The potential use of nanozyme in aging and age-related diseases.

The effects of an increasingly elderly population are among the most far-reaching in 21st-century society. The growing healthcare expense is mainly attributable to the increased incidence of chronic illnesses that accompany longer life expectancies. Different ideas have been put up to explain aging, but it is widely accepted that oxidative damage to proteins, lipids, and nucleic acids contributes to the aging process. Increases in life expectancy in all contemporary industrialized cultures are accompanied by sharp increases in the prevalence of age-related diseases such as cardiovascular and neurological conditions, type 2 diabetes, osteoporosis, and cancer. Therefore, academic and public health authorities should prioritize the development of therapies to increase health span. Nanozyme (NZ)-like activity in nanomaterials has been identified as promising anti-aging nanomedicines. More than that, nanomaterials displaying catalytic activities have evolved as artificial enzymes with high structural stability, variable catalytic activity, and functional diversity for use in a wide range of biological settings, including those dealing with age-related disorders. Due to their inherent enzyme-mimicking qualities, enzymes have attracted significant interest in treating diseases associated with reactive oxygen species (ROS). The effects of NZs on aging and age-related disorders are summarized in this article. Finally, prospects and threats to enzyme research and use in aging and age-related disorders are offered.

Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.

BACKGROUND: Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data. METHODS: We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models. RESULTS: Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6). CONCLUSIONS: We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes. SIGNIFICANCE: Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

A comparative study of CTG monitoring one hour before labor in infants born with and without asphyxia.

BACKGROUND AND AIM: Asphyxia is a condition arising when the infant is deprived of oxygen, causing Fetal brain damage or death, which is associated with hypoxia and hypercapnia. Although fetal Cardiotocography (CTG) can show the Fetal health status during labor, some studies have reported cases of fetal asphyxia despite reassuring CTGs. This study hence aimed to compare FHR Monitoring and uterine contractions in the last hour before delivered between two groups of infants born with and without asphyxia. METHODOLOGY: The study was conducted on 70 pregnant women who delivered Taleghani and Al-Zahra academic teaching hospitals of Tabriz for labor in 2020-2021. RESULTS: The study data showed no significant difference between mothers of infants with and without asphyxia in terms of demographics (p > 0.05). The prevalence of asphyxia was significantly higher only in mothers with the gravidity of 3 and 4 (p = 0.003). In terms of the methods for labor induction, the use of oxytocin was more common among mothers of infants with asphyxia (74.3%) than in those of infants without asphyxia (p = 0.015). The results also revealed a significant difference between infants with and without asphyxia in the Apgar score (first, fifth, and tenth minutes), need for neonatal resuscitation, umbilical cord artery Acidosis (pH, bicarbonate, and BE), and severity of HIE between two groups of infants with asphyxia and without asphyxia (p < 0.0001). The comparison of fetal CTG 0 to 20 min before the delivery indicated that normal variability was observed in 71.4% of infants born with asphyxia, whereas this figure for infants born without asphyxia was 91.4% (p = 0.031). However, the results showed no significant difference between the two groups of infants in any of the tstudied indicators at 20 and 40 min before the labor(p > 0.05). There was a significant difference between the two groups of infants in terms of deceleration at 40 and 60 min before the labor, as it was observed in 53.6% of infants born with asphyxia and only 11.1% of those born without asphyxia. The results also demonstrated a significant difference between the two groups in the type of deceleration (p = 0.025). Pearson and Spearman correlation coefficients showed a significant and direct relationship between interpretation the CTG of the three Perinatologists(p < 0.0001, r > 0.8). CONCLUSION: The study results demonstrated a significant difference between infants born with asphyxia and those born without asphyxia in variability at 0 to 20 min before the labor and deceleration at 40 to 60 min before the labor.

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategies play an important role to fight against the adverse conditions and improve survival. Therefore, timing, and more specifically early diagnosis of the illness, is crucially important for an effective treatment. Studies have indicated that vital signals such as heart rate variability (HRV) could provide potential prognostic biological markers that can help with early detection of sepsis before it is clinically diagnosed through its actual symptoms. Therefore, this study employs neonatal and pediatric electrocardiogram (ECG) to extract 52 hourly sets of linear and non-linear features from the HRV, starting from 24 hours prior to the clinical diagnosis of sepsis in patients with positive blood cultures (n=14). Similar sets of features were also obtained from a non-sepsis control group to create an evaluation benchmark (n=14).In particular, this study initially demonstrates how the variations within the 24 hours values of specific HRV feature-sets could effectively reveal prognostic information about the evolution of sepsis, prior to the actual clinical diagnosis. Moreover, this study demonstrates that differences in the values of a particular set of features at 22 hours before the actual clinical diagnosis/symptoms can be reliably used to train a convolutional neural network for automatic classification between the individuals in the sepsis and non-sepsis groups with 88.89±7.86% accuracy.

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategies play an important role to fight against the adverse conditions and improve survival. Therefore, timing, and more specifically early diagnosis of the illness, is crucially important for an effective treatment. Studies have indicated that vital signals such as heart rate variability (HRV) could provide potential prognostic biological markers that can help with early detection of sepsis before it is clinically diagnosed through its actual symptoms. Therefore, this study employs neonatal and pediatric electrocardiogram (ECG) to extract 52 hourly sets of linear and non-linear features from the HRV, starting from 24 hours prior to the clinical diagnosis of sepsis in patients with positive blood cultures (n=14). Similar sets of features were also obtained from a non-sepsis control group to create an evaluation benchmark (n=14).In particular, this study initially demonstrates how the variations within the 24 hours values of specific HRV featuresets could effectively reveal prognostic information about the evolution of sepsis, prior to the actual clinical diagnosis. Moreover, this study demonstrates that differences in the values of a particular set of features at 22 hours before the actual clinical diagnosis/symptoms can be reliably used to train a convolutional neural network for automatic classification between the individuals in the sepsis and non-sepsis groups with 88.89±7.86% accuracy.Clinical relevance- Results suggest potential early diagnosis of sepsis through real-time automatic classification of HRV features as prognostic indicators in clinical ECG recordings.

Comparison of the Effects of Fenugreek Vaginal Cream and Ultra Low- Dose Estrogen on Atrophic Vaginitis.

INTRODUCTION: Atrophic vaginitis is a common problem in postmenopausal women and results from decreased levels of blood estrogen. It is associated with symptoms of itching, burning, dyspareunia, and postmenopausal bleeding. The present study evaluated the effects of fenugreek extract on atrophic vaginitis. MATERIALS AND METHODS: This randomized controlled clinical trial was performed on 60 postmenopausal women in Ardabil, Iran, in 2018. The participants were selected using block randomization with the allocation ratio 1:1. Those in the intervention group received 0.5g (the applicator filled to the half-full mark) fenugreek vaginal cream 5% twice a week for 12 weeks. The control group received conjugated estrogens vaginal cream at the dose of 0.625 mg (the applicator filled to the half-full mark) containing 0.3 mg of conjugated estrogens. Atrophic vaginitis was evaluated before and after the treatment through clinical examination, clinical signs, and measurement of Vaginal Maturation Index (VMI). FINDINGS: After the 12-week intervention and modification of the baseline score, the mean (standard error) score for atrophic vaginitis signs was 3.100 (1.43-4.75). This difference was statistically significant in intragroup comparison and in favor of the control group in intergroup comparison (p=0.001). VMI was less than 49% in 86.7% and 46.7% of the participants in the intervention and control groups, respectively. This was a significant difference in favor of the control group (p=0.001). CONCLUSION: The results of this study showed that total fenugreek extract could be effective in treating signs of atrophic vaginitis, but it was not as effective as ultra-low-dose estrogen.

Effects of journal therapy counseling with anxious pregnant women on their infants' sleep quality: a randomized controlled clinical trial.

BACKGROUND: Sleep is especially important for infants, since it stimulates the development of neural connections in their brains. Psychological stress such as anxiety could affect sleep quality. This study investigated the effects of journal therapy counseling sessions on the infants' sleep quality based on mothers' perception (primary outcome), maternal anxiety, infants' anthropometric and developmental parameters, and the frequency of exclusive breastfeeding (secondary outcomes). METHODS: A total of 70 healthy women with gestational age of 28-31 weeks participated in this randomized controlled trial. The participants were randomly allocated into intervention and control groups using randomized block design. Three in-person journal therapy sessions and three telephone counseling sessions (2 between in-person sessions and 1 one month postpartum) were provided to those in the intervention group, while the control group only received routine care. The Infant Sleep Questionnaire (ISQ), Exclusive Breastfeeding Checklist, and Infant Anthropometric Parameters Checklist were completed at two and four months postpartum. The Beck Anxiety Inventory (BAI) was completed during pregnancy, at the end of the intervention, and at two and four months postpartum, and the Ages and Stages Questionnaire (ASQ) was completed at 4 months postpartum. Data were analyzed using chi-square, independent t-test, ANCOVA and repeated measure ANOVA. RESULTS: There was no significant difference between the two groups in demographic characteristics and baseline anxiety scores. The mean sleep quality score in infants two months of age (MD: -4.2; 95%CI: - 1.1 to - 7.2; P = 0.007) and four months of age (MD: -5.5; 95%CI: - 8.4 to - 2.7; P < 0.001) was significantly lower in the intervention group than that of those in the control group. Based on the repeated measure ANOVA results, the mean postpartum anxiety score of mothers in the intervention group was significantly lower than that of those in the control group (AMD: -7.7; 95%CI: - 5.5 to - 10.1; P < 0.001). There was no significant difference between the two groups regarding other outcomes including the frequency of exclusive breastfeeding, and anthropometric and developmental parameters (P > 0.05). CONCLUSION: Journal therapy can decrease mothers' anxiety and improve the infants' sleep quality based on their perception. However, further studies are required before drawing any definitive conclusion. TRIAL REGISTRATION NUMBER: Iranian Registry of Clinical Trials (IRCT): IRCT20120718010324N45. Date of registration: August 11, 2018. URL: https://en.irct.ir/trial/33211.

The effect of distraction techniques on pain and stress during labor: a randomized controlled clinical trial.

BACKGROUND: Pain control and the stress associated with labor and delivery are among the most important issues of health care system. Use of distraction techniques during childbirth is reported to reduce pain and stress of labor. However, there is a limited publication that looked on the effect of distraction techniques on labor pain and stress. Thus, the aim of this study was to determine the effect of distraction techniques on labor pain and stress (primary outcomes), fear of childbirth, length of delivery stages, first minute Apgar score and oxytocin consumption (secondary outcomes). METHODS: A randomized controlled clinical trial was conducted on 68 pregnant women. Participants were allocated into two groups (intervention and control groups) by blocked randomization. Participants in the intervention group received distraction techniques in four sessions. Questionnaires of Fear of Childbirth (W-DEQ version A) and PSS once were completed before intervention and again at the 36th week for the W-DEQ and in the active phase for the PSS through interviews. The pain was assessed through VAS during active phase per hour. The length of delivery stages was recorded in the partograph chart. Data were analyzed by independent t-test and ANCOVA. RESULTS: The mean of perceived stress during labor in the intervention group was significantly less than that of the control group (AMD: -3.2; 95% CI: - 0.8 to - 6.0; P = 0.01). The mean (SD) of pain intensity during labor was less than in the intervention and control groups compare to the control group (6.2 vs 7.5; P < 0.001). There was no significant difference between the two groups in terms of fear of childbirth score (AMD: 5.3; 95% CI: 13.0 to - 6.0; P = - 2.3). Moreover, there was no statistically significant difference between the two groups in terms of the active phase of labor (P = 0.504), second stage of delivery (P = 0.928), total length of delivery (P = 0.520), Apgar score (P = 1.000) and frequency of oxytocin consumption (P = 0.622). CONCLUSION: According to the results, distraction techniques can reduce the pain and stress of labor, but further studies by using the distraction techniques are needed to reach a decisive conclusion. TRIAL REGISTRATION: IRCT2017042910324N39; Name of registry: Iranian Registry of Clinical Trials; Registered 11 September 2017. URL of registry: https://fa.irct.ir/user/trial/10814/view. Date of enrolment of the first participant to the trial: September 2017.

Identifying Optimal Features from Heart Rate Variability for Early Detection of Sepsis in Pediatric Intensive Care.

Sepsis as bacterial infection is the most common and costly causes of mortality in critically ill patients. The early diagnosis of sepsis is significantly important for effective treatment. In this study, over a period of two years, the electrocardiogram of nearly 500 pediatric and neonate patients with heart diseases were collected in 24 hours before diagnosis. The collected data of 22 patients were studied including 11 sepsis patients with positive blood cultures and 11 non-sepsis patients. After extracting the HRV (Heart Rate Variability) signal, 28 linear and nonlinear features according to previous research were extracted. By using the relative entropy method as a feature selection technique, the extracted features were evaluated for their ability to discriminate the data in sepsis and non-sepsis groups, and the best features were entered into the classification process. Using the four classification models of SVM, LDA, KNN and Decision Tree, the accuracy of 86.36% was obtained with Decision Tree for discrimination of sepsis patients from other patients.