Validating a computable phenotype for nephrotic syndrome in children and adults using PCORnet® data

Background: Primary nephrotic syndromes are rare diseases which impedes adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet® CDM at 3 institutions from Jan 1, 2009 to Jan 1, 2018, where a random selection of 50 cases and 50 non-cases (individuals not meeting case criteria seen within the same calendar year and within five years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 non-cases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results: The algorithm identified a total of 2,708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009-2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI: 97-99%), 79% (95% CI: 74-85%), and 0.9 (0.84-0.97), respectively. The most common causes of false positive classification were secondary FSGS (9/39) and lupus nephritis (9/39). Conclusion: This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.

Performance validity testing via telehealth and failure rate in veterans with moderate-to-severe traumatic brain injury: A veterans affairs TBI model systems study

BACKGROUND: The COVID-19 pandemic has led to increased utilization of teleneuropsychology (TeleNP) services. Unfortunately, investigations of performance validity tests (PVT) delivered via TeleNP are sparse. OBJECTIVE: The purpose of this study was to examine the specificity of the Reliable Digit Span (RDS) and 21-item test administered via telephone METHOD: Participants were 51 veterans with moderate-to-severe traumatic brain injury (TBI). All participants completed the RDS and 21-item test in the context of a larger TeleNP battery. Specificity rates were examined across multiple cutoffs for both PVTs. RESULTS: Consistent with research employing traditional face-to-face neuropsychological evaluations, both PVTs maintained adequate specificity (i.e., > 90%) across previously established cutoffs. Specifically, defining performance invalidity as RDS < 7 or 21-item test forced choice total correct < 11 led to < 10%false positive classification errors. CONCLUSIONS: Findings add to the limited body of research examining and provide preliminary support for the use of the RDS and 21-item test in TeleNP via telephone. Both measures maintained adequate specificity in veterans with moderate-to-severe TBI. Future investigations including clinical or experimental “feigners” in a counter-balanced cross-over design (i.e., face-to-face vs. TeleNP) are recommended.

Visual assessment of [18F]flutemetamol PET images can detect early amyloid pathology and grade its extent

Purpose To investigate the sensitivity of visual read (VR) to detect early amyloid pathology and the overall utility of regional VR. Methods [18F]Flutemetamol PET images of 497 subjects (ALFA+ N = 352; ADC N = 145) were included. Scans were visually assessed according to product guidelines, recording the number of positive regions (0–5) and a final negative/positive classification. Scans were quantified using the standard and regional Centiloid (CL) method. The agreement between VR-based classification and published CL-based cut-offs for early (CL = 12) and established (CL = 30) pathology was determined. An optimal CL cut-off maximizing Youden’s index was derived. Global and regional CL quantification was compared to VR. Finally, 28 post-mortem cases from the [18F]flutemetamol phase III trial were included to assess the percentage agreement between VR and neuropathological classification of neuritic plaque density. Results VR showed excellent agreement against CL = 12 (κ = .89, 95.2%) and CL = 30 (κ = .88, 95.4%) cut-offs. ROC analysis resulted in an optimal CL = 17 cut-off against VR (sensitivity = 97.9%, specificity = 97.8%). Each additional positive VR region corresponded to a clear increase in global CL. Regional VR was also associated with regional CL quantification. Compared to mCERADSOT-based classification (i.e., any region mCERADSOT > 1.5), VR was in agreement in 89.3% of cases, with 13 true negatives, 12 true positives, and 3 false positives (FP). Regional sparse-to-moderate neuritic and substantial diffuse Aβ plaque was observed in all FP cases. Regional VR was also associated with regional plaque density. Conclusion VR is an appropriate method for assessing early amyloid pathology and that grading the extent of visual amyloid positivity could present clinical value.

Perception of Hypertension Patients on Treatment in Tamiang Layang Hospital, Central Kalimantan, Indonesia

Hypertension is a significant health problem considering the complications it causes. The research objective was to describe hypertensive patients' perceptions of their treatment at the inpatient installation of RSUD Tamiang Layang in 2019. Quantitative research methods with descriptive research design were applied with sampling with non-probability sampling is purposive sampling, amounting to 30 respondents—the process of collecting data using a questionnaire instrument. The data obtained were then processed and analyzed using Univariate Analysis. The results showed that 93.33% of respondents had a positive perception classification of their treatment, and as many as 6.67% of respondents had a negative perception classification. It was concluded that hypertensive patients' perception towards treatment in the inpatient room at RSUD Tamiang Layang in 2019 has a positive classification of treatment, which means that most patients know and understand hypertension treatment both pharmacologically and non-pharmacologically, although it is still not optimal.

Students’ Interest towards the Use of Watching Health Movie in Increasing Their English Pronunciation

The problem statement of this thesis what is the students’ interest toward the use of movie in improving pronunciation ability in English Students of IAIN Palopo. The Objective of the research is to identify the students’ interest in using movie toward the students’ pronunciation ability. This research was a descriptive study. This study was applied after the students followed the pronunciation teaching through watching Health Movies activities. Instrument of the Data Research is questionnaire. This instrument was given to find out students’ interest in the use of movie. The questionnaire aims to find the students’ interest by using movie. The questionnaire was composed based on ARCS component (Attention, Relevance, Confidence, and satisfaction) and the list construct agree with the researcher’s needs. The questionnaire used Likert Scale. The questionnaire distributed to the respondent after the last treatment of using movie in teaching pronunciation. This research has 10 positive and 10 negative statements. The questionnaire was distributed to the students of experimental group after giving treatment to know their interest toward the implementation of movie in teaching pronunciation. The questionnaire determined whether the students have positive attitude or not. Based on the result of the questionnaire on the students’ interest, the analysis of questionnaire showed the mean score of students’ interest was 89.4 or equivalent to strongly positive classification. This leads to the conclusion that the use of movie can increase the interests of the English students at IAIN Palopo in learning pronunciation .

Utilizing deep learning and graph mining to identify drug use on Twitter data

Background The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. Methods Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. Results To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC’s of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. Conclusion Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased accuracy scores and improves the predictive capability.

A machine learning approach to predict pancreatic islet grafts rejection versus tolerance

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

Dysfunction of categorization of emotional faces in people with schizophrenia

People with schizophrenia often show deficits in recognizing facial emotions, which contributes to poor social functioning. In this experiment we directly investigated how 20 people being treated for schizophrenia categorized emotional faces. In a control group of healthy people who had no mental illness, happy faces were classified faster than sad faces, that is, there was a positive classification advantage. However, this phenomenon was not present for inverted faces. Compared with the control group, the people with schizophrenia categorized emotional faces more slowly, with less accuracy, and without a positive classification advantage, except for an overall delayed response for inverted rather than right-way-up conditions. Although face inversion delayed the categorization of neutral faces in the group with schizophrenia, inversion effects for both happy and sad faces did not differ between the 2 groups. These results suggest a dysfunction of categorization of emotional faces in people with schizophrenia, although these individuals could adopt the same criterion pattern emotions as the control group did on faces shown inverted and the right way up. Our findings provide new evaluation evidence for practitioners treating people with schizophrenia.

Automatic Sorting of Dwarf Minke Whale Underwater Images

A predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subspecies) occurs annually in the Australian waters of the northern Great Barrier Reef in June–July, which has been the subject of a long-term photo-identification study. Researchers from the Minke Whale Project (MWP) at James Cook University collect large volumes of underwater digital imagery each season (e.g., 1.8TB in 2018), much of which is contributed by citizen scientists. Manual processing and analysis of this quantity of data had become infeasible, and Convolutional Neural Networks (CNNs) offered a potential solution. Our study sought to design and train a CNN that could detect whales from video footage in complex near-surface underwater surroundings and differentiate the whales from people, boats and recreational gear. We modified known classification CNNs to localise whales in video frames and digital still images. The required high classification accuracy was achieved by discovering an effective negative-labelling training technique. This resulted in a less than 1% false-positive classification rate and below 0.1% false-negative rate. The final operation-version CNN-pipeline processed all videos (with the interval of 10 frames) in approximately four days (running on two GPUs) delivering 1.95 million sorted images.