Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis.

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet's robust feature extraction capabilities with ConvNeXt's advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study's methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

Nutritional conditions affecting of selenium nanoparticles synthesized by Fusarium oxysporum (CCASU-2023-F9), and their biological activities against mycotoxin-producing fungi isolated from animal feed.

One of the most promising biologically based nanomanufacturing processes is the production of selenium nanoparticles (SeNPs) by fungi. The use of these biosynthesized nanoparticles in agricultural practices has emerged as a new approach for controlling pathogen growth and mycotoxin production. In the present study, different chemical and physical parameters were investigated for the growth of Fusarium oxysporum (CCASU-2023-F9) to increase selenite reduction and obtain the highest yield of selenium nanoparticles (SeNPs). Fusarium oxysporum (CCASU-2023-F9) exhibited tolerance to up to 1 mM sodium selenite (Na2SeO3), accompanied by red coloration of the medium, which suggested the reduction of selenite and the formation of selenium nanoparticles (SeNPs). Reduced selenite was quantified using inductively coupled plasma‒mass spectrometry (ICP-MS), and the results revealed that Fusarium oxysporum (CCASU-2023-F9) is able to transform 45.5% and 50.9% of selenite into elemental selenium by using fructose and urea as the best carbon and nitrogen sources, respectively. An incubation temperature of 30 °C was the best physical condition at which 67.4% of the selenite was transformed into elemental selenium. The results also indicated that pH 7 was the optimum pH, as it displayed 27.2% selenite reduction with a net dry weight of 6.8 mg/mL. Increasing the concentration of sulfate resulted in a significant increase in selenite reduction, as it reached a maximum value of 75.3% at 0.15% g/ml sulfate. The maximum reduction in sodium selenite content was 85.2% at a C/N ratio of 2:1. The biosynthesized SeNPs exhibited antifungal activity against several fungi, such as Aspergillus flavus, Aspergillus niger, and Fusarium oxysporum, that were isolated from animal and poultry feed. Elevated SeNP concentrations (10500 ppm) significantly inhibited fungal growth. SeNPs at a concentration of 5000 ppm inhibited aflatoxin production (B1, B2, G1, and G2) by A. flavus, in addition to inhibiting mycotoxin production (T2 toxin, fumonisin B1, zearaleone, fusarin C, and moniliformin) by F. oxysporum. In conclusion, the results revealed favorable nutritional conditions for the maximum production of SeNPs by Fusarium oxysporum (CCASU-2023-F9) and indicated the marked inhibitory effect of SeNPs on mycotoxins that contaminate animal feed, causing serious consequences for animal health, and that lead to improving the quality of commercially produced animal feed. The obtained results can serve as a basis for commercial applicability.

Discharging patients home from the intensive care unit: A new trend.

Discharging patients directly to home from the intensive care unit (ICU) is becoming a new trend. This review examines the feasibility, benefits, challenges, and considerations of directly discharging ICU patients. By analyzing available evidence and healthcare professionals' experiences, the review explores the potential impacts on patient outcomes and healthcare systems. The practice of direct discharge from the ICU presents both opportunities and complexities. While it can potentially reduce costs, enhance patient comfort, and mitigate complications linked to extended hospitalization, it necessitates meticulous patient selection and robust post-discharge support mechanisms. Implementing this strategy successfully mandates the availability of home-based care services and a careful assessment of the patient's readiness for the transition. Through critical evaluation of existing literature, this review underscores the significance of tailored patient selection criteria and comprehensive post-discharge support systems to ensure patient safety and optimal recovery. The insights provided contribute evidence-based recommendations for refining the direct discharge approach, fostering improved patient outcomes, heightened satisfaction, and streamlined healthcare processes. Ultimately, the review seeks to balance patient-centered care and effective resource utilization within ICU discharge strategies.

An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.

Factors affecting timing of hypothyroidism following radioactive iodine therapy (RAIT) for patients with Graves' disease: A 12-month observational study.

BACKGROUND: This retrospective study analyzed factors influencing hypothyroidism development after radioactive iodine therapy for Graves' disease. PATIENTS AND METHODS: Three hundred and three patients with Graves' disease treated with radioactive iodine (RAI) from 2013 to 2022 at two Egyptian hospitals were included. Data collected included demographics, lab values, thyroid imaging, RAI doses, and outcomes. Patients were followed for ≥1 year to assess hypothyroidism onset. RESULTS: At the end of 1 year, around 79.5% of the individuals developed hypothyroidism while 12.5% continued to experience hyperthyroidism. The onset of hypothyroidism occurred earlier in those with thyroid volume (≤75.5 cm 3 ), lower thyroid weight (≤84.7 g), thyroid uptake (≤18.8%), and higher RAI dose/volume (≥0.1022 mCi/ml) ( P  < 0.001). Additionally, there was a correlation between anti-thyroid peroxidase (anti-TPO) antibodies and faster development of hypothyroidism compared to those who were negative for antibodies (2.9 vs 8.9 months, P  = 0.001). When considering factors in analysis it was found that anti-TPO antibodies were the only independent predictor, for developing hypothyroidism (hazard risk 30.47, P  < 0.001). Additionally, thyroid volume and uptake independently predicted successful treatment outcomes ( P  < 0.05). CONCLUSION: Positive anti-TPO antibodies strongly predict hypothyroidism risk after RAI therapy for Graves' disease. Smaller thyroid size, lower uptake, and higher RAI dose/volume correlate with earlier hypothyroidism onset but are less significant predictors than anti-TPO status. Findings can guide RAI therapy personalization to optimize outcomes.

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture.

Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.

Optimizing classification of diseases through language model analysis of symptoms.

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization-Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

Coronary Artery Dissection and Myocarditis Caused by Eosinophilic Granulomatosis with Polyangiitis (EGPA): A Case Report.

Eosinophilic granulomatosis with polyangiitis (EGPA) also referred to as Churg-Strauss syndrome is a rare vasculitis of the small to medium vessels. We present a rare case of acute coronary artery dissection brought on by EGPA, which generally has a poor prognosis. A 41-year-old male with history of bronchial asthma presented to the emergency room with a 2-week history of dyspnea, cough with clear phlegm, and fever. For the past eight months he had experienced episodes with similar symptoms relieved by steroids. CT chest showed bilateral upper lobe patchy opacities with extensive workup for infectious etiology being negative. He had peripheral eosinophilia with sinusitis. He had acute coronary syndrome and Coronary angiogram showed Right coronary artery dissection. After making a diagnosis of EGPA based on American college of Rheumatology criteria, he was successfully treated with high dose immunosuppression. Coronary artery dissection is a fatal and uncommon complication of EGPA which is usually diagnosed postmortem. Early recognition of this condition ante mortem and aggressive treatment can be lifesaving as demonstrated in our case.

Early stent fractures in superficial femoral artery resulting multiple pseudoaneurysm formation within one year: a case report.

BACKGROUND: Though fracture is known complication of stenting, pseudoaneurysm asscoiated with stent fracture is an extremely rare complication. This has previoulsy been described to occur at least one or more years following initial stent placement. Here we present a case of multi-site stent fracture leading to two separate SFA pseudoaneurysms within one year of placement, successfully treated with covered stents. CASE PRESENTATION: A 72-year-old male presented with severe claudication of his left lower extremity (Rutherford 3), found to have long segment SFA chronic total occlusion (CTO). Patient successfully underwent endovascular revascularization. Follow-up duplex ultrasound (US) at one year demonstrated a focus of severe in-stent restenosis (ISR). During repeat angiogram for treatment of the stenosis, stent fracture and pseudoaneurysm was seen in the distal SFA, which was treated successfully with a self-expanding covered stent. Additional stent fractures and pseudoanerusyms were subseuqently identified on follow-up, necessitating a third angiogram, and these were successfully repaired using overlapping covered stents, without further recurrence. CONCLUSIONS: Superficial femoral artery stent fractures leading to pseudoaneurysms are extremely rare, particularly within first year of stent placement. Endovascular repair with covered stents has proven to be an effective treatment option with decreased procedural morbidity compared to surgical repair.

A Case of Herpes Simplex Virus Meningitis in an Immunocompromised Individual: Avoiding Common Diagnostic Pitfalls.

Herpes simplex virus meningoencephalitis (HSV ME) is a severe viral infection that affects the brain and surrounding tissues. It is caused primarily by HSV type 1 (HSV-1) virus. This condition requires prompt recognition and treatment due to its potential for significant morbidity and mortality. We aim to highlight the importance of avoiding common diagnostic pitfalls in identifying HSV meningoencephalitis, especially in immunocompromised individuals. We present a case of a 34-year-old immunocompromised patient with HSV meningoencephalitis, emphasizing key clinical features and diagnostic strategies that helped us reach an accurate diagnosis. By sharing this case, we aim to enhance awareness and improve the management of HSV meningoencephalitis in similar patient populations, leading to better outcomes.

COVID-19-associated MRSA infective endocarditis and mitral valve perforation: a case report.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic, leading to significant morbidity and mortality. The interplay between COVID-19 and other medical conditions can complicate diagnosis and management, necessitating further exploration. CASE PRESENTATION: This case report presents a patient with COVID-19 who developed infective endocarditis (IE) and mitral valve perforation caused by methicillin-resistant Staphylococcus aureus on a native mitral valve. Notably, the patient did not exhibit typical IE risk factors, such as intravenous drug use. However, he did possess risk factors for bacteremia, including a history of diabetes mellitus and recent steroid use due to the COVID-19 infection. The diagnosis of IE was crucially facilitated by transesophageal echocardiography. CONCLUSIONS: This case highlights the potential association between COVID-19 and the development of infective endocarditis. Prompt evaluation using transesophageal echocardiography is vital when there is a high suspicion of IE in COVID-19 patients. Further research is required to elucidate the precise relationship between COVID-19 and IE.

Biosynthesis approach of copper nanoparticles, physicochemical characterization, cefixime wastewater treatment, and antibacterial activities.

The aim of this paper is the green synthesis of copper nanoparticles (Cu NPs) via Quinoa seed extract. X-ray diffraction (XRD) results confirmed the production of the pure crystalline face center cubic system of the Cu NPs with an average crystallite size of 8.41 nm. Infrared spectroscopy (FT-IR) analysis confirmed the capping and stabilization of the Cu NPs bioreduction process. UV visible spectroscopy (UV-Vis). surface plasmon resonance revealed the absorption peak at 324 nm with an energy bandgap of 3.47 eV. Electrical conductivity was conducted assuring the semiconductor nature of the biosynthesized Cu NPs. Morphological analysis was investigated confirming the nano-characteristic properties of the Cu NPs as polycrystalline cubic agglomerated shapes in scanning electron microscopy (SEM) analysis. Transmission electron microscopy (TEM) analysis also was used to assess the cubic shapes at a particle size of 15.1 ± 8.3 nm and a crystallinity index about equal to 2.0. Energy dispersive spectroscopy (EDX) was conducted to investigate the elemental composition of the Cu NPs. As a potential utility of the biosynthesized Cu NPs as nano adsorbents to the removal of the Cefixime (Xim) from the pharmaceutical wastewater; adsorption studies and process parameters were being investigated. The following strategic methodology for maximum Xim removal was conducted to be solution pH 4, Cu NPs dosage 30 mg, Xim concentration 100 mg/L, and absolute temperature 313 K. The maximum monolayer adsorption capacity was 122.9 mg/g according to the Langmuir isothermal model, and the kinetic mechanism was pseudo-second-order. Thermodynamic parameters also were derived as spontaneous chemisorption endothermic processes. Antibacterial activity of the Xim and Xim@Cu NPs was investigated confirming they are highly potent against each Gram-negative and Gram-positive bacterium.

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study's training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew's correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.

Quality Improvement Initiative in a Community Hospital to Reduce Central Line Device Utilization Rate.

Background The intensive care unit (ICU) in a community hospital in southwest Minnesota saw a steady increase in central line-associated bloodstream infections (CLABSI) and an increase in the utilization of central lines. The baseline CLABSI rate was 11.36 at the start of the project, which was the highest in the last five years. The corresponding device utilization rate (DUR) was 64%, which increased from a pre-COVID pandemic rate of 45%. Aim The aim of this project was to decrease the ICU DUR by 37.5% from a baseline of 64% to 40% within six months without adversely impacting staff satisfaction. Methods A multidisciplinary team using the define, measure, analyze, improve, and control (DMAIC) methodology reviewed the potential causes of the increased use of central lines in the ICU. The team identified the following major causal themes: process, communication, education, and closed-loop feedback. Once the root causes were determined, suitable countermeasures were identified and implemented to address these barriers. These included reviewing current guidelines, enhanced care team rounding, staff education, and the creation of a vascular access indication algorithm. The team met biweekly to study the current state, determine the future state, evaluate feedback, and guide implementation. Results The pandemic saw a surge in the number of severely ill patients in the ICU, which may have caused an increase in the DUR. The project heightened the awareness of the increased DUR and its impact on the CLABSI rate. The initiation of discussion around this project led to an immediate decline in DUR via increased awareness and focus. As interventions were introduced and implemented, the DUR continued to decrease at a steady rate. Post implementation, the DUR met the project goal of less than 40%. The team continued to track progress and monitor feedback. The DUR continued to meet the goal for three months post implementation. Since the start of the project, there have been no CLABSI events reported. This effort has positively impacted safety and patient outcomes. Conclusions Through a defined process, the central line utilization rate in our ICU was decreased to 37.5% to meet the target goal and has been sustained.

Exploring the effects of pandemics on transportation through correlations and deep learning techniques.

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.

An Unusual Case of Escherichia Coli Meningitis in an Immunocompetent Adult.

Spontaneous meningitis caused by Gram-negative bacilli is rare in adults. It typically occurs after a neurosurgical procedure or head injury but may also be related to the presence of a neurosurgical device, cerebrospinal fluid (CSF) leak syndrome, or seen in immunosuppressed patients. Escherichia coli (E. coli) is the leading cause of Gram-negative bacilli meningitis. We describe the case of a 47-year-old man who was hospitalized for spontaneous, community-acquired E. coli meningitis, which is unusual to see in an immunocompetent adult. CSF analysis was consistent with bacterial meningitis; his blood culture was positive for E. coli. Within 24 hours of initiation of antibiotics, his status improved.

Case Report of Acute Q Fever with Hepatitis Progressing to Chronic Q Fever with Endocarditis.

A 35-year-old male greenhouse worker presented with myalgia, fatigue, and fever. Initially, he was thought to have an unspecified viral infection and was treated with conservative therapy. However, the patient's symptoms persisted, and he reported additional symptoms of mild abdominal pain and headaches. Laboratory evaluation was significant for elevated liver enzymes. Due to concern for acute hepatitis and persistent fever the patient was hospitalized. During his hospital course, no infectious etiology was found to explain his symptoms. After discharge from the hospital, additional testing showed positive serology for Q fever IgG phase II antibody (1:8192) and phase II antibody IgM (>1:2048). He was treated with doxycycline and had a good clinical response. Upon follow-up, he had worsening Phase I IgG serologies. Transesophageal echo demonstrated vegetations consistent with endocarditis.

The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study.

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.

Climate change-related knowledge and attitudes among a sample of the general population in Egypt.

Introduction: Identifying the public awareness and risk perception regarding climate change, are fundamental preliminary steps in determining gaps and paving the way for awareness campaigns that address climate change causes and counteraction mitigation measures. However, few studies were conducted in Egypt; thus, the researchers conducted the current cross-sectional study among a sample of the Egyptian population to identify general knowledge and perception about climate change and its effects, as well as attitudes toward mitigation measures. Methods: An exploratory population-based electronic-open survey, was conducted among 527 members of the general population between January and April 2022, using a convenience sampling technique. A pre-tested 2-page (screen) electronic included three sections: sociodemographic characteristics, global warming/climate change-related knowledge, and attitude toward climate change mitigation. Results: The average global warming knowledge score was 12 ± 3. More than 70% (71.1%) of the participants were knowledgeable (percentage score >70%). Approximately half of the enrolled participants (48.2%) agreed that everyone is vulnerable to the effects of global warming/climate change. More than three-quarters (78.3%) of the participants agreed that carbon emissions from vehicles and industrial methane emissions were the first factors that contributed to climate change, followed by the ozone holes (731%). Global warming/climate change-related knowledge was statistically higher in participants aged of >30 years, married participants, urban residents, highly educated individuals, and employed individuals (p-value ≤ 0.05). Approximately 80% of the participants agreed that responding to the questionnaire drew their attention to the topic of climate change and its effects. More than two-thirds of those polled agreed that increasing public transportation use could help mitigate the effects of climate change/global warming, followed by the materials used and the direction of construction. Conclusion: More than two-thirds of the participants were knowledgeable regarding climate change. Social media and the internet were the main sources of information. However, participants need to get the information in a different way that could help in changing their attitude positively toward the issue of climate change mitigation. The current study recommends the need for various initiatives that work should be launched.

Overlap of diabetic ketoacidosis and hyperosmolar hyperglycemic state.

Diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemia state (HHS) are two life-threatening metabolic complications of diabetes that significantly increase mortality and morbidity. Despite major advances, reaching a uniform consensus regarding the diagnostic criteria and treatment of both conditions has been challenging. A significant overlap between these two extremes of the hyperglycemic crisis spectrum poses an additional hurdle. It has well been noted that a complete biochemical and clinical patient evaluation with timely diagnosis and treatment is vital for symptom resolution. Worldwide, there is a lack of large-scale studies that help define how hyperglycemic crises should be managed. This article will provide a comprehensive review of the pathophysiology, diagnosis, and management of DKA-HHS overlap.