Survey and classification of functional characteristics in neural network technique for the diagnosis of ischemic heart disease: A systematic review

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results. Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content. Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network. Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians� acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients� real, accurate, and flowing data in these systems. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved

Survey and classification of functional characteristics in neural network technique for the diagnosis of ischemic heart disease: A systematic review

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results. Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content. Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network. Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians� acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients� real, accurate, and flowing data in these systems. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved

Structural and functional analyses of minimal phosphopeptides targeting the polo-box domain of polo-like kinase 1

Polo-like kinase-1 (Plk1) has a pivotal role in cell proliferation and is considered a potential target for anticancer therapy. The noncatalytic polo-box domain (PBD) of Plk1 forms a phosphoepitope binding module for protein-protein interaction. Here, we report the identification of minimal phosphopeptides that specifically interact with the PBD of human PLK1, but not those of the closely related PLK2 and PLK3. Comparative binding studies and analyses of crystal structures of the PLK1 PBD in complex with the minimal phosphopeptides revealed that the C-terminal SpT dipeptide functions as a high-affinity anchor, whereas the N-terminal residues are crucial for providing specificity and affinity to the interaction. Inhibition of the PLK1 PBD by phosphothreonine mimetic peptides was sufficient to induce mitotic arrest and apoptotic cell death. The mode of interaction between the minimal peptide and PBD may provide a template for designing therapeutic agents that target PLK1.National Institutes of Health (U.S.) (Grant R01 GM60594)National Cancer Institute (U.S.)National Institutes of Health (U.S.) (Contract N01-CO-12400)National Institutes of Health (U.S.) (HHSN261200800001E

HIV-1 Enhancing Effect of Prostatic Acid Phosphatase Peptides Is Reduced in Human Seminal Plasma

We recently reported that HIV-1 infection can be inhibited by innate antimicrobial components of human seminal plasma (SP). Conversely, naturally occurring peptidic fragments from the SP-derived prostatic acid phosphatase (PAP) have been reported to form amyloid fibrils called “SEVI” and enhance HIV-1 infection in vitro. In order to understand the biological consequence of this proviral effect, we extended these studies in the presence of human SP. PAP-derived peptides were agitated to form SEVI and incubated in the presence or absence of SP. While PAP-derived peptides and SEVI alone were proviral, the presence of 1% SP ablated their proviral activity in several different anti-HIV-1 assays. The anti-HIV-1 activity of SP was concentration dependent and was reduced following filtration. Supraphysiological concentrations of PAP peptides and SEVI incubated with diluted SP were degraded within hours, with SP exhibiting proteolytic activity at dilutions as high as 1∶200. Sub-physiological concentrations of two prominent proteases of SP, prostate-specific antigen (PSA) and matriptase, could degrade physiological and supraphysiological concentrations of PAP peptides and SEVI. While human SP is a complex biological fluid, containing both antiviral and proviral factors, our results suggest that PAP peptides and SEVI may be subject to naturally occurring proteolytic components capable of reducing their proviral activity

Investigation of Griffithsin's Interactions with Human Cells Confirms Its Outstanding Safety and Efficacy Profile as a Microbicide Candidate

Many natural product-derived lectins such as the red algal lectin griffithsin (GRFT) have potent in vitro activity against viruses that display dense clusters of oligomannose N-linked glycans (NLG) on their surface envelope glycoproteins. However, since oligomannose NLG are also found on some host proteins it is possible that treatment with antiviral lectins may trigger undesirable side effects. For other antiviral lectins such as concanavalin A, banana lectin and cyanovirin-N (CV-N), interactions between the lectin and as yet undescribed cellular moieties have been reported to induce undesirable side effects including secretion of inflammatory cytokines and activation of host T-cells. We show that GRFT, unlike CV-N, binds the surface of human epithelial and peripheral blood mononuclear cells (PBMC) through an exclusively oligosaccharide-dependent interaction. In contrast to several other antiviral lectins however, GRFT treatment induces only minimal changes in secretion of inflammatory cytokines and chemokines by epithelial cells or human PBMC, has no measureable effect on cell viability and does not significantly upregulate markers of T-cell activation. In addition, GRFT appears to retain antiviral activity once bound to the surface of PBMC. Finally, RNA microarray studies show that, while CV-N and ConA regulate expression of a multitude of cellular genes, GRFT treatment effects only minimal alterations in the gene expression profile of a human ectocervical cell line. These studies indicate that GRFT has an outstanding safety profile with little evidence of induced toxicity, T-cell activation or deleterious immunological consequence, unique attributes for a natural product-derived lectin

A smart wearable device for monitoring and self-management of diabetic foot: A proof of concept study

Background and Objective: Diabetic foot is one of the important complications of diabetes, which is occurred due to the destructive parameters in different anatomical sites of feet. Management and monitoring of these parameters are very important to decrease or prevent foot ulcers. We aimed to develop a smart wearable device to monitor these parameters to prevent diabetic foot. Methods: Following literature review and expert panel discussions, we considered pressure, temperature and humidity to develop the system. During these sessions, we also developed the system architecture and determined the required technologies. We also developed a mobile application. Finally, all sensors were evaluated for accurate monitoring of pressure, temperature and humidity. A standard protocol was used to evaluate each of these sensors. To this end, five people (four with diabetes and one healthy person) participated. They did a series of movements including walking, sitting, and standing. We considered the pressure measured by Pedar system as the gold standard. Furthermore, we changed the environment temperature and humidity during several experiments and considered the environment temperature and humidity as gold standard. We compared the measured values by sensors with these gold standards. Results: The evaluation indicated the accurate performance of pressure, humidity and temperature sensors. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the system to provide alarms based on the pressure measured using Pedar were 100, 50, 92.5, 91.8, and 100 , respectively. The performance of temperature sensors in smart shoes was confirmed by slight differences compared to thermometers. Relatively equal values of humidity measured by two sensors on the left and right feet and the increased difference with the environment humidity showed the exact humidity measured using these sensors. Conclusion: This smart shoes monitors pressure, humidity, and temperature of patients� feet and sends this data to their smart phone by the Bluetooth module. Furthermore, it controls these parameters; as each of these parameters exceeds the defined threshold, alerts are given to patients for self-management. © 2020 Elsevier B.V

Survey of the patients' perspectives and preferences in adopting telepharmacy versus in-person visits to the pharmacy: a feasibility study during the COVID-19 pandemic

Background Following the coronavirus disease 2019 (COVID-19) pandemic, the health authorities recommended the implementation of strict social distancing and complete lockdown regulations to reduce disease spread. The pharmacists quickly adopted telemedicine (telepharmacy) as a solution against this crisis, but awareness about this technology is lacking. Therefore, the purpose of this research was to explore the patients' perspectives and preferences regarding telepharmacy instead of traditional in-person visits. Methods An electronic questionnaire was designed and sent to 313 patients who were eligible for the study (from March to April 2021). The questionnaire used five-point Likert scales to inquire about motivations for adopting telepharmacy and in-person visits, their perceived advantages and disadvantages, and the declining factors of telepharmacy. Finally, the results were descriptively analyzed using SPSS 22. Results Of all 313 respondents, a total of 241 (77) preferred appointments via telepharmacy while 72 (23) preferred in-person services. There was a significant difference between the selection percentage of telepharmacy and in-person services (chi-square 91.42; p < 0.0001). Preference bout the telepharmacy system versus in-person visits to the pharmacy was associated with factors such as "reducing the incidence of contagious disease" (4.41; +/- 0.78), "spending less time receiving pharmaceutical services" (4.24; +/- 0.86)), and "traveling a shorter distance for receiving pharmaceutical services" (4.25; +/- 0.86). "Reducing costs" (90.87), "saving time" (89.21), and "reducing the incidence of contagious disease" (87.13) were the most important reasons for choosing telepharmacy services. Also, "face-to-face communication with the pharmacist" (25), "low internet bandwidth" (25), and "reduction of patients' anxiety and the increase of their peace of mind" (23.61) were the most important reasons for choosing in-person visits. Conclusion Survey data indicate that most participants are likely to prefer the use of telepharmacy, especially during crises such as the current COVID-19 pandemic. Telepharmacy can be applied as an important means and a crucial service to lessen the load on healthcare organizations and expand drug supply shelters in pharmacies. However, there are still substantial hurdles to overcome in order to successfully implement the telemedicine platform as part of mainstream practice

Design and development of a web-based registry for Coronavirus (COVID-19) disease

Background: The 2019 coronavirus (COVID-19) is a highly contagious disease associated with a high morbidity and mortality worldwide. The accumulation of data through a prospective clinical registry enables public health authorities to make informed decisions based on real evidence obtained from surveillance of COVID-19. This registry is also fundamental to providing robust infrastructure for future research surveys. The purpose of this study was to design a registry and its minimum data set (MDS), as a valid and reliable data source for reporting and benchmarking COVID-19. Methods: This cross sectional and descriptive study provides a template for the required MDS to be included in COVID-19 registry. This was done by an extensive literature review and 2 round Delphi survey to validate the content, which resulted in a web-based registry created by Visual Studio 2019 and a database designed by Structured Query Language (SQL). Results: The MDS of COVID-19 registry was categorized into the administrative part with 3 sections, including 30 data elements, and the clinical part with 4 sections, including 26 data elements. Furthermore, a web-based registry with modular and layered architecture was designed based on final data classes and elements. Conclusion: To the best of our knowledge, COVID-19 registry is the first designed instrument from information management perspectives in Iran and can become a homogenous and reliable infrastructure for collecting data on COVID-19. We hope this approach will facilitate epidemiological surveys and support policymakers to better plan for monitoring patients with COVID-19. © Iran University of Medical Sciences

Comparing machine learning algorithms for predicting COVID-19 mortality

Background The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. Methods In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. Results The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03, 90.70, 94.23, 95.10, and 99.02, respectively. Conclusion It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians

Benefits, barriers, and facilitators of using speech recognition technology in nursing documentation and reporting: A cross-sectional study

Background and AimNursing reports are necessary for clinical communication and provide an accurate reflection of nursing assessments, care provided, changes in clinical status, and patient-related information to support the multidisciplinary team to provide individualized care. Nurses always face challenges in recording and documenting nursing reports. Speech recognition systems (SRS), as one of the documentation technologies, can play a potential role in recording medical reports. Therefore, this study seeks to identify the barriers, benefits, and facilitators of utilizing speech recognition technology in nursing reports. Materials and MethodsThis cross-sectional was conducted through a researcher-made questionnaire in 2022. Invitations were sent to 200 ICU nurses working in the three educational hospitals of Imam Reza (AS), Qaem and Imam Zaman in Mashhad city (Iran), 125 of whom accepted our invitation. Finally, 73 nurses included the study based on inclusion and exclusion criteria. Data analysis was performed using SPSS 22.0. ResultsAccording to the nurses, "paperwork reduction" (3.96, +/- 1.96), "performance improvement" (3.96, +/- 0.93), and "cost reduction" (3.95, +/- 1.07) were the most common benefits of using the SRS. "Lack of specialized, technical, and experienced staff to teach nurses how to work with speech recognition systems" (3.59, +/- 1.18), "insufficient training of nurses" (3.59, +/- 1.11), and "need to edit and control quality and correct documents" (3.59, +/- 1.03) were the most common barriers to using SRS. As well as "ability to fully review documentation processes" (3.62, +/- 1.13), "creation of integrated data in record documentation" (3.58, +/- 1.15), "possibility of error correction for nurses" (3.51, +/- 1.16) were the most common facilitators. There was no significant relationship between nurses' demographic information and the benefits, barriers, and facilitators. ConclusionsBy providing information on the benefits, barriers, and facilitators of using this technology, hospital managers, nursing managers, and information technology managers of healthcare centers can make more informed decisions in selecting and implementing SRS for nursing report documentation. This will help to avoid potential challenges that may reduce the efficiency, effectiveness, and productivity of the systems