The Karachi intracranial stenosis study (KISS) Protocol: an urban multicenter case-control investigation reporting the clinical, radiologic and biochemical associations of intracranial stenosis in Pakistan.

Background: Intracranial stenosis is the most common cause of stroke among Asians. It has a poor prognosis with a high rate of recurrence. No effective medical or surgical treatment modality has been developed for the treatment of stroke due to intracranial stenosis. We aim to identify risk factors and biomarkers for intracranial stenosis and to develop techniques such as use of transcranial doppler to help diagnose intracranial stenosis in a cost-effective manner. Methods/Design: The Karachi Intracranial Stenosis Study (KISS) is a prospective, observational, case-control study to describe the clinical features and determine the risk factors of patients with stroke due to intracranial stenosis and compare them to those with stroke due to other etiologies as well as to unaffected individuals. We plan to recruit 200 patients with stroke due to intracranial stenosis and two control groups each of 150 matched individuals. The first set of controls will include patients with ischemic stroke that is due to other atherosclerotic mechanisms specifically lacunar and cardioembolic strokes. The second group will consist of stroke free individuals. Standardized interviews will be conducted to determine demographic, medical, social, and behavioral variables along with baseline medications. Mandatory procedures for inclusion in the study are clinical confirmation of stroke by a healthcare professional within 72 hours of onset, 12 lead electrocardiogram, and neuroimaging. In addition, lipid profile, serum glucose, creatinine and HbA1C will be measured in all participants. Ancillary tests will include carotid ultrasound, transcranial doppler and magnetic resonance or computed tomography angiogram to rule out concurrent carotid disease. Echocardiogram and other additional investigations will be performed at these centers at the discretion of the regional physicians. Discussion: The results of this study will help inform locally relevant clinical guidelines and effective public health and individual interventions

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Deep Ensembling for Perceptual Image Quality Assessment

Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpose as they learn features differently. After extensive experiments, we have designed a deep architecture containing two CNN architectures as its sub-units. Moreover, a self-collected image database BIQ2021 is proposed with 12,000 images having natural distortions. The self-collected database is subjectively scored and is used for model training and validation. It is demonstrated that synthetic distortion databases cannot provide generalization beyond the distortion types used in the database and they are not ideal candidates for general-purpose image quality assessment. Moreover, a large-scale database of 18.75 million images with synthetic distortions is used to pretrain the model and then retrain it on benchmark databases for evaluation. Experiments are conducted on six benchmark databases three of which are synthetic distortion databases (LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the performance is demonstrated using perceptually weighted rank correlation to indicate the perceptual superiority of the proposed approach. Multiple experiments are conducted to validate the generalization performance of the proposed model by training on different subsets of the databases and validating on the test subset of BIQ2021 database

4-Methyl-3-nitropyridin-2-amine

In the title compound, C6H7N3O2, the dihedral angle between the nitro group and the pyridine ring is 15.5 (3)° and an intramolecular N—H...O hydrogen bond occurs. In the crystal, inversion dimers linked by two N—H...N hydrogen bonds occur, resulting in R22(8) rings. The packing is stabilized by aromatic π–π stacking [centroid–centroid distance = 3.5666 (15) Å] and a short N—O...π contact is seen

Synthesis and Characterization of Silver Nanoparticle-Polydimethylsiloxane (Ag-NP-PDMS) Stretchable Conductive Nanocomposites

A number of different research methodologies have been developed to increase the conductivity and mechanical properties of stretchable or flexible conductors. One of the promising techniques recommended for applying metallic nanoparticles (NPs) to PDMS (polydimethylsiloxane) substrate is to develop a thin-film that gives possible conductivity and good mechanical strain. This article discusses the preparation of silver nanoparticles using the chemical reduction method with silver nitrate as the precursor, and uses glucose as a reducing agent. In addition, polyvinyl pyrrolidone (PVP) is used to prevent the nanoparticles’ oxidation and agglomeration once they have been synthesized successfully. Moreover, we utilize the power of diethylamine to accelerate the evolution of nanoparticles, and deionized water is used to prevent any possible contamination. The prepared Ag-NPs are then deposited on the solidified PDMS substrate through sintering. A multimeter is used to measure the electrical resistance. Ag-NPs are confirmed by UV-Vis at a 400-nm peak. Furthermore, we discuss the surface morphologies, particle sizes and thicknesses of the film and substrate when studied using different microscopy techniques. The prepared stretchable conductor is found to be suitable to use in biosensing and electronic devices

GEARS: A Genetic Algorithm Based Machine Learning Technique to Develop Prediction Models

Abstract.-The development of new prediction models to identify potential modified residues are based on different machine learning methods. Primary sequences, biochemical properties of the amino acids and 3D structural information of proteins are used to evolve prediction models. The information about the significant residues to govern different biological processes has not been considered yet to develop a prediction model. MAPRes is an efficient tool which has been utilized to mine significant residues and association patterns for surrounding amino acids of some specific modifications on hydroxyl and amino group such as phosphorylation and acetylation. The primary sequences of the proteins and association patterns of surrounding amino acids of modified residues may use to train new dataset for the development of an efficient and reliable prediction model. Biophysical and biochemical properties of the amino acids are also important parameters for the prediction of the modified residues. This study proposes, GEARS (Genetic Evolution of ClAssifers by Learning Residue Rules and Sequences), a classifier rule learning model, which considered different machine learning techniques such as ANNs, HMM and MAPRes were considered for the development of GEARS model. The GEARS, by combining these models, will have the capacity to reduce the false negative and positive predictions