Diagnosis of Fungal Keratitis: Comparison
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Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by Ming-Tse Kuo.

Fungal keratitis (FK) is one of the most devastating corneal infectious diseases caused by opportunistic infection of fungi. 

FK has an abysmal visual prognosis, potentially leading to blindness, and thus requires accurate diagnosis. 

  • fungal keratitis
  • Risk factors
  • Diagnosis

1. Introduction

Fungal keratitis (FK) is one of the most devastating microbial keratitis with the worst visual prognosis, potentially leading to blindness [1,2][1][2]. Over 40% of microbial keratitis cases are caused by fungal infection in several tropical and subtropical countries [3,4,5,6][3][4][5][6]. The clinical characteristics of FK are mild pain, the insidious growth of fungal pathogens in the deep cornea, and difficulty in differentiating it from other kinds of microbial keratitis early on. Therefore, the early and accurate diagnosis of FK by means of clinical features is sometimes impossible due to patients’ delayed visits or the great similarities of FK with other types of microbial keratitis in the early stages of the disease. In addition, conventional laboratory approaches using smears (direct microscopy) and microbial cultures fail to serve as reliable diagnostic tools in many cases. Direct microscopy is limited by examiners’ experience, while the culture approach is time-consuming and incapable of isolating fastidious fungal pathogens. As a result, several severe complications, such as hypopyon, glaucoma, iris atrophy, cataract, corneal melting, corneal perforation, and endophthalmitis [6[6][7],7], may occur in patients with FK.
In addition to the problems of current clinical and laboratory diagnosis, the medical treatment of FK is also full of challenges. Approximately one-third of FK patients were refractory to antifungal agents and ultimately required therapeutic keratoplasty [8,9][8][9]. Currently available antifungal agents have limitations not only in the drug-dependent capacities of corneal penetration but also in species-dependent fungicidal capacity. Tracing the predictors of medical treatment failure, including old age, trauma, large lesions, deep infiltrates, positive fungal culture results, day 6 repeated fungal culture-positive results, Aspergillus species isolation, and higher minimum inhibitory concentrations (MICs) to natamycin [10[10][11][12],11,12], identification of the fungal pathogen plays a critical role in adopting surgical management in time.
In order to rescue the vision of those patients with a delayed visit, minimize the risk of medical treatment failure, and quickly determine the need for surgical management, precise diagnosis is the decisive factor. Omics technology advancements, such as genomics, proteomics, and metabolomics, have been exploring the biochemical changes in ocular surface samples of FK over the past two decades. Through the collective characterization and quantification of pools of biochemical molecules, omics technologies provide several alternative methods for diagnosing and monitoring FK. Among these technologies, genomics approaches have been widely proven to be good alternatives to conventional smear and culture methods, while proteomic and metagenomic FK also show promising results.

2. Pathogens and Risk Factors of FK

Common fungal pathogens are frequently morphologically classified into filamentous and yeast-like fungi [13], with filamentous fungi being more common than yeast-like fungi worldwide. The fungal pathogen of FK is generally opportunistic corneal infection predisposed to corneal surface trauma [9]. According to the result of the Asia Cornea Society Infectious Keratitis Study [14], FK was the second common microbial keratitis with slightly fewer cases than bacterial keratitis (BK) (FK: BK = 33%: 38%). However, among the 2831 microorganisms isolated from patients with suspected microbial keratitis, the top 3 pathogens were Fusarium spp. (18%), Pseudomonas spp. (10%), and Aspergillus spp. (8%). In addition to Fusarium and Aspergillus, Curvularia spp., Alternaria spp., and Candida spp. were also commonly reported pathogens of FK [15,16,17,18][15][16][17][18].
Climate also plays a major role in pathogen determination. The proportions of common pathogens of FK in temperate regions are different from those in tropical and subtropical regions. For example, in the temperate climate of a Danish population, 52% of FK patients were infected with Candida, 20% with Fusarium, 16% with Aspergillus, and 12% with mixed filamentous fungi [19].
The major risk factors of FK include a tropical climate, the rainy season, trauma, being an agriculture worker, and a rural area [3], whereas the other risk factors are associated with urbanization, including contact lens wear, ocular surface disease, and immunocompromised status, such as diabetic mellitus and corticosteroid exposure [16,19,20][16][19][20]. Although the tropical climate is a major risk of FK, increased incidences have been reported in temperate climate regions in recent years [3,21][3][21].

3. Clinical Diagnosis of FK

Corneal ulceration features of FK identified by slit lamp biomicroscope include feathery/serrated/irregular margin, raised slough, dry texture, satellite lesions, and colorization. Among these features, the presentation of a feathery margin raised slough, and colorization was significantly and independently associated with FK. However, the feathery margin, agreed by most ophthalmologists, is the hallmark that distinguishes FK from BK [22,23][22][23]. In addition to corneal ulceration, corneal endothelial plaque and hypopyon were sometimes present in FK patients with deep corneal invasion. Even with the above clinical morphologies, the overall diagnostic accuracy is less than 70%, even when impressed by a corneal specialist [23].
In vivo confocal microscopy is a novel tool to non-invasively diagnose FK in a clinical office [24]. Under in vivo confocal microscopy, the presence of highly reflective lines with numerous interlocking branches could be identified as filamentary FK, whereas the exhibition of hyper-reflective deposits or pseudofilaments could be recognized as yeast-form FK [25]. However, diagnostic accuracy is highly dependent on observers’ experiences, with only moderate sensitivity (71.4%), even for experienced observers [25].

4. Laboratory Diagnosis of FK

Microbial culture via corneal scraping samples is still the current gold standard for diagnosing FK, while a smear by a KOH wet mount or a gram stain was recommended as an additional clinical routine for rapidly confirming FK [26,27,28,29][26][27][28][29]. Microbial culture can identify fungal species and facilitate an antifungal susceptibility test, but it is time-consuming and fails to detect fastidious fungal pathogens [27,30,31][27][30][31]. On the other hand, direct microscopic examination is currently the fastest diagnostic method, given that light microscopy and associated reagents are equipped in the clinical office. However, the results of direct microscopy for FK have highly variable sensitivity, possibly due to variation in examiners’ experiences [26,27,29,31][26][27][29][31]. In a four-case preliminary study [32], measurement of the tear β-D-glucan level was reported to have a high sensitivity for diagnosing FK, but there is no further study to validate such a finding. Fortunately, molecular tests based on different omics approaches for diagnosing FK are gaining popularity in recent years and show great promise in routine clinical practices.

5. Perspectives of an Omics Approach in FK

Omics approaches still have significant room for improvement in diagnosing FK, monitoring the interaction between hosts and pathogens, guiding the treatment of FK, and evaluating the treatment effects in FK events. Currently, a genomic approach for diagnosing FK may not guarantee absolute accuracy to species-level identification because most of the diagnostic methods focus on one DNA fragment, such as rRNA genes and ITSs. However, following technical advancements of next-generation sequencing and the accumulation of whole-genome sequencing databases, simultaneous multi-fragment amplification and detection will be possible by DNA-based molecular techniques for rapid and precise species-level identification with the coverage of critical anti-fungal genes. High-throughput sequencing techniques, including shot-gun and target-enrichment sequencing, will be powerful metagenomic solutions for elucidating the fungal pathogen–microbiota and pathogen–patient relationships during FK events, respectively. In addition, the personalized tear proteomics using MOLDI-TOF-MS and LC-MS techniques will help physicians to precisely monitor expressions of virulent factors from fungal pathogens and the defense response of patients. Among the predictors of medical treatment failure, positive fungal culture results, day 6 repeated fungal culture-positive results, Aspergillus species isolation, and MIC to natamycin lead to worse outcomes, and a lower threshold for surgical intervention should be adopted. Genomic and metagenomic approaches may provide alternative diagnoses for FK patients with false-negative results in conventional approaches. In addition, the ability to identify fungal species with a higher risk of antifungal resistance can help physicians determine whether to adopt surgical intervention early on or implement a more aggressive medical treatment. A metagenomics approach for monitoring pathogenic conjunctival flora in FK may help guide the antibiotic regimen to prevent bacterial superinfection. The insidious course of FK may cause physicians to overlook the disease progression of FK. A tear proteomics approach for identifying up-regulated and down-regulated proteins released from the ocular surface or fungal pathogens could help physicians to determine the stage of fungal infection and the response of ocular surface immunity. In the cloud era, different types of clinical information (risk factors and clinical photos) and laboratory data (smears, cultures, and omics surveys) from an increasing amount of FK cases will be collected more efficiently. Data mining for FK cases will help physicians develop a prognosis prediction model and evolve an optimal decision model to guide FK treatment. 

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