Response of various histological types of locally advanced rectal cancer to neoadjuvant multimodality therapy.

Objectives: To determine the response of various histological types of locally advanced rectal cancer to neoadjuvant multimodality therapy. METHODS: The non-randomised, quasi-experimental retrospective cohort study was conducted at the Combined Military Hospital, Rawalpindi, Pakistan, and comprised data of patients treated between January 1, 2020, to September 30, 2021. The data retrieved related to histologically proven and locally advanced rectal cancer patients aged 18-70 years receiving neoadjuvant chemoradiotherapy. Radiotherapy dose was 45 gray to pelvis with a boost to gross tumour of 5.4 gray in 3 fractions by using volumetric arc therapy concurrently with capecitabine 625mg/m² daily. A magnetic resonance imaging scan of pelvis with contrast was done at 5-10 weeks before surgery. Histological response to neoadjuvant treatment of various histological types was evaluated using the Rectal Cancer Regression Grade. Data was analysed using SPSS 22. RESULTS: Of the 182 patients evaluated, 108(59.34%) were included; 64(59.3%) males and 44(40.7%) females. The overall mean age was 45.4±5.2 years. Regression status was grade 1 in 24(22%) patients, grade 2 in 43(40%) and grade 3 in 41(38%) (p=0.074). There were 12(11.11%) patients with signet ring cell and 10(83.3%) showed pathological tumour regression. There were 17(15.74%) patients with mucinous variant, and 12(70.5%) had tumour regression. There were 79(73.15%) patients with adenocarcinoma, and 59(74.6%) of them showed tumour regression. . CONCLUSIONS: There was less tumour regression in mucinous and signet ring cell variants of adenocarcinoma. Modification and intensification of neoadjuvant therapy may be required in such histologies.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory.

AIMS AND OBJECTIVES: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive insilico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. METHODS: For this purpose, we (a) investigated associated drugs and pathways for MetS, (b) employed eight different similarity measures to construct eight gene regulatory networks, (c) chose an optimal network, where a maximum number of drug targets were central, (d) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (e) employed these candidate genes to propose suitable drugs. RESULTS: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. CONCLUSION: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

Whole-genome sequencing as a first-tier diagnostic framework for rare genetic diseases.

Rare diseases affect nearly 300 million people globally with most patients aged five or less. Traditional diagnostic approaches have provided much of the diagnosis; however, there are limitations. For instance, simply inadequate and untimely diagnosis adversely affects both the patient and their families. This review advocates the use of whole genome sequencing in clinical settings for diagnosis of rare genetic diseases by showcasing five case studies. These examples specifically describe the utilization of whole genome sequencing, which helped in providing relief to patients via correct diagnosis followed by use of precision medicine.