wymer James , Olney Nicholas , Gwathmey Kelly , Ajroud-Driss Senda , Hayat Ghazala , Heiman-Patterson Terry , Cerri Federica , Fournier Christina , Glass Jonathan , Sherman Alex , Fiecas Mark , Walk David 
Published: November 2024

AbstractBackground and ObjectivesTimes to clinically relevant events are a valuable outcome in observational and interventional studies, complementing linear outcomes such as functional rating scales and biomarkers. In ALS, there are several clinically relevant events. We developed dynamic prediction models for several of these times to events that can be used for clinical trial modeling and personal planning.MethodsLandmark time-to-event analysis was implemented to determine the effect of patient characteristics on disease progression. Longitudinal data from 1557 participants in the ALS Natural History Consortium dataset were used. Five outcomes in the ALS disease progression were considered: loss of ambulation, loss of speech, gastrostomy, non-invasive ventilation (NIV) use, and continuous NIV use. Covariates in our models include age at diagnosis, sex, onset location, riluzole use, diagnostic delay, ALSFRS-R scores at the landmark time, and ALSFRS-R rates of change from baseline. Internal and external validation techniques were used.ResultsFor each of our models and landmark times, we present risk prediction intervals for random sets of patient characteristics. We demonstrate our models’ application for an individual’s personal predicted time-to-event. Our internal and external validation metrics indicate good concordance and overall performance. The time to loss of speech models perform the best for each metric in terms of both internal and external validation.DiscussionLandmarking is an efficient, individualized risk prediction model that is intuitive for both clinicians and patients. Importantly, landmarking can be used for clinical trial modeling, personal planning, and development of real-world evidence of the impacts of treatment interventions.
Neural synchrony in the pain connectome predicts chronic pain severity and interactions with mental health outcomes: A transdiagnostic study using magnetoencephalography and multivariate modeling
Authors: Saberi Majid , Ventresca Matthew , Zamyadi Rouzbeh , Zhang Jing , Vartanian Oshin , Jetly Rakesh , Bhat Venkat , Rhind Shawn G , Don Richardson J , Dunkley Benjamin T 
Published: November 2024

AbstractChronic pain is a pervasive condition that involves complex neurobiological mechanisms and frequently co-occurs with mental health challenges such as anxiety, depression, and posttraumatic stress disorder (PTSD), interacting significantly with psychiatric outcomes. Despite its prevalence, the neural mechanisms of chronic pain and its interactions with mental health remain poorly understood – however, functional brain imaging studies suggest the “pain connectome”, a collection of regions amongst numerous brain networks, plays a role in mediating the subjective perception of pain, and serves aspects of psychopathology. Here, we tested whether neural synchrony, a key mechanism in brain network communication, was predictive of self-reported chronic pain severity in a group of Canadian Armed Forces (CAF) service members and Veterans (n = 99) and examined if brain network functioning within the pain connectome interacts with symptoms of mental health challenges. We applied a 5-minute eyes-open resting-state magnetoencephalography (MEG) paradigm combined with multivariate modelling using partial least squares regression (PLSR). MEG is a powerful electrophysiological technique for imaging neural activity, including synchrony and network interactions, and PLSR allows exploration of complex multivariate data with high dimensionality to extract primary and interacting effects of interest in brain data. Pain severity was moderately correlated with the continuum of depression, anxiety, and PTSD symptoms, with mental health outcomes highly correlated between each other. We identified significant positive and negative associations between neural synchrony in the pain connectome and chronic pain severity. Beta oscillations were primarily related to pain severity and showed little-to-no interaction with mental health outcomes. Similarly, theta band synchrony exhibited a more specific and prominent association with the pain-anxiety interaction. Conversely, high frequency gamma synchrony was associated with chronic pain severity and showed significant effects for pain-anxiety, pain-depression, and pain-PTSD interactions. Additionally, predictive modeling using machine learning revealed that cross-spectral synchrony could reliably predict chronic pain severity in individual cases, suggesting it as a robust neurobiological marker for chronic pain. It also demonstrated moderate predictive accuracy in indexing comorbid interactions between pain and mental health. Our findings show that complex, multidimensional patterns of neural synchrony in the pain connectome mediate both chronic pain intensity and its interaction with mental health, offering new avenues for targeted and personalized therapeutic interventions and objectively tracking treatment efficacy.
GOOGLE TRENDS IN THE DIAGNOSIS OF METEOSENSITIVITY
Authors: Yarovaya Ksenia , Hnatiuk Iryna , Solodovnikova Yuliia , Son Anatoliy 
Published: November 2024

AbstractIntroductionPrimary headache disorders, including tension-type headaches (TTH) and migraines, are highly prevalent and pose a significant socio-economic burden. Environmental triggers, such as weather changes, are commonly believed to provoke these headaches.MethodsThis study aims to explore the role of geomagnetic activity as a potential trigger for TTH and migraines. Using Google Trends data, we tracked public interest in the search terms “migraine” and “headache” in Ukraine from 2019 to 2023. We analyzed the Ap index, which measures geomagnetic activity, alongside the search data to explore potential correlations.ResultsA total of 261 observations were made. The analysis revealed a negative correlation between the popularity of search queries for “migraine” and “headache” and geomagnetic activity, with increased search interest during periods of low geomagnetic activity. TTH patients were found to be more sensitive to low geomagnetic activity compared to those with migraines, suggesting different pathophysiological mechanisms between these two types of headaches. While the correlation is statistically significant, it remains relatively weak, indicating that geomagnetic fluctuations may contribute to headache triggers but are not the sole factor.ConclusionsThe findings suggest a potential link between geomagnetic activity and the incidence of primary headaches, with TTH being more responsive to these fluctuations than migraines. The study underscores the need for further research into the impact of geomagnetic activity on human health and headache disorders.What is already known on this topicPrimary headache disorders, such as tension-type headaches (TTH) and migraines, are commonly triggered by environmental factors, but the role of geomagnetic activity remains underexplored despite its known effects on human health, like circadian rhythms and blood pressure.What this study addsThis study identifies a significant negative correlation between low geomagnetic activity and increased public interest in headache-related terms, suggesting that geomagnetic fluctuations may act as environmental triggers for headaches, particularly TTH. It highlights distinct sensitivities between TTH and migraines to geomagnetic changes and demonstrates the utility of Google Trends as a tool for exploring public health patterns related to environmental factors.How this study might affect research, practice or policyThis study highlights geomagnetic activity as a potential environmental trigger for primary headaches, encouraging further research into its role and distinct impacts on TTH and migraines. It underscores the utility of tools like Google Trends for public health surveillance, offering insights for healthcare practices and policy development to better address headache prevention.
Determining the role of lifestyle factors in healthy cognitive preservation in SuperAgers: A systematic review
Authors: Watson Philippa , Smith Megan , Koychev Ivan , Gallacher John , Bauermeister Sarah 
Published: November 2024

AbstractBackgroundA subset of older adults, known as SuperAgers (SAs), exhibit exceptional resilience to these effects, displaying cognitive abilities at the same level or exceeding those 20-30 years younger. To date however, there is little understanding as to which factors may be responsible for SA enhanced cognitive abilities in old age. This systematic review aimed to identify and evaluate the evidence for an association between lifestyle factors and SA status.MethodsA systematic literature search in accordance with the PRISMA guidelines was conducted across MedLine, Embase, Ovid, Global Health, APA PsychArticles, and PsycINFO from inception to 06/11/23 of studies investigating the link between SA status and one or more lifestyle factors.ResultsA systematic search identified 13 studies which met the inclusion criteria. Eleven investigated the relationship between two or more lifestyle factors with the most common lifestyle factor examined being mental health followed by physical activity, social engagement and smoking. Mixed results were observed across the included studies while social engagement and mental health emerged as the most likely lifestyle factors to be positively associated with SA status, there was considerable heterogeneity in the measures used to assess different lifestyle factors.ConclusionNo clear conclusions could be drawn as to which lifestyle factors are associated with SA status due to scarcity of studies and heterogeneity in the measurement of different lifestyle factors.
Machine learning for predicting severe dengue, Puerto Rico
Authors: Madewell Zachary J. , Rodriguez Dania M. , Thayer Maile B. , Rivera-Amill Vanessa , Paz-Bailey Gabriela , Adams Laura E. , Wong Joshua M. 
Published: November 2024

AbstractBackgroundDistinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. Traditional warning signs recommended by the World Health Organization (WHO) offer a practical approach for clinicians but have limitations in sensitivity and specificity. This study evaluates the performance of machine learning (ML) models compared to WHO- recommended warning signs in predicting severe dengue among laboratory-confirmed cases in Puerto Rico.MethodsWe analyzed data from Puerto Rico’s Sentinel Enhanced Dengue Surveillance System (May 2012–August 2024), using 40 clinical, demographic, and laboratory variables. Nine ML models, including Decision Trees, K-Nearest Neighbors, Naïve Bayes, Support Vector Machines, Artificial Neural Networks, AdaBoost, CatBoost, LightGBM, and XGBoost, were trained using 5-fold cross-validation and evaluated with area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A subanalysis excluded hemoconcentration and leukopenia to assess performance in resource-limited settings. An AUC-ROC value of 0.5 indicates no discriminative power, while a value closer to 1.0 reflects better performance.ResultsAmong the 1,708 laboratory-confirmed dengue cases, 24.3% were classified as severe. Gradient boosting algorithms achieved the highest predictive performance, with AUC-ROC values exceeding 94% for CatBoost, LightGBM, and XGBoost. Feature importance analysis identified hemoconcentration (≥20% increase during illness or ≥20% above baseline for age and sex), leukopenia (white blood cell count <4,000/mm³), and timing of presentation to a healthcare facility at 4–6 days post-symptom onset as key predictors. Excluding hemoconcentration and leukopenia did not significantly affect model performance. Individual warning signs like abdominal pain and restlessness had sensitivities of 79.0% and 64.6%, but lower specificities of 48.4% and 59.1%, respectively. Combining ≥3 warning signs improved specificity (80.9%) while maintaining moderate sensitivity (78.6%), resulting in an AUC-ROC of 74.0%.ConclusionsML models, especially gradient boosting algorithms, outperformed traditional warning signs in predicting severe dengue. Integrating these models into clinical decision-support tools could help clinicians better identify high-risk patients, guiding timely interventions like hospitalization, closer monitoring, or the administration of intravenous fluids. The subanalysis excluding hemoconcentration confirmed the models’ applicability in resource-limited settings, where access to laboratory data may be limited.
Multicountry genomic analysis underscores regional cholera spread in Africa
Authors: Mboowa Gerald , Matteson Nathaniel Lucero , Tanui Collins Kipngetich , Kasonde Mpanga , Kamwiziku Guyguy Kusanzangana , Akanbi Olusola Anuoluwapo , Chitio Jucunú Johane Elias , Kagoli Mathews , Essomba René Ghislain , Ayitewala Alisen , Ssewanyana Isaac , Ngo Bitoungui Valentina Josiane , Amuri Adrienne Aziza , Azman Andrew S , Babatunde Olajumoke Atinuke , Akenji Blaise Mboringong , Broban Anaïs , Malembaka Espoir Bwenge , Ongole Francis , Chukwu Chimaobi Emmanuel , Ismael Nália , Kapona Otridah , Laurindo Osvaldo , Mbala Placide Kingebeni , Etoundi Mballa Georges Alain , Miambo Imelda Carlos Zulfa , Mwanyongo Alex Ansaye , Najjuka Grace , Mutale Joseph , Musonda Kunda , Niyonzima Allan Muruta , Nyenje Mirriam Ethel , Popoola Michael , Shempela Doreen Mainza , Medi Sike Christiane , Sitoe Sofião Manjor , Wanjohi Dorcas Waruguru , Welo Placide Okitayemba , Yelewa Mtisunge , Yennan Sebastian , Ziba Lucius ,   CholGEN Consortium, Bitilinyu-Bangoh Joseph Ephram , Chilengi Roma , Hadja Hamsatou , Idris Jide , Langa José Paulo Maurício , Mukadi-Bamuleka Daniel , Nabadda Susan , Debes Amanda K , Sack David A , Kaseya Jean , Tebeje Yenew Kebede , Wohl Shirlee , Tessema Sofonias Kifle 
Published: November 2024

ABSTRACTCholera remains a significant public health burden in many countries in sub-Saharan Africa, though the exact mechanisms of bacterial emergence and spread remain largely undefined. We generated genomic data from 728Vibrio choleraeO1 isolates predominantly collected between 2019-2024 to create the largest dataset ofV. choleraegenomes sequenced locally in Africa. This dataset enabled us to interrogate recent patterns of spread, including the rapid circulation of the AFR15 lineage associated with unusually large outbreaks in Southern Africa. We provide evidence for the movement of the AFR15 lineage into new African Member States and confirm previously observed differences inV. choleraetransmission dynamics in West versus East Africa, though cross-border transmission is prevalent on both sides of the continent. Despite observed differences, evolutionary processes are similar across lineages and we find no evidence for significant changes in antimicrobial resistance genotypes. Overall, our findings emphasize the importance of regionally coordinated cross-border surveillance and interventions, while also demonstrating the critical role of locally generated genomic data in understanding the spread of cholera in Africa.