Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing.

Dysregulated response of cytokines can result in hyper-inflammatory conditions called cytokine storm,commonly seen in sepsis and septic shock..The current study assessed hemoadsorption device - CytoSorb® for its efficiency as an adjuvant in treating patients suffering from septic shock. Total 8 patients enrolled were subjected to hemoadsorption therapy along with the standard treatment.The results of the study revealed significant improvement in clinical and haemodynamic parameters before and after initiation of CytoSorb® therapy. Out of 8 patients, 6 showed a positive outcome while 2 did not survive.The results of this study show that the use of hemoadsorption therapy can be considered in critically ill ICU patients as a safe and effective adjuvant therapy along with the standard treatment for a better outcome in patients.

A 30 years, 4th Gravida with 3 abortions with history of 8 months amenorrhea was admitted to the hospital with chief complaints of leaking per vagina since 4 hours and was not associated with pain abdomen or bleeding per vaginum. Perceiving decreased fetal movements since 6 hours. She had 3 previous missed abortions followed by D and E. In the present pregnancy, gestational age was 32 weeks at the time of admission.Patient's general condition was stable, all other investigation were found to be normal her pulse was 100 beats/min, tachycardia present, BP -100/70mmHg. On obstetric examination, uterus was 30 weeks size, 1-2 contraction lasting for 15-20 seconds, Breech presentation, FHR was 124 per minute, regular, decreased liquor clinically. On vulvovaginal examination-Frank leaking per vagina present.Ultrasound showed single live intrauterine pregnancy of 29 weeks 3 days with breech presentation with oligohydramnios, Placenta was at fundal region, Biophysical profile was 6/8, FHR 124 bpm.After taking high risk consent in view of fetal prematurity, patient was posted for Emergency LSCS. And extracted a single live preterm female baby of weight of 1.45 kg by breech.During the cesarean section, on opening abdomen lower segment was found to be congested with torturous vessels. So we suspected missed diagnosis of placenta previa. After delivery of the baby, we found succenturiate lobe of the placenta occupying lower uterine segment with vessels running across the membrane.

The SARS-CoV-2 viral genome is mutating and evolving into new variations, including the recently discovered Delta and Omicron. In this study, we used and evaluated our notion of differential targeting of SARS-CoV-2 variant genomes by microRNAs (miRNAs) of the infected human pancreas. We discovered that even with UTR mutations, the Delta, Omicron, and original Wuhan variations' genomes would be differentially targeted by the host pancreas cell's native miRNAs in the same way. The miRNAs have a distinct Minimal Free Energy (MFE) with the different viral types' genomes, but they are nonetheless responsible for the activation of Diabetes-associated genes.