Investigating the Effects of Combined Physical-cognitive Exercises on Executive Functions: A Home-based Exercise Approach

Purpose: Improving brain functions through physical exercises has been the focus of research in recent years. Accordingly, it is important to examine the kind of physical exercises and brain functions that are affected. This study aims to examine the effect of integrated physical cognitive exercises at home on the executive functions of adults. Methods: This was a field trial study, in which 28 people were examined in 2 groups. People in the experimental group participated in combined cognitive and physical exercises for 16 sessions, while the control group did their daily routines. Inhibition components were evaluated by the go/no-go test and working memory through the N-back test at the beginning and end of the training period. The data were analyzed via factorial analysis of variance through the SPSS software, version 19. Results: The findings indicated that the experimental condition, compared to the control condition, caused a significant improvement in the correct inhibition (P=0.002) and total correct response (P=0.047) components in the go/no-go test, along with the commission errors in the N-back test (P=0.003). Conclusion: The results showed that the combined physical and cognitive exercise had a positive effect on the core executive functions (attentional inhibition and working memory) and could prevent the performance decrement caused by quarantine and the reduction of daily activities that people were facing

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

Comparison of Laboratory Findings and Incidence Rate of Renal Failure With and Without Cardiopulmonary Bypass Machine After Coronary Artery Bypass Graft

Objectives: Acute kidney insuffciency is a prevalent and serious disease that follows coronary artery bypass graft (CABG). One of the important symptoms of acute renal failure (ARF) is the increased level of urea and serum creatinine. This study examined the rate of renal failure in patients undergoing on-pump and off-pump CABG. Materials and Methods: In this descriptive–comparative survey, we selected the patients undergoing heart surgery. Levels of urea, creatinine, sodium, potassium and urinary output were controlled and recorded in the frst days of admission and ICU discharge. Data collection tool was a checklist, the frst part included demographic information and the second part was related to the information on kidney function. The data were analyzed using SPSS version 21.0. Results: The fndings of this study showed a statistically signifcant difference in terms of age and the incidence of renal failure based on the increased levels of urea and serum creatinine before and after CABG (P0.05). Conclusions: Patients’ age was an important factor for kidney insuffciency following CABG. Type of the surgery (on- and off-pump) and gender had no influence on the incidence rate of ARF. Stronger measures to protect the kidneys in older patients may reduce this high-risk complication

No soldiers left behind: an IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

Assessing the role of anastomotic level in low anterior resection (LAR) surgery among rectal cancer patients in the development of LAR syndrome: a systematic review study

Abstract Background The etiology of LARS has not been elaborated on clearly. Studies have reported neoadjuvant therapy, low-lying rectal cancers, adjuvant therapy and anastomotic leakage as risk factors for the development of LARS. Anastomotic level has also been proposed as a possible risk factor; However, there have been conflicting results. This study aims to evaluate the role of the level of anastomosis as a potential risk factor for the development of LARS. Method A systematic literature search was conducted on Pubmed, Scopus, Embase, and Web of Science databases using Mesh terms and non-Mesh terms from 2012 to 2023. Original English studies conducted on rectal cancer patients reporting of anastomotic level and LARS status were included in this study. Eligible studies were assessed regarding quality control with Joanna-Briggs Institute (JBI) questionnaires. Results A total of 396 articles were found using the research queries, and after applying selection criteria 4 articles were selected. A sample population of 808 patients were included in this study with a mean age of 61.51 years with male patients consisting 59.28% of the cases. The Mean assessment time was 15.6 months which revealed a mean prevalence of 48.89% for LAR syndrome. Regression analysis revealed significantly increased risk of LAR syndrome development due to low anastomosis level in all 4 studies with odds ratios of 5.336 (95% CI:3.197–8.907), 3.76 (95% CI: 1.34–10.61), 1.145 (95% CI: 1.141–2.149) and 2.11 (95% CI: 1.05–4.27) for low anastomoses and 4.34 (95% CI: 1.05–18.04) for ultralow anastomoses. Conclusions LARS is a long-term complication following surgery, leading to reduced quality of life. Low anastomosis level has been reported as a possible risk factor. All of the studies in this systematic review were associated with an increased risk of LARS development among patients with low anastomosis

Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization

Purpose: Extracting water equivalent diameter (DW), as a good descriptor of patient size, from the CT localizer before the spiral scan not only minimizes truncation errors due to the limited scan field-of-view but also enables prior size-specific dose estimation as well as scan protocol optimization. This study proposed a unified methodology to measure patient size, shape, and attenuation parameters from a 2D anterior-posterior localizer image using deep learning algorithms without the need for labor-intensive vendor-specific calibration procedures. Methods: 3D CT chest images and 2D localizers were collected for 4005 patients. A modified U-NET architecture was trained to predict the 3D CT images from their corresponding localizer scans. The algorithm was tested on 648 and 138 external cases with fixed and variable table height positions. To evaluate the performance of the prediction model, structural similarity index measure (SSIM), body area, body contour, Dice index, and water equivalent diameter (DW) were calculated and compared between the predicted 3D CT images and the ground truth (GT) images in a slicewise manner. Results: The average age of the patients included in this study (1827 male and 1554 female) was 53.8 ± 17.9 (18–120) years. The DW, tube current, and CTDIvol measured on original axial images in the external 138 cases group were significantly larger than those of the external 648 cases (P < 0.05). The SSIM and Dice index calculated between the prediction and GT for body contour were 0.998 ± 0.001 and 0.950 ± 0.016, respectively. The average percentage error in the calculation of DW was 2.7 ± 3.5 %. The error in the DW calculation was more considerable in larger patients (p-value < 0.05). Conclusions: We developed a model to predict the patient size, shape, and attenuation factors slice-by-slice prior to spiral scanning. The model exhibited remarkable robustness to table height variations. The estimated parameters are helpful for patient dose reduction and protocol optimization

Sugarcane bagasse ex-situ catalytic fast pyrolysis for the production of Benzene, Toluene and Xylenes (BTX)

The ex-situ catalytic pyrolysis of sugarcane bagasse with various HZSM-5 (23, 50, and 80) catalysts was studied in a tandem micro reactor-GC/MS at 400 degrees C, 450 degrees C, 500 degrees C and 550 degrees C with a catalyst to biomass (C/B) ratios ranging from 2 to 23. The yields of benzene, toluene and xylenes (BTX) were significantly affected by pyrolysis temperature and C/B ratio. The highest BTX yield of 22% was obtained for the HZSM-5 (23) catalyst at C/B ratio of 12.5 and a temperature of 475 degrees C. Finally, an experimental design was performed to determine the optimal process conditions for BTX yields

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Background Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID‐19 using a DL‐based model. Purpose This study aimed to evaluate the performance of deep privacy‐preserving federated learning (DPFL) in predicting COVID‐19 outcomes using chest CT images. Methods After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold‐out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold‐out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. Results The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79–0.85) and (95% CI: 0.77–0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models ( p ‐value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. Conclusion The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi‐institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.</p

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests