Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia.

Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.

Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study.

BACKGROUND: Pancreatic neuroendocrine tumors (PNETs) are one of the most common endocrine tumors, and liver metastasis (LMs) are the most common location of metastasis from PNETS; However, there is no valid nomogram to predict the diagnosis and prognosis of liver metastasis (LMs) from PNETs. Therefore, we aimed to develop a valid predictive model to aid physicians in making better clinical decisions. METHODS: We screened patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2010-2016. Feature selection was performed by machine learning algorithms and then models were constructed. Two nomograms were constructed based on the feature selection algorithm to predict the prognosis and risk of LMs from PNETs. We then used the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration plot and consistency index (C-index) to evaluate the discrimination and accuracy of the nomograms. Kaplan-Meier (K-M) survival curves and decision curve analysis (DCA) were also used further to validate the clinical efficacy of the nomograms. In the external validation set, the same validation is performed. RESULTS: Of the 1998 patients screened from the SEER database with a pathological diagnosis of PNET, 343 (17.2%) had LMs at the time of diagnosis. The independent risk factors for the occurrence of LMs in PNET patients included histological grade, N stage, surgery, chemotherapy, tumor size and bone metastasis. According to Cox regression analysis, we found that histological subtype, histological grade, surgery, age, and brain metastasis were independent prognostic factors for PNET patients with LMs. Based on these factors, the two nomograms demonstrated good performance in model evaluation. CONCLUSION: We developed two clinically significant predictive models to aid physicians in personalized clinical decision-makings.

Immune disguise: the mechanisms of Neu5Gc inducing autoimmune and transplant rejection.

Organ (stem cell) transplantation is the most effective treatment for advanced organ failure. Neu5Gc (N-hydroxyacetylneuraminic acid) is a pathogenic non-human sialic acid, which is very similar to the molecular structure of Neu5Ac (N-acetylneuraminic acid) in human body. Neu5Gc has the function of "immune disguise", which is the main obstacle to transplantation. Gene knockout such as cytidine monophosphate-N-acetylneuraminidase (CMAH) reduces donor antigenicity, making xenotransplantation from fiction to reality. Exploring the immune disguise event in this emerging field has become a hot topic in the research of transplantation immune tolerance mechanism.

Inguinal hernias in patients on continuous ambulatory peritoneal dialysis: is tension-free mesh repair feasible?

BACKGROUND: Continuous ambulatory peritoneal dialysis (CAPD), which often causes a common complication such as abdominal wall hernia, is a prevalent alternative therapy for end-stage renal failure patients. However, relevant studies are somewhat rare, and the peritoneal dialysis (PD) protocol during the perioperative period is still controversial. The aim of this study was to evaluate the effectiveness and perioperative management of tension-free mesh repair for inguinal hernias in CAPD patients. METHODS: Between January 2013 and December 2019, 18 CAPD patients with 20 inguinal hernias who underwent tension-free mesh repair were retrospectively analyzed. Data on demographics, perioperative features, the perioperative dialysis protocol and surgical complications were collected and assessed. RESULTS: All hernias were diagnosed after the start of CAPD, and the median duration from PD onset to hernia formation was 16 months (2-61 months). All patients underwent successful tension-free mesh repair, including 17 Lichtenstein and 3 anterior Kugel procedures. The median operation time was 62.5 min, and the median postoperative hospital stay was 3 days. Fifteen patients received low-exchange volumes and high-frequency exchanges from 1 to 3 days after surgery for 2 weeks with gradual resumption of the original CAPD regimen within 4 weeks. Complications included seroma (n = 2) and hematoma (n = 1). No wound or mesh infection or recurrence occurred during the follow-up period. CONCLUSIONS: Tension-free mesh repair is safe and feasible for inguinal hernias in CAPD patients, The Lichtenstein mesh repair should be the first choice, and anterior Kugel repair may be considered an effective procedure. Bridging hemodialysis seems unnecessary except for emergency surgery.

A preliminary multicenter evaluation of endoscopic sublay repair for ventral hernia from China.

BACKGROUND: For ventral hernia, endoscopic sublay repair (ESR) may overcome the disadvantages of open sublay and laparoscopic intraperitoneal onlay mesh repair. This retrospective study presents the preliminary multicenter results of ESR from China. The feasibility, safety, and effectiveness of ESR were evaluated; its surgical points and indications were summarized. METHODS: The study reviewed 156 ventral hernia patients planned to perform with ESR in ten hospitals between March 2016 and July 2019. Patient demographics, hernia characteristics, operative variables, and surgical results were recorded and analyzed. RESULTS: ESR was performed successfully in 153 patients, 135 with totally extraperitoneal sublay (TES) and 18 with transabdominal sublay (TAS). In 19 patients, TES was performed with the total visceral sac separation (TVS) technique, in which the space separation is carried out along the peritoneum, avoiding damage to the aponeurotic structure. Endoscopic transversus abdominis release (eTAR) was required in 17.0% of patients, and only 18.3% of patients required permanent mesh fixation. The median operative time was 135 min. Most patients had mild pain and resume eating soon after operation. No severe intraoperative complications occurred. Bleeding in the extraperitoneal space occurred in two patients and was stopped by nonsurgical treatment. Seroma and chronic pain were observed in 5.23 and 3.07% of patients. One recurrence occurred after TAS repair for an umbilical hernia. CONCLUSION: ESR is feasible, safe, and effective for treating ventral hernias when surgeons get the relevant surgical skills, such as the technique of "partition breaking," TVS, and eTAR. Small-to-medium ventral hernias are the major indications.

Quantitative proteomics suggest a potential link between early embryonic death and trisomy 16.

Activation of extracellular signal-regulated kinase (ERK) signalling, alteration of the uterine microenvironment and a reduction in human chorionic gonadotrophin production have been linked with fetal trisomy 16-induced early embryonic death (EED). However, the detailed biological mechanism of EED remains unclear. Using quantitative proteomics we successfully screened differentially expressed proteins in the villous tissues from patients with EED and fetal trisomy 16 (EEDT16), patients with EED but normal fetal chromosomes (EEDNC) and patients undergoing elective abortion with normal fetal chromosomes (EANC) as the reference group. Compared with the reference group, we identified 337 and 220 differentially expressed proteins in EEDT16 patients and EEDNC patients respectively; these were involved in critical biological processes including immune response, superoxide metabolism, inflammatory responses and so on. We found that differential expression of immunological function-related molecules, such as human leukocyte antigen-g (HLA-G), HLA-C, Fc Fragment Of IgG Receptor III (FcγR III), also named CD16, interleukin 18 (IL-18) and transforming growth factor ß1 (TGF-ß1), might induce EED in both EEDT16 and EEDNC patients. More severe immunological dysfunction was observed in EEDT16 patients than that in EEDNC patients. Furthermore, differential expression of implantation and invasion-related molecules, such as cytochrome b-245 light chain (CYBA), neutrophil cytosol factor 2 (NCF2), Mitogen-activated protein kinase kinase kinase 4 (MAP3K4), matrix metalloproteinase 2 (MMP2), MMP9 and tumour necrosis factor α (TNF-α) might induce EED in both EEDT16 and EEDNC patients, although more severe dysfunction in the implantation and invasion ability of villous tissues was observed in EEDT16 patients.

Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition.

Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research.

[Clinical value of human papillomavirus genotyping testing in the diagnosis of cervical intraepithelial neoplasia].

OBJECTIVE: To Investigate the value of HPV genotyping in diagnosis of cervical intraepithelial neoplasia (CIN). METHODS: From July 2012 to February 2013, 200 women from Tianjin Medical University General Hospital and 244 women from Affiliated Hospital of the Chinese People's Armed Forces Logistics College were selected to be HPV genotyping test and thin liqid-based cytology test. Consequently, 132 samples were performed colposcopy test and cervical biopsy. RESULTS: HPV prevalence was 26.4% (117/444) in this study. The infection of one type HPV was more common. The top 5 of HPV types were HPV16, 58, 33, 18, and 52. The top 5 of the risk for CIN II and above followed HPV16, 33, 39, 52 and 18. There was no significant difference between age and HPV positive rate (χ² = 0.948, P > 0.05). Multiple infection and cervical lesions rank correlation analysis (r = 0.132, P > 0.05). For CIN II and above disease, cytology positive rate was 90% (44/49), and HPV positive rate was 96% (47/49)cytology combine HPV positive rate was 98% (48/49, χ² = 0.063, P > 0.05). CONCLUSIONS: HPV infection should increasing trends with age. Cytology test and HPV genotyping test had good consistency. The combination of them can improve the sensitivity for high-grade lesions.

Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of the Dynamic Brain Functional Networks.

BACKGROUND: Alzheimer's Disease (AD), identified as the most common type of dementia, presents considerable heterogeneity in clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS: We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 AD patients, 441 MCI patients, and 1160 normal controls (NC). Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method and comprehensively assessed their similarity and differences. RESULTS: Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in both their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as well as those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and these MCI have a more severe cognitive decline rate. CONCLUSIONS: This study quantifies neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identifies disease subtypes in AD and MCI using a comprehensive resting-state fMRI multicenter dataset. It provides new insights for developing early prevention and personalized treatment strategies for AD.

Excellent Charge Separation of NCQDs/ZnS Nanocomposites for the Promotion of Photocatalytic H2 Evolution.

Carbon Quantum dots (CQDs) are widely studied because of their good optical and electronic characteristics and because they can easily generate photocarriers. Nitrogen-doped CQDs (NCQDs) may exhibit improved hydrophilic, optical, and electron-transfer properties, which are conducive to photocatalytic hydrogen evolution. In this paper, NCQD-modified ZnS catalysts were successfully prepared. Under the irradiation of the full spectrum, the H2 evolution rate of the optimal catalyst 0.25 wt % NCQDs/ZnS achieves 5.70 mmol g-1 h-1, which is 11.88, 43.84, and 5.14 times the values of ZnS (0.48 mmol g-1 h-1), NCQDs (0.13 mmol g-1 h-1), and CQDs/ZnS (1.11 mmol g-1 h-1), respectively. Furthermore, it shows good stability, indicating that the modification of NCQDs prevents the photocorrosion and oxidation of ZnS. The enhanced performance is due to NCQD loading, which promotes the separation of photogenerated carriers, optimizes the structures, and increases the specific surface area. This work highlights the fact that NCQD-modified ZnS may afford a new strategy to synthesize ZnS-based photocatalysts with enhanced H2 production performance.

Phase registration based on matching of phase distribution characteristics and its application in FDOCT.

Phase fluctuations in a two-transverse-dimensional scanning Fourier domain optical coherence tomography (FDOCT) seriously affect in vivo phase related applications. The phase difference between two A-scans sampled at the same scanning position or adjacent scanning position is acquired by matching of the phase distribution characteristics on the surface of two A-scans. Finger and palm scanning experiments are performed and defocused images of finger and palm are recovered based on Fresnel scalar diffraction algorithm by using phase compensated OCT complex signals. To further prove the performance of the proposed method, human eye scanning experiments are also performed and blood flow images of retina are extracted from the phase registration results. The accurate, fast and simple phase compensation method is critical for in vivo phase related applications.

Non-invasive detection of the content of white blood cells in the blood of humans based on dynamic spectrum.

Objective. Changes in white blood cell content have been shown to be useful in determining whether the body is in a healthy state. We propose an improved data processing and modeling approach, which helps to accommodate blood component content detection and improve prediction accuracy.Approach. In this experiment, the finger-end transmission method was used for spectral measurement, and we collected a total of 440 sample data. In this paper, we first use the method of CEEMDAN combined with wavelet threshold to denoise the PPG signal, and then use the integral method to extract the spectral features, which makes up for the defects of the single-edge method using incomplete data and the deviation of the slope of the rising segment from the actual signal. We further improve the screening of samples and wavelengths, and used PLS regression modeling combine the double nonlinear correction method to build the most stable and universal model.Main results. The model has been applied to 332 subjects' finger transmission spectral data to predict the concentration of leukocytes. The correlation coefficient of the final training set result was 0.927, and the root mean square error (RMSE) is 0.569×109l-1, the correlation coefficient of the prediction set result is 0.817, and the RMSE is 0.826×109l-1, which proves the practicability of the proposed method.Significance. We propose a non-invasive method for detecting leukocyte concentration in blood that can also be generalized to detect other blood components.

Adropin attenuates pancreatitis­associated lung injury through PPARγ phosphorylation­related macrophage polarization.

Acute pancreatitis (AP)­associated lung injury (ALI) is a critical complication of AP. Adropin is a regulatory protein of immune metabolism. The present study aimed to explore the immunomodulatory effects of adropin on AP­ALI. For this purpose, serum samples of patients with AP were collected and the expression levels of serum adropin were detected using ELISA. Animal models of AP and adropin knockout (Adro­KO) were constructed, and adropin expression in serum and lung tissues was investigated. The levels of fibrosis and apoptosis were evaluated using hematoxylin and eosin staining, Masson's staining and immunohistochemistry of in lung tissue. M1/M2 type macrophages in the lungs were detected using immunofluorescence staining, western blot analysis and reverse transcription­quantitative PCR. As shown by the results, adropin expression was decreased in AP. In the Adro­KO + L­arginine (L­Arg) group, macrophage infiltration, fibrosis and apoptosis were increased. The expression of peroxisome proliferator­ activated receptor γ (PPARγ) was downregulated, and the macrophages exhibited a trend towards M1 polarization in the Adro­KO + L­Arg group. Adropin exogenous supplement attenuated the levels of fibrosis and apoptosis in the model of AP. Adropin exogenous supplement also increased PPARγ expression by the regulation of the phosphorylation levels, which was associated with M2 macrophage polarization. On the whole, the findings of the present study suggest that adropin promotes the M2 polarization of lung macrophages and reduces the severity of AP­ALI by regulating the function of PPARγ through the regulation of its phosphorylation level.

Adropin deficiency worsens TNBS-induced colitis.

The aim of this study was to describe the effects of adropin deficiency on the distribution, phenotype and pathological phenotype of macrophages in colonic and mesenteric tissues of AdrKO (Enho-/-) mice, so as to explore the mechanism of adropin deficiency in spontaneous and experimental colitis. In this study, RNA-seq and metabonomics were used to screen the regulatory mechanism of adropin on the phenotypic transformation of macrophages. We found that adropin levels in active UC patients were significantly lower than those in normal subjects and remission UC patients, and at the same time, a large number of proinflammatory M1-type macrophages were infiltrated in the mesenteric tissue of colonic tissues from UC and CD patients. At the same time, spontaneous colitis occurred in Enho-/- (adropin-deficient)C57BL/6 mice, and there was an imbalance of M2 â†’ M1 polarization of macrophages in colon and mesentery of Enho-/- mice. In vivo, it has showed that adropin deficiency could exacerbate the pathological phenotype of colitis induced by TNBS. In vitro, adropin was used to intervene RAW264.7 macrophages, and then combined analysis of RNA-seq and metabolomics demonstrated that adropin regulated lipid metabolism of macrophages through PPARγ, thus promoting the repolarization of macrophages from M1 to M2. Adropin deficiency led to an imbalance in the phenotypic distribution of macrophages infiltrating the colon and mesenteric tissues, namely, an increase in M1 type, which led to the occurrence and development of colitis.

[Phase synchronization analysis of EEG signal during audio-visual stimulation].

EEG Synchronization is considered the conformity of the brain functional areas. Advanced brain function requires many nervous systems with a specific function in relevant brain regions (areas) to achieve integration and coordination at different levels. In this paper, a new method for phase synchronization analysis-Mutually Approximate Entropy is proposed to process different frequency band of EEG signal during audio-visual stimulation and get Similar results with the method of Synchronization Index and Mutual Information Entropy. This showed that the Mutually Approximate Entropy can lead to a good indication of the phase synchronization between two leads. The paper also explored the brain reaction zone by the results of the phase synchronization analysis. The research work lays the foundation for the brain-computer interface design.

Two-point Fixation for Biological Mesh in Laparoscopic Inguinal Hernia Repair.

OBJECTIVE: To evaluate the clinical outcomes and safety of two-point fixation for biological mesh in laparoscopic totally extraperitoneal (TEP) inguinal hernia repair. STUDY DESIGN: Case series. PLACE AND DURATION OF STUDY: The first Affiliated Hospital of Fujian Medical University Hospital, Fuzhou, China, from January to December 2019. METHODOLOGY: A total of 38 patients with a primary inguinal hernia who underwent laparoscopic TEP repair with a small intestine submucosal matrix biological mesh were included. A novel two-point fixation method was performed at the level of 2 cm above the upper margin of the hernia ring. The mesh was fixed at 3 cm medial and lateral to the inferior epigastric artery. The recurrence rate, surgical site infection rate, postoperative chronic pain, hematoma/seroma, and chronic pain were evaluated. RESULTS: There was no conversion to open procedure. The surgical time was 60.0 (range 35-72) min, and the time of mesh fixation was 4.00 (range 2.5-6.0) minutes. All patients were discharged on the first postoperative day and had similar pain scores (VAS score = 1). Hematoma/seroma was detected in only three (7.9%) patients. No infection or recurrence was observed. CONCLUSION: The two-point fixation for biological mesh is reliable and easy to perform. Further study with a larger sample size may be needed to validate it. KEY WORDS: Inguinal hernia, Laparoscopy, Biological mesh, Surgical mesh, Herniorrhaphy, Two-point fixation.

Noninvasive detection and analysis of human globulin based on dynamic spectrum.

Noninvasive detection of blood components is the most ideal and effective method to prevent and detect many clinical diseases. However, the accuracy of noninvasive detection based on the spectrum is not always satisfactory. The influence of various interferences in measurement limits the accuracy of the analysis. The dynamic spectrum theory can theoretically eliminate the individual differences and measurement environment influence and improve measurement accuracy. The concentration of globulin is closely related to the status of the immune system, which is of great significance for clinical diagnosis. This paper improves the signal-to-noise ratio from all links of dynamic spectrum data processing to realize the noninvasive detection of globulin. Through reasonable pretreatment, extraction, quality evaluation, and variable screening, the valid information of the spectrum gets maximum utilization. Finally, using the partial least squares prediction model to predict globulin concentration. The results show that the model established by dynamic spectrum treated by this method has a good predictive performance for globulin. The correlation coefficient of the prediction set is 0.962, the root-mean-square error of the prediction set is only 1.058 g/L, the correlation coefficient of the calibration set is 0.996, and the root-mean-square error of the calibration set is 0.332 g/L. The experimental results show that reasonable data processing of dynamic spectrum can effectively improve the signal-to-noise ratio of the data, make the established model have good prediction accuracy and performance, and realize the high-precision prediction globulin. This paper provides a complete research idea and method for the noninvasive detection of blood components. It is hopeful to realize the noninvasive quantitative detection of trace components in blood.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.

Prognostic value of neutrophil-to-lymphocyte ratio in colorectal cancer liver metastasis: A meta-analysis of results from multivariate analysis.

We aimed to evaluate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) in colorectal cancer liver metastasis (CRLM). A comprehensive search was conducted across PubMed, MedLine, and the Cochrane Library databases for articles published from January 1, 2000 to April 30, 2022 that investigated the long-term prognostic value of NLR in CRLM; only studies with multivariate analyses were enrolled. Hazard ratio (HR) with 95% confidence interval (CI) was used to determine the effect size. A total of 2,522 patients in 12 studies were selected; the meta-analysis demonstrated that elevated NLR correlated with poor overall survival (OS) and disease-free survival (DFS) (HR, 1.95, 95%CI, 1.698-2.223, P < 0.01; HR, 1.80, 95%CI, 1.363-2.363, P < 0.01; respectively). The 5-year OS and disease-free survival rates were higher in the patients with normal NLR than in patients with high NLR (47% vs. 27%, P < 0.01; 37% vs. 6%, P < 0.01, respectively). Further analysis of clinicopathological parameters indicated no significant difference between the patients with and without elevated NLR. Begg's and Egger's tests for publication bias revealed no significant publication bias (P = 0.891 and P = 0.926, respectively). Multivariate analysis revealed that NLR had an excellent prognostic ability in CRLM, which can be used in deciding the treatment and predicting the clinical outcomes. Further multicenter randomized controlled trials are required to verify this conclusion.

Automatic estimation of point-spread-function for deconvoluting out-of-focus optical coherence tomographic images using information entropy-based approach.

This paper proposes an automatic point spread function (PSF) estimation method to de-blur out-of-focus optical coherence tomography (OCT) images. The method utilizes Richardson-Lucy deconvolution algorithm to deconvolve noisy defocused images with a family of Gaussian PSFs with different beam spot sizes. Then, the best beam spot size is automatically estimated based on the discontinuity of information entropy of recovered images. Therefore, it is not required a prior knowledge of the parameters or PSF of OCT system for de-convoluting image. The model does not account for the diffraction and the coherent scattering of light by the sample. A series of experiments are performed on digital phantoms, a custom-built phantom doped with microspheres, fresh onion as well as the human fingertip in vivo to show the performance of the proposed method. The method may also be useful in combining with other deconvolution algorithms for PSF estimation and image recovery.