Search results for: clinical prediction rule

Authors: Fatemeh Sadeghi, Peyman Sheikhzadeh

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Introduction: Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm was recently developed to suppress excessive noise by applying a relative difference penalty. The aim of this study was to investigate the effect of various strengths of noise penalization factor in the BSREM algorithm under different acquisition duration and lesion sizes in order to determine an optimum penalty factor by considering both quantitative and qualitative image evaluation parameters in clinical uses. Materials and Methods: The NEMA IQ phantom and 15 clinical whole-body patients with prostate cancer were evaluated. Phantom and patients were injected withGallium-68 Prostate-Specific Membrane Antigen(68 Ga-PSMA)and scanned on a non-time-of-flight Discovery IQ Positron Emission Tomography/Computed Tomography(PET/CT) scanner with BGO crystals. The data were reconstructed using BSREM with a β-value of 100-500 at an interval of 100. These reconstructions were compared to OSEM as a widely used reconstruction algorithm. Following the standard NEMA measurement procedure, background variability (BV), recovery coefficient (RC), contrast recovery (CR) and residual lung error (LE) from phantom data and signal-to-noise ratio (SNR), signal-to-background ratio (SBR) and tumor SUV from clinical data were measured. Qualitative features of clinical images visually were ranked by one nuclear medicine expert. Results: The β-value acts as a noise suppression factor, so BSREM showed a decreasing image noise with an increasing β-value. BSREM, with a β-value of 400 at a decreased acquisition duration (2 min/ bp), made an approximately equal noise level with OSEM at an increased acquisition duration (5 min/ bp). For the β-value of 400 at 2 min/bp duration, SNR increased by 43.7%, and LE decreased by 62%, compared with OSEM at a 5 min/bp duration. In both phantom and clinical data, an increase in the β-value is translated into a decrease in SUV. The lowest level of SUV and noise were reached with the highest β-value (β=500), resulting in the highest SNR and lowest SBR due to the greater noise reduction than SUV reduction at the highest β-value. In compression of BSREM with different β-values, the relative difference in the quantitative parameters was generally larger for smaller lesions. As the β-value decreased from 500 to 100, the increase in CR was 160.2% for the smallest sphere (10mm) and 12.6% for the largest sphere (37mm), and the trend was similar for SNR (-58.4% and -20.5%, respectively). BSREM visually was ranked more than OSEM in all Qualitative features. Conclusions: The BSREM algorithm using more iteration numbers leads to more quantitative accuracy without excessive noise, which translates into higher overall image quality and lesion detectability. This improvement can be used to shorter acquisition time.

Keywords: BSREM reconstruction, PET/CT imaging, noise penalization, quantification accuracy

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Authors: Chi Wu, Dean Wu, Wen-Te Liu, Cheng-Yu Tsai, Shin-Mei Hsu, Yin-Tzu Lin, Ru-Yin Yang

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Background: The results of previous studies compared the benefits and risks of receiving insomnia medication. However, the effects between hypnotic drugs used and enhancement of sleep quality were still unclear. Objective: The aim of this study is to establish a prediction model for hypnotic drugs' dosage used for insomnia subjects and associated the relationship between sleep stage ratio change and drug types. Methodologies: According to American Academy of Sleep Medicine (AASM) guideline, sleep stages were classified and transformed to hypnogram via the polysomnography (PSG) in a hospital in New Taipei City (Taiwan). The subjects with diagnosis for insomnia without receiving hypnotic drugs treatment were be set as the comparison group. Conversely, hypnotic drugs dosage within the past three months was obtained from the clinical registration for each subject. Furthermore, the collecting subjects were divided into two groups for training and testing. After training convolution neuron network (CNN) to predict types of hypnotics used and dosages are taken, the test group was used to evaluate the accuracy of classification. Results: We recruited 76 subjects in this study, who had been done PSG for transforming hypnogram from their sleep stages. The accuracy of dosages obtained from confusion matrix on the test group by CNN is 81.94%, and accuracy of hypnotic drug types used is 74.22%. Moreover, the subjects with high ratio of wake stage were correctly classified as requiring medical treatment. Conclusion: CNN with hypnogram was potentially used for adjusting the dosage of hypnotic drugs and providing subjects to pre-screening the types of hypnotic drugs taken.

Keywords: convolution neuron network, hypnotic drugs, insomnia, polysomnography

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Authors: Karla Lebedoff, Susan Walsh, Michelle Bain

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Eczema is a chronic atopy condition requiring long-term daily management in children. Written action plans for other chronic atopic conditions, such as asthma and food allergies, are widely recommended and distributed to pediatric patients' parents and caregivers, seeking to improve clinical outcomes and become empowered to manage the patient's ever-changing symptoms. Written action plans for eczema, referred to as "asthma of the skin," are not routinely used in practice. Parents of children suffering from eczema rarely receive a written action plan to follow, and commendations supporting eczema action plans are inconsistent. Pediatric patients between birth and 18 years old who were followed for eczema at an urban Midwest community hospital were eligible to participate in this quality improvement project. At the initial visit, parents received instructions on individualized eczema action plans for their child and completed two validated surveys: Health Confidence Score (HCS) and Patient-Oriented Eczema Measure (POEM). Pre- and post-survey responses were collected, and clinical symptom presentation at follow-up were outcome determinants. Project implementation was guided by Institute for Healthcare Improvement's Step-up Framework and the Plan-Do-Study-Act cycle. This project measured clinical outcomes and parent confidence in self-management of their child's eczema symptoms with the responses from 26 participant surveys. Pre-survey responses were collected from 36 participants, though ten were lost to follow-up. Average POEM scores improved by 53%, while average HCS scores remained unchanged. Of seven completed in-person follow-up visits, six clinical progress notes documented improvement. Individualized eczema action plans can be seamlessly incorporated into primary and specialty care visits for pediatric patients suffering from eczema. Following a patient-specific eczema action plan may lessen the daily physical and mental burdens of uncontrolled eczema for children and parents, managing symptoms that chronically flare and recede. Furthermore, incorporating eczema action plans into practice potentially reduces the likely underestimated $5.3 billion economic disease burden of eczema on the U.S. healthcare system.

Keywords: atopic dermatitis, eczema action plan, eczema symptom management, pediatric eczema

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

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Authors: Priyadarsini Samal, Rajesh Singla

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Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

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Authors: Liane J. Ioannou, Jonathan Serpell, Cino Bendinelli, David Walters, Jenny Gough, Dean Lisewski, Win Meyer-Rochow, Julie Miller, Duncan Topliss, Bill Fleming, Stephen Farrell, Andrew Kiu, James Kollias, Mark Sywak, Adam Aniss, Linda Fenton, Danielle Ghusn, Simon Harper, Aleksandra Popadich, Kate Stringer, David Watters, Susannah Ahern

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BACKGROUND: There are significant variations in the management, treatment and outcomes of thyroid cancer, particularly in the role of: diagnostic investigation and pre-treatment scanning; optimal extent of surgery (total or hemi-thyroidectomy); use of active surveillance for small low-risk cancers; central lymph node dissections (therapeutic or prophylactic); outcomes following surgery (e.g. recurrent laryngeal nerve palsy, hypocalcaemia, hypoparathyroidism); post-surgical hormone, calcium and vitamin D therapy; and provision and dosage of radioactive iodine treatment. A proven strategy to reduce variations in the outcome and to improve survival is to measure and compare it using high-quality clinical registry data. Clinical registries provide the most effective means of collecting high-quality data and are a tool for quality improvement. Where they have been introduced at a state or national level, registries have become one of the most clinically valued tools for quality improvement. To benchmark clinical care, clinical quality registries require systematic measurement at predefined intervals and the capacity to report back information to participating clinical units. OBJECTIVE: The aim of this study was to develop a core set clinical indicators that enable measurement and reporting of quality of care for patients with thyroid cancer. We hypothesise that measuring clinical quality indicators, developed to identify differences in quality of care across sites, will reduce variation and improve patient outcomes and survival, thereby lessening costs and healthcare burden to the Australian community. METHOD: Preparatory work and scoping was conducted to identify existing high quality, clinical guidelines and best practice for thyroid cancer both nationally and internationally, as well as relevant literature. A bi-national panel was invited to participate in a modified Delphi process. Panelists were asked to rate each proposed indicator on a Likert scale of 1–9 in a three-round iterative process. RESULTS: A total of 236 potential quality indicators were identified. One hundred and ninety-two indicators were removed to reflect the data capture by the Australian and New Zealand Thyroid Cancer Registry (ANZTCR) (from diagnosis to 90-days post-surgery). The remaining 44 indicators were presented to the panelists for voting. A further 21 indicators were later added by the panelists bringing the total potential quality indicators to 65. Of these, 21 were considered the most important and feasible indicators to measure quality of care in thyroid cancer, of which 12 were recommended for inclusion in the final set. The consensus indicator set spans the spectrum of care, including: preoperative; surgery; surgical complications; staging and post-surgical treatment planning; and post-surgical treatment. CONCLUSIONS: This study provides a core set of quality indicators to measure quality of care in thyroid cancer. This indicator set can be applied as a tool for internal quality improvement, comparative quality reporting, public reporting and research. Inclusion of these quality indicators into monitoring databases such as clinical quality registries will enable opportunities for benchmarking and feedback on best practice care to clinicians involved in the management of thyroid cancer.

Keywords: clinical registry, Delphi survey, quality indicators, quality of care

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Authors: Samaila Muazu Bawa

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Hydration process, crack potential and setting time of concrete grade C30, C40 and C50 were separately monitored using non-contact electrical resistivity apparatus, a plastic ring mould and penetration resistance method respectively. The results show highest resistivity of C30 at the beginning until reaching the acceleration point when C50 accelerated and overtaken the others, and this period corresponds to its final setting time range, from resistivity derivative curve, hydration process can be divided into dissolution, induction, acceleration and deceleration periods, restrained shrinkage crack and setting time tests demonstrated the earliest cracking and setting time of C50, therefore, this method conveniently and rapidly determines the concrete’s crack potential. The highest inflection time (ti), the final setting time (tf) were obtained and used with crack time in coming up with mathematical models for the prediction of concrete’s cracking age for the range being considered. Finally, ANSYS numerical simulations supports the experimental findings in terms of the earliest crack age of C50 and the crack location that, highest stress concentration is always beneath the artificially introduced expansion joint of C50.

Keywords: concrete hydration, electrical resistivity, restrained shrinkage crack, ANSYS simulation

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

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Authors: Mir Mohammad Reza Hosseini

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In new years immune checkpoint inhibitors have gathered care as being one of the greatest talented kinds of immunotherapy on the prospect. There has been a specific emphasis on the immune checkpoint molecules, cytotoxic T-lymphocyte antigen-4 (CTLA-4) and programmed cell death protein 1 (PD-1). In 2011, ipilimumab, the primary antibody obstructive an immune checkpoint (CTLA4) was authorized. It is now documented that recognized tumors have many devices of overpowering the antitumor immune response, counting manufacture of repressive cytokines, staffing of immunosuppressive immune cells, and upregulation of coinhibitory receptors recognized as immune checkpoints. This was fast followed by the growth of monoclonal antibodies directing PD1 (pembrolizumab and nivolumab) and PDL1 (atezolizumab and durvalumab). Anti-PD1/PDL1 antibodies have developed some of the greatest extensively set anticancer therapies. We also compare and difference their present place in cancer therapy and designs of immune-related toxicities and deliberate the role of dual immune checkpoint inhibition and plans for the organization of immune-related opposing proceedings. In this review, the employed code and present growth of numerous immune checkpoint inhibitors are abridged, while the communicating device and new development of Immune checkpoint inhibitors in cancer therapy-based synergistic therapies with additional immunotherapy, chemotherapy, phototherapy, and radiotherapy in important and clinical educations in the historical 5 years are portrayed and tinted. Lastly, we disapprovingly measure these methods and effort to find their fortes and faintness based on pre-clinical and clinical information.

Keywords: checkpoint, cancer therapy, PD-1, PDL-1, CTLA4, immunosuppressive

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Authors: M. Patterson, R. Bond, K. Cowan, M. Mulvenna, C. Reid, F. McMahon, P. McGowan, H. Cormican

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This paper outlines the design of a simulator to allow for the optimisation of clinical workflows through a pathology laboratory and to improve the laboratory’s efficiency in the processing, testing, and analysis of specimens. Often pathologists have difficulty in pinpointing and anticipating issues in the clinical workflow until tests are running late or in error. It can be difficult to pinpoint the cause and even more difficult to predict any issues which may arise. For example, they often have no indication of how many samples are going to be delivered to the laboratory that day or at a given hour. If we could model scenarios using past information and known variables, it would be possible for pathology laboratories to initiate resource preparations, e.g. the printing of specimen labels or to activate a sufficient number of technicians. This would expedite the clinical workload, clinical processes and improve the overall efficiency of the laboratory. The simulator design visualises the workflow of the laboratory, i.e. the clinical tests being ordered, the specimens arriving, current tests being performed, results being validated and reports being issued. The simulator depicts the movement of specimens through this process, as well as the number of specimens at each stage. This movement is visualised using an animated flow diagram that is updated in real time. A traffic light colour-coding system will be used to indicate the level of flow through each stage (green for normal flow, orange for slow flow, and red for critical flow). This would allow pathologists to clearly see where there are issues and bottlenecks in the process. Graphs would also be used to indicate the status of specimens at each stage of the process. For example, a graph could show the percentage of specimen tests that are on time, potentially late, running late and in error. Clicking on potentially late samples will display more detailed information about those samples, the tests that still need to be performed on them and their urgency level. This would allow any issues to be resolved quickly. In the case of potentially late samples, this could help to ensure that critically needed results are delivered on time. The simulator will be created as a single-page web application. Various web technologies will be used to create the flow diagram showing the workflow of the laboratory. JavaScript will be used to program the logic, animate the movement of samples through each of the stages and to generate the status graphs in real time. This live information will be extracted from an Oracle database. As well as being used in a real laboratory situation, the simulator could also be used for training purposes. ‘Bots’ would be used to control the flow of specimens through each step of the process. Like existing software agents technology, these bots would be configurable in order to simulate different situations, which may arise in a laboratory such as an emerging epidemic. The bots could then be turned on and off to allow trainees to complete the tasks required at that step of the process, for example validating test results.

Keywords: laboratory-process, optimization, pathology, computer simulation, workflow

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Authors: Felicia Segeth, Florian G. Klein, Lea Berger, Andreas Kolk, Per S. Holm

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The entry of oncolytic viruses (OVs) into clinical application opens groundbreaking changes in current and future treatment regimens. However, despite their potent anti-cancer activity in vitro, clinical studies revealed limitations of OVs as monotherapy. The same applies to CDK 4/6 inhibitors (CDK4/6i) targeting cell cycle as well as bromodomain and extra-terminal domain inhibitors (BETi) targeting gene expression. In this study, the anti-tumoral effect of XVir-N-31, an YB-1 dependent oncolytic adenovirus, was evaluated in combination with Ribociclib, a CDK4/6i, and JQ1, a BETi. The head and neck squamous cell carcinoma (HNSCC) cell lines Fadu, SAS, and Cal-33 were used. DNA replication and gene expression of XVir-N-31 was measured by RT-qPCR, protein expression by western blotting, and cell lysis by SRB assays. Treatment with CDK4/6i and BETi increased viral gene expression, viral DNA replication, and viral particle formation. The data show that the combination of oncolytic adenovirus XVir-N-31 with CDK4/6i & BETi acts highly synergistic in cancer cell lysis. Furthermore, additional molecular analyses on this subject demonstrate that the positive transcription elongation factor P-TEFb plays a decisive role in this regard, indicating an influence of the combinational therapy on gene transcription control. The combination of CDK4/6i & BETi and XVir-N-31 is an attractive strategy to achieve substantial cancer cell killing and is highly suitable for clinical testing.

Keywords: adenovirus, BET, CDK4/6, HNSCC, P-TEFb, YB-1

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Authors: Wan-Ting Kuo, Yi-Wen Liu, Hsiao-Sheng Liu

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Annual incidence of bladder cancer increases in the world and occurs frequently in the male. Most common type is transitional cell carcinoma (TCC) which is treated by transurethral resection followed by intravesical administration of agents. In clinical treatment of bladder cancer, chemotherapeutic drugs-induced apoptosis is always used in patients. However, cancers usually develop resistance to chemotherapeutic drugs and often lead to aggressive tumors with worse clinical outcomes. Approximate 70% TCC recurs and 30% recurrent tumors progress to high-grade invasive tumors, indicating that new therapeutic agents are urgently needed to improve the successful rate of overall treatment. Nonapoptotic program cell death may assist to overcome worse clinical outcomes. Autophagy which is one of the nonapoptotic pathways provides another option for bladder cancer patients. Autophagy is reported as a potent anticancer therapy in some cancers. First of all, we established a mouse orthotopic bladder tumor formation model in order to create a similar tumor microenvironment. IVIS system and micro-ultrasound were utilized to noninvasively monitor tumor formation. In addition, we carried out intravesical treatment in our animal model to be consistent with human clinical treatment. In our study, we carried out intravesical instillation of the autophagy inducer in mouse orthotopic bladder tumor to observe tumor formation by noninvasive IVIS system and micro-ultrasound. Our results showed that bladder tumor formation is suppressed by the autophagy inducer, and there are no significant side effects in the physiology of mice. Furthermore, the autophagy inducer upregulated autophagy in bladder tissues of the treated mice was confirmed by Western blot, immunohistochemistry, and immunofluorescence. In conclusion, we reveal that a novel autophagy inducer with low side effects suppresses bladder tumor formation in our mouse orthotopic bladder tumor model, and it provides another therapeutic approach in bladder cancer patients.

Keywords: bladder cancer, transitional cell carcinoma, orthotopic bladder tumor formation model, autophagy

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Authors: Hassan Waseem

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Cytomegalovirus (CMV) is ubiquitously distributed viral agent responsible for different clinical manifestations that may vary according to the immunologic status of the patient. CMV can cause morbidity and mortality among fetuses and patients with compromised immune system. A cross-sectional study was carried out in Islamabad to investigate the prevalence and risk factors associated with CMV infection among pregnant women. Blood samples of 172 pregnant women visiting Mother and Child Healthcare, Pakistan Institute of Medical Sciences (PIMS) Islamabad were taken. In present study, serum samples of the women were checked for CMV-specific IgG and IgM antibodies by enzyme linked immunosorbent assay (ELISA). Clinical, obstetrical and socio-demographical characteristics of the women were collected by using structured questionnaires. Out of 172 pregnant women included in the study, 171 (99.4%) were CMV specific IgG positive and 30 (17.4%) were found positive for CMV-IgM antibodies. The CMV has taken an endemic form in Pakistan so, routine screening of CMV among pregnant women is recommended.

Keywords: Cytomegalovirus, blood transfusion, ELISA, seroprevalence

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Authors: Lojain Suliman, Xinrong Liu, Xiaohan Zhou

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Since most tunnels are built in crowded metropolitan settings, the excavation process must take place in highly condensed locations, including high-density cities. In this way, the tunnels are typically located close together, which leads to more interaction between the parallel existing tunnels, and this, in turn, leads to more settlement. This research presents an examination of the impact of a large-scale tunnel excavation on two forms of settlement: surface settlement and settlement surrounding the tunnel. Additionally, research has been done on the properties of interactions between two and three parallel tunnels. The settlement has been evaluated using three primary techniques: theoretical modeling, numerical simulation, and data monitoring. Additionally, a parametric investigation on how distance affects the settlement characteristic for parallel tunnels with short distances has been completed. Additionally, it has been observed that the sequence of excavation has an impact on the behavior of settlements. Nevertheless, a comparison of the model test and numerical simulation yields significant agreement in terms of settlement trend and value. Additionally, when compared to the FEM study, the suggested analytical solution exhibits reduced sensitivity in the settlement prediction. For example, the settlement of the small tunnel diameter does not appear clearly on the settlement curve, while it is notable in the FEM analysis. It is advised, however, that additional studies be conducted in the future employing analytical solutions for settlement prediction for parallel tunnels.

Keywords: settlement, FEM, analytical solution, parallel tunnels

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Authors: F. O. Adeosun, I. O. Orubuloye, O. O. Babalola

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Although caesarean section has greatly improved obstetric care throughout the world, in developing countries there is a great aversion to caesarean section. This study was carried out to examine the rate at which pregnant women with pre-eclampsia choose caesarean section over natural birth. A cross-sectional study was conducted among 500 pre-eclampsia antenatal clients seen at the States University Teaching Hospitals in the last one year. The sample selection was purposive. Information on their educational background, beliefs and attitudes were collected. Data analysis was presented using simple percentages. Out of 500 women studied, 38% favored caesarean section while 62% were against it. About 89% of them understood what caesarean section is, 57.3% of those who understood what caesarean section is will still not choose it as an option. Over 85% of the women believed caesarean section is done for medical reasons. If caesarean section is given as an option for childbirth, 38% would go for it, 29% would try religious intervention, 5.5% would not choose it because of fear, while 27.5% would reject it because they believe it is culturally wrong. Majority of respondents (85%) who favored caesarean delivery are aware of the risk attached to choosing virginal birth but go an extra mile in sourcing funds for a caesarean session while over 64% cannot afford the cost of caesarean delivery. It is therefore pertinent to encourage research in prediction methods and prevention of occurrence, since this would assist patients to plan on how to finance treatment.

Keywords: caesarean section, choice, cost, pre eclampsia, prediction methods

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: N. Nagalakshmi, Indira Bairy, M. Atulya, Jesil Mathew

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S. aureus has the ability to colonize and form biofilms on implanted biomaterials, which is difficult to disrupt, and current antimicrobial therapies for biofilms have largely proven unsuccessful in complete eradication of biofilm. The present study is aimed to determine the lysostaphin activity against biofilm producing MRSA clinical strains. The minimum biofilm inhibition activity of lysostaphin was studied against twelve strong biofilm producing isolates. The biofilm was produced in 96-wells micro-titer plate and biofilm was treated with lysostaphin (0.5 to 16 µg/ml), vancomycin (0.5 to 64 µg/ml) and linezolid (0.5 to 64 µg/ml). The biofilm inhibitory concentration of lysostaphin was found between 4 to 8 µg/ml whereas vancomycin and linezolid inhibited at concentration between 32 to 64 µg/ml. Results indicate that lysostaphin as potential antimicrobial activity against biofilm at lower concentration is comparable with routine antibiotics like vancomycin and linezolid.

Keywords: biofilm, lysostaphin, MRSA, minimum biofilm inhibitory concentration

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Rachid El Khoury

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Chronic pain is perhaps the most burdensome health issue facing the planet. Our understanding of the pathophysiology of chronic pain has increased substantially over the past 25 years, including but not limited to changes in the brain. However, we still do not know why chronic pain develops in some people and not in others. Most of the recent developments in pain science, that have direct relevance to clinical management, relate to our understanding of the role of the brain, the role of the immune system, or the role of cognitive and behavioral factors. Although the Biopsychosocial model of pain management was presented decades ago, the Bio-reductionist model remains, unfortunately, at the heart of many practices across professional and geographic boundaries. A large body of evidence shows that nociception is neither sufficient nor necessary for pain. Pain is a conscious experience that can certainly be, and often is, associated with nociception, however, always modulated by countless neurobiological, environmental, and cognitive factors. This study will clarify the current misconceptions of chronic pain concepts, and their misperceptions by clinicians. It will also attempt to bridge the considerable gap between what we already know on pain but somehow disregarded, the development in pain science, and clinical practice.

Keywords: chronic pain, nociception, biopsychosocial, neuroplasticity

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Authors: Aliyah Dosani, Kerri Alderson

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Two different simulations using standardized patients were developed to reinforce content and foster undergraduate nursing students’ practice and development of interpersonal skills in difficult clinical situations in the classroom. The live actor simulations focused on fostering interpersonal skills, traditionally considered by students to be simple and easy. However, seemingly straightforward interactions can be very stressful, particularly in women’s complex social/emotional situations. Supporting patients in these contexts is fraught with complexity and high emotion, requiring skillful support, assessment and intervention by a registered nurse. In this presentation, the personal and professional perspectives of the development, incorporation, and execution of the live actor simulations will be discussed, as well as the inclusion of student perceptions, and the learning gained by the involved faculty.

Keywords: adult learning, interpersonal skill development, simulation learning, teaching and learning

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Authors: Abdelmalk Brahma

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Drying is a phenomenon that accompanies the hardening of hydraulic materials. It can, if it is not prevented, lead to significant spontaneous dimensional variations, which the cracking is one of events. In this context, cracking promotes the transport of aggressive agents in the material, which can affect the durability of concrete structures. Drying shrinkage develops over a long period almost 30 years although most occurred during the first three years. Drying shrinkage stabilizes when the material is water balance with the external environment. The drying shrinkage of cementitious materials is due to the formation of capillary tensions in the pores of the material, which has the consequences of bringing the solid walls of each other. Knowledge of the shrinkage characteristics of concrete is a necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable shrinkage movement in reinforced or prestressed concrete and the appropriate steps can be taken in design to accommodate this movement. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes in consideration the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: drying, hydraulic concretes, shrinkage, modeling, prediction

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Authors: Heiko Jessen, Laura Tanus, Slobodan Ruzicic

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Objectives: The efficacy of Stribild®, an integrase strand transfer inhibitor (INSTI) -based STR, has been evaluated in randomized clinical trials and it has demonstrated durable capability in terms of achieving sustained suppression of HIV-1 RNA-levels. However, differences in monitoring frequency, existing selection bias and profile of patients enrolled in the trials, may all result in divergent efficacy of this regimen in routine clinical settings. The aim of this study was to assess the virologic outcomes, safety and tolerability profile of Stribild® in a routine clinical setting. Methods: This was a retrospective monocentric analysis on HIV-1-infected patients, who started with or were switched to Stribild®. Virological failure (VF) was defined as confirmed HIV-RNA>50 copies/ml. The minimum time of follow-up was 24 weeks. The percentage of patients remaining free of therapeutic failure was estimated using the time-to-loss-of-virologic-response (TLOVR) algorithm, by intent-to-treat analysis. Results: We analyzed the data of 197 patients (56 ART-naïve and 141 treatment-experienced patients), who fulfilled the inclusion criteria. Majority (95.9%) of patients were male. The median time of HIV-infection at baseline was 2 months in treatment-naïve and 70 months in treatment-experienced patients. Median time [IQR] under ART in treatment-experienced patients was 37 months. Among the treatment-experienced patients 27.0% had already been treated with a regimen consisting of two NRTIs and one INSTI, whereas 18.4% of them experienced a VF. The median time [IQR] of virological suppression prior to therapy with Stribild® in the treatment-experienced patients was 10 months [0-27]. At the end of follow-up (median 33 months), 87.3% (95% CI, 83.5-91.2) of treatment-naïve and 80.3% (95% CI, 75.8-84.8) of treatment-experienced patients remained free of therapeutic failure. Considering only treatment-experienced patients with baseline VL<50 copies/ml, 83.0% (95% CI, 78.5-87.5) remained free of therapeutic failure. A total of 17 patients stopped treatment with Stribild®, 5.4% (3/56) of them were treatment-naïve and 9.9% (14/141) were treatment-experienced patients. The Stribild® therapy was discontinued in 2 (1.0%) because of VF, loss to follow-up in 4 (2.0%), and drug-drug interactions in 2 (1.0%) patients. Adverse events were in 7 (3.6%) patients the reason to switch from therapy with Stribild® and further 2 (1.0%) patients decided personally to switch. The most frequently observed adverse events were gastrointestinal side effects (20.0%), headache (8%), rash events (7%) and dizziness (6%). In two patients we observed an emergence of novel resistances in integrase-gene. The N155H evolved in one patient and resulted in VF. In another patient S119R evolved either during or shortly upon switch from therapy with Stribild®. In one further patient with VF two novel mutations in the RT-gene were observed when compared to historical genotypic test result (V106I/M and M184V), whereby it is not clear whether they evolved during or already before the switch to Stribild®. Conclusions: Effectiveness of Stribild® for treatment-naïve patients was consistent with data obtained in clinical trials. The safety and tolerability profile as well as resistance development confirmed clinical efficacy of Stribild® in a daily practice setting.

Keywords: ART, HIV, integrase inhibitor, stribild

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Authors: Ishapathik Das

Abstract:

The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Maria L. G. Lourenço, Keylla H. N. P. Pereira, Viviane Y. Hibaru, Fabiana F. Souza, João C. P. Ferreira, Simone B. Chiacchio, Luiz H. A. Machado

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The neonatal care of cats and dogs represents a challenge to veterinarians due to the small size of the newborns and their physiological particularities. In addition, many Veterinary Medicine colleges around the world do not include neonatology in the curriculum, which makes it less likely for the veterinarian to have basic knowledge regarding neonatal care and worsens the clinical care these patients receive. Therefore, lack of assistance and negligence have become frequent in the field, which contributes towards the high mortality rates. This study aims at describing cases and the main conditions pertaining to the neonatology clinical routine in cats and dogs, highlighting the importance of specialized care in this field of Veterinary Medicine. The study included 808 neonates admitted to the São Paulo State University (UNESP) Veterinary Hospital, Botucatu, São Paulo, Brazil, between January 2018 and November 2019. Of these, 87.3% (705/808) were dogs and 12.7% (103/808) were cats. Among the neonates admitted, 57.3% (463/808) came from emergency c-sections due to dystocia, 8.7% (71/808) cane from vaginal deliveries with obstetric maneuvers due to dystocia, and 34% (274/808) were admitted for clinical care due to neonatal conditions. Among the neonates that came from emergency c-sections and vaginal deliveries, 47.3% (253/534) was born in respiratory distress due to severe hypoxia or persistent apnea and required resuscitation procedure, such as the Jen Chung acupuncture point (VG26), oxygen therapy with mask, pulmonary expansion with resuscitator, heart massages and administration of emergency medication, such as epinephrine. On the other hand, in the neonatal clinical care, the main conditions and alterations observed in the newborns were omphalophlebitis, toxic milk syndrome, neonatal conjunctivitis, swimmer puppy syndrome, neonatal hemorrhagic syndrome, pneumonia, trauma, low weight at birth, prematurity, congenital malformations (cleft palate, cleft lip, hydrocephaly, anasarca, vascular anomalies in the heart, anal atresia, gastroschisis, omphalocele, among others), neonatal sepsis and other local and systemic bacterial infections, viral infections (feline respiratory complex, parvovirus, canine distemper, canine infectious traqueobronchitis), parasitical infections (Toxocara spp., Ancylostoma spp., Strongyloides spp., Cystoisospora spp., Babesia spp. and Giardia spp.) and fungal infections (dermatophytosis by Microsporum canis). The most common clinical presentation observed was the neonatal triad (hypothermia, hypoglycemia and dehydration), affecting 74.6% (603/808) of the patients. The mortality rate among the neonates was 10.5% (85/808). Being knowledgeable about neonatology is essential for veterinarians to provide adequate care for these patients in the clinical routine. Adding neonatology to college curriculums, improving the dissemination of information on the subject, and providing annual training in neonatology for veterinarians and employees are important to improve immediate care and reduce the mortality rates.

Keywords: neonatal care, puppies, neonatal, conditions

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