Nevin Manimala

F1000Res. 2025 Sep 9;14:805. doi: 10.12688/f1000research.167786.2. eCollection 2025.

ABSTRACT

BACKGROUND: Effective performance monitoring is essential for improving academic productivity, especially in private higher education institutions. This study aimed to establish the perception of academic staff toward performance monitoring and how these perceptions influence academic staff performance in private chartered universities in Western Uganda. Guided by Expectancy Theory and Self-Determination Theory, the study explored how monitoring practices affect motivation and performance outcomes.

METHODS: A convergent parallel mixed-methods design was employed. The quantitative strand involved 386 academic staff selected from five private chartered universities using stratified random sampling. Data were collected using structured questionnaires. In the qualitative strand, semi-structured interviews were conducted with 10 purposively selected Deans of Faculties. Quantitative data were analyzed using descriptive statistics, Pearson correlation, and regression analysis, while qualitative data were analyzed thematically.

RESULTS: Findings revealed that 58.2% of academic staff had a moderately positive perception of existing performance monitoring practices. Pearson correlation analysis showed a moderate positive relationship between perception of performance monitoring and academic staff performance (r = .476, p < 0.01). Regression analysis further indicated that perception of performance monitoring significantly predicted academic staff performance (β = 0.394, p = 0.000), explaining 18.7% of the variance (R ² = 0.187). Qualitative data revealed challenges such as irregular feedback, lack of transparency, and limited involvement of academic staff in developing performance indicators.

CONCLUSIONS: The study concludes that academic staff performance can be enhanced when performance monitoring systems are perceived as fair, transparent, and participatory. It recommends the standardization of performance evaluation procedures, improved feedback mechanisms, and the active involvement of academic staff in setting performance targets. Strengthening these areas could improve motivation, engagement, and overall academic performance in private universities across the region.

PMID:41287836 | PMC:PMC12640481 | DOI:10.12688/f1000research.167786.2

F1000Res. 2025 Sep 10;14:902. doi: 10.12688/f1000research.169601.1. eCollection 2025.

ABSTRACT

The increasing prevalence of vector-borne diseases like West Nile virus (WNV) highlights the critical need for predictive modeling tools that can guide public health decision-making, particularly given the absence of effective vaccines. We developed a modular computational framework that simulates and analyzes WNV transmission dynamics through compartmental models capturing the intricate ecological interactions among avian hosts, mosquito vectors, and human populations. Our system integrates epidemiological parameters with customizable intervention mechanisms, facilitating the assessment of scenario-specific mitigation approaches. Distinguishing itself from conventional static models, this framework enables users to model dynamic, time-sensitive interventions including targeted mosquito control and strategic bird population management-the two principal containment strategies currently employed against WNV. Using simulations that reflect realistic outbreak scenarios, we evaluated how varying intervention intensities and implementation timings affect epi- demic progression. Our findings reveal that early implemented, dual-target strategies addressing both vector populations and avian reservoirs can substantially reduce transmission dynamics and minimize human exposure risk. This framework serves as a comprehensive decision-support platform for policymakers and vector control agencies, delivering mechanistic insights into the effectiveness of non-pharmaceutical interventions against zoonotic pathogens within complex ecological systems. The tool’s modular design and scenario-testing capabilities make it particularly valuable for proactive outbreak preparedness and evidence-based intervention planning.

PMID:41287835 | PMC:PMC12640483 | DOI:10.12688/f1000research.169601.1

F1000Res. 2025 Nov 14;14:1040. doi: 10.12688/f1000research.166837.2. eCollection 2025.

ABSTRACT

BACKGROUND: In an increasingly competitive healthcare sector, delivering exceptional services is crucial for enhancing patient satisfaction and fostering long-term loyalty. However, the direct impact of leadership and governance on patient experience and retention remains underexplored. This study examines the relationships between corporate governance, service quality, patient satisfaction, and loyalty, with a particular focus on entrepreneurial leadership in healthcare organizations in Indonesia.

METHODS: A cross-sectional survey was conducted with 384 executive patients across hospitals in Indonesia. Data were analyzed using structural equation modeling (SEM), with p-values reported to indicate statistical significance.

RESULTS: The findings reveal that entrepreneurial leadership significantly enhances both corporate governance and service quality. Corporate governance positively influences patient satisfaction and loyalty. Service quality was found to moderately influence patient satisfaction, but had a stronger, direct effect on patient loyalty. Additionally, patient satisfaction emerged as a critical mediator in the development of patient loyalty.

CONCLUSIONS: This study provides valuable insights for healthcare managers and policymakers seeking to improve organizational performance and patient retention. Theoretically, it advances the understanding of how leadership and governance frameworks impact patient retention and service quality in the Indonesian context. Practically, the study offers actionable recommendations for hospital administrators to enhance patient satisfaction and loyalty by implementing entrepreneurial leadership, robust governance practices, and continuous service improvement.

PMID:41287832 | PMC:PMC12640491 | DOI:10.12688/f1000research.166837.2

Drug Healthc Patient Saf. 2025 Nov 19;17:253-264. doi: 10.2147/DHPS.S564658. eCollection 2025.

ABSTRACT

BACKGROUND: Globally, healthcare systems are currently facing a significant challenge in terms of antibiotic resistance. Healthcare professionals actively participate in the process of prescribing, dispensing and administering antibiotics.

OBJECTIVE: We examined the knowledge, attitudes and practices regarding antibiotic use and antibiotic resistance among healthcare professionals working in a tertiary hospital located in Monrovia, Liberia.

METHODS: A hospital-based cross-sectional survey was carried out from January to June, 2023 involving 61 healthcare workers at the ELWA Hospital, Liberia. A purposive sample of healthcare workers across diverse professional roles was surveyed using a structured questionnaire on antibiotic use and resistance. Data were analyzed in SPSS v25 using descriptive statistics to summarize participant characteristics and inferential tests to explore variable associations.

RESULTS: Participants ages ranged from 20 to 60 years (mean = 40.7 ± 5) and nurses constituted the majority professional group (59%). Most respondents (68.9%) disagreed that antibiotics are effective against viral infections (OR = 0.45; p = 0.020). However, 36.1% believed antibiotics could be stopped when symptoms resolve and 24.6% believed leftover antibiotics could be reused (OR = 0.33; p = 0.002). Majority, 72.1% and 70.5%, reported never using antibiotics for body pain or headaches, respectively (OR = 3.67; p = 0.001 and OR = 4.78; p < 0.001). Despite this, 39.3% admitted to sometimes or always storing leftover antibiotics and 39.3% agreed or strongly agreed that stopping antibiotics early is safe (OR = 0.36; p = 0.016).

CONCLUSION: The study identified persistent gaps in healthcare workers’ knowledge, attitudes, and practices regarding antibiotic use and resistance, despite encouraging awareness in some areas. Misconceptions such as premature discontinuation and reuse of leftover antibiotics were common. Findings underscore the need for targeted education and strengthened stewardship programs in Liberia’s healthcare settings.

PMID:41287831 | PMC:PMC12640598 | DOI:10.2147/DHPS.S564658

Sci Rep. 2025 Nov 24;15(1):41522. doi: 10.1038/s41598-025-25311-x.

ABSTRACT

In recent years, the widespread dissemination of fake news on social media has raised concerns about its impact on public opinion, trust, and decision-making. Addressing the limitations of traditional detection methods, this study introduces a hybrid deep learning approach that enhances the identification of fake news. The objective is to improve detection accuracy and model robustness by combining a Long Short-Term Memory (LSTM) network for contextual feature extraction with a Convolutional Gaussian Perceptron Neural Network (CGPNN) for classification. To further optimize performance, we integrated a metaheuristic Moth-Flame Whale Optimization (MFWO) algorithm for hyperparameter tuning. Experimental evaluation was conducted on four benchmark datasets ISOT, Fakeddit, BuzzFeedNews, and FakeNewsNet using standardized preprocessing techniques and TF-IDF-based text representation. Results show that the proposed model outperforms existing methods, achieving up to 98% accuracy, 95% F1-score, and statistically significant improvements (p < 0.05) over transformer-based and graph neural network models. These findings suggest that the hybrid framework effectively captures linguistic patterns and textual irregularities in deceptive content. The proposed method offers a scalable and efficient solution for fake news detection with practical applications in social media monitoring, digital journalism, and public awareness campaigns. Overall, the framework delivers 3-8% higher accuracy and F1-score compared to state-of-the-art approaches, demonstrating both robustness and practical applicability for large-scale fake news detection.

PMID:41285851 | DOI:10.1038/s41598-025-25311-x

Nat Commun. 2025 Nov 24;16(1):10376. doi: 10.1038/s41467-025-65316-8.

ABSTRACT

The chaotic dynamics of small-scale vorticity plays a key role in understanding and controlling turbulence, with direct implications for energy transfer, mixing, and coherent structure evolution. However, measuring or controlling its dynamics remains a major conceptual and experimental challenge due to its transient and chaotic nature. Here we use a combination of experiments, theory, and simulations to show that small magnetic particles of different densities, exploring flow regions of distinct vorticity statistics, can act as effective probes for measuring and forcing turbulence at its smallest scale. The interplay between the magnetic torque, from an externally controllable magnetic field, and hydrodynamic stresses, from small-scale turbulent vorticity, uncovers an extremely rich phenomenology. Notably, we present the first experimental and numerical observation of stochastic resonance for particles in turbulence, where turbulent fluctuations, remarkably acting as an effective noise, enhance the particle rotational response to external forcing. We identify a pronounced resonant peak in the particle rotational phase lag when the applied rotating magnetic field matches the characteristic intensity of small-scale turbulent vortices. Furthermore, we reveal a novel symmetry-breaking mechanism: an oscillating magnetic field with zero mean angular velocity can counterintuitively induce net particle rotation in zero-mean vorticity turbulence. Our findings pave the way to developing flexible techniques for manipulating particle dynamics in complex flows. The discovered mechanism enables a novel magnetic resonance-based approach to be developed for measuring turbulent vorticity. In this approach, particles act as probes, emitting a detectable magnetic field that can characterize turbulence even under conditions that are optically inaccessible.

PMID:41285830 | DOI:10.1038/s41467-025-65316-8

Nat Commun. 2025 Nov 24;16(1):10037. doi: 10.1038/s41467-025-65943-1.

ABSTRACT

The surface tectonic style of rocky planets controls interior evolution, geologic activity, dynamo action, atmospheric composition, and thus, habitability. In the solar system, Earth is unique in exhibiting plate tectonics, but it also displays a protracted history of diverse tectonic regimes. To understand the dynamics of the coupled plate-mantle system, here we explore 2D hemispheric-scale thermochemical mantle convection models with self-consistent magmatism that reach the statistical steady state. By statistical analysis of model predictions, we quantitatively distinguish between six tectonic regimes, including the mobile, stagnant, sluggish, plutonic-squishy and episodic lids. In addition, we discover the episodic-squishy lid regime, characterized by alternating episodes of plutonic-squishy lid and mobile-lid behavior. By mapping out these regimes over a wide parameter space, we constrain the conditions for potential regime transitions during planetary cooling. Based on geological evidence of Earth’s tectonic history, our results point to decreasing effective lithospheric strength during planetary evolution, consistent with previously-proposed physical weakening mechanisms. We also suggest an important role of the episodic-squishy lid for early Earth and present-day Venus. Thus, our study helps to understand the tectonic history of terrestrial planets as they cool over time.

PMID:41285820 | DOI:10.1038/s41467-025-65943-1

NPJ Parkinsons Dis. 2025 Nov 24. doi: 10.1038/s41531-025-01214-6. Online ahead of print.

ABSTRACT

Previous studies link lower physical activity with incident Parkinson’s disease (PD) but rely on self-reported data and fail to address reverse causation. This study used accelerometer-derived daily step count, an objective measure of physical activity, to examine its association with incident PD in the UK Biobank, within successive periods of follow-up. PD cases were identified through hospital inpatient and death data, and associated with machine learning-derived step counts using Cox regression models, adjusted for age, sex, demographic and lifestyle factors. For 94,696 valid participants and a median follow-up of 7.9 years, 407 incident PD cases occurred. Every 1000 steps higher were associated with 8% lower risk of PD (hazard ratio 0.92, 95% confidence interval 0.89-0.94). However, this association was no longer statistically significant when excluding follow-up periods closer to the time of accelerometer wear, suggesting that low activity may be an early marker, but not a risk factor for PD.

PMID:41285792 | DOI:10.1038/s41531-025-01214-6

Nat Commun. 2025 Nov 24;16(1):10372. doi: 10.1038/s41467-025-65373-z.

ABSTRACT

Understanding the interaction between complex urban environments and human mobility flow patterns underpins adaptive transport systems, resilient communities, and sustainable urban developments, yet inter-regional origin-destination mobility flow information from traditional surveys are costly to update. The satellite imagery offers up-to-date information on urban sensing and opens avenues to examine urban morphology-mobility dynamics. This study develops a deep learning model, Imagery2Flow for predicting fine-grained human mobility flows in urban areas using 10 to 30-meter medium resolution satellite imagery in a timely and low-cost manner. Extensive experiments demonstrate good performance and flexible spatial-temporal generalizability on the top-10 largest metropolitan statistical areas of the United States. Through exploring the spatial heterogeneous effects, we investigate the urban factors (centrality and compactness) influencing human movement flow distributions, enhancing our comprehension of their interactions. The spatial transferability of Imagery2Flow helps reduce regional inequality by informing decisions in data-poor regions, learning from data-rich ones. Interestingly, the typologies of urban sprawl can help explain the cross-city model generalization capability. The temporal transferability proves that human dynamics of cities and the process of urbanization can be well captured from the observed built environment by remote sensing.

PMID:41285790 | DOI:10.1038/s41467-025-65373-z

Nutr Diabetes. 2025 Nov 24;15(1):50. doi: 10.1038/s41387-025-00405-7.

ABSTRACT

BACKGROUND AND OBJECTIVE: High fasting plasma glucose (HFPG) is a major risk factor for diseases, posing a serious public health challenge. This study examines the global burden of 13 non-communicable diseases (NCDs) attributed to HFPG.

METHODS: We used the 2021 GBD Study to analyze deaths and DALYs linked to HFPG( > 4.90-5.30 mmol/L). Socio-Demographic Index (SDI) was used to assess development levels, with subgroup analyses by geography, year, gender, and SDI.

RESULTS: In 2021, HFPG contributed to 5.15 million deaths and 151.95 million DALYs globally. From 1990 to 2021, the estimated annual percentage change (EAPC) in deaths and DALYs were 0.11 and 0.55, respectively. Diabetes, ischemic heart disease (IHD), and stroke accounted for the most deaths (1.66, 1.35, and 0.84 million). Liver cancer, chronic kidney disease (CKD), and pancreatic cancer showed the fastest mortality increases, with EAPCs of 1.90, 1.69, and 1.34, respectively. IHD and stroke had declining mortality burdens, with EAPCs of -0.13 and -0.98.

CONCLUSION: Over 30 years, HFPG-related NCDs have increased globally. Diabetes, IHD, and stroke remain the top burdens, while liver cancer, CKD, and pancreatic cancer are rising fastest. The disease burden in men is higher than in women, except for people with Alzheimer’s disease and other dementias and people with blindness and vision loss (BVL).

PMID:41285708 | DOI:10.1038/s41387-025-00405-7