The overall success in the complex group after the first procedure was 87.2% versus 88.3% (P<0.05), and after redo procedure it was 90.4% vs 94.7% (P<0.05). There were three complications (pericardial perforation 2; cardioembolism 1) only in the complex group. The fluoroscopy time for complex was longer than that of the standard procedure (25.10 ± 6.32 versus 15.23 ± 5.33 min, P = 2.54).
Arrhythmias requiring complex electrophysiological procedure for ablation have a comparable success rate to standard ablation procedure but at the cost of extra hardware, complications and fluoroscopy time.
Arrhythmias requiring complex electrophysiological procedure for ablation have a comparable success rate to standard ablation procedure but at the cost of extra hardware, complications and fluoroscopy time.
In India, huge mortality occurs due to cardiovascular diseases (CVDs) as these diseases are not diagnosed in early stages. Machine learning (ML) algorithms can be used to build efficient and economical prediction system for early diagnosis of CVDs in India.
A total of 1670 anonymized medical records were collected from a tertiary hospital in South India. Seventy percent of the collected data were used to train the prediction system. Five state-of-the-art ML algorithms (k-Nearest Neighbours, Naïve Bayes, Logistic Regression, AdaBoost and Random Forest [RF]) were applied using Python programming language to develop the prediction system. https://www.selleckchem.com/products/3-methyladenine.html The performance was evaluated over remaining 30% of data. The prediction system was later deployed in the cloud for easy accessibility via Internet.
ML effectively predicted the risk of heart disease. The best performing (RF) prediction system correctly classified 470 out of 501 medical records thus attaining a diagnostic accuracy of 93.8%. Sensitivity and specificity were observed to be 92.8% and 94.6%, respectively. The prediction system attained positive predictive value of 94% and negative predictive value of 93.6%. The prediction model developed in this study can be accessed at http//das.southeastasia.cloudapp.azure.com/predict/.
ML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected.
ML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected.
With virtually dried out new antibiotic discovery pipeline, emergence and spread of antimicrobial resistance is a cause for global concern. Colistin, a cyclic polypeptide antibiotic, often regarded as last resort for multi drug resistance gram-negative bacteria, is also rendered ineffective by horizontal transfer of resistance genes. Surveillance of colistin resistance in GNB is essential to ascertain molecular epidemiology.
Whole genome sequencing (WGS) of an unusual colistin resistant urinary isolate of Escherichia coli was performed using Illumina MiSeq platform using 2x250bp V2 chemistry by following the manufactures protocol (Illumina Inc. USA). Multiple web-based bio-informatic tools were utilized to ascertain antibiotic resistant genes.
An approximate 5.4 Mb of genome of the urinary isolate AFMC_UC19 was sequenced successfully. Mobile colistin resistance gene (mcr) on the plasmid responsible for horizontal spread was absent in the isolate.
Colistin resistance has been reported previously in Klebsiella pneumoniae and it is a rare occurrence in Escherichia coli in Indian setting. Although the isolate lack mcr mediated colistin resistance, emergence and spread of colistin resistant in gram-negative bacteria pose a threat.
Colistin resistance has been reported previously in Klebsiella pneumoniae and it is a rare occurrence in Escherichia coli in Indian setting. Although the isolate lack mcr mediated colistin resistance, emergence and spread of colistin resistant in gram-negative bacteria pose a threat.
The purpose of this prospective observational study is to analyse posture-induced cyclotorsion in eyes undergoing conventional phacoemulsification with toric intraocular lens (IOL) implantation and femtolaser-assisted cataract surgery (FLACS) using the Verion image-guided system.
Cyclotorsion was assessed in patients who underwent conventional phacoemulsification with toric IOL implantation and FLACS between June 2017 and November 2017 with registration of iris architecture, limbal and bulbar conjunctival blood vessels acquired preoperatively using the Verion Reference Unit (the patient in sitting position) and intraoperatively under the microscope using the digital marker of the Verion image-guided system with the patient in supine position.
Forty-four eyes of 30 patients (21 men and 9 women) were included with the mean age of 56.5±17.1 (range, 19-89; median, 62) years. The mean cyclotorsion induced by change in posture from sitting to supine position was 5.84±3.25° (range, 1-17; median, 5). Overall, clockwise (CW) rotation (59.1%) was noted to be more common than counter clockwise (CCW) rotation (40.9%). Furthermore, CW rotation was more common in men than in women, and CCW rotation was significantly more common in women. Patients who underwent bilateral sequential cataract surgery showsimilar cyclorotation (CW or CCW) in both eyes more often than mixed rotation (85.7% vs 14.3%).
Significant cyclotorsion can occur in supine position during cataract surgery. Accurate assessment of the amount and direction of cyclotorsion aids in appropriate alignment of the toric IOL for optimal visual outcomes.
Significant cyclotorsion can occur in supine position during cataract surgery. Accurate assessment of the amount and direction of cyclotorsion aids in appropriate alignment of the toric IOL for optimal visual outcomes.
Proficiency in laparoscopy is gradually achieved. After initial simulation, it is safe to move to real patients. Simulation improves the basic attributes of laparoscopy, and its non-availability hampers training. Virtual reality and commercial simulators are exorbitantly expensive. Cheaper non-commercial latest, mobile phone-based simulators appear ergonomically unsuitable. A need for a no-cost, home-based laparoscopic endotrainer was felt by authors.
The authors proposed the concept of smart TV and smart phone-based laparoscopy trainer (STELA), an almost zero cost, lightweight indigenous, cable-less box-type endotrainer, with a smart phone housed on the model, projecting to smart TV via Wi-fi direct. The simulation timings on STELA were compared with Universal Beetel endotrainer by a group of surgeons and residents using identical tasks like object transfer (OT) and knot making (KM).
Data were analysed using SPSS, version 23.There was no significant difference in the mean timings of the residents (p>0.05) on two endotrainers, for both tasks, and of surgeons for OT. Surgeons took significantly longer time (p<0.05) in KM on STELA. Highest correlation (r=+.848) (<.05) was seen for KM on both devices by residents.
STELA is a viable, technologically advanced, no cost alternative to the non-commercial cumbersome simulators especially for beginners.
STELA is a viable, technologically advanced, no cost alternative to the non-commercial cumbersome simulators especially for beginners.Neurology practice has faced many challenges since Jean-Martin Charcot established its sacred tenets. Artificial Intelligence (AI) promises to revolutionize the time-tested neurology practice in unimaginable ways. link2 AI can now diagnose stroke from CT/MRI scans, detect papilledema and diabetic retinopathy from retinal scans, interpret electroencephalogram (EEG) to prognosticate coma, detect seizure well before ictus, predict conversion of mild cognitive impairment to Alzheimer's dementia, classify neurodegenerative diseases based on gait and handwriting. Clinical practice would likely change in near future to accommodate AI as a complementary tool. The clinician should be prepared to change the perception of AI from nemesis to opportunity.Currently, most critical care information is not expressed automatically at a granular level, rather is continually assessed by overindulged Intensive Care Unit (ICU) staff. Furthermore, due to different confounding morbidities and the uniqueness of the ICU setting, it is difficult to protocolize treatment regimens in the ICU. In highly complex ICU setting where man and resource management becomes extremely challenging, definite advancements are required to implement Artificial Intelligence (AI) for prognosticating the course of the disease to aid in informed decision-making. AI is the intelligence of a computer or computer-supervised robot to execute a piece of work commonly associated with intelligent beings, wherein the machines go beyond the realms of normal information processing by adding the characteristics of learning, sound reasoning, and weighting of the inputs. AI recognizes circuitous, relational time-series blueprint within datasets and this reasoning of analysis transcends conventional threshold-based analysis adapted in ICU protocols. AI works on the principle of a more complex form of Machine Learning by Artificial Neural Networks (ANN). These information-processing paradigms use multidimensional arrays called tensors which aid in 'learning' and 'weighting' all the information made available to it, thereby converting normal machine learning into Deep Learning. Here, the use of AI for data mining in complex ICU settings for protocol formulation and temporal representation and reasoning is discussed.Precision medicine has brought in many changes to the practise of medicine. The omics-based development of biomarkers and pharmaco-omics-based drug development programmes are evidences for the advancement. However, the field where it has proved to be most useful is in the development of various modalities of treatment in oncology. Various drugs targeting vascular endothelial growth factor, epidermal growth factor, tyrosine kinase receptor and rat sarcoma mutations have come to the forefront proving to be beneficial in many cancers. Some of the classic drugs developed using this concept include trastuzumab, bevacizumab, cetuximab and panitumumab among others. Precision medicine has been put to best use in the COVID-19 pandemic through use of various biomarkers such as IL-6 and c-reactive protein in assessing severity of disease, for development of various therapies and also to judge efficacy of vaccines. Precision medicine is also finding its place in management of infectious diseases, chronic diseases such as asthma, connective tissue diseases, cardiovascular diseases, diabetes and obesity. India has also made its presence felt in the field by launching various initiatives such as the Indian genome project and Indian cancer genome atlas. Numerous challenges still exist to the future of precision medicine such as cost involved, ethics, security of the Big data, merger of various platforms to integrate data and also availability of trained manpower to manage the data and algorithms. link3 This new age medicine is a big step forward for mankind and hopefully it will bring more benefits for both patients and the caregivers in the near future.
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