Can Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces

The ability to identify whether a user is “zoning out” (mind wandering) from video has many HCI (e.g., distance learning, high-stakes vigilance tasks). However, it remains unknown how well humans can perform this task, how they compare to automatic computerized approaches, and how a fusion of the two might improve accuracy. We analyzed videos of users’ faces and upper bodies recorded 10s prior to self-reported mind wandering (i.e., ground truth) while they engaged in a computerized reading task. We found that a state-of-the-art machine learning model had comparable accuracy to aggregated judgments of nine untrained human observers (area under receiver operating characteristic curve [AUC] = .598 versus .589). A fusion of the two (AUC = .644) outperformed each, presumably because each focused on complementary cues. Furthermore, adding more humans beyond 3–4 observers yielded diminishing returns. We discuss implications of human–computer fusion as a means to improve accuracy in complex tasks.

Serum D-Lactate, a Novel Serological Biomarker, Is Promising for the Diagnosis of Periprosthetic Joint Infection

Background: Although many markers are used for diagnosis of periprosthetic joint infection (PJI), serological screening and diagnosis for PJI are still challenging. We evaluated the performance of serum D-lactate and compared it with ESR, coagulation-related biomarkers and synovial D-lactate for the diagnosis of PJI.Methods: Consecutive patients with preoperative blood and intraoperative joint aspiration of a prosthetic hip or knee joint before revision arthroplasty were prospectively included. The diagnosis of PJI was based on the criteria of the Musculoskeletal Infection Society, and the diagnostic values of markers were estimated based on receiver operating characteristic (ROC) curves by maximizing sensitivity and specificity using optimal cutoff values.Results: Of 52 patients, 26 (50%) were diagnosed with PJI, and 26 (50%) were diagnosed with aseptic failure. ROC curves showed that serum D-lactate, fibrinogen (FIB) and ESR had equal areas under the curve (AUCs) of 0.80, followed by D-dimer and fibrin degradation product, which had AUCs of 0.67 and 0.69, respectively. Serum D-lactate had the highest sensitivity of 88.46% at the optimal threshold of 1.14 mmol/L, followed by FIB and ESR, with sensitivities of 80.77% and 73.08%, respectively, while there were no significant differences in specificity (73.08%, 73.08% and 76.92%, respectively). Conclusion: Serum D-lactate showed similar performance to FIB and ESR for diagnosis of PJI. The advantages of serum D-lactate are pathogen-specific, highly sensitive, minimally invasive and rapidly available making serum D-lactate useful as a point-of-care screening test for PJI.

Prediction of distant metastasis of spinal and pelvic osteosarcoma by constructing and validating nomogram:a retrospective study based on SEER database

Background:Spinal and pelvic osteosarcoma is a rare type of all osteosarcomas,and distant metastasis is an important factor for poor prognosis of this disease. There are no similar studies on prediction of distant metastasis of spinal and pelvic osteosarcoma. We aim to construct and validate a nomogram to predict the risk of distant metastasis of spinal and pelvic osteosarcoma.Methods:We collected the data on patients with spinal and pelvic osteosarcoma from the Surveillance, Epidemiology, and End Results(SEER) database retrospectively. The Kaplan-Meier curve was used to compare differences in survival time between patients with metastasis and non-metastasis. Total patients were randomly divided into training cohort and validation cohort. The risk factor of distant metastasis were identified via the least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic analysis. The nomogram we constructed were validated internally and externally by C-index, calibration curves,receiver operating characteristic(ROC) curve and Decision curve analysis (DCA).Results:The Kaplan-Meier curve showed that the survival time of non-metastatic patients was longer than that of metastatic patients(P＜0.001).All patients(n=358) were divided into training cohort(n=269) and validation cohort(n=89).The LASSO regression selected five meaningful variables in the training cohort. The multivariate logistic regression analysis demonstrated that surgery(yes,OR=0.175, 95%CI=0.095-0.321,p=0.000) was the independent risk factors for distant metastasis of patients with spinal and pelvic osteosarcoma. The C-index and calibration curves showed the good agreement between the predicted results and the actual results. The area under the receiver operating characteristic curve(AUC) values were 0.748(95%CI=0.687-0.817) and 0.758(95%CI=0.631-0.868) in the training and validation cohorts respectively. The DCA showed that the nomogram has a good clinical usefulness and net benefit.Conclusion:No surgery is the independent risk factor of distant metastasis of spinal and pelvic osteosarcoma. The nomogram we constructed to predict the probability of distant metastasis of patients with spinal and pelvic osteosarcoma is reliable and effective by internal and external verification.

Clinical Significance and Potential Role of LSM4 Overexpression in Hepatocellular Carcinoma: An Integrated Analysis Based on Multiple Databases

Background: Hepatocellular carcinoma (HCC) is a solid tumor with high recurrence rate and high mortality. It is crucial to discover available biomarkers to achieve early diagnosis and improve the prognosis. The effect of LSM4 in HCC still remains unrevealed. Our study is dedicated to exploring the expression of LSM4 in HCC, demonstrating its clinical significance and potential molecular mechanisms.Methods: Clinical information and LSM4 expression values of HCC were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Survival analysis and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prognostic and diagnostic significance of LSM4. Calculating pooled standardized mean difference (SMD) and performing summary receiver operating characteristic (sROC) curve analysis to further determine its expression status and diagnostic significance. LSM4-related co-expressed genes (CEGs) were obtained and explored their clinical significance in HCC. LSM4-associated pathways were identified through Gene set enrichment analysis (GSEA).Results: Up-regulated LSM4 was detected in HCC tissues (SMD = 1.56, 95% CI: 1.29–1.84) and overexpressed LSM4 had excellent distinguishing ability (AUC = 0.91, 95% CI: 0.88–0.93). LSM4 was associated with clinical stage, tumor grade, and lymph node metastasis status (p < 0.05). Survival analysis showed that high LSM4 expression was related to poor overall survival (OS) of HCC patients. Cox regression analysis suggested that high LSM4 expression may be an independent risk factor for HCC. We obtained nine up-regulated CEGs of LSM4 in HCC tissues, and six CEGs had good prognostic and diagnostic significance. GSEA analysis showed that up-regulated LSM4 was closely related to the cell cycle, cell replication, focal adhesion, and several metabolism-associated pathways, including fatty acid metabolism.Conclusion: Overexpressed LSM4 may serve as a promising diagnostic and prognostic biomarker of HCC. Besides, LSM4 may play a synergistic effect with CEGs in promoting the growth and metastasis of HCC cells via regulating crucial pathways such as cell cycle, focal adhesion, and metabolism-associated pathways.

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

Triglyceride to HDL-C Ratio Is Associated with Plasma D-Dimer Levels in Different Types of Pancreatitis: A Retrospective Single-Centered Study in China

BackgroundThis study aims to evaluate the relationship between D-dimer and dyslipidemia, especially triglyceride to HDL-C ratio (TG/HDL-C) in different types of pancreatitis. We analyzed the D-dimer and dyslipidemia levels in acute pancreatitis (AP), recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP). Material and MethodsA single-centered retrospective study was conducted on 1013 patients diagnosed with AP, RAP or CP. Only patients hospitalized within 24 h of onset were included, and 204 patients were enrolled in pancreatitis groups. 68 normal persons without pancreatitis, malignant diseases, pregnancy, or organ failure, who had health check-ups, were enrolled in the control group. Blood samples were taken within 24h of admission. The epidemiology and etiology were analyzed. D-dimer and dyslipidemia levels were compared between different types of pancreatitis. Furthermore, the area under the receiver-operating characteristic curve (AUC) was used to estimate the validity of the predictor and to define optimal cut-off points for prediction.ResultsWe found that D-dimer and TG/HDL-C ratio could distinguish mild AP (MAP) and non-MAP in AP and RAP patients. The D-dimer level was related to TG/HDL-C ratio and severity of pancreatitis, with the coefficient correlation of 0.379 and 0.427(p<0.01), respectively. TG/HDL-C was related to D-dimer in different types of pancreatitis. Multivariate analysis was conducted in the parameters at admission like alcohol abuse, dyslipidemia and coagulation disturbance in distinguishing AP and RAP groups from the control group, and the parameter like diabetes in RAP and CP groups significantly increased compared with that of the control group. ConclusionsThe value of D-dimer level and TG/HDL-C ratio in predicting the severity of AP and RAP was confirmed but there was no significant difference between CP group and the control group. The D-dimer level was related to dyslipidemia and TG/HDL-C ratio.

Association of ROX Index with Mechanical Ventilator Use in Sepsis Patients in the Emergency Department

Detecting sepsis patients who are at a high-risk of mechanical ventilation is important in emergency departments (ED). The respiratory rate oxygenation (ROX) index is the ratio of tissue oxygen saturation/fraction of inspired oxygen to the respiratory rate. This study aimed to investigate whether the ROX index could predict mechanical ventilator use in sepsis patients in an ED. This retrospective observational study included quick sequential organ failure assessment (qSOFA) ≥ 2 sepsis patients that presented to the ED between September 2019 and April 2020. The ROX and ROX-heart rate (HR) indices were significantly lower in patients with mechanical ventilator use within 24 h than in those without the use of a mechanical ventilator (4.0 [3.2–5.4] vs. 10.0 [5.9–15.2], p < 0.001 and 3.9 [2.7–5.8] vs. 10.1 [5.4–16.3], p < 0.001, respectively). The area under the receiver operating characteristic (ROC) curve of the ROX and ROX-HR indices were 0.854 and 0.816 (both p < 0.001). The ROX and ROX-HR indices were independently associated with mechanical ventilator use within 24 h (adjusted hazard ratio = 0.78, 95% CI: 0.68–0.90, p < 0.001 and adjusted hazard ratio = 0.87, 95% CI 0.79–0.96, p = 0.004, respectively). The 28-day mortality was higher in the low ROX and low ROX-HR groups. The ROX and ROX-HR indices were associated with mechanical ventilator use within 24 h in qSOFA ≥ 2 patients in the ED.

CTRP3 as a novel biomarker in the plasma of Saudi children with autism

Background C1q/tumor necrosis factor-related protein-3 (CTRP3) has diverse functions: anti-inflammation, metabolic regulation, and protection against endothelial dysfunction. Methods The plasma level of CTRP3 in autistic patients (n = 32) was compared to that in controls (n = 37) using ELISA. Results CTRP3 was higher (24.7% with P < 0.05) in autistic patients than in controls. No association was observed between CTRP3 and the severity of the disorder using the Childhood Autism Rating Scale (CARS). A positive correlation between CARs and the age of patients was reported. Receiver operating characteristic (ROC) analysis demonstrated a low area under the curve (AUC) for all patients (0.636). Low AUCs were also found in the case of severe patients (0.659) compared to controls, but both values were statistically significant (P ≤ 0.05). Despite the small sample size, we are the first to find an association between CTRP3 and autism spectrum disorder (ASD).

Characterising the Area Under the Curve Loss Function Landscape

One of the most common metrics to evaluate neural network classifiers is the area under the receiver operating characteristic curve (AUC). However, optimisation of the AUC as the loss function during network training is not a standard procedure. Here we compare minimising the cross-entropy (CE) loss and optimising the AUC directly. In particular, we analyse the loss function landscape (LFL) of approximate AUC (appAUC) loss functions to discover the organisation of this solution space. We discuss various surrogates for AUC approximation and show their differences. We find that the characteristics of the appAUC landscape are significantly different from the CE landscape. The approximate AUC loss function improves testing AUC, and the appAUC landscape has substantially more minima, but these minima are less robust, with larger average Hessian eigenvalues. We provide a theoretical foundation to explain these results. To generalise our results, we lastly provide an overview of how the LFL can help to guide loss function analysis and selection.