Search results for: predictive microbiology

Authors: Suwapitch Chalongkulasak, Teerasak E-Kobon, Pramote Chumnanpuen

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Acne vulgaris is a common skin disease mainly caused by the Gram–positive pathogenic bacterium, Propionibacterium acnes. This bacterium stimulates inflammation process in human sebaceous glands. Giant African snail (Achatina fulica) is alien species that rapidly reproduces and seriously damages agricultural products in Thailand. There were several research reports on the medical and pharmaceutical benefits of this snail mucus peptides and proteins. This study aimed to in silico predict multifunctional bioactive peptides from A. fulica mucus peptidome using several bioinformatic tools for determination of antimicrobial (iAMPpred), anti–biofilm (dPABBs), cytotoxic (Toxinpred), cell membrane penetrating (CPPpred) and anti–quorum sensing (QSPpred) peptides. Three candidate peptides with the highest predictive score were selected and re-designed/modified to improve the required activities. Structural and physicochemical properties of six anti–P. acnes (APA) peptide candidates were performed by PEP–FOLD3 program and the five aforementioned tools. All candidates had random coiled structure and were named as APA1–ori, APA2–ori, APA3–ori, APA1–mod, APA2–mod and APA3–mod. To validate the APA activity, these peptide candidates were synthesized and tested against six isolates of P. acnes. The modified APA peptides showed high APA activity on some isolates. Therefore, our biomimetic mucus peptides could be useful for preventing acne vulgaris and further examined on other activities important to medical and pharmaceutical applications.

Keywords: Propionibacterium acnes, Achatina fulica, peptidomes, antibacterial peptides, snail mucus

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Md. Najiur Rahman

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This hypothesis reveals a profound and symmetrical connection that goes beyond the boundaries of quantum physics and cosmology, revolutionizing our understanding of the fundamental building blocks of the cosmos, given its name ‘The Theory of the Mystery’. This theory has an elegantly simple equation, “R = ∆r / √∆m” which establishes a beautiful and well-crafted relationship between the radius (R) of an elementary particle or galaxy, the relative change in radius (∆r), and the mass difference (∆m) between related entities. It is fascinating to note that this formula presents a super synchronization, one which involves the convergence of every basic particle and any single celestial entity into perfect alignment with its respective mass and radius. In addition, we have a Supporting equation that defines the mass-radius connection of an entity by the equation: R=√m/N, where N is an empirically established constant, determined to be approximately 42.86 kg/m, representing the proportionality between mass and radius. It provides precise predictions, collects empirical evidence, and explores the far-reaching consequences of theories such as General Relativity. This elegant symmetry reveals a fundamental principle that underpins the cosmos: each component, whether small or large, follows a precise mass-radius relationship to exert gravity by a universal law. This hypothesis represents a transformative process towards a unified theory of physics, and the pursuit of experimental verification will show that each particle and galaxy is bound by gravity and plays a unique but harmonious role in shaping the universe. It promises to reveal the great symphony of the mighty cosmos. The predictive power of our hypothesis invites the exploration of entities at the farthest reaches of the cosmos, providing a bridge between the known and the unknown.

Keywords: unified theory, quantum gravity, mass-radius relationship, dark matter, uniform gravity

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: John Masani Nduko, Joseph Wafula Matofari

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Many spontaneously fermented milk products are produced in Kenya, where they are integral to the human diet and play a central role in enhancing food security and income generation via small-scale enterprises. Fermentation enhances product properties such as taste, aroma, shelf-life, safety, texture, and nutritional value. Some of these products have demonstrated therapeutic and probiotic effects although recent reports have linked some to death, biotoxin infections, and esophageal cancer. These products are mostly processed from poor quality raw materials under unhygienic conditions resulting to inconsistent product quality and limited shelf-lives. Though very popular, research on their processing technologies is low, and none of the products has been produced under controlled conditions using starter cultures. To modernize the processing technologies for these products, our study aims at describing the microbiology and biochemistry of a representative Kenyan spontaneously fermented milk product, Mursik using modern biotechnology (DNA sequencing) and their chemical composition. Moreover, co-creation processes reflecting stakeholders’ experiences on traditional fermented milk production technologies and utilization, ideals and senses of value, which will allow the generation of products based on common ground for rapid progress will be discussed. Knowledge of the value of clean starting raw material will be emphasized, the need for the definition of fermentation parameters highlighted, and standard equipment employment to attain controlled fermentation discussed. This presentation will review the available information regarding traditional fermented milk (Mursik) and highlight our current research work on the application of molecular approaches (metagenomics) for the valorization of Mursik production process through starter culture/ probiotic strains isolation and identification, and quality and safety aspects of the product. The importance of the research and future research areas on the same subject will also be highlighted.

Keywords: lactic acid bacteria, high throughput biotechnology, spontaneous fermentation, Mursik

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Bummo Ahn

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Palpation is widely used to measure soft tissue firmness or stiffness in the living condition in order to apply detection, diagnosis, and treatment of tumors, scar tissue, abnormal muscle tone, or muscle spasticity. Since these methods are subjective and depend on the proficiency level, it is concluded that there are other diagnoses depending on the condition of the experts and the results are not objective. The mechanical property obtained by using the elasticity of the tissue is important to calculate a predictive variable for monitoring abnormal tissues. If the mechanical load such as reaction force on the foot increases in the same region under the same conditions, the mechanical property of the tissue is changed. Therefore, objective diagnosis is possible not only for experts but also for patients using this quantitative information. Furthermore, the portable system also allows non-experts to easily diagnose at home, not in hospitals or institutions. In this paper, we introduce a portable palpation system that can be used to measure the mechanical properties of human tissue, which can be applied to monitor diabetic foot ulceration patients with measuring the mechanical property change of foot tissue. The system was designed to be smaller and portable in comparison with the conventional palpation systems. It is consists of the probe, the force sensor, linear actuator, micro control unit, the display module, battery, and housing. Using this system, we performed validation experiments by applying different palpations (3 and 5 mm) to soft tissue (silicone rubber) and measured reaction forces. In addition, we estimated the elastic moduli of the soft tissue against different palpations and compare the estimated elastic moduli that show similar value even if the palpation depths are different.

Keywords: palpation probe, portable, diabetic foot ulceration, monitoring, mechanical property

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Garima Joshi, Pratap Sharan, V. Sreenivas, Nand Kumar, Kameshwar Prasad, Ashima N. Wadhawan

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Schizophrenia is a debilitating disorder and is marked by cognitive impairment, which deleteriously impacts the social and professional functioning along with the quality of life of the patients and the caregivers. Often the cognitive symptoms are in their prodromal state and worsen as the illness progresses; they have proven to have a good predictive value for the prognosis of the illness. It has been shown that intensive cognitive rehabilitation (CR) leads to improvements in the healthy as well as cognitively-impaired subjects. As the majority of population in India falls in the lower to middle socio-economic status and have low education levels, using the existing packages, a majority of which are developed in the West, for cognitive rehabilitation becomes difficult. The use of technology is also restricted due to the high costs involved and the limited availability and familiarity with computers and other devices, which pose as an impedance for continued therapy. Cognitive rehabilitation in India uses a plethora of retraining methods for the patients with schizophrenia targeting the functions of attention, information processing, executive functions, learning and memory, and comprehension along with Social Cognition. Psychologists often have to follow an integrative therapy approach involving social skills training, family therapy and psychoeducation in order to maintain the gains from the cognitive rehabilitation in the long run. This paper reviews the methodologies and cognitive retaining programs used in India. It attempts to elucidate the evolution and development of methodologies used, from traditional paper-pencil based retraining to more sophisticated neuroscience-informed techniques in cognitive rehabilitation of deficits in schizophrenia as home-based or supervised and guided programs for cognitive rehabilitation.

Keywords: schizophrenia, cognitive rehabilitation, neuropsychological interventions, integrated approached to rehabilitation

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Authors: Recep Kesli, Gulsah Asik, Cengiz Demir, Onur Turkyilmaz

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Objective: Stenotrophomonas maltophilia (S. maltophilia) has recently emerged as an important nosocomial microorganism. Treatment of invasive infections caused by this organism is problematic because this microorganism is usually resistant to a wide range of commonly used antimicrobials. We aimed to evaluate clinical isolates of S. maltophilia in respect to sampling sites and antimicrobial resistant. Method: During a two years period (October 2013 and September 2015) eighteen samples collected from the intensive care unit (ICU) patients hospitalized in Afyon Kocatepe University, ANS Practice and Research Hospital. Identification of the bacteria was determined by conventional methods and automated identification system-VITEK 2 (bio-Mérieux, Marcy l’toile, France). Antibacterial resistance tests were performed by Kirby Bauer disc (Oxoid, England) diffusion method following the recommendations of CLSI. Results: Eighteen S. maltophilia strains were identified as the causative agents of different infections. The main type of infection was lower respiratory tract infection (83,4 %); three patients (16,6 %) had bloodstream infection. While, none of the 18 S. maltophilia strains were found to be resistant against to trimethoprim sulfametaxasole (TMP-SXT) and levofloxacine, eight strains 66.6 % were found to be resistant against ceftazidim. Conclusion: The isolation of S.maltophilia starains resistant to TMP-SXT is vital. In order to prevent or minimize infections due to S. maltophilia such precuations should be utilized: Avoidance of inappropriate antibiotic use, prolonged implementation of foreign devices, reinforcement of hand hygiene practices and the application of appropriate infection control practices. Microbiology laboratories also may play important roles in controlling S. maltophilia infections by monitoring the prevalence, continuously, the provision of local antibiotic resistance paterns data and the performance of synergistic studies also may help to guide appropirate antimicrobial therapy choices.

Keywords: Stenotrophomonas maltophilia, trimethoprim-sulfamethoxazole, antimicrobial resistance, Stenotrophomonas spp.

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Authors: Larissa Burruano, Sergey Grabovyj, Irina Nguen

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The study aimed to assess the level of adherence to anti retroviral therapy (ART) and to examine the relationship between adherence and risk behavior factor (drug use) among patients infected with HIV. The patients with newly diagnosed or established HIV infection under follow-up at the Sumskij Regional Centre for AIDS Prevention in Ukraine were eligible for this study. Medical records were used to measure the patient’s adherence to medication. Measurements were obtained at month 6 and at month 12 to calculate the number of medication omission during the past 30 days: (on a 2-point scale – once until three in a month – were considered adherent, three and more in a month – were considered non-adherent). Of the 50 study participants, 27 (54.0%) were men and 23 (46.0%) women. The mean age is 35.2 years (SD= 5.1). A majority of the patients (82.0%) is in the age group of 25-30 years. The main level of adherence was 74.0% and 66.0% at 6 and 12 months, respectively. The main routes of HIV transmission were drug injection among men 12 (44.4%) and sexual contact among women 11 (47.8%). Univariate analyses indicated that patients who had lower level of education were more likely to have been non-adherent at month 6- (X2 =5.1, n=50, p < .05) and at month 12 (X2 = 4.34, n=50, p < .05). Multivariate tests showed that only age (OR= 1.163 [95% CI 0.98–1.370]) was significant independent predictor of treatment adherence, while gender, education, employment status were not predictive for the risk of developing non-compliance. There was not a significant interaction between non-adherence and intravenous drug use. Consistent with these findings, younger people were more likely to have missed a dose of their medication because they had a greater sense of invulnerability than older patients. The study indicates that the socio demographic characteristic should be taken into an account in the future research regarding adherence in the case of HIV infection. If the patient anti retroviral adherence can be improved by qualitatively better medical care in all regions of the Ukraine, behavioral changes in the population can to be expected in the long term.

Keywords: HIV, antiretroviral therapy, adherence, Ukraine, Eastern Europe

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Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Yohannes Bisa Biramo

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This study examined the meditational role of surface acting and deep acting on the relationship between calling orientation and work engagement of teachers in secondary schools of Wolaita Zone, Wolaita, Ethiopia. A predictive non-experimental correlational design was performed among 300 secondary school teachers. Stratified random sampling followed by a systematic random sampling technique was used as the basis for selecting samples from the target population. To analyze the data, Structural Equation Modeling (SEM) was used to test the association between the independent variables and the dependent variables. Furthermore, the goodness of fit of the study variables was tested using SEM to see and explain the path influence of the independent variable on the dependent variable. Confirmatory factor analysis (CFA) was conducted to test the validity of the scales in the study and to assess the measurement model fit indices. The analysis result revealed that calling was significantly and positively correlated with surface acting, deep acting and work engagement. Similarly, surface acting was significantly and positively correlated with deep acting and work engagement. And also, deep acting was significantly and positively correlated with work engagement. With respect to mediation analysis, the result revealed that surface acting mediated the relationship between calling and work engagement and also deep acting mediated the relationship between calling and work engagement. Besides, by using the model of the present study, the school leaders and practitioners can identify a core area to be considered in recruiting and letting teachers teach, in giving induction training for newly employed teachers and in performance appraisal.

Keywords: calling, surface acting, deep acting, work engagement, mediation, teachers

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Authors: Yuexin Liu, Gordon B. Mills, Russell R. Broaddus, John N. Weinstein

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The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to the high-stage diseases. However, there are no available biomarkers that predict EEC patient staging at the time of diagnosis. We aim to develop a predictive scheme to help in this regards. Using reverse-phase protein array expression profiles for 210 EEC cases from The Cancer Genome Atlas (TCGA), we constructed a Protein Scoring of EEC Staging (PSES) scheme for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MD Anderson Cancer Center (MDACC) using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used. PSES robustly distinguished high- from low-stage tumors in the TCGA cohort (area under the ROC curve [AUC]=0.74; 95% confidence interval [CI], 0.68 to 0.82) and in the validation cohort (AUC=0.67; 95% CI, 0.58 to 0.76). Even among grade 1 or 2 tumors, PSES was significantly higher in high- than in low-stage tumors in both the TCGA (P = 0.005) and MDACC (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in high-stage tumors. PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Keywords: endometrial carcinoma, protein, protein scoring of EEC staging (PSES), stage

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Authors: Mohan Bilikallahalli Sannathimmappa, Vinod Nambiar, Rajeev Aravindakshan

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Background: Image-based teaching-learning module is innovative student-centered andragogy. The objective of our study was to explore medical students’ perception of effectiveness of image-based learning strategy in promoting their lifelong learning skills and evaluate its impact on improving students’ exam grades. Methods: A prospective single-cohort study was conducted on undergraduate medical students of the academic year 2021-22. The image-based teaching-learning module was assessed through pretest, posttest, and exam grades. Students’ feedback was collected through a predesigned questionnaire on a 3-point Likert Scale. The reliability of the questionnaire was assessed using Cronbach’s alpha coefficient test. In-Course Exam-4 results were compared with In-Course Exams 1, 2, and 3. Correlation coefficients were worked out wherever relevant to find the impact of the exercise on grades. Data were collected, entered into Microsoft Excel, and statistically analyzed using SPSS version 22. Results: In total, 127 students were included in the study. The posttest scores of the students were significantly high (24.75±) as compared to pretest scores (8.25±). Students’ opinion towards the effectiveness of image-based learning in promoting their lifelong learning skills was overwhelmingly positive (Cronbach’s alpha for all items was 0.756). More than 80% of the students indicated image-based learning was interesting, encouraged peer discussion, and helped them to identify, explore, and revise key information and knowledge improvement. Nearly 70% expressed image-based learning enhanced their critical thinking and problem-solving skills. Nine out of ten students recommended image-based learning module for future topics. Conclusion: Overall, Image-based learning was found to be effective in achieving undergraduate medical students learning outcomes. The results of the study are in favor of the implementation of Image-based learning in Microbiology courses. However, multicentric studies are required to authenticate our study findings.

Keywords: active learning, knowledge, medical education, microbes, problem solving

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Authors: Zixuan Zhao, Lingbin Du, Le Wang, Youqing Wang, Yi Yang, Jingjun Chen, Hengjin Dong

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Objectives: Few results from public attitudes for lung cancer screening are available both in China and abroad. This study aimed to identify preferred lung cancer screening modalities in a Chinese population and predict uptake rates of different modalities. Materials and Methods: A discrete choice experiment questionnaire was administered to 392 Chinese individuals aged 50–74 years who were at high risk for lung cancer. Each choice set had two lung screening options and an option to opt-out, and respondents were asked to choose the most preferred one. Both mixed logit analysis and stepwise logistic analysis were conducted to explore whether preferences were related to respondent characteristics and identify which kinds of respondents were more likely to opt out of any screening. Results: On mixed logit analysis, attributes that were predictive of choice at 1% level of statistical significance included the screening interval, screening venue, and out-of-pocket costs. The preferred screening modality seemed to be screening by low-dose computed tomography (LDCT) + blood test once a year in a general hospital at a cost of RMB 50; this could increase the uptake rate by 0.40 compared to the baseline setting. On stepwise logistic regression, those with no endowment insurance were more likely to opt out; those who were older and housewives/househusbands, and those with a health check habit and with commercial endowment insurance were less likely to opt out from a screening programme. Conclusions: There was considerable variance between real risk and self-perceived risk of lung cancer among respondents, and further research is required in this area. Lung cancer screening uptake can be increased by offering various screening modalities, so as to help policymakers further design the screening modality.

Keywords: lung cancer, screening, China., discrete choice experiment

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Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Richa Sinha, Mohammad Irfan Javed, Sanjay Singh

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Introduction: Good antimicrobial prescribing reduces length of stay in hospital, risk of adverse events, antimicrobial resistance, and unnecessary hospital expenditure. The aim of this prospective audit was to identify any problems with antimicrobial prescribing including documentation of the relevant aspects as well as appropriateness of antibiotics use. The audit was conducted on the surgical wards in a teaching hospital in the UK. Methods: Standards included the indication, duration, choice, and prescription of antibiotic should be in line with current Regional Guidelines and should be clearly documented on the prescription chart. There should be an entry in each patients’ medical record of the diagnosis and indication for each acute antibiotic prescription issued. All prescriptions should clearly document the route, frequency and dose of antibiotic. Data collection was done for 2 weeks in the month of March 2014. A proforma including all the questions above was completed for all the patients. The results were analysed using Excel. Results: 35 patients in total were selected for the audit. 85.7% of patients had indication of antibiotic documented on the prescription chart and 68.5% of patients had indication documented in the notes. The antibiotic used was in line with hospital guidelines in 45.7% of patients, however, in a further 28.5% of patients the reason for the antibiotic prescription was microbiology approved. Therefore, in total 74.2% of patients had been prescribed appropriate antibiotics. The duration of antibiotic was documented in 68.6% of patients and the antibiotic was reviewed in 37.1% of patients. The dose, frequency and route was documented clearly in 100% of patients. Conclusion: Overall, prescribing can be improved on the surgical wards in this hospital. Only 37.1% of patients had clear documentation of a review of antibiotics. It may be that antibiotics have been reviewed but this should be clearly highlighted on the prescription chart or the notes. Failure to review antibiotics can lead to poor patient care and antimicrobial resistance and therefore it is important to address this. It is also important to address the appropriateness of antibiotics as inappropriate antibiotic prescription can lead to failure of treatment as well as antimicrobial resistance. The good points from the audit was that all patients had clear documentation of dose, route and frequency which is extremely important in the administration of antibiotics. Recommendations from this audit included to emphasize good antimicrobial prescribing at induction (twice yearly), an antimicrobial handbook for junior doctors, and re-audit in 6 months time.

Keywords: prescribing, antimicrobial, indication, duration

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Nouira Mariem, Ennigrou Samir

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Background and aims: Healthcare-associated infections (HAI) represent a major public health problem worldwide. They represent one of the most serious adverse events in health care. The objectives of our study were to estimate the prevalence of HAI at the Charles Nicolle Hospital (CNH) and to identify the main associated factors as well as to estimate the frequency of antibiotic use. Methods: It was a cross-sectional study at the CNH with a unique passage per department (October-December 2018). All patients present at the wards for more than 48 hours were included. All patients from outpatient consultations, emergency, and dialysis departments were not included. The site definitions of infections proposed by the Centers for Disease Control and Prevention (CDC) were used. Only clinically and/or microbiologically confirmed active HAIs were included. Results: A total of 318 patients were included, with a mean age of 52 years and a sex ratio (female/male) of 1.05. A total of 41 patients had one or more active HAIs, corresponding to a prevalence of 13.1% (95% CI: 9.3%-16.9%). The most frequent site infections were urinary tract infections and pneumonia. Multivariate analysis among adult patients (>=18 years) (n=261) revealed that infection on admission (p=0.01), alcoholism (p=0.01), high blood pressure (p=0.008), having at least one invasive device inserted (p=0.004), and history of recent surgery (p=0.03), increased the risk of HAIs significantly. More than 1 of 3 patients (35.4%) were under antibiotics on the day of the survey, of which more than half (57.4%) were under two or more types of antibiotics. Conclusion: The prevalence of HAIs and antibiotic prescriptions at the CNH were considerably high. An infection prevention and control committee, as well as the development of an antibiotic stewardship program with continuous monitoring using repeated prevalence surveys, must be implemented to limit the frequency of these infections effectively.

Keywords: prevalence, healthcare associated infection, antibiotic, Tunisia

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Authors: Ali Ghorbanipour

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The aim of this study is to investigate the drug resistance pattern and the value of the MIC (minimum inhibitory concentration)method to reduce the impact of infectious diseases and the slow development of resistance. Method: The study was conducted on clinical specimens collected between 2018 to 2021. identification of isolates and antibiotic susceptibility testing were performed using conventional biochemical tests. Antibiotic resistance was determined using kibry-Bauer disk diffusion and MIC by E-test methods comparative with microdilution plate elisa method. Results were interpreted according to CLSI. Results: Out of 249600 different clinical specimens, 18720 different pathogenic bacteria by overall detection ratio 7.7% were detected. Among pathogen bacterial were Gram negative bacteria (70%,n=13000) and Gram positive bacteria(30%,n=5720).Medically relevant gram-negative bacteria include a multitude of species such as E.coli , Klebsiella .spp , Pseudomonas .aeroginosa , Acinetobacter .spp , Enterobacterspp ,and gram positive bacteria Staphylococcus.spp , Enterococcus .spp , Streptococcus .spp was isolated . Conclusion: Our results highlighted that the resistance ratio among Gram Negative bacteria and Gram positive bacteria with different infection is high it suggest constant screening and follow-up programs for the detection of antibiotic resistance and the value of MIC drug susceptibility reporting that provide a new way to the usage of resistant antibiotic in combination with other antibiotics or accurate weight of antibiotics that inhibit or kill bacteria. Evaluation of wrong medication in the expansion of resistance and side effects of over usage antibiotics are goals. Ali ghorbanipour presently working as a supervision at the microbiology department of Massoud medical laboratory. Iran. Earlier, he worked as head department of pulmonary infection in firoozgarhospital, Iran. He received master degree in 2012 from Fergusson College. His research prime objective is a biologic wound dressing .to his credit, he has Published10 articles in various international congresses by presenting posters.

Keywords: antimicrobial profile, MIC & MBC Method, microplate antimicrobial assay, E-test

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Authors: Junaid Mahmood Alam, Sana Anwar, Sarah Sughra Asghar

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Chronic hepatitis or Hepatitis C viral (HCV) infection has been identified as one of the factors that could elicit autoimmune disease resulting in the development of auto-antibodies. Furthermore, HCV is implicated in contravening of forbearance to antigens, therefore, inciting auto-reactivity. In this regard, several near and past studies noted the prevalence of thyroid dysfunction and production of anti-thyroid antibodies (ATAb) such as anti-thyroid peroxidase (AntiTPO) and anti-thyroglobulin (AntiTG) in patients with HCV. Likewise, one of the etiologies of augmentation of thyroid disease is basically interferon therapy for HCV infections, for which a number of autoimmune diseases have been noted including Grave’s disease, Hishimoto thyroiditis. A prospectively case-control study was therefore carried out at department of clinical biochemistry lab services and chemical pathology in collaboration with department of clinical microbiology, at Liaquat National Hospital and Medical College, Karachi Pakistan for the period January 2015 to December 2017. Two control groups were inducted for comparison purpose, control group 1 = without HCV infection and with thyroid disorders (n = 20), control group 2 = with HCV infection and without thyroid disorders (n = 20), whereas HCV infected were n = 40 where more than half were noted to be positive for either of HCV IgG and Ag. In HCV group, patients with existing sub-clinical hypothyroidism and clinical hyperthyroidism were less than 5%. Analysis showed the presence of AntiTG in 12 HCV patients (30%), AntiTPO in 15 (37.5%) and both AntiTG and antiTPO in 10 patients (25%). Only 3 patients were found with the history of anti-thyroid auto-antibodies (7.5%) and one with parents and relatives with auto-immune disorders (2.5%). Patients that remained untreated were 12 (30%), under treatment 18 (45%) and with complete-course of treatment 10 (25%). As per review of the literature, meta-analysis of evident data and cross-sectional studies of selective cohorts (as studied in presented research), thyroid connection is designated as one of the most recurrent endocrine ailment associated with chronic HCV infection. Moreover, it also represents an extrahepatic disease in the continuum of HCV syndrome. In conclusion, HCV patients were more likely to encompass thyroid disorders especially related to development of either of ATAb or both antiTG and AntiTPO.

Keywords: Hepatitis C viral (HCV) infection, anti-thyroid antibodies, anti-thyroid peroxidase antibodies, anti-thyroglobulin antibodies

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Authors: Osama Zayed

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Aim of the work: The aim of this study is to assess the predictors of pelvic vascular injuries in patients with pelvic fractures from major blunt trauma. Methods: This study was conducted as a tool-assessment study. Forty six patients with pelvic fractures from major blunt trauma will be recruited to the study arriving to department of emergency, Suez Canal University Hospital. Data were collected from questionnaire including; personal data of the studied patients and full medical history, clinical examinations, outcome measures (The Physiological and Operative Severity Score for enumeration of Mortality and morbidity (POSSUM), laboratory and imaging studies. Patients underwent surgical interventions or further investigations based on the conventional standards for interventions. All patients were followed up during conservative, operative and post-operative periods in the hospital for interpretation the predictive scores of vascular injuries. Results: Significant predictors of vascular injuries according to computed tomography (CT) scan include age, male gender, lower Glasgow coma (GCS) scores, occurrence of hypotension, mortality rate, higher physical POSSUM scores, presence of ultrasound collection, type of management, higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) POSSUM scores, presence of abdominal injuries, and poor outcome. Conclusions: There was higher frequency of males than females in the studied patients. There were high probability of morbidity and low probability of mortality among patients. Our study demonstrates that POSSUM score can be used as a predictor of vascular injury in pelvis fracture patients.

Keywords: predictors, pelvic vascular injuries, pelvic fractures, major blunt trauma, POSSUM

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Authors: Dawit Abay Tesfamariam

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Ethiopia’s Climate- Resilient Green Economy strategy focuses mainly on generating and utilization of Renewable Energy (RE). The data collected in 2016 by Ethiopian Electric Power (EEP) indicates that the intermittent RE sources on the grid from solar and wind energy were only 8 % of the total energy produced. On the other hand, the EEP electricity generation plan in 2030 indicates that 36 % of the energy generation share will be covered by solar and wind sources. Thus, a case study was initiated to model and compute the balance and consumption of electricity in three different scenarios: 2016, 2025, and 2030 using the Energy PLAN Model (EPM). Initially, the model was validated using the 2016 annual power-generated data to conduct the EPM analysis for two predictive scenarios. The EPM simulation analysis using EPM for 2016 showed that there was no significant excess power generated. Hence, the model’s results are in line with the actual 2016 output. Thus, the EPM was applied to analyze the role of energy storage in RE in Ethiopian grid systems. The results of the EPM simulation analysis showed there will be excess production of 402 /7963 MW average and maximum, respectively, in 2025. The excess power was dominant in all months except in the three rainy months of the year (June, July, and August). Consequently, based on the validated outcomes of EPM indicates, there is a good reason to think about other alternatives for the utilization of excess energy and storage of RE. Thus, from the scenarios and model results obtained, it is realistic to infer that; if the excess power is utilized with a storage mechanism that can stabilize the grid system; as a result, the extra RE generated can be exported to support the economy. Therefore, researchers must continue to upgrade the current and upcoming energy storage system to synchronize with RE potentials that can be generated from RE.

Keywords: renewable energy, storage, wind, energyplan

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: Ofonime Ogba, Augustine Akpan

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Objectives and Goals: The study was aimed at determining pathogenic microbial carriage on the external body parts of Camponotus consobrinus which is also known as the banded sugar ant because of its liking for sugar and sweet food. The level of pathogenic microbial carriage of Camponotus consobrinus in association to the environment in which they have been collected is not known. Methods: The ants were purposively collected from four locations including the kitchens, bedroom of various homes, food shops, and bakeries. The sample collection took place within the hours of 6:30 pm to 11:00 pm. The ants were trapped in transparent plastic containers of which sugar, pineapple peels, sugar cane and soft drinks were used as bait. The ants were removed with a sterile spatula and put in 10mls of peptone water in sterile universal bottles. The containers were vigorously shaken to wash the external surface of the ant. It was left overnight and transported to the Microbiology Laboratory, University of Calabar Teaching Hospital for analysis. The overnight peptone broths were inoculated on Chocolate agar, Blood agar, Cystine Lactose Electrolyte-Deficient agar (CLED) and Sabouraud dextrose agar. Incubation was done aerobically and in a carbon dioxide jar for 24 to 48 hours at 37°C. Isolates were identified based on colonial characteristics, Gram staining, and biochemical tests. Results: Out of the 250 Camponotus consobrinus caught for the study, 90(36.0%) were caught in the kitchen, 75(30.0%) in the bedrooms 40(16.0%) in the bakery while 45(18.0%) were caught in the shops. A total of 82.0% prevalence of different microbial isolates was associated with the ants. The kitchen had the highest number of isolates 75(36.6%) followed by the bedroom 55(26.8%) while the bakery recorded the lowest number of isolates 35(17.1%). The profile of micro-organisms associated with Camponotus consobrinus was Escherichia coli 73(30.0%), Morganella morganii 45(18.0%), Candida species 25(10.0%), Serratia marcescens 10(4.0%) and Citrobacter freundii 10(4.0%). Conclusion: Most of the Camponotus consobrinus examined in the four locations harboured potential pathogens. The presence of ants in homes and shops can facilitate the propagation and spread of pathogenic microorganisms. Therefore, the development of basic preventive measures and the control of ants must be taken seriously.

Keywords: Camponotus consobrinus, potential pathogens, microbial isolates, spread

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Authors: A. Kherraf, M. Bouziane, A. Drighil, L. Azzouzi, R. Habbal

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Objective: Myxomas are rare heart tumors most commonly found in the left atrium. The purpose of this observation is to report a rare case of myxoma of the right atrium revealed by pulmonary embolism. Observation: A 34-year-old patient with no history presented to the emergency room with sudden onset dyspnea. Clinical examination showed arterial pressure at 110/70mmHg, tachycardia at 110bpm, and 90% oxygen saturation. The ECG enrolled in incomplete right bundle branch block. The radio-thorax was normal. Echocardiography revealed the presence of a large homogeneous intra-OD mass, contiguous to the inter-atrial septum, prolapsing through the tricuspid valve, and causing mild tricuspid insufficiency, with dilation of the right ventricle and retained systolic function with PAPs estimated at 45mmHg. A chest scan was performed, revealing the presence of right segmental pulmonary embolism. The patient was put under anticoagulant and underwent surgical resection of the mass; its pathological examination concluded to a myxoma. The post-operative consequences were simple, without recurrence of the mass after one year follow-up. Discussion: Myxomas represent 50% of heart tumors. Most often, they originate in the left atrium, and more rarely in the right atrium or the ventricles. Myxoma of the right atrium can be responsible for life-threatening pulmonary embolism. The most predictive factor for embolization remains the morphology of the myxomas; papillary or villous myxomas are the most friable. Surgery is the standard treatment, with regular postoperative follow-up to detect recurrence. Conclusion: Myxomas of the right atrium are a rare location for these tumors. Pulmonary embolism is the main complication and should routinely involve careful study of the right chambers on echocardiography.

Keywords: pulmonary embolism, myxoma, right atrium, heart tumors

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Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

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Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

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Authors: S. Da Costa, D. Páez, F. Sánchez

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We examine the causal role of positive affect on creativity, the association of creativity or innovation in the ideation phase with functional emotional regulation, successful adjustment to stress and dispositional emotional creativity, as well as the predictive role of creativity for positive emotions and social adjustment. The study examines the effects of modification of positive affect on creativity. Participants write three poems, narrate an infatuation episode, answer a scale of personal growth after this episode and perform a creativity task, answer a flow scale after creativity task and fill a dispositional emotional creativity scale. High and low positive effect was induced by asking subjects to write three poems about high and low positive connotation stimuli. In a neutral condition, tasks were performed without previous affect induction. Subjects on the condition of high positive affect report more positive and less negative emotions, more personal growth (effect size r = .24) and their last poem was rated as more original by judges (effect size r = .33). Mediational analysis showed that positive emotions explain the influence of the manipulation on personal growth - positive affect correlates r = .33 to personal growth. The emotional creativity scale correlated to creativity scores of the creative task (r = .14), to the creativity of the narration of the infatuation episode (r = .21). Emotional creativity was also associated, during performing the creativity task, with flow (r = .27) and with affect balance (r = .26). The mediational analysis showed that emotional creativity predicts flow through positive affect. Results suggest that innovation in the phase of ideation is associated with a positive affect balance and satisfactory performance, as well as dispositional emotional creativity is adaptive.

Keywords: affectivity, creativity, induction, innovation, psychological factors

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