A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate.

Assessment of Primary Healthcare Providers' Knowledge and Practices in Addressing Asymptomatic Hyperuricemia and Gout in the Asir Region of Saudi Arabia.

Introduction and aim Gout, the most common form of inflammatory arthritis, arises from hyperuricemia, a condition where elevated levels of uric acid lead to the deposition of monosodium urate (MSU) crystals in the joints. Nevertheless, it's important to note that not all cases of hyperuricemia result in gout. Methodology This cross-sectional study was conducted in the Asir region of Saudi Arabia, targeting primary healthcare physicians (PHPs) specializing in family medicine and general practice. The study utilized a modified electronic questionnaire, inspired by similar studies and aligned with recent guidelines, to assess PHPs' knowledge and practices concerning asymptomatic hyperuricemia (AH) and gout. The questionnaire encompassed the PHPs' demographic data and their knowledge and practices for AH and gout management. Results Out of 201 participating PHPs, the majority were male (68.2%), predominantly aged 25-34 years (73.1%), and practicing as general practitioners (61.2%). A significant proportion of PHPs had less than five years of experience (63.7%). In terms of education, 36.8% attended continuing medical education (CME) on AH or gout, and 66.7% were aware of the related management guidelines. The study revealed that the total knowledge score among PHPs averaged 5.18 out of seven, indicating a moderate level of knowledge. However, their practice level was moderate, with a mean practice score of 6.75 out of 12. The study also found no significant differences in knowledge scores based on gender, age, or years of experience, but significant variations were noted based on medical specialty. Conclusion There is a moderate level of knowledge and practice among PHPs in managing AH and gout in the Asir region. Despite adequate knowledge levels, there appears to be a gap in implementing this knowledge into practice, particularly in long-term management strategies. The findings emphasize the need for ongoing medical education and specialized training programs to bridge these gaps. The study provides a valuable framework for identifying and addressing similar challenges in other regions and medical practices.

An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification.

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset's mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA's performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.

Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.

Endoscopic Sleeve Gastroplasty in Those With a Body Mass Index of 27-30.

INTRODUCTION: Endoscopic sleeve gastroplasty (ESG) is safe and effective in patients with a body mass index (BMI) more than 30, with few cases reported in patients with overweight (BMI 27-30). However, evidence is lacking in the overweight group because the procedure is not currently performed routinely for such patients. In this study, we aim to evaluate the safety and efficacy of ESG in patients with a BMI between 27 and 30 who failed other weight loss modalities and/or had weight-related comorbidities. METHODS: This was a subgroup analysis of data pertaining to adults with a BMI between 27 and 30 who underwent ESG as a primary weight loss intervention. Data were abstracted from our longitudinal, prospective single-center registry. We analyzed weight loss, comorbidity resolution, adverse events, revisions, and quality of life using the Bariatric Analysis and Reporting Outcome System. RESULTS: Of 3,797 ESG procedures, 656 patients (17%) had a BMI of 27-30. The mean age was 33 ± 9 years and women comprised 94% (n = 616) of the sample. The mean % total weight loss at 6, 12, 24, and 36 months after ESG was 11.0 ± 7.2, 15.5 ± 6.3, 15.1 ± 8.3%, and 13.3 ± 9.9%, respectively. Eight of 22 patients with diabetes (36%) and 9 of 51 patients (18%) with hypertension experienced complete remission. Two patients were hospitalized with bleeding. Twenty-three patients (3.5%) underwent revision to laparoscopic sleeve gastrectomy or repeat ESG. Six more patients underwent suture removal. A total of 214 of 261 patients (82%) rated quality of life after ESG as good or better. DISCUSSION: ESG seems to be well tolerated, safe, and effective in patients with a BMI of 27-30.

Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model.

In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction.

Long-term outcomes of laparoscopic sleeve gastrectomy in those with class I obesity: safety, efficacy, and quality of life.

BACKGROUND: Class I obesity carries significant morbidity and mortality risk similar to higher grades of obesity, and persons with class I obesity have a high risk of progression to class II and III obesity. While bariatric surgery has made strides in safety and efficacy, it remains inaccessible for persons with class I obesity (body mass index [BMI] of 30-35 kg/m2). OBJECTIVES: To assess safety, weight loss durability, co-morbidity resolution, and quality of life after laparoscopic sleeve gastrectomy (LSG) in persons with class I obesity. SETTING: Multidisciplinary medical center that specializes in obesity management. METHODS: A longitudinal prospective single-surgeon registry was queried for data pertaining to persons with class I obesity who underwent primary LSG. Primary endpoint was weight loss. Secondary endpoints included change in obesity-related co-morbidities, adverse events, and post hoc analysis of symptoms of gastroesophageal reflux disease (GERD) and Bariatric Analysis and Reporting Outcome System results. Follow-up was divided into short- (1-3 yr), intermediate- (4-7 yr), and long-term (8-12 yr). We evaluated percent excess weight loss (%EWL) using linear mixed models adjusting for age, sex, years since operation, and baseline BMI. Least-squares means estimates and 95% confidence intervals (CI) were generated. RESULTS: Of 13,863 bariatric procedures, a total of 1851 patients were included. Mean baseline BMI, age, and male:female ratio were 32.6 ± 2.1 kg/m2, 33.7 ± 9.2 years, and 1:5, respectively. Adjusted mean %EWL (95% CI) at short-, intermediate-, and long-term follow-up were 111% (95% CI, 91%-131%), 110% (95% CI, 89%-131%), and 141% (95% CI, 57%-225%), respectively. Of 195 patients with type 2 diabetes, 59% experienced complete remission, and of 168 patients with hypertension, 43% experienced complete remission. Being on oral antidiabetes medication was a significant predictor of sustained remission compared with being on insulin or combination therapy (P < .001). Sixty-nine patients had symptoms of GERD before surgery, which improved in 55 (79.7%). Thirty-three patients developed de novo symptoms of GERD. The average Bariatric Analysis and Reporting Outcome System score was 4.5 ± 1.7, with 83% of participants rating their quality of life after surgery as good, very good, or excellent. CONCLUSION: Those with class I obesity who undergo LSG experience normalization of weight, sustained remission of co-morbidity, and good quality of life without significant risk of morbidity or mortality.

The Neuronal Cotransmission: Mechanistic Insights From the Autonomic Nervous System.

It is now scientifically accepted that neurons have the ability to release multiple transmitter substances simultaneously, yet, cotransmission's functionality is still limited to the scientific community. Acetylcholine is released by the noradrenergic neurons, and then the acetylcholine works prejunctionally in the promotion of the noradrenaline release. This hypothesis significantly challenged the previous idea of autonomic transmission as being a simple process that had a single transmitter. Norepinephrine was thought to be the single transmitter at the sympathetic neurovascular junction according to "Dale's principle". However, more evidence of the involvement of other neurotransmitters has been shown by many researchers in conjunction with Dale's principle and established terms such as adrenergic, purinergic, and peptidergic nerves. With the discovery of cotransmission, we now understand the existence of more than one neurotransmitter at a sympathetic neurovascular junction.