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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]

Introduction: Health expenditures in survivors of myocardial infarction (MI) causes increased financial burden. Secondary prevention strategy can be planned with knowledge of health-related quality of life (HRQoL) in survivors of MI. Aim: To study HRQOL using Mac New Heart questionnaire. Objective: Female survivors of MI will undergo the questionnaire and the scores across physical, social and emotional domains will be noted. Material and Methods: Observational study. Female survivors of MI attending follow up in cardiac outpatient department between January 2017 and January 2018 were subjected to MacNew Heart Disease HRQoL questionnaire. Statistical Analysis Used: Pearson's correlation coefficient; software used was SPSS 22.0 version. Results: Mean age 60 years; mean duration since MI – 7.88 months; 74% were married and 88% on vegetarian diet. Those women with mean age of 40 years, vegetarian and married had better mean scores. The emotional score improved over a period of time whereas the physical and social score remained the same. Conclusion: Female survivors above 40 years showed poor scores across all three domains and therefore need early cardiac rehabilitation as also long-term follow up.

Background: Anemia is highly prevalent in low- and middle-income countries, where prevalence of acute coronary syndrome (ACS) is also rising. Evidence indicates that baseline anemia status can prognosticate ACS. However, the Global Registry of Acute Coronary Events (GRACE) score that is popularly used all over the world does not include information on anemia. Objectives: Our objective was to investigate if anemia at admission, along with the GRACE score, improves the prediction of adverse outcomes within 6 months in rural Indian patients of ACS. Methods: We enrolled 200 ACS patients at the Acharya Vinoba Bhave Rural Hospital—a rural, tertiary care hospital in central India. Patients were followed for 6 months for death and major adverse cardiac event (MACE). Improvement in the prediction of adverse events by including anemia in addition to the GRACE score was quantified using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI) and the net reclassification index (NRI). Results: There were 31 deaths due to MACE and an additional 28 non-fatal MACE events during follow-up. Baseline hemoglobin was strongly and independently associated with both outcomes even after adjusting for a multivariable propensity score. For the outcome of death and death/MACE there was a moderate improvement in the AUC of 1% and 6%, respectively. However, for these outcomes the IDI for baseline hemoglobin was 6% (p = 0.03) and 12% (p << 0.0001), respectively, while the NRI was 0.50 (p = 0.01) and 0.78 (p << 0.0001), respectively. Conclusions: Inclusion of baseline anemia in addition to the GRACE score improves prognostication of ACS patients.

Introduction: The information on patient characteristics and outcome in patients requiring invasive mechanical ventilation (IMV) is critical for better use of resources and clinical decision making in a rural ICU. Objective: To understand characteristics and outcome of patients on IMV. Design: This is a retrospective study in patients admitted in medical intensive care unit of a rural hospital who were on IMV during August 2013 to February 2015. Adult patients with failing respiratory drive and/or those who failed oxygen therapy or NIV (non invasive ventilation) were considered eligible for invasive ventilation. Patients exclusively on NIV were excluded (reason for exclusion was to study the outcome in an expensive intervention like IMV). Patients who were weaned and extubated and subsequently shifted to medicine ward were considered “good” outcome and “adverse” (not-extubated) if they died or sought discharge against medical advice. Outcome measure: All-cause mortality during ICU stay. Results: A total of 505 patients, of which 74.7% were male with mean age of 52 years (IQ range 38–65 years). Comorbidities were seen in 76.4% patients; significantly higher in not-extubated (94.85% vs 5.15%) (p = 0.008). The ICU stay, days on ventilation and total hospital stay were 5 (3–9) days, 2 (1–5) days and 5(3–9) days respectively. Primary cause for IMV was sepsis, neurological, cardiac, renal and respiratory and others like envenomation, drug overdose, organophosphate poisoning, etc. Hypertension and diabetes were the commonest co-morbidities. Conclusion: The mortality in patients requiring invasive ventilation support from low-resource setting is high.

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]