SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm.

The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%.

A Single-Dose Intramuscular Nanoparticle Vaccine With or Without Prior Intrauterine Priming Triggers Specific Uterine and Colostral Mucosal Antibodies and Systemic Immunity in Gilts but Not Passive Protection for Suckling Piglets.

An effective single-dose vaccine that protects the dam and her suckling offspring against infectious disease would be widely beneficial to livestock animals. We assessed whether a single-dose intramuscular (i.m.) porcine epidemic diarrhea virus (PEDV) vaccine administered to the gilt 30 days post-breeding could generate mucosal and systemic immunity and sufficient colostral and mature milk antibodies to protect suckling piglets against infectious challenge. The vaccine was comprised of polymeric poly-(lactide-co-glycolide) (PGLA)-nanoparticle (NP) encapsulating recombinant PEDV spike protein 1 (PEDVS1) associated with ARC4 and ARC7 adjuvants, a muramyl dipeptide analog and a monophosphoryl lipid A (MPLA) analog, respectively (NP-PEDVS1). To establish whether prior mucosal exposure could augment the i.m. immune response and/or contribute to mucosal tolerance, gilts were immunized with the NP-PEDVS1 vaccine via the intrauterine route at breeding, followed by the i.m. vaccine 30 days later. Archived colostrum from gilts that were challenged with low-dose PEDV plus alum was used as positive reference samples for neutralizing antibodies and passive protection. On day 100 of gestation (70 days post i.m. immunization), both vaccinated groups showed significant PEDVS1-specific IgG and IgA in the serum, as well as in uterine tissue collected on the day of euthanasia. Anti-PEDVS1 colostral IgG antibody titers collected at farrowing were significantly higher relative to the negative control gilts indicating that the NP vaccine was effective in contributing to the colostral antibodies. The PEDVS1-specific colostral IgA and anti-PEDVS1 IgG and IgA antibodies in the mature milk collected 6 days after farrowing were low for both vaccinated groups. No statistical differences between the vaccinated groups were observed, suggesting that the i.u. priming vaccine did not induce mucosal tolerance. Piglets born to either group of vaccinated gilts did not receive sufficient neutralizing antibodies to protect them against infectious PEDV at 3 days of age. In summary, a single i.m. NP vaccine administered 30 days after breeding and a joint i.u./i.m. vaccine administered at breeding and 30 days post-breeding induced significant anti-PEDVS1 immunity in systemic and mucosal sites but did not provide passive protection in suckling offspring.

Interplay between cellular metabolism and DNA viruses.

Viruses as intracellular pathogens take over the host metabolism and reprogram to facilitate optimal virus production. DNA viruses can cause alterations in several metabolic pathways, including aerobic glycolysis also known as the Warburg effect, pentose phosphate pathway activation, and amino acid catabolism such as glutaminolysis, nucleotide biosynthesis, lipid metabolism, and amino acid biosynthesis. The available energy for productive infection can be increased in infected cells via modification of different carbon source utilization. This review discusses the metabolic alterations of the DNA viruses that will be the basis for future novel therapeutic approaches.

Biosensor as an alternative diagnostic method for rabies virus detection: A literature review.

Rabies virus as a neurotropic agent causes rabies in humans and animals. Rabies virus transmission usually occurs through direct contact with saliva of rabid animals. However, serological and molecular tests commonly are used in diagnosing rabies but all the detection methods of rabies have some limitations. It is necessary to develop a rapid, effective, and low-cost biosensor as an alternative tool to detect rabies virus. In this review, we studied related biosensor researches to rabies virus detection for comparing it with other detection test including serological and molecular methods. Given that very limited studies have been conducted in this field, biosensors as quick, effective, and high sensitivity tools can be used in diagnostic of rabies as an alternative tool instead of other detection methods. According to the important role of rapid detection of rabies in the control of infection and public health measures, development of a biosensor as a quick tool can be very significant in the diagnosis of rabies.

Clinical Symptoms and Types of Samples Are Critical Factors for the Molecular Diagnosis of Symptomatic COVID-19 Patients: A Systematic Literature Review.

BACKGROUND: Currently, a novel coronavirus found in 2019 known as SARS-CoV-2 is the etiological agent of the COVID-19 pandemic. Various parameters including clinical manifestations and molecular evaluation can affect the accuracy of diagnosis. This review aims to discuss the various clinical symptoms and molecular evaluation results in COVID-19 patients, to point out the importance of onset symptoms, type, and timing of the sampling, besides the methods that are used for detection of SARS-CoV-2. METHODS: A systematic literature review of current articles in the Web of Science, PubMed, Scopus, and EMBASE was conducted according to the PRISMA guideline. RESULTS: Of the 12946 patients evaluated in this investigation, 7643 were confirmed to be COVID-19 positive by molecular techniques, particularly the RT-PCR/qPCR combined technique (qRT-PCR). In most of the studies, all of the enrolled cases had 100% positive results for molecular evaluation. Among the COVID-19 patients who were identified as such by positive PCR results, most of them showed fever or cough as the primary clinical signs. Less common symptoms observed in clinically confirmed cases were hemoptysis, bloody sputum, mental disorders, and nasal congestion. The most common clinical samples for PCR-confirmed COVID-19 patients were obtained from throat, oropharyngeal, and nasopharyngeal swabs, while tears and conjunctival secretions seem to be the least common clinical samples for COVID-19 diagnosis among studies. Also, different conserved SARS-CoV-2 gene sequences could be targeted for qRT-PCR detection. The suggested molecular assay being used by most laboratories for the detection of SARS-CoV-2 is qRT-PCR. CONCLUSION: There is a worldwide concern on the COVID-19 pandemic and a lack of well-managed global control. Hence, it is crucial to update the molecular diagnostics protocols for handling the situation. This is possible by understanding the available advances in assays for the detection of the SARS-CoV-2 infection. Good sampling procedure and using samples with enough viral loads, also considering the onset symptoms, may reduce the qRT-PCR false-negative results in symptomatic COVID-19 patients. Selection of the most efficient primer-probe for target genes and samples containing enough viral loads to search for the existence of SARS-CoV-2 helps detecting the virus on time using qRT-PCR.

Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study.

BACKGROUND: Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models. OBJECTIVE: This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task. METHODS: We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded. RESULTS: The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability. CONCLUSIONS: Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

Neural Network-Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study.

BACKGROUND: It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. OBJECTIVE: The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user's activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. METHODS: We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. RESULTS: Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. CONCLUSIONS: Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual's level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Correlation of low serum vitamin-D with uterine leiomyoma: a systematic review and meta-analysis.

BACKGROUND: There is a growing body of evidence on low serum vitamin-D levels and the risk of uterine leiomyomas (UL). Therefore, this systematic review and meta-analysis was conducted to investigate the association between serum vitamin D levels and UL occurrence. METHODS: Searches were systematically conducted of the electronic databases PubMed, Scopus, EMBASE, Web of Science (ISI), Cochrane library, Ovid, and Google Scholar to identify relevant studies from inception until February 6, 2020. Heterogeneity across the included studies was examined using Cochran's Q and I-square (I2). Data was pooled using random effects modeling and expressed as standardized mean differences (SMDs). RESULTS: Nine eligible studies with a total of 1730 participants (835 patients with UL and 895 controls) were included in the current meta-analysis. Pooled results with random effects modeling indicated that serum vitamin D levels were significantly lower in patients with UL than in the control group (n = 9, SMD = - 0.67; 95% CI, - 0.98, - 0.35, p < 0.001; I2 = 89.3%, p < 0.001). Based on the findings of subgroup analyses, it was found that the SMD values across the included studies from Asia (n = 4, SMD = - 1.20; 95% CI, - 1.45, - 0.96, p < 0.001; I2 = 30.6%, p = 0.229) were lower than those from Europe (n = 3, SMD = - 0.34; 95% CI, - 0.49, - 0.18, p < 0.001; I2 = 0.0%, p = 0.602) and Africa (n = 2, SMD = - 0.13; 95% CI, - 0.29, 0.04, p = 0.128; I2 = 0.0%, p = 0.417), although the difference was not significant in Africa. Publication year was also found to be a potential contributor's variable in the pooled SMD using the meta-regression method (t = - 3.00, p = 0.02). CONCLUSIONS: To the best of our knowledge, the current meta-analysis showed for the first time that serum vitamin D levels were significantly lower in women with UL in selected populations.

Diagnostic Accuracy of Shear Wave Elastography as a Non-invasive Biomarker of High-Risk Non-alcoholic Steatohepatitis in Patients with Non-alcoholic Fatty Liver Disease.

In this study, we evaluated the diagnostic accuracy of shear wave elastography (SWE) for differentiating high-risk non-alcoholic steatohepatitis (hrNASH) from non-alcoholic fatty liver and low-risk non-alcoholic steatohepatitis (NASH). Patients with non-alcoholic fatty liver disease scheduled for liver biopsy underwent pre-biopsy SWE. Ten SWE measurements were obtained. Biopsy samples were reviewed using the NASH Clinical Research Network Scoring System and patients with hrNASH were identified. Receiver operating characteristic curves for SWE-based hrNASH diagnosis were charted. One hundred sixteen adult patients underwent liver biopsy at our institution for the evaluation of non-alcoholic fatty liver disease. The area under the receiver operating characteristic curve of SWE for hrNASH diagnosis was 0.73 (95% confidence interval: 0.61-0.84, p < 0.001). The Youden index-based optimal stiffness cutoff value for hrNASH diagnosis was calculated as 8.4 kPa (1.67 m/s), with a sensitivity of 77% and specificity of 66%. SWE may be useful for the detection of NASH patients at risk of long-term liver-specific morbidity and mortality.

Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study.

BACKGROUND: It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. OBJECTIVE: The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. METHODS: Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. RESULTS: The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. CONCLUSIONS: This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.

Shear bond strength of ceramic and metal brackets bonded to enamel using color-change adhesive.

BACKGROUND: This study aimed to compare the shear bond strength (SBS) of ceramic and metal brackets bonded to the enamel using Grengloo color-change adhesive and Transbond XT conventional composite. MATERIALS AND METHODS: In this in vitro, experimental study, 120 extracted human premolars were divided into four groups: (1) Grengloo with metal brackets, (2) Grengloo with ceramic brackets, (3) Transbond XT with metal brackets, and (4) Transbond XT with ceramic brackets. After bracket bonding, the teeth were subjected to 500 thermal cycles and incubated at 37°C for 24 h. The SBS was measured and the data were analyzed using two-way ANOVA. Mann-Whitney U test was used to compare the Adhesive Remnant Index (ARI) scores. was statistically significant (P < 0.001). RESULTS: The effect of type of bracket (metal/ceramic) on SBS was not statistically significant (P = 0.368). However, the effect of type of composite on SBS was statistically significant (P < 0.001) and the SBS of Grengloo was higher than that of Transbond XT. No significant difference was found in ARI scores among the groups. CONCLUSION: Regarding the high SBS and the safe region of bond failure in Grengloo composite, this color-change adhesive can be a suitable alternative in cases that require high bond strength. Furthermore, the color contrast of Grengloo can make composite removal easier than Transbond XT with color match of composite and teeth.

Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data.

Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period. We trained and tested models using EHR data from 14,407 and 3,009 patients, respectively. The best model achieved an AUROC of 0.719, outperforming the simple, competitive baseline of relying prediction based on the last BP measure alone (0.634). Perhaps surprisingly, recurrent neural networks did not outperform a simple logistic regression for this task, suggesting that linear models should be included as strong baselines for predictive tasks using EHR.

Documentation of Medical Records in Hospitals of Mazandaran University of Medical Sciences in 2014: a Quantitative Study.

INTRODUCTION: Documentation of patient care in medical record formats is always emphasized. These documents are used as a means to go on treating the patients, staff in their own defense, assessment, care, any legal proceedings and medical science education. Therefore, in this study, each of the data elements available in patients' records are important and filling them indicates the importance put by the documenting teams, so it has been dealt with the documentation the patient records in the hospitals of Mazandaran province. METHOD: This cross-sectional study aimed to review medical records in 16 hospitals of Mazandaran University of Medical Sciences (MazUMS). In order to collection data, a check list was prepared based on the data elements including four forms of the admission, summary, patients' medical history and progress note. The data recording was defined as "Yes" with the value of 1, lack of recording was defined as "No" with the value of 2, and "Not applied" with the value of 0 for the cases in which the mentioned variable medical records are not applied. RESULTS: The overall evaluation of the documentation was considered as 95-100% equal to "good", 75-94% equal to "average" and below -75% equal to "poor". Using the stratified random sample volume formula, 381 cases were reviewed. The data were analyzed by the SPSS version 19 and descriptive statistics. RESULTS: The results showed that %62 of registration and all the four forms were in the "poor" category. There was no big difference in average registration among the hospitals. Among the educational groups Gynecology and Infectious were equal and had the highest average of documentation of %68. In the data categories, the highest documentation average belonged to the verification, %91. CONCLUSION: According to the overall assessment in which the rate of documentation was in the category "week", we should make much more efforts to reach better conditions. Even if a data element is recognized meaningless, unnecessary and repetitive by the in charge of documentation, it should not be neglected and skipped. In order to solve the problems of these types, it is suggested to discuss the medical records forms and elements that seem unnecessary in the related committees.

The effect of allopurinol on lowering blood pressure in hemodialysis patients with hyperuricemia.

BACKGROUND: Hyperuricemia has been associated with the development of high blood pressure (BP). We studied the effects of allopurinol therapy in hyperuricemic hemodialysis (HD) patients with high BP. MATERIALS AND METHODS: This single-blind, randomized cross-over clinical study involved 55 HD patients with serum uric acid level > 6.5 (men) and > 5.5 mg/dL (women). They were randomly divided in two groups, each of which went through two phases. Group-1 in phase-1 received 100 mg/day orally of allopurinol for three months; while Group-2 was given whatever medication they received prior to the study. After two months of washing period, the groups were crossed-over. The BP levels were measured before and after HD during the eight months study period. RESULTS: Fifty-three patients completed the study (33 men and 20 women, with mean age of 55.8 years). Uric acid levels decreased significantly during the12 weeks of allopurinol therapy (7.71 ± 1.53 to 5.2 ± 1.2 P < 0.005). Overall, after the 12 weeks of allopurinol therapy, systolic and diastolic BP also significantly decreased in allopurinol group, 15.8% (139 to 117, P < 0.0005) and 8.6% (81 to 74, P <.0005), respectively. There were not significant changes in body mass index, blood urea nitrogen, creatinine, albumin, cholesterol, triglyceride, hemoglobin, liver enzymes and serum electrolytes level after treatment. Patients treated with allopurinol had a significant increase in the quality of dialysis (KT/V) (P: 0.043). CONCLUSIONS: In HD patients, allopurinol treatment reduced BP. The results indicate a new potential therapeutic approach for controlling BP in HD patients.

Prevalence of metabolic syndrome in a hemodialysis population.

INTRODUCTION: The role of metabolic syndrome (MS) in hemodialysis population has not been thoroughly studied. This study aimed to determine the prevalence of MS and to identify its correlates among hemodialysis patients. MATERIALS AND METHODS: This cross-sectional study was conducted on patients in a hemodialysis center. The MS was defined according the Adult Treatment Panel III criteria. Clinical data of the patients were collected and blood samples were studied to measure fasting blood glucose and lipid profile. RESULTS: Eighty hemodialysis patients, including 47 men (58.8%) and 33 women (41.2%) with a mean age of 55.6 ± 15.6 years, were enrolled in this study. Metabolic syndrome was diagnosed in 23 patients (28.7%). Hypertension was present in 55 patients (68.8%). Fifteen patients (18.8%) were diabetic, 24 (30.0%) had a high serum triglyceride, 22 (27.5%) had a low high-density lipoprotein cholesterol, and 20 (25.0%) had evidence of abdominal obesity. Patients with MS had significantly higher body mass indexes (P < .001), fasting blood glucose levels (P < .001), and triglyceride levels (P = .004). Metabolic syndrome was not associated with gender, age, and duration of hemodialysis. Men showed significant abnormality in glucose metabolism (P = .008). Prevalence of low high-density lipoprotein cholesterol was significantly higher in the women than in the men (P = .02). CONCLUSIONS: The prevalence of MS in our hemodialysis patients was relatively high, with the most common element being hypertension. We suggest that there needs to be a new set of criteria defined for MS in hemodialysis patients.