Comparisons of postoperative outcomes of laparoscopic versus open surgery using inverse probability of treatment weighting analysis: an evidence from Iran.

BACKGROUND: Colorectal cancer has created a significant burden worldwide, including in Iran. Open and laparoscopic surgery are important treatment methods for this disease. The aim of this study is to compare postoperative outcomes of laparoscopic versus open surgery in Iran, with a particular emphasis on controlling confounding factors. METHODS: To control confounding factors in between-group comparisons of observational studies, a method based on propensity scores was used. The current study was conducted on 916 patients with colorectal cancer in the city of Shiraz between the years 2011 to 2022. The required data regarding treatment outcomes, type of surgery, demographic characteristics, and clinical factors related to cancer was extracted from the Colorectal Cancer Research Center of Shiraz University of Medical Sciences. To control confounding factors, we used the Inverse Probability of Treatment Weighting (IPTW) as one of the analytical approaches based on Propensity Score analysis. After IPTW analysis, univariate logistic regression was used for treatment effect estimation. Stata 17 was used for statistical analysis. RESULTS: After controlling for 24 clinical and demographic covariates, negative post-operative outcomes were significantly lower in laparoscopic than open surgery. There were significant differences between the two groups of surgery in the percentages of death due to cancer (P < 0.01), recurrence (P < 0.01), and metastasis (P < 0.05). The treatment effect univariate logistic regression analysis indicated that laparoscopic surgery reduced the risk of negative postoperative outcomes including death due to cancer (OR = 0.411, P < 0.01), recurrence (OR = 0.343, P < 0.01) and metastasis (OR = 0.611, P < 0.05) compared to open surgery. CONCLUSIONS: In terms of postoperative outcomes including cancer-related mortality, recurrence, and metastasis, the laparoscopic surgery outperformed open surgery. Therefore, further development of laparoscopic surgery can lead to better health outcomes for the population and optimize the utilization of healthcare resources.

Frequency of Visual Fields Needed to Detect Glaucoma Progression: A Computer Simulation Using Linear Mixed Effects Model.

PRCIS: Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression. PURPOSE: It is challenging to maintain a balance between the frequency of visual field testing and the long-term costs that may result from insufficient treatment of glaucoma patients. This study aims to simulate real-world circumstances of visual field data to determine the optimum follow-up scheme for the timely detection of glaucoma progression using a linear mixed effects model (LMM). MATERIALS AND METHODS: An LMM with random intercept and slope was used to simulate the series of mean deviation sensitivities over time. A cohort study including 277 glaucoma eyes that were followed for 9.0±1.2 years was used to derive residuals. Data were generated from patients with early-stage glaucoma having various regular and irregular follow-up scenarios and different rates of visual field loss. For each condition, 10,000 series of eyes were simulated, and one confirmatory test was conducted to identify progression. RESULTS: By doing one confirmatory test, the percentage of incorrect progression detection decreased considerably. The time to detect progression was shorter for eyes with an evenly spaced 4-monthly schedule, particularly in the first 2 years. From then onward, results from twice-a-year testing were similar to results from examinations scheduled 3 times per year. CONCLUSIONS: Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression. This approach could be considered for improving glaucoma monitoring. Moreover, simulating data using LMM may provide a better estimate of the disease progression time.

Evaluating the Effect of Jammer Radiation on Learning and Memory in Male Rats.

Background: Previous studies shown that mobile phone can impairment of working memory in humans. Objective: In this study, the effect of radiofrequency radiation emitted from common mobile jammers have been studied on the learning and memory of rats. Material and Methods: In this prospective study, 90 Sprague-Dawley rats, were divided into 9 groups (N=10): Control, Sham1st (exposed to a switched-off mobile jammer device at a distance of 50 or 100 cm/1 day, 2 hours), Sham2nd (similar to Sham1st, but for 14 days, 2 h/day), Experimental1st -50 cm/1 day &100 cm/1 day (exposed to a switched-on device at a distance of 50 or 100 cm for 2 hours), Experimental2nd (similar to experimental1st, but for 14 days, 2 h/day). The animals were tested for learning and memory the next day, by the shuttle box. The time that a rat took to enter the dark part was considered as memory. Results: Mean short-term memory was shorter in the experimental- 50 cm/1 day than control and sham- 50 cm/1 day (P=0.034), long-term memory was similar. Mean short- and long-term memory were similar in the experimental- 100 cm/1 day, control and sham- 100 cm/1 day (P>0.05). Mean short-term memory was similar in experimental- 50 cm/14 days, control, and sham- 50 cm/14 days (P=0.087), but long-term learning memory was shorter in the radiated group (P=0.038). Mean short- and long-term were similar among experimental-100 cm/14 days, control or sham 100 cm/14 days (P>0.05). Conclusion: Rats exposed to jammer device showed dysfunction in short- and long-term memory, which shown the unfavorable effect of jammer on memory and learning. Our results indicated that the distance from radiation source was more important than the duration.

Pointwise and Region-Wise Course of Visual Field Loss in Patients With Glaucoma.

Purpose: Accurate assessment of visual field (VF) trend may help clinicians devise the optimum treatment regimen. This study was conducted to investigate the behavior of VF sequences using pointwise and region-wise linear, exponential, and sigmoid regression models. Materials and Methods: In a retrospective cohort study, 277 eyes of 139 patients with glaucoma who had been followed for at least 7 years were investigated. Linear, exponential, and sigmoid regression models were fitted for each VF test location and Glaucoma Hemifield Test (GHT) region to model the trend of VF loss. The model with the lowest root mean square error (RMSE) was selected as the best fit. Results: The mean age (standard deviation [SD]) of the patients was 59.9 years (9.8) with a mean follow-up time of 9.3 (0.7) years. The exponential regression had the best fit based on pointwise and region-wise approaches in 39.3% and 38.1% of eyes, respectively. The results showed a better performance based on sigmoid regression in patients with initial VF sensitivity threshold greater than 22 dB (71.6% in pointwise and 62.2% in region-wise approaches). The overall RMSE of the region-wise regression model was lower than the overall RMSE of the pointwise model. Conclusions: In the current study, nonlinear regression models showed a better fit compared to the linear regression models in tracking VF loss behavior. Moreover, findings suggest region-wise analysis may provide a more appropriate approach for assessing VF deterioration. Translational Relevance: Findings may confirm a nonlinear progression of VF deterioration in patients with glaucoma.

The pre- and postoperative neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios: The comparison of laparoscopy and laparotomy in colorectal cancer patients.

INTRODUCTION: The management of inflammation after colorectal surgery is important to decrease the susceptibility to postoperative complications. Platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) in the peripheral blood are known as two important inflammatory markers. In this study we evaluated and compared colorectal laparoscopy and laparotomy based on the PLR and NLR. METHODS: Totally, 76 patients were divided into two groups including patients who underwent laparoscopy (45 cases) or laparotomy (31 cases). The PLR and NLR were calculated based on cell blood count analysis of preoperative and postoperative day (POD) one and three in both groups. Statistical analysis was performed using SPSS software version 22. RESULTS: The PLR and NLR have no significant association with age, gender and tumor site (p > 0.05). However, both ratios were significantly increased in laparotomy patients at POD1 compared with the laparoscopy patients (p < 0.05). According to the two by two comparisons, the preoperative and postoperative PLR were significantly different in the laparotomy group (p < 0.05) but not in the laparoscopy group (p > 0.05). However, the preoperative and postoperative NLR were significantly different in both laparoscopy and laparotomy groups (p < 0.05). CONCLUSION: The NLR and PLR markers indicated that laparoscopy can be a better choice for colorectal surgery due to lower induction of inflammation compared with laparotomy.

Safflower (Carthamus tinctorius L.) oil could improve abdominal obesity, blood pressure, and insulin resistance in patients with metabolic syndrome: A randomized, double-blind, placebo-controlled clinical trial.

ETHNOPHARMACOLOGICAL RELEVANCE: Carthamus tinctorius L. (Safflower) has been widely recommended to treat metabolic disorders in traditional herbal medicine in Persia, China, Korea, Japan, and other East-Asian countries. The anti-hypercholesterolemic and antioxidant effects of this plant have been well documented, but its protective effects against Metabolic Syndrome (MetS) have not been fully illustrated. AIM OF THE STUDY: The present study aimed to evaluate the effects of safflower oil on MetS risk factors. MATERIALS AND METHODS: In this randomized, double-blind, placebo-controlled clinical trial, 67 patients with MetS were administered either divided 8 g safflower oil or placebo daily for 12 weeks. All patients were advised to follow their previous diets and physical activities. RESULTS: Safflower oil resulted in a significant reduction in waist circumference (-2.42 ± 3.24 vs. 0.97 ± 2.53, p<0.001), systolic blood pressure (-8.80 ± 9.77 vs. -2.26 ± 8.56, p = 0.021), diastolic blood pressure (-3.53 ± 7.52 vs. -0.70 ± 6.21, p = 0.041), fasting blood sugar (-5.03 ± 10.62 vs. 2.94 ± 7.57, p = 0.003), and insulin resistance (-0.59 ± 1.43 vs. 0.50 ± 1, p = 0.012), but an increase in adiponectin level (0.38 ± 0.99 vs. -0.09 ± 0.81, p = 0.042) in the treatment group in comparison to the placebo group. The results revealed a direct relationship between leptin level and Body Mass Index (BMI) in both groups (p<0.001). In addition, increase in BMI resulted in a non-significant decrease in adiponectin level in both groups. Moreover, no significant difference was observed between the two groups regarding lipid profiles, leptin serum level, serum creatinine concentration, and other outcomes. CONCLUSION: Safflower oil without lifestyle modification improved abdominal obesity, blood pressure, and insulin resistance in patients with MetS.

Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering Methods.

BACKGROUND: Comparison of gene expression algorithms may be beneficial for obtaining disease pattern or grouping patients based on the gene expression profile. The current study aimed to investigate whether the knowledge within these data is able to group the ovarian cancer patients with similar disease pattern. METHODS: Four different clustering methods were applied on 20 genes expression data of 37 women with ovarian cancer. All selected genes in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and etc. Comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were used to evaluate the performance of clustering methods. RESULTS: Six out of 20 genes (IFN-γ, Foxp3, IL-4, BCL-2, Oct4 and survivin) selected by the Laplacian score showed key roles in the development of ovarian cancer and their prognostic values were clinically and statistically confirmed. The results indicated proper capability of the expression pattern of these genes in grouping the patients with similar prognosis, i.e. patients alive after 5 years or dead (62.12%). CONCLUSION: The results revealed the better performance for k-means and hierarchical clustering methods, and confirmed the fact that by using the expression profile of these genes, patients with similar behavior can be grouped in the same cluster with acceptable accuracy level. Certainly, the useful information from these data may contribute to the prediction of prognosis in ovarian cancer patients along with other features of patients.
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Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.

PURPOSE: The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation. METHODS: The study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014-2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients' prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria. RESULTS: Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions. CONCLUSION: Data mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.

Evaluation of the efficacy of Zataria multiflora essential oil versus albendazole in patients infected with liver cystic echinococcosis: A nonrandomized clinical trial.

BACKGROUND: Cystic echinococcosis (CE) is a life-threatening disease in many countries. Albendazole, as the drug of choice for medical treatment of CE, is accompanied by adverse effects and may be ineffective in 20%-40% of cases; hence, new and more effective compounds are urgently needed to optimize the management of the disease. This study was performed to evaluate the efficacy of Zataria multiflora essential oil (ZMEO) versus albendazole against human liver CE. MATERIALS AND METHODS: In this nonrandomized and single-blinded clinical trial, thirty patients who were infected with liver CE were divided into two groups (15 in each) and treated with albendazole (800 mg daily) and ZMEO (60 mg daily), respectively. Albendazole and ZMEO were administered orally for 180 consecutive days. The volume of hydatid cysts was measured by ultrasonography before and 2, 4 and 6 months after the start of treatment. Simultaneously, biochemical analysis was performed on the blood samples of patients to assess the possible side effects of the two treatment regimens. RESULTS: Two, 4 and 6 months after the start of treatment, ZMEO indicated a significantly higher ability in reduction of the volume of the hydatid cysts, compared to albendazole (P < 0.05). The mean values of aspartate aminotransferase, alanine transaminase and alkaline phosphatase were also significantly lower in the patients treated with ZMEO in comparison to those treated with albendazole (P < 0.05). No clinical adverse effects were observed in the patients treated by ZMEO. CONCLUSION: From the point of view of efficacy and safety, ZMEO indicated a significant superiority to albendazole. Hence, ZMEO may be considered as an alternative for albendazole in the medical treatment of liver CE.

Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study.

Random selection of initial centroids (centers) for clusters is a fundamental defect in K-means clustering algorithm as the algorithm's performance depends on initial centroids and may end up in local optimizations. Various hybrid methods have been introduced to resolve this defect in K-means clustering algorithm. As regards, there are no comparative studies comparing these methods in various aspects, the present paper compared three hybrid methods with K-means clustering algorithm using concepts of genetic algorithm, minimum spanning tree, and hierarchical clustering method. Although these three hybrid methods have received more attention in previous researches, fewer studies have compared their results. Hence, seven quantitative datasets with different characteristics in terms of sample size, number of features, and number of different classes are utilized in present study. Eleven indices of external and internal evaluating index were also considered for comparing the methods. Data indicated that the hybrid methods resulted in higher convergence rate in obtaining the final solution than the ordinary K-means method. Furthermore, the hybrid method with hierarchical clustering algorithm converges to the optimal solution with less iteration than the other two hybrid methods. However, hybrid methods with minimal spanning trees and genetic algorithms may not always or often be more effective than the ordinary K-means method. Therefore, despite the computational complexity, these three hybrid methods have not led to much improvement in the K-means method. However, a simulation study is required to compare the methods and complete the conclusion.

Comparison of four variable selection methods to determine the important variables in predicting the prognosis of traumatic brain injury patients by support vector machine.

BACKGROUND: Large amounts of information have called for increased computational complexity. Data dimension reduction is therefore critical to preliminary analysis. In this research, four variable selection (VS) methods are compared to obtain the important variables in predicting the prognosis of traumatic brain injury (TBI) patients. MATERIALS AND METHODS: In a retrospective follow-up study, 741 TBI patients who were hospitalized for at least 2 days and had a Glasgow Coma Scale score of at least one were followed. Their clinical data recorded during intensive care unit (ICU) admission and eight-category extended GOS conditions 6 months after discharge were utilized here. Two filter- and two wrapper-based VS methods were applied for comparison. A support vector machine (SVM) classifier was then used, and the sensitivity, specificity, accuracy, and the area under the receiver characteristic curve (AUC) values were calculated. RESULTS: Theoretically, the variables selected by sequential forward selection (SFS) method would better predict the prognosis (AUC = 0.737, 95% confidence interval [0.701, 0.772], specificity = 89.2%, sensitivity = 58.9% and accuracy = 79.1%) than the others. Genetic algorithm (GA), minimum redundancy maximum relevance (MRMR), and mutual information method were in the next orders, respectively. CONCLUSION: The use of an SVM classifier on optimal subsets given by GA and SFS reveals that wrapper-based methods perform better than filter-based methods in our data set, although all selected subsets, except for the MRMR, were clinically accepted. In addition, for prognosis prediction of TBI patients, a small subset of clinical records during ICU admission is enough to achieve an accepted accuracy.

Unsupervised neural network for evaluating the ability of the SF-36 instrument to differentiate individuals.

BACKGROUND: Health-related quality of life (HRQoL) and well-being refer to the positive, subjective state that is contrary to illness. HRQoL instruments include some common questionnaires, which may often be understood differently depending on the level of individuals' knowledge. AIMS: To investigate the ability of 36 Short Form Health Survey (SF-36) as a well-known questionnaire in evaluating people's well-being. METHODS: We compared unsupervised artificial neural networks with a self-organized map learning algorithm and k-means clustering method. Understanding of the content of the questionnaire was also checked according to age group and sex. The study included 1087 people aged > 18 years (640 healthy individuals and 447 patients with chronic diseases) in Shiraz, Islamic Republic of Iran between 2011 and 2013. RESULTS: The eight subscale scores of the SF-36 instrument were not able to evaluate the well-being of people. The ability of all 36 items in the questionnaire was > 60% in both self-organized map and k-means methods. The self-organized map learning algorithm evaluated people better than the k-means clustering method, based on the accuracy rate in prediction. The SF-36 instrument was better understood by young people. CONCLUSIONS: Differences in people's health conditions may not appear on the SF-36 subscale scores; therefore, the findings from the subscale scores of SF-36 should be cautiously interpreted.

The Effect of Aromatherapy by Lavender Oil on Infant Vaccination Pain: a Double Blind Randomized Controlled Trial.

Introduction: Exposure to noxious stimuli can cause pain in infants. This study was conducted to evaluate the effects of the lavender oil inhalation on the pain resulting from the pentavalent vaccination. Methods: This clinical trial consisted of two groups: the lavender oil group with 42 infants and the placebo group with 57 infants. The healthy infants without congenital abnormalities in need of pentavalent vaccine also participated in our study. The infants started the lavender oil or placebo aromatherapy one minute before injection. The pain was assessed three times, using the Neonatal Infant pain Scale (NIPS): before vaccination, 15 s, and 5 min after vaccination. Also, the duration of crying was measured in both groups. Results: At baseline, the two groups were similar in relation to the NIPS scores. While, after 5 minutes, the NIPS score was significantly lower in the lavender group. Based on the repeated measures analysis, the NIPS score changed over time totally. However, the two groups were significantly different in relation to the NIPS score over time. The duration of crying was 75.47 (60.675) second in the lavender group and 105.22 (75.739) s in the control group. The statistical test showed a significant difference between the two groups. Conclusion: A low concentration of the lavender oil inhalation can reduce the pain and improve soothing in the infants with the pentavalent vaccine injection.

Network Clustering and Bibliometrics of Pharmacology and Pharmacy Research Outputs Published by Iranian Authors.

OBJECTIVE: Evidence-based practice in medical sciences needs to publish confidential evidence that strongly depends on the research publications. This bibliometrics and network analytic study aims to evaluate the research publications of Iranian authors in pharmacology and pharmacy. METHODS: Through the pharmacology and pharmacy category of Web of Science (WOS), all published articles affiliated with an Iranian researcher as an author were retrieved. Full records of retrieved articles in the WOS, including author name and affiliation, journal name, citation number, cited references, and keywords, were exported to a plain text file. Network analysis through VOSviewer was used for mapping the characteristics of the retrieved articles. All statistical analyses were done using the Microsoft Excel and SPSS version 25. FINDINGS: The total number of Iran's publications (citations) rose from 1557 articles (10,085 citations) in 2000-2009 years to 6271 articles (77791 citations) in 2010-2018 years. Tehran University of Medical Sciences was the most productive university. The total number of RCTs rose from 82 publications in 2000-2009 to 278 publications in 2010-2018. The same numbers for systematic reviews and meta-analyses were four publications in 2000-2009 and 169 publications in 2010-2018. The five major topics of researches in pharmacology and pharmacy were drug delivery, basic pharmacology, oxidative stress, animal study, and molecular aspect of pharmacy. CONCLUSION: This study showed a marked increasing rate of publications and received citations by Iranians in pharmacology and pharmacy. After 2010, the rate of articles in the high-impact journals had growth. Furthermore, research articles in the highest level of evidence were more published by Iranians.

The Relationship Between Body Mass Index (BMI) and Menstrual Disorders at Different Ages of Menarche and Sex Hormones.

OBJECTIVE: The present research aimed to study the relationship between body mass index and menstrual disorders at different ages of menarche and sex hormones. METHODS: In this cross-sectional study, 2000 girls aged between 9 and 18 in all levels were selected through cluster sampling in Shiraz. Data were collected using demographic characteristics, menstrual disorders, body mass index and hormones' measure questionnaires. To analyze the data, we used SPSS 16 and Chi-square test. RESULTS: A total of 1024 (51.2%) out of 2000 subjects had normal BMI and the smallest group belonged to 26 subjects (1.3%) with BMI ≤ 30. There is a significant relationship between body mass index, menstrual cycle length (p = 0.006), spotting (p = 0.005), passing clots (p = 0.001) and menstrual bleeding (p = 0.04), and this relationship is insignificant between body mass index and duration of bleeding (p = 0.95), amenorrhea (p = 0.03), dysmenorrhea (p = 0.26) and menstrual regularity (p = 0.95). Investigating the relationship between body mass index and some of sex hormones shows that there is no significant relationship among BMI and TSH (p = 0.94), FSH (p = 0.21), LH (p = 0.21), Prolactin (p = 0.97), Testosterone (p = 0.66), and DHEAS (p = 0.94). CONCLUSIONS: A significant relationship among BMI and menstrual cycle length, spotting, passing clots and menstrual bleeding, and was insignificant with sex hormones.

Distribution and Rates of Visual Field Loss across Different Disease Stages in Primary Open-Angle Glaucoma.

PURPOSE: To identify patterns and rates of visual field (VF) loss in primary open-angle glaucoma (POAG) across different levels of severity. DESIGN: Retrospective, observational case series. PARTICIPANTS: Visual fields of 278 eyes of 139 patients with POAG (9 years of follow-up with ∼17 visits) from the Rotterdam Eye Hospital in The Netherlands were analyzed to identify patterns and rates of VF loss. MAIN OUTCOME MEASURES: Rate of VF decline for the entire VF, each region, and test point. Hemifield asymmetric rate if VF decline for each region and test point. METHODS: Total deviation (TD) values were extracted from the Humphrey VF Analyzer (Carl Zeiss Meditec, Dublin, CA). Eyes were stratified into 3 glaucoma stages by means of the mean deviation (MD): better than -6 decibels (dB), worse than -6 dB and better than -12 dB, and worse than -12 dB. Each hemifield was divided into 5 regions according to the Glaucoma Hemifield Test (GHT): central, paracentral, nasal, and peripheral arcuates 1 and 2. Point-wise and region-wise asymmetric patterns of VF loss and rate of VF loss were identified by comparing the values in the superior hemifield and the inferior hemifield at each severity level using a generalized estimating equation. RESULTS: The mean age of the patients was 60.2±10.3 years (mean ± standard deviation [SD]). The rate of MD loss, for all eyes taken together, was -0.11 dB/year. In the cross-sectional analysis, in eyes in the early and moderate stages, central and peripheral arcuate 2 regions in the superior hemifield were worse than their inferior counterpart, whereas in the advanced stage all GHT regions in the superior hemifield were significantly worse than the corresponding regions in the inferior hemifield (P ≤ 0.05). In the longitudinal analysis, there was no significant difference in the rate of VF loss between the GHT regions in the superior and inferior hemifields. CONCLUSIONS: Our findings suggest that in POAG, VF damage is worse in the superior hemifield than in the inferior hemifield.

Effect of Lavender Oil Aroma in the Early Hours of Postpartum Period on Maternal Pains, Fatigue, and Mood: A Randomized Clinical Trial.

BACKGROUND: Busy care providers focus on the serious complications of postpartum period. This issue causes the seemingly trivial complications, such as mother's pains, fatigue, and psychological status, to be less taken into account. The study aimed to determine the effect of lavender oil aroma in the early hours of postpartum period on maternal pains, fatigue, and mood in primiparous mothers. METHODS: This randomized clinical trial was conducted on 56 participants; 29 in intervention group and 27 in control group. The intervention group received lavender oil in three doses during the first 24 h after delivery. Sesame oil was used in the control group. Intensity of pain, fatigue, and distress level was measured by visual analog scale before and after the interventions. Besides, mood status was assessed through the positive and negative affect schedule. RESULTS: The mean age of all the participants was 23.88 ± 3.88 years. After the first intervention and also in the tomorrow morning assessment, significant differences were found between the two groups regarding perineal pain (P = 0.004, P < 0.001), physical pain (P < 0.001), fatigue (P = 0.02, P < 0.001), and distress scores (P < 0.001). In addition, significant differences were found concerning the mean scores of positive (P < 0.001) and negative (P = 0.007, P < 0.001) moods between the two groups after the interventions. Repeated measures analyses showed that the two groups were significantly different over time in all the evaluated variables. CONCLUSIONS: Lavender oil aromatherapy starting in the first hours of postpartum period resulted in better physical and mood status compared to nonaromatic group.

K Important Neighbors: A Novel Approach to Binary Classification in High Dimensional Data.

K nearest neighbors (KNN) are known as one of the simplest nonparametric classifiers but in high dimensional setting accuracy of KNN are affected by nuisance features. In this study, we proposed the K important neighbors (KIN) as a novel approach for binary classification in high dimensional problems. To avoid the curse of dimensionality, we implemented smoothly clipped absolute deviation (SCAD) logistic regression at the initial stage and considered the importance of each feature in construction of dissimilarity measure with imposing features contribution as a function of SCAD coefficients on Euclidean distance. The nature of this hybrid dissimilarity measure, which combines information of both features and distances, enjoys all good properties of SCAD penalized regression and KNN simultaneously. In comparison to KNN, simulation studies showed that KIN has a good performance in terms of both accuracy and dimension reduction. The proposed approach was found to be capable of eliminating nearly all of the noninformative features because of utilizing oracle property of SCAD penalized regression in the construction of dissimilarity measure. In very sparse settings, KIN also outperforms support vector machine (SVM) and random forest (RF) as the best classifiers.

Identifying the Prognosis Factors in Death after Liver Transplantation via Adaptive LASSO in Iran.

Despite the widespread use of liver transplantation as a routine therapy in liver diseases, the effective factors on its outcomes are still controversial. This study attempted to identify the most effective factors on death after liver transplantation. For this purpose, modified least absolute shrinkage and selection operator (LASSO), called Adaptive LASSO, was utilized. One of the best advantages of this method is considering high number of factors. Therefore, in a historical cohort study from 2008 to 2013, the clinical findings of 680 patients undergoing liver transplant surgery were considered. Ridge and Adaptive LASSO regression methods were then implemented to identify the most effective factors on death. To compare the performance of these two models, receiver operating characteristic (ROC) curve was used. According to the results, 12 factors in Ridge regression and 9 ones in Adaptive LASSO regression were significant. The area under the ROC curve (AUC) of Adaptive LASSO was equal to 89% (95% CI: 86%-91%), which was significantly greater than Ridge regression (64%, 95% CI: 61%-68%) (p < 0.001). As a conclusion, the significant factors and the performance criteria revealed the superiority of Adaptive LASSO method as a penalized model versus traditional regression model in the present study.

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.

BACKGROUND: Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. OBJECTIVES: This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. METHODS: The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). RESULTS: The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes' order by DT method was more consistent with the clinical literature. CONCLUSIONS: The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.