A DFT investigation on structural, electronic, magnetic, optical, elastic and hydrogen storage properties of Ru-based hydride-perovskites XRuH (X = Cr, V, Ni).

The present investigation delves into various physical properties exhibited by CrRuH, VRuH and NiRuH. Notably adopting a stable cubic configuration, both compounds manifest a distinct metallic demeanor characterized by an absolute absence of band gap. In-depth analysis through Total Density of States (TDOS) and Partial Density of States (PDOS) justify this metallic conduct by distinctly showcasing peak conductivity at the Fermi level.

Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.

Background And Purpose: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learning (ML)-based approaches, such as random survival forests (RSFs) and, more recently, deep learning (DL) have been proposed; however, these techniques are largely black-box in nature, limiting explainability. We compared CPH, RSF and DL to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients receiving radiotherapy using pre-treatment covariables.

Enhancing supercapacitor performance of Ni-Co-Mn metal-organic frameworks by compositing it with polyaniline and reduced graphene oxide.

Metal-organic frameworks (MOFs) are one of the most sought-after materials in the domain of supercapacitors and can be tailored to accommodate diverse compositions, making them amenable to facile functionalization. However, their intrinsic specific capacitance as well as energy density is minimal, which hinders their usage for advanced energy storage applications. Therefore, herein, we have prepared six electrodes, , Ni-Co-Mn MOFs, polyaniline (PANI), and reduced graphene oxide (rGO) along with their novel nanocomposites, , C, C, and C, comprising MOFs : PANI : rGO in a mass ratio of 100 : 1 : 0.

Voxel-Wise Comparison of Co-Registered Quantitative CT and Hyperpolarised Gas Diffusion-Weighted MRI Measurements in IPF.

The patterns of idiopathic pulmonary fibrosis (IPF) lung disease that directly correspond to elevated hyperpolarised gas diffusion-weighted (DW) MRI metrics are currently unknown. This study aims to develop a spatial co-registration framework for a voxel-wise comparison of hyperpolarised gas DW-MRI and CALIPER quantitative CT patterns. Sixteen IPF patients underwent He DW-MRI and CT at baseline, and eleven patients had a 1-year follow-up DW-MRI.

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI.

Changes in the prevalence of diabetes, prediabetes and associated risk factors in rural Baluchistan; a secondary analysis from repeated surveys (2002-2017).

To observe trends of diabetes and its associated risk factors from health surveys 2002-2017 in rural areas of Baluchistan-Pakistan and a secondary analysis based on community based health surveys of Baluchistan conducted between 2001-02, 2009-10, and 2016-17. A total of 4250 participants were included in this combined analysis, 2515 from 2001-2002, 1377 from 2009-2010 and 358 from 2016-2017 survey year. In each survey, detailed information of baseline parameters were noted on a predesigned questionnaire.

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

Background: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

Background: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton ( H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.

Purpose: Develop a generalizable CNN for lung segmentation in H-MRI, robust to pathology, acquisition protocol, vendor, and center.

Towards survival prediction of cancer patients using medical images.

Survival prediction of a patient is a critical task in clinical medicine for physicians and patients to make an informed decision. Several survival and risk scoring methods have been developed to estimate the survival score of patients using clinical information. For instance, the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores are developed for the survival prediction of heart patients.

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

Background: Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton ( H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE).

Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI.

Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques.

Glycemic status and general characteristics among individuals with undiagnosed diabetes; findings from second National Diabetes Survey of Pakistan 2016-2017 (NDSP 08).

Background And Aim: To observe the glycemic status among individuals with undiagnosed diabetes in urban and rural areas of all four provinces of Pakistan.

Methods: The findings of this study are obtained from the second National Diabetes Survey of Pakistan (NDSP) 2016-2017, a nationwide epidemiological survey. Out of 12,486 individuals approached, 10,834 participants agreed to be included in the second NDSP.

NDSP-10: The Cardiometabolic Risk Profile with Various Degrees of Dysglycemia in Younger and Older Adults: Findings from the Second National Diabetes Survey of Pakistan 2016-2017.

To assess and compare the cardiometabolic risk profile of the Pakistani population between younger (<45 years) and older adults (≥45 years). This is a substudy of a nationally representative community-based epidemiological second National Diabetes Survey of Pakistan (NDSP) 2016-2017. Out of 12,486 individuals approached, 10,834 participants agreed to be included (response rate 87%).

Plant-Mediated Green Synthesis of Zinc Oxide Nanoparticles Using Peel Extract of for Boosting Seed Germination of Seeds.

High demand of food for rapidly increasing population requires novel but ecofriendly fertilizers. Green reducing and capping agents are being explored to minimize production cost and toxicity of chemicals in synthesis of nanoparticles (NPs) which could be used to increase the production of crops and plants. In present research, Zinc Oxide Nanoparticles (ZnO NPs) are produced by employing an eco-friendly, simple and efficient green route using peel extract of .

Palbociclib in combination with aromatase inhibitors in patients ≥ 75 years with oestrogen receptor-positive, human epidermal growth factor receptor 2 negative advanced breast cancer: A real-world multicentre UK study.

Background: Breast cancer incidence increases with age and real-world data is essential to guide prescribing practices in the older population. The aim of this study was to collect large scale real-world data on tolerability and efficacy of palbociclib + AI in the first line treatment of ER+/HER2-advanced breast cancer in those aged ≥75 years.

Methods: 14 cancer centres participated in this national UK retrospective study.

Pancreatic cystic neoplasms and post-inflammatory cysts: interobserver agreement and diagnostic performance of MRI with MRCP.

Purpose: We aimed to answer several clinically relevant questions; (1) the interobserver agreement, (2) diagnostic performance of MRI with MRCP for (a) branch duct intraductal papillary mucinous neoplasms (BD-IPMN), mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN), (b) distinguishing mucinous (BD-IPMN and MCN) from non-mucinous cysts, and (c) distinguishing three pancreatic cystic neoplasms (PCN) from post-inflammatory cysts (PIC).

Methods: A retrospective analysis was performed at a tertiary referral center for pancreatic diseases on 71 patients including 44 PCNs and 27 PICs. All PCNs were confirmed by surgical pathology to be 17 BD-IPMNs, 13 MCNs, and 14 SCNs.

Deep learning in structural and functional lung image analysis.

The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis.

NDSP 09: Risk Assessment of Pakistani Individual for Diabetes (RAPID) - Findings from Second National Diabetes Survey of Pakistan (NDSP) 2016-2017.

Objective: To elucidate the effectiveness of Risk Assessment of Pakistani individuals with diabetes (RAPID) tool in epidemiological and population-based second National Diabetes Survey of Pakistan (NDSP) 2016-2017 for identifying risk of developing type 2 diabetes.

Methodology: This observational study was a sub-analysis of the second National Diabetes Survey of Pakistan (NDSP) 2016-2017 conducted from February 2016 to August 2017 in all four provinces of Pakistan. Ethical approval was obtained from National Bioethics Committee Pakistan.

Relationship between diabetic retinopathy and diabetic nephropathy; A longitudinal follow-up study from a tertiary care unit of Karachi, Pakistan.

Objective: To determine the development and progression of diabetic retinopathy in subjects with diabetic nephropathy.

Methodology: This retrospective longitudinal follow up study was conducted in outpatient department of Baqai Institute of Diabetology and Endocrinology (BIDE), a tertiary care diabetes unit of Karachi Pakistan, from January 2005 to December 2016. Type 2 diabetic subjects with newly diagnosed diabetic nephropathy (DN) and sex-age matched controls were identified from the electronic database of the institute, Health Management System (HMS).

Glycated hemoglobin (HbA1c) as diagnostic criteria for diabetes: the optimal cut-off points values for the Pakistani population; a study from second National Diabetes Survey of Pakistan (NDSP) 2016-2017.

Aim: Glycated hemoglobin (HbA1c) cut-off values as diagnostic tool in diabetes and prediabetes with its concordance to oral glucose tolerance test (OGTT) in Pakistani population.

Methodology: Data for this substudy was obtained from second National Diabetes Survey of Pakistan (NDSP) 2016-2017. With this survey, 10 834 individuals were recruited and after excluding known subjects with diabetes, 6836 participants fulfilled inclusion criteria for this study.

Radical accelerated radiotherapy for non-small cell lung cancer (NSCLC): A 5-year retrospective review of two dose fractionation schedules.

Background And Purpose: Numerous fractionation regimes are used for inoperable NSCLC patients not suitable for stereotactic ablative radiotherapy. Continuous hyperfractionated accelerated radiotherapy (CHART, 54 Gy, 36 fractions over 12 days) and hypofractionated accelerated radiotherapy (55 Gy, 20 fractions over 4 weeks) are recommended UK schedules. In this single-centre retrospective analysis, we compare both fractionation schemes for patients treated at our institution from 2010 to 15.

Facile synthesis and antimicrobial activity of CdS-AgS nanocomposites.

In this study CdS-AgS nanocomposites for antibacterial activity were synthesized via facile co-precipitation method using PVP as capping agent. The prepared nanocomposites have particle sizes in the range of 50-100 nm (SEM) and PVP addition has good influence on the morphology of nanocomposites. The antimicrobial activity of pure AgS, CdS and CdS-AgS composites was evaluated against Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli.

Imaging of regional ventilation: Is CT ventilation imaging the answer? A systematic review of the validation data.

Computed Tomography Ventilation Imaging (CTVI) is an experimental imaging modality that derives regional lung function information from non-contrast respiratory-correlated CT datasets. Despite CTVI being extensively studied in cross-modality imaging comparisons, there is a lack of consensus on the state of its clinical validation in humans. This systematic review evaluates the CTVI clinical validation studies to date, highlights their common strengths and weaknesses and makes recommendations.

Optimized health care for subjects with type 1 diabetes in a resource constraint society: A three-year follow-up study from Pakistan.

Background: Inadequate health infrastructure and poverty especially in rural areas are the main hindrance in the optimal management of subjects with type 1 diabetes (T1D) in Pakistan.

Aim: To observe effectiveness of diabetes care through development of model clinics for subjects with T1D in the province of Sindh Pakistan.

Methods: A welfare project with name of "Insulin My Life", was started in province of Sindh, Pakistan.