Publications by authors named "S Sundar Rao"

The purpose of this study was to identify whether the preoperative hemoglobin to albumin ratio (HAR) could predict the prognosis of patients who underwent colorectal cancer (CRC) radical resection. This study enrolled 4018 consecutive CRC patients, calculating HAR as the hemoglobin count divided by albumin count. Patients were divided into the high and low HAR groups based on a cut-off value (0.

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Introduction: Disaccharidases produced by the small intestinal brush border facilitate digestion of dietary carbohydrates. If deficient, they can cause carbohydrate malabsorption resulting in several abdominal symptoms. Our aim was to examine the prevalence of disaccharidase deficiency and correlate this with abdominal symptoms in adult patients with chronic abdominal symptoms.

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Persons with blindness and low vision experience increased fall and injury risk beyond atypical biomechanics and balance impairments. Falling risk doubles with blindness, and more than triples with depth perception losses. Despite this, physical therapy focuses on musculoskeletal injuries postevent rather than taking a proactive and preventative approach for persons with blindness and low vision.

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Introduction: To evaluate the enamel abrasion effects of soft, ultra-soft, and nano-bristle toothbrushes using atomic force microscopy (AFM) to guide toothbrush selection for optimal enamel preservation.

Methods: This in vitro study involved 45 extracted human teeth (central and lateral incisors), randomly assigned to three groups (n=15 each): Group I (nano-bristle), Group II (ultra-soft bristle), and Group III (soft bristle). Each specimen underwent 10,000 brushing cycles with a standardized 2 N force to simulate one year of brushing.

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Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data.

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