The Diversity, Resistance Profiles and Plasmid Content of Klebsiella spp. Recovered from Dairy Farms Located around Three Cities in Pakistan.

The rise of antimicrobial resistance (AMR) in bacterial pathogens such as Klebsiella pneumoniae (Kp) is a pressing public health and economic concern. The 'One-Health' framework recognizes that effective management of AMR requires surveillance in agricultural as well as clinical settings, particularly in low-resource regions such as Pakistan. Here, we use whole-genome sequencing to characterise 49 isolates of Klebisella spp. (including 43 Kp) and 2 presumptive Providencia rettgeri isolates recovered from dairy farms located near 3 cities in Pakistan-Quetta (n = 29), Faisalabad (n = 19), and Sargodha (n = 3). The 43 Kp isolates corresponded to 38 sequence types (STs), and 35 of these STs were only observed once. This high diversity indicates frequent admixture and limited clonal spread on local scales. Of the 49 Klebsiella spp. isolates, 41 (84%) did not contain any clinically relevant antimicrobial resistance genes (ARGs), and we did not detect any ARGs predicted to encode resistance to carbapenems or colistin. However, four Kp lineages contained multiple ARGs: ST11 (n = 2), ST1391-1LV (n = 1), ST995 (n = 1) and ST985 (n = 1). STs 11, 1391-1LV and 995 shared a core set of five ARGs, including blaCTX-M-15, harboured on different AMR plasmids. ST985 carried a different set of 16 resistance genes, including blaCTX-M-55. The two presumptive P. rettgeri isolates also contained multiple ARGs. Finally, the four most common plasmids which did not harbour ARGs in our dataset were non-randomly distributed between regions, suggesting that local expansion of the plasmids occurs independently of the host bacterial lineage. Evidence regarding how dairy farms contribute to the emergence and spread of AMR in Pakistan is valuable for public authorities and organizations responsible for health, agriculture and the environment, as well as for industrial development.

Improving Convergence Speed of Bat Algorithm Using Multiple Pulse Emissions along Multiple Directions.

Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and BAT Algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others.

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing.

Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

Widespread prevalence and molecular epidemiology of tet(X4) and mcr-1 harboring Escherichia coli isolated from chickens in Pakistan.

The emergence and spread of plasmid-mediated tigecycline resistance gene tet(X4) and colistin resistance gene mcr-1 in Escherichia coli (E. coli) pose a potential threat to public health, due to the importance of colistin and tigecycline for treating serious clinical infections. However, the characterization of bacteria coharboring both genes was few reported. Here, we described the molecular epidemiology of tet(X4) and mcr-1 harboring E. coli strains of chicken origin in Pakistan, with methods including PCR, antimicrobial susceptibility testing, DNA transfer assays, plasmid replicon typing, whole-genome sequencing and bioinformatics analysis. The tet(X4) gene was identified in 36 isolates exhibiting high levels of tigecycline resistance (MICs, 16-128 mg/L). Worryingly, 24 of the 36 tet(X4)-bearing isolates were confirmed as colistin resistance, positive for plasmid-borne mcr-1. We observed the prevalence of tet(X4)-bearing IncFII plasmid with mcr-1-bearing IncI2 plasmid in 12 E. coli isolates, with a high co-transfer frequency except for one strain PK8233, in which tet(X4)- and mcr-1-bearing plasmids were non-transferable. Coexistence of tet(X4)-bearing IncFII plasmid with mcr-1-carrying multidrug-resistant (MDR) IncHI2 plasmid was also identified in 10 E. coli isolates, and a relatively low co-transfer frequency was obtained except PK8575, in which mcr-1 was non-transferable. The transferability of pPK8275-tetX in PK8275 and pPK8233-tetX in PK8233, that could transfer from E. coli J53 to C600 by conjugation, was interfered by certain factors in PK8275 and PK8233. This may provide new insights to prevent and control the spread of antibiotic resistance genes. Two strains were reported to co-carry tet(X4)-positive IncQ1 plasmid and mcr-1-positive IncI2 plasmid. Convergence of tet(X4) and mcr-1 genes in E. coli by conjugative or mobilizable plasmids may lead to potentially widespread transmission of such resistance genes, which may incur antibiotic-resistance crisis globally.

Emergence of Plasmid-Mediated Resistance Genes tet(X) and mcr-1 in Escherichia coli Clinical Isolates from Pakistan.

The emergence of tet(X) represents a significant threat to human health. In this study, we aimed to investigate the genomic characterizations of tet(X)-positive clinical Escherichia coli isolates and provide genomic insight into the dissemination of antibiotic-resistant bacteria in clinical settings. Four tet(X)-positive E. coli isolates, PK5074, PK5086, PK5095, and PK5097, from 100 human clinical isolates were identified by PCR and were resistant to tigecycline. tet(X) genes were in IncFII plasmids in 4 E. coli isolates. Worryingly, PK5074 also carried an mcr-1-bearing IncHI2 plasmid. Notably, a relatively high cotransfer frequency of tet(X) and mcr-1 in PK5074 was found. PK5086, PK5095, and PK5097 were categorized into sequence type 410 (ST410) and indicated clonal dissemination of tet(X)-positive strains in hospitals, but tet(X)-bearing plasmids in PK5086, PK5095, and PK5097 were nontransferable. We present the first report of clinical E. coli isolates harboring tet(X) in South Asia. Our results support the implication of humans as a potential reservoir for tet(X)-harboring E. coli. We provide insight into the dissemination of tet(X) and mcr-1 in a clinical setting and highlight the current transmission of both critical resistance genes globally. IMPORTANCE Global transmission of plasmid-mediated tigecycline resistance gene tet(X)-bearing Escherichia coli strains incurs a public health concern. However, the research focusing on the prevalence of tet(X)-positive isolates in clinical specimens is still rare, and to our knowledge, there is no such report from South Asia. Here, we characterized four E. coli clinical isolates harboring tet(X) of human origin in Pakistan and demonstrated clonal dissemination of tet(X)-positive isolates in hospitals. We report the emergence of an mcr-1-bearing IncHI2 plasmid together with a tet(X)-positive IncFII plasmid in one clinical isolate. Cotransfer of tet(X)- and mcr-1-carrying plasmids is worrying and warrants further investigations.

Internet of medical things embedding deep learning with data augmentation for mammogram density classification.

Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.

Brain tumor detection and multi-classification using advanced deep learning techniques.

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

Eptifibatide and risk of bleeding after failed thrombolysis.

The safety of adjunct eptifibatide in the setting of rescue angioplasty (PTCA) with or without stenting after full-dose thrombolytic therapy is not well defined. Our study was undertaken to assess the risk of hemorrhagic complications following use of eptifibatide in patients undergoing rescue PTCA/stenting following failed thrombolysis. Clinical records of 43 consecutive patients (53% males) who received eptifibatide during rescue PTCA/stenting following full-dose fibrinolytic therapy were reviewed. Data were collected for: timing of rescue PTCA following fibrinolytic use; concomitant use of other antiplatelet agents; hospital length of stay; in-hospital mortality; and incidence of bleeding complications. Bleeding complications were categorized as major or minor according to Thrombolysis in Myocardial Infarction (TIMI) study group criteria. Overall bleeding complications developed in 13 patients (30%), with 4 patients (9%) experiencing major bleeding. Univariate predictors of major bleeding complications were: older age; female sex; lower baseline platelet count; and time to initiation of eptifibatide following failed thrombolysis. On multivariate analysis, the only predictors of bleeding were gender (27% in females versus 3% in males; odds ratio, 1.7; 95% confidence interval, 0.1-0.9) and time to initiation of eptifibatide following failed thrombolysis (4.6 +/- 2 hours versus 11 +/- 9 hours; p<0.04; 95% confidence interval, 2.1-11.4). Use of potent antiplatelet agents during rescue PTCA/stenting results in an increased risk of bleeding. Careful attention to predictors of bleeding and, in particular, delaying eptifibatide administration following full-dose fibrinolytic use may result in the reduction of major and minor bleeding complications.