Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder in which individuals often face social difficulties as well as language and communication challenges. Micro-expressions are extremely brief changes in facial expression. Moreover, the micro-expressions exhibited by individuals with ASD frequently represent an accurate reflection of their internal feelings. Therefore, using the Cinemetrics method to extract micro-expressions from ASD patients in movies and targeting them for detection can help doctors make early diagnosis of ASD patients. In this paper, we establish a dataset of micro-expressions of ASD patients in movies, use the improved YOLOv8-SMART algorithm for target detection, and compare it with other target detection algorithms without improvement. The comparison results prove that our algorithm effectively improves the recognition of micro-expressions, which provides reference value for future practical applications in the task of micro-expression recognition in ASD patients.

Objectives: COVID-19 remains a significant healthcare burden. Leveraging the combined power of clinical trial data and big data from the real world, this study elucidated baseline factors predictive of subsequent outcomes relating to severe COVID-19 disease (SD) and the effect of nirmatrelvir/ritonavir (Tx), a protease inhibitor, on disease progression. Methods: We retrospectively analyzed data from the Evaluation of Protease Inhibition for COVID-19 in High-Risk Patients (EPIC-HR) clinical trial (NCT04960202) to discern observational associations between baseline factors and subsequent SD outcome. Baseline factors, including demographics, clinical laboratory results, symptoms, medical history, vital signs, and electrocardiogram features, were studied using machine learning (ML) for their importance in predicting hospitalization or death through Day 28, with Tx effects analyzed statistically. Generalizability of results was evaluated using real-world data (RWD) Optum Electronic Health Records. Results: Modeling indicated Tx was the greatest predictor of whether a patient progressed to SD. The most important baseline factors associated with increased risk of SD were elevated baseline (1) viral load (VL; > ~4 log10 copies/mL), (2) hsCRP (> ~1 mg/dL), (3) ferritin (> ~280 ug/L), (4) haptoglobin (> ~210 mg/dL), and (5) increased age (> ~48 years). Tx reduced VL and abnormally high hsCRP and haptoglobin to greater extents than placebo at the measured time points. RWD validation supported findings on increased risk with elevated hsCRP and ferritin and increased age (no data were available on VL and haptoglobin). Conclusion: ML analysis identified critical baseline factors immediately before or at the beginning of COVID-19 infection predictive of progression to SD in adults that are common to a heterogeneous population. This study provides insights on multivariate signatures of COVID-19 disease progression and Tx effects, which may aid future studies and inform treatment decision making.

Bacterial and archaea genome sequencing and assembly are trivial tasks nowadays. After assembling contigs and scaffolds from a genome, the subsequent step is annotation. An annotation evidencing the expected features, like rRNA, tRNA, and CDS, is a signal of the quality of our sequencing and assembly. Different techniques to obtain and reproduce DNA samples, as well as sequencing and assembly of genomes, can impact the quality of a genome’s expected features. The Pannotator tool was conceived as an aid annotation tool focusing on the differences between assembling and its reference genome. Some of the key features for bacterial genome annotations are the subcellular location and immunological potential of a CDS. Instead of reimplementing the prediction of these features in Pannotator, we leveraged the capabilities of our microservice to provide them. In the end, Medpipe software was not modified, and Pannotator underwent minor changes to incorporate the subcellular location and immunological potential of all exported proteins annotated by the tool. Moreover, our Medpipe microservice can also be incorporated into other software. The Medpipe microservice is open to anyone, not only to our Pannotator tool. The successful integration of Medpipe to Pannotator, powered by the Medpipe microservice, offers a powerful approach to advanced genomic analysis. The Medpipe microservice, built on Kotlin with the Spring Boot framework, is instrumental in the automation of Medpipe processing. It achieves this using REST endpoints, such as the execution of Medpipe in an asynchronous manner, status retrieval, and prediction generation, which enhance the modularity and scalability of the microservice. The availability of endpoint documentation, detailed request examples, and logs make our microservice user-friendly. The results of this integration demonstrate the value of the information provided by Medpipe, enriching genomic annotation with additional details, such as the density of mature epitopes (MED) and protein subcellular location classification. The Pannotator has evolved beyond basic function annotation and now provides data on immunological potential, structure, and subcellular location after being integrated with our microservice. The Medpipe microservice is available at https://github.com/santosardr/medpipe-ms.git.