Miracle-dz/UniFAR-FTUs huggingface dataset:
PIP2 Improves Cerebral Blood Flow in a Mouse Model of Alzheimer’s Disease

Abstract Alzheimer’s disease (AD) is a leading cause of dementia and a substantial healthcare burden. Despite this, few treatment options are available for controlling AD symptoms. Notably, neuronal activity-dependent increases in cortical cerebral blood flow (CBF; functional hyperemia) are attenuated in AD patients, but the associated pathological mechanisms are not fully understood at the molecular level. A fundamental mechanism underlying functional hyperemia is activation of capillary endothelial inward-rectifying K+ (Kir2.1) channels by neuronally derived potassium (K+), which evokes a retrograde capillary-to-arteriole electrical signal that dilates upstream arterioles, increasing blood delivery to downstream active regions. Here, using a mouse model of familial AD (5xFAD), we tested whether this impairment in functional hyperemia is attributable to reduced activity of capillary Kir2.1 channels. In vivo CBF measurements revealed significant reductions in whisker stimulation (WS)-induced and K+-induced hyperemic responses in 5xFAD mice compared with age-matched controls. Notably, measurements of whole-cell currents in freshly isolated 5xFAD capillary endothelial cells showed that Kir2.1 current density was profoundly reduced, suggesting a defect in Kir2.1 function. Because Kir2.1 activity absolutely depends on binding of phosphatidylinositol 4,5-bisphosphate (PIP2) to the channel, we hypothesized that capillary Kir2.1 channel impairment could be corrected by exogenously supplying PIP2. As predicted, a PIP2 analog restored Kir2.1 current density to control levels. More importantly, systemic administration of PIP2 restored K+-induced CBF increases and WS-induced functional hyperemic responses in 5xFAD mice. Collectively, these data provide evidence that PIP2-mediated restoration of capillary endothelial Kir2.1 function improves neurovascular coupling and CBF in the setting of AD.
MMP9 and IGFBP1 Regulate Tumor Immune and Drive Tumor Progression in Clear Cell Renal Cell Carcinoma

Immunotherapy is a novel approach and has been used in various diseases, especially in cancers. Recently, immunotherapy has gradually been used to treat advanced clear cell renal cell carcinoma (ccRCC) or metastatic ccRCC. However, the efficacy of immunotherapy is not satisfying due to the influence of the tumor microenvironment. In this study, we mainly focused on the abundance and function of tumor-infiltrating immune cells (TIICs). Monocyte and TNM stage were identified as independent prognostic factors via CIBERSORT and Cox regression analysis. Then, ccRCC patients were divided into high risk/TNMhighMonocyteslow cluster and low risk/TNMlowMonocyteshigh cluster. Further differential gene analysis, protein-protein interaction (PPI) network, and survival analysis screened nine hub genes between the above two clusters. MMP9 and IGFBP1 were selected for further study through sample validation. Moreover, gene set enrichment analysis revealed that MMP9 and IGFBP1 were involved in tumor immune via mediating cell surface receptor signal pathway, cytokine production pathway, or monocyte signal pathway. In conclusion, these findings suggested that monocyte acted as a protective factor and MMP9/IGFBP1 played a vital role in tumor immune, which might become potential novel biomarkers and therapeutic targets for immunotherapy in ccRCC.
Fear Associated with COVID-19 in Patients with Neovascular Age-Related Macular Degeneration

Purpose Since the beginning of the COVID-19 pandemic, news related to the pandemic has created a feeling of fear, particularly among high-risk groups including elderly patients. This study aimed to assess the fear associated with COVID-19 and to evaluate the fear of vision decrease related to the delay of treatment in neovascular age-related macular degeneration patients (nAMD) during the pandemic. Patients and Methods This is a prospective cross-sectional study of 160 actively treated patients with nAMD enrolled between September and November 2020 at a tertiary hospital in Québec, Canada. For each participant, demographic and clinical data were collected. The anxiety was rated in a questionnaire composed of two sections: the Fear of COVID-19 Scale (FCV-19S) and eight additional questions to assess ophthalmology-related COVID-19 statements. Results The mean ± standard deviation level of FCV-19S was 17.05±4.38. In the multivariable analysis, it was significantly higher in women (p<0.001) and lower in patients with a high school education vs elementary school (p=0.009). In the ophthalmology-related statements, 16% feared vision loss because of difficulties in maintaining regular follow-ups during the pandemic. The female gender was significantly associated with a higher tendency to postpone their appointment (p=0.03). No association was found between the patients’ underlying disease characteristics and higher fear of vision loss. Conclusion Despite the massive impact of the pandemic, anxiety related to COVID-19 and delaying ophthalmology treatments remained relatively low in nAMD patients. Greater explanations to address this fear may reduce anxiety level, especially among female patients and those with an elementary school education.
Using collective variables to drive molecular dynamics simulations

A software framework is introduced that facilitates the application of biasing algorithms to collective variables of the type commonly employed to drive massively parallel molecular dynamics (MD) simulations. The modular framework that is presented enables one to combine existing collective variables into new ones, and combine any chosen collective variable with available biasing methods. The latter include the classic time-dependent biases referred to as steered MD and targeted MD, the temperature-accelerated MD algorithm, as well as the adaptive free-energy biases called metadynamics and adaptive biasing force. The present modular software is extensible, and portable between commonly used MD simulation engines.
Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

Abstract Objective Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. Methods This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). Results The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P < .001). The best-predicting model utilized 1726 variables with AUC = 0.932 (95% CI, 0.922–0.941). The model was robust across multiple prediction windows and subgroups by demographics, points of historical most recent clinical contact, and depression diagnosis history. Conclusions Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.
Monte Carlo Simulation of Errors for N-localizer Systems in Stereotactic Neurosurgery: Novel Proposals for Improvements

Introduction: Frame-based stereotaxis has been widely utilized for precise neurosurgical procedures throughout the world for nearly 40 years. The N-localizer is an integral component of most of the extant systems. Analysis of targeting errors related to the N-localizer has not been carried out in sufficient detail. We highlight these potential errors and develop methods to reduce them. Methods: N-localizer systems comprising three and four N-localizers of various geometries were analyzed using Monte Carlo (MC) simulations. The simulations included native and altered geometric dimensions (Width [W] x Height [H]). Errors were computed using the MC simulations that included the x- and y-axes of vertically oriented rods, that altered the W/H ratio, and that added a fourth N-localizer to a three N-localizer system. Results: The inclusion of an overdetermined system of equations and the geometries of the N-localizer systems had significant effects on target errors. Root Mean Square Errors (RMS-e) computed via millions of MC iterations for each study demonstrated that errors were reduced by (1) inclusion of the x- and y-coordinates of the vertically oriented rods, (2) a greater triangular area enclosed by the diagonal fiducials of the N-localizer system (stereotactic triangle), (3) a larger W/H ratio, and (4) an N-localizer system that comprised four N-localizers. Conclusion: Monte Carlo simulations of Root Mean Square error (RMS-e) is a useful technique to understand targeting while using N-localizer systems in stereotactic neurosurgery. The application of vertical rod positions enhances computational accuracy and can be performed on any N-localizer system. Keeping the target point within the stereotactic triangle enclosed by the diagonal rods can also reduce errors. Additional optimizations of N-localizer geometry may also reduce potential targeting errors. Further analysis is needed to confirm these findings which may have clinical importance.
β-elemene enhances the antitumor activity of erlotinib by inducing apoptosis through AMPK and MAPK pathways in TKI-resistant H1975 lung cancer cells

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) significantly improve the outcome of non-small-cell lung cancer (NSCLC) patients with EGFR mutations, however, most TKI-treated patients will develop resistance to TKIs. β-elemene, extracted from Curcuma aromatica Salisb. , has been widely used to treat various malignant tumors, including TKI-resistant NSCLC, but, the effects and the molecular mechanisms remain unclear. In this study, the NCI-H1975 cell line harboring double mutations L858R/T790M was treated with varying concentrations of β-elemene and/or erlotinib. The effects of β-elemene on cell proliferation, migration, apoptosis, and the expression of relevant proteins of NCI-H1975 cells were evaluated. The results revealed that β‑elemene significantly inhibited the growth, colony formation capacity, wound healing ability of NCI-H1975 cells, and improved the sensitivity of NCI-H1975 cells to erlotinib. Compared with erlotinib alone, β-elemene plus erlotinib significantly promoted the apoptosis of NCI-H1975 cells, accompanied by the down-regulated expression of P-mTOR, P-EGFR, CHOP proteins and up-regulated expression of P-AMPKα and Bax proteins. Taken together, these findings demonstrate that β-elemene suppresses the proliferation and migration of TKI-resistant H1975 cells, and enhances the antitumor activity of erlotinib by inducing apoptosis through AMPK and MAPK pathways in TKI-resistant H1975 lung cancer cells, indicating that β-elemene is a promising anti-cancer therapeutic candidate for TKI-resistant NSCLC.
Application of Enrichment Factor to Assessment of Zinc Enrichment/Depletion in Farming Soils

Abstract Zinc (Zn) content was assayed in the farming soils of Suszec commune (agricultural and mining district in southern Poland). Its content in the soils (n=1227) assayed by means of atomic absorption spectrophotometry (AAS) ranged from 22.8 to 177.1 mg/kg. The enrichment factor (EF) calculated by using calcium as a reference element fell within a range of 0.66–2.63. The mean values of the enrichment factor classified the soil as slightly contaminated with zinc. The values of EF less than 1 pointing to zinc deficiency in the soil were found for 52% of the samples tested. Acid soils with low humus content showed a tendency for zinc‐deficient samples to significantly increase in number.
Numerical modelling on fate and transport of nitrate in an unsaturated system under non-isothermal condition

Nitrate pollution in groundwater arising from wastewater and fertiliser application through vadose zone is a major problem and it causes a prime risk to groundwater-based drinking water supplies. In this study, a one-dimensional numerical model is developed to investigate the nitrogen species transport in unsaturated porous media along with mass transfer of oxygen from gaseous phase to aqueous phase. Further, the temperature-dependent nitrogen transformation rates, oxygen solubility in an aqueous phase from gaseous phase, and oxygen diffusion coefficient have been successfully incorporated. Results suggest that the temperature-dependent oxygen diffusion coefficient is significant in case of temperature variation between 30 and 45 °C, whereas the temperature-dependent oxygen solubility and transformation rate are found to be the critical parameters influencing the nitrogen species transport when the temperature variation is between 10 and 30 °C. In the temperature range between 30 and 45 °C, the model performance marginally improved compared with that of the same under isothermal condition. But in the case of variation between 10 and 30 °C, the nitrate nitrogen concentration has higher variation during non-isothermal conditions when compared with isothermal conditions. Moreover, the results suggest that the dissolved oxygen concentration is also significantly affected by non-isothermal conditions.
Neuropsychiatric Symptoms of Alzheimer’s Disease in Down Syndrome and Its Impact on Caregiver Distress

Background: Neuropsychiatric symptoms (NPS) are non-cognitive manifestations common to dementia and other medical conditions, with important consequences for the patient, caregivers, and society. Studies investigating NPS in individuals with Down syndrome (DS) and dementia are scarce. Objective: Characterize NPS and caregiver distress among adults with DS using the Neuropsychiatric Inventory (NPI). Methods: We evaluated 92 individuals with DS (≥30 years of age), divided by clinical diagnosis: stable cognition, prodromal dementia, and AD. Diagnosis was determined by a psychiatrist using the Cambridge Examination for Mental Disorders of Older People with Down’s Syndrome and Others with Intellectual Disabilities (CAMDEX-DS). NPS and caregiver distress were evaluated by an independent psychiatrist using the NPI, and participants underwent a neuropsychological assessment with Cambridge Cognitive Examination (CAMCOG-DS). Results: Symptom severity differed between-groups for delusion, agitation, apathy, aberrant motor behavior, nighttime behavior disturbance, and total NPI scores, with NPS total score being found to be a predictor of AD in comparison to stable cognition (OR for one-point increase in the NPI = 1.342, p = 0.012). Agitation, apathy, nighttime behavior disturbances, and total NPI were associated with CAMCOG-DS, and 62% of caregivers of individuals with AD reported severe distress related to NPS. Caregiver distress was most impacted by symptoms of apathy followed by nighttime behavior, appetite/eating abnormalities, anxiety, irritability, disinhibition, and depression (R2 = 0.627, F(15,76) = 8.510, p < 0.001). Conclusion: NPS are frequent and severe in individuals with DS and AD, contributing to caregiver distress. NPS in DS must be considered of critical relevance demanding management and treatment. Further studies are warranted to understand the biological underpinnings of such symptoms.
Band Structures of Narrow Zigzag Silicon Carbon Nanoribbons

A first-principles investigation on the electronic properties of the silicon carbon nanoribbons (SiC NRs) having zigzag-shaped edges passivated by hydrogen with widths up to 1.7 nm is presented. It is found that, for the zigzag SiC NRs wider than 0.6 nm, the ground state is a ferrimagnetic state that has two different direct band gaps for the spin-up and the spin-down channels near the X point, and thus it is semiconducting instead of half metallic. We also found that the ferromagnetic (FM) state is metallic and has higher energy than the ferrimagnetic state for very narrow zigzag SiC NRs, though the energies of the FM metallic state and the ferrimagnetic semiconducting state are almost equivalent as the width increases. Furthermore, we found that the energies of nonmagnetic semiconducting states that have only one direct band gap were much higher than those of the FM and the ferrimagnetic states. On the other hand, the zigzag SiC NRs narrower than 0.6 nm have only a nonmagnetic semiconducting state that .
Medial gastrocnemius muscle fascicles shorten throughout stance during sprint acceleration

The compliant nature of distal limb muscle‐tendon units is traditionally considered suboptimal in explosive movements when positive joint work is required. However, during accelerative running, ankle joint net mechanical work is positive. Therefore, this study aims to investigate how plantar flexor muscle‐tendon behavior is modulated during fast accelerations. Eleven female sprinters performed maximum sprint accelerations from starting blocks, while gastrocnemius muscle fascicle lengths were estimated using ultrasonography. We combined motion analysis and ground reaction force measurements to assess lower limb joint kinematics and kinetics, and to estimate gastrocnemius muscle‐tendon unit length during the first two acceleration steps. Outcome variables were resampled to the stance phase and averaged across three to five trials. Relevant scalars were extracted and analyzed using one‐sample and two‐sample t‐tests, and vector trajectories were compared using statistical parametric mapping. We found that an uncoupling of muscle fascicle behavior from muscle‐tendon unit behavior is effectively used to produce net positive mechanical work at the joint during maximum sprint acceleration. Muscle fascicles shortened throughout the first and second steps, while shortening occurred earlier during the first step, where negative joint work was lower compared with the second step. Elastic strain energy may be stored during dorsiflexion after touchdown since fascicles did not lengthen at the same time to dissipate energy. Thus, net positive work generation is accommodated by the reuse of elastic strain energy along with positive gastrocnemius fascicle work. Our results show a mechanism of how muscles with high in‐series compliance can contribute to net positive joint work.
Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales

Background The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained. Objective This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data. Methods We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP. Results The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation. Conclusions Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.
SFRmaker and Linesink‐Maker: Rapid Construction of Streamflow Routing Networks from Hydrography Data

Groundwater models have evolved to encompass more aspects of the water cycle, but the incorporation of realistic boundary conditions representing surface water remains time‐consuming and error‐prone. We present two Python packages that robustly automate this process using readily available hydrography data as the primary input. SFRmaker creates input for the MODFLOW SFR package, while Linesink‐maker creates linesink string input for the GFLOW analytic element program. These programs can reduce weeks or even months of manual effort to a few minutes of execution time, and carry the added advantages of reduced potential for error, improved reproducibility and facilitation of step‐wise modeling through reduced dependency on a particular conceptual model or discretization. Two real‐world examples at the county to multi‐state scales are presented.
Nanofils ferromagnétiques auto-assemblés en matrice d'oxyde : croissance, épitaxie verticale et propriétés magnétiques

Cette these presente l'elaboration et l'etude de nanofils ferromagnetiques de cobalt, nickel et d'alliages cobalt-nickel epitaxies en matrice de titanate de strontium et de baryum. Les fils sont elabores par auto-assemblage lors de depots sequentiels par ablation laser pulse. Tout d'abord, les parametres de croissance permettant de controler le diametre des fils et leur densite sont mis en evidence en modelisant la croissance de l'hetero-structure par simulations Monte-Carlo cinetique. Ensuite, on montre que les fils sont dilates axialement et relaxes radialement. L'origine de l'etat dilate est expliquee en adaptant le modele de Frenkel-Kontorova a notre situation et les inhomogeneites de deformation des nanofils sont decrites en analysant des cartographies de l'espace reciproque. La dilatation cree une anisotropie magnetique, par couplage magneto-elastique, qui, dans le cas du nickel, peut compenser l'anisotropie de forme des fils. Enfin, pour des fils de Co0.4Ni0. 6 de diametre superieur a quatre nanometres, la temperature de blocage de l'assemblee de fils est superieure a la temperature ambiante et la barriere d'energie du renversement magnetique est de l'ordre d'un electronvolt, ce qui est interessant pour d'eventuelles applications, par exemple en enregistrement de donnees.