Background: Colorectal cancer (CRC) is the third most common cancer in the world. Non-coding RNAs or microRNAs (miRNAs; miRs) biomarkers can play a role in cancer carcin-ogenesis and progression. Specific KRAS and EGFR mutation are associated with CRC develop-ment playing a role in controlling the cellular process as epigenetic events. Circulating serum miRs can serve for early diagnosis, monitoring, and prognosis of CRC as biomarkers but it is still unclear, clinically. Objective: To determine potential biomarkers of circulating serum miR-133b and miR-206 in CRC patients Methods: Bioinformatic prediction of microRNA was screened followed by TargetScanHu-man7.2, miRTar2GO, miRDB, MiRanda, and DIANA-microT-CDS. Forty-four CRC serum (19 locally advanced, 23 distant advanced CRC) and 12 normal serum samples were subsequently extracted for RNA isolation, cDNA synthesis, and miR validation. The candidate circulating se-rum miR-133b and miR-206 were validated resulting in a relative expression via quantitative RT-PCR. Relative expression was normalized to the spike-internal control and compared to normal samples as 1 using the 2-ΔΔCt method in principle. Results: Our results represented 9 miRs of miR-206, miR-155-5p, miR-143-3p, miR-193a-3p, miR-30a-5p, miR-30d-5p, miR-30e-5p, miR-543, miR-877-5p relate to KRAS-specific miRs, whereas, 9 miRs of miR-133b, miR-302a-3p, miR-302b-3p, miR-302d-3p, miR-302e, miR-520a-3p, miR-520b, miR-520c-3p and miR-7-5p relevance to EGFR-specific miRs by using the bioin-formatic prediction tools. Our results showed a decreased expression level of circulating serum miR-133b as well as miR-206 associating with CRC patients (local and advanced metastasis) when compared to normal (P < 0.05), significantly. Conclusion: The circulating serum miR-133b and miR-206 can serve as significant biomarkers for monitoring the clinical outcome of progression with metastatic CRC patients. Increased drug-responsive CRC patients associated with crucial molecular intervention should be further ex-plored, clinically.

This study aimed to develop a local dataset of abnormal RBC morphology from confirmed cases of anemia and thalassemia in Thailand, providing a foundation for medical image analysis and future AI-assisted diagnostics. Blood smear samples from six hematological disorders were collected between April and May 2025, with twelve regions of interest segmented into approximately 34,000 single-cell images. To characterize cell variability, a convolutional autoencoder was applied to extract latent features, while ellipse fitting was used to quantify cell geometry. Expert hematologists validated representative clusters to ensure clinical accuracy, and data augmentation was employed to address class imbalance and expand rare morphological types. From the dataset, 14,089 high-quality single-cell images were used to classify RBC morphology into 36 clinically meaningful categories. Unlike existing datasets that rely on limited or curated samples, this dataset reflects population-specific characteristics and morphological diversity relevant to Southeast Asia. The results demonstrate the feasibility of establishing scalable and interpretable datasets that integrate computational methods with expert knowledge. The proposed dataset serves as a robust resource for advancing hematology research and contributes to bridging traditional diagnostics with AI-driven clinical support systems.

Red blood cell (RBC) morphology is critical for diagnosing hematological disorders, particularly in regions such as Southeast Asia where anemia and thalassemia are highly prevalent. However, manual microscopic assessment is labor-intensive, subjective, and dependent on expert availability, while existing automated methods often rely on small, curated datasets that fail to represent real-world smear variability. This study proposes a hybrid framework integrating preprocessing, unsupervised autoencoding, k-means clustering, ellipse fitting, expert-in-the-loop validation, and targeted data augmentation to establish a robust RBC labeling pipeline. High-resolution smear images from confirmed anemia and thalassemia cases were processed to extract over 14,000 single-cell patches, filtered systematically into quality-assured subsets. Latent features from a CNN autoencoder enabled clustering into 80 morphological groups, quantified using ellipse-based geometric metrics and validated by hematology experts. Data augmentation addressed class imbalance, expanding rare morphologies while preserving realistic cell structure. The resulting dataset captures clinically relevant morphological diversity specific to the Thai population and provides a scalable, interpretable framework for medical analysis and future AI model development in hematology.