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The birth of a child with an increased number of blood vessels in the umbilical cord (UC) is a rare event and has been previously reported to be associated with a higher incidence of congenital anomalies.

A case of a healthy female infant with an intra-uterine growth restriction (IUGR), born from a bicornuate uterus at 37 weeks of gestation and weighing 2500 g with a diagnosis of five-vessel UC made post-natally, on gross examination of the UC and confirmed by histopathological examination is documented. In particular, sections from the UC showed four umbilical arteries (UA) and one umbilical vein (UV). A review of literature was performed.

The physical examination of the baby after birth was normal. Caffeic Acid Phenethyl Ester solubility dmso To the best of our knowledge, this is the first case to document the association of five-vessel UC with IUGR and the Mullerian anomaly of bicornuate uterus.

The detection of supernumerary blood vessels in the UC mandates comprehensive workup for associated congenital anomalies but this case suggests that such finding might not always serve as an ominous sign for an adverse perinatal outcome.

The detection of supernumerary blood vessels in the UC mandates comprehensive workup for associated congenital anomalies but this case suggests that such finding might not always serve as an ominous sign for an adverse perinatal outcome.Brain-computer interface (BCI) is getting increasing attention where classification of motor imagery (MI) using electroencephalography (EEG) signal plays a vital role. In traditional EEG-based BCI setup, after applying pre-processing like band-pass filtering and spatial filtering, features are extracted and are fed to the classifier. However, most of the traditional features are extracted from a single time window, which is usually the full-time frame of a cue-based MI signal. Such features are usually statistical characteristics like log-variance of the whole-time signal. Thus, the information, which is localised in time and crucial for subject-specific MI classification, is not best captured. In this work, a new time-localised approach is proposed where multiple time windows are used for feature extraction. We have developed a number of feature representations using those time windows. Our experimental results corroborate that the proposed approach can achieve higher accuracy of classification when compared to methods using conventional features using the same platform.The current research was undertaken to design stealth liposomes of 5-Fluorouracil for reducing its cardiotoxicity and prolong the half-life by developing long-circulating liposomes. The liposomes were prepared by the NH4EDTA gradient method, where EDTA is used as a cardioprotectant. Ascorbyl-6-palmitate was also used which helped for the synergistic effect of 5-Fluorouracil to counteract the cancer cells and provide promising application in the treatment of breast cancer cells. Taguchi design was used for screening of formulation and HSPC phospholipid was selected. The drug-excipient compatibility was checked through FTIR which showed all the excipients were compatible with the drug. The formulation was optimized by using 32 factorial design. The drug to lipid ratio (15) and Ascorbyl-6-Palmitate concentration (15 mg) were selected. The vesicle size of the prepared liposomes was found to be 70.12 ± 0.58 nm and uniform distribution was observed. The zeta potential and entrapment efficiency of the stealth liposomes were found -16.28 mV and 92 ± 0.007% respectively. In-vitro drug release study of formulation showed drug release of 63.50 ± 0.94% in 24 hrs. The formulation was sterilized by 0.22 µm Mixed cellulose esters (MCE) membrane filter and passed sterility test. Moreover, a biodistribution study was performed by Fluorescence microscopy and by HPLC method, which showed formulation was circulated for 24 hours. Finally, a cell line study indicated that prepared formulation possess greater anti-tumour activity. The cardiotoxicity study revealed that the stealth liposomes have minimum cardiotoxicity as compare to the plain drug.

It is uncertain whether ventilation in patients with acute respiratory failure related to coronavirus disease 2019 (COVID-19) differs from that in patients with acute respiratory distress syndrome (ARDS) from another origin.

We undertook two literature searches in PubMed to identify observational studies reporting on ventilation management--one in patients with acute respiratory failure related to COVID-19, and one in patients with ARDS from another origin. The searches identified 14 studies in patients with acute respiratory failure related to COVID-19, and 8 studies in patients with ARDS from another origin.

In patients with acute respiratory failure related to COVID-19, ventilation management seems to be similar to that of patients with ARDS from another origin. The future lies in studies focused on personalized treatment of ARDS of all origins, including COVID-19.

In patients with acute respiratory failure related to COVID-19, ventilation management seems to be similar to that of patients with ARDS from another origin. The future lies in studies focused on personalized treatment of ARDS of all origins, including COVID-19.To evaluate the usability of the COMPASS application with mixed-methodology, using a citizen science approach. Parents/tutors of 10-11 years old children attending a primary school in Barcelona, Spain, were invited to take part in the study. We conducted semi-structured interviews on a subset (n = 7) of participants, two weeks after using the app for the first time. A list of suggestions of improvement was extracted from the interviews. The System Usability Scale (SUS, range 0-100) was administered to all participants before and after the improvements were implemented. We provide both a quantitative analysis (t-test of change in SUS scores) and a qualitative thematic analysis of the interviews. A total of 22 participants were included in the study. The mean score before implementation of changes was 68.5 (Standard deviation, SD = 11.1), and improved to 73.1 (10.5) (p-value = 0.025). Regarding the qualitative assessment, we obtained 24 codes and grouped them into 3 categories. It uncovered problems in the installation phase and the main barriers to use lack of time and the need for the app to evolve.