Optimizing lung cancer classification through hyperparameter tuning.

Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.

Dietary caffeine intake is associated with favorable metabolic profile among apparently healthy overweight and obese individuals.

BACKGROUND: Recent studies have revealed some conflicting results about the health effects of caffeine. These studies are inconsistent in terms of design and population and source of consumed caffeine. In the current study, we aimed to evaluate the possible health effects of dietary caffeine intake among overweight and obese individuals. METHODS: In this cross-sectional study, 488 apparently healthy individuals with overweight and obesity were participated. Dietary intake was assessed by a Food Frequency Questionnaire (FFQ) and the amount of dietary caffeine was calculated. Body composition was determined by bioelectrical impedance analysis (BIA). Enzymatic methods were used to evaluate serum lipid, glucose, and insulin concentrations. RESULTS: Those at the highest tertile of dietary caffeine intake had lower percentage of fat mass, higher fat free mass and appetite score (P < 0.05). Also, lower total cholesterol (TC) and low density lipoprotein cholesterol (LDL-c) was observed in higher tertiles of dietary caffeine intake compared with lower tertiles. In multinomial adjusted models, those at the second tertile of dietary caffeine intake were more likely to have higher serum insulin (P = 0.04) and lower homeostatic model assessment of insulin resistance (HOMA-IR) values compared with first tertile (P = 0.03) in crude model. While, in the age, body mass index (BMI), sex, physical activity, socio-economic status (SES) and energy intake -adjusted model (Model III), those at the third tertile of dietary caffeine intake were more likely to have low serum LDL concentrations [odds ratio (OR) = 0.957; CI = 0.918-0.997; P = 0.04]. With further adjustment to dietary vegetable, fiber and grain intake, those at the third tertile of dietary caffeine intake were more likely to have low systolic blood pressure (SBP), LDL and high HDL levels compared with those at the first tertile (P < 0.05). CONCLUSION: High intakes of dietary caffeine was associated with lower LDL, SBP, insulin resistance and higher HDL concentrations among overweight and obese individuals. However, due to observational design of the study, causal inference is impossible and further studies are warranted to confirm our findings.

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia.

Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals' lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data.

Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image-patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image-patch thermograms.

Preventing overloaded dissemination in healthcare applications using NonDelay tolerant dissemination technique.

Wearable Sensors (WSs) are widely used in healthcare applications to monitor patient health. During the data transmission, dissemination requires additional time to transmit the details with minimum computation difficulties. The existing techniques consume high overloaded while transmitting data in healthcare applications. The research problem is overcome by applying the non-delay-tolerant dissemination technique (NDTDT) to prevent overloaded dissemination and augment immediate, swift message delivery. The dissemination techniques utilize the intelligent decision-making process to provide the accumulated details to the healthcare center. The proposed approach is reliable in mitigating the errors due to inconsistent and discrete sensing intervals between the WSs. The constraints due to delay and interrupted transmission losses are reduced by selecting appropriate slots for WS information handling. This technique aims at maximizing the delivery of accumulated WS information through non-submissive or underlay dissemination. The method is designed to reduce dissemination delay and maximize successful message delivery. Two variations, sensors and data flows, validate the proposed NDTDT system's performance. The model increases the delivery rate by 0.91% and 0.932%, the dissemination probability by 0.964% and 0.98%, and the final metrics involved are an average delay of 12.78 ms and 11.67 ms.

Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems.

A Multi-Criteria Recommender System (MCRS) represents users' preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains. However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product. Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS. Initially, we build a model by assigning weights to multi-criteria features and then learn the preferences on each criteria during similarity computation among users through a genetic algorithm. This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making. Finally, products are recommended after making predictions. The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures.

Numerical and Experimental Analysis of Horizontal-Axis Wind Turbine Blade Fatigue Life.

Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine's service life by improving the turbine blades' fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade's fatigue life is calculated using Morrow's equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade's fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation.

Thermal and environmental optimization of an intercooled gas turbine toward a sustainable environment.

In this article, in order to achieve a sustainable environment, the optimization of a GT equipped with intercooling of the compression process is discussed. To limit the exergy destruction in intercooling cooling process and also to reduce the heat dissipation in the environment, an ORC system is applied for heat recovery and more power generation. Decision variables include CPR, first stage CPR, TIT, intercooler effectiveness, HRVG pressure, and superheating degree. During a parametric study, the effect of decision variables on operating factors including exergy efficiency, TCR, and the normalized emission rate of environmental pollutants are investigated. Finally, by performing bi-objective optimization and considering exergy efficiency and TCR as OFs, optimal performance conditions are determined. Finally, it is observed that in optimum conditions, exergy efficiency is 33% and TCR is 0.9 $/s.

Functions, mechanisms, and clinical applications of lncRNA LINC00857 in cancer pathogenesis.

Emerging data indicated that long noncoding RNAs (lncRNAs) are crucial players in the biological processes via regulating epigenetics, transcription, and protein translation. A novel lncRNA, LINC00857, was indicated to upregulate in several types of cancer. In addition, LINC00857 was functionally related to the modulation of the cancer-linked behaviors, including invasion, migration, proliferation, epithelial-mesenchymal transition (EMT), cell cycle, and apoptosis. The importance of LINC00857 in cancer onset and development proposed that LINC00857 has major importance in the cancer progression and may be considered as a novel prognostic/diagnostic biomarker as well as a treatment target. Here, we retrospectively investigate the available progress in biomedical research investigating the functions of LINC00857 in cancer, focusing on finding the molecular mechanisms affecting various cancer-related behaviors and exploring its clinical applications.

Supporting wound healing by mesenchymal stem cells (MSCs) therapy in combination with scaffold, hydrogel, and matrix; State of the art.

Non-healing wounds impose a huge annual cost on the survival of different countries and large populations in the world. Wound healing is a complex and multi-step process, the speed and quality of which can be changed by various factors. To promote wound healing, compounds such as platelet-rich plasma, growth factors, platelet lysate, scaffolds, matrix, hydrogel, and cell therapy, in particular, with mesenchymal stem cells (MSCs) are suggested. Nowadays, the use of MSCs has attracted a lot of attention. These cells can induce their effect by direct effect and secretion of exosomes. On the other hand, scaffolds, matrix, and hydrogels provide suitable conditions for wound healing and the growth, proliferation, differentiation, and secretion of cells. In addition to generating suitable conditions for wound healing, the combination of biomaterials and MSCs increases the function of these cells at the site of injury by favoring their survival, proliferation, differentiation, and paracrine activity. In addition, other compounds such as glycol, sodium alginate/collagen hydrogel, chitosan, peptide, timolol, and poly(vinyl) alcohol can be used along with these treatments to increase the effectiveness of treatments in wound healing. In this review article, we take a glimpse into the merging scaffolds, hydrogels, and matrix application with MSCs therapy to favor wound healing.

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.

Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.

Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure.

Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder-Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network's depth.

Impact of endogenous stress on albumin structure in systemic lupus erythematosus (SLE) patients.

Systemic lupus erythematosus (SLE) is an inflammatory, autoimmune disorder of unknown etiology. The inflammatory stress in SLE patients may modify macromolecules and produce structural/functional abnormalities. The present study is aimed at examining the consequences of stresses on the structure of albumin in SLE patients. Albumin was isolated from the sera of SLE/healthy subjects. Multiple physicochemical techniques were used to elucidate, structure of albumin. Advanced glycation end products in SLE patients' albumin were identified by the AGE specific fluorescence. Quenching of tryptophan, tyrosine fluorescence and surface protein hydrophobicity was observed in SLE patients' albumin. Protein-bound carbonyls were elevated while free thiol, lysine, arginine, and alpha helicity was found to be decreased in SLE albumin. Furthermore, changes in the secondary structure of SLE albumin were observed as shift in the position of amide I/II bands. Functionality of SLE albumin was also compromised as its cobalt-binding ability was substantially declined. Adduction of moieties was detected by dynamic light scattering (DLS) and confirmed by matrix assisted laser desorption/ionization. DLS, thioflavin T and transmission electron microscopy results confirmed aggregates in SLE patients' albumin. This study may be helpful in understanding the role of modified albumin in the cofounding pathologies associated with SLE.

Oral antimicrobial peptides: Types and role in the oral cavity.

Antimicrobial peptides (AMPs) are a wide-ranging class of host-defense molecules that act early to contest against microbial invasion and challenge. These are small cationic peptides that play an important in the development of innate immunity. In the oral cavity, the AMPs are produced by the salivary glands and the oral epithelium and serve defensive purposes. The aim of this review was to discuss the types and functions of oral AMPs and their role in combating microorganisms and infections in the oral cavity.

Advances in Nanotechnology for Restorative Dentistry.

Rationalizing has become a new trend in the world of science and technology. Nanotechnology has ascended to become one of the most favorable technologies, and one which will change the application of materials in different fields. The quality of dental biomaterials has been improved by the emergence of nanotechnology. This technology manufactures materials with much better properties or by improving the properties of existing materials. The science of nanotechnology has become the most popular area of research, currently covering a broad range of applications in dentistry. This review describes the basic concept of nanomaterials, recent innovations in nanomaterials and their applications in restorative dentistry. Advances in nanotechnologies are paving the future of dentistry, and there are a plenty of hopes placed on nanomaterials in terms of improving the health care of dental patients.