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Vaccine fatigue and influenza vaccination trends across Pre-, Peri-, and Post-COVID-19 periods in the United States using epic’s cosmos database

Introduction Influenza vaccination is a critical public health measure, especially amidst the challenges of the COVID-19 pandemic. This study uses Epic’s Cosmos database to analyze influenza vaccination trends across demographic groups in the United States, examining the effects of the COVID-19 pandemic and “vaccine fatigue” on influenza vaccination rates. Methods This retrospective cross-sectional study analyzes influenza vaccination rates pre- (1 May 2018–31 May 2019), peri- (1 May 2020–31 May 2021), and post-COVID-19 (1 May 2023–31 May 2024). A two-proportion z-test and Cohen’s h test were calculated to assess statistical significance. Results Influenza vaccination rates increased peri-COVID (+1.99% point change, h = 0.04, p < 0.001) and decreased post-COVID (−6.41% point change, h = 0.14, p < 0.001) relative to pre-COVID. The largest changes were observed in the following groups: 5−18 year olds (−13.92% point change, h = 0.31, p < 0.001), 19−26 year olds (−9.91% point change, h = 0.25, p < 0.001), American Indian or Alaska Native (−8.11% point change, h = 0.18, p < 0.001), Other Races (−7.36% point change, h = 0.17, p < 0.001), White (−6.89% point change, h = 0.15, p < 0.001), and the South U.S. census region (−7.21% point change, h = 0.17, p < 0.001). Conclusion Post-pandemic influenza vaccination compliance decreased relative to pre-pandemic, especially among younger age groups, certain racial groups, and in the southern U.S. These findings suggest that the COVID-19 pandemic negatively impacted influenza vaccination compliance. While overall trends aligned with publicly available data, absolute counts may be under-reported within Epic due to incomplete documentation of vaccine administrations outside of Epic-affiliated systems.

PARP12-mediated mono-ADP-ribosylation as a checkpoint for necroptosis and apoptosis

Necroptosis and apoptosis are two alternatively regulated cell death pathways. Activation of RIPK1 upon engagement of TNFR1 by TNFα may promote necroptosis by interacting with RIPK3 or apoptosis by activating caspases. RIPK1 is extensively regulated by a variety of dynamic posttranslational modifications which control its kinase activity and formation of downstream complexes to mediate necroptosis and apoptosis. Here, we investigate the functional significance and mechanism by which PARP12, a mono-ADP-ribosyltransferase, interacts with RIPK1 and RIPK3 in cells stimulated by IFNγ and TNFα. We show that PARP12 catalyzes the mono-ADP-ribosylation (MARylation) of RIPK1 in both the intermediate domain and the kinase domain, as well as the MARylation of RIPK3. PARP12 deficiency reduces necroptosis by inhibiting the activation of RIPK1 kinase and its interaction with RIPK3, as well as sensitizes to apoptosis by promoting the binding of RIPK1 with caspase-8. Thus, upon induction by IFNs, PARP12 may function as a cellular checkpoint that controls RIPK1 to promote necroptosis and inhibit apoptosis. Importantly, while PARP12 is a known interferon-stimulated gene (ISG), PARP12 deficiency promotes the expression of a subset of ISGs and confers protection against influenza A virus-induced mortality in mice. Our study demonstrates that PARP12 is an important modulator of cellular antiviral response.

Placental inflammation is increased in gestational diabetes mellitus: The role of inflammasome NLRP-3 and chemokine scavenger decoy receptor D6

Background Gestational diabetes mellitus is characterized by low-grade systemic inflammation. Placental inflammation in gestation diabetes mellitus has not been extensively investigated yet. Objectives Aims of this study were to analyze: a) serum levels of Th-1 cytokines and D6-specific chemokines in women with gestation diabetes mellitus, compared to normal pregnant women; b) placental expression of the inflammasome NLR family pyrin domain containing 3 (NLRP-3) and the chemokines scavenger decoy D6 receptor. Methods Serum samples collected between 24 and 28 weeks of pregnancy from singleton pregnancies with gestational diabetes mellitus and gestational age-matched normal pregnant women were analyzed by bead-based multiplex assays for chemokine (C-C motif) ligand 2 (CCL2), chemokine (C-C motif) ligand 4 (CCL4), interferon gamma (IFN-γ), C-C motif chemokine ligand 11 (CCL11) and tumor necrosis factor alpha (TNF-α) levels. Placental samples from GDM and controls were analysed by immunohistochemistry and multiplex spatial immunofluorescence for protein expression of NLR family pyrin domain containing 3 (NLRP-3), interleukin-1 beta (IL-1β) and chemokines scavenger decoy D6 receptor. Results GDM women (n = 25) showed higher serum levels of CCL-2 (p < 0.01), CCL-4 (p < 0.05) and IFN-γ (p < 0.05) compared to controls (n = 25). Placental expression of NLRP-3 was significantly higher in GDM women (n = 10) compared to controls (n = 7; p < 0.05) while only a trend of increase of IL-1β and D6 expression was observed in GDM compared to normal placentas. Conclusions GDM is characterized by higher serum levels of pro-inflammatory cytokines with consistent over-expression of the inflammasome NLRP-3 in placental tissues compared to normal pregnancy.

Dual-laser “808 and 1,064 nm” strategy that circumvents the Achilles’ heel of photothermal therapy

Breast cancer has now overtaken lung cancer as the “world’s leading cancer,” yet detecting and implementing effective therapies remains a significant challenge. Substantial advances have been made in photothermal therapy (PTT), where photosensitizers use photonic energy to induce localized hyperthermia for cancer eradication. This pioneering approach is gaining traction in clinical settings. However, traditional PTT faces inherent limitations, including the risk of damage to neighboring healthy tissues and potential inflammatory responses due to overheating. Drawing inspiration from the distinct characteristics of aggregation-induced emission the small molecule, PM331, was chosen for study. This donor–acceptor–donor system displays good photothermal conversion efficiencies (40% and 66%) upon excitation at 808 nm and 1,064 nm, respectively. It is also characterized by attractive optical features in the second near-infrared (NIR-II) window. Using nanoparticles containing PM331, PM331@F127 , we have developed a PTT strategy, termed dual-laser PTT (DLPTT), that involves successive excitation using 808 nm and 1,064 nm lasers guided by both NIR-II fluorescence and photoacoustic imaging. The DLPTT strategy involves two steps. First, it initiates DNA damage and downregulates heat shock protein expression as the result of an initial brief irradiation with an 808 nm laser. This is then followed by irradiation with a 1,064 nm laser to ablate tumor cells while minimizing inflammation and harm to surrounding healthy tissues. Based on the findings reported here, we suggest that DLPTT could represent an attractive approach to precision medicine and one that could make PTT more amenable to clinical implementation.

An image and text-based fake news detection with transfer learning

Fake news has emerged as a significant problem in today’s information age, threatening the reliability of information sources. Detecting fake news is crucial for maintaining trust and ensuring access to factual information. While deep learning offers solutions, most approaches focus on the text, neglecting the potential of visual information, which may contradict or misrepresent the accompanying text. This research proposes a multimodal classification approach that combines text and images to improve fake news detection, particularly in low-resource settings where labeled data is scarce. We leverage CLIP, a model that understands relationships between images and text, to extract features from both modalities. These features are concatenated and fed into a simple one-layer multi-layer perceptron (MLP) for classification. To enhance data efficiency, we apply LoRA (Low-Rank Adaptation), a parameter-efficient fine-tuning technique, to the CLIP model. We also explore the effects of integrating features from other models. The model achieves an 83% accuracy when using LoRA in classifying whether an image and its accompanying text constitute fake or factual news. These results highlight the potential of multimodal learning and efficient fine-tuning techniques for robust fake news detection, even with limited data.

A quantitative geospatial analysis of the risk that Boko Haram will target a school

We provide a novel quantitative geospatial analysis of school attacks perpetrated by Boko Haram in Nigeria. Such attacks are used by Boko Haram to kidnap boys (for potential use as child soldiers and suicide bombers) and girls (for potential use as domestic servants, as sex slaves, and suicide bombers). We first build a novel geospatially tagged data set spanning almost 15 years (July 2009 to April 2023) of data not only on Boko Haram attacks on schools (our dependent variable) but also a set of 15 independent variables (or features) about other attacks by Boko Haram, locations of security installations, as well as socioeconomic and geospatial characteristics of the regions around these schools. Second, we develop a univariate statistical analysis of this data, showing strong links between three broad factors affecting attacks on schools: Security presence in and around a school, the Boko Haram Activity in the area around a school, and the Socioeconomic characteristics of the region around a school. Third, we train several predictive machine learning models and assess their predictive efficacy. The results show that some of these models can accurately quantify the likelihood that a school will be at risk of a Boko Haram attack. In addition, they cast light on the features that are most important in making such predictions. We then analyze learned decision trees to identify some conditions on the independent variables that help predict Boko Haram attacks on school. Fourth, we use these decision trees to formulate multivariate hypotheses that we investigate further from a statistical perspective. We find that Security presence near schools, Activity of Boko Haram in regions, and the Socioeconomic factors characterizing the region a school is in are all significant predictors of attacks. We conclude with a policy recommendation.

Correction: Prevalence of chronic kidney disease among young people living with HIV in Sub Saharan Africa: A systematic review and meta-analysis

Exploring the potential of artificial intelligence in individualized cognitive training: A systematic review

To tackle the challenge of responders heterogeneity, Cognitive Training (CT) research currently leverages AI Techniques for providing individualized curriculum rather than one-size-fits-all designs of curriculum. Our systematic review explored these new generations of adaptive methods in computerized CT and analyzed their outcomes in terms of learning mechanics (intra-training performance) and effectiveness (near, far and everyday life transfer effects of CT). A search up to June 2023 with multiple databases selected 19 computerized CT studies using AI techniques for individualized training. After outlining the AI-based individualization approach, this work analyzed CT setting (content, dose, etc.), targeted population, intra-training performance tracking, and pre-post-CT effects. Half of selected studies employed a macro-adaptive approach mostly for multiple-cognitive domain training while the other half used a micro-adaptive approach with various techniques, especially for single-cognitive domain training. Two studies emphasized the favorable influence on CT effectiveness, while five underscored its capacity to enhance the training experience by boosting motivation, engagement, and offering diverse learning pathways. Methodological differences across studies and weaknesses in their design (no control group, small sample, etc.) were observed. Despite promising results in this new research avenue, more research is needed to fully understand and empirically support individualized techniques in cognitive training.

Multitarget inhibition of CDK2, EGFR, and tubulin by phenylindole derivatives: Insights from 3D-QSAR, molecular docking, and dynamics for cancer therapy

Cancer remains one of the leading causes of death globally, presenting significant challenges to healthcare systems due to its complexity and the limitations of current therapeutic strategies. Despite advancements in anticancer drug development, monotherapies often fail to provide long-term efficacy due to the emergence of drug resistance. This resistance is primarily due to the activation of compensatory pathways in cancer cells, which allows them to bypass the effects of single-target therapies. To overcome this, targeting multiple key proteins simultaneously has emerged as a promising strategy to enhance therapeutic outcomes and address resistance mechanisms. In this study, 2-Phenylindole derivatives were explored as MCF7 breast cancer cell line inhibitors using 3D-QSAR modeling to design more effective compounds. The CoMSIA/ SEHDA model demonstrated high reliability (R² = 0.967) and a strong Leave-One-Out cross-validation coefficient (Q² = 0.814), further validated by external testing (R²Pred = 0.722). Six new compounds with potent inhibitory activity were designed, and their favorable ADMET profiles were confirmed. Molecular docking studies revealed that the newly designed compounds exhibited better binding affinities (−7.2 to −9.8 kcal/mol) to key cancer-related targets (CDK2, EGFR, and Tubulin) compared to the reference drug and the most active molecule (molecule 39) in the dataset. Additionally, 100 ns molecular dynamics simulations confirmed the stability of the best-docked complexes, highlighting their potential as promising candidates for anticancer drug development.

Tire Deformation-Based Regulation of Braking Torque in Manual Wheelchairs Equipped with Reverse Locking Modules

Moving in a manual wheelchair involves overcoming various architectural and terrain barriers. One of the obstacles that most burdens the muscular system and generates a high risk of instability is the climb up a slope. This article presents a comprehensive regulation method that allows for achieving the desired braking torque of the locking module based solely on tire deformation measurements, rather than the previously used contact force. To address the research problem, a research method was developed, consisting of three experimental tests and one mathematical analysis. The experiments included the measurement of the sliding force moment (E1), braking torque (E2), and tire deformation (E3). Using these methods, a measurement procedure was formulated to allow the measurement of the braking torque generated by the reverse locking module through tire deformation. Research on braking torque Mh showed that for wheelchairs with 24’’x1’’ wheels and a tire pressure of 4-7 bar, tire deformation eT, depending on the diameter of the pressing roller, ranges from mm to mm. For a constant roller diameter of 70 mm, to achieve a torque of 7.5 Nm, the deformation was mm, and for 12 Nm – mm. The sliding force FZ increased by 57% with the user’s mass rising from 50 kg to 90 kg (from N to N at a pressure of 7 bar). ANOVA analysis confirmed that both the nominal contact force FdN and the diameter of the roller dr had a significant impact on the braking torque Mh. Verification of the developed mathematical model of braking torque as a function of tire deformation showed an error range of 3% to 7%.

Investigating the dynamics and uncertainties in portfolio optimization using the Fourier-Millen transform

Many investors and financial managers view portfolio optimisation as a critical step in the management and selection processes. This is due to the fact that a portfolio fundamentally comprises a collection of uncertain securities, such as equities. For this reason, having a solid understanding of the elements responsible for these uncertainties is absolutely necessary. Investors will always look for a portfolio that can handle the required amount of risk while still producing the desired level of expected returns. This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. These models make use of physical analyses, such as the Fourier transform, wavelet transforms and the Fourier–Mellin transform. Motivated by their use in medical analysis and detection, the purpose of this research was to analyse the efficacy of these methods in establishing the primary factors that go into optimising a particular portfolio. These geometric features are input into artificial neural networks, including convolutional and recurrent networks. These are then compared with other algorithms, such as vector autoregression, in portfolio optimisation tests. By testing these models on real-world data obtained from the US stock market, we were able to obtain preliminary findings on their utility.

Correction: FRESH extrusion 3D printing of type-1 collagen hydrogels photocrosslinked using ruthenium

Preacclimatization and base excess rather than deficit?

Spatio-temporal heterogeneity of metro ridership under major epidemic conditions

The COVID-19 epidemic has significantly altered travelers' behavior, therefore influenced how land use impacts subway ridership. This paper investigates these changes by employing a Geographically and Temporally Weighted Regression (GTWR) model to analyze the spatial and temporal impacts throughout the pandemic. The findings reveal that the outbreak notably reduced metro trip generation across all land use types except residential. Post-pandemic, the influence of workplace, park and green space, and educational land uses in the city center increased. Additionally, workplace land use in rapidly developing areas emerged as a critical factor in boosting metro travel post-epidemic. These insights suggest that commuting, school travel, and outdoor recreation are primary drivers of subway ridership recovery. These results can assist local governments and metro managers in optimizing land use planning and development strategies in the future.

Risk of ischemic stroke associated with anti-rheumatic agents in patients with rheumatoid arthritis: A nationwide population-based case-control study

Introduction Patients with rheumatoid arthritis (RA) face a significantly higher risk of major adverse cardiovascular events, including stroke, due to the pivotal role of inflammation in atherosclerosis and thrombosis. Various anti-rheumatic agents may influence stroke risk either by increasing or decreasing it, and this effect remains unclear. This study aimed to investigate the association between different anti-rheumatic agents and the risk of incident stroke in patients with RA using a nationwide claims database. Methods In this nested case-control study, the Korean Health Insurance Review and Assessment data of 35,133 patients newly diagnosed with seropositive RA from January 2011 to December 2020 were used. Incident ischemic stroke cases were identified and matched with randomly selected controls at a 1:4 ratio. The usage of anti-rheumatic agents was measured from the date of RA diagnosis to the index date and stratified in terms of exposure time and duration. The risk of stroke associated with each anti-rheumatic agent was estimated using conditional logistic regression and adjusted for comorbidities and concomitant drug use. Results Of the 35,133 patients, 1,386 (3.9%) cases were newly diagnosed with new-onset stroke. Thus, 1,384 stroke cases and 5,499 controls with newly diagnosed RA were included in the analysis. Current exposure to sulfasalazine (aOR: 0.79, 95% CI: 0.65–0.97) and hydroxychloroquine (aOR: 0.83, 95% CI: 0.72–0.96) was associated with a decreased risk of stroke, while current exposure to glucocorticoids (aOR: 1.71, 95% CI: 1.46–2.00) and tocilizumab (aOR: 3.47, 95% CI: 1.70–7.08) was related to an increased risk of stroke. Conclusion In this nationwide cohort study of patients with RA, treatment with sulfasalazine and hydroxychloroquine was associated with a decreased risk of stroke, while glucocorticoids and tocilizumab were linked to an increased risk of stroke. The association of tocilizumab with stroke should be cautiously interpreted due to the statistical limitations.

Reply to Kleinsasser and Burtscher: Superimposed acclimatization and adaptation to low and high altitudes in highlanders compared to lowlanders

Pathways to mental health services across local health systems in sub-Saharan Africa: Findings from a systematic review

Globally, over 280 million individuals suffer from mental disorders, and almost 85% in low-resource settings do not receive any therapy. In sub-Saharan Africa (SSA), many patients are forced to either live with untreated mental illness or seek care from traditional or religious leaders due to the high treatment cost. This literature review identifies pathways to access mental health services and proposed a collaborative model for care across SSA. We systematically searched five electronic databases (Embase and MEDLINE via OVID, CINAHL, PsycINFO, and Global Index Medicus) using the following search terms, ‘pathways to care’, ‘mental disorders,’ and ‘sub-Saharan Africa’ for primary studies reporting on pathways to care for mental disorders in SSA. There were no restrictions on the study’s date. Overall, the electronic database search produced 3399 search results, of which we retrieved 194 articles for full-text screening and 29 studies included in the analysis. This study finds that traditional and faith-based healers play an integral role in the pathway to care; more than 70% used traditional and religious healers as the first point of care for mental health care. The median duration for the delay in seeking treatment in a health facility was six months. Patients who sought care from traditional and faith healers were found to have experienced the most prolonged delay without treatment. Age, gender, level of education, marital status, and geographical location were some of the factors associated with the pathway choice. Patients who sought care from traditional and faith healers as the first point of care were found to have experienced the most extended delay without treatment when they arrived at the hospital. The study proposes and recommends a new model for collaboration between biomedical, traditional and faith-based healers that focuses on education through training and adopting a new referral framework.

Continuous respiratory rate monitoring through temporal fusion of ECG and PPG signals

Respiratory rate (RR) is an important vital sign indicating various pathological conditions, such as clinical deterioration, pneumonia, and adverse cardiac arrest. Traditional RR measurement methods are normally intrusive and inconvenient for ubiquitous continuous monitoring. There have been studies on RR estimation by extracting respiratory modulated components (RMCs) from wearable accessible noninvasive cardiovascular signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG), with RR estimated from each RMC or fused RMCs derived from either ECG or PPG. However, there is few study on robust continuous RR estimation with the combination of all kinds of RMCs from both ECG and PPG in the time domain. In this study, we propose the temporal fusion of RMCs extracted from both ECG and PPG signals to estimate RR with the aim to improve estimation performance. We extracted six RMCs from ECG and PPG, identified those RMCs of high quality with the respiratory quality index, fused the identified ones into one respiratory signal with principal component analysis, and estimated the RR from the fused signal. Validation on two public datasets - the Capnobase dataset (42 subjects) and the BIDMC dataset (53 subjects) - showed that the proposed method attained a mean absolute error (MAE) of 1.39 breaths/min and 3.29 breaths/min for RR estimation, respectively, achieving an average 11.61% reduction in MAE compared to existing state-of-the-art approaches. This demonstrates that temporal fusion of the RMCs of wearable ECG and PPG can improve the performance of RR estimation.

Correction for Feng et al., Phosphorylation of dedicator of cytokinesis 1 (Dock180) at tyrosine residue Y722 by Src family kinases mediates EGFRvIII-driven glioblastoma tumorigenesis

Drone-assisted time-varying magnetic field analysis for fault diagnosis in grounding grids

Grounding grids are essential for ensuring the safety of power substations, but their performance can degrade due to corrosion, fractures, or other faults. Traditional fault diagnosis methods are time-consuming, labor-intensive, and require physical access to substations, posing safety risks. This paper introduces a drone-based approach for magnetic field sensing to diagnose grounding grid faults, significantly reducing operational risks and improving efficiency. However, the movement of the drone introduces time-varying electromagnetic interference (EMI) from substation equipment and the drone itself, complicating the isolation of grounding grid signals. To address this problem, we propose a time-varying un-mixing technique combined with the Fast Independent Component Analysis (FastICA) algorithm to effectively suppress the EMI and extract the grounding grid signals. Simulation results demonstrate the efficacy of the proposed technique in separating grounding grid signals under time-varying conditions, outperforming the FastICA algorithm by 96.36% and the Independent Vector Analysis (IVA) by 41.17% at a block length of 4000 and ΓΔk=0.05. These results highlight the robustness and applicability of the proposed approach for real-world grounding grid fault diagnosis, ensuring accuracy and safety in EMI-rich environments. However, the performance of the proposed technique degrades at higher values of ΓΔk, which represents the speed of the flying drone.