Rhodesmullins7873
Ischemic stroke (IS) is one of the leading causes of morbidity and mortality worldwide. Circulating microRNAs have a potential as minimally invasive biomarkers for disease prediction, diagnosis, and prognosis. In this study, we sought to use different machine learning algorithms to identify an optimal model of microRNA by integrating the expression data of pre-selected microRNAs for discriminating patients with IS from controls.
The expression level of microRNAs in the peripheral blood of 50 patients with IS and 50 matched controls were assessed through real-time polymerase chain reaction (qRT-PCR). Machine learning algorithms, including artificial neural network, random forest, extreme gradient boosting, and support vector machine (SVM) were employed via R 3.6.3 software to establish diagnostic models for IS.
The IS group had significantly increased expression levels of miR-19a (P<0.001), miR-148a (P<0.001), miR-320d (P=0.003), and miR-342-3p (P<0.001) compared with the control group. MiR-148a, miR-342-3p, miR-19a, and miR-320d yielded areas under the receiver operating characteristic curve (AUC) of 0.872, 0.844, 0.721, and 0.673, respectively, with 0.740, 0.940, 0.740, and 0.840 sensitivity and 0.920, 0.640, 0.600, and 0.440 specificity, respectively. Model miR-148a+miR-342-3p+miR-19a had the best predictive value when analyzed via SVM algorithm with AUC, sensitivity, and specificity values of 0.958, 0.937, and 0.889, respectively.
The diagnostic value of the combination of miR-148a, miR-342-3p, and miR-19a through SVM algorithm has the potential to serve as a feasible approach to promote the diagnosis of IS.
The diagnostic value of the combination of miR-148a, miR-342-3p, and miR-19a through SVM algorithm has the potential to serve as a feasible approach to promote the diagnosis of IS.
Machine learning (ML) techniques are being increasingly adopted in the medical field.
We developed a deep neural network (DNN) model and applied 2 well-known ML algorithms, logistic regression and random forest, in predicting motor outcome at 6 months after stroke.
In the present study, by using 14 input variables which are easily measured by clinicians, we developed ML models and investigated their applicability to predicting motor outcome in hemiplegic stroke patients. We retrospectively analyzed data of 1,056 consecutive stroke patients. Favorable outcomes of the upper and lower limbs were defined as a modified Brunnstrom classification (MBC) score of ≥5 (able to perform activities of daily living with the affected upper limb) and a functional ambulation category (FAC) score of ≥4 (able to walk without guardian's assistance), respectively. Poor outcomes of the upper and lower limbs were defined as MBC and FAC scores of <5 and <4, respectively. We developed 3 ML algorithms, namely the DNN, logistic regression, and random forest.
Regarding the prediction of upper limb function, for the DNN model, the area under the curve (AUC) was 0.906. For the logistic regression and random forest models, the AUC were 0.874 and 0.882, respectively. For the prediction of lower limb function, for the DNN, logistic regression, and random forest models, the AUCs were 0.822, 0.768, and 0.802, respectively.
We demonstrated that the ML algorithms, particularly the DNN, can be useful for predicting motor outcomes in the upper and lower limbs at 6 months after stroke.
We demonstrated that the ML algorithms, particularly the DNN, can be useful for predicting motor outcomes in the upper and lower limbs at 6 months after stroke.
To characterize differences in disposition arrangement among rehab-eligible stroke patients at a Comprehensive Stroke Center before and during the COVID-19 pandemic.
We retrospectively analyzed a prospective registry for demographics, hospital course, and discharge dispositions of rehab-eligible acute stroke survivors admitted 6 months prior to (10/2019-03/2020) and during (04/2020-09/2020) the COVID-19 pandemic. The primary outcome was discharge to an inpatient rehabilitation facility (IRF) as opposed to other facilities using descriptive statistics, and IRF versus home using unadjusted and adjusted backward stepwise logistic regression.
Of the 507 rehab-eligible stroke survivors, there was no difference in age, premorbid disability, or stroke severity between study periods (p>0.05). There was a 9% absolute decrease in discharges to an IRF during the pandemic (32.1% vs. 41.1%, p=0.04), which translated to 38% lower odds of being discharged to IRF versus home in unadjusted regression (OR 0.62, 95%CI 0.42-0.92, p=0.016). The lower odds of discharge to IRF persisted in the multivariable model (aOR 0.16, 95%CI 0.09-0.31, p<0.001) despite a significant increase in discharge disability (median discharge mRS 4 [IQR 2-4] vs. 2 [IQR 1-3], p<0.001) during the pandemic.
Admission for stroke during the COVID-19 pandemic was associated with a significantly lower probability of being discharged to an IRF. This effect persisted despite adjustment for predictors of IRF disposition, including functional disability at discharge. Potential reasons for this disparity are explored.
Admission for stroke during the COVID-19 pandemic was associated with a significantly lower probability of being discharged to an IRF. This effect persisted despite adjustment for predictors of IRF disposition, including functional disability at discharge. Potential reasons for this disparity are explored.
Xerostomia is one of the multidimensional effects of cancer therapy that can impact on patients' quality of life. This study aims to evaluate the reliability and validity properties of the xerostomia questionnaire.
The data were retrieved from a randomized, interventional and longitudinal follow-up study of 72 head and neck cancer patients on active oral radiotherapy. Data were retrieved by the Xerostomia Questionnaire, the EORTC-QLQ C30 and EORT QLQ HN35. Data analyzes included intrinsic credibility of affinity Cronbach alpha factor, which is related to validity and cross-sectional validity.
The Cronbach's alpha index of the Greek version of the questionnaire was high. Explicitly, the total scale of the questionnaire had an α=0.97, and the individual dimensions - Degree of dryness and the quality of life had an alpha of 0.97 and 0.98 respectively. The xerostomia dimension of the questionnaire has a high positive correlation with QLQ C30 overall (r=0.61, p<0.001), and with the HN35 xerostomia dimension (r=0.63, p<0.001) which demonstrate its' concurrent validity. The dimensions of the xerostomia questionnaire were highly correlated with the EORTC-QLQ C30 dimensions which supports its' predictive validity. The level of xerostomia and the quality of life varied according to the level of pain of the patients (p<0.001) that support the cross-sectional validity of the questionnaire.
The Greek version of the xerostomia questionnaire is a reliable and valid tool for use in head and neck cancer patients on active oral radiotherapy.
The Greek version of the xerostomia questionnaire is a reliable and valid tool for use in head and neck cancer patients on active oral radiotherapy.Since the high cost and low power generation hinder the overall practical application of microbial fuel cells (MFCs), numerous attempts have been made in the field of cathode materials to enhance the electrical performance of MFCs because they directly catalyze the oxygen reduction reactions (ORR). To choose a proper cathode material, following principles such as ORR activity, conductivity, cost-efficiency, durability, surface area, and accessibility should be taken into consideration. In preparation of cathode materials, versatile materials have been chosen, synthesized, or modified to achieve an improvement in power generation of MFCs. The most widely applied cathode materials could be categorized into three classes, namely carbon-base materials, metal-based materials, and biocatalysts. This review summarizes the utilization, development, and the cost of cathode materials applied in MFCs and tries to highlight the effective modification methods of cathode materials which have helped in achieving enhanced power generation of MFCs in recent years.The disposal of large amounts of biogas residue from anaerobically digested waste is a burden on environment protection. iCRT3 clinical trial Porous biochars (BCs) were synthesized from biogas residue at three pyrolysis temperatures (300 °C, 550 °C, and 800 °C) and used to catalyze H2O2 for the degradation of benzene, toluene, ethylbenzene, xylene isomers (ortho, para, and meta), and styrene (BTEXS) to develop a new use for biogas residues. The prepared BCs were characterized through scanning electron microscopy, Brunauer-Emmett-Teller method, Fourier transform infrared spectrometry, and X-ray photoelectron spectroscopy. Results showed that BC800/H2O2 had the highest BTEXS degradation performance over 6 h. The degradation kinetic data were most consistent with the pseudo-second-order model. The different catalytic effect of the three BCs pyrolyzed at different temperatures were attributed to the dominant active sites (C-O/C-OH/C=C/C=O groups, pyridinic N, and graphitic N) that induced the production of reactive oxygen species (ROS). ROS-quenching experiments indicated that the degradation of BTEXS by BC300/H2O2, BC550/H2O2, and BC800/H2O2 involved ∙OH, ∙O2-, and 1O2. ∙OH was the dominant ROS in BC300/H2O2 and BC550/H2O2, and 1O2 was the dominant ROS in BC800/H2O2. Our findings provided new insight into the different catalytic mechanisms for BC production at different pyrolysis temperatures and demonstrated that a porous BC catalyst with high utilization value could be prepared from biogas residue and could hold considerable potential for application in BTEXS treatment in the future.Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed 'meta-metabolome ecology.' Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering and variations in putative biochemical transformations. We refer to this phenomenon as 'thermodynamic redundancy,' which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable concepts, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics.Anticoagulant rodenticides (ARs) used to control mammalian pest populations cause secondary exposure of predatory species throughout much of the world. It is important to understand the drivers of non-target AR exposure patterns as context for assessing long-term effects and developing effective mitigation for these toxicants. In Australia, however, little is known about exposure and effects of ARs on predators. We detected AR residues in 74% of 50 opportunistically collected carcasses of the Tasmanian wedge-tailed eagle (Aquila audax fleayi), an endangered apex predator. In 22% of birds tested, or 31% of those exposed, liver concentrations of second generation ARs (SGARs) were >0.1 mg/kg ww. Eagles were exposed to flocoumafen, a toxicant only available from agricultural suppliers, at an exceptionally high rate (40% of birds tested). Liver SGAR concentrations were positively associated with the proportion of agricultural habitat and human population density in the area around where each eagle died. The high exposure rate in a species not known to regularly prey upon synanthropic rodents supports the hypothesis that apex predators are vulnerable to SGARs.
