Diagnosis Of Fungal Infections Research Articles

A-257 Revolutionizing Fungal Infection Diagnosis: A Sensitive, Cost-Effective, and Time-Efficient Solution

Abstract Background Fungal infection detection is essential for optimizing therapeutic strategies, preventing complications, enhancing overall health, and alleviating the financial burden, and preventing transmission. According to the CDC, fungal illness resulted in 75,000 hospital admissions and 9 million outpatient visits annually, with 7,199 deaths predicted in the United States in 2021. The rising prevalence of fungal infections, influenced by factors such as population expansion and evolving treatment strategies poses a growing challenge to achieving an early and accurate diagnosis. There is the crucial need for improved detection methods to promptly identify various fungal infections, including Candida species, spanning from skin issues to potentially fatal systemic diseases, ultimately reducing associated morbidity and mortality. Severe and profound infections with life-threatening potential are common in individuals with compromised immune systems and those who are hospitalized. This risk is notably elevated among patients with organ transplants, immunosuppressive treatments, diabetes, recent broad-spectrum antibiotic usage, catheter utilization, and extended hospitalization periods. Our advancement seeks to introduce innovative methods leveraging carbon-based nanomaterials for the detection of fungal infections, offering distinct advantages including cost-effectiveness, ease of operation, and time efficiency compared to conventional techniques. Methods Our technology utilizes carbon-based nano materials, a promising advancement in nanotechnology to detect fungal infections, especially Candida manifestations. The samples are brought into contact with pre-probed carbon-based nano materials sensors maintained at a specific annealing temperature, facilitating nucleic acid hybridization. Subsequently, alterations in the carbon nanotube's electrical signal are observed, indicating a change in conductivity. This change can allow for the measurement of a voltage difference across a continuous flow of electricity, correlating with the concentration of targets present in the sample, thereby enabling the detection of specific microorganisms. No change was noticed in electrical properties of carbon-based nano material when negative experiments were conducted. Results The initial findings showed the presence of specific microorganisms within the sample in less than 15 minutes with carbon-based nano materials sensors. Comparative analysis of the electrical signal magnitude values obtained from carbon nanotube measurements against the corresponding CT values from quantitative PCR (qPCR) provides insights into the correlation between the electrical readouts and molecular quantification, offering a comprehensive evaluation of the nanotube-based detection system's performance in diagnostics procedure. Conclusions Unlike conventional blood culture and qPCR, our technology, delivers outcomes with heightened sensitivity, cost-effectiveness, and time efficiency. Our team's uncomplicated sample collection method notably reduces the intricacies of sample preparation, enabling the proper disposal of both the sample container and sensor part without requiring disassembly. Enhanced sensitivity, coupled with the economical and rapid detection capabilities of advanced technologies like carbon-based nano materials, is a promising alternative for the accurate and efficient diagnosis of fungal infections. Consequently, the implementation of such technology not only enhances diagnostic efficiency but also contributes to better patient outcomes through prompt and targeted therapeutic interventions.

A-274 The Yield of the Limulus Amebocyte Lysate (LAL), Which is Used for Endotoxin Detection and Fungal Diagnosis is Increased by Caffeine Collection Buffer

Abstract Background Limulus Amebocyte Lysate (LAL), which is a major component of Fungal infection diagnosis kit, is used for the adjunct diagnosis of invasive fungal infection. This study used different blood collection buffers to find the optimal blood collection buffer to maximize the yield of LAL. The PBS collection buffer was found to prevent degranulation during blood collection procedure and the PBS-derived Limulus Amebocyte Lysate (LAL) was observed to work in both the chromogenic test and the turbidimetric method assays. A similar phenomenon was observed with the caffeine collection buffer. Methods To further confirm this observation, we used a caffeine collection buffer to collect the blood of horseshoe crabs in the 2023 bleeding season (from July to October). Once a week, 3 crabs were bled for a total of 30 crabs. The blood of each crab was collected using the caffeine collection buffer and the 3% NaCl collection buffer, respectively. The cell pellets were resuspended into LAL reagent water (LRW) or 5 mM CaCl2. The final LAL activity was tested by both chromogenic and turbidimetric assay methods. Results The results showed that a caffeine collection buffer could prevent degranulation for over 1 hr and the activity yield of caffeine-derived LAL is much higher than that of the 3% NaCl solution. There was no discernible impact upon activity yield of crab size, period of the bleed season, or the vendors. Interestingly, caffeine-derived LAL was observed to work in both the chromogenic and turbidimetric assays. The enzyme characteristic of the LAL was also described. Conclusions Caffeine collection buffer prevents amebocyte degranulation during blood collection and processing. The activity of caffeine LAL is much higher than that of 3% NaCl. Caffeine LAL works in both chromogenic test and turbidimetric assay.

Artificial Intelligence Applications in Medical Mycology: Current and Future

The application of artificial intelligence (AI) in the medical mycology field represents a new era in the diagnosis and management of fungal infections. AI technologies, particularly machine learning (ML) and deep learning (DL) methods, enhance diagnostic accuracy by leveraging large datasets and complex algorithms. This review examines current applications of AI in laboratory and clinical settings for fungal diagnostics. In the laboratory, AI models analyze microscopic images from potassium hydroxide (KOH) examinations, fungal culture tests, and histopathologic slides, which improves the detection rates of fungal pathogens significantly. In the clinical setting, AI assists the diagnosis of fungal infections using medical images, exhibiting high efficacy in binary classification tasks. However, challenges include small sample sizes, class imbalances, reliance on expert-labeled data, and the black box nature of AI models. Explainable AI offers potential solutions by providing human-comprehensible insights into AI decisionmaking processes. In addition, human-computer collaboration can enhance diagnostic accuracy, particularly for less experienced clinicians. The development of generative AI models, e.g., large language models and multimodal AI, promises to create extensive datasets and integrate various data sources for comprehensive diagnostics. Addressing these limitations through prospective clinical validation and continuous feedback will be essential for realizing the full potential of AI in medical mycology.

The role of thrombocyte indices in early determination of sepsis agents in newborns

Purpose: Neonatal sepsis is an important cause of morbidity and mortality. The signs and symptoms of neonatal sepsis are nonspecific, and there is no ideal marker for diagnosis. Our study aimed to investigate the role of platelet indices in determining agents in sepsis. Materials and Methods: A total of 90 records with sepsis data were examined retrospectively, demographic and clinical characteristics of gram-positive, gram-negative and Candida infection patients were evaluated by complete blood counts at the time of diagnosis and on the fifth day of infection and platelet amount (PLT), mean platelet volume (MPV), plateletcrit (PCT) and distribution width (PDW) values were compared by ROC curve analysis. Results: No statistical difference was found between the groups in terms of gender, gestational week, birth weight, surfactant use, need for mechanical ventilator treatment, and necrotizing enterocolitis. When infection was first detected, the Area Under Curve (AUC) of PCT, PLT and MPV (without thrombocytopenia) values in detecting gram-positive bacterial infection were 0.764, 0.765 and 0.792 respectively. Conclusion: The increase in PCT and PLT values at the time of initial diagnosis can be used to detect gram-positive bacterial infections. A decrease in MPV can be used for early diagnosis of fungal infections without thrombocytopenia.

Strengthening Fungal Infection Diagnosis and Treatment: An In-depth Analysis of Capabilities in Honduras.

Invasive fungal infections (IFIs) are a major public health concern in low- and middle-income countries (LMICs) due to limited diagnostic and treatment resources, leading to high morbidity and mortality. Despite their significant global burden, IFIs are underrecognized and underdiagnosed in LMICs. This study evaluates the diagnostic and therapeutic capacities for managing IFI in Honduras, a country with unique health care challenges. From March to December 2023, a comprehensive survey was conducted across multiple health care centers in Honduras. The survey, reviewed for content and clarity by local medical institutions, targeted medical microbiologists and clinicians to assess various aspects of fungal disease diagnosis and treatment. Data included the availability and use of diagnostic tools and antifungal therapies, identifying gaps and limitations in current practices. The survey revealed that Candida spp (97.4%) and Aspergillus spp (35.9%) were the most concerning pathogens. Although microscopy and culture methods were available in most institutions, their application in suspected IFI cases was inconsistent, and antifungal susceptibility testing was rarely performed. Advanced diagnostic techniques, such as antigen detection, were available in only a few institutions, while antibody detection and polymerase chain reaction testing were entirely absent. All hospitals had access to at least 1 triazole antifungal, typically fluconazole, but there was a notable scarcity of more potent antifungals, including amphotericin B formulations and echinocandins. The limited use of available diagnostic tools and the restricted availability of essential antifungals were identified as major barriers to effective IFI management. This study highlights significant gaps in the diagnostic and therapeutic capabilities for managing IFI in Honduras. The underutilization of basic diagnostic tools, the inaccessibility of advanced testing methods, and the limited availability of essential antifungal medications underscore the urgent need for capacity-building initiatives, infrastructure improvements, and policy reforms. Addressing these deficiencies is critical for enhancing the management of IFI in Honduras, with broader implications for similar LMIC settings. These findings can inform targeted interventions and resource allocation to improve outcomes for patients with IFI.

Utilizing Next-Generation Sequencing: Advancements in the Diagnosis of Fungal Infections.

Next-generation sequencing (NGS) has emerged as a promising tool for diagnosing fungal infections. It enables the identification of a wide range of fungal species and provides more accurate and rapid results than traditional diagnostic methods. NGS-based approaches involve the sequencing of DNA or RNA from clinical samples, which can be used to detect and identify fungal pathogens in complex clinical samples. The development of targeted gene panels and whole-genome sequencing has allowed for identifying genetic markers associated with antifungal drug resistance, enabling clinicians to tailor patient treatment options. NGS can also provide insights into the pathogenesis of fungal infections and aid in discovering novel drug targets. Although NGS has some limitations, such as cost and data analysis, it can potentially revolutionize the future diagnosis and treatment of fungal infections.

Exploring the potential of chitin and chitosan in nanobiocomposites for fungal immunological detection and antifungal action

Chitin is a polymer of N-acetylglucosamine and an essential component of the fungal cell wall. Chitosan is the deacetylated form of chitin and is also important for maintaining the integrity of this structure. Both polysaccharides are widely distributed in nature and have been shown to have a variety of applications in biomedicine, including their potential in immune sensing and as potential antifungal agents. In addition, chitin has been reported to play an important role in the pathogen-host interaction, involving innate and adaptive immune responses. This paper will explore the role of chitin and chitosan when incorporated into nanobiocomposites to improve their efficacy in detecting fungi of medical interest and inhibiting their growth. Potential applications in diagnostic and therapeutic medicine will be discussed, highlighting their promise in the development of more sensitive and effective tools for the early diagnosis of fungal infections. This review aims to highlight the importance of the convergence of nanotechnology and biology in addressing public health challenges.

Clinical Impact of Plasma Metagenomic Next-Generation Sequencing on Infection Diagnosis and Antimicrobial Therapy in Immunocompromised Patients.

Clinical impact of plasma metagenomic next-generation sequencing (mNGS) on infection diagnosis and antimicrobial therapy in immunocompromised patients with suspected infection remains unclear. Between March and December 2022, 424 cases with fever, infection history, mechanical ventilation, or imaging abnormalities underwent plasma mNGS testing at a single center. Eleven patients have received solid organ transplantation, and the remaining patients were categorised into febrile neutropenia (FN), non-neutropenia (NN), and non-haematologic disease (NTHD) groups based on immunosuppression severity. The diagnostic rate of infection and the utilisation of antimicrobial agents based on mNGS were assessed. The use of mNGS significantly improved the diagnostic rates for fungi in the FN (56.1%, P = 0.003) and NN (58.8%, P = 0.008) groups versus the NHD group (33.3%). Positive impacts associated with therapy were significantly greater than negative impacts across all three groups (all P < 0.001), and the utilisation of escalation therapy was significantly more frequent in the FN group than in the NN groups (P = 0.006). Over 70% of cases with negative mNGS results across the three groups underwent de-escalation therapy, with >1/3 being discontinued, preventing antimicrobial overuse. Plasma mNGS has a clinically confirmed positive impact in immunocompromised patients with neutropenia, improving the diagnosis of fungal infections and antimicrobial therapy.

Insights into invasive fungal infection diagnostic and treatment capacities in tertiary care centres of Germany.

In Germany, the growing incidence of invasive fungal infections (IFIs) is a significant health concern, particularly impacting individuals with compromised immune systems due to factors like increasing transplant recipients, an ageing population, and heightened use of immunosuppressive medications. Diagnosing IFI remains challenging, and the integration of biomarker assays into clinical practice is difficult. Antifungal resistance, exemplified by pan-antifungal-resistant Candida auris cases, adds complexity to treatment. This study aims to provide a concise overview of the diagnostic and treatment landscape for IFI in Germany, identifying areas for improvement and paving the way for targeted interventions. Data were collected using an online electronic case report form from October 2021 to February 2023. The survey included questions about institutional practices related to fungal infection diagnosis and treatment, with invitations extended to researchers nationwide. The study surveyed 58 hospitals across Germany. Notably, 77.6% managed high-risk patients for IFI. While 86% had onsite microbiology labs, a significant difference was noted for high-risk patients (93% in specialized hospitals versus 62% in others). Microscopy services had 96% coverage, while overall access to culture was 96%. Antigen tests had 96% coverage, and antibody access was reported at 98%. PCR testing was available at 98%. Imaging access showed no significant access differences. Variability existed in amphotericin B formulations based on patient profiles. Therapeutic drug monitoring was more common in high-risk patient institutions (89.5% versus 50.0%). All analysed institutions reported access to surgery (100%). Addressing identified disparities in diagnostic and therapeutic resources for IFI is crucial to improving patient outcomes. The study calls for ongoing research and collaboration to optimize strategies for the prevention and treatment of IFI, emphasizing the importance of equitable access to resources, especially in high-risk patient populations.

Current status and new experimental diagnostic methods of invasive fungal infections after hematopoietic stem cell transplantation.

Invasive fungal infections (IFIs) are common and life-threatening complications in post-hematopoietic stem cell transplantation (post-HSCT) recipients, Severe IFIs can lead to systemic infection and organ damage, which results in high mortality in HSCT recipients. With the development of the field of fungal infection diagnosis, more and more advanced non-culture diagnostic tools have been developed, such as glip biosensors, metagenomic next-generation sequencing, Magnetic Nanoparticles and Identified Using SERS via AgNPs+ , and artificial intelligence-assisted diagnosis. The advanced diagnostic approaches contribute to the success of HSCT and improve the overall survival of post-HSCT leukemia patients by supporting therapeutical decisions. This review provides an overview of the characteristics of two high-incidence IFIs in post-HSCT recipients and discusses some of the recently developed IFI detection technologies. Additionally, it explores the potential application of cationic conjugated polymer fluorescence resonance energy transfer (CCP-FRET) technology for IFI detection. The aim is to offer insights into selecting appropriate IFI detection methods and gaining an understanding of novel fungal diagnostic approaches in laboratory settings.

Classification of Microscopic Fungi Images Using Vision Transformers for Enhanced Detection of Fungal Infections

Fungi play a pivotal role in our ecosystem and human health, serving as both essential contributors to environmental sustainability and significant agents of disease. The importance of precise fungi detection cannot be overstated, as it underpins effective disease management, agricultural productivity, and the safeguarding of global food security. This research explores the efficacy of vision transformer-based architectures for the classification of microscopic fungi images of various fungal types to enhance the detection of fungal infections. The study compared the pre-trained base Vision Transformer (ViT) and Swin Transformer models, evaluating their capability in feature extraction and fine-tuning. The incorporation of transfer learning and fine-tuning strategies, particularly with data augmentation, significantly enhances model performance. Utilizing a comprehensive dataset with and without data augmentation, the study reveals that Swin Transformer, particularly when fine-tuned, exhibits superior accuracy (98.36%) over ViT model (96.55%). These findings highlight the potential of vision transformer-based models in automating and refining the diagnosis of fungal infections, promising significant advancements in medical imaging analysis.

Nucleic-Acid-Based Molecular Fungal Diagnostics: A Way to a Better Future.

The world has seen a tremendous increase in the number of fungal infections during the past two decades. Recently, the World Health Organisation released the pathogen priority list for fungal infections, signifying the importance of these infections in the fields of research and public health. Microbiology laboratories demand an upgrade in the diagnostic system to keep up with the increased burden of these infections. Diagnosis of fungal infections using conventional techniques has always faced limitations in terms of specificity, sensitivity, and turnaround time. Although these methods are the core pillars of the diagnosis, there is an increased need for molecular approaches. Molecular techniques have revolutionised the field of fungal diagnostics. The diverse array of molecular techniques, including techniques like Polymerase Chain Reaction (PCR), have emerged as a cornerstone in fungal diagnostics. Molecular techniques have transformed fungal diagnostics, providing powerful tools for the rapid and accurate identification of pathogens. As these technologies continue to evolve, their integration into routine clinical practice holds the promise of improving patient outcomes through timely and targeted antifungal interventions. This review will cover the molecular approaches involved in fungal diagnostics, moving from the basic techniques to the advanced-level nucleic-acid-based molecular approaches providing a high throughput and decreased turnaround time for the diagnosis of serious fungal infections.

Pan-Indian Clinical Registry of Invasive Fungal Infections Among Patients in the Intensive Care Unit: Protocol for a Multicentric Prospective Study.

Fungal infections are now a great public health threat, especially in those with underlying risk factors such as neutropenia, diabetes, high-dose steroid treatment, cancer chemotherapy, prolonged intensive care unit stay, and so on, which can lead to mycoses with higher mortality rates. The rates of these infections have been steadily increasing over the past 2 decades due to the increasing population of patients who are immunocompromised. However, the data regarding the exact burden of such infection are still not available from India. Therefore, this registry was initiated to collate systematic data on invasive fungal infections (IFIs) across the country. The primary aim of this study is to create a multicenter digital clinical registry and monitor trends of IFIs and emerging fungal diseases, as well as early signals of any potential fungal outbreak in any region. The registry will also capture information on the antifungal resistance patterns and the contribution of fungal infections on overall morbidity and inpatient mortality across various conditions. This multicenter, prospective, noninterventional observational study will be conducted by the Indian Council of Medical Research through a web-based data collection method from 8 Advanced Mycology Diagnostic and Research Centers across the country. Data on age, gender, clinical signs and symptoms, date of admission, date of discharge or death, diagnostic tests performed, identified pathogen details, antifungal susceptibility testing, outcome, and so on will be obtained from hospital records. Descriptive and multivariate statistical methods will be applied to investigate clinical manifestations, risk variables, and treatment outcomes. These Advanced Mycology Diagnostic and Research Centers are expected to find the hidden cases of fungal infections in the intensive care unit setting. The study will facilitate the enhancement of the precision of fungal infection diagnosis and prompt treatment modalities in response to antifungal drug sensitivity tests. This registry will improve our understanding of IFIs, support evidence-based clinical decision-making ability, and encourage public health policies and actions. Fungal diseases are a neglected public health problem. Fewer diagnostic facilities, scanty published data, and increased vulnerable patient groups make the situation worse. This is the first systematic clinical registry of IFIs in India. Data generated from this registry will increase our understanding related to the diagnosis, treatment, and prevention of fungal diseases in India by addressing pertinent gaps in mycology. This initiative will ensure a visible impact on public health in the country. DERR1-10.2196/54672.

A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood.

Background: Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. Methods: A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-speed camera and a deep learning analysis section. Results: For training data, the sensitivity and specificity of the convolutional neural network model were 93.5% (92.7-94.2%) and 99.5% (99.1-99.5%), respectively. For validating data, the sensitivity and specificity were 81.3% (80.0-82.5%) and 99.4% (99.2-99.6%), respectively. Cryptococcal cells were found in 22.07% of blood samples. Conclusion: This high-speed microscopy system can analyze fungal pathogens in blood samples rapidly with high sensitivity and specificity and can help dramatically accelerate the diagnosis of fungal infectious diseases.

Fungal infections in liver cirrhosis.

Liver cirrhosis is a chronic condition that is associated with a variety of complications across organ systems. Patients with cirrhosis also suffer from immune dysfunction, which may predispose them to catastrophic bacterial and fungal infections. Bacterial infections in liver cirrhosis have been well-documented, however, data remains scarce regarding fungal infections. Candida and Aspergillus have been reported as the most common pathogens among patients with cirrhosis, causing both invasive and non-invasive infections. However, other pathogens such as Coccidioides, Pneumocystis, Cryptococcus, and Rhizopus have been increasing in incidence. Diagnosis of fungal infection is often difficult, particularly in regards to distinguishing colonization from invasive infection. Serum markers such as beta-D-glucan (BDG) and galactomannan are beneficial diagnostic tools in conjunction with fungal cultures and imaging modalities. Bronchoscopy with bronchoalveolar lavage (BAL) or lung biopsy can be useful adjuncts as well. Liver transplantation is another important consideration as invasive fungal infection (IFI) is a contraindication to transplant surgery. Additionally, patients are at increased risk for infection due to immunosuppression in the post-transplant period. We aim to discuss the mechanisms responsible for immune dysfunction in advanced liver disease, the epidemiology of fungal infections in this population, as well as presentations and management considerations pertaining to specific pathogens and antifungal regimens.

Data analysis of patients with positive mould or dimorphic fungal cultures from sterile sites.

Moulds and dimorphic fungi are increasingly recognised as pathogens carrying high morbidity and mortality in critically ill and immune-compromised patients. The lack of surveillance data limits our understanding of these infections. To determine the distribution, patient characteristics, risk factors, therapy and treatment outcome in patients with positive mould or dimorphic fungal cultures from sterile sites at a tertiary hospital in central South Africa. All moulds or dimorphic fungi cultured from sterile specimens from 1 July 2014 to 30 June 2017 were identified retrospectively. Laboratory and clinical records were reviewed. Information collected included gender and age, type of specimen collected for investigation, specific fungi isolated, underlying conditions, other contributing risk factors, treatment and outcome of the patients. Forty-eight patient records were analysed. Male and female patients were equally distributed. The mean age was 40.5 years (range 7-78 years). Aspergillus spp. were most commonly isolated. The most common underlying condition was HIV infection, followed by haematological conditions. Twenty-six (54.2%) patients received treatment involving antifungal therapy alone (n = 19; 73.1%), surgery alone (n = 5; 19.2%) or a combined medical and surgical approach (n = 2; 7.7%). Twenty-two (45.8%) patients received no treatment. The overall mortality rate was 25.0% (n = 12). The diagnosis of fungal infections remains challenging. In the current study, moulds and dimorphic fungi were isolated from at-risk patients' specimens. Despite treatment with appropriate antifungal agents, the associated mortality rate was high. This study contributes to the growing body of knowledge on these potentially life-threatening infections.

Invasive Aspergillosis Mimicking Tolosa-Hunt Syndrome: A Case Report

Invasive fungal infection remains a major cause of morbidity and mortality in immunocompromised patients. Invasive fungal sinusitis can present as a Tolosa-Hunt syndrome (THS) or orbital apex, leading to frequent misdiagnosis. Accurate diagnosis of fungal infection invading the cavernous sinus or orbital apex is essential to reduce mortality through early antifungal treatment and reduce the risk of worsening with steroid treatment due to misdiagnosis of THS. Herein, we report a case of invasive fungal sinusitis mimicking THS.

MeFunX: A novel meta-learning-based deep learning architecture to detect fungal infection directly from microscopic images

Fungal infections are a growing threat to human health. They can lead to a range of health problems and can be life-threatening. There are many impediments to the traditional diagnosis of fungal infections, such as a diminishing number of clinical mycologists, expensive procedures, high time consumption, and requirements for sensitivity and specificity. Therefore, early diagnosis of fungal infection is critical to effective treatment. In this paper, a novel meta-learning-based deep learning architecture, termed MeFunX, is proposed for the early detection of fungal infections from microscopic images. MeFunX architecture consisted of two convolutional neural network-based models as base learners and XGBoost as the meta-learner. To assess the proposed approach, standard metrics namely, accuracy, precision, recall and f1-score, were used. The fungal disease identification performance of MeFunX was compared with state-of-the-art architectures like VGG16, InceptionV3, ResNet, AlexNet, DenseNet, and EfficientNet. In addition, MeFunX was also benchmarked against its base learners and other meta-learning model with EfficientNet and ResNet as the base learners to demonstrate the effectiveness of the meta-learning architecture. Rigorous experimentation clearly signifies the superior performance of MeFunX, which achieved an overall accuracy of 92.49 % for the early diagnoses of fungal infections in microscopic images.

Fungal Infections in the Caribbean: A Review of the Literature to Date.

The most common fungal infections reported from the Caribbean include dermatophytosis, candidiasis, pneumocystis, aspergillosis, histoplasmosis, and cryptococcosis. The Caribbean is hyperendemic for histoplasmosis, with high population exposures. Fungal infections are a significant public health problem in the Caribbean, with rates varying depending on the specific country or region. In Trinidad and Tobago, the fungal burden accounts for 3.3% of the 1.4 million population, while in Jamaica, with a population of 2.9 million, over 57,600 people suffer from fungal infections each year. A study in the Dominican Republic estimated that approximately 221,027 (2%) of over 10 million people have a serious fungal infection. Fungal infections accounts for 21.9% of all skin infections in Haiti. The diagnosis of fungal infections in the Caribbean can be challenging, as access to laboratory testing and specialized medical services is limited in many areas. Access to antifungal medications can also be a challenge in some areas, and antifungal resistance has been reported.

Modulatory immune responses in fungal infection associated with organ transplant - advancements, management, and challenges.

Organ transplantation stands as a pivotal achievement in modern medicine, offering hope to individuals with end-stage organ diseases. Advancements in immunology led to improved organ transplant survival through the development of immunosuppressants, but this heightened susceptibility to fungal infections with nonspecific symptoms in recipients. This review aims to establish an intricate balance between immune responses and fungal infections in organ transplant recipients. It explores the fundamental immune mechanisms, recent advances in immune response dynamics, and strategies for immune modulation, encompassing responses to fungal infections, immunomodulatory approaches, diagnostics, treatment challenges, and management. Early diagnosis of fungal infections in transplant patients is emphasized with the understanding that innate immune responses could potentially reduce immunosuppression and promise efficient and safe immuno-modulating treatments. Advances in fungal research and genetic influences on immune-fungal interactions are underscored, as well as the potential of single-cell technologies integrated with machine learning for biomarker discovery. This review provides a snapshot of the complex interplay between immune responses and fungal infections in organ transplantation and underscores key research directions.