Fabrication and characterization of unique sustain modified chitosan nanoparticles for biomedical applications.

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 IC50 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.

Evaluation of the Antibacterial, Anti-Cervical Cancer Capacity, and Biocompatibility of Different Graphene Oxides.

Cancer stands as one of the deadliest diseases in human history, marked by an inferior prognosis. While traditional therapeutic methods like surgery, chemotherapy, and radiation have demonstrated success in inhibiting tumor cell growth, their side effects often limit overall benefits and patient acceptance. In this regard, three different graphene oxides (GO) with variations in their degrees of oxidation were studied chemically and tissue-wise. The accuracy of the synthesis of the different GO was verified by robust techniques using X-ray photoelectron spectroscopy (XPS), as well as conventional techniques such as infrared spectroscopy (FTIR), RAMAN spectroscopy, and X-ray diffraction (XRD). The presence of oxygenated groups was of great importance. It affected the physicochemical properties of each of the different graphene oxides demonstrated in the presence of new vibrational modes related to the formation of new bonds promoted by the graphitization of the materials. The toxicity analysis in the Hep-2 cell line of graphene oxide formulations at 250 µg/mL on the viability and proliferation of these tumor cells showed low activity. GO formulations did not show high antibacterial activity against Staphylococcus aureus and Escherichia coli strains. However, the different graphene oxides showed biocompatibility in the subdermal implantation model for 30, 60, and 90 days in the biomodels. This allowed healing by restoring hair and tissue architecture without triggering an aggressive immune response.

Adopting the International Consensus on ANA Patterns (ICAP) classification for reporting: the experience of Italian clinical laboratories.

The indirect immunofluorescence assay on HEp-2 cells (HEp-2 IFA) is still considered the reference method to detect anti-nuclear antibodies (ANA) because of its high sensitivity and represents a relevant tool for the diagnosis of autoimmune rheumatic diseases. During the last decade, the International Consensus on ANA Patterns (ICAP) initiative promoted harmonization and understanding of HEp-2 IFA staining pattern nomenclature, as well as promoting their use in patient care by providing interpretation for HEp-2 IFA test results. In conjunction with a nationwide survey on the evolution of autoantibody diagnostics in autoimmune rheumatic diseases, we focused on the adherence of the Italian laboratories to the ICAP nomenclature analyzing its lights and shadows. The recent ICAP-oriented report, largely used today among Italian laboratories, also represents a further step in harmonizing and improving communication with the clinicians, adding value to laboratory findings and helping with critical clinical decisions.

Detection and antibiotic resistance of diarrheagenic Escherichia coli from patients with diarrhea in Ulaanbaatar, Mongolia.

INTRODUCTION: Diarrheal diseases are common with worldwide distribution, and diarrheagenic Escherichia coli (DEC) strains are the main causative agents. The present study aimed to define the association of various pathotypes of E. coli from diarrheal patients in Mongolia. METHODOLOGY: A total of 341 E. coli strains were isolated from the stool of diarrheal patients. Bacterial susceptibility to antimicrobial agents was determined by the Kirby Bauer disk diffusion method. DEC isolates were identified by HEp-2 cell adherence assay and multiplex polymerase chain reaction (PCR). RESULTS: DEC pathogens were detected in 53.7% of 341 E. coli isolates. Enteroaggregative E. coli (EAEC) was the most common DEC pathotype identified by HEp-2 adherence assay and multiplex PCR methods in 97 samples (28.4%), followed by atypical enteropathogenic E. coli (EPEC) in 50 samples (14.7%), diffusely adherent E. coli (DAEC) in 25 samples (7.3%), enterohaemorrhagic E. coli (EHEC) in 6 samples (1.8%), enterotoxigenic E. coli (ETEC) in 4 samples (1.2%), and enteroinvasive E. coli (EIEC) in 1 sample (0.3%). DEC strains had > 50% antibiotic resistance against cephalothin, ampicillin, and trimethoprim/sulfamethoxazole. All tested DEC strains were susceptible to imipenem. Among the 183 DEC strains, 27 (14.8%) were extended spectrum beta-lactamase producing isolates, and 125 (68.3%) isolates were multiple drug resistant. CONCLUSIONS: We have identified six pathotypes of DEC from the clinical isolates tested and concluded that a high prevalence of antimicrobial resistance was observed in these pathotypes. EAEC was the most common pathotype identified and this is the first report of EHEC identification in Mongolia.

The effect of thymoquinone and propranolol combination on epidermoid laryngeal carcinoma cell.

PURPOSE: We aimed to evaluate the effects of thymoquinone and propranolol on Hep-2 cells representing laryngeal Ca cell type in comparison with cisplatin. We also evaluated their combined effects. METHODS: Apoptotic effects were directly analyzed via mitochondrial membrane potential and caspase-3 assays. In addition, effects on apoptosis and cell cycle via Bcl-2, Bax, P53, and Cyclin D1 mRNA expressions and effects on angiogenesis via VEGFA mRNA expression were evaluated by RT-qPCR. RESULTS: According to our results, it was determined that the anticancer effects of thymoquinone on Hep-2 cells were higher than propranolol. Our JC-1 and caspase-3 results showed an effect close to cisplatin, especially for 50 µM thymoquinone. Significant differences were also obtained in Bcl-2, Bax, P53, and cyclin D1 results for similar concentrations compared to the control. No effect of thymoquinone was seen for VEGFA. Propranolol alone had no significant effect on JC-1 and Caspase-3. Propranolol had an effect on Bcl-2, Bax mRNA expressions compared to the control, only at 250 µM concentration. Propranolol and its combinations increased VEGFA mRNA expression-like cisplatin. CONCLUSION: Thymoquinone induced apoptosis and blocked the cell cycle in Hep-2 cells. The effects of propranolol, which was reported to have an antiangiogenesis effect in some studies, on apoptosis and cell cycle were limited except at high concentrations. For this cell line, why propranolol causes an increase in VEGFA expression should be evaluated extensively. Thymoquinone shows promise for cancer therapy, but studies need to be designed in vivo to evaluate the effects more reliably.

SIFT-CNN: When Convolutional Neural Networks Meet Dense SIFT Descriptors for Image and Sequence Classification.

Despite the success of hand-crafted features in computer visioning for many years, nowadays, this has been replaced by end-to-end learnable features that are extracted from deep convolutional neural networks (CNNs). Whilst CNNs can learn robust features directly from image pixels, they require large amounts of samples and extreme augmentations. On the contrary, hand-crafted features, like SIFT, exhibit several interesting properties as they can provide local rotation invariance. In this work, a novel scheme combining the strengths of SIFT descriptors with CNNs, namely SIFT-CNN, is presented. Given a single-channel image, one SIFT descriptor is computed for every pixel, and thus, every pixel is represented as an M-dimensional histogram, which ultimately results in an M-channel image. Thus, the SIFT image is generated from the SIFT descriptors for all the pixels in a single-channel image, while at the same time, the original spatial size is preserved. Next, a CNN is trained to utilize these M-channel images as inputs by operating directly on the multiscale SIFT images with the regular convolution processes. Since these images incorporate spatial relations between the histograms of the SIFT descriptors, the CNN is guided to learn features from local gradient information of images that otherwise can be neglected. In this manner, the SIFT-CNN implicitly acquires a local rotation invariance property, which is desired for problems where local areas within the image can be rotated without affecting the overall classification result of the respective image. Some of these problems refer to indirect immunofluorescence (IIF) cell image classification, ground-based all-sky image-cloud classification and human lip-reading classification. The results for the popular datasets related to the three different aforementioned problems indicate that the proposed SIFT-CNN can improve the performance and surpasses the corresponding CNNs trained directly on pixel values in various challenging tasks due to its robustness in local rotations. Our findings highlight the importance of the input image representation in the overall efficiency of a data-driven system.

Role of curcumin in selected head and neck lesions. Limitations on the use of the Hep-2 cell line: A critical review.

Neoplastic diseases of the upper respiratory airways, as well as head and neck cancers, are a frequent cause of death and significantly affect the quality of life of both patients and survivors. As the frequency increases, new and improved treatment techniques are sought. Promising properties in this respect are expressed by a natural compound - curcumin. Along with its derivatives, it was found useful in the treatment of a series of cancers. Curcumin was found to be effective in clinical trials and in vitro, in vivo anticancer experiments. Nanoformulations (e.g., poly(lactide-co-glycolic acid)-based nanoparticles, nanoemulsions), and modifications of curcumin, as well as its combinations with other substances (e.g., catechins, cisplatin) or treatments (e.g., radiotherapy or local use in inhalation), were found to enhance the antitumor effect. This review aims to summarize the recent findings for the treatment of head and neck diseases, especially squamous cell carcinomas (HNSCCs), including drawing attention to the constant use of the misidentified Hep-2 cell line and proposing databases purposed at eliminating this problem. Moreover, this manuscript focuses on pointing out the molecular mechanisms of therapy that have been reached and emphasizing the shortcomings that still need to be addressed.

Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study.

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.

How to report the antinuclear antibodies (anti-cell antibodies) test on HEp-2 cells: guidelines from the ICAP initiative.

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.

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus.

BACKGROUND: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. METHODS: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. RESULTS: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (κ) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. CONCLUSIONS: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning.

In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods.

The clinical relevance of anti-dsDNA antibodies determined by the Elia™ dsDNA assay in patients with negative indirect immunofluorescence on the HEp-2 cell.

OBJECTIVES: Data on the clinical importance of the detection of anti-dsDNA antibodies in patients with negative indirect immunofluorescence on the HEp-2 cell (IIF) are sparse and are especially not available for all common commercially available assays. This study aimed to assess the clinical significance of anti-dsDNA antibodies determined by the Elia™ dsDNA assay in patients with negative IIF. METHODS: We retrospectively examined the medical records of 234 consecutive subjects with detectable anti-dsDNA antibodies determined by the Elia™ dsDNA assay. RESULTS: A total of 124 subjects with detectable anti-dsDNA autoantibodies were IIF-negative, but yielded positive or borderline results in the Elia™ CTD screen assay for antinuclear antibodies (ANA). Within this group, 6/49 IIF-negative patients (12%) with ANA-associated systemic autoimmune rheumatic disorders (AASARD) and 118/185 subjects (64%) with various other diseases (Non-AASARD) were identified. There was no statistically significant difference with regard to the concentrations of anti-dsDNA antibodies (p=0.53) between the AASARD and the Non-AASARD group. Within the AASARD group, four patients diagnosed with systemic lupus erythematosus (SLE, treated), discoid lupus erythematosus (untreated), indetermined connective tissue disease (untreated) and polymyositis (treated) had positive anti-dsDNA autoantibodies, whereas two patients with treated SLE, thereby one in remission, had borderline concentrations of anti-dsDNA antibodies. CONCLUSIONS: Our findings suggest that the detection of anti-dsDNA antibodies in IIF-negative patients can be of clinical relevance in some cases. Our results further support the combined use of IIF and solid-phase assays in screening algorithms for ANA, in order to avoid overlooking potentially important autoantibody entities.

A proteomic repertoire of autoantigens identified from the classic autoantibody clinical test substrate HEp-2 cells.

BACKGROUND: Autoantibodies are a hallmark of autoimmune diseases. Autoantibody screening by indirect immunofluorescence staining of HEp-2 cells with patient sera is a current standard in clinical practice. Differential diagnosis of autoimmune disorders is based on commonly recognizable nuclear and cytoplasmic staining patterns. In this study, we attempted to identify as many autoantigens as possible from HEp-2 cells using a unique proteomic DS-affinity enrichment strategy. METHODS: HEp-2 cells were cultured and lysed. Total proteins were extracted from cell lysate and fractionated with DS-Sepharose resins. Proteins were eluted with salt gradients, and fractions with low to high affinity were collected and sequenced by mass spectrometry. Literature text mining was conducted to verify the autoantigenicity of each protein. Protein interaction network and pathway analyses were performed on all identified proteins. RESULTS: This study identified 107 proteins from fractions with low to high DS-affinity. Of these, 78 are verified autoantigens with previous reports as targets of autoantibodies, whereas 29 might be potential autoantigens yet to be verified. Among the 107 proteins, 82 can be located to nucleus and 15 to the mitotic cell cycle, which may correspond to the dominance of nuclear and mitotic staining patterns in HEp-2 test. There are 55 vesicle-associated proteins and 12 ribonucleoprotein granule proteins, which may contribute to the diverse speckled patterns in HEp-2 stains. There are also 32 proteins related to the cytoskeleton. Protein network analysis indicates that these proteins have significantly more interactions among themselves than would be expected of a random set, with the top 3 networks being mRNA metabolic process regulation, apoptosis, and DNA conformation change. CONCLUSIONS: This study provides a proteomic repertoire of confirmed and potential autoantigens for future studies, and the findings are consistent with a mechanism for autoantigenicity: how self-molecules may form molecular complexes with DS to elicit autoimmunity. Our data contribute to the molecular etiology of autoimmunity and may deepen our understanding of autoimmune diseases.

Deep learning based HEp-2 image classification: A comprehensive review.

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, the methods are organized with a deep network usage based taxonomy. The core idea, notable achievements, and key strengths and weaknesses of each method are critically analyzed. Furthermore, a concise review of the existing HEp-2 datasets that are commonly used in the literature is given. The paper ends with a discussion on novel opportunities and future research directions in this field. It is hoped that this paper would provide readers with a thorough reference of this novel, challenging, and thriving field.

A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.

Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated task due to the heterogeneity of these cellular images. Hence, an automated classification scheme appears to be necessary. However, the majority of the available methods prefer to utilize the supervised learning approach for this problem. The need for thousands of images labelled manually can represent a difficulty with this approach. The first contribution of this work is to demonstrate that classifying HEp-2 cell images can also be done using the unsupervised learning paradigm. Unlike the majority of the existing methods, we propose here a deep learning scheme that performs both the feature extraction and the cells' discrimination through an end-to-end unsupervised paradigm. We propose the use of a deep convolutional autoencoder (DCAE) that performs feature extraction via an encoding-decoding scheme. At the same time, we embed in the network a clustering layer whose purpose is to automatically discriminate, during the feature learning process, the latent representations produced by the DCAE. Furthermore, we investigate how the quality of the network's reconstruction can affect the quality of the produced representations. We have investigated the effectiveness of our method on some benchmark datasets and we demonstrate here that the unsupervised learning, when done properly, performs at the same level as the actual supervised learning-based state-of-the-art methods in terms of accuracy.

Human platelet lysate efficiency, stability, and optimal heparin concentration required in culture of mammalian cells.

BACKGROUND: Fetal bovine serum (FBS) has been used to support the growth and proliferation of mammalian cells for decades. Owing to several risk factors associated with FBS, several trials have been conducted to evaluate substitutes to FBS with the same efficiency and the lower risk issues. METHODS: In this study, human platelet lysate (HPL) derived from activated human platelets was evaluated as an alternative to FBS due to the associated risk factors. To evaluate the efficiency of the preparation process, platelet count was performed before and after activation. The concentrations of several growth factors and proteins were measured to investigate HPL efficiency. HPL stability was studied at regular intervals, and optimal heparin concentration required to prevent gel formation in various media was determined. The biological activity of HPL and FBS was compared by evaluating the growth performance of Vero and Hep-2 cell lines. RESULTS: Result of platelet count assay revealed the efficiency of HPL preparation process. Growth factor concentrations in HPL were significantly higher than those in FBS, while the protein content of HPL was lower than that of FBS. Stability study data showed that the prepared HPL was stable for up to 15 months at -20℃. Ideal heparin concentration to be used in different media was dependent on calcium concentration. Results of cell viability assay showed that HPL was superior to FBS in supporting the growth and proliferation of Vero and Hep-2 cells. CONCLUSION: The HPL prepared by the mechanical activation of platelets may serve as an efficient alternative to FBS in cell culture process.

Blocking long noncoding RNA MALAT1 restrained the development of laryngeal and hypopharyngeal carcinoma.

PURPOSE: The long non-coding RNA MALAT1 is a predictive marker in several solid tumors with highly conserved sequences. However, the role of non-coding RNA in development of laryngeal or hypopharyngeal cancer remains unclear. METHODS: Tumor tissues and adjacent non-cancer tissues of 24 patients were collected. We detected the expression of MALAT1 in laryngeal cancer tissues and hypopharyngeal cancer tissues. Moreover, we developed a MALAT1 silencing model in human laryngeal tumor cells by transfecting MALAT1 small interfering RNA into human laryngeal carcinoma cell line Hep-2 and pharyngeal carcinoma cell line FaDu with Lipofectamine 2000 system. Cell cycle analysis, Cell Counting Kit-8 assay, Transwell assay, quantitative reverse transcription PCR, and wound-healing assays were performed to evaluate the impact of MALAT1 depletion on laryngeal or hypopharyngeal cancer cell's growth, proliferation, apoptosis, invasion and migration. RESULTS: MALAT1 was significantly up-regulated in laryngeal and hypopharyngeal carcinoma cells. MALAT1 down-regulation induced the increased apoptosis of both cell lines and suppressed cells' proliferation. Cells were arrested in G1/G2 phase and cells of S phase were significantly decreased. Down-regulation of MALAT1 expression can also inhibit the migration and invasion of laryngeal squamous cell carcinoma cell (Hep-2) and hypopharyngeal cancer cell (FaDu). CONCLUSION: In summary, our deactivation model of MALAT1 disentangled the active function of it as a regulator of gene expression governing the hallmarks of laryngeal and hypopharyngeal cancer. Blocking this long non-coding RNA may restrain the development of laryngeal cancer.

A multi-centre study for standardization of antinuclear antibody indirect immunofluorescence screening with automated system.

INTRODUCTION: ndirect immunofluorescence assay (IFA) using HEp-2 as substrate plays a consolidate role for the detection and measurement of ANA, which is currently considered as the reference method for detection. Manual operation is still very common in China, therefore, the need of standardization and automation for ANA-IFA detecting has been highlighted. OBJECTIVE: The current multi-center study is aimed to evaluate if HELIOS (AESKU Diagnostics, Wendelsheim, Germany) contributes to comparability of ANA screening results among different labs，and establish application specification of HELIOS for standardization of ANA detection. METHODS: ANA detection by manual IFA method and HELIOS on 230 clinical serum samples in eight laboratories. The performance to discriminate positive/negative screening results, endpoint titer estimation and pattern recognition were evaluated in HELIOS and manual visual. RESULTS: The positive coincident rate for ANA detection by manual IFA ranges from 87.7% to 97.8%, the negative coincidence rate ranges from 68.8% to 100%, the correctly estimated titer evaluation were 80 to 171 cases, the correct pattern in 146 to 161 cases, respectively. The positive coincident rate of HELIOS for ANA detection ranges from 91.2% to 97.7%, the negative coincidence rate ranges from 96.5% to 100%, the correctly estimated titer evaluation were 145 to 157 cases, the correct pattern in 123 to 140 cases, respectively. CONCLUSION: HELIOS could provide accurate diagnostic results, this include not only positive/negative results, but also endpoint titer, common patterns. The application of this system can help to promote standardization of ANA detection.