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Biological membranes present an essential constituent of living cells. Their main role is to separate the interior of a cell from its surrounding, however allowing the selective transfer of specific material through it. Configuration changes of membranes are often correlated with important biological processes [1-7].

Fact-finding Investigation for the Activation of the Legal-forensic Nursing Consultancy Desk at the Order of Nursing Professions of the Province of Avellino

A 24-year-old male presented with epistaxis and gingival bleeding, physical examination revealed pallor and purpuric spots on the left upper limb. Laboratory findings showed pancytopenia. As part of the etiological workup for pancytopenia, a bone marrow aspirate was performed, revealing moderately cellular marrow with rare megakaryocytes and intranuclear inclusions in proerythroblasts. Parvovirus B19 testing in the bone marrow was conducted via nucleic acid extraction followed by PCR.

Present piece of idea exhibits to divert attention towards automated high precision Life Support System (LSS) instead of manual one using medical intelligence devices while treating and diagnosis to the patient, where Ventilator, inhaler and respiratory control is most important factor during operation, surgeries and in other likewise medical emergency situations to maintain proper saturation in patient lungs to sustain their lives. This work gives idea, how we can design A.I based Inhaler System for the same. 

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

A poison is a substance capable of causing illness or harm to a living organism upon contact or introduction to the body. Toxins and venoms are poisons of biological origin, with the latter usually reserved to describe the bites or stings of poisonous animals. In India, the suicide rate is 18.5 suicide deaths for every 100,000 people. The majority (79%) of suicides occur in low- and middle-income countries. Ingestion of poison is one of the most common modes of suicide in low and middle-income countries such as India [1].

Background: The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. Aim: To clarify and figure out food habits and different lifestyle pattern among a sample of Palestinian women attending the nutrition center in the Gaza Strip. Methods: A cross-sectional study involved 116 women were randomly chosen by purposive census sampling. They aged 25 to 60 years with a diagnosis of obesity. The data collected by interview questionnaire that included social factors, lifestyle habits, and health/disease history. Results: Age group variable has distributed significantly (p=0.024), (31.0%) of morbid obese were ≥35 years group compared to (13.5%) of morbid obese amongst the younger group. The higher intakes of legumes (OR: 2.134, P=0.003), nuts (OR: 3.917, P=0.019), eggs (OR: 6.840, P=0.009), fast foods (OR: 4.461, P=0.005), and soda drinks (OR: 2.230, P=0.004) were the risk factors linked to the increased risk of morbid obesity. Moreover, the higher intakes of legumes (OR: 8.439, P=0.011), eggs (OR: 6.900, P=0.041), chips (OR: 5.049, P=0.012), sugar (OR: 2.068, P=0.011), and fast foods (OR: 3.029, P=0.025) are risk factors of chronic diseases. Conclusion: The study identified several lifestyle factors and improper dietary habits associated with obesity among women in Gaza city. There is a great need to change these habits to avoid the increased risk of obesity. Gene-environment interaction can explain the high incidence of obesity. A national plan of action to overcome obesity is urgently needed to reduce its economic and health burden

We report the case of a 29-year-old male referred to our surgical department for evaluation of two progressively enlarging lumbar masses with an eight-month history.

Objectives: Natural disasters confront individuals, communities and governments with the challenge of rebuilding and addressing psychosocial sequelae. With the increasing number of natural disasters it is timely to evaluate the efficacy of interventions and strategies to address the mental health needs of individuals and the community. Method: An evaluation of literature related to the psychological impact of natural disasters, treatment efficacy and government strategies to confront the social and psychological impact of natural disasters for the period 1983-2016 was undertaken. Results: Epidemiological studies following natural disasters despite the use of differing psychological measures demonstrate significant psychological morbidity - anxiety (7-42%), complicated grief (28-41%), depression (6.5-38%), post-traumatic stress disorder (11-89%) and substance misuse (1.3-24%). Intervention studies post-disaster demonstrate efficacy variability. Conclusions: The increase in the number and impact of meteorological and hydrological events since the 1980s and the psychological, social and economic consequences of these events has resulted in the development and implementation of government policies to confront the immediate and long-term adverse outcomes. The focus is typically on resources and infrastructure redevelopment with less focus on social and mental health interventions with long-term evaluation of interventions uncommon. The consequence of natural disasters emphasizes the importance developing strategies to ensure effective evaluated psychosocial interventions are available across at-risk communities.

Objective: To provide a rigorous, multidisciplinary synthesis of the epidemiological, genetic, immunological, and environmental factors contributing to Premature Ovarian Failure (POF), with particular attention to regional disparities, occupational exposures, the impact of chemotherapy, the occurrence and recurrence of endometrosis, and emerging fertility preservation strategies. Design: A structured literature review with an emphasis on recent advances in genetic and immunological understanding.Setting: Academic research and clinical insights from multidisciplinary contributors.Patients: Individuals diagnosed with POF as reported in the literature.Interventions: Review of literature concerning epidemiology, genetic mutations, immunological disorders, and surgical outcomes linked to POF.Main outcome measures: Identification of both established and emerging risk factors, validation of genetic and immunological markers, and clarification of diagnostic and preventive clinical approaches.Results: The prevalence of POF varies globally, affecting 1% of women under 40. Genetic factors, particularly mutations in the FMR1 and BMP15 genes, play a significant role, alongside autoimmune diseases. Chemotherapy is a leading iatrogenic cause, while endometriosis and ovarian cyst surgeries significantly contribute to diminished ovarian reserve.Conclusion: POF is a multifactorial condition with rising incidence in specific subgroups. Improved early detection, standardized biomarker use, and expanded access to fertility preservation are essential. Targeted genomic and occupational risk screening may enable personalized interventions. Further genomic studies are needed to elucidate rare mutations and their impact.