Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics.

The United States is the world's largest oil/gas methane emitter according to current national reports. Reducing these emissions is a top priority in the US government's climate action plan. Here, we use a 2010 to 2019 high-resolution inversion of surface and satellite observations of atmospheric methane to quantify emission trends for individual oil/gas production regions in North America and relate them to production and infrastructure. We estimate a mean US oil/gas methane emission of 14.8 (12.4 to 16.5) Tg a-1 for 2010 to 2019, 70% higher than reported by the US Environmental Protection Agency. While emissions in Canada and Mexico decreased over the period, US emissions increased from 2010 to 2014, decreased until 2017, and rose again afterward. Increases were driven by the largest production regions (Permian, Anadarko, Marcellus), while emissions in the smaller production regions generally decreased. Much of the year-to-year emission variability can be explained by oil/gas production rates, active well counts, and new wells drilled, with the 2014 to 2017 decrease driven by reduction in new wells and the 2017 to 2019 surge driven by upswing of production. We find a steady decrease in the oil/gas methane intensity (emission per unit methane gas production) for almost all major US production regions. The mean US methane intensity decreased from 3.7% in 2010 to 2.5% in 2019. If the methane intensity for the oil/gas supply chain continues to decrease at this pace, we may expect a 32% decrease in US oil/gas emissions by 2030 despite projected increases in production.

Electrical stimulation induced self-related auditory hallucinations correlate with oscillatory power change in the default mode network.

Self-related information is crucial in our daily lives, which has led to the proposal that there is a specific brain mechanism for processing it. Neuroimaging studies have consistently demonstrated that the default mode network (DMN) is strongly associated with the representation and processing of self-related information. However, the precise relationship between DMN activity and self-related information, particularly in terms of neural oscillations, remains largely unknown. We electrically stimulated the superior temporal and fusiform areas, using stereo-electroencephalography to investigate neural oscillations associated with elicited self-related auditory hallucinations. Twenty-two instances of auditory hallucinations were recorded and categorized into self-related and other-related conditions. Comparing oscillatory power changes within the DMN between self-related and other-related auditory hallucinations, we discovered that self-related hallucinations are associated with significantly stronger positive power changes in both alpha and gamma bands compared to other-related hallucinations. To ensure the validity of our findings, we conducted controlled analyses for factors of familiarity and clarity, which revealed that the observed effects within the DMN remain independent of these factors. These results underscore the significance of the functional role of the DMN during the processing of self-related auditory hallucinations and shed light on the relationship between self-related perception and neural oscillatory activity.

Prior Knowledge Uses Prestimulus Alpha Band Oscillations and Persistent Poststimulus Neural Templates for Conscious Perception.

Prior knowledge has a profound impact on the way we perceive the world. However, it remains unclear how the prior knowledge is maintained in our brains and thereby influences the subsequent conscious perception. The Dalmatian dog illusion is a perfect tool to study prior knowledge, where the picture is initially perceived as noise. Once the prior knowledge was introduced, a Dalmatian dog could be consciously seen, and the picture immediately became meaningful. Using pictures with hidden objects as standard stimuli and similar pictures without hidden objects as deviant stimuli, we investigated the neural representation of prior knowledge and its impact on conscious perception in an oddball paradigm using electroencephalogram (EEG) in both male and female human subjects. We found that the neural patterns between the prestimulus alpha band oscillations and poststimulus EEG activity were significantly more similar for the standard stimuli than for the deviant stimuli after prior knowledge was provided. Furthermore, decoding analysis revealed that persistent neural templates were evoked after the introduction of prior knowledge, similar to that evoked in the early stages of visual processing. In conclusion, the current study suggests that prior knowledge uses alpha band oscillations in a multivariate manner in the prestimulus period and induces specific persistent neural templates in the poststimulus period, enabling the conscious perception of the hidden objects.SIGNIFICANCE STATEMENT The visual world we live in is not always optimal. In dark or noisy environments, prior knowledge can help us interpret imperfect sensory signals and enable us to consciously perceive hidden objects. However, we still know very little about how prior knowledge works at the neural level. Using the Dalmatian dog illusion and multivariate methods, we found that prior knowledge uses prestimulus alpha band oscillations to carry information about the hidden object and exerts a persistent influence in the poststimulus period by inducing specific neural templates. Our findings provide a window into the neural underpinnings of prior knowledge and offer new insights into the role of alpha band oscillations and neural templates associated with conscious perception.

Superior Attentional Efficiency of Auditory Cue via the Ventral Auditory-thalamic Pathway.

Auditory commands are often executed more efficiently than visual commands. However, empirical evidence on the underlying behavioral and neural mechanisms remains scarce. In two experiments, we manipulated the delivery modality of informative cues and the prediction violation effect and found consistently enhanced RT benefits for the matched auditory cues compared with the matched visual cues. At the neural level, when the bottom-up perceptual input matched the prior prediction induced by the auditory cue, the auditory-thalamic pathway was significantly activated. Moreover, the stronger the auditory-thalamic connectivity, the higher the behavioral benefits of the matched auditory cue. When the bottom-up input violated the prior prediction induced by the auditory cue, the ventral auditory pathway was specifically involved. Moreover, the stronger the ventral auditory-prefrontal connectivity, the larger the behavioral costs caused by the violation of the auditory cue. In addition, the dorsal frontoparietal network showed a supramodal function in reacting to the violation of informative cues irrespective of the delivery modality of the cue. Taken together, the results reveal novel behavioral and neural evidence that the superior efficiency of the auditory cue is twofold: The auditory-thalamic pathway is associated with improvements in task performance when the bottom-up input matches the auditory cue, whereas the ventral auditory-prefrontal pathway is involved when the auditory cue is violated.

Specific serum autoantibodies predict the development and progression of Alzheimer's disease with high accuracy.

Autoimmunity plays a key role in the pathogenesis of Alzheimer's disease (AD). However, whether autoantibodies in peripheral blood can be used as biomarkers for AD has been elusive. Serum samples were obtained from 1,686 participants, including 767 with AD, 146 with mild cognitive impairment (MCI), 255 with other neurodegenerative diseases, and 518 healthy controls. Specific autoantibodies were measured using a custom-made immunoassay. Multivariate support vector machine models were employed to investigate the correlation between serum autoantibody levels and disease states. As a result, seven candidate AD-specific autoantibodies were identified, including MAPT, DNAJC8, KDM4D, SERF1A, CDKN1A, AGER, and ASXL1. A classification model with high accuracy (area under the curve (AUC) = 0.94) was established. Importantly, these autoantibodies could distinguish AD from other neurodegenerative diseases and out-performed amyloid and tau protein concentrations in cerebrospinal fluid in predicting cognitive decline (P < 0.001). This study indicated that AD onset and progression are possibly accompanied by an unappreciated serum autoantibody response. Therefore, future studies could optimize its application as a convenient biomarker for the early detection of AD.

Tumor-derived small extracellular vesicles facilitate omental metastasis of ovarian cancer by triggering activation of mesenchymal stem cells.

BACKGROUND: Omental metastasis is the major cause of ovarian cancer recurrence and shortens patient survival, which can be largely attributed to the dynamic evolution of the fertile metastatic microenvironment driven by cancer cells. Previously, we found that adipose-derived mesenchymal stem cells (ADSCs) undergoing a phenotype shift toward cancer-associated fibroblasts (CAFs) participated in the orchestrated omental premetastatic niche for ovarian cancer. Here, we aim to elucidate the underlying mechanisms. METHODS: Small extracellular vesicles were isolated from ovarian cancer cell lines (ES-2 and its highly metastatic subline, ES-2-HM) and patient ascites using ultracentrifugation. Functional experiments, including Transwell and EdU assays, and molecular detection, including Western blot, immunofluorescence, and RT-qPCR, were performed to investigate the activation of ADSCs in vitro. High-throughput transcriptional sequencing and functional assays were employed to identify the crucial functional molecules inducing CAF-like activation of ADSCs and the downstream effector of miR-320a. The impact of extracellular vesicles and miR-320a-activated ADSCs on tumor growth and metastasis was assessed in subcutaneous and orthotopic ovarian cancer xenograft mouse models. The expression of miR-320a in human samples was evaluated using in situ hybridization staining. RESULTS: Primary human ADSCs cocultured with small extracellular vesicles, especially those derived from ES-2-HM, exhibited boosted migration, invasion, and proliferation capacities and elevated α-SMA and FAP levels. Tumor-derived small extracellular vesicles increased α-SMA-positive stromal cells, fostered omental metastasis, and shortened the survival of mice harboring orthotopic ovarian cancer xenografts. miR-320a was abundant in highly metastatic cell-derived extracellular vesicles, evoked dramatic CAF-like transition of ADSCs, targeted the 3'-untranslated region of integrin subunit alpha 7 and attenuated its expression. miR-320a overexpression in ovarian cancer was associated with omental metastasis and shorter survival. miR-320a-activated ADSCs facilitated tumor cell growth and omental metastasis. Depletion of integrin alpha 7 triggered CAF-like activation of ADSCs in vitro. Video Abstract CONCLUSIONS: miR-320a in small extracellular vesicles secreted by tumor cells targets integrin subunit alpha 7 in ADSCs and drives CAF-like activation, which in turn facilitates omental metastasis of ovarian cancer.

GGC expansions in NOTCH2NLC contribute to Parkinson disease and dopaminergic neuron degeneration.

BACKGROUND AND PURPOSE: The role of GGC repeat expansions within NOTCH2NLC in Parkinson's disease (PD) and the substantia nigra (SN) dopaminergic neuron remains unclear. Here, we profile the NOTCH2NLC GGC repeat expansions in a large cohort of patients with PD. We also investigate the role of GGC repeat expansions within NOTCH2NLC in the dopaminergic neurodegeneration of SN. METHODS: A total of 2,522 patients diagnosed with PD and 1,085 health controls were analyzed for the repeat expansions of NOTCH2NLC by repeat-primed PCR and GC-rich PCR assay. Furthermore, the effects of GGC repeat expansions in NOTCH2NLC on dopaminergic neurons were investigated by using recombinant adeno-associated virus (AAV)-mediated overexpression of NOTCH2NLC with 98 GGC repeats in the SN of mice by stereotactic injection. RESULTS: Four PD pedigrees (4/333, 1.2%) and three sporadic PD patients (3/2189, 0.14%) were identified with pathogenic GGC repeat expansions (larger than 60 GGC repeats) in the NOTCH2NLC gene, while eight PD patients and one healthy control were identified with intermediate GGC repeat expansions ranging from 41 to 60 repeats. No significant difference was observed in the distribution of intermediate NOTCH2NLC GGC repeat expansions between PD cases and controls (Fisher's exact test p-value = 0.29). Skin biopsy showed P62-positive intranuclear NOTCH2NLC-polyGlycine (polyG) inclusions in the skin nerve fibers of patient. Expanded GGC repeats in NOTCH2NLC produced widespread intranuclear and perinuclear polyG inclusions, which led to a severe loss of dopaminergic neurons in the SN. Consistently, polyG inclusions were presented in the SN of EIIa-NOTCH2NLC-(GGC)98 transgenic mice and also led to dopaminergic neuron loss in the SN. CONCLUSIONS: Overall, our findings provide strong evidence that GGC repeat expansions within NOTCH2NLC contribute to the pathogenesis of PD and cause degeneration of nigral dopaminergic neurons.

Selective N-Alkylation of Aminobenzenesulfonamides with Alcohols for the Synthesis of Amino-(N-alkyl)benzenesulfonamides Catalyzed by a Metal-Ligand Bifunctional Ruthenium Catalyst.

[(p-Cymene)Ru(2,2'-bpyO)(H2O)] was proven to be an efficient catalyst for the synthesis of amino-(N-alkyl)benzenesulfonamides via selective N-alkylation of aminobenzenesulfonamides with alcohols. It was confirmed that functional groups in the bpy ligand are crucial for the activity of catalysts. Furthermore, the utilization of this catalytic system for the preparation of a biologically active compound was presented.

Task demands modulate pre-stimulus alpha frequency and sensory template during bistable apparent motion perception.

Despite ambiguous environmental inputs, top-down attention biases our subjective perception toward the preferred percepts, via modulating prestimulus neural activity or inducing prestimulus sensory templates that carry concrete internal sensory representations of the preferred percepts. In contrast to frequent changes of behavioral goals in the typical cue-target paradigm, human beings are often engaged in a prolonged task state with only 1 specific behavioral goal. It remains unclear how prestimulus neural signals and sensory templates are modulated in the latter case. To answer this question in the present electroencephalogram study on human subjects, we manipulated sustained task demands toward one of the 2 possible percepts in the bistable Ternus display, emphasizing either temporal integration or segregation. First, the prestimulus peak alpha frequency, which gated the temporal window of temporal integration, was effectively modulated by task demands. Furthermore, time-resolved decoding analyses showed that task demands biased neural representations toward the preferred percepts after the full presentation of bottom-up stimuli. More importantly, sensory templates resembling the preferred percepts emerged even before the bottom-up sensory evidence were sufficient enough to induce explicit percepts. Taken together, task demands modulate both prestimulus alpha frequency and sensory templates, to eventually bias subjective perception toward the preferred percepts.

Pre-stimulus gamma power in human posteromedial cortex shows supra-modal mechanisms in predicting the amplitude and latency of task-induced suppression.

Upon repetitively performing the same well-practiced task on identical bottom-up stimuli, our performance still varies. Although it has been well documented that elevated pre-stimulus baseline activity in the human default-mode network impairs the subsequent task performance, it remains unknown (i) the fine-grained temporal dynamics and (ii) whether the underlying neural dynamics are supra-modal or modality-specific. We utilized intracranial recordings in the human posteromedial cortex (PMC) during a simple visual and an auditory detection task. Our findings suggested that the pre-stimulus gamma power in PMC predicted the subsequent task performance. Critically, the higher the pre-stimulus gamma power, the longer it took for it to be suppressed, and the less suppressed it was during the task performance, which eventually resulted in deleterious effects on task performance, i.e. longer reaction times. These fine-grained temporal dynamics were consistent between the visual and auditory simple detection task. In addition, a direct comparison between the visual and auditory modality showed that the between-modality difference emerged during the recovery period from the maximal gamma suppression back to the baseline. Taken together, the present results contribute novel spatio-temporal mechanisms in human PMC on how simple detection performance varies across multiple repetitions, irrespective of the sensory modality involved.

Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project.

BACKGROUND: Objective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project. METHODS: The research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain. CONCLUSION: Pilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Peripheral extracellular vesicles in neurodegeneration: pathogenic influencers and therapeutic vehicles.

Neurodegenerative diseases (NDDs) such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis epitomize a class of insidious and relentless neurological conditions that are difficult to cure. Conventional therapeutic regimens often fail due to the late onset of symptoms, which occurs well after irreversible neurodegeneration has begun. The integrity of the blood-brain barrier (BBB) further impedes efficacious drug delivery to the central nervous system, presenting a formidable challenge in the pharmacological treatment of NDDs. Recent scientific inquiries have shifted focus toward the peripheral biological systems, investigating their influence on central neuropathology through the lens of extracellular vesicles (EVs). These vesicles, distinguished by their ability to breach the BBB, are emerging as dual operatives in the context of NDDs, both as conveyors of pathogenic entities and as prospective vectors for therapeutic agents. This review critically summarizes the burgeoning evidence on the role of extracerebral EVs, particularly those originating from bone, adipose tissue, and gut microbiota, in modulating brain pathophysiology. It underscores the duplicity potential of peripheral EVs as modulators of disease progression and suggests their potential as novel vehicles for targeted therapeutic delivery, positing a transformative impact on the future landscape of NDD treatment strategies. Search strategy A comprehensive literature search was conducted using PubMed, Web of Science, and Scopus from January 2000 to December 2023. The search combined the following terms using Boolean operators: "neurodegenerative disease" OR "Alzheimer's disease" OR "Parkinson's disease" OR "Amyotrophic lateral sclerosis" AND "extracellular vesicles" OR "exosomes" OR "outer membrane vesicles" AND "drug delivery systems" AND "blood-brain barrier". MeSH terms were employed when searching PubMed to refine the results. Studies were included if they were published in English, involved human subjects, and focused on the peripheral origins of EVs, specifically from bone, adipose tissue, and gut microbiota, and their association with related diseases such as osteoporosis, metabolic syndrome, and gut dysbiosis. Articles were excluded if they did not address the role of EVs in the context of NDDs or did not discuss therapeutic applications. The titles and abstracts of retrieved articles were screened using a dual-review process to ensure relevance and accuracy. The reference lists of selected articles were also examined to identify additional relevant studies.

Ozone pollution in the North China Plain spreading into the late-winter haze season.

Surface ozone is a severe air pollution problem in the North China Plain, which is home to 300 million people. Ozone concentrations are highest in summer, driven by fast photochemical production of hydrogen oxide radicals (HOx) that can overcome the radical titration caused by high emissions of nitrogen oxides (NOx) from fuel combustion. Ozone has been very low during winter haze (particulate) pollution episodes. However, the abrupt decrease of NOx emissions following the COVID-19 lockdown in January 2020 reveals a switch to fast ozone production during winter haze episodes with maximum daily 8-h average (MDA8) ozone concentrations of 60 to 70 parts per billion. We reproduce this switch with the GEOS-Chem model, where the fast production of ozone is driven by HOx radicals from photolysis of formaldehyde, overcoming radical titration from the decreased NOx emissions. Formaldehyde is produced by oxidation of reactive volatile organic compounds (VOCs), which have very high emissions in the North China Plain. This remarkable switch to an ozone-producing regime in January-February following the lockdown illustrates a more general tendency from 2013 to 2019 of increasing winter-spring ozone in the North China Plain and increasing association of high ozone with winter haze events, as pollution control efforts have targeted NOx emissions (30% decrease) while VOC emissions have remained constant. Decreasing VOC emissions would avoid further spreading of severe ozone pollution events into the winter-spring season.

Effect of biopolymer chitosan on manganese immobilization improvement by microbial­induced carbonate precipitation.

Microbially induced carbonate precipitation (MICP), as an eco-friendly and promising technology that can transform free metal ions into stable precipitation, has been extensively used in remediation of heavy metal contamination. However, its depressed efficiency of heavy metal elimination remains in question due to the inhibition effect of heavy metal toxicity on bacterial activity. In this work, an efficient, low-cost manganese (Mn) elimination strategy by coupling MICP with chitosan biopolymer as an additive with reduced treatment time was suggested, optimized, and implemented. The influences of chitosan at different concentrations (0.01, 0.05, 0.10, 0.15 and 0.30 %, w/v) on bacterial growth, enzyme activity, Mn removal efficiency and microstructure properties of the resulting precipitation were investigated. Results showed that Mn content was reduced by 94.5 % within 12 h with 0.15 % chitosan addition through adsorption and biomineralization as MnCO3 (at an initial Mn concentration of 3 mM), demonstrating a two-thirds decrease in remediation time compared to the chitosan-absent system, whereas maximum urease activity increased by ∼50 %. Microstructure analyses indicated that the mineralized precipitates were spherical-shaped MnCO3, and a smaller size and more uniform distribution of MnCO3 is obtained by the regulation of abundant amino and hydroxyl groups in chitosan. These results demonstrate that chitosan accelerates nucleation and tunes the growth of MnCO3 by providing nucleation sites for mineral formation and alleviating the toxicity of metal ions, which has the potential to upgrade MICP process in a sustainable and effective manner. This work provides a reference for further understanding of the biomineralization regulation mechanism, and gives a new perspective into the application of biopolymer-intensified strategies of MICP technology in heavy metal contamination.

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices.

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis.

INTRODUCTION: The cognitive impairment patterns and the association with Alzheimer's disease (AD) in mental disorders remain poorly understood. METHODS: We analyzed data from 486,297 UK Biobank participants, categorizing them by mental disorder history to identify the risk of AD and the cognitive impairment characteristics. Causation was further assessed using Mendelian randomization (MR). RESULTS: AD risk was higher in individuals with bipolar disorder (BD; hazard ratio [HR] = 2.37, P < 0.01) and major depressive disorder (MDD; HR = 1.63, P < 0.001). MR confirmed a causal link between BD and AD (ORIVW = 1.098), as well as obsessive-compulsive disorder (OCD) and AD (ORIVW = 1.050). Cognitive impairments varied, with BD and schizophrenia showing widespread deficits, and OCD affecting complex task performance. DISCUSSION: Observational study and MR provide consistent evidence that mental disorders are independent risk factors for AD. Mental disorders exhibit distinct cognitive impairment prior to dementia, indicating the potential different mechanisms in AD pathogenesis. Early detection of these impairments in mental disorders is crucial for AD prevention. HIGHLIGHTS: This is the most comprehensive study that investigates the risk and causal relationships between a history of mental disorders and the development of Alzheimer's disease (AD), alongside exploring the cognitive impairment characteristics associated with different mental disorders. Individuals with bipolar disorder (BD) exhibited the highest risk of developing AD (hazard ratio [HR] = 2.37, P < 0.01), followed by those with major depressive disorder (MDD; HR = 1.63, P < 0.001). Individuals with schizophrenia (SCZ) showed a borderline higher risk of AD (HR = 2.36, P = 0.056). Two-sample Mendelian randomization (MR) confirmed a causal association between BD and AD (ORIVW = 1.098, P < 0.05), as well as AD family history (proxy-AD, ORIVW = 1.098, P < 0.001), and kept significant after false discovery rate correction. MR also identified a nominal significant causal relationship between the obsessive-compulsive disorder (OCD) spectrum and AD (ORIVW = 1.050, P < 0.05). Individuals with SCZ, BD, and MDD exhibited impairments in multiple cognitive domains with distinct patterns, whereas those with OCD showed only slight declines in complex tasks.

A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort.

INTRODUCTION: Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear. METHODS: One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed. RESULTS: A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181) level. A model based on dual-task gait, dual-task smooth pursuit, prosaccade, and anti-saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p-tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824. DISCUSSION: Combining dual-task gait and dual-task eye-tracking analysis is feasible for the detection of CI. HIGHLIGHTS: This is the first study to report the efficiency of integrated parameters of dual-task gait and eye-tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye-tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual-task gait, smooth pursuit, prosaccade, and anti-saccade, achieving the best area under the curve of 0.987 for CI detection.

Body mass index and pressure injuries risk in hospitalized adult patients: A dose-response analysis.

BACKGROUND: The association between underweight and pressure injuries (PIs) has been established in several studies. However, there is a lack of well-designed research investigating the connection between overweight and obesity with these injuries. OBJECTIVE: This meta-analysis aims to investigate the dose-response relationship between body mass index (BMI) and the risk of PIs in adult hospitalized patients. METHODS: PubMed, Web of Science, and MEDLINE Databases were searched from inception to May 2024. Observational articles with at least three BMI categories were included in the study. BMI was defined as underweight, normal weight, overweight, and morbid obesity for the meta-analysis. The non-linear relationship between BMI and the risk of PIs in hospitalized adults was investigated using restricted cubic spline models. Fractional polynomial modeling was used. RESULTS: Eleven articles reporting at least 3 categories of BMI met the inclusion criteria, including 31,389 participants. Compared to patients with normal weight, those with underweight, obesity, and morbid obesity exhibited an increased risk of PIs, with odds ratios of 1.70 (95%CI:1.50-1.91), 1.12 (95%CI:1.02-1.24), 1.70 (95%CI:1.13-2.55), respectively. A J-shaped dose-response model was established for the relationship between PI risk and BMI (Pnon-linearity < 0.001, Plinearity = 0.745). CONCLUSION: The J-shaped dose-response pattern revealed that underweight, obesity and morbid obesity heightened the risk of PIs in hospitalized adults. Lower and higher BMI values may signify an increased risk for PIs, particularly among the elderly with lower BMI, providing valuable guidance for medical staff.

An integrated analysis identifies six molecular subtypes of pancreatic ductal adenocarcinoma revealing cellular and molecular landscape.

Pancreatic ductal adenocarcinoma (PDA) has been found to have a high mortality rate. Despite continuous efforts, current histopathological classification is insufficient to guide individualized therapies of PDA. We first define the molecular subtypes of PDA (MSOP) based on a meta-cohort of 845 samples from 11 PDA datasets. We then performed functional analyses involving immunity, fibrosis and metabolism. We recognized six molecular subtypes with different survival statistics and molecular composition. The squamous basal-like (SBL) subtype had a poor prognosis and high infiltration of ENO1+ (Enolase 1)/ADM+ (Adrenomedullin) cancer-associated fibroblasts (CAFs). The immune mesenchymal-like (IML) subtype and the normal mesenchymal-like (NML) subtype were characterized by genes associated with extracellular matrix (ECM) activities and immune responses, having favorable prognoses. IML was featured by elevated exhausted immune signaling and inflammatory CAFs infiltration, whereas NML was featured with myofibroblastic CAFs infiltration. The exocrine-like (EL) subtype was high in exocrine signals, while the pure classical-like (PCL) subtype lacked immunocytes infiltration. The quiescent-like (QL) subtype had diminished metabolic signaling and high infiltration of NK cells. SBL, IML and NML were enriched in innate anti-PD-1 resistance signatures. In sum, this MSOP depicts a vivid cell-to-molecular atlas of the tumor microenvironment of PDA and might facilitate to design a precise combination of therapies that target immunity, metabolism and stroma.

Downregulation of TOP2 modulates neurodegeneration caused by GGGGCC expanded repeats.

GGGGCC repeats in a non-coding region of the C9orf72 gene have been identified as a major genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. We previously showed that the GGGGCC expanded repeats alone were sufficient to cause neurodegeneration in Drosophila. Recent evidence indicates that GGGGCC expanded repeats can modify various gene transcriptomes. To determine the role of these genes in GGGGCC-mediated neurotoxicity, we screened an established Drosophila model expressing GGGGCC expanded repeats in this study. Our results showed that knockdown of the DNA topoisomerase II (Top2) gene can specifically modulate GGGGCC-associated neurodegeneration of the eye. Furthermore, chemical inhibition of Top2 or siRNA-induced Top2 downregulation could alleviate the GGGGCC-mediated neurotoxicity in Drosophila assessed by eye neurodegeneration and locomotion impairment. By contrast, upregulated Top2 levels were detected in Drosophila strains, and moreover, TOP2A level was also upregulated in Neuro-2a cells expressing GGGGCC expanded repeats, as well as in the brains of Sod1G93A model mice. This indicated that elevated levels of TOP2A may be involved in a pathway common to the pathophysiology of distinct ALS forms. Moreover, through RNA-sequencing, a total of 67 genes, involved in the pathways of intracellular signaling cascades, peripheral nervous system development, and others, were identified as potential targets of TOP2A to modulate GGGGCC-mediated neurodegeneration.