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Results of the anti-nuclear antibodies-indirect immunofluorescence assay (anti-cell antibodies test) on HEp-2 cell substrates should be communicated to clinicians in a standardized way, adding value to laboratory findings and helping with critical clinical decisions. This paper proposes a test report based on the practices informed by 118 laboratories in 68 countries, with recommendations from the International Consensus on ANA Patterns (ICAP) group. Major focus is placed on the report format containing endpoint titers, immunofluorescence patterns together with anti-cell (AC) nomenclature, remarks on follow-up or reflex testing, and possible other autoantibody associations. ISO 15,189 directives were integrated into the test report. Special situations addressed include serum screening dilutions and endpoint titers, relevance of immunofluorescence patterns with special attention to cytoplasmic patterns, mixed and compound patterns, and how to report different titers corresponding to multiple patterns or autoantibodies in the same sample. This paper suggests a subtitle for the HEp-2-IIFA, namely anti-cell antibodies test, which could gradually substitute the original outdated ANA nomenclature. This ICAP pro forma report represents a further step in harmonizing the way relevant clinical information could be provided by laboratories.

Convolutional neural networks (CNN) are widely used in computer vision and medical image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly for downsampling the feature maps by aggregating features from local regions. Pooling can help CNN to learn invariant features and reduce computational complexity. Although the max and the average pooling are the widely used ones, various other pooling techniques are also proposed for different purposes, which include techniques to reduce overfitting, to capture higher-order information such as correlation between features, to capture spatial or structural information, etc. As not all of these pooling techniques are well-explored for medical image analysis, this paper provides a comprehensive review of various pooling techniques proposed in the literature of computer vision and medical image analysis. In addition, an extensive set of experiments are conducted to compare a selected set of pooling techniques on two different medical image classification problems, namely HEp-2 cells and diabetic retinopathy image classification. Experiments suggest that the most appropriate pooling mechanism for a particular classification task is related to the scale of the class-specific features with respect to the image size. As this is the first work focusing on pooling techniques for the application of medical image analysis, we believe that this review and the comparative study will provide a guideline to the choice of pooling mechanisms for various medical image analysis tasks. In addition, by carefully choosing the pooling operations with the standard ResNet architecture, we show new state-of-the-art results on both HEp-2 cells and diabetic retinopathy image datasets.

Chitosan (CS) is a biopolymer that offers a wide range in biomedical applications due to its biocompatibility, biodegradability, low toxicity and antimicrobial activity. Syringaldehyde (1) is a naturally occurring organic compound characterized by its use in multiple fields such as pharmaceuticals, food, cosmetics, textiles and biological applications. Herein, development of chitosan derivative with physicochemical and anticancer properties via Schiff base formation from the reaction of chitosan with sustainable eco-friendly syringaldehyde yielded the (CS-1) derivative. Moreover, in the presence of polyethylene glycol diglycidyl ether (PEGDGE) or sodium tripolyphosphate (TPP) as crosslinkers gave chitosan derivatives (CS-2) and (CS-3NPs) respectively. The chemical structures of the new chitosan derivatives were confirmed using different tools. (CS-3NPs) nanoparticle showed improvement in crystallinity, and (CS-2) derivative revealed the highest thermal stability compared to virgin chitosan. The cytotoxicity activity of chitosan and its derivatives were evaluated against HeLa (human cervical carcinoma) and HEp-2 (Human Larynx carcinoma) cell lines. The highest cytotoxicity activity was exhibited by (CS-3NPs) compared to virgin chitosan against HeLa cell growth inhibition and apoptosis of 90.38 ± 1.46% and 30.3% respectively and IC 50 of 108.01 ± 3.94 µg/ml. From the above results, it can be concluded that chitosan nanoparticle (CS-3NPs) has good therapeutic value as a potential antitumor agent against the HeLa cancer cell line.

The morphological patterns in indirect immunofluorescence assay on HEp-2 cells (HEp-2 IFA) reflect the autoantibodies in the sample. The International Consensus on ANA Patterns (ICAP) classifies 30 relevant patterns (AC-0 to AC-29). AC-4 (fine speckled nuclear pattern) is associated to anti-SS-A/Ro, anti-SS-B/La, and several autoantibodies. Anti-SS-A/Ro samples may contain antibodies to Ro60 and Ro52. A variation of AC-4 (herein designated AC-4a), characterized by myriad discrete nuclear speckles, was reported to be associated with anti-SS-A/Ro. The plain fine speckled pattern (herein designated AC-4b) seldom was associated with anti-SS-A/Ro. This study reports the experience of four expert laboratories on AC-4a and AC-4b. Anti-Ro60 monoclonal antibody A7 was used to investigate the HEp-2 IFA pattern. Records containing concomitant HEp-2 IFA and SS-A/Ro tests from Durand Laboratory, Argentina ( = 383) and Fleury Laboratory, Brazil ( = 144,471) were analyzed for associations between HEp-2 IFA patterns and disease-associated autoantibodies (DAA): double-stranded DNA, Scl-70, nucleosome, SS-B/La, Sm, and U1-RNP. A total of 381 samples from Dresden Technical University (TU-Dresden), Germany, were assayed for HEp-2 IFA and DAA. Monoclonal A7 recognized Ro60 in Western blot and immunoprecipitation, and yielded the AC-4a pattern on HEp-2 IFA. Analyses from Durand Laboratory and Fleury Laboratory yielded compatible results: AC-4a was less frequent (8.9% and 2.7%, respectively) than AC-4b (26.1% and 24.2%) in HEp-2 IFA-positive samples. Reactivity to SS-A/Ro occurred in 67.6% and 96.3% of AC-4a-pattern samples against 23% and 6.8% of AC-4b pattern samples. Reciprocally, AC-4a occurred in 24% and 47.1% of anti-SS-A/Ro-positive samples, and in 3.8% and 0.1% of anti-SS-A/Ro-negative samples. Data from TU-Dresden show that the AC-4a pattern occurred in 69% of 169 anti-SS-A/Ro-monospecific samples (62% of all anti-SS-A/Ro-positive samples) and in 4% of anti-SS-A/Ro-negative samples, whereas anti-SS-A/Ro occurred in 98.3% of AC-4a samples and in 47.9% of AC-4b samples. In all laboratories, coexistence of anti-SS-B/La, but not other DAA, in anti-SS-A/Ro-positive samples did not disturb the AC-4a pattern. AC-4a was predominantly associated with anti-Ro60 antibodies. This study confirms the association of AC-4a pattern and anti-SS-A/Ro in opposition to the AC-4b pattern. The results of four international expert laboratories support the worldwide applicability of these AC-4 pattern variants and their incorporation into ICAP classification under codes AC-4a and AC-4b, respectively. The AC-4 pattern should be maintained as an umbrella pattern for cases in which one cannot discriminate AC-4a and AC-4b patterns. The acknowledgment of the AC-4a pattern should add value to HEp-2 IFA interpretation.

The International Consensus on ANA Patterns (ICAP) recently codified the AC-30 nuclear pattern, characterized by fine speckled nuclear texture and metaphase chromatin staining. Although formally recognized, its clinical significance remains unclear. Quantitative methods and objective outcome measures for characterizing AC-30 have not been established. A retrospective analysis was conducted using archived HEp-2 ANA images. AC-30 was defined by unanimous scoring from three blinded experts. AC-1 and AC-2 served as comparator sets. Clinical diagnoses and serological data were retrieved from hospital records. Pixel classification was performed using ilastik, and per-nucleus intensity features were extracted with CellProfiler. Image-level separation was assessed by partitioning nuclei into high- and low-intensity groups via Isolation Forest. Statistical comparisons used Welch's t-tests and Spearman's rank correlation. The AC-30 group included 183 images (AC-1, n = 183; AC-2, n = 207). Within AC-30, 57.4% had non-AID or unknown diagnoses, 26.8% had other autoimmune diseases, and 15.8% had ANA-associated rheumatic disease (AARD). In ENA-negative AC-30, AARD accounted for 7.7% (6/78), compared to 18.3% (21/115) in ENA-negative AC-2. RA accounted for 16.9% (31/183) in AC-30, consistent across ENA strata. AC-30 displayed lower per-nucleus intensities than AC-2 (all p < 0.0001) and reduced per-image ΔMaxIntensity (0.177 vs. 0.252, p < 0.0001), while ΔMeanIntensity was similar (0.068 vs. 0.067, p = 0.549). AC-30 is quantitatively dimmer than AC-2, with reduced nuclear brightness and peak-intensity separation. In this retrospective cohort, ENA-negative AC-30 was associated with fewer AARD cases. RA accounted for ~17% of AC-30, and anti-CCP and RF remained informative markers. The ilastik-CellProfiler workflow enables auditable ANA quantification.

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs’ manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells’ existence.

Purpose: Development of pharmaceutical dosage forms of natural products has gained great interest recently. Propolis is a natural product with various active compounds and multiple pharmacological activities. Its resinous nature and low bioavailability were obstacles in the optimum use of this magnificent natural product. Aim: This study evaluates the effect of using liposomes as a drug delivery system on the enhancement of the cytotoxic effect of propolis on squamous cell carcinoma cell lines (Hep-2) of head and neck. Methods: An optimized liposomal formulation of propolis was prepared using the conventional thin film hydration method 1, 2. The prepared (Hep-2) cell line was treated with different concentrations of propolis and optimized propolis liposomes for 24 h. The effect of both propolis and propolis liposomes on cell line was investigated using MTT assay, cytological examination, and nuclear morphometric analysis. The effect of the drugs on the cell apoptosis was evaluated using Annexin V. Results: The findings revealed that both propolis and propolis liposomes have a cytotoxic effect on Hep-2 cell line through induction of apoptosis. The effect was dose dependent. However, a statistically significant enhancement in propolis-mediated apoptosis on Hep-2 cells was elucidated due to encapsulation within the prepared liposomes. Conclusion: Liposome is a powerful tool for enhancing the cytotoxicity of propolis against Hep-2 cell line.

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a nuclear transcription factor that is activated by reactive oxygen species (ROS). Recent studies reported that hyperactivation of the Nrf2 pathway creates an environment that favors the survival of normal as well as malignant cells, protecting them against oxidative stress, chemotherapeutic agents, and radiotherapy. SUMO1/sentrin/SMT3 specific peptidase 3 (SENP3) reverses sumoylation of small ubiquitin-like modifier (SUMO)-conjugates. We demonstrated that Nrf2 was detected in the nuclei of laryngeal carcinoma cells, but not in cells of tissues surrounding the cancer, which correlated with the appearance of SENP3 in the nuclei. Silencing of Nrf2 in laryngeal carcinoma cell line Hep-2 significantly reduced cell viability and enhanced apoptosis rates under cisplatin, 5-fluorouracil (5-FU) and phenethyl isothiocyanate (PEITC) exposure. Cisplatin exposure induced ROS stress in Hep-2 cells in a time-dependent manner and was accompanied by increased Nrf2 and SENP3 protein accumulations, an effect reversed by the addition of the antioxidant N-acetyl-cysteine (NAC). Silencing of SENP3 led to reduced Nrf2 protein levels, whereas overexpression of SENP3 led to concomitant enhanced transcription of the Nrf2 target genes HO-1, NQO1, GCLC and GSTM1. Immunoprecipitation showed that overexpressed Nrf2 and SENP3 could be precipitated together, indicating that they were intracellular bound to each other. Our data identified intranuclear activation of Nrf2 is triggered by cisplatin-induced ROS development through the activity of SENP3. These findings provide novel insights into the Nrf2 reduced cancer cell response to the chemotherapy of laryngeal carcinoma.

Accurate detection of anti-aminoacyl-tRNA synthetase autoantibodies (ASA) is central to the diagnosis of antisynthetase syndrome (ASyS). Although immunoprecipitation (IP) remains the historical reference method, its limited availability in routine clinical laboratories necessitates reliance on commercially available immunoassays. The diagnostic performance of these assays varies by specificity and cut-off interpretation. We aimed to improve the post-analytical interpretation of ASA positivity in routine practice. We conducted a retrospective single-center study including 125 patients with at least one positive ASA detected by line-blot immunoassay (LIA) between January 2023 and September 2025. Thirty-three patients fulfilled clinician-based diagnostic criteria for ASyS, while 92 served as controls. Additional immunological data included indirect immunofluorescence on HEp-2 cells (IIF-HEp-2) and anti-Ro52 autoantibodies. Variables associated with ASyS were incorporated into multivariable logistic regression models. A diagnostic probability score was derived from the final model and evaluated using receiver operating characteristic (ROC) curve analysis. Semi-quantitative LIA intensity alone showed limited discriminatory capacity, particularly in cases with low-intensity bands. In contrast, compatible cytoplasmic IIF-HEp-2 patterns and anti-Ro52 co-positivity significantly improved discrimination. The final multivariable model integrating semi-quantitative LIA intensity, IIF-HEp-2 cytoplasmic pattern (AC-19/20), and anti-Ro52 co-positivity achieved an AUC of 0.94 (95% CI 0.89-0.99). Stratification into low-, intermediate-, and high-probability zones provided clinically interpretable diagnostic categories. In ASA-positive patients, probability-based integration of routinely available immunoassay results improves the interpretation of LIA positivity and enhances discrimination between ASyS and alternative diagnoses.