Owensvilhelmsen1663

Short-term (up to one year) beneficial effects of intra-articular viscosupplementation with HA in early primary knee OA can be seen with a decreasing trend in the intensity of pain and an increasing trend in improving the physical functioning and health-related quality of life.

Short-term (up to one year) beneficial effects of intra-articular viscosupplementation with HA in early primary knee OA can be seen with a decreasing trend in the intensity of pain and an increasing trend in improving the physical functioning and health-related quality of life.

For the success of any program, its implementation plays a crucial role. Community health workers are of immense importance for malaria elimination from India.

This study was aimed to assess the knowledge gaps and the responsible factors for mitanins' knowledge on various aspects of and problems faced by mitanins during their work.

Structured interviewer-based questionnaire was used to collect the data, and ordinal regression was applied to analyze the data.

Only 26% of the mitanins were having a good knowledge attitude and practices (KAP) score about malaria. Malaria endemicity of area [odds ratio (OR) = 0.26, 95% CI = 0.13-0.50),

< 0.001] and education (OR = 0.35, 95% CI = 0.18-0.69,

= 0.002) were the two significant factors affecting the KAP of mitanins.

This study shows that prioritizing education while recruiting the mitanins and training them in the low endemic areas with a focus on malaria, which will help achieve the malaria elimination goal.

This study shows that prioritizing education while recruiting the mitanins and training them in the low endemic areas with a focus on malaria, which will help achieve the malaria elimination goal.

Clear cell renal cell carcinoma (ccRCC) is expected in the elderly and poor prognosis. We aim to explore prognostic factors of ccRCC in the elderly and construct a nomogram to predict cancer-specific survival (CSS) in elderly patients with ccRCC.

Clinicopathological information for all elderly patients with ccRCC from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression models were used to identify the independent risk factors for CSS. A new nomogram was constructed to predict CSS at 1-, 3-, and 5 years in elderly patients with ccRCC based on independent risk factors. Subsequently, we used the consistency index (C-index), calibration curves, and the area under the receiver operating curve (AUC) and decision curve analysis (DCA) to test the prediction accuracy of the model.

A total of 33,509 elderly patients with ccRCC were enrcRCC with good accuracy and reliability, providing clinical guidance for patients and physicians.

In this study, we explored prognostic factors in elderly patients with ccRCC. We found that age, sex, marriage, TNM stage, surgery, and tumor size were independent risk factors for CSS. We constructed a new nomogram to predict CSS in elderly patients with ccRCC with good accuracy and reliability, providing clinical guidance for patients and physicians.Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criteria. A recent study has indicated the potential of language analysis for AD diagnosis. In this study, we proposed a novel feature purification network that can improve the representation learning of transformer model further. Though transformer has made great progress in generating discriminative features because of its long-distance reasoning ability, there is still room for improvement. There exist many common features that are not indicative of any specific class, and we rule out the influence of common features from traditional features extracted by transformer encoder and can get more discriminative features for classification. We apply this method to improve transformer's performance on three public dementia datasets and get improved classification results markedly. Specifically, the method on Pitt datasets gets state-of-the-art (SOTA) result.This study was to explore the application of MRI based on artificial intelligence technology combined with neuropsychological assessment to the cognitive impairment of patients with neurological cerebrovascular diseases. A total of 176 patients were divided into a control group, a vascular cognitive impairment non-dementia (VCIND) group, a vascular dementia (VD) group, and an Alzheimer's disease (AD) group. All patients underwent MRI and neuropsychological evaluation and examination, and an improved fuzzy C-means (FCM) clustering algorithm was proposed for MRI processing. It was found that the segmentation accuracy (SA) and similarity (KI) data of the improved FCM algorithm used in this study were higher than those of the standard FCM algorithm, bias-corrected FCM (BCFCM) algorithm, and rough FCM (RFCM) algorithm (p less then 0.05). In the activities of daily living (ADL), the values in the VCIND group (23.55 ± 6.12) and the VD group (28.56 ± 3.1) were higher than that in the control group (19.17 ± 3.67), so the hippocampal volume was negatively correlated with the ADL (r = -0.872, p less then 0.01). In the VCIND group (52.4%), VD group (31%), and AD group (26.1%), the proportion of patients with the lacunar infarction distributed on both sides of the brain and the number of multiple cerebral infarction lesions (76.2, 71.4, and 71.7%, respectively) were significantly higher than those in the control group (23.9 and 50%). In short, the improved FCM algorithm showed a higher segmentation effect and SA for MRI of neurological cerebrovascular disease. In addition, the distribution, number, white matter lesions, and hippocampal volume of lacunar cerebral infarction were related to the cognitive impairment of patients with cerebrovascular diseases.

This study aimed to test empirically whether (1) the local impact of the COVID-19 pandemic was associated with increases in intimate partner aggression (IPA) and heavy drinking, and (2) heavy drinking moderated the association between COVID-19 stress and IPA perpetration.

Participants were 510 individuals (approximately 50% who endorsed a sexual or gender minority identity) recruited via Qualtrics Research Services in April 2020, during the height of shelter-in-place (SiP) restrictions across the United States. They completed a questionnaire battery that included measures of COVID-19 stressors, physical and psychological IPA perpetration, and heavy drinking.

Rates of physical and psychological IPA perpetration significantly increased after implementation of SiP restrictions which aimed to mitigate the transmission of COVID-19. COVID-19 stress was significantly and positively associated with physical and psychological IPA perpetration; however, COVID-19 stress was positively associated with physical IPA perpetration among non-heavy drinking, but not heavy drinking, participants.

Drawn from a large sample of participants of diverse sexual identities, findings tentatively implicate COVID-19 stress as a critical correlate of IPA perpetration and suggest that "low risk" individuals (i.e., non-heavy drinkers) should not be overlooked. These data provide preliminary support for the usefulness of public health polices and individual-level interventions that target stress, heavy drinking, and their antecedents.

Drawn from a large sample of participants of diverse sexual identities, findings tentatively implicate COVID-19 stress as a critical correlate of IPA perpetration and suggest that "low risk" individuals (i.e., non-heavy drinkers) should not be overlooked. These data provide preliminary support for the usefulness of public health polices and individual-level interventions that target stress, heavy drinking, and their antecedents.Since the COVID-19 pandemic, the unprecedented use of facemasks has been requiring for wearing in daily life. By wearing facemask, human exhaled breath aerosols and inhaled environmental exposures can be efficiently filtered and thus various filtration residues can be deposited in facemask. Therefore, facemask could be a simple, wearable, in vivo, onsite and noninvasive sampler for collecting exhaled and inhalable compositions, and gain new insights into human health and environmental exposure. In this review, the recent advances in developments and applications of in vivo facemask sampling of human exhaled bacteria, viruses, proteins, and metabolites, and inhalable facemask contaminants and air pollutants, are reviewed. New features of facemask sampling are highlighted. The perspectives and challenges on further development and potential applications of facemask devices are also discussed.Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra's algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution.The methanol-to-hydrocarbons (MTH) process, commonly catalyzed by zeolites, is of great commercial interest and therefore widely studied both in industry and academia. However, zeolite-based catalyst materials are notoriously hard to study at the nano-scale. Atom probe tomography (APT) is uniquely positioned among the suite of characterization techniques, as it can provide 3D chemical information with sub-nm resolution. learn more In this work, we have used APT to study the nano-scale coking behavior of zeolite SSZ-13 and its relation to bulk coke formation on the macro-/micro-scale studied with operando and in situ UV-vis spectroscopy and microscopy. Radial distribution function analysis (RDF) of the APT data revealed short carbon-carbon length scale affinities, consistent with the formation of larger aromatic molecules (coke species). Using nearest neighbor distribution (NND) analysis, an increase in the homogeneity of carbon was found with increasing time-on-stream. However, carbon clusters could not be isolated due to spatial noise and limited clustering.