Evaluation of Different Mycobacterial Species for Drug Discovery and Characterisation of Novel Inhibitors of Mycobacterium Tuberculosis

Tuberculosis (Tb) has plagued mankind for many centuries and is still a leading cause of death worldwide. A worrying development is the emergence of drug-resistant Tb that poses further challenges to the control of the disease. The global Tb burden and high mortality rate indicate that new drugs are needed for Tb treatment. While no new anti-Tb agents have been introduced to the market for about three decades, drugs with novel mechanisms of action can amend the current Tb treatment regimen and may provide an effective solution to drug resistance. The main objectives of this study were to identify an appropriate in vitro model that could be used for anti-Tb drug high-throughput screening (HTS), and to use this model to identify a novel candidate anti-tubercular drug and its cognate cellular target. A sensitive growth inhibition assay was set up with a GFP-labelled Tb vaccine strain, M. bovis BCG, using standard first and second line anti-tubercular drugs. HTS of the drug libraries was performed with various in vitro models to evaluate their efficacy for use in anti-Tb drug discovery. Approximately 50% of the M. tuberculosis inhibitors were not detected in screening with the surrogate species, M. smegmatis; whereas, only 21% of hits were not detected with M. bovis BCG. A comparative genomic study revealed that 97% of M. bovis BCG proteins, compared to 70% in M. smegmatis have conserved orthologues in M. tuberculosis H37Rv. Therefore, M. bovis BCG represented a more sensitive model than M. smegmatis for detecting anti-M. tuberculosis compounds. M. bovis BCG was then used to screen for novel anti-Tb agents by HTS of compound libraries and various plant extracts, followed by validation of new compounds in M. tuberculosis H37Ra. A number of novel M. tuberculosis inhibitors were identified, including sappanone A dimethyl ether from plant sources and compounds NSC112200 and NSC402959 from NIH chemical libraries. The inhibitors that were validated using M. tuberculosis H37Ra were also validated in the virulent Tb strain, M. tuberculosis H37Rv. In addition, their activity was further investigated using a suite of other clinically important human pathogens. One of the key anti-mycobacterial hits identified in this study, NSC402959, has previously been detected in screens for compounds that inhibit ribonuclease H (RNase H), an enzyme that is required for a number of essential cellular processes. NSC402959 inhibited RNase H proteins from HIV as well as from E. coli. Since HIV and Tb are major pandemics, previously-known anti-HIV RNase H compounds were imported and tested for their anti-proliferative activity towards M. tuberculosis H37Ra. HIV RNase H inhibitors, NSC35676, NSC112200, NSC133457 and NSC668394, exhibited good anti-mycobacterial activity in this study. In silico analysis suggested a plausible interaction of these inhibitors with mycobacterial RNase HI. A biochemical assay further confirmed NSC112200 to be specific against RNase HI from M. tuberculosis. These interesting inhibitors were not only structurally different from existing anti-Tb drugs but some of them were also non-toxic to mammalian cells and may have a unique mechanism of action. Thus, these compounds showed good potential for development as dual inhibitors of Tb and HIV; therefore, future studies in animal infection models to determine their dual anti-mycobacterial and anti-HIV activities are warranted

Native New Zealand plants with inhibitory activity towards Mycobacterium tuberculosis

<p>Abstract</p> <p>Background</p> <p>Plants have long been investigated as a source of antibiotics and other bioactives for the treatment of human disease. New Zealand contains a diverse and unique flora, however, few of its endemic plants have been used to treat tuberculosis. One plant, <it>Laurelia novae-zelandiae</it>, was reportedly used by indigenous Maori for the treatment of tubercular lesions.</p> <p>Methods</p> <p><it>Laurelia novae-zelandiae </it>and 44 other native plants were tested for direct anti-bacterial activity. Plants were extracted with different solvents and extracts screened for inhibition of the surrogate species, <it>Mycobacterium smegmatis</it>. Active plant samples were then tested for bacteriostatic activity towards <it>M. tuberculosis </it>and other clinically-important species.</p> <p>Results</p> <p>Extracts of six native plants were active against <it>M. smegmatis</it>. Many of these were also inhibitory towards <it>M. tuberculosis </it>including <it>Laurelia novae-zelandiae </it>(Pukatea). <it>M. excelsa </it>(Pohutukawa) was the only plant extract tested that was active against <it>Staphylococcus aureus</it>.</p> <p>Conclusions</p> <p>Our data provide support for the traditional use of Pukatea in treating tuberculosis. In addition, our analyses indicate that other native plant species possess antibiotic activity.</p

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Evaluation of Different Mycobacterial Species for Drug Discovery and Characterisation of Novel Inhibitors of Mycobacterium Tuberculosis

Tuberculosis (Tb) has plagued mankind for many centuries and is still a leading cause of death worldwide. A worrying development is the emergence of drug-resistant Tb that poses further challenges to the control of the disease. The global Tb burden and high mortality rate indicate that new drugs are needed for Tb treatment. While no new anti-Tb agents have been introduced to the market for about three decades, drugs with novel mechanisms of action can amend the current Tb treatment regimen and may provide an effective solution to drug resistance. The main objectives of this study were to identify an appropriate in vitro model that could be used for anti-Tb drug high-throughput screening (HTS), and to use this model to identify a novel candidate anti-tubercular drug and its cognate cellular target. A sensitive growth inhibition assay was set up with a GFP-labelled Tb vaccine strain, M. bovis BCG, using standard first and second line anti-tubercular drugs. HTS of the drug libraries was performed with various in vitro models to evaluate their efficacy for use in anti-Tb drug discovery. Approximately 50% of the M. tuberculosis inhibitors were not detected in screening with the surrogate species, M. smegmatis; whereas, only 21% of hits were not detected with M. bovis BCG. A comparative genomic study revealed that 97% of M. bovis BCG proteins, compared to 70% in M. smegmatis have conserved orthologues in M. tuberculosis H37Rv. Therefore, M. bovis BCG represented a more sensitive model than M. smegmatis for detecting anti-M. tuberculosis compounds. M. bovis BCG was then used to screen for novel anti-Tb agents by HTS of compound libraries and various plant extracts, followed by validation of new compounds in M. tuberculosis H37Ra. A number of novel M. tuberculosis inhibitors were identified, including sappanone A dimethyl ether from plant sources and compounds NSC112200 and NSC402959 from NIH chemical libraries. The inhibitors that were validated using M. tuberculosis H37Ra were also validated in the virulent Tb strain, M. tuberculosis H37Rv. In addition, their activity was further investigated using a suite of other clinically important human pathogens. One of the key anti-mycobacterial hits identified in this study, NSC402959, has previously been detected in screens for compounds that inhibit ribonuclease H (RNase H), an enzyme that is required for a number of essential cellular processes. NSC402959 inhibited RNase H proteins from HIV as well as from E. coli. Since HIV and Tb are major pandemics, previously-known anti-HIV RNase H compounds were imported and tested for their anti-proliferative activity towards M. tuberculosis H37Ra. HIV RNase H inhibitors, NSC35676, NSC112200, NSC133457 and NSC668394, exhibited good anti-mycobacterial activity in this study. In silico analysis suggested a plausible interaction of these inhibitors with mycobacterial RNase HI. A biochemical assay further confirmed NSC112200 to be specific against RNase HI from M. tuberculosis. These interesting inhibitors were not only structurally different from existing anti-Tb drugs but some of them were also non-toxic to mammalian cells and may have a unique mechanism of action. Thus, these compounds showed good potential for development as dual inhibitors of Tb and HIV; therefore, future studies in animal infection models to determine their dual anti-mycobacterial and anti-HIV activities are warranted

Evaluation of Different Mycobacterial Species for Drug Discovery and Characterisation of Novel Inhibitors of Mycobacterium Tuberculosis

Tuberculosis (Tb) has plagued mankind for many centuries and is still a leading cause of death worldwide. A worrying development is the emergence of drug-resistant Tb that poses further challenges to the control of the disease. The global Tb burden and high mortality rate indicate that new drugs are needed for Tb treatment. While no new anti-Tb agents have been introduced to the market for about three decades, drugs with novel mechanisms of action can amend the current Tb treatment regimen and may provide an effective solution to drug resistance. The main objectives of this study were to identify an appropriate in vitro model that could be used for anti-Tb drug high-throughput screening (HTS), and to use this model to identify a novel candidate anti-tubercular drug and its cognate cellular target. A sensitive growth inhibition assay was set up with a GFP-labelled Tb vaccine strain, M. bovis BCG, using standard first and second line anti-tubercular drugs. HTS of the drug libraries was performed with various in vitro models to evaluate their efficacy for use in anti-Tb drug discovery. Approximately 50% of the M. tuberculosis inhibitors were not detected in screening with the surrogate species, M. smegmatis; whereas, only 21% of hits were not detected with M. bovis BCG. A comparative genomic study revealed that 97% of M. bovis BCG proteins, compared to 70% in M. smegmatis have conserved orthologues in M. tuberculosis H37Rv. Therefore, M. bovis BCG represented a more sensitive model than M. smegmatis for detecting anti-M. tuberculosis compounds. M. bovis BCG was then used to screen for novel anti-Tb agents by HTS of compound libraries and various plant extracts, followed by validation of new compounds in M. tuberculosis H37Ra. A number of novel M. tuberculosis inhibitors were identified, including sappanone A dimethyl ether from plant sources and compounds NSC112200 and NSC402959 from NIH chemical libraries. The inhibitors that were validated using M. tuberculosis H37Ra were also validated in the virulent Tb strain, M. tuberculosis H37Rv. In addition, their activity was further investigated using a suite of other clinically important human pathogens. One of the key anti-mycobacterial hits identified in this study, NSC402959, has previously been detected in screens for compounds that inhibit ribonuclease H (RNase H), an enzyme that is required for a number of essential cellular processes. NSC402959 inhibited RNase H proteins from HIV as well as from E. coli. Since HIV and Tb are major pandemics, previously-known anti-HIV RNase H compounds were imported and tested for their anti-proliferative activity towards M. tuberculosis H37Ra. HIV RNase H inhibitors, NSC35676, NSC112200, NSC133457 and NSC668394, exhibited good anti-mycobacterial activity in this study. In silico analysis suggested a plausible interaction of these inhibitors with mycobacterial RNase HI. A biochemical assay further confirmed NSC112200 to be specific against RNase HI from M. tuberculosis. These interesting inhibitors were not only structurally different from existing anti-Tb drugs but some of them were also non-toxic to mammalian cells and may have a unique mechanism of action. Thus, these compounds showed good potential for development as dual inhibitors of Tb and HIV; therefore, future studies in animal infection models to determine their dual anti-mycobacterial and anti-HIV activities are warranted.</p

Driver Emotions Recognition Based on Improved Faster R-CNN and Neural Architectural Search Network

It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous vehicles. To address the issue, an automatic system is developed for driver’s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver’s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver’s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset

A Driver Gaze Estimation Method Based on Deep Learning

Car crashes are among the top ten leading causes of death; they could mainly be attributed to distracted drivers. An advanced driver-assistance technique (ADAT) is a procedure that can notify the driver about a dangerous scenario, reduce traffic crashes, and improve road safety. The main contribution of this work involved utilizing the driver’s attention to build an efficient ADAT. To obtain this “attention value”, the gaze tracking method is proposed. The gaze direction of the driver is critical toward understanding/discerning fatal distractions, pertaining to when it is obligatory to notify the driver about the risks on the road. A real-time gaze tracking system is proposed in this paper for the development of an ADAT that obtains and communicates the gaze information of the driver. The developed ADAT system detects various head poses of the driver and estimates eye gaze directions, which play important roles in assisting the driver and avoiding any unwanted circumstances. The first (and more significant) task in this research work involved the development of a benchmark image dataset consisting of head poses and horizontal and vertical direction gazes of the driver’s eyes. To detect the driver’s face accurately and efficiently, the You Only Look Once (YOLO-V4) face detector was used by modifying it with the Inception-v3 CNN model for robust feature learning and improved face detection. Finally, transfer learning in the InceptionResNet-v2 CNN model was performed, where the CNN was used as a classification model for head pose detection and eye gaze angle estimation; a regression layer to the InceptionResNet-v2 CNN was added instead of SoftMax and the classification output layer. The proposed model detects and estimates head pose directions and eye directions with higher accuracy. The average accuracy achieved by the head pose detection system was 91%; the model achieved a RMSE of 2.68 for vertical and 3.61 for horizontal eye gaze estimations

Driver Emotions Recognition Based on Improved Faster R-CNN and Neural Architectural Search Network

It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous vehicles. To address the issue, an automatic system is developed for driver&rsquo;s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver&rsquo;s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver&rsquo;s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset

The role of biochemical variations and genotype testing in determining the virological response of patients infected with hepatitis C virus

Background: In hepatitis C virus (HCV), infection viral and IL28B genotype along with many clinical and biochemical factors can influence response rates to pegylated interferon plus ribavirin (Peg-IFN-a/R) therapy and progression to chronic hepatitis C (CHC). Aims: The present study was conducted to determine the effect of biochemical and risk factors on treatment outcome in CHC patients in relation to their viral and host genotype. Settings and Design: The present study was a prospective Pe- IFN efficacy study consisting of Peg-IFN-a/R therapy for 24–48 weeks including 250 HCV infected patients. Materials and Methods: Biochemical parameters were determined by Beckman Coulter AU680 automated analyzer. HCV and Interleukin 28B (IL28B) genotyping were carried out by polymerase chain reaction-restriction fragment length polymorphism and viral load was determined by quantitative real-time PCR. Results: Wild outnumbered the variant genotypes in rs 12979860, rs 12980275, and rs 8099917 SNP of IL28B gene. Sustained virological response (SVR) SVR and viral genotype were significantly associated with age, hepatic steatosis, low-grade varices, and serum aspartate transaminase levels (at the end of treatment) (P < 0.05). In addition, SVR was significantly influenced by body mass index (BMI), insulin resistance, serum low-density lipoprotein , and ferritin levels (P < 0.05). Viral genotype 1 infected patients had higher serum cholesterol and triglyceride levels (P < 0.05). Conclusions: Although the IL28B sequence variation is the major factor that can influence response rates to antiviral therapy, viral and biochemical factors also have a definite role to play in the diagnosis, etiology, and treatment outcome in HCV-infected patients

EEDLABA: Energy-Efficient Distance- and Link-Aware Body Area Routing Protocol Based on Clustering Mechanism for Wireless Body Sensor Network

In medical environments, a wireless body sensor network (WBSN) is used to operate remotely, and sensor nodes are employed. It consists of sensor nodes installed on a human body to monitor a patient’s condition, such as heartbeat, temperature, and blood sugar level, and are functionalized and controlled by remote devices. A WBSN consists of nodes that are actually sensors in nature and are operated with a short range of communication. These sensor nodes are fixed with limited computation power and the main concern is energy consumption and path loss. In this paper, we propose a new protocol named energy-efficient distance- and link-aware body area (EEDLABA) with a clustering mechanism and compare it with the current link-aware and energy-efficient body area (LAEEBA) and distance-aware relaying energy-efficient (DARE) routing protocols in a WBSN. The proposed protocol is an extended type of LAEEBA and DARE in which the positive features have been deployed. The clustering mechanism has been presented and deployed in EEDLABA for better performance. To solve these issues in LAEEBA and DARE, the EEDLABA protocol has been proposed to overcome these. Path loss and energy consumption are the major concerns in this network. For that purpose, the path loss and distance models are proposed in which the cluster head (CH) node, coordinator (C) node, and other nodes, for a total of nine nodes, are deployed on a human body. The results have been derived from MATLAB simulations in which the performance of the suggested EEDLABA has been observed in assessment with the LAEEBA and DARE. From the results, it has been concluded that the proposed protocol can perform well in the considered situations for WBSNs