Prediction of future dementia among patients with mild cognitive impairment (MCI) by integrating multimodal clinical data.

Efficiently and objectively analyzing the complex, diverse multimodal data collected from patients at risk for dementia can be difficult in the clinical setting, contributing to high rates of underdiagnosis or misdiagnosis of this serious disorder. Patients with mild cognitive impairment (MCI) are especially at risk of developing dementia in the future. This study evaluated the ability of multi-modal machine learning (ML) methods, especially the Ensemble Integration (EI) framework, to predict future dementia development among patients with MCI. EI is a machine learning framework designed to leverage complementarity and consensus in multimodal data, which may not be adequately captured by methods used by prior dementia-related prediction studies. We tested EI's ability to predict future dementia development among MCI patients using multimodal clinical and imaging data, such as neuroanatomical measurements from structural magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge. For predicting future dementia development among MCI patients, on a held out test set, the EI-based model performed better (AUC = 0.81, F-measure = 0.68) than the more commonly used XGBoost (AUC = 0.68, F-measure = 0.57) and deep learning (AUC = 0.79, F-measure = 0.61) approaches. This EI-based model also suggested MRI-derived volumes of regions in the middle temporal gyrus, posterior cingulate gyrus and inferior lateral ventricle brain regions to be predictive of progression to dementia.

Aneurysmal Cavity in the Interventricular Septum by the Rupture of the Right Sinus of Valsalva: An Interesting Finding on Cardiac Imaging.

This case report documents a rare congenital anomaly in a 27-year-old man of African descent presenting with exertional chest discomfort and shortness of breath, diagnosed with a ruptured right sinus of Valsalva (RSOV) aneurysm dissecting into the interventricular septum (IVS), creating an aneurysmal cavity. Such occurrences are typically rare, with this type of aneurysm largely manifesting in the right atrium, making its presentation in the IVS without intracardiac communication exceptionally uncommon. Cardiac imaging, including transesophageal echocardiography and cardiac magnetic resonance imaging (CMR), played pivotal roles in visualizing the structural abnormality and planning the subsequent surgical intervention. The patient's treatment included heart failure optimization, followed by surgery to repair the aneurysmal cavity while preserving the native aortic valve. Postoperative challenges included a complete heart block managed by cardiac resynchronization therapy and an intracardiac defibrillator. The report underscores the importance of advanced imaging in diagnosing and managing rare cardiac anomalies, highlighting the aneurysm's unique rupture pattern and location.

Deep learning for the harmonization of structural MRI scans: a survey.

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.

Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.

This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices formation to handle real time (non-linear and non-stationary) data based feature extraction using Schrödinger equation. Features based adaptive classification is made possible through the Q-learning algorithm wherein optimal Q-values selection is done via EJAYA optimization algorithm. Fuzzy lattice is formed using x-ray image pixels and lattice Kinetic Energy (K.E.) is calculated using the Schrödinger equation. Feature vector lattices having highest K.E. have been used as an input features for the classifier. The classifier has been employed for pneumonia classification (normal, mild and severe) and Tuberculosis detection (presence or absence). A total of 3000 images have been used for pneumonia classification yielding an accuracy, sensitivity, specificity, precision and F-scores of 97.90%, 98.43%, 97.25%, 97.78% and 98.10%, respectively. For Tuberculosis 600 samples have been used. The achived accuracy, sensitivity, specificity, precision and F-score are 95.50%, 96.39%, 94.40% 95.52% and 95.95%, respectively. Computational time are 40.96 and 39.98 s for pneumonia and TB classification. Classifier learning rate (training accuracy) for pneumonia classes (normal, mild and severe) are 97.907%, 95.375% and 96.391%, respectively and for tuberculosis (present and absent) are 96.928% and 95.905%, respectively. The results have been compared with contemporary classification techniques which shows superiority of the proposed approach in terms of accuracy and speed of classification. The technique could serve as a fast and accurate tool for automated pneumonia and tuberculosis classification.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Efficient Control of Electron Localization and Probability Modulation with Synthesized Two-Color Intense Laser Pulses.

A coupled electron-nuclear dynamical study at attosecond time scale is performed on the HD+ and H2+ molecular ions under the influence of synthesized intense two-color electric fields. We have employed ω - 2ω and also, ω - 3ω two-color fields in the infrared/mid-infrared regime to study the different fragmentation processes originating from the interference of n - (n + i) (i = 1, 2) photon absorption pathways. The branching ratios corresponding to different photofragments are controlled by tuning the relative phase as well as intensity of the two-color pulses, while the effect of the initial nuclear wave function is also studied by taking an individual vibrational eigenstate or a coherent superposition of several eigenstates of HD+ and H2+. By comprehensive analysis, the efficacy of the two different types of synthesized two-color pulses (ω - 2ω and ω - 3ω) are analyzed with respect to one-color intense pulses in terms of controlling the probability modulation and electron localization asymmetry and compared with previous theoretical calculations and experimental findings. Through the detailed investigation, we have addressed which one is the major controlling knob to have better electron localization as well as probability modulation.

Efficient detection of p-nitrophenol via a polypyrrole flower-decorated nickel foam-based electrochemical sensor.

p-Nitrophenol (p-NP) is known as a common contaminant found in wastewater, agricultural runoff, and industrial effluents which can degrade water quality and cause potential carcinogenic and toxic effects on the human body. Its detection is essential for public health, industrial safety, environmental protection, and regulatory compliance, underscoring its broad applicability. In this study, a novel electrochemical sensor based on polypyrrole (PPy) flowers assembled via nanotubes was developed for the sensitive determination of p-NP. The nickel (Ni) foam modified with PPy flowers functioned as the working electrode and showed selectivity toward p-NP in a phosphate buffer medium at pH 7.0. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were utilized for the sensitive determination of p-NP. Under the optimum conditions, the peak currents of DPV versus the concentrations of p-NP in the range of 0.01-20 nM showed a good linear relationship (R2 = 0.9943), and the limit of detection (LOD) was calculated to be 7.18 pM (signal-to-noise ratio of 3, S/N = 3). The fabricated electrochemical p-NP sensor exhibited high sensitivity, a low detection limit, and a low response time. The recoveries of p-NP in real samples of groundwater and tap water using the PPy Fls/Ni foam electrode were in the range of 91.0-108.4% with a relative standard deviation (RSD) in the range of 6.65%. Consequently, the PPy Fls/Ni foam electrode could be applied as a rapid, precise, and sensitive electrochemical sensor platform for aqueous p-NP quantification and determination.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events.

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Toxic Epidermal Necrolysis After COVID-19 Vaccination: A Case Report and Review of Literature.

Following vaccination for COVID-19, various cutaneous adverse reactions (CARs) are reported. Here is an Asian male in late 50's who developed necrotic skin with mucosal involvement 10 days following booster dose of ChAdOx1 nCov-19 vaccination. Based on disease course and morphology, toxic epidermal necrolysis (TEN) was suspected. The patient developed respiratory distress and was intubated, intravenous immunoglobulin (IVIG) administered at 2 g/kg body weight following which skin lesions healed in fourth week, the patient was discharged after 50 days of intensive care unit (ICU) stay. Severe CARs are rare following vaccination, of two components in ChAdOx1nCoV-19 adenoviral vector vaccine, virotopes cause T-cell mediated granulysin and granzyme B release leading to epidermal detachment and mucosal involvement of conducting airways causing respiratory failure. CARs can also occur in whom first and second dose was uneventful. Supportive therapy and prevention of sepsis are mainstay of management. Though the use of IVIG has shown conflicting results, our case was successfully managed with IVIG.

Portal Vein Reconstruction-trans Jugular Intrahepatic Portosystemic Shunt and Portal Vein Stenting in Patients With Extrahepatic Portal Vein Obstruction.

Extrahepatic portal vein thrombosis (EHPVO) is an uncommon cause of portal hypertension. In the long term, patients may develop portal cavernoma cholangiopathy (PCC). Up to 30%-40% of patients with EHPVO may not have shuntable veins and are often difficult to manage surgically. Interventional treatment including portal vein recanalisation-trans jugular intrahepatic portosystemic shunt (PVRecan-TIPS) has been used for patients with EHPVO. However, PV reconstruction-trans jugular intrahepatic portosystemic shunt (PVRecon-TIPS) and portal vein stenting are novel techniques for managing such patients with EHPVO with non-shuntable venous anatomy. In contrast to PVRecan-TIPS, PV reconstruction-TIPS (PVRecon-TIPS) is performed through intrahepatic collaterals. Here we present six cases of PCC who presented with recurrent acute variceal bleeding (AVB) and or refractory biliary stricture. They did not have any shuntable veins. PVRecon-TIPS was performed for five patients whilst PV stenting was done in one. Amongst the six patients, one died of sepsis whilst one who developed hyponatremia and hepatic encephalopathy was salvaged with conservative management. Following the procedure, they were started on anti-coagulation. Decompression of cavernoma was documented in all other patients. Biliary changes improved completely in 40% of patients.

Multi-omic integration reveals alterations in nasal mucosal biology that mediate air pollutant effects on allergic rhinitis.

BACKGROUND: Allergic rhinitis is a common inflammatory condition of the nasal mucosa that imposes a considerable health burden. Air pollution has been observed to increase the risk of developing allergic rhinitis. We addressed the hypotheses that early life exposure to air toxics is associated with developing allergic rhinitis, and that these effects are mediated by DNA methylation and gene expression in the nasal mucosa. METHODS: In a case-control cohort of 505 participants, we geocoded participants' early life exposure to air toxics using data from the US Environmental Protection Agency, assessed physician diagnosis of allergic rhinitis by questionnaire, and collected nasal brushings for whole-genome DNA methylation and transcriptome profiling. We then performed a series of analyses including differential expression, Mendelian randomization, and causal mediation analyses to characterize relationships between early life air toxics, nasal DNA methylation, nasal gene expression, and allergic rhinitis. RESULTS: Among the 505 participants, 275 had allergic rhinitis. The mean age of the participants was 16.4 years (standard deviation = 9.5 years). Early life exposure to air toxics such as acrylic acid, phosphine, antimony compounds, and benzyl chloride was associated with developing allergic rhinitis. These air toxics exerted their effects by altering the nasal DNA methylation and nasal gene expression levels of genes involved in respiratory ciliary function, mast cell activation, pro-inflammatory TGF-ß1 signaling, and the regulation of myeloid immune cell function. CONCLUSIONS: Our results expand the range of air pollutants implicated in allergic rhinitis and shed light on their underlying biological mechanisms in nasal mucosa.

APASL clinical practice guidelines on the management of acute kidney injury in acute-on-chronic liver failure.

Acute-on-chronic liver failure (ACLF) is a syndrome that is characterized by the rapid development of organ failures predisposing these patients to a high risk of short-term early death. The main causes of organ failure in these patients are bacterial infections and systemic inflammation, both of which can be severe. For the majority of these patients, a prompt liver transplant is still the only effective course of treatment. Kidneys are one of the most frequent extrahepatic organs that are affected in patients with ACLF, since acute kidney injury (AKI) is reported in 22.8-34% of patients with ACLF. Approach and management of kidney injury could improve overall outcomes in these patients. Importantly, patients with ACLF more frequently have stage 3 AKI with a low rate of response to the current treatment modalities. The objective of the present position paper is to critically review and analyze the published data on AKI in ACLF, evolve a consensus, and provide recommendations for early diagnosis, pathophysiology, prevention, and management of AKI in patients with ACLF. In the absence of direct evidence, we propose expert opinions for guidance in managing AKI in this very challenging group of patients and focus on areas of future research. This consensus will be of major importance to all hepatologists, liver transplant surgeons, and intensivists across the globe.

Remediation of pharmacophoric laboratory waste by using biodegradable carbon nanoparticles of bacterial biofilm origin.

Research laboratories generate a broad range of hazardous pharmacophoric chemical contaminants, from drugs to dyes used during various experimental procedures. In the recent past, biological methods have demonstrated great potential in the remediation of such contaminants. However, the presence of pharmacophoric chemicals containing antibiotics, xenobiotics, and heavy metals suppresses the growth and survivability of used microbial agents, thus decreasing the overall efficiency of biological remediation processes. Bacterial biofilm is a natural arrangement to counter some of these inhibitions but its use in a systemic manner, portable devices, and pollutant remediation plants post serious challenges. This could be countered by synthesizing a biodegradable carbon nanoparticle from bacterial biofilm. In this study, extracellular polymeric substance-based carbon nanoparticles (Bio-EPS-CNPs) were synthesized from bacterial biofilm derived from Bacillus subtilis NCIB 3610, as a model bacterial system. The produced Bio-EPS-CNPs were investigated for physiochemical properties by dynamic light scattering, optical, Fourier-transformed infrared, and Raman spectroscopy techniques, whereas X-ray diffraction study, scanning electron microscopy, and transmission electron microscopy were used to investigate structural and morphological features. The Bio-EPS-CNPs exhibited negative surface charge with spherical morphology having a uniform size of sub-100 nm. The maximum remediation of some laboratory-produced pharmacophoric chemicals was achieved through a five-round scavenging process and confirmed by UV/Vis spectroscopic analysis with respect to the used pharmacophore. This bioinspired remediation of used pharmacophoric chemicals was achieved through the mechanism of surface adsorption via hydrogen bonding and electrostatic interactions, as revealed by different characterizations. Further experiments were performed to investigate the effects of pH, temperature, stirring, and the protocol of scavenging to establish Bio-EPS-CNP as a possible alternative to be used in research laboratories for efficient removal of pharmacophoric chemicals by incorporating it in a portable, filter-based device.

A Comprehensive Youth Diabetes Epidemiological Data Set and Web Portal: Resource Development and Case Studies.

BACKGROUND: The prevalence of type 2 diabetes mellitus (DM) and pre-diabetes mellitus (pre-DM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth pre-DM/DM data. OBJECTIVE: We aimed to first build a high-quality, comprehensive epidemiological data set focused on youth pre-DM/DM. Subsequently, we aimed to make these data accessible by creating a user-friendly web portal to share them and the corresponding codes. Through this, we hope to address this significant gap and facilitate youth pre-DM/DM research. METHODS: Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to pre-DM/DM (fasting plasma glucose level ≥100 mg/dL or glycated hemoglobin ≥5.7%) for youth aged 12-19 years (N=15,149). We identified individual factors associated with pre-DM/DM risk using bivariate statistical analyses and predicted pre-DM/DM status using our Ensemble Integration (EI) framework for multidomain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth Online Dashboard (POND) to share the data and codes. RESULTS: We extracted 95 variables potentially relevant to pre-DM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of pre-DM/DM (P<.001, Bonferroni adjusted), including race or ethnicity, health insurance, BMI, added sugar intake, and screen time. Among these factors, 16 factors were also identified based on the EI methodology (Fisher P of overlap=7.06×106). In addition to those, the EI approach identified 11 additional predictive variables, including some known (eg, meat and fruit intake and family income) and less recognized factors (eg, number of rooms in homes). The factors identified in both analyses spanned across all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND. CONCLUSIONS: Using NHANES data, we built one of the largest public epidemiological data sets for studying youth pre-DM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of pre-DM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth pre-DM/DM studies.

Chimeric antigen receptor macrophages target and resorb amyloid plaques.

Substantial evidence suggests a role for immunotherapy in treating Alzheimer's disease (AD). While the precise pathophysiology of AD is incompletely understood, clinical trials of antibodies targeting aggregated forms of ß amyloid (Aß) have shown that reducing amyloid plaques can mitigate cognitive decline in patients with early-stage AD. Here, we describe what we believe to be a novel approach to target and degrade amyloid plaques by genetically engineering macrophages to express an Aß-targeting chimeric antigen receptor (CAR-Ms). When injected intrahippocampally, first-generation CAR-Ms have limited persistence and fail to significantly reduce plaque load, which led us to engineer next-generation CAR-Ms that secrete M-CSF and self-maintain without exogenous cytokines. Cytokine secreting "reinforced CAR-Ms" have greater survival in the brain niche and significantly reduce plaque load locally in vivo. These findings support CAR-Ms as a platform to rationally target, resorb, and degrade pathogenic material that accumulates with age, as exemplified by targeting Aß in AD.

Combination of EZH2 and ATM inhibition in BAP1-deficient mesothelioma.

BACKGROUND: More than half of mesothelioma tumours show alterations in the tumour suppressor gene BAP1. BAP1-deficient mesothelioma is shown to be sensitive to EZH2 inhibition in preclinical settings but only showed modest efficacy in clinical trial. Adding a second inhibitor could potentially elevate EZH2i treatment efficacy while preventing acquired resistance at the same time. METHODS: A focused drug synergy screen consisting of 20 drugs was performed by combining EZH2 inhibition with a panel of anti-cancer compounds in mesothelioma cell lines. The compounds used are under preclinical investigation or already used in the clinic. The synergistic potential of the combinations was assessed by using the Bliss model. To validate our findings, in vivo xenograft experiments were performed. RESULTS: Combining EZH2i with ATMi was found to have synergistic potential against BAP1-deficient mesothelioma in our drug screen, which was validated in clonogenicity assays. Tumour growth inhibition potential was significantly increased in BAP1-deficient xenografts. In addition, we observe lower ATM levels upon depletion of BAP1 and hypothesise that this might be mediated by E2F1. CONCLUSIONS: We demonstrated the efficacy of the combination of ATM and EZH2 inhibition against BAP1-deficient mesothelioma in preclinical models, indicating the potential of this combination as a novel treatment modality using BAP1 as a biomarker.

Coherent multi-band MIMO radar: robustness analysis to SSMF-based RF signal delivery.

A numerical evaluation is conducted to assess the impact of distributing radio frequency (RF) signals through optical fiber links on the performance of a coherent multi-band multiple-input multiple-output (MIMO) radar system. The analysis focuses on scenarios where the antennas are widely separated in comparison to the employed signal wavelengths. The development of a model to quantify the phase noise (PN) induced on each RF band due to the signal transmission through optical fiber links between the centralized base station and each radar peripheral is described. Monte Carlo simulation results are collected to estimate the key performance indicators (KPIs) for varying standard single-mode fiber (SSMF) length and different PN contributions. The main contributors to the PN are revealed to be chromatic dispersion (CD), double Rayleigh scattering (DRS), and mechanical vibrations. In a shipborne scenario, a significant performance degradation occurs only when the length of the fiber links reaches approximately 20 km. Further, the PN impact has also been studied in a shipborne scenario to analyze the robustness of the system for worse phase noise level assumptions. The results reveal excellent robustness of the proposed centralized acquisition and processing approach in the presence of both very long fiber links and economically employed RF oscillators.

Relationship Between Cognitive Impairment and Depression Among Middle Aged and Older Adults in Primary Care.

Objectives: To determine rates of previously undetected cognitive impairment among patients with depression in primary care. Methods: Patients ages 55 and older with no documented history of dementia or mild cognitive impairment were recruited from primary care practices in New York City, NY and Chicago, IL (n = 855). Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA) and depression with the Patient Health Questionnaire-8. Results: The mean age was 66.8 (8.0) years, 45.3% were male, 32.7% Black, and 29.2% Latinx. Cognitive impairment increased with severity of depression: 22.9% in persons with mild depression, 27.4% in moderate depression and 41.8% in severe depression (p = .0002). Severe depression was significantly associated with cognitive impairment in multivariable analysis (standardized ß = -.11, SE = 0.33, p < .0001). Discussion: Depression was strongly associated with previously undetected cognitive impairment. Primary care clinicians should consider screening, or expand their screening, for both conditions.

Microwave Doping of Sulfur and Iron in ß12 Borophene.

Borophene, a 2D material exhibiting unique crystallographic phases like the anisotropic atomic lattices of ß12 and X3 phases, has attracted considerable attention due to its intriguing Dirac nature and metallic attributes. Despite surpassing graphene in electronic mobility, borophene's potential in energy storage and catalysis remains untapped due to its inherent electrochemical and catalytic limitations. Elemental doping emerges as a promising strategy to introduce charge carriers, enabling localized electrochemical and catalytic functionalities. However, effective doping of borophene has been a complex and underexplored challenge. Here, an innovative, one-pot microwave-assisted doping method, tailored for the ß12 phase of borophene is introduced. By subjecting dispersed ß12 borophene in dimethylformamide to controlled microwave exposure with sulfur powder and FeCl3 as doping precursors, S- and Fe doping in borophene can be controlled. Employing advanced techniques including high-resolution transmission electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy, confirm successful sulfur and iron dopant incorporation onto ß12 borophene is confirmed, achieving doping levels of up to 11 % and 13 %, respectively. Remarkably, S- and Fe-doped borophene exhibit exceptional supercapacitive behavior, with specific capacitances of 202 and 120 F g-1, respectively, at a moderate current density of 0.25 A g-1.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA.

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.