Synthetic α-Helical Nanopore Reactor for Chemical Sensing.

The use of nanopores for the single-molecule sensing of folded proteins and biomacromolecules has recently gained attention. Here, we introduce a simplified synthetic α-helical transmembrane pore, pPorA, as a nanoreactor and sensor that exhibits functional versatility comparable to that of engineered protein and DNA nanopores. The pore, built from the assembly of synthetic 40-amino-acid-long peptides, is designed to contain cysteine residues within the lumen and at the pore terminus for site-specific chemical modification probed using single-channel electrical recordings.

Semantic segmentation of low magnification effusion cytology images: A semi-supervised approach.

Cytopathologists examine microscopic images obtained at various magnifications to identify malignancy in effusions. They locate the malignant cell clusters at a low magnification and then zoom in to investigate cell-level features at a high magnification. This study predicts the malignancy at low magnification levels such as 4X and 10X in effusion cytology images to reduce scanning time.

Impact of family-centred postnatal training on maternal and neonatal health and care practices in district hospitals in two states in India: a pre-post study.

Background And Objectives: The Care Companion Program (CCP) is an in-hospital multitopic skill-based training programme provided to families to improve postdischarge maternal and neonatal health. The states of Punjab and Karnataka in India piloted the programme in 12 district hospitals in July 2017, and no study to date has evaluated its impact.

Methods: We compared telephonically self-reported maternal and neonatal care practices and health outcomes before and after the launch of the CCP programme in 11 facilities.

Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data.

Ki-67 labelling index is a biomarker which is used across the world to predict the aggressiveness of cancer. To compute the Ki-67 index, pathologists normally count the tumour nuclei from the slide images manually; hence it is timeconsuming and is subject to inter pathologist variability. With the development of image processing and machine learning, many methods have been introduced for automatic Ki-67 estimation.

Proteomic profiling of medulloblastoma reveals novel proteins differentially expressed within each molecular subgroup.

Objectives: The objective of the study was to identify novel medulloblastoma (MB) biomarkers through proteomic profiling, correlate it with the molecular subgroups of MB and assess the clinical significance.

Methods: Archived paraffin embedded tumor tissue blocks from 118 MB patients, operated at our institute were retrieved. Clinical information was documented from the hospital database.