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. The code is accessible at https://github.com/Tinysqua/UWAFA-GAN.

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.

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.

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.

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.

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.

Research trends and hotspots in prostate cancer associated exosome: a bibliometric analysis.

Background: Prostate cancer is viewed as the second most common cancer in men worldwide. In our study, we used bibliometric analysis to construct a visual map of the relationship between prostate cancer and exosomes with the intent of uncovering research trends and current hotspots in this field. Method: We searched the Web of Science Core Collection for all publications in the prostate cancer associated with exosome field came out since 2010. With the assistance of bibliometric analysis software such as VOSviewer and CiteSpace, we conducted data extraction and analysis for countries/regions, institutions, authors, journals, references and keywords. Results: A bibliometric analysis of 990 publications was performed. Since 2010, the published quantity and cited frequency of the prostate cancer-associated exosome field have revealed an increasing tendency. In this field, we visualized the research trends by the means of analyzing the references and keywords. We obtained the statistical data: the total citations of publications have increased to 55,462, the average citation per article has reached 55.3 times, and the H-index has amounted to 110. Our findings supported that USA, China and Italy rank the top countries with both the maximum publications and strongest cooperations. Harvard Medical School, Cedars Sinai Med Ctr, Johns Hopkins University, are top institutions in the center of research as they are held to be. Thery C, Skog J and Taylor DD are the leading and outstanding professors and researchers. And top journals like Prostate, Plos One and Journal of Extracellular Vesicle expressed keen interests in this field. Based on our analysis and research, we believe that this field is attracting more and more attention and will focus on tumor bone metastasis, drug delivery, and tumor suppressor. Conclusion: In the past 12 years, researchers have dedicated their efforts to prostate cancer associated exosome. On the basis of previous studies, scientists are showing increasingly solicitude for the role of exosome in prostate cancer progression and potential therapy such as drug delivery.

Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis.

Introduction: Since the significant breakthroughs in artificial intelligence (AI) algorithms, the application of AI in bladder cancer has rapidly expanded. AI can be used in all aspects of the bladder cancer field, including diagnosis, treatment and prognosis prediction. Nowadays, these technologies have an excellent medical auxiliary effect and are in explosive development, which has aroused the intense interest of researchers. This study will provide an in-depth analysis using bibliometric analysis to explore the trends in this field. Method: Documents regarding the application of AI in bladder cancer from 2000 to 2022 were searched and extracted from the Web of Science Core Collection. These publications were analyzed by bibliometric analysis software (CiteSpace, Vosviewer) to visualize the relationship between countries/regions, institutions, journals, authors, references, keywords. Results: We analyzed a total of 2368 publications. Since 2016, the number of publications in the field of AI in bladder cancer has increased rapidly and reached a breathtaking annual growth rate of 43.98% in 2019. The U.S. has the largest research scale, the highest study level and the most significant financial support. The University of North Carolina is the institution with the highest level of research. EUROPEAN UROLOGY is the most influential journal with an impact factor of 24.267 and a total citation of 11,848. Wiklund P. has the highest number of publications, and Menon M. has the highest number of total citations. We also find hot research topics within the area through references and keywords analysis, which include two main parts: AI models for the diagnosis and prediction of bladder cancer and novel robotic-assisted surgery for bladder cancer radicalization and urinary diversion. Conclusion: AI application in bladder cancer is widely studied worldwide and has shown an explosive growth trend since the 21st century. AI-based diagnostic and predictive models will be the next protagonists in this field. Meanwhile, the robot-assisted surgery is still a hot topic and it is worth exploring the application of AI in it. The advancement and application of algorithms will be a massive driving force in this field.

Early clinical efficacy analysis of enhanced recovery following surgery combined with interscalene brachial plexus block for arthroscopic rotator cuff repair.

To explore the early clinical value of enhanced recovery after surgery (ERAS) with interscalene brachial plexus block (ISB) for arthroscopic rotator cuff repair (ARCR). We enrolled 240 patients who underwent arthroscopic rotator cuff repair, randomly divided into 3 groups (n = 80 each). Groups A, B, and C underwent only surgery, surgery + ERAS, and ISB + surgery + ERAS, respectively. We analyzed the clinical data and postoperative indicators for the 3 patient groups. Group comparisons of clinical data and postoperative indicators revealed no significant differences in clinical characteristics (P > .05). Group C showed superior Visual Analog Scale scores at 0-6 and 6-24 hours postoperatively (P < .05), and the shortest length of hospital stay (LOS) (P < .05). At 6 weeks and 3 months postoperatively, Constant-Murley shoulder score and University of California-Los Angeles scores were better in Groups B and C than in Group A (P < .05). Joint swelling was more common in Group A than in Groups B and C (P < .05) but with no significant difference in the incidence of postoperative stiffness (P > .05). ERAS can relieve postoperative pain, shorten LOS, and help restore shoulder joint mobility, thereby reducing postoperative swelling. ISB + ERAS optimized pain control and allowed a shorter LOS, but had similar effects on early functional recovery and complications.

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.

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation.

Introduction: Accurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion. Methods: The proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance. Results: Experimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost. Discussion: Our method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.

Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis.

Deep neural networks (DNNs) have been widely applied in the medical image community, contributing to automatic ophthalmic screening systems for some common diseases. However, the incidence of fundus diseases patterns exhibits a typical long-tailed distribution. In clinic, a small number of common fundus diseases have sufficient observed cases for large-scale analysis while most of the fundus diseases are infrequent. For these rare diseases with extremely low-data regimes, it is challenging to train DNNs to realize automatic diagnosis. In this work, we develop an automatic diagnosis system for rare fundus diseases, based on the meta-learning framework. The system incorporates a co-regularization loss and the ensemble-learning strategy into the meta-learning framework, fully leveraging the advantage of multi-scale hierarchical feature embedding. We initially conduct comparative experiments on our newly-constructed lightweight multi-disease fundus images dataset for the few-shot recognition task (namely, FundusData-FS). Moreover, we verify the cross-domain transferability from miniImageNet to FundusData-FS, and further confirm our method's good repeatability. Rigorous experiments demonstrate that our method can detect rare fundus diseases, and is superior to the state-of-the-art methods. These investigations demonstrate that the potential of our method for the real clinical practice is promising.

Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review.

Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.

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.

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.

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.

The role of autophagy in calcium oxalate kidney stone: A systematic review of the literature.

Background: Calcium oxalate kidney stone is one of the common diseases in the urinary system and has a high recurrence rate. Currently, the pathogenesis of kidney stone and the methods to prevent recurrence are still being investigated. Autophagy, as an event of cellular self-repair, has received attention in the field of kidney stone in recent years. In some current studies, autophagy has shown destructiveness and protectiveness in the pathogenesis of kidney stone. The inhibition or promotion of autophagy may be a key target for future kidney stone therapy. This systematic literature review discusses the function of autophagy in kidney stone pathogenesis in the context of current research and synthesizes the evidence analysis to provide a basis for new future therapies. Method: We systematically reviewed the literature during September 2021 according to the Preferred Reporting Items for Systematic Evaluation and Meta-Analysis (PRISMA) guidelines. Articles on studying the role of autophagy in the pathogenesis of calcium oxalate kidney stone were extracted from PubMed, MEDLINE, Embase and Scopus, including in vivo versus in vitro experiments. The study topic, language and publication date were not restricted. Two authors (Li and Zhou) searched and screened the literature. Results: We screened 18 articles from the 33 collected articles, of which 6 conducted in vitro cellular studies, four conducted animal studies, eight conducted cellular studies with animal studies, and five studied human specimens. In early studies, the literature generally concluded that autophagy is deleterious in the development of kidney stone. In 2020, the idea of the protectiveness of autophagy associated with kidney stone was first proposed and focused on targeting transcription factor EB. In addition, the interaction of autophagy with other cellular events and the regulation of signaling molecules are focused on in this paper. Conclusion: This systematic review provides advances in research on the role of autophagy in renal calculi. The current studies suggest that both upregulation and downregulation of autophagy may ameliorate injury in kidney stone models. The authors prefer the upregulation of autophagy as a future research direction for kidney stone treatment.

LMO3 downregulation in PCa: A prospective biomarker associated with immune infiltration.

Prostate cancer is the third leading cause of new cancer cases and the second most common tumor type in men globally. LMO3 has been stated to play a vital role in some cancers; however, the prognostic value of LMO3 in PCa remains vague. Here, we utilized various web databases to elucidate in detail the prognostic value and molecular functions of LMO3 in PCa. LMO3 expression was significantly decreased in PCa. Low LMO3 expression was associated with gender, age, and TNM grade and predicted a poor prognosis in PCa patients. Functional enrichment analysis suggested that LMO3 is engaged in the extracellular matrix and immune response. Moreover, LMO3 was positively correlated with immune infiltration levels and numerous immune markers. LMO3 may function as a prospective biomarker of immune infiltration in PCa.

Non-Coding RNAs: New Dawn for Diabetes Mellitus Induced Erectile Dysfunction.

Erectile dysfunction (ED) is a common sexual dysfunction in males, with multifactorial alterations which consist of psychological and organic. Diabetes mellitus (DM) induced erectile dysfunction (DMED) is a disconcerting and critical complication of DM, and remarkably different from non-diabetic ED. The response rate of phosphodiesterase type 5 inhibitor (PDE5i), a milestone for ED therapy, is far from satisfactory in DMED. Unfortunately, the contributing mechanisms of DMED remains vague. Hence, It is urgent to seek for novel prospective biomarkers or targets of DMED. Numerous studies have proved that non-coding RNAs (ncRNAs) play essential roles in the pathogenesis process of DM, which comprise of long non-coding RNAs (lncRNAs) and small non-coding RNAs (sncRNAs) like microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs). However, the implications of ncRNAs in DMED are still understudied. This review highlights the pathophysiology of DMED, summarizes identified mechanisms of ncRNAs associated with DMED and covers the topic of perspectives for ncRNAs in DMED.

Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning.

Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.