The Risk of Infection-Caused Mortality in Gastric Adenocarcinoma: A Population-Based Study.

Background: Gastric adenocarcinoma (GAC) is a deadly tumor. Postoperative complications, including infections, worsen its prognosis and may affect overall survival. Little is known about perioperative complications as well as modifiable and non-modifiable risk factors. Early detection and treatment of these risk factors may affect overall survival and mortality. Methods: We extracted GAC patient's data from the Surveillance, Epidemiology, and End Results (SEER) database and analyzed using Pearson's Chi-square, Cox regression, Kaplan-Meier, and binary regression methods in SPSS. Results: At the time of analysis, 59,580 GAC patients were identified, of which 854 died of infection. Overall, mean survival in months was better for younger patients, age < 50 years vs. ≥ 50 years (60.45 vs. 56.75), and in females vs. males (65.23 vs. 53.24). The multivariate analysis showed that the risk of infectious mortality was higher in patients with age ≥ 50 years (hazard ratio (HR): 3.137; 95% confidence interval (CI): 2.178 - 4.517), not treated with chemotherapy (HR: 1.669; 95% CI: 1.356 - 2.056), or surgery (HR: 1.412; 95% CI:1.132 - 1.761) and unstaged patients (HR: 1.699; 95% CI: 1.278 - 2.258). In contrast, the mortality risk was lower in females (HR: 0.658; 95% CI: 0.561 - 0.773) and married patients (HR: 0.627; 95% CI: 0.506 - 0.778). The probability of infection was higher in older patients (odds ratio (OR) of 2.094 in ≥ 50 years), other races in comparison to Whites and Blacks (OR: 1.226), lesser curvature, not other specified (NOS) as a primary site (OR: 1.325), and patients not receiving chemotherapy (OR: 1.258). Conclusion: Older, unmarried males with GAC who are not treated with chemotherapy or surgery are at a higher risk for infection-caused mortality and should be given special attention while receiving treatment.

Spontaneous spinal epidural hematoma following IV heparin and catheter-directed thrombolysis for deep vein thrombosis.

Spontaneous spinal epidural hematoma (SSEH) is a rare condition, and it usually presents with acute onset neck or back pain, progressive weakness, and other symptoms of spinal cord compression. Catheter-directed thrombolysis is one option for limbs threatened by iliofemoral venous thrombosis; other options, such as venous thrombectomy (either open or percutaneous), are also available. There are few reported cases of SSEH owing to catheter-directed thrombolysis for deep venous thrombosis (DVT). We present a case of a 65-year-old man who presented with left lower limb extensive iliofemoral DVT and received catheter-directed thrombolysis. The patient initially had rapid improvement in his symptoms with restoration of limb perfusion. However, within 6 hours of starting catheter-directed thrombolysis, the patient developed extensive SSEH and underwent emergent spinal decompression surgery with laminectomy of T11 to T12 with complete resolution of the neurological deficit. Clinicians should consider SSEH in differential diagnosis if the patient develops acute onset neck or back pain after catheter-guided thrombolysis for DVT.

Biopolymer­carbonaceous composites, progress, and adsorptive mitigation of water pollutants.

Chitin is the second most abundant natural biopolymer, which is composed of N-acetyl glucosamine units linked by ß-(1 â†’ 4) Chitosan is an N-deacetylated product of chitin. Properties of chitosan and chitin, such as biocompatibility, non-toxic nature, and biodegradability, make them successful alternatives for energy and environmental applications. However, their low mechanical properties, small surface area, reduced thermal properties, and greater pore volume restrict the potential for adsorption applications. Multiple investigations have demonstrated that these flaws can be prevented by fabricating chitosan and chitin with carbon-based composites. This review presents a comprehensive analysis of the fabrication of chitosan/chitin carbon-based materials. Furthermore, this review examines the prevalent technologies of functionalizing chitosan/chitin biopolymers and applications of chitin and chitosan as well as chitosan/chitin carbon-based composites, in various environmental fields (mitigating diverse water contaminants and developing biosensors). Also, the subsequent regeneration and reuse of adsorbents were also discussed. Finally, we summarize a concise overview of the difficulties and potential opportunities associated with the utilization of chitosan/chitin carbon-based composites as adsorbents to remove water contaminants.

The Shadows of Normal Flora on Minor Wounds: A Case Report of an Uncommon Pathogen With Potentially Lethal Consequences.

Staphylococcus lugdunensis is a gram-positive, coagulase-negative organism, typically found in the normal skin flora, predominantly colonizing the perineal region. It has gained recognition as an opportunistic pathogen capable of causing severe infections. This manuscript presents a case study of a 75-year-old female with multiple comorbidities, including hypertension, hyperlipidemia, atrial fibrillation on Xarelto, type 2 diabetes mellitus, hypothyroidism, and a bioprosthetic aortic valve. The patient exhibited symptoms of fever, chills, and lethargy following a dog scratch that resulted in wounds on the left lower extremity. Despite initial negative findings in the drug screen and unremarkable workup for other infectious etiologies, the patient's clinical course revealed the presence of S. lugdunensis in the blood cultures. Timely intervention with broad-spectrum intravenous antibiotics and a six-week course of cefazolin led to significant improvement without recurrence. Staphylococcus lugdunensis, previously considered a relatively benign microorganism, has become a significant player in infectious diseases, particularly causing skin and soft tissue infections and infective endocarditis (IE). It is considered an aggressive pathogen, especially in chronic immunocompromised personnel, with a high potential for morbidity and mortality. S. lugdunensis was found to be the fourth most common cause of IE. The manuscript discusses the epidemiology, clinical presentation, and management of S. lugdunensis infections, emphasizing the importance of early recognition and treatment to prevent potentially fatal outcomes.

Elevated Troponins and Diagnosis of Non-ST-Elevation Myocardial Infarction in the Emergency Department.

Background In the emergency department (ED), the diagnosis of non-ST-elevation myocardial infarction (NSTEMI) is primarily based on the presence or absence of elevated cardiac troponin levels, ECG changes, and clinical presentation. However, limited data exist regarding the incidence, clinical characteristics, and predictive value of different cardiac diagnostic tests and outcomes in patients with non-acute coronary syndrome (ACS)-related troponin elevation. Our study aimed to determine the percentage of patients with elevated troponin levels who had true ACS and identify various risk factors associated with true ACS in these patients. Methodology This was a single-center retrospective study. We performed a chart review of patients who presented to the ED from January 1, 2016, to December 31, 2017, and were admitted to the hospital with an elevated cardiac troponin I level in the first 12 hours after ED presentation with a diagnosis of NSTEMI. True ACS was defined as (a) patients with typical symptoms of ischemia and ECG ischemic changes and (b) patients with atypical symptoms of myocardial ischemia or without symptoms of ischemia and new segmental wall motion abnormalities on echocardiogram or evidence of culprit lesion on angiography. A logistic regression model was used to determine the association between risk factors and true ACS. Results A total of 204 patients were included in this study. The mean age of the study group was 67.4 ± 14.5 years; 53.4% (n = 109) were male, and 57.4% (n = 117) were Caucasian. In our study, 51% of patients were found to have true ACS, and the remaining 49% had a non-ACS-related elevation in troponins. Most patients without ACS had alternate explanations for elevated troponin levels. The presence of chest pain (odds ratio (OR) = 3.7, 95% confidence interval (CI) = 1.8-7.7, p = 0.001), tobacco smoking (OR = 4, 95% CI = 1.06-3.8, p = 0.032), and wall motion abnormalities on echocardiogram (OR = 3.8, 95% CI = 1.8-6.5, p = 001) were associated with increased risk of true ACS in patients with elevated troponins. Conclusions Cardiac troponin levels can be elevated in hospitalized patients with various medical conditions, in the absence of ACS. The diagnosis of ACS should not be solely based on elevated troponin levels, as it can lead to expensive workup and utilization of hospital resources.

Carcinoid Heart Syndrome With Metastatic Low-Grade Neuroendocrine Tumor of the Liver: A Rare Case.

We present a rare and complex case of a 76-year-old male patient with a history of low-grade neuroendocrine tumor (NET) of the small intestine, status post resection, who presented with recurrence of the tumor in the liver and subsequent carcinoid heart syndrome (CHS). The recurrent liver tumor caused severe tricuspid regurgitation and CHS, highlighting the rare association between NETs and CHS, particularly in the elderly population. This case underscores the importance of multidisciplinary care and close monitoring for patients with recurrent NETs and potential cardiac complications.

Corrigendum: Sesquiterpene lactones attenuate paclitaxel resistance via inhibiting MALAT1/STAT3/ FUT4 axis and P-Glycoprotein transporters in lung cancer cells.

[This corrects the article DOI: 10.3389/fphar.2022.795613.].

Aspiration Thrombectomy Using Inari FlowTriever System for Inferior Vena Cava Tumor Thrombus: A Case Report.

Pharmacomechanical therapy and catheter-directed thrombolysis are potent treatments for venous thromboembolism. However, limited data exist regarding the management of thrombi in the inferior vena cava (IVC). IVC thrombus resulting from tumors is a particularly uncommon condition. Managing IVC tumor thrombi poses even greater challenges, as conventional therapies such as systemic anticoagulation and thrombolysis are often ineffective. In this report, we present the case of a 73-year-old male with an inferior vena cava tumor thrombus successfully managed through aspiration thrombectomy utilizing the Inari FlowTriever system.

Spontaneous Coronary Artery Dissection in the Setting of Marijuana: A Case Report.

This case report presents a detailed examination of spontaneous coronary artery dissection (SCAD) in a 61-year-old Middle Eastern male with a history of marijuana use and essential hypertension. The patient's emergency presentation with loss of consciousness and subsequent diagnostics - including elevated troponins and distinctive electrocardiogram changes - led to the identification of extensive SCAD affecting multiple coronary arteries. The association between marijuana use and cardiovascular pathology is focal in this study, particularly considering the patient's positive test for tetrahydrocannabinol (THC) and significant smoking history. This case highlights the critical need for heightened awareness among clinicians regarding the implications of recreational marijuana use, particularly in individuals with predisposing cardiovascular risk factors. Furthermore, it illustrates the complexity of diagnosing and managing SCAD, a condition that may vary widely in its presentation and severity, necessitating a tailored approach to treatment that considers both the acute manifestations and underlying contributory factors such as substance use.

Low-Complexity 2D-DOD and 2D-DOA Estimation in Bistatic MIMO Radar Systems: A Reduced-Dimension MUSIC Algorithm Approach.

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field.

Robust polymer hybrid and assembly materials from structure tailoring to efficient catalytic remediation of emerging pollutants.

A massive amount of toxic substances and harmful chemicals are released every day into the outer environment, imposing serious environmental impacts on both land and aquatic animals. To date, research is constantly in progress to determine the best catalytic material for the effective remediation of these harmful pollutants. Hybrid nanomaterials prepared by combining functional polymers with inorganic nanostructures got attention as a promising area of research owing to their remarkable multifunctional properties deriving from their entire nanocomposite structure. The versatility of the existing nanomaterials' design in polymer-inorganic hybrids, with respect to their structure, composition, and architecture, opens new prospects for catalytic applications in environmental remediation. This review article provides comprehensive detail on catalytic polymer nanocomposites and highlights how they might act as a catalyst in the remediation of toxic pollutants. Additionally, it provides a detailed clarification of the processing of design and synthetic ways for manufacturing polymer nanocomposites and explores further into the concepts of precise design methodologies. Polymer nanocomposites are used for treating pollutants (electrocatalytic, biocatalytic, catalytic, and redox degradation). The three catalytic techniques that are frequently used are thoroughly illustrated. Furthermore, significant improvements in the method through which the aforementioned catalytic process and pollutants are extensively discussed. The final section summarizes challenges in research and the potential of catalytic polymer nanocomposites for environmental remediation.

Clinical Presentation, Diagnosis, Treatment, and Outcomes of Myocarditis in Children: A Tertiary Care Hospital Experience.

Background Clinical presentation, diagnosis, and treatment of myocarditis in children can be highly challenging, and results can vary greatly. Research on the precise processes of myocardial injury, including the effects of viral infections and newly identified variables like COVID-19, is still underway. Though treatment approaches, such as immunosuppressive therapy, are still debatable, diagnostic methods such as cardiac MRI and biomarkers show promise in improving diagnostic accuracy. The purpose of this study is to describe the spectrum of pediatric acute myocarditis, assess existing therapy approaches, and develop regional guidelines based on the experience of a tertiary care institution.  Methods Children diagnosed with acute myocarditis over a six-month period were included in this retrospective and descriptive hospital-based study. Data on demographics, clinical presentations, diagnostic tests, treatments, and results were gathered and examined. Descriptive statistics, non-parametric tests for categorical variables, and Spearman's correlation tests for continuous data were used in the statistical analysis, with a significance level of p < 0.05.  Results Of the 99 patients included, the mean age was 2.37 years, with males making up the majority (n = 54, 54.55%). Clinical symptoms typically included shortness of breath (n = 998, 99.0%), vomiting (n = 63, 63.6%), and chest pain (n = 6, 6.1%). High levels of troponin I (n = 70, 70.7%), cardiomegaly on a chest X-ray (n = 97, 97.0%), and different degrees of ventricular dysfunction were found in the laboratory and in imaging studies. Methylprednisolone (n = 84, 84.8%) and IV immunoglobulin (n = 54, 54.5%) were the most often used treatment modalities, and there were no appreciable differences in the two treatment groups' outcomes. A weak negative association (Spearman's rho = -0.211, p = 0.036) was found in the correlation study between the administration of methylprednisolone and length of stay (LOS), indicating possible benefits in terms of shortening hospital stays.  Conclusion This research offers a significant understanding of the clinical manifestation, treatment, and complications of acute myocarditis in children. Methylprednisolone administration seems to be linked to a shorter length of stay (LOS), despite disagreements over treatment approaches. To confirm these results and provide guidance for evidence-based management guidelines for pediatric myocarditis in our setup, more studies are necessary.

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.

Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces a novel deep learning-based model that accurately predicts EP. Our approach entails generating two distinct datasets for training and evaluating the model. We then use three distinct strategies to transform protein sequences to numerical representations: Dipeptide Deviation from Expected Mean (DDE), Dipeptide Composition (DPC), and Group Amino Acid (GAAC). Following that, we train and compare the performance of four advanced deep learning models algorithms: Ensemble Residual Convolutional Neural Network (ERCNN), Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The DDE encoding combined with the ERCNN model demonstrates the best performance on both datasets. This study demonstrates deep learning's potential for precisely predicting EP, which can considerably accelerate research and streamline drug discovery efforts. This analytical method has the potential to find new therapeutic targets and advance our understanding of EP activities in disease.

Blood cell image segmentation and classification: a systematic review.

Background: Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology: This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results: Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion: The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network.

Vascular endothelial growth factor (VEGF) is involved in the development and progression of various diseases, including cancer, diabetic retinopathy, macular degeneration and arthritis. Understanding the role of VEGF in various disorders has led to the development of effective treatments, including anti-VEGF drugs, which have significantly improved therapeutic methods. Accurate VEGF identification is critical, yet experimental identification is expensive and time-consuming. This study presents Deep-VEGF, a novel computational model for VEGF prediction based on deep-stacked ensemble learning. We formulated two datasets using primary sequences. A novel feature descriptor named K-Space Tri Slicing-Bigram position-specific scoring metrix (KSTS-BPSSM) is constructed to extract numerical features from primary sequences. The model training is performed by deep learning techniques, including gated recurrent unit (GRU), generative adversarial network (GAN) and convolutional neural network (CNN). The GRU and CNN are ensembled using stacking learning approach. KSTS-BPSSM-based ensemble model secured the most accurate predictive outcomes, surpassing other competitive predictors across both training and testing datasets. This demonstrates the potential of leveraging deep learning for accurate VEGF prediction as a powerful tool to accelerate research, streamline drug discovery and uncover novel therapeutic targets. This insightful approach holds promise for expanding our knowledge of VEGF's role in health and disease.Communicated by Ramaswamy H. Sarma.

An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.

Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.Communicated by Ramaswamy H. Sarma.

Insight into microplastics in the aquatic ecosystem: Properties, sources, threats and mitigation strategies.

Globally recognized as emergent contaminants, microplastics (MPs) are prevalent in aquaculture habitats and subject to intense management. Aquaculture systems are at risk of microplastic contamination due to various channels, which worsens the worldwide microplastic pollution problem. Organic contaminants in the environment can be absorbed by and interact with microplastic, increasing their toxicity and making treatment more challenging. There are two primary sources of microplastics: (1) the direct release of primary microplastics and (2) the fragmentation of plastic materials resulting in secondary microplastics. Freshwater, atmospheric and marine environments are also responsible for the successful migration of microplastics. Until now, microplastic pollution and its effects on aquaculture habitats remain insufficient. This article aims to provide a comprehensive review of the impact of microplastics on aquatic ecosystems. It highlights the sources and distribution of microplastics, their physical and chemical properties, and the potential ecological consequences they pose to marine and freshwater environments. The paper also examines the current scientific knowledge on the mechanisms by which microplastics affect aquatic organisms and ecosystems. By synthesizing existing research, this review underscores the urgent need for effective mitigation strategies and further investigation to safeguard the health and sustainability of aquatic ecosystems.

3D indoor modeling and game theory based navigation for pre and post COVID-19 situation.

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural facilities. There aren't any maps available for most installations, and there are no proven techniques to categorize features within indoor areas to provide location-based services. During a pandemic like COVID-19, the direct connection between the masses is one of the significant preventive steps. Hospitals are the main stakeholders in managing such situations. This study presents a novel method to create an adaptive 3D model of an indoor space to be used for localization and routing purposes. The proposed method infuses LiDAR-based data-driven methodology with a Quantum Geographic Information System (QGIS) model-driven process using game theory. The game theory determines the object localization and optimal path for COVID-19 patients in a real-time scenario using Nash equilibrium. Using the proposed method, comprehensive simulations and model experiments were done using QGIS to identify an optimized route. Dijkstra algorithm is used to determine the path assessment score after obtaining several path plans using dynamic programming. Additionally, Game theory generates path ordering based on the custom scenarios and user preference in the input path. In comparison to other approaches, the suggested way can minimize time and avoid congestion. It is demonstrated that the suggested technique satisfies the actual technical requirements in real-time. As we look forward to the post-COVID era, the tactics and insights gained during the pandemic hold significant value. The techniques used to improve indoor navigation and reduce interpersonal contact within healthcare facilities can be applied to maintain a continued emphasis on safety, hygiene, and effective space management in the long term. The use of three-dimensional (3D) modeling and optimization methodologies in the long-term planning and design of indoor spaces promotes resilience and flexibility, encouraging the adoption of sustainable and safe practices that extend beyond the current pandemic.

Public mental health through social media in the post COVID-19 era.

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions.

An enhanced diabetes prediction amidst COVID-19 using ensemble models.

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.