Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment.

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Predictors of Serotonin Syndrome in Acute Poisoning with 5-Hydroxytryptamine Modulators.

5-Hydroxytryptamine (5-HT) modulators are commonly prescribed medications with potentially life-threatening outcomes, particularly serotonin syndrome (SS). Early prediction of SS is critical not only to avoid lethal drug combinations but also to initiate appropriate treatment. The present work aimed to recognize the significant predictors of SS through a retrospective cross-sectional study that was conducted among patients exposed to an overdose of 5-HT modulators and admitted to a poison control center where 112 patients were enrolled. Of them, 21 patients were diagnosed with SS, and 66.7% of patients with SS were exposed to long-term co-ingestion. There was a noticeable surge in SS between April and May, and 52.4% of patients who suffered from SS were admitted after suicidal exposure (p < 0.05). Patients with SS showed severe presentation indicated by high-grade poison severity scores (PSS) and low Glasgow coma scales (GCS). PSS was a significant predictor of SS with an area under the curve of 0.879. PCO2, pulse, GCS, HCO3, and erythrocytic count were other significant predictors of SS. Combinations of serotonergic agents increase the likelihood of developing SS. Clinicians should be vigilant when prescribing a combination of serotonergic therapy, particularly for patients on illicit sympathomimetic and over-the-counter medications like dextromethorphan.

Hemorrhagic giant adrenal myelolipoma discovered incidentally: a case report.

Adrenal myelolipomas are rare, benign, nonfunctional tumors composed of mature adipose tissue and hematopoietic elements. Hemorrhage within an adrenal myelolipoma is an uncommon occurrence, and when it happens, it can present with various clinical manifestations. Here, we report a case of a hemorrhagic giant adrenal myelolipoma in a 45-year-old female that was discovered incidentally. We discuss the clinical presentation, radiological findings, surgical intervention, and postoperative outcomes in this case report.

Mitotic CDK1 and 4E-BP1 I: Loss of 4E-BP1 serine 82 phosphorylation promotes proliferative polycystic disease and lymphoma in aged or sublethally irradiated mice.

4E-BP1 is a tumor suppressor regulating cap-dependent translation that is in turn controlled by mechanistic target of rapamycin (mTOR) or cyclin-dependent kinase 1 (CDK1) phosphorylation. 4E-BP1 serine 82 (S82) is phosphorylated by CDK1, but not mTOR, and the consequences of this mitosis-specific phosphorylation are unknown. Knock-in mice were generated with a single 4E-BP1 S82 alanine (S82A) substitution leaving other phosphorylation sites intact. S82A mice were fertile and exhibited no gross developmental or behavioral abnormalities, but the homozygotes developed diffuse and severe polycystic liver and kidney disease with aging, and lymphoid malignancies after irradiation. Sublethal irradiation caused immature T-cell lymphoma only in S82A mice while S82A homozygous mice have normal T-cell hematopoiesis before irradiation. Whole genome sequencing identified PTEN mutations in S82A lymphoma and impaired PTEN expression was verified in S82A lymphomas derived cell lines. Our study suggests that the absence of 4E-BP1S82 phosphorylation, a subtle change in 4E-BP1 phosphorylation, might predispose to polycystic proliferative disease and lymphoma under certain stressful circumstances, such as aging and irradiation.

The outcome of COVID-19 patients in the intensive care unit in Sudan: A cross-sectional study.

Background and Aim: A major outbreak of coronavirus spread all over the world and gave rise to high mortality rate and high admission rate to intensive care unit (ICU). This cohort study aims to assess the outcome of COVID-19 patients in ICU and to investigate the factors associated with mortality. Method: This is a multicentered retrospective cohort study that was conducted among confirmed cases of COVID-19 patients, who were admitted to ICU in Sudan during March 2021. The data collection was done manually from the medical records of patients. Mortality rate and association and prediction of factors associated with mortality were obtained using Statistical Package for the Social Sciences software (SPSS) version 22. Results: The mortality rate among patients in this study was 70%. Using the chi-square test we found that age, needing intubation, developing Systemic inflammatory response syndrome, neurological complications, hematological complications, and cardiac complications have a significant association with the outcome. Conclusion: Majority of COVID-19 patients who were admitted to the ICU died. 55.8% of patients developed at least one complication during their stay in ICU. The age, the need for intubation, and developing of systematic inflammatory response syndrome (SIRS) are the factors that predict the mortality.

Diabetes disease prediction system using HNB classifier based on discretization method.

Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way - through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.

An Analysis of Body Language of Patients Using Artificial Intelligence.

In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.

A Generalized Laser Simulator Algorithm for Mobile Robot Path Planning with Obstacle Avoidance.

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment's borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm.

Effect of ozone versus naringin on testicular injury in experimentally induced ulcerative colitis in adult male albino rats.

Testicular dysfunction is caused by the continuous inflammation and oxidative stress that are present at the local site in ulcerative colitis (UC) spreading to the testes via systemic circulation. The influence of ozone and naringine on colitis-mediated testicular dysfunction was investigated in this study. Forty-eight adult male rats were divided into four groups: I control group, II dextran sodium sulfate (DSS) UC-induced group, III DSS+naringine, and IV DSS+ozone groups. UC was induced in groups II, III, and IV using 0.1 ml of 4% DSS in their drinking water per day for 6 days by gastric gavage. All animals were sacrificed 45 days from the start. Blood samples were obtained to estimate serum testosterone hormone. Testicular tissues were processed for measurement of tissue malondialdehyde (MDA) and examined by light and electron microscopes. Ultrastructurally, group II revealed a relatively thick basement membrane enveloping the seminiferous tubule. Sertoli cell cytoplasm appears rarified with wide intracellular spaces, vacuoles, and multiple lysosomes; distorted spermatogonia with electron dense nuclei and cytoplasm; and primary spermatocytes with small nuclei and electron dense cytoplasm. Abnormal sperm profiles were visible in middle pieces, mid, principle, and end pieces that were markedly affected with disorganization of axoneme and outer dense fibers. Leydig cells revealed dilated cisternae of smooth endoplasmic reticulum. Morphometric and statistical analyses were performed. Group III showed some improvement; however, group IV showed more improvement. The results indicated that ozone caused marked improvement than naringine against UC-induced testicular damage via their antioxidant and anti-inflammatory properties.

Does Kidney Transplantation Help Young Patients on Dialysis With Erectile Dysfunction? A Single-center Study.

OBJECTIVE: to investigate the effects of a successful KT on EF in male patients with ESRD. MATERIALS AND METHODS: A single-center cross-sectional prospective study to assess the erectile dysfunction in male patients one month before and one year after KT. We adopted a validated, self-administered translated International Index of Erectile Function (IIEF-15) questionnaire. A sub-analysis was performed by categorizing the cohort into 2 age groups: <50 and ≥50 yr. RESULTS: Between September 2017 and February 2021, 68 ESRD patients underwent kidney transplantation (KT), with a mean age of 48.9 ± 12.9 years. Thirty-one patients were below 50 years (Group I). The median hemodialysis duration was 12 months. Sixty-three patients (92.6%) had ED. The mean total IIEF-15 score before and after was 46.8/75 ±12.7 and 55.5 ±13, respectively (P <.001). Forty-six patients (67.6%) reported improved erectile function, 22 (32.4%) did not demonstrate any change, and no patient reported deterioration. Moreover, after KT, sexual desire, orgasm, and overall patient satisfaction improved significantly. Before KT, 83.8% and 100% of groups I and II patients had ED, which dropped to 22.6% and 86.5%, respectively, after KT. IIEF-15 scores improved in both groups. However, the improvement in ED was observed significantly in young patients with mild ED. CONCLUSION: KT positively impacts sexual function and improves erectile dysfunction, especially among young patients. The duration of dialysis before kidney transplantation had no impact on ED improvement after transplantation. The positive effect of transplantation on ED could encourage ESRD patients to undergo KT.

Body Language Analysis in Healthcare: An Overview.

Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through analyzing their body language. The development of technologies for recognizing and interpreting arm and leg gestures, facial features, and body postures is still in its early stage. More extensive research is needed using artificial intelligence (AI) techniques in disease detection. This paper presents a comprehensive survey of the research performed on body language processing. Upon defining and explaining the different types of body language, we justify the use of automatic recognition and its application in healthcare. We briefly describe the automatic recognition framework using AI to recognize various body language elements and discuss automatic gesture recognition approaches that help better identify the external symptoms of epidemic and pandemic diseases. From this study, we found that since there are studies that have proven that the body has a language called body language, it has proven that language can be analyzed and understood by machine learning (ML). Since diseases also show clear and different symptoms in the body, the body language here will be affected and have special features related to a particular disease. From this examination, we discovered that it is possible to specialize the features and language changes of each disease in the body. Hence, ML can understand and detect diseases such as pandemic and epidemic diseases and others.

ArtSeg-Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations.

Brightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by orders of magnitude compared to pixel-level annotation, but without substantially sacrificing the segmentation performance. We confirm that artifacts indeed exist with different shapes and sizes in three different brightfield microscopy image datasets, and distort downstream analyses such as nuclei segmentation, morphometry and fluorescence intensity quantification. We then demonstrate that our automated artifact removal ameliorates this problem. Such rapid cleaning of acquired images using the power of deep learning models is likely to become a standard step for all large scale microscopy experiments.

Live-cell microscopy or fluorescence anisotropy with budded baculoviruses-which way to go with measuring ligand binding to M4 muscarinic receptors?

M4 muscarinic acetylcholine receptor is a G protein-coupled receptor (GPCR) that has been associated with alcohol and cocaine abuse, Alzheimer's disease, and schizophrenia which makes it an interesting drug target. For many GPCRs, the high-affinity fluorescence ligands have expanded the options for high-throughput screening of drug candidates and serve as useful tools in fundamental receptor research. Here, we explored two TAMRA-labelled fluorescence ligands, UR-MK342 and UR-CG072, for development of assays for studying ligand-binding properties to M4 receptor. Using budded baculovirus particles as M4 receptor preparation and fluorescence anisotropy method, we measured the affinities and binding kinetics of both fluorescence ligands. Using the fluorescence ligands as reporter probes, the binding affinities of unlabelled ligands could be determined. Based on these results, we took a step towards a more natural system and developed a method using live CHO-K1-hM4R cells and automated fluorescence microscopy suitable for the routine determination of unlabelled ligand affinities. For quantitative image analysis, we developed random forest and deep learning-based pipelines for cell segmentation. The pipelines were integrated into the user-friendly open-source Aparecium software. Both image analysis methods were suitable for measuring fluorescence ligand saturation binding and kinetics as well as for screening binding affinities of unlabelled ligands.

A Study of Morphological Characteristics of Lung Fissures and Trachea in the Indian Population.

Tracheal length and lung anatomy have been rarely studied; however, the anatomy of the lung has been shown to vary significantly. Moreover, the surgery regarding trachea are few, and hence the surgeons do not have extensive experience in the trachea. OBJECTIVE: We aimed to study the variations of the lung anatomy and the relation between tracheal length and body height in the Indian population. MATERIALS AND METHODS: This is an observational study to observe the tracheal length in relation to body height and sex and gross morphological anatomy of the lung in 70 cadavers. The data was collected from the forensic department of Bangalore Medical College and Research Institute (BMCRI), and further analysis was done at Kidwai Memorial Institute of Oncology. RESULTS: Deviation from normal lung morphology was seen in 37.86% of the specimens studied. The tracheal length (average, 9.97 cm) correlated with the body length (average, 147.02 cm) with a Pearson coefficient of 0.806 (p value=0.001) Conclusion: The study of lung fissure morphology guides clinicians in understanding and planning lung disease treatment, especially lobectomy/segmentectomy surgeries. The information of the average length of the trachea with respect to body height in a given ethnicity will help during endotracheal intubation and tracheal surgical planning.

Factors Affecting Adherence to Phosphate-binding Medications among Patients with End-stage Kidney Disease in Makkah City.

Despite the evidence that the management of hyperphosphatemia depends heavily on adherence to phosphate-binding (PB) medications, many dialysis patients are non-adherent. Therefore, factors associated with non-adherence to PB medications should be identified and eliminated. This study aimed to identify and highlight factors influencing adherence to PB medications among patients with end-stage kidney disease (ESKD). A cross-sectional survey was conducted in the hemodialysis centers of three major governmental hospitals in Makkah City, Saudi Arabia. The World Health Organization's five dimensions of adherence to medication (patient, socioeconomic, condition, therapy, and health system) were used to guide the analysis. A multivariable logistic regression analysis was used to determine factors influencing adherence to PB medications among patients with ESKD. Three hundred and fifty-eight patients submitted completed questionnaires and were included in this study; of them, 87.99% were adherent to PB medications. The factors sex, adherence to dietary restrictions, and duration on dialysis were found to be significantly and positively associated with adherence to PB medications, whereas the factors difficulty to take medications and difficulty to adhere to a large number of tablets had significant and negative associations with adherence to PB medications. Hyperphosphatemia is a cause for concern as it leads to several life-threatening complications. The results of the present study encourage to recruit representative samples and consider more factors, such as patients' attitudes toward medications and provider-level factors, to inform policy and/or programmatic interventions that increase adherence to PB medications among patients with ESKD.

Prevalence of Non-adherence to Prescribed Medications among Hemodialysis Patients in Makkah City.

Patients with end-stage kidney disease (ESKD) are required to take multiple medications. Adherence to a complex regimen of medications is challenging and might lead to non-adherence. This study aimed to assess nonadherence to prescribed medications among patients with ESKD in Makkah City and determine the factors associated with a such behavior. A cross-sectional study was conducted at three governmental hospitals in Makkah City, Saudi Arabia. Descriptive statistics were performed to characterize participants, and a multivariable logistic regression analysis was used to determine factors associated with nonadherence to prescribed medications among patients with ESKD. In total, 358 patients have submitted completed surveys and were included in this study. A considerable number (45.25%) of participating patients were found to be nonadherent to prescribed medications. The factors: age, belief that taking medications as scheduled is important, adherence to dialysis sessions, and the number of comorbid diseases had significant and negative associations with nonadherence to prescribed medications. On the other hand, the factors: forgetfulness and having depression were significantly and positively associated with non-adherence to prescribed medications. Non-adherence to medications among patients on hemodialysis is a significant issue that leads to life-threatening complications. The factors identified as being significantly associated with nonadherence should be considered in designing future interventions to improve adherence to medications.

Reverse Axillary Mapping in Breast Cancer Using Blue Dye: A Tertiary Setup Experience.

The concept of reverse axillary mapping originated with the main purpose of reducing lymphedema. In this study, we test the advantage of reverse axillary mapping to delineate the arm-draining lymph nodes and their involvement in various stages of breast carcinoma. In this study, we also attempt to redefine the template for axillary dissection in breast cancer. During the period of September 30, 2020, to August 30, 2021, 46 patients were recruited to undergo a procedure in which isosulfan blue dye was injected into the upper arm and the axilla was explored to isolate the lymph nodes. The lymph nodes were submitted for examination histopathologically. The results conclusively showed that axillary lymph node metastasis was only influenced by the advanced stage of the disease (p=0.014) and the visualization of the lymphatics was independent of the stage, type of surgery, decubitus, or age. The study conclusively shows that attempts to preserve the upper limb-draining nodes in advanced stages would be futile and the preservation of such lymph nodes should be limited to the early stages of breast cancer.

Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.

Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While deep learning models have given encouraging results on this problem, the most powerful approaches have not yet been tested for attacking it. Here, we review and evaluate state-of-the-art very deep convolutional neural network architectures and training strategies for segmenting nuclei from brightfield cell images. We tested U-Net as a baseline model; considered U-Net++, Tiramisu, and DeepLabv3+ as latest instances of advanced families of segmentation models; and propose PPU-Net, a novel light-weight alternative. The deeper architectures outperformed standard U-Net and results from previous studies on the challenging brightfield images, with balanced pixel-wise accuracies of up to 86%. PPU-Net achieved this performance with 20-fold fewer parameters than the comparably accurate methods. All models perform better on larger nuclei and in sparser images. We further confirmed that in the absence of plentiful training data, augmentation and pretraining on other data improve performance. In particular, using only 16 images with data augmentation is enough to achieve a pixel-wise F1 score that is within 5% of the one achieved with a full data set for all models. The remaining segmentation errors are mainly due to missed nuclei in dense regions, overlapping cells, and imaging artifacts, indicating the major outstanding challenges.

In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer.

Improving effector activity of antigen-specific T cells is a major goal in cancer immunotherapy. Despite the identification of several effector T cell (TEFF)-driving transcription factors (TFs), the transcriptional coordination of TEFF biology remains poorly understood. We developed an in vivo T cell CRISPR screening platform and identified a key mechanism restraining TEFF biology through the ETS family TF, Fli1. Genetic deletion of Fli1 enhanced TEFF responses without compromising memory or exhaustion precursors. Fli1 restrained TEFF lineage differentiation by binding to cis-regulatory elements of effector-associated genes. Loss of Fli1 increased chromatin accessibility at ETS:RUNX motifs, allowing more efficient Runx3-driven TEFF biology. CD8+ T cells lacking Fli1 provided substantially better protection against multiple infections and tumors. These data indicate that Fli1 safeguards the developing CD8+ T cell transcriptional landscape from excessive ETS:RUNX-driven TEFF cell differentiation. Moreover, genetic deletion of Fli1 improves TEFF differentiation and protective immunity in infections and cancer.

Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator-Fuzzy Logic Algorithms and Sensor Fusion.

A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator-fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road's roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot's path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.