Evaluation of Antimicrobial Efficacy and Clinical Outcomes of Triphala and 2.5% Sodium Hypochlorite as Intraradicular Irrigants in Pulpectomy of Primary Teeth

A natural irrigation solution with a broad spectrum of antimicrobial coverage, triphala was selected for the pulpectomy procedure. Because of its natural ingredients, it is well-known for promoting tissue healing. It also supposedly has certain additional qualities as compared to usual irrigation solutions that are made chemically. Although 2.5% NaOCl is thought to be perfect since it meets most of the requirements for an irrigation solution but it cannot be optimized for pulpectomy procedure. Primary teeth that were recommended for pulpectomy underwent this randomized controlled experiment. Two groups of eighty-four primary teeth were randomly assigned to receive irrigations: triphala in Group A; 2.5% Sodium hypochlorite in Group B. Sample were taken from infected primary root canals. A sterile test tube with bhi broth as the transport media was used to collect pre- and post-irrigation samples using sterile absorbent paper tips. On agar media, microorganisms were cultivated and their mean colony count was assessed. Following the procedure, the patient's follow-up visits at one, two and three months were used to evaluate the clinical result. The post-microbial colony count was dramatically reduced (p<0.001) by both irrigation treatments. Triphala in Group A is demonstrating desirable efficacy. Clinical success was found satisfactory in both the groups studied (p<0.001). But statistically significant difference was not found (p=0.175). Considering undesirable properties of sodium hypochlorite triphala can be a better alternative as a root canal irrigants in pulpectomy of primary teeth.

GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.

Advancement in total hip implant: a comprehensive review of mechanics and performance parameters across diverse novelties

The UK's National Joint Registry (NJR) and the American Joint Replacement Registry (AJRR) of 2022 revealed that total hip replacement (THR) is the most common orthopaedic joint procedure. The NJR also noted that 10-20% of hip implants require revision within 1 to 10 years. Most of these revisions are a result of aseptic loosening, dislocation, implant wear, implant fracture, and joint incompatibility, which are all caused by implant geometry disparity. The primary purpose of this review article is to analyze and evaluate the mechanics and performance factors of advancement in hip implants with novel geometries. The existing hip implants can be categorized based on two parts: the hip stem and the joint of the implant. Insufficient stress distribution from implants to the femur can cause stress shielding, bone loss, excessive micromotion, and ultimately, implant aseptic loosening due to inflammation. Researchers are designing hip implants with a porous lattice and functionally graded material (FGM) stems, femur resurfacing, short-stem, and collared stems, all aimed at achieving uniform stress distribution and promoting adequate bone remodeling. Designing hip implants with a porous lattice FGM structure requires maintaining stiffness, strength, isotropy, and bone development potential. Mechanical stability is still an issue with hip implants, femur resurfacing, collared stems, and short stems. Hip implants are being developed with a variety of joint geometries to decrease wear, improve an angular range of motion, and strengthen mechanical stability at the joint interface. Dual mobility and reverse femoral head-liner hip implants reduce the hip joint's dislocation limits. In addition, researchers reveal that femoral headliner joints with unidirectional motion have a lower wear rate than traditional ball-and-socket joints. Based on research findings and gaps, a hypothesis is formulated by the authors proposing a hip implant with a collared stem and porous lattice FGM structure to address stress shielding and micromotion issues. A hypothesis is also formulated by the authors suggesting that the utilization of a spiral or gear-shaped thread with a matched contact point at the tapered joint of a hip implant could be a viable option for reducing wear and enhancing stability. The literature analysis underscores substantial research opportunities in developing a hip implant joint that addresses both dislocation and increased wear rates. Finally, this review explores potential solutions to existing obstacles in developing a better hip implant system.

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.

Phyllanthus emblica (Amla) methanolic extract regulates multiple checkpoints in 15-lipoxygenase mediated inflammopathies: Computational simulation and in vitro evidence

Amla (Phyllanthus emblica) has long been used in traditional folk medicine to prevent and cure a variety of inflammatory diseases. In this study, the antioxidant activity (DPPH scavenging and reducing power), anti-inflammatory activity (RBC Membrane Stabilization and 15-LOX inhibition), and anticoagulation activity (Serin protease inhibition and Prothrombin Time assays) of the methanolic extract of amla were conducted. Amla exhibited a substantial amount of phenolic content (TPC: 663.53 mg GAE/g) and flavonoid content (TFC: 418.89 mg GAE/g). A strong DPPH scavenging effect was observed with an IC50 of 311.31 µg/ml as compared to standard ascorbic acid with an IC50 of 130.53 µg/ml. In reducing power assay, the EC50 value of the extract was found to be 196.20 µg/ml compared to standard ascorbic acid (EC50 = 33.83 µg/ml). The IC50 value of the RBC membrane stabilization and 15-LOX assays was observed as 101.08 µg/ml (IC50 of 58.62 µg/ml for standard aspirin) and 195.98 µg/ml (IC50 of 19.62 µg/ml for standard quercetin), respectively. The extract also strongly inhibited serine protease (trypsin) activity with an IC50 of 505.81 µg/ml (IC50 of 295.44 µg/ml for standard quercetin). The blood coagulation time (PTT) was found to be 11.91 min for amla extract and 24.11 min for standard Warfarin. Thus, the findings of an in vitro study revealed that the methanolic extract of amla contains significant antioxidant, anti-inflammatory, and anticoagulation activity. Furthermore, in silico docking and simulation of reported phytochemicals of amla with human 15-LOXA and 15-LOXB were carried out to validate the anti-inflammatory activity of amla. In this analysis, epicatechin and catechin showed greater molecular interaction and were considerably stable throughout the 100 ns simulation with 15-lipoxygenase A (15-LOXA) and 15-lipoxygenase B (15-LOXB) respectively.

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator's skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.

PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection

The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing.

MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.

Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect

Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient's electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

Design and Performance Evaluation of a Novel Spiral Head-Stem Trunnion for Hip Implants Using Finite Element Analysis

With an expectation of an increased number of revision surgeries and patients receiving orthopedic implants in the coming years, the focus of joint replacement research needs to be on improving the mechanical properties of implants. Head-stem trunnion fixation provides superior load support and implant stability. Fretting wear is formed at the trunnion because of the dynamic load activities of patients, and this eventually causes the total hip implant system to fail. To optimize the design, multiple experiments with various trunnion geometries have been performed by researchers to examine the wear rate and associated mechanical performance characteristics of the existing head-stem trunnion. The objective of this work is to quantify and evaluate the performance parameters of smooth and novel spiral head-stem trunnion types under dynamic loading situations. This study proposes a finite element method for estimating head-stem trunnion performance characteristics, namely contact pressure and sliding distance, for both trunnion types under walking and jogging dynamic loading conditions. The wear rate for both trunnion types was computed using the Archard wear model for a standard number of gait cycles. The experimental results indicated that the spiral trunnion with a uniform contact pressure distribution achieved more fixation than the smooth trunnion. However, the average contact pressure distribution was nearly the same for both trunnion types. The maximum and average sliding distances were both shorter for the spiral trunnion; hence, the summed sliding distance was approximately 10% shorter for spiral trunnions than that of the smooth trunnion over a complete gait cycle. Owing to a lower sliding ability, hip implants with spiral trunnions achieved more stability than those with smooth trunnions. The anticipated wear rate for spiral trunnions was 0.039 mm³, which was approximately 10% lower than the smooth trunnion wear rate of 0.048 mm³ per million loading cycles. The spiral trunnion achieved superior fixation stability with a shorter sliding distance and a lower wear rate than the smooth trunnion; therefore, the spiral trunnion can be recommended for future hip implant systems.

Surface Acoustic Wave (SAW) Sensors for Hip Implant: A Numerical and Computational Feasibility Investigation Using Finite Element Methods

In this paper, a surface acoustic wave (SAW) sensor for hip implant geometry was proposed for the application of total hip replacement. A two-port SAW device was numerically investigated for implementation with an operating frequency of 872 MHz that can be used in more common radio frequency interrogator units. A finite element analysis of the device was developed for a lithium niobate (LiNBO3) substrate with a Rayleigh velocity of 3488 m/s on COMSOL Multiphysics. The Multiphysics loading and frequency results highlighted a good uniformity with numerical results. Afterwards, a hip implant geometry was developed. The SAW sensor was mounted at two locations on the implant corresponding to two regions along the shaft of the femur bone. Three discrete conditions were studied for the feasibility of the implant with upper- and lower-body loading. The loading simulations highlighted that the stresses experienced do not exceed the yield strengths. The voltage output results indicated that the SAW sensor can be implanted in the hip implant for hip implant-loosening detection applications.

Automated measurement of penile curvature using deep learning-based novel quantification method

Objective

Develop a reliable, automated deep learning-based method for accurate measurement of penile curvature (PC) using 2-dimensional images.

Materials and methods

A set of nine 3D-printed models was used to generate a batch of 913 images of penile curvature (PC) with varying configurations (curvature range 18° to 86°). The penile region was initially localized and cropped using a YOLOv5 model, after which the shaft area was extracted using a UNet-based segmentation model. The penile shaft was then divided into three distinct predefined regions: the distal zone, curvature zone, and proximal zone. To measure PC, we identified four distinct locations on the shaft that reflected the mid-axes of proximal and distal segments, then trained an HRNet model to predict these landmarks and calculate curvature angle in both the 3D-printed models and masked segmented images derived from these. Finally, the optimized HRNet model was applied to quantify PC in medical images of real human patients and the accuracy of this novel method was determined.

Results

We obtained a mean absolute error (MAE) of angle measurement <5° for both penile model images and their derivative masks. For real patient images, AI prediction varied between 1.7° (for cases of ∼30° PC) and approximately 6° (for cases of 70° PC) compared with assessment by a clinical expert.

Discussion

This study demonstrates a novel approach to the automated, accurate measurement of PC that could significantly improve patient assessment by surgeons and hypospadiology researchers. This method may overcome current limitations encountered when applying conventional methods of measuring arc-type PC.

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.

Evaluation of Antioxidant and Anticoagulation Activity of Piper chaba Hunter Stem

Background: Piper chaba Hunter, a flowering vine of the Piperaceae family, has long been used in South Asian countries for culinary purposes and traditionally in fat-rich meat preparation. The curative potential of this herb is of great interest to be studied. Objectives: The antioxidant and anticoagulation potential, as well as total phenolic and flavonoid content, were evaluated using cold and boiled water extract separately from the dried and ground stem. Methods: Antioxidant potential was evaluated by 2, 2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay and ferric reducing antioxidant power (FRAP) assay. The anticoagulation activity was evaluated by serine protease inhibition assay and prothrombin time (PT) assay. Folin–Ciocalteu (FC) reagent and aluminum complex (AlCl3 ) were used to assess total phenolic content and total flavonoid content, respectively. Results: DPPH scavenging assay revealed the IC50 value of 125.52 µg.mL-1 and 157.94 µg.mL-1 for boiled and cold water extract, respectively. Potent ferric reducing potential (FRAP) was observed as 142.87 µM and 135.37 µM of ferrous equivalent per 100 µg for boiled and cold water extract, respectively. The IC50 value of serine protease inhibitory activity was found as 182 µg.mL-1 and 161.12 µg.mL-1 for cold and boiled water extract, respectively. The PT time was 27.00 min for boiled water extract and 24.68 min for cold water extract. Significant phenolic and flavonoid content was also found in the test sample. Conclusion: P. chaba stem extract possesses potent antioxidant and anticoagulation activity, which can neutralize oxidative free radicals and have a vasodilation effect in oxidative and inflammatory diseases.

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.

Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature

An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals' foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.

Incisional hernia following colorectal cancer surgery according to suture technique: Hughes Abdominal Repair Randomized Trial (HART)

BACKGROUND

Incisional hernias cause morbidity and may require further surgery. HART (Hughes Abdominal Repair Trial) assessed the effect of an alternative suture method on the incidence of incisional hernia following colorectal cancer surgery.

METHODS

A pragmatic multicentre single-blind RCT allocated patients undergoing midline incision for colorectal cancer to either Hughes closure (double far-near-near-far sutures of 1 nylon suture at 2-cm intervals along the fascia combined with conventional mass closure) or the surgeon's standard closure. The primary outcome was the incidence of incisional hernia at 1 year assessed by clinical examination. An intention-to-treat analysis was performed.

RESULTS

Between August 2014 and February 2018, 802 patients were randomized to either Hughes closure (401) or the standard mass closure group (401). At 1 year after surgery, 672 patients (83.7 per cent) were included in the primary outcome analysis; 50 of 339 patients (14.8 per cent) in the Hughes group and 57 of 333 (17.1 per cent) in the standard closure group had incisional hernia (OR 0.84, 95 per cent c.i. 0.55 to 1.27; P = 0.402). At 2 years, 78 patients (28.7 per cent) in the Hughes repair group and 84 (31.8 per cent) in the standard closure group had incisional hernia (OR 0.86, 0.59 to 1.25; P = 0.429). Adverse events were similar in the two groups, apart from the rate of surgical-site infection, which was higher in the Hughes group (13.2 versus 7.7 per cent; OR 1.82, 1.14 to 2.91; P = 0.011).

CONCLUSION

The incidence of incisional hernia after colorectal cancer surgery is high. There was no statistical difference in incidence between Hughes closure and mass closure at 1 or 2 years.

REGISTRATION NUMBER

ISRCTN25616490 (http://www.controlled-trials.com).

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.

The role of liquefied petroleum gas in decarbonizing India: fresh evidence from wavelet-partial wavelet coherence approach

India is predominantly a fossil fuel-intensive South Asian country that has traditionally settled for higher economic gains at the expense of lower environmental quality. However, in the contemporary era, it has become essential for India to come up with viable solutions that can enable the nation to transform its economy into a low-carbon one. Although replacing fossil fuel use with renewable energy sources is assumed to be the ideal pathway to decarbonizing the Indian economy, achieving this clean energy transition involves a long-term process. Thus, the Indian government should rather consider adoption of interim solutions to the environmental pollution problems faced by the nation. Against this backdrop, this study looks at whether enhancing the consumption level of liquefied petroleum gas, a relatively cleaner fossil fuel, can help India reduce its carbon dioxide emissions figures and attain environmentally sustainable economic growth. The econometric analysis is designed as per the theoretical framework of the environmental Kuznets curve hypothesis whereby the effects of economic growth on carbon dioxide emissions are examined controlling for liquefied petroleum gas consumption in the context of India between 1990 and 2018. Based on the findings from the autoregressive distributed lag model bounds test analysis, it is witnessed that there are long-run cointegrating relationships among per capita levels of carbon dioxide emissions, real gross domestic product, and liquefied petroleum gas consumption of India. Besides, the environmental Kuznets curve hypothesis is found to be valid only in the short run; however, it does not sustain in the long run since the economic growth-carbon dioxide emissions nexus is observed to follow a U-shaped relationship in the long run. Moreover, higher liquefied petroleum gas consumption is found to boost carbon dioxide emissions in the short run while reducing it in the long run. Furthermore, the findings from the wavelet and partial wavelet coherence and causality analyses also advocate in favor of promoting the use of liquefied petroleum gas in India in order to significantly curb the energy use-related carbon dioxide emission figures of the nation. Hence, considering these important findings, this study recommends that the Indian government should design policies for augmenting liquefied petroleum gas into the national energy mix and also adopt relevant green economic growth strategies in order to facilitate environmentally-sustainable growth of its economy.

An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning

Tai Chi has been proven effective in preventing falls in older adults, improving the joint function of knee osteoarthritis patients, and improving the balance of stroke survivors. However, the effect of Tai Chi on human gait dynamics is still less understood. Studies conducted in this domain only relied on statistical and clinical measurements on the time-series gait data. In recent years machine learning has proven its ability in recognizing complex patterns from time-series data. In this research work, we have evaluated the performance of several machine learning algorithms in classifying the walking gait of Tai Chi masters (people expert on Tai Chi) from the normal subjects. The study is designed in a longitudinal manner where the Tai Chi naive subjects received 6 months of Tai Chi training and the data was recorded during the initial and follow-up sessions. A total of 57 subjects participated in the experiment among which 27 were Tai Chi masters. We have introduced a gender, BMI-based scaling of the features to mitigate their effects from the gait parameters. A hybrid feature ranking technique has also been proposed for selecting the best features for classification. The research reports 88.17% accuracy and 93.10% ROC AUC values from subject-wise 5-fold cross-validation for the Tai Chi masters' vs normal subjects' walking gait classification for the "Single-task" walking scenarios. We have also got fairly good accuracy for the "Dual-task" walking scenarios (82.62% accuracy and 84.11% ROC AUC values). The results indicate that Tai Chi clearly has an effect on the walking gait dynamics. The findings and methodology of this study could provide preliminary guidance for applying machine learning-based approaches to similar gait kinematics analyses.

Does anthropogenic upstream water withdrawal impact on downstream land use and livelihood changes of Teesta transboundary river basin in Bangladesh?

This article evaluates the impact of upstream water withdrawal on downstream land use and livelihood changes in the Teesta River basin, using a combination of geospatial and social data. Results show that water bodies gradually decreased, indicating a low volume of water discharge from upstream of the Teesta River basin due to the construction of several barrages. During the study period, a significant change in the area of water bodies was observed between 2012 and 2016, from 881 to 1123 Ha, respectively. The cropland area increased because farmers changed their cropping practice due to water scarcity and floods. Trend analyses of riverbank erosion and accretion patterns suggest an increase in accretion rates compared to the rate of riverbank erosion. A household survey was conducted using a self-administered questionnaire where 450 respondents have participated (farmers: 200 and fishermen: 250). Survey results show that most of the farmers (65.5%) and fishermen (76.8%) think that the construction of upstream barrages caused harm to them. The majority of farmers and fishermen feel water scarcity, mainly in the dry season. We found that a large number of participants in the study area are willing to change their occupations. Furthermore, participants observed that many local people are migrating or willing to migrate to other places nowadays. Our study also found that farmers who face water scarcity in their area are more likely to change their location than their counterparts, while those who face problems in their cultivation are less likely to move. On the other hand, upstream barrages, fishing effects, and getting support in crisis significantly predict fishermen's occupation changes. We believe our results provide essential information on the significance of transboundary water-sharing treaties, sustainable water resource management, and planning.

OUP accepted manuscript

Motivation

Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield.

Results

We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.

COVID-19 infection localization and severity grading from chest X-ray images

The immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved outstanding performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation of Deep Learning models with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by an elegant human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.

OR53. Relation between platelet distribution width level with left ventricle global longitudinal strain on acute myocard infarct patient

Background and aims

In patients with acute myocardial infarction (AMI), global longitudinal strain (GLS) is a more sensitive marker than left ventricular ejection fraction (LVEF) in assessing left ventricular function, and the prognostic impact of marker of platelet distribution width (PDW) has also been reported in this setting. The aim of this study was to investigate the relationship between PDW and GLS in patients with AMI.

Methods and results

A total of 38 patients with AMI (31 males and 7 females), both with ST segment elevation (STEMI) and non ST segment elevation (NSTEMI) were included. Blood samples were drawn at presentation and echocardiography was performed within 24 hours of presentation. Median PDW levels was 16.0%, and median GLS was -9.9%. A positive correlation was found between PDW levels and GLS (r = 0.339, p = 0.037).

Conclusion

Among patients with AMI, elevated PDW levels were associated with poor left ventricular function. This finding suggests that determining PDW levels within 24 hours following AMI may have beneficial prognostic value in predicting left ventricular function.

Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

930 Impact of the COVID-19 Pandemic on Higher General Surgical Training – A West of Scotland Experience

Introduction

COVID-19 pandemic has caused significant disruption in all aspects of training. Our aim was to explore the degree of impact caused by the pandemic on higher general surgical trainees.

Method

All higher general surgical trainees in a single UK deanery were invited to participate in an online, voluntary, anonymous survey via SurveyMonkey.

Results

64 (72.7%) of the trainees responded. 39.1% were ST3/4, 29.7% were ST5/6, 20.3% were ST7/8 and 10.9% were out of training (maternity & research). Thirty-five (55.6%) worked in district general hospitals. Forty (68.9%) trainees felt that they had fewer opportunities to be primary surgeon. Forty-two (67.7%) trainees did not have access to laparoscopic simulation trainers. Fifty-two (88.1%) trainees had their courses and 2 (3.4%) had their FRCS part 2 exam postponed. 16 (27.1%) trainees reported they had been off-sick, with a median of 7 days off (range 3-35 days). Thirty-three (55.9%) trainees felt more stressed due to the pandemic and 35 (59.4%) had symptoms of burnout.

Conclusions

The COVID-19 pandemic has had an unprecedented impact on all aspects of higher surgical training. The most noticeable impact has been on the reduction in the confidence in laparoscopic and endoscopic skills.

Death Due To Electrocution- An Autopsy Based Study at Chattogram Medical College Morgue House

As we less frequently encounter cases of death due to electrocution, less attention is given to them. These all have significant impact on morbidity and mortality of the common people of different ages. This autopsy based retrospective study (from January 2014 to December 2016) was carried out by the history of the case, inquest report and by doing thorough autopsy of each of the cases at Chattogram Medical College Mortuary, Bangladesh. Fifty (50) cases of electrocution accounted for 1.23% of the total 4020 autopsies. Male victims i.e. 43(86%) outnumbered the females 7(14%). The majorly affected age group was 21-30 (24 cases) followed by 31-40 years (13 cases) and 41-50 years (5 cases). The commonest place of occurrence was on the street side in 33 cases (66%) followed by home 17 (34%). High tension wire i.e. in 28 cases (56%) were the main causative agents followed by home appliances 16 (32%) and water pump 6 (12%). In relation to distribution of entry and exit wounds, we observed evidence of both entry and exit wounds in 32 cases (64%) followed by no entry or exit wounds in 11 (22%) and entry wounds only in 7 (14%). As per this study, entry wounds were present in the upper limbs in 34 cases (68%) followed by head-neck (7 cases) and lower limbs (3 cases). We also observed maximum exit wounds were in the lower limbs i.e. in 36 cases (72%) followed by upper limbs (5 cases) and chest-abdomen (2 cases). Considering manner of death, we observed all the cases of electrocution i.e. 50 cases (100%) were of accidental. Electrocution accounts for a smaller proportion of all unnatural deaths which could be prevented by adequate awareness and adopting safety measures.

Does nosocomial COVID-19 result in increased 30-day mortality? A multi-centre observational study to identify risk factors for worse outcomes in patients with COVID-19

This study aimed to determine whether nosocomial coronavirus disease 2019 (COVID-19) has a worse outcome compared with community-acquired COVID-19. This was a prospective cohort study of all hospitalized patients with confirmed COVID-19 in three acute hospitals on 9th April 2020. Patients were followed-up for at least 30 days. Nosocomial infection was defined as a positive swab after 7 days of admission. In total, one hundred and seventy-three patients were identified, and 19 (11.0%) had nosocomial infection. Thirty-two (18.5%) patients died within 30 days (all cause) of a positive swab test; there were no significant differences in 30-day all-cause mortality rates between the three groups (i.e. patients admitted with suspected COVID-19, patients with incidental COVID-19 and patients with nosocomial COVID-19): 21.1% vs 17.6% vs 21.6% (P=0.755). Nosocomial COVID-19 is not associated with increased mortality compared with community-acquired COVID-19.

Detection of Vascular Changes in Systemic Lupus Erythematosus by Carotid Duplex Study in A Tertiary Cardiac Hospital

Systemic lupus erythematosus (SLE) is a common autoimmune connective tissue disorder and mainly affected female patients. This cross sectional study was performed in the department of Cardiology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh from July 2008 to June 2012. A total fifty (50) SLE patients were diagnosed on the basis of ACR criteria, having no cardiovascular symptoms. Another 50 age-matched normal individuals were included to compare with SLE group. Congenital vascular disease, ischaemic heart disease, congenital heart disease, rheumatic heart disease, hypothyroidism and any other inflammatory disease along with SLE were excluded from study. All patients were evaluated by Carotid duplex study. Mean age of SLE was 26.70±7.3 and mean age of normal subject was 25.64±8.01. Most of the SLE patients were female (about 92%) and male (about 8%). And about 94% was female in normal group and 6% was male. In Right common carotid arteries (RCCA), mean Intema medial thickness (IMT) was 0.86±0.10 IN SLE group and 0.73±0.06 in normal group. In LCCA, mean IMT was 0.89±0.14 in SLE group and 0.76±0.10 in normal group. IMT in SLE group was increased than control group. There was a significant difference (p=0.001) in both right and left side. The percentage rate of change in PSV and EDV of Carotid arteries of the SLE group was significantly higher than the control group (Both left and right side p=0.001). In RCCA, the PSV was 91.72±19.46 in SLE group and 62.60±6.66 in normal group (p=0.001). And EDV was 27.02±8.23 in SLE group and 16.48±2.32 in normal group (p=0.001). In LCCA, the PSV was 82.06±22.28 in SLE group and 60.36±7.54 in normal group (p=0.001). And EDV was 27.82±6.61 in SLE group and 18.08±2.69 in normal group (p=0.001). In LICA, mean PSV was 83.46±23.54 in SLE group and 60.36±7.54 in normal group (p=0.001). And EDV was 29.36±8.56 in SLE group and 18.08±2.69 in normal group (p=0.001). In RICA, mean PSV was 61.56±7.66 in SLE group and 62.16±5.35 in normal group (p=0.651) which was not significant. And EDV was 26.36±2.26 in SLE group and 19.00±2.17 in normal group (p=0.001). But majority of the vessels showed significant P value which signifies that vascular changes were more evident in SLE group than normal control group. SLE patients with carotid artery blood flow velocity and structural changes in endothelial function changes more evident than control group. Compared with the normal control group, IMT, PSV and EDV were significantly higher in SLE group, the difference was statistically significant (P<0.05). Vascular changes are common in SLE when clinically asymptomatic. Carotid duplex study is a non invasive tool for early detection of vascular changes to prevent stroke in SLE patients.

Towards next generation cannulation simulators

Background: Cannulation, in extracorporeal membrane oxygenation (ECMO), is the act of inserting a cannula through the body¹. For femoral veins, femoral arteries, and the jugular vein, the cannula stops at the inferior vena cava (IVC) beside the hepatic vein and at the beginning of the distal aorta, and the superior vena cava at the right atrium, respectively. Cannulation is considered a critical operation and requires intensive training. Simulation-based training (SBT) is the gold standard, allowing for training in risk-free, versatile, and realistic environments². A research collaboration was established between Hamad Medical Corporation and Qatar University College of Engineering to support the development of the ECMO training programme. Initially an ECMO machine simulator was developed with thermochromic ink to simulate blood and modules that simulate common emergencies practitioners may face during ECMO runs³. This cannulation simulator is now being designed to close the gap in the market in relation to cost and fidelity^4,5. Methods: The cannulation simulator is composed of several modules. Firstly, a 3D-printed femoral pad mold was constructed to facilitate the production of cannulation pads (Figure 1(a), (c)). Secondly, cannulation pads were designed so they are anatomically correct and ultrasound compatible. For the arteries, the superficial artery was added at the access point to simulate possible incorrect routes for the cannula. Furthermore, the orientation of the veins and arteries were set to further resemble the human anatomy, where the arteries are situated above the veins (Figure 1(a), (b)). In addition to the implementation of a closed loop linking the jugular to the femoral, cannulation access points with a pump connected to a tank between them to regulate the flow. The blood flow in the arteries was enhanced with a pump to simulate a pulsatile flow while the flow in the veins is laminar as seen in the single loop implementation (Figure 1(h)). The connection of the pump to the embedded system is shown in Figure 1(g). The junctional point in the IVC was designed in the venous loop to allow for two cannulas to pass and an alternative path simulating the renal vein was added. A force sensing resistor (FSR) was connected to detect and measure incorrect entry of the guide-wire as this, in real-time scenarios, could cause internal bleeding to the patient (Figure 1(g)). Lastly, the Y-connector showing the renal vein entry is shown in Figure 1(d) and (e). Results: Tests were done on the system namely on the FSR to recalibrate it in the presence of liquid. Tests on the pulsatile flow were conducted to optimize for realism in terms of pressure. Since both jugular and femoral cannulation access points are included, the simulator can be used for training for all ECMO modes including veno-arterial and veno-venous. After testing, the main limitations of the current prototype include the flexibility of the tubes, limits on FSR measurements, and the rigidity of the available 3D printing material. Conclusion: After implementing the stated features, the anticipated outcome is a realistic and cost-efficient ECMO cannulation simulator.

Quality of life of cervical cancer patients after completion of treatment - A study among Bangladeshi women

Cervical cancer is the main cause of malignancy-related death among women living in developing countries. The aim of this study is to evaluate the quality of life (QOL) among Bangladeshi cervical cancer survivors and its relationships with demographic and disease related factors A cross-sectional study was carried out onlone hundred nine consecutive cervical cancer survivors in National Institute of cancer Research and Hospital, Dhaka from September 2014 to february 2015 using European organization-for Research and treatment of cancer core Questionnaires (QOL-C30 and QOL- CX24). Demographic condition like education level, occupation and disease related factors like stages, treatment modality and duration of follow-up time were taken as investigating factors against functional scales. Cronbach's alpha was calculated to asses' internal consistency among items. Cervical cancer survivors stated a moderate QOL. Sub-domains of QOL score and global health status were significantly associated with physical function(PF) scales (p=.000), fatigue (p=.045), nausea and vomiting (p=.000), Appetite loss (p=.001), constipation (p=.005), symptom experience (p=.005) and menopausal symptoms (p=.015). QOL mean score were negatively associated with emotional function(EF) scales, pain, fatigue, nausea, appetite loss and financial problems. Education level showed significant association with physical function(PF) (p=.001), emotional function(EF) (p=.027), Cognitive function(CF) (p=.000) and sexual function (p=.001). Duration (Follow-up) time was significance association with PF (p=.005), EF (p=.012), symptoms experience (p=.001). Although, the QOL in cervical cancer survivors was moderate, treatment of related symptoms and improvement of demographic condition can influence the QOL and survivors improve the care of cervical cancer. So, improve the QOL among cervical cancer survivors.

Abstract OT2-01-15: PARTNER - Randomised, phase II/III trial to evaluate the safety and efficacy of the addition of olaparib to platinum-based neoadjuvant chemotherapy in triple negative and/or germline BRCA mutated breast cancer patients

Background: Triple Negative Breast Cancers (TNBC) are a biologically diverse and aggressive sub-group. Early effective treatment can lead to cure. Current standard treatment is systemic chemotherapy either pre-/post-definitive surgery. No specific targeted therapies are available for TNBC. There are phenotypic and molecular similarities between germline BRCA (gBRCA) breast cancer and TNBC. In TNBC 10%-20% harbour gBRCA mutations. In gBRCA patients, and potentially other homologous recombination deficiencies, these already compromised pathways allow drugs called PARP inhibitors (olaparib) to work particularly effectively.

Aims: To establish if the addition of olaparib to neoadjuvant platinum-based chemotherapy for TNBC and/or gBRCA breast cancer is safe and improves efficacy.

Trial design: 3-stage open label randomised phase II/III trial of neoadjuvant olaparib +/- platinum containing chemotherapy followed by clinicians' choice of anthracycline regimen. Stage 1 and 2, patients are randomised (1:1:1) to either control (3 weekly carboplatin AUC5/weekly paclitaxel 80mg/m2 chemotherapy - 4 cycles) or one of two research arms which uses the same chemotherapy regimen but with two different schedules of olaparib 150mg BD). Stage 3: patients are randomised (1:1) to either control arm or to the research arm selected in stage 2.

Primary outcome measures:

Stage 1: safety of the addition of olaparib to chemotherapy. Prophylactic G-CSF is mandatory.

Stage 2: pathological complete response (pCR) in each of the two research arms. At the end of stage 2, one of the research arms will be dropped.

Stage 3: pCR at surgery after neoadjuvant treatment. pCR - defined as no residual invasive carcinoma within the breast (ductal carcinoma in situ permitted) AND no evidence of metastatic disease within the lymph nodes.

Eligibility:

•Aged 16 to 70.

•Written informed consent.

•Histologically confirmed invasive breast cancer.

•Clinical stage T1-4 N0-2 (tumour or metastatic node diameter&gt;10mm)

•Confirmed ER-negative and HER2-negative or gBRCA mutation positive, irrespective of hormone status.

•Performance Status 0-1

Statistical Methods: Stage 1, Safety: both research arms combined. Stage 2, Schedule selection criteria: pCR rate and completion rate of olaparib protocol treatment. It is a “pick-the winner” design with 53 patients in each research arm. This allows a 90% power, 5% one-sided significance level to test null hypothesis of pCR ≤35% versus an alternative hypothesis of pCR ≥55% in each of the research arms.

Stage 3, Efficacy: anticipated pCR ∼45-55% for all trial patients and ∼50-60% for gBRCA patients. The trial is powered to detect an absolute improvement of 15% (all patients) and 20% (gBRCA patients) by adding olaparib to chemotherapy (enriched design). TNBC patient recruitment will be capped, to ensure the required number of gBRCA patients are enrolled. Enrichment design is applied with the overall significance level 0.05(α)=0.025(αall)+ 0.025(αgBRCA) and 80% power.

Present accrual: 1 [Trial opened: 23rd May 2016]

Target accrual: 527 (TNBC 307; gBRCA 220)

Contact information: Dr. Jean Abraham; Email: ja344@medschl.cam.ac.uk.

Citation Format: Abraham JE, Vallier A-L, Qian W, Grybowicz L, Thomas S, Mahmud S, Harvey C, McAdam K, Hughes-Davies L, Roylance R, Copson E, Brown J, Provenzano E, Tischkowitz M, Earl HM. PARTNER - Randomised, phase II/III trial to evaluate the safety and efficacy of the addition of olaparib to platinum-based neoadjuvant chemotherapy in triple negative and/or germline BRCA mutated breast cancer patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr OT2-01-15.

The effects of power toothbrushing on periodontal inflammation in a Canadian nursing home population: A randomized controlled trial

OBJECTIVES

The aim of this study was to investigate whether twice-daily use of a rotating-oscillating power toothbrush (Oral-B Professional Care 1000^™ ) in nursing home (NH) residents over a 6-week period, compared to usual care (UC), would reduce periodontal inflammation.

METHODS

In this repeated measures single-blinded randomized controlled trial, 59 residents of one NH in Winnipeg, Canada, were randomized to receive either twice-daily tooth brushing with a rotating-oscillating power toothbrush (PB) or UC by caregivers. Consent was obtained from residents or their proxies. Participants had some natural teeth, periodontal inflammation, non-aggressive behaviour, no communicable diseases, were non-smokers and non-comatose. Outcomes were measured at baseline and 6 weeks, which included: inflammation (MGI, Lobene), bleeding (PBI, Loesche) and Plaque (Turesky). Comparisons of group changes in outcomes were analysed using an ANOVA with a repeated measure.

RESULTS

Of 59 original study participants, one withdrew, one died prior to study commencement and three died before study completion. All oral parameters improved significantly for the remaining 54 residents over time (P<.0001), with no differences between groups.

CONCLUSIONS

These results demonstrate that it is possible for caregivers to improve periodontal inflammation of residents over a 6-week period. Despite no significant group differences, periodontal inflammation of all study participants improved significantly, particularly in the reduction of bleeding, a direct measure of periodontal inflammation, which is a unique finding.

Structural Variations of Nose and Paranasal Sinuses in Various Sinonasal Pathologies: Tomographic Study of 50 Cases in Bangladeshi People

The aim of this study was to determine the frequency of structural variations in nose & paranasal sinuses in computed tomography in Bangladeshi people. This retrospective study was done at the Sir Salimullah Medical College Mitford Hospital and Apollo Hospitals, Dhaka, Bangladesh. Fifty (50) CT scan of Nose and Para nasal sinuses were collected from the patients presented with different sinonasal pathologies in OPD, IPD of both hospitals from July 2013 to June 2014. The scans were reviewed for the presence of different structural variations of nose and paranasal sinuses. The age range of the patients was 25 to 65 years. The most common anatomical variation in this study was hypertrophied inferior turbinate (82%) followed by ethmoidal bulla (70%), deviated nasal septum (64%), agar nasi cell (40%), concha bullosa (38%). In most of the patients we found more than one variation. There is wide range of anatomical variations in nose and paranasal sinuses which might be regarded as the aetiological factors of different sinonasal pathologies. To maximize patients' benefit and to avoid unexpected situations during surgeries as well as dreadful complications, individualized pre-planning through tomographic study should be considered.

Antithrombotic Effects of Five Organic Extracts of Bangladeshi Plants In Vitro and Mechanisms in In Silico Models

This research was carried out to investigate the thrombolytic effects of the methanolic extracts of five Bangladeshi plants. Phytochemical metabolites of those plants have been identified to elucidate whether the plant-derived metabolites are linked with the thrombolytic effects. Potential computer aided models were adopted in this study to find out a structure-function correlation between the phytochemical constituents and thrombolytic effects using the secondary metabolites as ligands and tissue plasminogen activator (t-PA) as receptor for the best fit ligand-receptor interaction.

A Psoriatic Arthritis Patient with Multiple Rare Complications – case report and review of literature

A 28 year old male patient of psoriasis was admitted in a tertiary care hospital of Bangladesh with typical skin lesions for 18 years, nail changes for 16 years, arthritis for 12 years and eye changes for 3 years. In addition he had other rare extra articular complications like severe aortic stenosis, moderate aortic regurgitation and mild mitral regurgitation. He had a positive family history of psoriasis. HLAB27 is positive and X-ray of hands and feet showed classical findings of psoriatic arthritis. We presented the case to show the early age of onset, severity of the disease with rapid progression and multiple extra articular complications. DOI: http://dx.doi.org/10.3329/pulse.v5i2.20266 Pulse Vol.5 July 2011 p.48-53

Color Doppler sonography and resistivity index in the differential diagnosis of hepatic neoplasm

The purpose of this study was to determine the usefulness of color doppler sonography and resistivity index (RI) in differentiating liver tumors. The study was carried out in the Department of Radiology and Imaging, Mymensingh Medical College Hospital, and Institute of Nuclear Medicine and Allied Sciences (INMAS), Mymensingh, Bangladesh, during the period of July 2009 to June 2011. Total 50 consecutive cases were studied. Among them 27 were hepatocellular carcinomas, 19 were metastatic tumors, 03 were hemangiomas and 01 was hepatic adenoma. Doppler sonographic findings were then correlated, case by case, with final diagnosis- either pathologically by USG guided Fine-needle aspiration or by other imaging modalities (e.g., CT scan and RBC liver scan for hepatic hemangioma). The RI value of hepatocellular carcinoma was 0.69±0.096 and in metastatic tumors 0.73±0.079. The results showed no significant difference between the RI of hepatocellular carcinomas and metastatic liver tumors but it was significantly higher than benign lesions (p<0.05). RI of hemangiomas was 0.49±0.64 and in one hepatic adenoma was 0.65. When RI was <0.6 for benign liver tumors and ≥0.6 for malignant tumors we calculated a sensitivity of 89.14%, specificity of 66.7%, accuracy of 85.71% positive predictive value of 97.62% and negative predictive value of 28.57% in differentiating benign and malignant tumors. Thirty four of 46(73.9%) malignant lesions had intratumoral flow and 25% of benign lesions also showed intratumoral flow. The difference of intratumoral flow between malignant and benign lesions was significant (p<0.01). Two of 4 benign lesions (50%) had peritumoral vascularity where 6% of the malignant tumors showed peritumoral vascularity. In conclusion, combined studies of the type of intra-and peri-tumoral flow signals in CDFI and the parameter of RI would be more helpful in the differential diagnosis of benign and malignant liver tumors.

Effects of Solid Waste and Industrial Effluents on Water Quality of Turag River at Konabari Industrial Area, Gazipur, Bangladesh

The study was conducted to investigate the effects of solid waste and industrial effluents on the water quality of Turag River. Both the upstream and downstream sampled water from the selected points were analyzed for color, odor, pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), biological oxygen demand (BOD), copper (Cu), cadmium (Cd), iron (Fe), lead (Pb) and zinc (Zn) concentrations. Results of the study showed that the color of water was light to dark black and emitted noxious smell due to the industrial effluents. The upstream water was slightly alkaline with comparatively high DO content while low concentration of other parameters. The water after the solid waste and effluents received points as well as middle and downstream points was slightly alkaline with higher levels of other parameters when compared with upstream point. The minimum and maximum values of pH, EC, TDS, DO and BOD were 7.24-7.61, 425-2277 ?S/cm, 239-1349 ppm, 1.22-3.66 ppm and -2.44-0.86 ppm, respectively. The continuous dumping of waste materials resulted in a marked increase in the concentration of metals in the river water varied in the order of Fe > Zn > Pb > Cu > Cd. The study concluded that the downstream water in the river was almost polluted and unsuitable for human consumption and aquaculture purposes.DOI: http://dx.doi.org/10.3329/jesnr.v5i2.14817 J. Environ. Sci. & Natural Resources, 5(2): 213-218 2012