Background: KH-type splicing regulatory protein (KHSRP, also called KSRP), a versatile RNA-binding protein, plays a critical role in various physiological and pathological conditions through modulating gene expressions at multiple levels. However, the role of KSRP in clear cell renal cell carcinoma (ccRCC) remains poorly understood. Methods: KSRP expression was detected by a ccRCC tissue microarray and evaluated by an in silico analysis. Cell loss-of-function and gain-of-function, colony-formation, anoikis, and transwell assays, and an orthotopic bioluminescent xenograft model were conducted to determine the functional role of KRSP in ccRCC progression. Micro (mi)RNA and complementary (c)DNA microarrays were used to identify downstream targets of KSRP. Western blotting, quantitative real-time polymerase chain reaction, and promoter- and 3-untranslated region (3'UTR)-luciferase reporter assays were employed to validate the underlying mechanisms of KSRP which aggravate progression of ccRCC. Results: Our results showed that dysregulated high levels of KSRP were correlated with advanced clinical stages, larger tumor sizes, recurrence, and poor prognoses of ccRCC. Neural precursor cell-expressed developmentally downregulated 4 like (NEDD4L) was identified as a novel target of KSRP, which can reverse the protumorigenic and prometastatic characteristics as well as epithelial-mesenchymal transition (EMT) promotion by KSRP in vitro and in vivo. Molecular studies revealed that KSRP can decrease NEDD4L messenger (m)RNA stability via inducing mir-629-5p upregulation and directly targeting the AU-rich elements (AREs) of the 3’UTR. Moreover, KSRP was shown to transcriptionally suppress NEDD4L via inducing the transcriptional repressor, Wilm's tumor 1 (WT1). In the clinic, ccRCC samples revealed a positive correlation between KSRP and mesenchymal-related genes, and patients expressing high KSRP and low NEDD4L had the worst prognoses. Conclusion: The current findings unveil novel mechanisms of KSRP which promote malignant progression of ccRCC through transcriptional inhibition and post-transcriptional destabilization of NEDD4L transcripts. Targeting KSRP and its pathways may be a novel pharmaceutical intervention for ccRCC.

Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes. Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size. The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes. Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 μm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.

Introduction: Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images. Methods: This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ [SurePath]), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total number of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses. Results: The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78–0.91 vs. 0.75–0.88, p = 0.451–0.578). Notably, it substantially reduced both scanning time (137.2–635.0 s vs. 332.6–1,278.8 s, p < 0.05) and image file size (0.51–2.10 GB vs. 1.16–3.10 GB, p < 0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%). Conclusion: We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.

RNA interference (RNAi) is a biological process in which RNA molecules are involved in sequence-specific suppression of gene expression, via small RNA triggers derived from double-stranded RNA that can target specific genes; it is a natural process that plays a role in both the regulation of protein synthesis and in immunity. Discovery of RNAi by Fire and Mello in 1998 had a profound impact on unraveling novel aspects of eukaryotic biology. RNA interference (RNAi) has proven to be an immensely useful tool for studying gene function and validation of potential drug targets in almost all organisms. A great advance in parasitic protozoa was achieved by the experimental demonstration of RNAi in Trypanosoma brucei, and in other protists such as Leishmania braziliensis, Entamoeba histolytica and Giardia lamblia/intestinalis. These organisms exhibit numerous differences beyond the core 'dicer' and 'slicer' activities, thereby expanding knowledge of the evolutionary diversification of this pathway in eukaryotes. When present, RNAi has led to new technologies for engineering powerful and facile knockdowns in gene expression, revolutionizing biomedical research and opening clinical potentialities. In this review, we discuss the distribution of RNAi pathways, their biological roles, and experimental applications in protozoan parasites.

Treating diabetic wounds effectively remains a significant clinical challenge. Emerging studies suggest that microRNAs (miRNAs) play crucial roles in various physiological and pathological processes and hold promise as therapeutic tools. This study investigates the miRNA expression profile in keratinocytes using a cell model of diabetic wounds. Microarray analysis revealed that 43 miRNAs from wounded keratinocytes incubated under diabetic conditions (high glucose/hypoxia) exhibited a two-fold change in expression compared to those incubated under normal conditions (low glucose/normoxia). Quantitative RT-PCR confirmed significant differences in the expression of eight miRNAs, with miR-3138 and miR-3679-5p being further analyzed for their roles in keratinocyte migration. Transfection with a miR-3138 mimic and a miR-3679-5p inhibitor indicated that upregulation of miR-3138 and downregulation of miR-3679-5p enhance keratinocyte migration in both normal and diabetic wounds. Pathway and gene ontology (GO) analyses identified potential pathways and functional annotations associated with miR-3138 and miR-3679-5p in diabetic wound healing. Potential human gene targets of miR-3138 and miR-3679-5p were predicted using a three-way comparison of the TargetScan, miRDB, and DIANA databases. This study elucidates the miRNA expression signature of human keratinocytes in a diabetes-like environment, providing deeper insights into the pathogenesis of diabetic wounds.