No abstract

No abstract

Background: Skin hyperpigmentation usually results from an increased number, or activity, of melanocytes. The degree of pigmentation of skin depends on the amount and type of melanin, degree of skin vascularity, presence of carotene, and thickness of the stratum corneum. Common causes of hyperpigmentation include post-inflammatory hyperpigmentation, melasma, solar lentigines, ephelides (freckles), and café-au-lait macules. Some skin tumors can be hyperpigmented as basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). Stem cell factor (SCF) is a growth factor and its interaction with its receptor, c-kit, is well known to be critical to the survival of melanocytes. Methods: This study was carried out on 60 patients complaining of hyperpigmented skin lesions (20 melasma, 20 solar lentigines, and 20 freckles) and 36 patients with skin tumors (14 BCC, 12 SCC, and 10 MM). Punch skin biopsies were taken from the previous lesions. Immunohistochemical staining of these samples was done using the stem cell factor (SCF). Results: There was positive expression of SCF in all cases of melasma, solar lentigines and freckles with significant increase in the intensity of expression in the lesional areas than the non-lesional ones (P=0.004). There was also a statistically significant increase in the expression of SCF in BCC and melanoma tumor cells. Conclusion: SCF has a great role in skin hyperpigmented disorders and this can be used as a target for the developing of new antipigmentary lines of treatment by inhibiting SCF. SCF can also be involved in the emergence of some skin tumors.

No abstract

No abstract

X‐ray repair cross‐complementing group 1 (XRCC1), a coordinator protein of the DNA repair complex, is thought to be involved in cancer progression. This case–control study aimed to investigate the association of two biallelic single‐nucleotide polymorphisms (SNPs; Arg399Gln, Arg194Trp) of the XRCC1 gene with its tissue expression level and breast cancer (BC) risk in Egyptian women. This study included 100 BC female patients (case group 1) and 100 healthy females (control group 2). The XRCC1 tissue expression was assessed by immunohistochemistry (IHC). Genotyping of the two XRCC1 SNPs (Arg399Gln, Arg194Trp) using real‐time polymerase chain reaction (PCR) was also conducted. The XRCC1 expression level was significantly lower in cancerous tissues than adjacent non‐cancerous tissues (p < .001). The XRCC1 399Gln/Gln genotype, 399Gln allele, the dominant, and recessive models were significantly associated with lower XRCC1 expression in breast cancerous tissues and increased risk for BC (3.390‐, 1.965‐, 2.241‐, and 2.429‐folds, respectively). The XRCC1 399Gln/Gln genotype was associated with lower incidence of advanced tumor grade (OR: 0.06; 95%CI: 0.01–0.74; p = .028). Conversely, the XRCC1 Arg194Trp polymorphism did not show any significant association with either XRCC1 expression in breast cancer tissues or BC risk in all genetic models. The XRCC1 haplotypes, 399Gln/194Arg and 399Gln/194Trp, were associated with 1.800‐ and 1.675‐folds risk for BC, respectively. The XRCC1 gene polymorphism (Arg399Gln) is associated with reduced XRCC1 tissue expression and enhanced BC risk with a well‐differentiated nature in Egyptian women. Moreover, XRCC1 haplotypes, 399Gln/194Arg and 399Gln/194Trp, were associated with increased BC risk.

In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be utilized for developing smart agriculture to determine the exact location of the diseased part of the leaf on farmland in an efficient manner. There is no exception that convolutional neural networks (CNNs) have achieved the latest accomplishment in many aspects of human life and the farming sector. Semantic image segmentation is considered the main problem in computer vision. Despite tremendous progress in applications, approximately all semantic image segmentation algorithms fail to achieve sufficient hash results because of the absence of details sensitivity, problems in assessing the global similarity of image pixels, or both. Methods of post-processing improvement, as a wonderfully critical means of improving the underlying flaws mentioned above from algorithms, depend almost on Conditional Random Fields (CRFs). Therefore, plant disease prediction plays important role in the premature notification of the disease to alleviate its effects on disease forecast investigation purposes in the smart farming arena. Hence, this work proposes an efficient IoT-based plant disease recognition system using semantic segmentation methods such as FCN-8 s, CED-Net, SegNet, DeepLabv3, and U-Net with the CRF method to allocate disease parts in leaf crops. Evaluation of this network and comparison with other networks of the state art. The experimental results and their comparisons proclaim over F1-score, sensitivity, and intersection over union (IoU). The proposed system with SegNet and CRFs gives high results compared with other methods. The superiority and effectiveness of the mentioned improvement method, as well as its range of implementation, are confirmed through experiments.

ObjectiveTo evaluate the impact of a luteinising hormone-releasing hormone (LHRH) agonist, goserelin acetate (GA), on surgical blood loss during transurethral resection of the prostate (TURP), as well as its histopathological effect on prostatic microvessel density (MVD).Patients and methodsPatients who underwent TURP due to benign prostatic enlargement (60–100 mL) were randomly subdivided into two equal groups according to whether they received preoperative GA administration (3.6 mg; group A) or not (group B). Evaluation parameters were operative time, weight of resected prostatic tissue, perioperative haematocrit (HCT) changes, estimation of intraoperative blood loss, and suburethral and stromal prostatic MVD. Effects of GA on prostate weight and any possible side-effects were also monitored.ResultsIn all, 35 and 33 patients were included in groups A and B, respectively. Operative time and HCT values’ changes were significantly less in group A (P < 0.05). Also, operative blood loss (both total and adjusted per weight of resected tissue) was lower in group A, at a mean (SD) of 178.13 (77.71) mL and 3.74 (1.52) mL/g vs 371.75 (91.09) mL and 8.59 (2.42) mL/g (P < 0.001). The median MVD in both suburethral [8 vs 11 vessels/high-power field (HPF)] and stromal tissues (9 vs 17 vessels/HPF) were significantly lower in group A (P < 0.001). Side-effects were minimal.ConclusionA single dose of GA, a LHRH agonist, before TURP is safe and effective in reducing surgical blood loss. It significantly reduced MVD in both suburethral and stromal nodular prostatic tissues without regional discrepancy.