Plasma Amino Acids May Improve Prediction Accuracy of Cerebral Vasospasm after Aneurysmal Subarachnoid Haemorrhage

Aneurysmal subarachnoid haemorrhages (aSAH) account for 5% of strokes and continues to place a great burden on patients and their families. Cerebral vasospasm (CVS) is one of the main causes of death after aSAH, and is usually diagnosed between day 3 and 14 after bleeding. Its pathogenesis remains poorly understood. To verify whether plasma concentration of amino acids have prognostic value in predicting CVS, we analysed data from 35 patients after aSAH (median age 55 years, IQR 39–62; 20 females, 57.1%), and 37 healthy volunteers (median age 50 years, IQR 38–56; 19 females, 51.4%). Fasting peripheral blood samples were collected on postoperative day one and seven. High performance liquid chromatography-mass spectrometry (HPLC-MS) analysis was performed. The results showed that plasma from patients after aSAH featured a distinctive amino acids concentration which was presented in both principal component analysis and direct comparison. No significant differences were noted between postoperative day one and seven. A total of 18 patients from the study group (51.4%) developed CVS. Hydroxyproline (AUC = 0.7042, 95%CI 0.5259–0.8826, p = 0.0248) and phenylalanine (AUC = 0.6944, 95%CI 0.5119–0.877, p = 0.0368) presented significant CVS prediction potential. Combining the Hunt-Hess Scale and plasma levels of hydroxyproline and phenylalanine provided the model with the best predictive performance and the lowest leave-one-out cross-validation of performance error. Our results suggest that plasma amino acids may improve sensitivity and specificity of Hunt-Hess scale in predicting CVS.

OKN-007 Alters Protein Expression Profiles in High-Grade Gliomas: Mass Spectral Analysis of Blood Sera

Current therapies for high-grade gliomas, particularly glioblastomas (GBM), do not extend patient survival beyond 16–22 months. OKN-007 (OKlahoma Nitrone 007), which is currently in phase II (multi-institutional) clinical trials for GBM patients, and has demonstrated efficacy in several rodent and human xenograft glioma models, shows some promise as an anti-glioma therapeutic, as it affects most aspects of tumorigenesis (tumor cell proliferation, angiogenesis, migration, and apoptosis). Combined with the chemotherapeutic agent temozolomide (TMZ), OKN-007 is even more effective by affecting chemo-resistant tumor cells. In this study, mass spectrometry (MS) methodology ESI-MS, mass peak analysis (Leave One Out Cross Validation (LOOCV) and tandem MS peptide sequence analyses), and bioinformatics analyses (Ingenuity® Pathway Analysis (IPA®), were used to identify up- or down-regulated proteins in the blood sera of F98 glioma-bearing rats, that were either untreated or treated with OKN-007. Proteins of interest identified by tandem MS-MS that were decreased in sera from tumor-bearing rats that were either OKN-007-treated or untreated included ABCA2, ATP5B, CNTN2, ITGA3, KMT2D, MYCBP2, NOTCH3, and VCAN. Conversely, proteins of interest in tumor-bearing rats that were elevated following OKN-007 treatment included ABCA6, ADAMTS18, VWA8, MACF1, and LAMA5. These findings, in general, support our previous gene analysis, indicating that OKN-007 may be effective against the ECM. These findings also surmise that OKN-007 may be more effective against oligodendrogliomas, other brain tumors such as medulloblastoma, and possibly other types of cancers.

Model averaging with the hybrid model: An asymptotic study and demonstration

In this paper, we present a new model averaging technique that can be applied in medical research. The dataset is first partitioned by the values of its categorical explanatory variables. Then for each partition, a model average is determined by minimising some form of squared errors, which could be the leave-one-out cross-validation errors. From our asymptotic optimality study and the results of simulations, we demonstrate under several high-level assumptions and modelling conditions that this model averaging procedure may outperform jackknife model averaging, which is a well-established technique. We also present an example where a cross-validation procedure does not work (that is, a zero-valued cross-validation error is obtained) when determining the weights for model averaging.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1,700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Statistical Seasonal Forecasting of Tropical Cyclone Landfall on South China Utilizing Preseason Predictors

Predicting tropical cyclone (TC) activities has been a topic of great interest and research. Many existing seasonal forecasting models of TC predict the numbers of TC geneses and landfalls based on the environmental factors in the peak TC season. Here, we utilize the mainstream reanalysis datasets in 1979–2005 and propose a statistical seasonal forecasting model, namely the SYSU model, for predicting the number of TC landfalls on South China based on the preseason environmental factors. The multiple linear regression analysis shows that the April sea level pressure over the tropical central Pacific, the March-April mean sea surface temperature southwest to Australia, the March 850-hPa zonal wind east to Japan, and the April 500-hPa zonal wind over Bay of Bengal are the significant predictors. The model is validated by the leave-one-out cross validation and recent 15-year observations (2006–2020). The correlation coefficient between the modeled results and observations reaches 0.87 (p < 0.01). The SYSU model exhibits 90% hit rate (38 out of 42) in 1979–2020. The Antarctic Oscillation, and the variations of the western North Pacific subtropical high and Intertropical Convergence Zone could be the possible physical linkages or mechanisms. The model demonstrates an operational potential in the seasonal forecasting of TC landfall on South China.

Brainstem Cavernous Malformations Management: Microsurgery vs. Radiosurgery, a Meta-Analysis

Given the rareness of available data, we performed a systematic review and meta-analysis on therapeutic strategy microsurgical resection and stereotactic radiosurgery (SRS) for brainstem cavernous malformations (BSCMs) and assessed mortality, permanent neurological deficits (PNDs), rebleeding rate, and patients who require reintervention to elucidate the benefits of each treatment modality. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) were used for protocol development and manuscript preparation. After applying all inclusion and exclusion criteria, six remaining articles were included in the final manuscript pool. In total, this meta-analysis included 396 patients, among them 168 patients underwent microsurgical treatment and 228 underwent SRS. Findings of the present meta-analysis suggest that regarding the total group of patients, in terms of mortality, late rebleeding rate, and PNDs, there was no superiority of the one method over the other. Applying the leave-one-out method to our study suggests that with low robust of the results for the bleeding rate and patients who require reintervention outcome factor, there was no statistical difference among the surgical and SRS treatment. Microsurgical treatment of BSCMs immediately eliminates the risk of rehemorrhage; however, it requires complete excision of the lesion and it is associated with a similar rate of PNDs compared with SRS management. Apparently, SRS of BSCMs causes a marked reduction in the risk of rebleeding 2 years after treatment, but when compared with the surgical treatment, there was not any remarkable difference.

Neural Collaborative with Sentence BERT for News Recommender System

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.

Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

<p class="Abstract"><span id="page629R_mcid43" class="markedContent"><span dir="ltr">Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.</span></span></p>

Functional Connectivity and Speech Entrainment Speech Entrainment Improves Connectivity Between Anterior and Posterior Cortical Speech Areas in Non-fluent Aphasia

Background Speech entrainment (SE), the online mimicking of an audio-visual speech model, has been shown to increase speech fluency in individuals with non-fluent aphasia. One theory that may explain why SE improves speech output is that it synchronizes functional connectivity between anterior and posterior language regions to be more similar to that of neurotypical speakers. Objectives The present study tested this by measuring functional connectivity between 2 regions shown to be necessary for speech production, and their right hemisphere homologues, in 24 persons with aphasia compared to 20 controls during both free (spontaneous) speech and SE. Methods Regional functional connectivity in participants with aphasia were normalized to the control data. Two analyses were then carried out: (1) normalized functional connectivity was compared between persons with aphasia and controls during free speech and SE and (2) stepwise linear models with leave-one-out cross-validation including normed functional connectivity during both tasks and proportion damage to the left hemisphere as independent variables were created for each language score. Results Left anterior–posterior functional connectivity and left posterior to right anterior functional connectivity were significantly more similar to connectivity of the control group during SE compared to free speech. Additionally, connectivity during free speech was more associated with language measures than connectivity during SE. Conclusions Overall, these results suggest that SE promotes normalization of functional connectivity (i.e., return to patterns observed in neurotypical controls), which may explain why individuals with non-fluent aphasia produce more fluent speech during SE compared to spontaneous speech.

Increased persuadability and credulity in people with corpus callosum dysgenesis

Corpus callosum dysgenesis is one of the most common congenital neurological malformations. Despite being a clear and identifiable structural alteration of the brains white matter connectivity, the impact of corpus callosum dysgenesis on cognition and behavior has remained unclear. Here we build upon past clinical observations in the literature to define the clinical phenotype of corpus callosum dysgenesis better using unadjusted and adjusted group differences compared with a neurotypical sample on a range of social and cognitive measures that have been previously reported to be impacted by a corpus callosum dysgenesis diagnosis. Those with a diagnosis of corpus callosum dysgenesis (n = 22) demonstrated significantly higher persuadability, credulity, and insensitivity to social trickery than neurotypical (n = 86) participants, after controlling for age, sex, education, autistic-like traits, social intelligence, and general cognition. To explore this further, machine learning, utilizing a set neurotypical sample for training the normative covariance structure of our psychometric variables, was used to test whether these dimensions possessed the capability to discriminate between a test-set of neurotypical and corpus callosum dysgenesis participants. We found that participants with a diagnosis of corpus callosum dysgenesis were best classed within dimension space along the same axis as persuadability, credulity, and insensitivity to social trickery after controlling for age and sex, with Leave-One-Out-Cross-Validation across 250 training-set permutations providing a mean accuracy of 71.7 percent. These results have wide-reaching implications for a) the characterization of corpus callosum dysgenesis, and b) the role of the corpus callosum in social inference.