The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or ...death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.
Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent ...scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved.
Risk stratification tools are useful to provide appropriate clinical care for older patients with pneumonia. This study aimed to compare a Frailty Index (FI) with pneumonia severity measures, ...CURB-65, and the Pneumonia Severity Index (PSI), for predicting mortality and persistent disability after pneumonia.
Single-center prospective cohort study.
The study included 190 patients aged ≥65 years who were hospitalized with pneumonia at a university hospital in Korea between October 2019 and September 2020.
At admission, a 50-item deficit-accumulation FI (range: 0-1), CURB-65 (range: 0-5), and PSI (range: 0-395) scores were calculated. The outcomes were death and a composite outcome of death or decline in ability to perform daily activities and physical task 6 months later.
The median age was 79 years (interquartile range: 74-85), and 70 (36.8%) patients were women. The patients who died (n = 53) had higher FI (median, 0.46 vs 0.20; P < .011), CURB-65 score (median, 3 vs 2; P = .001), and PSI score (median, 149 vs 116; P < .001) than those who did not. The C-statistics (95% confidence intervals) for 6-month mortality were 0.69 (0.61-0.77) for the FI, 0.62 (0.53-0.71) for CURB-65, and 0.71 (0.62-0.79) for the PSI (P = .019). The C-statistics for the 6-month composite outcome were 0.73 (0.65-0.81) for the FI, 0.64 (0.55-0.73) for CURB-65, and 0.69 (0.60-0.77) for the PSI (P = .096). The C-statistics improved when the FI was added to CURB-65 (from 0.64 to 0.74; P = .003) and to the PSI (from 0.69 to 0.75; P = .044) for the composite outcome.
Measuring frailty provides additive value to widely used pneumonia severity measures in predicting death or persistent hospitalization-associated disability in older adults after pneumonia hospitalization. Early recognition of frailty may be useful to identify those who require in-hospital and post-acute care interventions for functional recovery.
This work presents a hashing-based algorithm for Private Set Intersection (PSI) in the honest-but-curious setting. The protocol is generic, modular and provides both asymptotic and concrete ...efficiency improvements over existing PSI protocols. If each player has m elements, our scheme requires only $O(m \secpar)$ communication between the parties, where $\secpar$ is a security parameter. Our protocol builds on the hashing-based PSI protocol of Pinkas et al. (USENIX 2014, USENIX 2015), but we replace one of the sub-protocols (handling the cuckoo "stash'') with a special-purpose PSI protocol that is optimized for comparing sets of unbalanced size. This brings the asymptotic communication complexity of the overall protocol down from $ømega(m \secpar)$ to $O(m\secpar)$, and provides concrete performance improvements (10-15% reduction in communication costs) over Kolesnikov et al. (CCS 2016) under real-world parameter choices. Our protocol is simple, generic and benefits from the permutation-hashing optimizations of Pinkas et al. (USENIX 2015) and the Batched, Relaxed Oblivious Pseudo Random Functions of Kolesnikov et al. (CCS 2016).
To improve the identification and subsequent intervention of COVID-19 patients at risk for ICU admission, we constructed COVID-19 severity prediction models using logistic regression and artificial ...neural network (ANN) analysis and compared them with the four existing scoring systems (PSI, CURB-65, SMARTCOP, and MuLBSTA). In this prospective multi-center study, 296 patients with COVID-19 pneumonia were enrolled and split into the General-Ward-Care group (N = 238) and the ICU-Admission group (N = 58). The PSI model (AUC = 0.861) had the best results among the existing four scoring systems, followed by SMARTCOP (AUC = 0.770), motified-MuLBSTA (AUC = 0.761), and CURB-65 (AUC = 0.712). Data from 197 patients (training set) were analyzed for modeling. The beta coefficients from logistic regression were used to develop a severity prediction model and risk score calculator. The final model (NLHA2) included five covariates (consumes alcohol, neutrophil count, lymphocyte count, hemoglobin, and AKP). The NLHA2 model (training: AUC = 0.959; testing: AUC = 0.857) had similar results to the PSI model, but with fewer variable items. ANN analysis was used to build another complex model, which had higher accuracy (training: AUC = 1.000; testing: AUC = 0.907). Discrimination and calibration were further verified through bootstrapping (2000 replicates), Hosmer-Lemeshow goodness of fit testing, and Brier score calculation. In conclusion, the PSI model is the best existing system for predicting ICU admission among COVID-19 patients, while two newly-designed models (NLHA2 and ANN) performed better than PSI, and will provide a new approach for the development of prognostic evaluation system in a novel respiratory viral epidemic.
•PSI can effectively predict the severity risk of adult COVID-19 patients.•The NLHA2 model with only five variables had a similar predictive effect as PSI scoring.•The prediction model constructed by artificial neural network algorithm performed better than models.•Deep learning algorithms have great potential in clinical prediction.
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The aim of the present study was to improve the accuracy and reliability of ORIF in patients with condylar head fractures (CHFs) by developing a design for patient specific fixators, navigation and ...repositioning guides, as well as the algorithms of their clinical application.
14 patients with 16 CHFs were treated by ORIF with the use of CAD/CAM technology. After virtual reduction of the bony fragments, the appropriate length and diameter of the screws was chosen. In biomechanically unfavorable cases (type p) patient specific reinforcement plates were used together with the positional screws for reinforcement of the bone-fixator system. And in cases of severely comminuted fractures patient specific 3-D plate was applied.
The CT data, obtained immediately after the operation revealed the good anatomical reduction. Any deviations of the small fragments noted were near 1 mm in all cases. Postoperative clinical examination at 3 months follow up showed good occlusion and mouth opening not less than 3 cm in all patients. The lateral and anterior mobility of the mandible was restored with small limitations of protrusive mobility in 1 case. All the patients were satisfied with the outcomes.
The application of the CAD/CAM technologies and the new design of the surgical guides and patient specific reinforcement plates for CHFs helps to improve the accuracy and quality of fragments reduction and stability of fixation with minimal risks of intraoperative complications.
CAD/CAM technologies improve the clinical effectiveness of treatment patients with the CHFs.
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•GoFDR predicts GO functions using sequence alignment prepared from PSI-BLAST.•GoFDR identifies the Functional Discriminating Residues for each target GO.•GoFDR applies score-probability table to ...convert raw scores to probabilities.•GoFDR ranked one of the best methods in CAFA2 experiment.
In this study, we developed a method named GoFDR for predicting Gene Ontology (GO)-based protein functions. The input for GoFDR is simply a query sequence-based multiple sequence alignment (MSA) produced by PSI-BLAST. For each GO term annotated to the sequences in the MSA, GoFDR identifies a number of functionally discriminating residues (FDRs) specific to the GO term, and scores the query sequence using a position specific scoring matrix (PSSM) constructed for the FDRs. The raw score is then converted into a probability score according to a score-to-probability table prepared from training sequences. GoFDR outperformed three sequence-based methods for predicting GO functions in a benchmark of 18,520 sequences. In addition, GoFDR was ranked one of the top methods according to the preliminary evaluation report released by the 2nd Critical Assessment of Function Annotation (CAFA2) project. Finally, we applied GoFDR to the complete human proteome sequences, and showed that the predictions made by GoFDR with high confidence significantly expanded current annotations of human proteome. As such, GoFDR is of great value not only for annotating protein functions in newly sequenced genomes, but also for characterizing the function of proteins of interest.
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As an indispensable component of various living organisms, the antioxidant proteins have been studied for anti-aging and prevention of various diseases, such as altitude sickness, coronary heart ...disease, and even cancer. However, the traditional experimental methods for identifying the antioxidant proteins are very expensive and time-consuming. Thus, to address the challenge, a new predictor, named ANOX, was developed in this study. Multiple features, such as frequency matrix features (FRE), amino acid and dipeptide composition (AADP), evolutionary difference formula features (EEDP), k-separated bigrams (KSB), and PSI-PRED secondary structure (PRED), were extracted to generate the original feature space. To find the optimized feature subset, the Max-Relevance-Max-Distance (MRMD) algorithm was implemented for feature ranking and our model received the best performance with the top 1170 features. Rigorous tests were performed to evaluate the performance of ANOX, and the results showed that ANOX achieved a major improvement in the prediction accuracy of the antioxidant proteins (AUC:0.930 and 0.935 using 5-fold cross-validation or the jackknife test) compared to the state-of-the-art predictor AOPs-SVM (AUC:0.869 and 0.885). The dataset used in this study and the source code of ANOX are all available at https://github.com/NWAFU-LiuLab/ANOX.
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•An SVM-based model was proposed to identify the antioxidant proteins.•Multiple features were extracted to generate the original feature space.•The Max-Relevance-Max-Distance algorithm is implemented for feature selection.•Our model performs better than the state-of-the art predictor.
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•Improvisers showed distinct network connectivity when categorizing musical structures.•Improvisers showed inter-network connectivity in the α-band before a motor-response.•Connectivity between DMN ...and VN was strongly linked to improvisation expertise.•More experienced musicians showed stronger connectivity between DMN, DAN and CCN.
Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.
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