Infectious Illness Research Articles

Exploring the Impact of Personalized Nutritional Approaches on Metabolism and Immunity: A Systematic Review of Various Nutrients and Dietary Patterns.

The systematic review investigates the impact of different nutrients and dietary patterns on metabolism and immunityto answer the research question: "Can personalized nutritional approaches boost immunity?" The importance of diet in supporting the immune system has come to light in today's environment, where a strong immune system is crucial for protection against infectious illnesses, as highlighted by the COVID-19 pandemic. This systematic review adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Four databases were screened for relevant data published in 2022-2023: PubMed, PubMed Central (PMC), MEDLINE, and Cochrane Library. Inclusion and exclusion criteria were utilized, and 13 papers were finalized after screening and employing the quality appraisal tool Cochrane Bias assessment for randomized controlled trials (RCT). Personalized nutrition can strengthen immunity and enhance overall health by adjusting dietary recommendations and following a person's genetic makeup, lifestyle, and health state. An adequate supply of vitamins, minerals, proteins, and fatty acidsas well as an optimum caloric intake are essential for immune health, and individual requirements can vary significantly due to genetic factors, lifestyle choices, and underlying health conditions. Personalized nutrition considers these factors, enabling tailored dietary recommendations to address specific nutrient needs and optimize nutrient intake, leading to better health outcomes. The review concludes that personalized nutrition is more effective than a one-size-fits-all approach in boosting immunity, and its potential impact on health and immune function is highly important.

Fusing Deep Immuno Net and Random Forest to Improve the Detection of Dengue Fever: A Computational Strategy for Prompt and Precise Diagnosis

Dengue fever is a worldwide health issue caused by the virus. Effective medical management and avoidance of severe consequences need fast and precise Dengue detection. This research uses advanced image pre-processing, deep learning methods using convolutional neural networks (CNNs), and feature selection to improve Dengue diagnosis. Our first study focuses on improving input images using cutting-edge image enhancement methods, including Adaptive Contrast Enhancement using Histogram Equalization (ACE-HE)during pre-processing. This improves visual data for processing, enabling Dengue identification. Feature extraction, using a CNN architecture optimized for Dengue detection, is our core technique. We also use current neural network designs like EfficientNet and transformers to extract subtle characteristics needed for effective diagnosis. Our approach uses computational evolutionary algorithms and neural structure searches to identify the most relevant features from the large pool of retrieved information. These characteristics are gradually fused to provide a complete depiction of Dengue's complicated patterns. Dengue detection is completed using high-performance classifiers including Random Forest variations and ensemble approaches. Our architecture is adaptable and resilient, achieving above 97.92% BCCD dataset classification accuracy. This technology represents a major advance in Dengue diagnosis and meets the worldwide requirement for rapid and accurate infectious illness identification. Our strategy uses the latest computer vision and neural network technology to produce dependable and effective tools for rapid and accurate Dengue diagnosis, addressing a crucial worldwide healthcare issue.

Measles - United States, January 1, 2020-March 28, 2024.

Measles is a highly infectious febrile rash illness and was declared eliminated in the United States in 2000. However, measles importations continue to occur, and U.S. measles elimination status was threatened in 2019 as the result of two prolonged outbreaks among undervaccinated communities in New York and New York City. To assess U.S. measles elimination status after the 2019 outbreaks and to provide context to understand more recent increases in measles cases, CDC analyzed epidemiologic and laboratory surveillance data and the performance of the U.S. measles surveillance system after these outbreaks. During January 1, 2020-March 28, 2024, CDC was notified of 338 confirmed measles cases; 97 (29%) of these cases occurred during the first quarter of 2024, representing a more than seventeenfold increase over the mean number of cases reported during the first quarter of 2020-2023. Among the 338 reported cases, the median patient age was 3 years (range=0-64 years); 309 (91%) patients were unvaccinated or had unknown vaccination status, and 336 case investigations included information on ≥80% of critical surveillance indicators. During 2020-2023, the longest transmission chain lasted 63 days. As of the end of 2023, because of the absence of sustained measles virus transmission for 12 consecutive months in the presence of a well-performing surveillance system, U.S. measles elimination status was maintained. Risk for widespread U.S. measles transmission remains low because of high population immunity. However, because of the increase in cases during the first quarter of 2024, additional activities are needed to increase U.S. routine measles, mumps, and rubella vaccination coverage, especially among close-knit and undervaccinated communities. These activities include encouraging vaccination before international travel and rapidly investigating suspected measles cases.

SARS-CoV-2 infects cells lining the blood-retinal barrier and induces a hyperinflammatory immune response in the retina via systemic exposure.

SARS-CoV-2 has been shown to cause wide-ranging ocular abnormalities and vision impairment in COVID-19 patients. However, there is limited understanding of SARS-CoV-2 in ocular transmission, tropism, and associated pathologies. The presence of viral RNA in corneal/conjunctival tissue and tears, along with the evidence of viral entry receptors on the ocular surface, has led to speculation that the eye may serve as a potential route of SARS-CoV-2 transmission. Here, we investigated the interaction of SARS-CoV-2 with cells lining the blood-retinal barrier (BRB) and the role of the eye in its transmission and tropism. The results from our study suggest that SARS-CoV-2 ocular exposure does not cause lung infection and moribund illness in K18-hACE2 mice despite the extended presence of viral remnants in various ocular tissues. In contrast, intranasal exposure not only resulted in SARS-CoV-2 spike (S) protein presence in different ocular tissues but also induces a hyperinflammatory immune response in the retina. Additionally, the long-term exposure to viral S-protein caused microaneurysm, retinal pigmented epithelium (RPE) mottling, retinal atrophy, and vein occlusion in mouse eyes. Notably, cells lining the BRB, the outer barrier, RPE, and the inner barrier, retinal vascular endothelium, were highly permissive to SARS-CoV-2 replication. Unexpectedly, primary human corneal epithelial cells were comparatively resistant to SARS-CoV-2 infection. The cells lining the BRB showed induced expression of viral entry receptors and increased susceptibility towards SARS-CoV-2-induced cell death. Furthermore, hyperglycemic conditions enhanced the viral entry receptor expression, infectivity, and susceptibility of SARS-CoV-2-induced cell death in the BRB cells, confirming the reported heightened pathological manifestations in comorbid populations. Collectively, our study provides the first evidence of SARS-CoV-2 ocular tropism via cells lining the BRB and that the virus can infect the retina via systemic permeation and induce retinal inflammation.

Predicting Multiple Diseases with Machine Learning and Streamlit: Enhancing Healthcare Digitally

Machine learning (ML) and streamlit technologies have emerged as strong tools in the field of healthcare, with the ability for predicting and diagnosing many diseases with impressive precision and speed. This research study examines the transformational potential of ML algorithms combined with streamlit for disease prediction, highlighting their numerous uses and significant impact on healthcare delivery. Predictive models can be constructed using broad datasets and powerful machine learning techniques to anticipate the onset, development, and treatment results of many diseases, ranging from chronic ailments to infectious illnesses. The inclusion of streamlit creates an easy-to-use user experience for medical personnel and patients alike, allowing for smooth engagement with models of prediction and improving decision-making processes. In addition, the use of such prediction models has the potential to improve early detection, personalized therapy techniques, and resource management in healthcare systems. However, the application of ML-based disease prediction systems raises concerns about data privacy, model clarity, and ethical consequences. By overcoming these obstacles and seizing the opportunities provided by ML and streamlit, the future of healthcare holds enormous promise for improving patient outcomes and expanding medical research.

Genetic Characterization, Pathogenicity, and Epidemiology Analysis of Three Sub-Genotype Pigeon Newcastle Disease Virus Strains in China.

Pigeon Newcastle disease (ND) is a serious infectious illness caused by the pigeon Newcastle disease virus (NDV) or Paramyxovirus type 1 (PPMV-1). Genotype VI NDV is a primary factor in ND among Columbiformes (such as pigeons and doves). In a recent study, eight pigeon NDV strains were discovered in various provinces in China. These viruses exhibited mesogenic characteristics based on their MDT and ICPI values. The complete genome sequences of these eight strains showed a 90.40% to 99.19% identity match with reference strains of genotype VI, and a 77.86% to 80.45% identity match with the genotype II vaccine strain. Additionally, analysis of the F gene sequence revealed that these NDV strains were closely associated with sub-genotypes VI.2.2.2, VI.2.1.1.2.1, and VI.2.1.1.2.2. The amino acid sequence at the cleavage site of the F protein indicated virulent characteristics, with the sequences 112KRQKRF117 and 112RRQKRF117 observed. Pigeons infected with these sub-genotype strains had a low survival rate of only 20% to 30%, along with lesions in multiple tissues, highlighting the strong spread and high pathogenicity of these pigeon NDV strains. Molecular epidemiology data from the GenBank database revealed that sub-genotype VI.2.1.1.2.2 strains have been prevalent since 2011. In summary, the findings demonstrate that the prevalence of genotype VI NDV is due to strains from diverse sub-genotypes, with the sub-genotype VI.2.1.1.2.2 strain emerging as the current epidemic strain, highlighting the significance of monitoring pigeon NDV in China.

The Relationship Between Feeding Patterns and Maternal Knowledge about Nutrition with the Incidence of Stunting in Children Age 0-5 Years in the Working Area of the Gedongan Health Center, Mojokerto City

<p>Stunting is a prolonged state of chronic malnutrition. This disorder is characterized by having a body length-for-age or body height-for-age measurement that falls below the -2 standard deviation established by the World Health Organization (WHO). The etiology of stunting is multifaceted, encompassing factors such as nutrition, feeding practices, infectious illnesses, and household income. Maternal understanding of nutrition, including dietary factors, is also associated with the occurrence of stunting. This study aimed to establish the relationship between feeding habits and maternal knowledge of nutrition with the prevalence of stunting in the operational region of the Gedongan Health Center, located in Mojokerto City. This study is an observational analytical study employing a cross-sectional design. The study’s population consisted of all women with children residing in the working area of the Gedongan Health Center. This study encompassed 144 participants who completed the Child Feeding Questionnaire (CFQ) regarding eating practices and a questionnaire assessing maternal knowledge of nutrition. The objective was to examine the relationship between these factors to height and age. The chi-square test results indicate a statistically significant association between feeding practices and stunting (<em>p</em> = 0.0001) and maternal knowledge about nutrition and stunting (<em>p</em> = 0.0001). The survey revealed that most children had been provided with appropriate feeding patterns, and most moms had a commendable nutritional understanding. The study’s findings indicate a noteworthy relationship between feeding behaviors and mothers’ nutrition knowledge.</p>

Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches.

Osteoporosis, arthritis, and fractures are examples of orthopedic illnesses that not only significantly impair patients' quality of life but also complicate and raise the expense of therapy. It has been discovered in recent years that the pathophysiology of orthopedic disorders is significantly influenced by the microbiota. By employing machine learning and deep learning techniques to conduct a thorough analysis of the disease-causing microbiome, we can enhance our comprehension of the pathophysiology of many illnesses and expedite the creation of novel treatment approaches. Today's science is undergoing a revolution because to the introduction of machine learning and deep learning technologies, and the field of biomedical research is no exception. The genesis, course, and management of orthopedic disorders are significantly influenced by pathogenic microbes. Orthopedic infection diagnosis and treatment are made more difficult by the lengthy and imprecise nature of traditional microbial detection and characterization techniques. These cutting-edge analytical techniques are offering previously unheard-of insights into the intricate relationships between orthopedic health and pathogenic microbes, opening up previously unimaginable possibilities for illness diagnosis, treatment, and prevention. The goal of biomedical research has always been to improve diagnostic and treatment methods while also gaining a deeper knowledge of the processes behind the onset and development of disease. Although traditional biomedical research methodologies have demonstrated certain limits throughout time, they nevertheless rely heavily on experimental data and expertise. This is the area in which deep learning and machine learning approaches excel. The advancements in machine learning (ML) and deep learning (DL) methodologies have enabled us to examine vast quantities of data and unveil intricate connections between microorganisms and orthopedic disorders. The importance of ML and DL in detecting, categorizing, and forecasting harmful microorganisms in orthopedic infectious illnesses is reviewed in this work.

Cushing's disease presenting with recurrent abscesses followed by post-remission hyperthyroidism.

We report a case of a 59-year-old woman with Cushing's disease who developed hyperthyroidism following treatment of hypercortisolaemia. The patient with a history of recurrent hospitalisations caused by multi-sited soft tissue abscesses was admitted with sepsis. Both her medical history and physical examination suggested Cushing's syndrome. The initial hormonal diagnostic process, conducted after sepsis treatment, brought forth conflicting results. However, hormonal assessment repeated 3 months later indicated pituitary hypercortisolaemia, which was confirmed through bilateral inferior petrosal sinus sampling and was successfully treated with transsphenoidal pituitary surgery. Three months after the surgery, the patient was readmitted to our epartment with symptoms of hyperthyroidism, which was confirmed by laboratory tests. Thyroid scintiscans indicated Graves' disease. However, the absence of anti-thyroid stimulating hormone antibodies suggested other etiologies of hyperthyroidism. Eventually, the patient underwent radioiodine therapy. Currently, her condition is improving and she has had no recurrence of abscesses, severe infections, or hyperthyroidism. In conclusion, while clinical manifestation of hypercortisolaemia might be non-specific, its treatment may trigger the development of autoimmune diseases. The presence of recurrent severe infections should prompt physicians to consider the possibility of hypercortisolaemia. Chronic hypercortisolism is debilitating and can lead to significant disability. Dexamethasone suppression testing in patients with active or recent severe inflammatory or infectious illnesses may produce misleading or confusing results. Clinicians should be aware of the potential development of autoimmune diseases following successful treatment of hypercortisolaemia.

Low early life socioeconomic status and susceptibility to the common cold in adulthood: The moderating role of negative affective reactivity.

Socioeconomic disadvantage during childhood and adolescence is associated with higher risk for many physical health problems, including infectious disease, throughout the lifespan. Greater negative affective (NA) reactivity has shown similar links to greater risk for physical health conditions and altered patterns of biological functioning associated with acute respiratory infection as socioeconomic disadvantage; yet their interactive effects on physical health outcomes have not been examined. Thus, the present study examined whether NA reactivity accentuated the link between early socioeconomic disadvantage and susceptibility to the common cold. Participants were 212 adults (42% female, 18-55 years old) who completed measures of childhood socioeconomic status (SES; parental home ownership) and were subsequently exposed to a virus that causes the common cold. Participants then remained quarantined for 5 days, during which multiple indicators of viral infection and clinical illness were assessed. Before and after quarantine, participants completed a laboratory stress task to assess NA reactivity. NA reactivity moderated the relationship between parental home ownership and clinical cold diagnosis in adulthood (b = -0.11, p = .018), such that fewer years of parental home ownership was associated with increased odds for developing a cold only among adults who had greater NA reactivity (OR = 0.89, 95% confidence interval, CI [0.82, 0.96]), but not among those who had lower NA reactivity (OR = 1.01, 95% CI [0.94, 1.09]). These findings suggest that how individuals negatively react to psychosocial stressors in adulthood may exacerbate the impact of childhood SES on acute infection susceptibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prevalence and Causes of Blood Deferral in Tertiary Care Teaching Hospital Islamabad

Background: A blood transfusion is a vital and potentially life-saving operation in modern therapeutic and surgical procedures; yet, it requires a sufficient supply of safe blood from a healthy donor. In addition to laboratory testing of blood bags for infectious illnesses, appropriate, safe, and healthy donor selection is required. On the other hand, the loss of valuable blood components that are accessible for transfusion results from fit donors deferring. Understanding the reasons for both temporary and permanent deferrals can help us prevent this, put our knowledge into practice. The current study aimed to assess the incidence and reasons behind blood donor deferrals at Islamabad teaching hospital that provides tertiary care. Methods: This cross-sectional study was conducted at Dr. Akbar Niazi Teaching Hospital’s blood bank. All Blood donors reporting to donate blood at a blood bank between the periods of 1st September 2020 to 24th December 2021 were included in the study. The data was obtained by interviewing the donors using specially designed Performa. The collected data of deferred blood donors was analyzed using SPSS version 21. Results: Out of a total of 2054 blood donors, 523 (25.5%) were deferred due to different reasons and ineligible to donate blood. Out of 523 deferred donors, 455(87%) were males and 68(13%) were females. The average age of the blood donors was 26(4.2%) and most blood donors were between 20 to 40 years age. The common causes for deferral were low hemoglobin (41.3%), recent COVID-19 vaccination (12.6%) and increased TLC (7.1%). Hepatitis B virus 39(7.5%), Hepatitis C virus (4.4%), HIV 5(1.0%) and Syphilis 18 (3.4%). Conclusion: The most frequent reasons for delay were elevated TLC count, recent COVID-19 immunization, and low hemoglobin levels. The cornerstone of a safe blood supply is the donor education on self-exclusion and strict selection criteria.

Association Between Ethnicity and Mortality Outcomes in Patients with COVID-19: A Mayo Clinic VIRUS Registry Cohort Study.

Hispanic populations face significant disparities in health and healthcare.The COVID-19 pandemic highlighted and sometimes further exacerbated these disparities. We conducted a multisite, retrospective cohort study of 6494 hospitalized adult patients admitted between March 2020 and January 2022 with a diagnosis of COVID-19 from five sites including academic hospitals in MN, AZ, and FL and community hospitals in MN and WI. This is an ancillary project of the Viral Infection and Respiratory illness Universal Study (VIRUS) registry, supplemented by electronic health record data. We compared in-hospital mortality and length of stay outcomes between Hispanic and non-Hispanic populations admitted with COVID-19 using propensity matched scores to account for imbalances in demographic and clinical covariables. Among a total of 6494 patients, 512 (7.9%) patients were reported deceased and 5982 (92.1%) alive at discharge. We did not find a statistically significant difference between in-hospital mortality nor length of stay between the two groups.

Pharmacological and Therapeutic Features of COVID - 19 Infection in Hodeidah, Yemen

Background: The symptoms and signs of coronavirus diseases 2019 (COVID-19) present at illness onset vary, but over the course of the disease, most persons with COVID-19 will experience the following: fever, cough, fatigue, anorexia, shortness of breath, hypoxia, sputum production, and myalgia. During the first year (2020), there was no effective cure for COVID-19 infection and the most common treatment for patients was supportive therapy. Ministry of Public Health and Population in Yemen presented national guideline that is extracted from other countries and World Health Organization (WHO). Objective: Therefore, in this research, we discuss the pharmacological and therapeutic approaches for management of COVID-19 during its early period of rampage, using availability of medicines in Yemeni local pharmaceutical marketing. On the other hand, the efficacy of therapeutic was monitored. Methodology: 505 patients with COVID-19 from Hodeidah showed that respiratory infection illness can range from mild to critical. Mild to moderate was 386 case (78.93 %), severe cases was 73 cases (15.13 %) and critical cases was 28 cases (5.93 %). The major criteria for cases admission namely clinical examination, case definition, clinical investigation, radiological finding, hematological and biochemical finding and molecular biology assay namely real – time - polymerase chain reaction (RT- PCR). The pharmacological and therapeutic properties of supportive therapy of COVID-19 included" antipyretic agent, antiviral (oseltamivir) analgesic agent, glucocorticoid (dexamethasone), anticoagulant agent (enoxaparin), anti-cough (acetylcysteine), antibiotics broad spectrum to treat the secondary infection namely azithromycin, ceftriaxone, ceftazidime, piperacillin and tazobactam, meropenam, and moxifloxacin. In addition, oxygen therapy is the major treatment that used in treatment of severe and critical cases. Also, vitamins (C and D) and mineral namely zinc were prescribed and proton pump inhibitor (PPI) (pantaprazol), anti-emetic (Ondansetron). Fluid therapy namely ringer lactate or normal saline were used in management of COVID -19. Results: The results showed that the age of the patients included in this study between 3- 92 years with 4.0 to 1.0 males: females. 49 patients were admitted in COVID-19 isolation department of Hodeidah, Yemen. 25/49 cases (54.34% ) were recovered with national guideline, the average of RR pre - treatment was 30 ± 5 breath / minute and reduced to 23 ± 2 breath / minute post – treatment with significantly statistically different (p < 0.05). On the other hand, the clinical symptoms namely difficult in breathing, cough, fever, and headache reduced to absent absolutely (100 %). In addition, the results showed that the average of WBC pre - treatment was 20 ± 4 (×109/L) and reduced to 7 ± 2 (×109/L) post – treatment with significantly statistically different (p < 0.05). Also, lymphocytes decreased with COVID -19 infection to 2 % with average (10±5%) and improved post - treatment to 20 %. In addition, neutrophil increased to 95 % with average (80±15%) and improved to 70 % post-treatment. the average of WBC pre - treatment was 20 ± 4 (×109/L) and reduced to 7 ± 2 (×109/L) post-treatment with significantly statistically different (p < 0.05). The outcome finding of management that 19 cases (3.88 %) of severe and 6 cases (1.22 %) of critical were recovered. On the other hand, 1 case of severe and 21 cases of critical COVID-19 were died. Conclusion: In spite of lack of specific antiviral drugs, the approach of pharmacology and therapeutics had good impact at level of mild to moderate cases, and severe cases while the critical cases needs to develop at level critical cases in intensive care unit (ICU) with mechanical ventilation (MV) procedure.

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

Tuberculosis (TB) is a chronic infectious illness. Pulmonary TB is still a global threat and concern. Currently, TB is among the top 10 causes of death in the world in 2015 and it is estimated that there are around 10.4 million new cases of TB in the world. 6 countries account for around 60% of new cases in the world: India, Indonesia, China, Nigeria, Pakistan, and South Africa. Globally, the TB death rate was around 22% of total world deaths from 2000 to 2015. This study introduces an innovative tool, the "Microscope Camera for Enhanced Mycobacterium Tuberculosis Counting in Acid-Fast Bacteria Sputum through Image Processing with Thresholding Method," designed to revolutionize the examination of tuberculosis (TB) samples. In response to the global threat of Pulmonary Tuberculosis, the tool employs a state-of-the-art CMOS-HD (Complementary metal-oxide-semiconductor High Definition) camera module and leverages the Python application for efficient image processing. The primary objective is to address the limitations faced by conventional diagnostic methods, particularly the challenges associated with the Rapid Molecular Test Method due to its dependence on expensive equipment, thereby making it less feasible for implementation in resource-constrained public health centers. The study underscores the persistent threat of TB as a global health concern, emphasizing its prevalence, with approximately 10.4 million new cases worldwide. Focusing on Ziehl-Neelsen stain sputum samples, the research seeks to elevate the accuracy of TB diagnosis by proposing a streamlined approach. This involves integrating a digital microscope system with a CMOS-HD camera module and utilizing advanced image processing techniques, specifically the Python Thresholding method. In the methodology, the research outlines a comprehensive data collection and analysis process. Mechanical and block diagrams illustrate the design of the module, while the system flow diagram elucidates the sequence of steps involved in TB sample analysis. The data analysis section employs metrics such as average, percentage error, and success percentage, providing a quantitative assessment of the system's performance. The results reveal an encouraging average system accuracy of 85.30%, demonstrating the potential of the developed tool to enhance the diagnostic process. However, the study transparently acknowledges areas for improvement, particularly in addressing discrepancies between manual and system calculations. The conclusion emphasizes the tool's promising role in assisting healthcare workers in detecting and counting TB bacteria in sputum, underscoring the need for ongoing refinements to achieve a definitive 100% accuracy and eliminate the possibility of misdiagnoses.

Emerging Infectious Diseases: A Persistent Threat to the Community

Infections that have recently surfaced in populations whose incidence is expanding quickly or poses as a potential threat to do so in the near future are considered emerging infectious diseases. Infectious agents that were previously unknown or unidentified may be the source of emerging illnesses. Globally, infectious illnesses pose a serious threat to both public health and the stability of economies. They have been among the top causes of death and disability for ages, posing increasing risks to human advancement and health security. The ongoing reappearance of old, neglected, and infectious disease epidemics with worldwide implications adds to the threat posed by infectious illnesses. At least thirty novel human-pathogenic infectious pathogens have evolved in the last 35 years; the majority of them being zoonotic, and research has indicated a strong correlation between the origins of these organisms and ecological, environmental, and socioeconomic factors.

Automated Life Stage Classification of Malaria Using Deep Learning

INTRODUCTION: Malaria, an infectious illness spread by mosquitoes, is a serious hazard to humans and animals, with an increasing number of cases recorded yearly. Prompt and precise diagnosis, as well as preventative actions, are critical for effectively combating this condition. Malaria is now diagnosed using standard techniques. Microscopy of blood smears, which consists of small pictures, is used by trained specialists to identify diseased cells and define their life phases. The World Health Organisation (WHO) has approved this microscopy-based malaria diagnostic method. Drawing a blood sample from the finger, pricking it, spreading it onto a clean glass slide, and allowing it to dry naturally are all steps in the method. Thin blood smears were previously used to identify parasites under the microscope, but thick blood smears are utilized when parasite levels are low. OBJECTIVES: Due to its reliance on medical knowledge, high prices, time-consuming nature, and unsatisfactory outcomes, this technique has significant disadvantages. However, as deep learning algorithms progress, these activities may be completed more effectively and with fewer human resources. METHODS: This study demonstrates the usefulness of transfer learning, a type of deep learning, in categorizing microscopic pictures of parasitized versus uninfected malaria cells. Six models were evaluated using the publicly accessible NIH dataset, proving the usefulness of the suggested technique. RESULTS: VGG19 model fared better than its competitors, obtaining 95.05% accuracy, 92.83% precision, 96.88% sensitivity, 93.46% specificity, and 94.81% F1-score. CONCLUSION: This categorization of malaria cell photos will benefit microscopists in particular, as it will improve their workflow and provide a viable alternative for detecting malaria using microscopic cell images.

A SYSTEMATIC REVIEW ON MALARIA DISEASE AND ITS TREATMENTS FOCUS ON ARTEMETHER DRUG

Malaria is one of the infectious illnesses of the highest scientific significance and importance to international health organizations. Plasmodium falciparum, the parasite responsible for a highly severe form of the disease in Africa, has traditionally received the most attention. However, in the last two decades, the Plasmodium vivax parasite, which is linked to a large number of cases in Latin America, the Middle East, South and Southeast Asia, the Horn of Africa, and Oceania, has sparked enormous interest, owing to published evidence that it can cause severe malaria, among other things. Malaria is a worldwide public health problem, with a saw 247 million cases recorded in 2021. African countries accounted for approximately 94% of all reported cases. So far, over 200 distinct varieties of protozoa have been discovered and identified, with at least 13 of them being pathogenic to humans. The malaria parasite's life cycle is a complex process involving a mosquito with the species Anopheles and a vertebrate host.
 Artemether is a lipid-soluble artemisinin derivative. It is available in both oral and intramuscular forms. It is also available as a fixed-dose formulation with lumefantrine. Artemether-lumefantrine is one of the ACTs authorized by the WHO and is being used by the majority of countries transitioning from less effective medications to ACTs. However, the emergence of antimalarial medication resistance poses a significant challenge to malaria control. It works by killing the pathogenic organisms responsible for malaria. It is known as an antimalarial medication, and it is more effective than quinine therapies.

Phenolic compounds of Theobroma cacao L. show potential against dengue RdRp protease enzyme inhibition by In-silico docking, DFT study, MD simulation and MMGBSA calculation.

Currently, there is no antiviral medication for dengue, a potentially fatal tropical infectious illness spread by two mosquito species, Aedes aegypti and Aedes albopictus. The RdRp protease of dengue virus is a potential therapeutic target. This study focused on the in silico drug discovery of RdRp protease inhibitors. To assess the potential inhibitory activity of 29 phenolic acids from Theobroma cacao L. against DENV3-NS5 RdRp, a range of computational methods were employed. These included docking, drug-likeness analysis, ADMET prediction, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations. The aim of these studies was to confirm the stability of the ligand-protein complex and the binding pose identified during the docking experiment. Twenty-one compounds were found to have possible inhibitory activities against DENV according to the docking data, and they had a binding affinity of ≥-37.417 kcal/mol for DENV3- enzyme as compared to the reference compound panduratin A. Additionally, the drug-likeness investigation produced four hit compounds that were subjected to ADMET screening to obtain the lead compound, catechin. Based on ELUMO, EHOMO, and band energy gap, the DFT calculations showed strong electronegetivity, favouravle global softness and chemical reactivity with considerable intra-molecular charge transfer between electron-donor to electron-acceptor groups for catechin. The MD simulation result also demonstrated favourable RMSD, RMSF, SASA and H-bonds in at the binding pocket of DENV3-NS5 RdRp for catechin as compared to panduratin A. According to the present findings, catechin showed high binding affinity and sufficient drug-like properties with the appropriate ADMET profiles. Moreover, DFT and MD studies further supported the drug-like action of catechin as a potential therapeutic candidate. Therefore, further in vitro and in vivo research on cocoa and its phytochemical catechin should be taken into consideration to develop as a potential DENV inhibitor.

Telemedicine and Pediatric Care in Rural and Remote Areas of Middle-and-Low-Income Countries: Narrative Review

ObjectivesCaring for children in low- and middle-income countries (LMIC) can be challenging. This review article aims to explore role of telemedicine in supporting pediatric care in LMIC.MethodologyA narrative review of existing English and Spanish literature was conducted to assess role of telemedicine to support pediatric care in LMIC.ResultsBeside medical education and direct pediatric care, telemedicine can provide sub-specialties consultations without extra burden on families. Additionally, telemedicine can help in lowering under-5 mortality by supporting neonatal care, infectious illnesses, and non-communicable diseases (NCDs). Telemedicine can be a gate for universal coverage for all children at a lower cost. For over a decade, it has been implemented successfully and sustained in a few LMIC. However, challenges in implementing telemedicine are enormous. Still, opportunities arise by using simpler technology, low-width band internet, smartphones, instant messaging applications and solar energy. COVID-19 pandemic facilitated acceptance and applicability of telemedicine worldwide including LMIC. Nevertheless, governments must regulate telemedicine by issuing policies and ensuring employment of local experts when possible to meet local resources and cultural competency.ConclusionTelemedicine has proven successful in improving pediatrics care. Many LMIC should take advantage of this innovation to promote equity and access to high quality pediatric care.

Bifurcation analysis of a delayed stochastic HBV epidemic model: Cell-to-cell transmission

In the present study, we develop a model for hepatitis B virus (HBV) that integrates cell-to-cell transmission of the virus, CTL immunological response, delay effects, and the effect of fluctuating environmental noise. The inheritance randomness of the system shifting the system from a deterministic to another system consists of stochastic differential equations (SDEs). After model formulation, the dynamical aspects of the model are investigated by using the key threshold quantity R0. If the threshold value R0 is assumed less than unity, then it is proven that the solution trajectories are fluctuating around the disease-free state (DFS) of the associated deterministic model. The fluctuation of the trajectories around the DFS explains the phenomenon of extinction of the stochastic models. It is also proved that the trajectories of the SDE model oscillate around the endemic state of the underlying ODE system when R0s>1 explains the sure persistence theory of the proposed model. One notable finding of the present manuscript is the positive association between the fluctuating amplitude and the intensities of the white noises. This understanding can provide useful control tools for managing the worst dynamic(s) of the infection. The exact value of the parameter for time delay determines the stability of the model under consideration. By analyzing the time-delayed parameter, it was identified that the model is stable by keeping the value of the parameter below the crucial value that causes the Hopf bifurcation. This conclusion is supported by the numerical examples having epidemiological significance. Furthermore, the findings suggest that stochastic environmental disturbance can impact the spread of infectious illnesses. Notably, the high levels of noise can help to slow down the spread of epidemics in the population.