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Influence of threshold selection strategy on the prognostic accuracy of chest CT severity score for mortality prediction of COVID-19 patients
No full textImpact of interferon-induced transmembrane protein 3 gene rs12252 polymorphism on COVID-19 mortality
No full textBackground and aims: Interferon-induced transmembrane protein 3 (IFITM3) plays a critical role in the adaptive and innate immune response by preventing membrane hemifusion between the host and viral cell cytoplasm. This study aimed to evaluate whether IFITM3 rs12252 polymorphism is related to an increased mortality rate of coronavirus disease 2019 (COVID-19). Methods: The IFITM3 rs12252 polymorphism was genotyped using the amplification refractory mutation systempolymerase chain reaction (ARMS-PCR) in 548 dead and 630 improved patients positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: In the present study, the minor allele frequency of IFITM3 rs12252 (C) was significantly more frequent in dead patients than in improved cases. The results of the multivariate logistic regression analysis indicated that the lower lipid profiles, PCR Ct value, 25-hydroxyvitamin D, and uric acid and higher levels of erythrocyte sedimentation rate (ESR), liver enzymes, and creatinine, and IFITM3 rs12252 CC genotypes were related to the COVID-19 infection mortality. Conclusions: In summary, our findings suggested a possible link between the mortality of COVID-19 infection, the CC genotypes of IFITM3 rs12252, and clinical parameters. Further investigations are required worldwide to prove the link relationship of COVID-19 mortality with host genetic factors
Neurological manifestations in patients with COVID-19: A systematic review and meta-analysis
No full textIntroduction The intensification of coronavirus disease 2019 (COVID-19) complications, severe symptoms, and high mortality rate has led researchers to focus on this significant issue. While respiratory and cardiac complications have been described as high-risk manifestations in patients with COVID-19, neurological complications can also enhance mortality. This study aimed to evaluate the prevalence of neurological complications arises from SARS-CoV-2 and assess the mortality rate from neurological complications. Material and Methods Literature review was conducted by searching in PubMed/Medline, Web of Sciences, and Embase. After performing search strategies with relevant terms, a number of articles were excluded, including review articles, systematic review or meta-analysis, duplicate publication of same researchers, congress abstracts, animal studies, case reports, case series, and articles reporting a history of neurological features prior to COVID-19 infection. After retrieving the data, statistical analysis was performed using the STATA Version 14 software. Results From 4455 retrieved publications, 20 articles were selected for further analysis. Among 18,258 included patients, 2791 showed neurological symptoms, which were classified into different groups. Headache, confusion, and fatigue were reported as the most non-specific neurological features in confirmed COVID-19 patients. Psychiatric symptoms, CNS disorders, cerebrovascular disorders, CNS inflammatory disorders, PNS disorders, neuromuscular disorders, etc., were defined as specific neurological manifestations. The pooled prevalence of neurological manifestations and mortality rate of COVID-19 patients with neurological features were estimated to be 23.0 (95 CI: 17.8-29.2) and 29.1 (95 CI: 20.3-39.8), respectively. Conclusion Neurological manifestations may commonly happen in patients with COVID-19. This study reported a high prevalence of neurological complications and mortality rates in COVID-19 patients. Therefore, patients with COVID-19 who indicated neurological symptoms should be taken seriously and should receive early treatment to prevent undesirable events
Anti-biofilm properties of eucalyptol in combination with antifungals against Candida albicans isolates in patients with hematological malignancy (vol 204, 295, 2022)
No full textPrediction of successful aging using ensemble machine learning algorithms
No full textBackground Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA. Methods In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naive Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models. Results The experimental results revealed that the predictive system has been more successful in predicting SA with a 93 precision, 92.40 specificity, 87.80 sensitivity, 90.31 F-measure, 89.62 accuracy, and a ROC of 96.10, using a five-fold cross-validation procedure. Conclusions Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes
Blastocystis sp. in Small Ruminants: A Universal Systematic Review and Meta-analysis
No full textPurpose The present review was done to evaluate the prevalence and subtype distribution of Blastocystis infection among small ruminants, at a global perspective. Methods Systematic search was performed in PubMed, Scopus, Google Scholar, and Web of Science until 30th January 2022 and total estimates along with 95 confidence intervals (CIs) were computed using a random-effects model. Results Ultimately, the required data were extracted from 25 papers including 19 datasets for each animal. Among 3125 sheep, the Blastocystis prevalence was 25.3 (95 CI 16.1-37.4) (10 countries), being lower in comparison to that in 2869 examined goats 20.5% (95% CI 11-35.1%) (12 countries). Regarding STs distribution, fourteen genetically diverse STs of Blastocystis (ST1-ST5, ST7, ST10, ST12, ST14, ST15, ST21, ST23, ST24, ST26) have been reported in sheep, and the highest pooled prevalence was related to ST10 11 datasets, 57.8% (95% CI 43.7-70.8%), followed by ST14 8 datasets, 28.4% (95% CI 20.2-38.4%), and ST7 2 datasets, 21.1% (95% CI 4.5-60.3%). Compared to sheep, more STs (ST1, ST3-ST7, ST10, ST12, ST14, ST21, ST23-ST26, and ST32) were reported from goats, and the highest weighted frequency was related to ST10 6 datasets, 45.1% (95% CI: 25.6-66.2%), followed by ST7 2 datasets, 40.4% (95% CI 30-51.7%), and ST14 4 datasets, 29% (95% CI 15.5-47.7%). Out of ten known zoonotic STs reported for Blastocystis (ST1-ST9, and ST12), 7 were isolated from sheep (ST1-ST5, ST7, and ST12) and 7 were reported from goats (ST1, ST3-ST7, ST12). Conclusions Overall, Blastocystis epidemiology in sheep and goats is yet to be elucidated and demands more comprehensive studies
Dosimetry of small photon fields in the presence of bone heterogeneity using MAGIC polymer gel, Gafchromic film, and Monte Carlo simulation
No full textBackground: The presence of heterogeneity within the radiation field increases the challenges of small field dosimetry. In this study, the performance of MAGIC polymer gel was evaluated in the dosimetry of small fields beyond bone heterogeneity. Materials and methods: Circular field sizes of 5, 10, 20 and 30 mm were used and Polytetrafluoroethylene with density of 2.2 g/cm(3) was used as the bone equivalent material. The PDD curves, beam profiles, and penumbra widths were measured using MAGIC polymer gel, EBT2 film, and Monte Carlo simulation. Results: The maximum differences between MAGIC and EBT2 are 6.1, 4.7, 2.4, and 2.2 for PDD curves at 5, 10, 20, and 30 mm circular fields, respectively. The dose differences and distance to agreement between MAGIC and MC were within 1.89/0.46 mm, 1.66/0.43 mm, 1.28/0.77 mm, and 1.31/0.81 mm for beam profile values behind bone heterogeneity at 5, 10, 20, and 30 mm field sizes, respectively. Conclusion: The results presented that the MAGIC polymer gel dosimeter is a proper instrument for dosimetry beyond high density heterogeneity
Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer
No full textBACKGROUND: Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. METHODS: A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers. including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB). and adaptive boosting (AdaBoost). were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity. sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. RESULTS: The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy. mean specificity, mean sensitivity. mean area under the curve. and mean F1-score of 88.37. 86.24, 89.72. 88.11, and 89.91. respectively. gained the best performance. CONCLUSIONS: The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine
Towards the First Multiepitope Vaccine Candidate against Neospora caninum in Mouse Model: Immunoinformatic Standpoint
No full textNeospora caninum is an economically significant parasite among livestock, particularly in dairy cattle herds, causing storm abortions. Vaccination seems necessary to limit the infection and its harsh consequences. This is the first steps towards developing a multiepitope vaccine candidate against N. caninum using in silico approaches. High-ranked mouse MHC-binding and shared linear B-cell epitopes from six proteins (SRS2, MIC3, MIC6, GRA1, IMP-1, and profilin) as well as IFN-gamma-inducing epitopes (from SAG1) were predicted, screened, and connected together through appropriate linkers. Finally, RS-09 protein (TLR4 agonist) and histidine tag were added to N- and C-terminal of the vaccine sequence, yielding 486 residues in length. Physicochemical properties showed a stable (instability index: 27.23), highly soluble, antigenic (VaxiJen score: 0.9554), and nonallergenic candidate. Secondary structure of the multiepitope protein included 58.85 random coil, 20.99 extended strand, and 20.16 alpha helix. Also, the tertiary structure was predicted, and further analyses validated a stable interaction between the vaccine model and mouse TLR4 (binding score: -1261.6). Virtual simulation of immune profile demonstrated potently stimulated humoral (IgG+IgM) and cell-mediated (IFN-gamma) responses upon multiepitope vaccine injection. Altogether, a potentially immunogenic vaccine candidate was developed using several N. caninum proteins, with the capability to elicit IFN-gamma upsurge and other components of cellular immunity, and can be used in prophylactic purposes against neosporosis
Predictive modeling for COVID-19 readmission risk using machine learning algorithms
No full textIntroduction The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. Results The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). Conclusions Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients