Search

Introduction: Sepsis is a clinical condition that results from infection and systemic inflammatory response syndrome, which can progress to severe sepsis and septic shock. This condition leads to myocardial dysfunction and increased mortality rates, particularly in cases of septic shock. Inotropic agents are beneficial in improving cardiac contractility and cardiac output in septic shock. Need for the study: It is important to address myocardial dysfunction in sepsis and septic shock, as these conditions significantly contribute to patient morbidity and mortality. Calcium desensitisation is a key factor in the pathophysiology of septic myocardial depression, leading to impaired cardiac function. Levosimendan, a novel calcium sensitiser, offers a promising therapeutic option due to its unique pharmacologic and biologic profile. By enhancing calcium sensitivity in cardiac myocytes, levosimendan may improve cardiac function without the detrimental side-effects associated with traditional inotropic agents. Aim: The research protocol was planned with an aim to analyse the impact of intravenous levosimendan on Right Ventricular (RV) dysfunction in patients with septic shock. Materials and Methods: This prospective observational study aims to assess the effect of levosimendan on the improvement of RV dysfunction in patients with septic shock who are admitted to the intensive care unit of Acharya Vinoba Bhave Rural Hospital, DMIHER (DU) Sawangi (Meghe), Wardha, Maharashtra, India from July 2023 to March 2025, 45 patients who meet the inclusion criteria will be recruited. Transthoracic Echocardiography (TTE) will be performed using standard measures to calculate RV function. Patients diagnosed with RV dysfunction will be administered levosimendan, followed by a re-evaluation of RV function at designated intervals. Statistical analysis will be conducted using R software, and a paired t-test will be employed to determine significant differences at pre- and post-intervention timelines for the outcome variables of echocardiographic parameters, including Tricuspid Annular Plane Systolic Excursion (TAPSE), EF, RV Fractional Area Change (FAC), and Tricuspid Regurgitant jet Velocity (TRV), at a 5% level of significance.

Real teeth or dental image datasets are a valuable resource that is transforming the field of dentistry by enabling automation, improving diagnostics and accelerating research and development.This article presents a comprehensive dataset containing 9,562 images of healthy teeth (noncarious) from children aged 1 to 14 years. The images capture different views of the teeth, including maxillary (upper) and mandibular (lower) arches, front, right, left, and occlusal (biting surface) views. These images are stored under eight subcategories in the Mendeley repository, a platform for research data. The potential application of this dataset involves using machine learning to analyze the dental condition. This could provide a faster analysis and facilitate remote assessment of dental conditions in underserved areas. Overall, this dataset seems like a promising tool for advancing dental care through the power of machine learning.

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers • A novel method for pneumothorax segmentation from chest X-rays. • A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation. • A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays.The proposed method is tested against other pneumothorax segmentation techniques using a dataset of ‘chest X-rays’ with ‘pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

Background Septic shock is a life-threatening condition characterized by systemic inflammation and organ dysfunction, with fluid resuscitation being a cornerstone of management. However, indiscriminate fluid administration can lead to fluid overload and worsen outcomes. Extravascular lung water (EVLW) estimation by lung ultrasound has emerged as a promising tool for guiding fluid therapy in septic shock, allowing clinicians to assess pulmonary edema and tailor resuscitation strategies accordingly. Methods This prospective observational study aims to evaluate the utility of EVLW estimation by lung ultrasound in guiding fluid resuscitation therapy in adult patients with septic shock admitted to a rural tertiary care teaching hospital. Eligible patients admitted to the intensive care unit (ICU) will undergo baseline demographic and clinical assessments, including lung ultrasound, to quantify EVLW using B-line analysis. Fluid resuscitation therapy will be initiated based on EVLW findings, with subsequent adjustments guided by repeat lung ultrasound examinations at 6, 12, 24, and 48 hours post-initiation. Outcome measures include changes in mean PaO2/FiO2 ratios, respiratory parameters, renal function, fluid balance, and mortality rates. Expected Outcome We anticipate that EVLW-guided fluid resuscitation therapy will lead to more precise and tailored management of septic shock, potentially reducing the incidence of fluid overload, ARDS, and renal dysfunction. By optimizing fluid management strategies based on individual patient characteristics and responses, we aim to improve clinical outcomes and enhance the delivery of care for patients with septic shock.

Introduction: Sepsis is a clinical condition that results from infection and systemic inflammatory response syndrome, which can progress to severe sepsis and septic shock. This condition leads to myocardial dysfunction and increased mortality rates, particularly in cases of septic shock. Inotropic agents are beneficial in improving cardiac contractility and cardiac output in septic shock. Need for the study: It is important to address myocardial dysfunction in sepsis and septic shock, as these conditions significantly contribute to patient morbidity and mortality. Calcium desensitisation is a key factor in the pathophysiology of septic myocardial depression, leading to impaired cardiac function. Levosimendan, a novel calcium sensitiser, offers a promising therapeutic option due to its unique pharmacologic and biologic profile. By enhancing calcium sensitivity in cardiac myocytes, levosimendan may improve cardiac function without the detrimental side-effects associated with traditional inotropic agents. Aim: The research protocol was planned with an aim to analyse the impact of intravenous levosimendan on Right Ventricular (RV) dysfunction in patients with septic shock. Materials and Methods: This prospective observational study aims to assess the effect of levosimendan on the improvement of RV dysfunction in patients with septic shock who are admitted to the intensive care unit of Acharya Vinoba Bhave Rural Hospital, DMIHER (DU) Sawangi (Meghe), Wardha, Maharashtra, India from July 2023 to March 2025, 45 patients who meet the inclusion criteria will be recruited. Transthoracic Echocardiography (TTE) will be performed using standard measures to calculate RV function. Patients diagnosed with RV dysfunction will be administered levosimendan, followed by a re-evaluation of RV function at designated intervals. Statistical analysis will be conducted using R software, and a paired t-test will be employed to determine significant differences at pre- and post-intervention timelines for the outcome variables of echocardiographic parameters, including Tricuspid Annular Plane Systolic Excursion (TAPSE), EF, RV Fractional Area Change (FAC), and Tricuspid Regurgitant jet Velocity (TRV), at a 5% level of significance. [ABSTRACT FROM AUTHOR]

Covid-19 is a novel pandemic disease with no potential vaccine treatment or medicine, the world is facing currently as of now. The death toll has increased to several lakhs and recovery rate is comparatively very less, was initially spotted in Wuhan (China). This spreads through close contact with people and socializing. The number of infected people varies with different parts of the world In our particular country India we are going through the lock down period which is the only vaccine to promote “social distancing” The hurdle arose due to the widespread of corona is major economy loss in combo with innocent lives. In this manuscript, we are visualizing the dataset which is publicly available to map, differentiate and separate the data in order to segregate the places that are most prone and perform basic regression to identify and predict the increasability of the counts from the dataset.

The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows for early detection of the disease and the implementation of preventative measures. Disease severity estimation based on digital picture analysis, where the pictures are obtained from the rice field using mobile devices, is one of the most effective control strategies. This paper offers a method for quantifying the severity of three rice crop diseases (brown spot, blast, and bacterial blight) that can determine the stage of plant disease. A total of 1200 images of rice illnesses and healthy images make up the input dataset. With the help of agricultural experts, the diseased zone was labeled according to the disease type using the Make Sense tool. More than 75% of the images in the dataset correspond to one disease label, healthy plants represent more than 15%, and multiple diseases represent 5% of the images labeled. This paper proposes a novel artificial intelligence rice grade model that uses an optimized faster-region-based convolutional neural network (FRCNN) approach to calculate the area of leaf instances and the infected regions. EfficientNet-B0 architecture was used as a backbone as the network shows the best accuracy (96.43%). The performance was compared with the CNN architectures: VGG16, ResNet101, and MobileNet. The model evaluation parameters used to measure the accuracy are positive predictive value, sensitivity, and intersection over union. This severity estimation method can be further deployed as a tool that allows farmers to obtain perfect predictions of the disease severity level based on lesions in the field conditions and produce crops more organically.

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks; and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution; while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets. [ABSTRACT FROM AUTHOR]

The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows for early detection of the disease and the implementation of preventative measures. Disease severity estimation based on digital picture analysis, where the pictures are obtained from the rice field using mobile devices, is one of the most effective control strategies. This paper offers a method for quantifying the severity of three rice crop diseases (brown spot, blast, and bacterial blight) that can determine the stage of plant disease. A total of 1200 images of rice illnesses and healthy images make up the input dataset. With the help of agricultural experts, the diseased zone was labeled according to the disease type using the Make Sense tool. More than 75% of the images in the dataset correspond to one disease label, healthy plants represent more than 15%, and multiple diseases represent 5% of the images labeled. This paper proposes a novel artificial intelligence rice grade model that uses an optimized faster-region-based convolutional neural network (FRCNN) approach to calculate the area of leaf instances and the infected regions. EfficientNet-B0 architecture was used as a backbone as the network shows the best accuracy (96.43%). The performance was compared with the CNN architectures: VGG16, ResNet101, and MobileNet. The model evaluation parameters used to measure the accuracy are positive predictive value, sensitivity, and intersection over union. This severity estimation method can be further deployed as a tool that allows farmers to obtain perfect predictions of the disease severity level based on lesions in the field conditions and produce crops more organically. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a growing health concern, particularly in older adults, due to its high prevalence and association with increased morbidity and mortality. Early detection and effective management are crucial to slowing disease progression and reducing complications such as cardiovascular events and end-stage renal disease (ESRD). Traditional biomarkers, including serum creatinine and estimated glomerular filtration rate (eGFR), often have limitations in older populations, where age-related physiological changes can obscure early signs of kidney dysfunction. Advanced biomarkers offer a more precise and comprehensive understanding of kidney health, providing insights into pathological processes such as inflammation, fibrosis, oxidative stress, and tubular injury. These biomarkers have the potential to enhance early diagnosis, predict disease progression, and inform personalized treatment approaches, particularly in the elderly. This review explores the current landscape of advanced biomarkers for CKD in older adults, highlighting their clinical utility and limitations. Key biomarkers, including those related to inflammation (C-reactive protein, interleukin-6), fibrosis (transforming growth factor-beta, collagen degradation products), oxidative stress (F2-isoprostanes, malondialdehyde), and tubular injury (kidney injury molecule-1, neutrophil gelatinase-associated lipocalin), are examined in the context of CKD. Emerging technologies, such as multi-omics and machine learning, are also discussed as they offer new opportunities for biomarker discovery and integration into clinical practice. While challenges remain, including the need for longitudinal studies and better standardization, advanced biomarkers hold promise for transforming CKD management in older adults, paving the way for earlier detection, better risk stratification, and more targeted therapeutic interventions., Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Pradeep et al.)

Metabolic syndrome (MetS) is a complex condition characterized by abdominal obesity, insulin resistance, dyslipidemia, and hypertension, all of which increase the risk of cardiovascular disease and type 2 diabetes. The gut microbiome plays a significant role in metabolic health, influencing digestion, immune function, and energy metabolism. When the gut microbiota becomes imbalanced due to poor diet and antibiotic use, it can lead to systemic inflammation, insulin resistance, and abnormal lipid metabolism, which are central features of MetS. This review explores the connection between gut microbial imbalances and MetS, focusing on the impact of the gut microbiome on metabolic health. Supplementation therapies targeting the gut microbiome, such as probiotics, prebiotics, synbiotics, and postbiotics, are evaluated for their potential to improve metabolic parameters in MetS patients. These interventions hold promise for enhancing insulin sensitivity, reducing inflammation, and improving lipid profiles. However, further research is needed to optimize these approaches for managing MetS. Understanding how to leverage the gut microbiome could lead to innovative, non-invasive treatments for this growing global health concern., Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Reddy et al.)