The Magic Triangle in Oral Potentially Malignant Disorders: Vitamin D, Vitamin D Receptor, and Malignancy.

OPMDs (oral potentially malignant disorders) are a group of disorders affecting the oral mucosa that are characterized by aberrant cell proliferation and a higher risk of malignant transformation. Vitamin D (VitD) and its receptor (VDR) have been extensively studied for their potential contributions to the prevention and therapeutic management of various diseases and neoplastic conditions, including oral cancer. Observational studies suggest correlations between VitD deficiency and higher cancer risk, worse prognosis, and increased mortality rates. Interestingly, emerging data also suggest a link between VitD insufficiency and the onset or progression of OPMDs. Understanding the role of the VitD-VDR axis not only in established oral tumors but also in OPMDs might thus enable early detection and prevention of malignant transformation. With this article, we want to provide an overview of current knowledge about OPMDs and VitD and investigate their potential association and ramifications for clinical management of OPMDs.

First Report of Red Leaf Midrib lesions on Sugarcane Caused by Lasiodiplodia theobromae in China.

Sugarcane (Saccharum officinarum) is an economically important crop and is extensively planted across China. In August 2020, leaf midribs with red lesions were observed on cultivar 'Yunzhe 081609' in Kaiyuan (103.27°E, 23.71°N), Yunnan, Southwestern China. In July to August 2021, similar symptoms were observed on cultivar 'Liucheng 05-136' in Hechi (108.48°E, 24.47°N), Guangxi, and on cultivars 'Yingyu 91-59' and 'Yunzhe 081609' in Lingcang (99.45°E, 23.33°N), Yunnan. Initially symptoms appeared as red spots on the leaf midribs, which gradually expanded, forming elongated red lesions. At high severity, the leaves broke and hung down. Disease incidence of leaves was estimated at 30 to 50% across the locations. To identify the etiology of this disease, three symptomatic leaves were collected from cultivars 'Liucheng 05-136', 'Yingyu 91-59', and 'Yunzhe 081609', respectively. Symptomatic leaf midribs were cut to small fragments (3 × 5 mm), surface sterilized with 70% ethanol for 30 s followed by 1% NaClO for 1 min, rinsed with sterilized distilled water three times, air dried on sterile filter paper, plated on potato dextrose agar (PDA), and incubated at 28°C in the dark. Ten isolates with similar morphological characteristics were obtained. Colonies on PDA were white to grayish-white with aerial mycelium growing initially upward and then forming clusters. After 10 days, mycelia turned to grayish black. Immature conidia were initially hyaline, aseptate, and ellipsoid. Mature conidia became dark brown, septate, longitudinal striate, and measured 21.2 to 25.8 × 11.4 to 16.4 µm (n = 30). Morphologically, the isolates were identified as Lasiodiplodia theobromae (Alves et al. 2008). For molecular identification, genomic DNA of four representative isolates (LTGX1, LTGX2, LTYN1 and LTYN2) was extracted using the Ezup Column Fungi Genomic DNA Purification kit. The internal transcribed spacer (ITS) region of rDNA, translation elongation factor 1-alpha (TEF-1α) gene, and ß-tubulin (TUB) gene were amplified with primer pairs ITS1/ITS4 for ITS, EF1-728F/EF1-986R for TEF-1α, and Bt2a/Bt2b for TUB, respectively (Glass and Donaldson 1995; Carbone and Kohn 1999; White et al. 1990), and then sequenced. The ITS (ON533336-ON533339), TEF-1α (ON939550-ON939553) and TUB (OP747306-OP747309) sequences were deposited in GenBank. BLAST searches showed >99% nucleotide identity to the sequences of ex-type isolate CBS 164.96 of L. theobromae (ITS, 99.8% to AY640255; TEF-1α, 99.9% to AY640258; TBU, 100% to EU673110). Phylogenetic analysis using maximum likelihood based on the combined ITS, TEF-1α, and TUB sequences of the isolates and reference sequences of Lasiodiplodia spp. downloaded from the GenBank indicated the isolates obtained in this study formed a clade strongly supported based on bootstrap values (100%) to the ex-type isolate CBS 164.96 sequences of L. theobromae. For pathogenicity tests, three healthy 6-month-old potted sugarcane leaf midribs of cultivar 'Yunzhe 081609' were wounded with a sterile needle, then inoculated using 8-mm mycelial agar plugs from a 10-day-old culture of strain LTYN1, and covered with wet cotton to maintain high relative humidity. Sterile PDA plugs were used as controls. Plants were placed in a greenhouse at 28 to 32°C. The test was conducted twice. Five days after inoculation, red lesions appeared on the inoculated leaf midribs. These symptoms were similar to those observed in the field. The leaves used for negative controls remained symptomless. The same fungus (L. theobromae) was re-isolated from all inoculated-symptomatic tissues; and isolates had the same morphological traits mentioned above. The DNA sequence data of these isolates was also similar than the original isolates. The association of L. theobromae with S. officinarum was recorded earlier in Cuba (Urtiaga, 1986), Myanmar (Thaung, 2008) and the Philippines (Reinking, 1919). Leaf midribs with red lesions caused by Colletotrichum falcatum has already been described around the world (Costa et al. 2021; Hossain et al. 2021; Xie et al. 2019). All together, this information indicates that L. theobromae is one of the causal agent of the red lesions symptoms on the sugarcane leaf midribs. To our knowledge, this is the first report of L. theobromae causing red lesions on leaf midribs of sugarcane in China. Further research will focus on developing management strategies to control this disease effectively.

Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives.

Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of diabetic retinopathy resulting in severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy stages. There is a need of an automated screening tool for the early detection and treatment of patients with diabetic retinopathy. This paper focuses on the segmentation of red lesions using nested U-Net Zhou et al. (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 2018) followed by removal of false positives based on the sub-image classification method. Different sizes of sub-images were studied for the reduction in false positives in the sub-image classification method. The network could capture semantic features and fine details due to dense convolutional blocks connected via skip connections in between down sampling and up sampling paths. False-negative candidates were very few and the sub-image classification network effectively reduced the falsely detected candidates. The proposed framework achieves a sensitivity of [Formula: see text], precision of [Formula: see text], and F1-Score of [Formula: see text] for the DIARETDB1 data set Kalviainen and Uusutalo (Medical Image Understanding and Analysis, Citeseer, 2007). It outperforms the state-of-the-art networks such as U-Net Ronneberger et al. (International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015) and attention U-Net Oktay et al. (Attention u-net: Learning where to look for the pancreas, 2018).

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy.

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Mathematical morphology for microaneurysm detection in fundus images.

AIM: Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. METHODS: The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. RESULTS: The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. CONCLUSION: In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.