Identifying the genetic association between the cerebral cortex and fibromyalgia.

Fibromyalgia (FM) is a central sensitization syndrome that is strongly associated with the cerebral cortex. This study used bidirectional two-sample Mendelian randomization (MR) analysis to investigate the bidirectional causality between FM and the cortical surface area and cortical thickness of 34 brain regions. Inverse variance weighted (IVW) was used as the primary method for this study, and sensitivity analyses further supported the results. The forward MR analysis revealed that genetically determined thinner cortical thickness in the parstriangularis (OR = 0.0567 mm, PIVW = 0.0463), caudal middle frontal (OR = 0.0346 mm, PIVW = 0.0433), and rostral middle frontal (OR = 0.0285 mm, PIVW = 0.0463) was associated with FM. Additionally, a reduced genetically determined cortical surface area in the pericalcarine (OR = 0.9988 mm2, PIVW = 0.0085) was associated with an increased risk of FM. Conversely, reverse MR indicated that FM was associated with cortical thickness in the caudal middle frontal region (ß = -0.0035 mm, PIVW = 0.0265), fusiform region (ß = 0.0024 mm, SE = 0.0012, PIVW = 0.0440), the cortical surface area in the supramarginal (ß = -9.3938 mm2, PIVW = 0.0132), and postcentral regions (ß = -6.3137 mm2, PIVW = 0.0360). Reduced cortical thickness in the caudal middle frontal gyrus is shown to have a significant relationship with FM prevalence in a bidirectional causal analysis.

Altered whole brain functional activity in patients with fibromyalgia.

OBJECTIVES: Fibromyalgia (FM) is a chronic pain disorder that takes a severe physical and psychological toll on patients and severely reduces their quality of life. In recent years, an increasing number of studies have used functional magnetic resonance imaging (fMRI) to investigate its pathogenesis. However, a recent summary analysis of functional connectivity in patients with FM is lacking. METHODS: We searched bibliographic databases, including PubMed, Web of Science (from inception until September 1st, 2022). Two separate researchers assessed the bias and quality of the studies. In order to further explain the core mechanism for FM, the abnormal brain function of FM was investigated by Activation Likelihood Estimation (ALE) analysis. RESULTS: Twenty-six FM publications (1,056 subjects) were eligible to be included in an ALE analysis. We found that the anterior cingulate (ACC) and insula (Ins) were abnormally active in patients with FM. In particular, the peak coordinates of (8,46,4) and (-46, -4,10) correspond to brain regions that were less active than healthy individuals. Furthermore, the Z-values were 4.46 and 4.97, while the p-values were 4.06 and 3.38. Surprisingly, we found that the degree of pain was negatively correlated with the activation of Ins (SDM-Z = -2.714). CONCLUSIONS: This study demonstrates abnormal brain activation which could lead to increased sensitivity of pain in patients with FM. The study sheds light on the central mechanisms of FM and provides the basis for further research.

Establishment and validation of a nomogram to predict postoperative anemia after total hip arthroplasty.

BACKGROUND: Anemia is a common complication of total hip arthroplasty (THA). In this study, we evaluated the preoperative risk factors for postoperative anemia after THA and developed a nomogram model based on related preoperative and intraoperative factors. METHODS: From January 2020 to May 2023, 927 THA patients at the same medical center were randomly assigned to either the training or validation cohort. The correlation between preoperative and intraoperative risk factors and postoperative anemia after THA was evaluated using univariate and multivariate logistic regression analysis. A nomogram was developed using these predictive variables. The effectiveness and validation for the clinical application of this nomogram were evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: Through univariate and multivariate logistic regression analysis, 7 independent predictive factors were identified in the training cohort: Lower body mass index (BMI), extended operation time, greater intraoperative bleeding, lower preoperative hemoglobin level, abnormally high preoperative serum amyloid A (SAA) level, history of cerebrovascular disease, and history of osteoporosis. The C-index of the model was 0.871, while the AUC indices for the training and validation cohorts were 84.4% and 87.1%, respectively. In addition, the calibration curves of both cohorts showed excellent consistency between the observed and predicted probabilities. The DCA curves of the training and validation cohorts were high, indicating the high clinical applicability of the model. CONCLUSIONS: Lower BMI, extended operation time, increased intraoperative bleeding, reduced preoperative hemoglobin level, elevated preoperative SAA level, history of cerebrovascular disease, and history of osteoporosis were seven independent preoperative risk factors associated with postoperative anemia after THA. The nomogram developed could aid in predicting postoperative anemia, facilitating advanced preparation, and enhancing blood management. Furthermore, the nomogram could assist clinicians in identifying patients most at risk for postoperative anemia.

68Ga-cyc-DX600 PET/CT in ACE2-targeted tumor imaging.

PURPOSE: For the tumor-specific ACE2 expression, this research aimed to establish and verify ACE2-targeted PET imaging in differentiating tumors with distinct ACE2 expression. METHODS: 68Ga-cyc-DX600 was synthesized as tracer of ACE2 PET. NOD-SCID mice were used to prepare the subcutaneous tumor models with HEK-293 or HEK-293T/hACE2 cells to verify ACE2 specificity, with other kinds of tumor cells to evaluate the diagnostic efficiency for ACE2 expression, additionally, immunohistochemical analysis and western blot were used to certify the findings on ACE2 PET, which was then performed on four cancer patients and compared with FDG PET. RESULTS: The metabolic clearance of 68Ga-cyc-DX600 was initially completed in 60 min, realizing an ACE2-dependent and organ-specific background of ACE2 PET; meanwhile, tracer uptake of subcutaneous tumor models was of a definite dependence on ACE2 expression (r = 0.903, p < 0.05), and the latter served as the primary factor when ACE2 PET was used for the differential diagnosis of ACE2-related tumors. In pre-clinical practice, a comparable tumor-to-background ratio was acquired in ACE2 PET of a lung cancer patient at 50 and 80 min post injection; the quantitative values of ACE2 PET and FDG PET were negatively correlated (r = - 0.971 for SUVmax, p = 0.006; r = - 0.994 for SUVmean, p = 0.001) in an esophageal cancer patient, no matter the primary lesion or metastasis. CONCLUSIONS: 68Ga-cyc-DX600 PET was an ACE2-specific imaging for the differential diagnosis of tumors and added complementary value to conventional nuclear medicine diagnosis, such as FDG PET on glycometabolism.

Retinal layers changes in patients with age-related macular degeneration treated with intravitreal anti-VEGF agents.

BACKGROUND: The purpose of this study was to investigate retinal layers changes in patients with age-related macular degeneration (AMD) treated with anti-vascular endothelial growth factor (anti-VEGF) agents and to evaluate if these changes may affect treatment response. METHODS: This study included 496 patients with AMD or PCV who were treated with anti-VEGF agents and followed up for at least 6 months. A comprehensive analysis of retinal layers affecting visual acuity was conducted. To eliminate the fact that the average thickness calculated may lead to differences tending to converge towards the mean, we proposed that the retinal layer was divided into different regions and the thickness of the retinal layer was analyzed at the same time. The labeled data will be publicly available for further research. RESULTS: Compared to baseline, significant improvement in visual acuity was observed in patients at the 6-month follow-up. Statistically significant reduction in central retinal thickness and separate retinal layer thickness was also observed (p < 0.05). Among all retinal layers, the thickness of the external limiting membrane to retinal pigment epithelium/Bruch's membrane (ELM to RPE/BrM) showed the greatest reduction. Furthermore, the subregional assessment revealed that the ELM to RPE/BrM decreased greater than that of other layers in each region. CONCLUSION: Treatment with anti-VEGF agents effectively reduced retinal thickness in all separate retinal layers as well as the retina as a whole and anti-VEGF treatment may be more targeted at the edema site. These findings could have implications for the development of more precise and targeted therapies for AMD treatment.

Effect of weight loss on erectile function in men with overweight or obesity: A meta-analysis of randomised controlled trials.

Obesity and overweight are independent risk factors of erectile dysfunction (ED). It is controversial whether weight loss can improve erectile function. We thereby performed this meta-analysis to clarify the therapeutic effect of weight loss on erectile function in overweight or obese men. Literature search was conducted on databases including Cochrane Library, Embase, Web of Science and PubMed to obtain all relevant English articles published before March 1, 2021. The primary outcome was final International Index of Erectile Function (IIEF) score or change in IIEF score. The secondary outcome was final body weight and body mass index (BMI) or change in body weight and BMI. After screening, 5 studies with 619 participants were enrolled in our meta-analysis. Our result showed that the mean difference in body weight between weight loss group and control group was -18.07 kg with p < .01, and the mean difference in BMI was -9.6 kg/m2 with p < .01. The mean difference of IIEF between weight loss group and control group was 1.99 with p < .01. This meta-analysis showed that weight loss could improve erectile function in overweight or obese men. Losing weight could serve as an adjuvant therapy for ED.

Probiotics ameliorates glycemic control of patients with diabetic nephropathy: A randomized clinical study.

OBJECTIVE: This research aimed to explore the effects of probiotic administration on glycemic control and renal function in patients with diabetic nephropathy (DN). METHODS: The 101 participants were randomly divided into two treatment groups and 76 patients were included in the final analysis. In 76 patients with diabetic nephropathy of type 2 diabetes, a randomized double-blind and placebo-controlled clinical trial was conducted to evaluate the administration of 3.2 × 109  CFU probiotic supplements per day (Bifidobacterium bifidum, 1.2 × 109  CFU, Lactobacillus acidophilus 4.2 × 109  CFU, Streptococcus thermophilus 4.3 × 109  CFU) for 12 weeks on glycemic control of patients, including fasting blood glucose, 2 h postprandial blood glucose, glycosylated hemoglobin (HbA1c), microalbuminuria/creatinine (mAlb/Cr) and estimated glomerular filtration rate (eGFR) levels. The placebo group daily received empty capsules filled with starch. RESULTS: After 12 weeks, the administration of probiotics demonstrated a significant reduction in fasting blood glucose (10.68 ± 3.24 mmol/L before vs. 7.81 ± 2.77 mmol/L after, p < 0.05), HbA1c (8.19 ± 1.60% before vs. 7.32 ± 1.20% after, p < 0.05) and mAlb/Cr (101.60 ± 22.17 mg/g before vs. 67.53 ± 20.11 mg/g after, p < 0.05), while only mAlb/Cr level was significantly lower in the probiotic group than in the placebo group after intervention (67.53 ± 20.11 mg/g vs. 87.71 ± 23.01, p < 0.05). Meanwhile, there was no significant reduction of 2 h postprandial blood glucose level (18.95 ± 5.23 mmol/L vs. 17.35 ± 6.28 mmol/L, p = 0.24) and eGFR (84.34 ± 6.97 ml/min vs. 82.8 ± 8.72 ml/min, p = 0.45) in patients before and after probiotic intake. In addition, the placebo group failed to show any significant change of these parameters. CONCLUSION: This clinical study revealed probiotic administration could ameliorate glycemic control of patients with diabetic nephropathy, potentiating its therapeutic potential in clinical application.

Role of JAK2 in the Pathogenesis of Diabetic Erectile Dysfunction and an Intervention With Berberine.

BACKGROUND: Oxidative stress is a significant contributor to the poor treatment efficacy on erectile dysfunction induced by diabetes mellitus (DMED). Thus, understanding the mechanism underlying oxidative stress will aid in the identification of novel therapeutic targets. AIM: To define the role of Janus kinase 2 (JAK2) in mediating oxidative stress in the corpus cavernosum smooth muscle cells (CCSMCs) and to investigate the therapeutic effect of monomeric berberine (BB), which inhibits JAK2, in the pathogenesis of DMED. METHODS: Streptozotocin was used to establish type I diabetic rat models and apomorphine tests were conducted to determine DMED rats. Eighteen DMED rats were divided into the DMED group and the DMED+BB group, whereas another 10 age-matched rats formed the control group. CCSMCs were isolated from the corpus cavernosum of rats and were treated with the JAK2 inhibitor alpha cyanano-(3,4-hydroxyl)N-benzophenylamine (AG490) and/or BB. OUTCOMES: Metabolic parameters; erectile function; histologic and molecular alterations. RESULTS: Erectile function was impaired and excessive oxidative stress was found in the DMED group. Excessive oxidative stress led to decreased expression level of phosphorylated endothelial nitric oxide synthase at serine 1177/endothelial nitric oxide synthase and increased expression level of Ras homolog gene family and Rho kinase 1/2. Meanwhile, the relative expression ratio of phosphorylated JAK2/JAK2 was significantly greater in the DMED group than that in the other groups. In vitro, oxidative stress was significantly reduced along with reduced intracellular calcium upon treatment with the JAK2 inhibitor, AG490. Moreover, the CCSMCs treated with BB showed changes similar to those upon treatment with AG490. In vivo experiments also confirmed that the erectile function of the DMED+BB group was improved, accompanied by decreased phosphorylated JAK2/JAK2 and decreased oxidative stress. CLINICAL TRANSLATION: JAK2 can be used as a therapeutic target and BB can be used as a potential drug for the clinical treatment of DMED. STRENGTHS AND LIMITATIONS: This study examines the promoting effect of JAK2 on oxidative stress occurrence in the corpus cavernosum and on the development of DMED in both animal experiments and cell experiments, as well evaluates the inhibitory effect of BB on JAK2 and its therapeutic effect on DMED. The main limitation of our current study is the lack of an appropriate means for activating JAK2. CONCLUSIONS: JAK2 can induce DMED by enhancing oxidative stress and BB can play a role in treating DMED by inhibiting JAK2 and reducing oxidative stress. Our study provides an option and an idea for further studies on the pathogenesis and treatment of DMED. Song J, Tang Z, Li H, et al. Role of JAK2 in the Pathogenesis of Diabetic Erectile Dysfunction and an Intervention With Berberine. J Sex Med 2019;16:1708-1720.

Comparison of intracytoplasmic sperm injection outcome with fresh versus frozen-thawed testicular sperm in men with nonobstructive azoospermia: a systematic review and meta-analysis.

PURPOSE: The purpose of the study is to explore testicular sperm cryopreservation in patients with nonobstructive azoospermia (NOA) whether affect the outcome of subsequent intracytoplasmic sperm injection (ICSI). METHODS: A systematic review and meta-analysis was conducted by searching the MEDLINE and EMBASE databases for relevant published studies in English language (1997-2017). Studies were eligible if they included the comparison of using fresh and frozen-thawed testicular sperm followed by ICSI. Two reviewers independently performed data extraction, quality assessment and assessed the risk of bias. The overall summary risk estimated the number of events. A meta-analysis was conducted using a random effects or fixed effects model analysis according to the test of heterogeneity. RESULTS: A total of 17 studies with 1,261 ICSI cycles were identified. Analysis of the present data showed no difference in the fertilization outcome when comparing fresh versus frozen-thawed spermatozoa (RR = 1.02, 95% CI 0.86-1.09). Similarly, no difference in CR (RR = 1.01, 95% CI 0.96-1.05), good embryo rate (RR = 1.01, 95% CI 0.95-1.09), and IR (RR = 0.93, 95% CI 0.66-1.30) was observed if the spermatozoa was fresh or frozen-thawed. Finally, no difference in CPR or LBR was noted when using fresh or frozen-thawed cycles were analyzed separately (RR = 1.03, 95% CI 0.86-1.24; RR 1.11, 95% CI 0.88-1.41, respectively). CONCLUSIONS: In men with NOA, the ICSI outcome is not affected by whether the retrieved testicular sperm is fresh or frozen. Sperm cryopreservation ought to be considered in every surgical sperm retrieval case, which remain feasible even in patients with few testicular sperm retrieved.

An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images.

Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.

Identification and characterization of a novel Toll-like receptor 4 homologue in blunt snout bream, Megalobrama amblycephala.

Toll-like receptors (TLRs) are central players in the innate immune system in response to a wide range of pathogen infection. Among various TLRs, TLR4 plays a key role in recognition of bacterial lipopolysaccharides (LPS). In the present study, we identified and characterized a novel TLR4 homologue (maTLR4b) in blunt snout bream (Megalobrama amblycephala) which was significantly distinct from previously reported M. amblycephala TLR4 (tentatively named maTLR4a). The results showed that the complete cDNA sequence of maTLR4b was 3261 bp with an open reading frame encoding a polypeptide of 820 amino acids, and that its genomic sequence was 3793 bp, which had 3 exons. Structurally, the deduced maTLR4b protein showed a typical TLR domain architecture, including a signal peptide, eight leucine-rich repeats (LRRs) in the extracellular region, a transmembrane domain, and a Toll-Interleukin 1 receptor (TIR) domain in the cytoplasmic region. Phylogenetic analysis revealed that all TLR4s from teleost fish formed a monophyletic clade. Both maTLR4a and maTLR4b were divided into two distinct branches, and showed the highest level of similarity with the grass carp TLR4.2 and TLR4.4 homologue, respectively. MaTLR4b was constitutively expressed in all healthy tissues tested although at different levels. After LPS stimulation, the expression levels were significantly up-regulated in spleen, and peaked at 4 h between maTLR4a and maTLR4b, but with a distinct and complementary expression patterns. Taken together, these results suggested that maTLR4b is indeed a functional homologue of TLR4 in other species, which may play vital role in innate immune.

Suppressing label noise in medical image classification using mixup attention and self-supervised learning.

Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevitably introduced in the medical image annotation, as the labeling process heavily relies on the expertise and experience of annotators. Meanwhile, DNNs suffer from overfitting noisy labels, degrading the performance of models. Therefore, in this work, we innovatively devise a noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification. Specifically, we incorporate contrastive learning and intra-group mixup attention strategies into vanilla supervised learning. The contrastive learning for feature extractor helps to enhance visual representation of DNNs. The intra-group mixup attention module constructs groups and assigns self-attention weights for group-wise samples, and subsequently interpolates massive noisy-suppressed samples through weighted mixup operation. We conduct comparative experiments on both synthetic and real-world noisy medical datasets under various noise levels. Rigorous experiments validate that our noise-robust method with contrastive learning and mixup attention can effectively handle with label noise, and is superior to state-of-the-art methods. An ablation study also shows that both components contribute to boost model performance. The proposed method demonstrates its capability of curb label noise and has certain potential toward real-world clinic applications.

ResNeXt-CC: a novel network based on cross-layer deep-feature fusion for white blood cell classification.

Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5-20.43% accuracy, 3.6-23.56% F1-score, 3.5-25.71% AUROC and 8.1-36.98% specificity, respectively.

SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation.

Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of ß cells under high insulin demand.

Microplastics as potential barriers to ultraviolet light emitting diode inactivation of MS2 bacteriophage: Influence of water-quality parameters.

Microplastics have emerged as a concerning contaminant in drinking water sources, potentially interacting with pathogenic microorganisms and affecting the disinfection processes. In this study, MS2 was selected as an alternative for the human enteric virus. The influence of microplastics polyvinylchloride (MPs-PVC) on ultraviolet light emitting diode (UV-LED) inactivation of MS2 was investigated under various water chemistry conditions, such as MPs-PVC concentration, pH, salinity, and humic acid concentration. The results revealed that higher concentrations of MPs-PVC led to the reduced inactivation of MS2 by decreased UV transmittance, hindering the disinfection process. Additionally, the inactivation efficiency of MS2 in the presence of MPs-PVC was influenced by pH, and acidic solution (pH at 4, 5, and 6) exhibited higher efficiency compared to alkaline solution (pH at 8 and 9) and neutral solution (pH at 7). The low Na+ concentrations (0-50 mM) had a noticeable effect on MS2 inaction efficiency in the presence of MPs-PVC, while the addition of Ca2+ posed an insignificant effect due to the preferential interaction with MPs-PVC. Furthermore, the inactivation rate of MS2 initially increased and then decreased with increasing the concentration of humic acid, which was significantly different without MPs-PVC. These findings shed light on the complex interactions between MPs-PVC and MS2 in the UV-LED disinfection process under various water-quality parameters, contributing to drinking water safety and treatment.

GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy.

Early-stage diabetic retinopathy (DR) presents challenges in clinical diagnosis due to inconspicuous and minute microaneurysms (MAs), resulting in limited research in this area. Additionally, the potential of emerging foundation models, such as the segment anything model (SAM), in medical scenarios remains rarely explored. In this work, we propose a human-in-the-loop, label-free early DR diagnosis framework called GlanceSeg, based on SAM. GlanceSeg enables real-time segmentation of MA lesions as ophthalmologists review fundus images. Our human-in-the-loop framework integrates the ophthalmologist's gaze maps, allowing for rough localization of minute lesions in fundus images. Subsequently, a saliency map is generated based on the located region of interest, which provides prompt points to assist the foundation model in efficiently segmenting MAs. Finally, a domain knowledge filtering (DKF) module refines the segmentation of minute lesions. We conducted experiments on two newly-built public datasets, i.e., IDRiD and Retinal-Lesions, and validated the feasibility and superiority of GlanceSeg through visualized illustrations and quantitative measures. Additionally, we demonstrated that GlanceSeg improves annotation efficiency for clinicians and further enhances segmentation performance through fine-tuning using annotations. The clinician-friendly GlanceSeg is able to segment small lesions in real-time, showing potential for clinical applications.

Cross-modal attention network for retinal disease classification based on multi-modal images.

Multi-modal eye disease screening improves diagnostic accuracy by providing lesion information from different sources. However, existing multi-modal automatic diagnosis methods tend to focus on the specificity of modalities and ignore the spatial correlation of images. This paper proposes a novel cross-modal retinal disease diagnosis network (CRD-Net) that digs out the relevant features from modal images aided for multiple retinal disease diagnosis. Specifically, our model introduces a cross-modal attention (CMA) module to query and adaptively pay attention to the relevant features of the lesion in the different modal images. In addition, we also propose multiple loss functions to fuse features with modality correlation and train a multi-modal retinal image classification network to achieve a more accurate diagnosis. Experimental evaluation on three publicly available datasets shows that our CRD-Net outperforms existing single-modal and multi-modal methods, demonstrating its superior performance.

UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-Scale Generation and Registration Enhancement.

Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies.

Multi-Learner Based Deep Meta-Learning for Few-Shot Medical Image Classification.

Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost of establishing high-quality medical datasets. Many FSL approaches have been proposed in natural image scenes. However, present FSL methods are rarely evaluated on medical images and the FSL technology applicable to medical scenarios need to be further developed. Meta-learning has supplied an optional framework to address the challenging FSL setting. In this paper, we propose a novel multi-learner based FSL method for multiple medical image classification tasks, combining meta-learning with transfer-learning and metric-learning. Our designed model is composed of three learners, including auto-encoder, metric-learner and task-learner. In transfer-learning, all the learners are trained on the base classes. In the ensuing meta-learning, we leverage multiple novel tasks to fine-tune the metric-learner and task-learner in order to fast adapt to unseen tasks. Moreover, to further boost the learning efficiency of our model, we devised real-time data augmentation and dynamic Gaussian disturbance soft label (GDSL) scheme as effective generalization strategies of few-shot classification tasks. We have conducted experiments for three-class few-shot classification tasks on three newly-built challenging medical benchmarks, BLOOD, PATH and CHEST. Extensive comparisons to related works validated that our method achieved top performance both on homogeneous medical datasets and cross-domain datasets.

Arthroscopic vs. open Broström-Gould for repairing anterior talofibular ligament: mid-term outcomes comparison.

Introduction: There have been few mid-term follow-up studies comparing arthroscopic and open Broström-Gould repair of the anterior talofibular ligament (ATFL). The purpose of this study was to evaluate the mid-term therapeutic effectiveness of arthroscopic ATFL repair with open Broström-Gould repair for chronic lateral ankle instability. Methods: We retrospectively reviewed the database of patients with chronic lateral ankle instability undergoing repair of the ATFL from June 2014 to June 2018. The choice of surgical approach will depend on computer-generated randomization. In total, 49 patients underwent the arthroscopic Brostrom-Gould technique (group AB), while the other 50 patients underwent the open Broström-Gould technique (group OB). The surgery duration, hospitalization time, postoperative complications, the preoperative/postoperative manual anterior drawer test (ADT), Visual analog scale (VAS) scores, American Orthopaedic Foot & Ankle Society (AOFAS) scores, Karlsson-Peterson (K-P) scores, and Tegner activity scores were collected for comparative analysis during the follow-up period of 48 months. Results: At the final follow-up, the clinical outcomes, including ADT, VAS, AOFAS, K-P, and Tegner activity scores, were significantly improved after either arthroscopic or open treatment. Specifically, the AOFAS and K-P scores in the group AB were significantly higher than those in the group OB at 6 months post-surgery (P < 0.05). Additionally, there were no significant differences in other clinical outcomes and postoperative complications between the two groups. Conclusions: Arthroscopic has predictable and good mid-term results after ATFL and may be a secure and effective alternative to open Broström-Gould repair.