Challenging pitfalls in frozen section pathology: a case of mandible ghost cell odontogenic carcinoma and the literature review.

BACKGROUND: Ghost cell odontogenic carcinoma (GCOC) is a rare malignancy characterized by the presence of ghost cells, preferably in the maxilla. Only slightly more than 50 case reports of GCOC have been documented to date. Due to the rarity of this tumor and its nonspecific clinical criteria, there is a heightened risk of misdiagnosis in clinical examination, imaging findings, and pathology interpretation. CASE PRESENTATION: A 50-year-old male patient presented to the hospital due to experiencing pain in his lower front teeth while eating for the past 2 months. Upon examination, a red, hard, painless mass was found in his left lower jaw, measuring approximately 4.0 cm × 3.5 cm. Based on the malignant histological morphology of the tumor and the abundant red-stained keratinized material, the preoperative frozen section pathology misdiagnosed it as squamous cell carcinoma (SCC). The surgical resection specimen pathology via paraffin section revealed that the tumor was characterized by round-like epithelial islands within the fibrous interstitium, accompanied by a large number of ghost cells and some dysplastic dentin with infiltrative growth. The malignant components displayed marked heterogeneity and mitotic activity. Additionally, a calcified cystic tumor component of odontogenic origin was observed. Hemorrhage, necrosis, and calcifications were present, with a foreign body reaction around ghost cells. Immunoreactivity for ß-catenin showed strong nuclear positivity in tumor cells, while immunostaining was completely negative for p53. The Ki67 proliferation index was approximately 30-40%. The tumor cells exhibited diffuse CK5/6, p63, and p40 immunoreactivity, with varying immunopositivity for EMA. Furthermore, no BRAFV600E mutation was identified by ARMS-PCR. The final pathology confirmed that the tumor was a mandible GCOC. CONCLUSION: We have reported and summarized for the first time the specific manifestations of GCOC in frozen section pathology and possible pitfalls in misdiagnosis. We also reviewed and summarized the etiology, pathological features, molecular characteristics, differential diagnosis, imaging features, and current main treatment options for GCOC. Due to its rarity, the diagnosis and treatment of this disease still face certain challenges. A correct understanding of the pathological morphology of GCOC, distinguishing the ghost cells and the secondary stromal reaction around them, is crucial for reducing misdiagnosis rates.

Fine-Tuning Electron-Donor Capability in the Basic Anion of Poly(ionic liquid) Frameworks for Revolutionizing Catalytic Synthesis of Ethyl Methyl Carbonate with Both Ultrahigh Catalytic Activity and Selectivity.

Ethyl methyl carbonate (EMC) is a crucial solvent extensively utilized in lithium-ion battery electrolytes; the transesterification of dimethyl carbonate (DMC) with ethanol is a pivotal reaction for EMC production. However, this reaction faces challenges due to the trade-off between catalytic activity and selectivity from the basic catalysts. In this issue, we report an innovative strategy through fine-tuning the electron-donor capability of the basic phenolate anion ([PhO]) in a novel poly(ionic liquid) (PIL) framework, as synthesized via an alkylation reaction between 1,3,5-tris(bromomethyl)benzene, biphenyldiimidazole, and N,N'-carbonyldiimidazole (CDI) to trigger targeted basicity that can directionally catalyze the transesterification of DMC with ethanol, so as to achieve both ultrahigh catalytic activity and selectivity toward EMC. By varying the substituent groups with electron-withdrawing and electron-donating effects on the phenolate anion, the PILs show expected changes in the catalytic performance, following well with the trend of charge density on these substituted phenolate anions. The optimized catalyst [CPIL-CDI][MeOPhO], induced by p-methoxyphenolate anions, allows an extraordinary EMC yield of 72.19% and an EMC selectivity of 91.48% under mild conditions without any process intensifications, suppressing all of the reported catalysts reported to date. Outcomes and approaches shown in this work have the potential to expedite the systematic design of cations and anions within PILs for industrial-scale EMC production through environmentally friendly transesterification processes.

Single cell analysis unveils B cell-dominated immune subtypes in HNSCC for enhanced prognostic and therapeutic stratification.

Head and neck squamous cell carcinoma (HNSCC) is characterized by high recurrence or distant metastases rate and the prognosis is challenging. There is mounting evidence that tumor-infiltrating B cells (TIL-Bs) have a crucial, synergistic role in tumor control. However, little is known about the role TIL-Bs play in immune microenvironment and the way TIL-Bs affect the outcome of immune checkpoint blockade. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, the study identified distinct gene expression patterns in TIL-Bs. HNSCC samples were categorized into TIL-Bs inhibition and TIL-Bs activation groups using unsupervised clustering. This classification was further validated with TCGA HNSCC data, correlating with patient prognosis, immune cell infiltration, and response to immunotherapy. We found that the B cells activation group exhibited a better prognosis, higher immune cell infiltration, and distinct immune checkpoint levels, including elevated PD-L1. A prognostic model was also developed and validated, highlighting four genes as potential biomarkers for predicting survival outcomes in HNSCC patients. Overall, this study provides a foundational approach for B cells-based tumor classification in HNSCC, offering insights into targeted treatment and immunotherapy strategies.

Perinatal exposure to traffic related air pollutants and the risk of infection in the first six months of life: a cohort study from a low-middle income country.

OBJECTIVE: There is limited study from low-and-middle income countries on the effect of perinatal exposure to air pollution and the risk of infection in infant. We assessed the association between perinatal exposure to traffic related air pollution and the risk of infection in infant during their first six months of life. METHODS: A prospective cohort study was performed in Jakarta, March 2016-September 2020 among 298 mother-infant pairs. PM2.5, soot, NOx, and NO2 concentrations were assessed using land use regression models (LUR) at individual level. Repeated interviewer-administered questionnaires were used to obtain data on infection at 1, 2, 4 and 6 months of age. The infections were categorized as upper respiratory tract (runny nose, cough, wheezing or shortness of breath), lower respiratory tract (pneumonia, bronchiolitis) or gastrointestinal tract infection. Logistic regression models adjusted for covariates were used to assess the association between perinatal exposure to air pollution and the risk of infection in the first six months of life. RESULTS: The average concentrations of PM2.5 and NO2 were much higher than the WHO recommended levels. Upper respiratory tract infections (URTI) were much more common in the first six months of life than diagnosed lower respiratory tract or gastro-intestinal infections (35.6%, 3.5% and 5.8% respectively). Perinatal exposure to PM2.5 and soot suggested increase cumulative risk of upper respiratory tract infection (URTI) in the first 6 months of life per IQR increase with adjusted OR of 1.50 (95% CI 0.91; 2.47) and 1.14 (95% CI 0.79; 1.64), respectively. Soot was significantly associated with the risk of URTI at 4-6 months age interval (aOR of 1.45, 95%CI 1.02; 2.09). All air pollutants were also positively associated with lower respiratory tract infection, but all CIs include unity because of relatively small samples. Adjusted odds ratios for gastrointestinal infections were close to unity. CONCLUSION: Our study adds to the evidence that perinatal exposure to fine particles is associated with respiratory tract infection in infants in a low-middle income country.

Bounded rationality in healthcare: unraveling the psychological factors behind patient satisfaction in China.

Introduction: Patient satisfaction is a crucial metric to gauge the quality of medical services, but the psychological factors influencing patient satisfaction remain insufficiently explored. Methods: This study examines these psychological factors by applying the theory of bounded rationality to 1,442 inpatients in Hangzhou, China, whose data were collected using a questionnaire. One-way ANOVA, correlation analysis, and hierarchical regression were used to analyze patient satisfaction and its associated factors. Additionally, the path analysis of the structural equation model revealed the mechanisms behind the key psychological factors that influenced patient satisfaction. Results: Medical risk perception, the social cognition of the medical environment, and social desirability bias had significant positive impacts on patient satisfaction. By contrast, negative emotions had a significant negative impact on patient satisfaction. Notably, patients' negative emotions had both a suppressive effect and a positive moderating effect on the relationship between medical risk perception and patient satisfaction. Similarly, social desirability bias had a suppressive effect on the correlation between the social cognition of the medical environment and patient satisfaction, albeit with a negative moderating effect. Discussion: These results suggest that when evaluating and improving patient satisfaction, accounting only for the factors that directly influence medical service quality is insufficient, as the indirect and moderating effects of patients' negative emotions and the social cognition of the medical environment must also be considered. Medical service providers should thus address patients' negative emotions, establish good doctor-patient relationships, optimize service environments, provide managers with medical risk education and training on negative emotions, and prioritize patient-centered care. Additionally, the government and relevant health departments should optimize medical policies, enhance fairness and accessibility, and create a positive social cognitive environment through public education and awareness campaigns.

Effects of general and spinal anesthesia on postoperative rehabilitation in older adults after lower limb surgery: a retrospective cohort study.

Objective: To investigate the effects of perioperative general anesthesia (GA) and spinal anesthesia (SA) on postoperative rehabilitation in elderly patients with lower limb surgery. Methods: This retrospective propensity score-matched cohort study included patients aged 65 years or older who underwent lower limb surgery between January 1, 2020, and May 31, 2023. The GA and SA were selected at the request of the orthopedic surgeon, patient, and their family members. The main outcomes included the incidence of the patient's inability to self-care at discharge, postoperative complications including pulmonary infection, thrombus of lower extremity veins, infection of incisional wound and delirium, length of hospital stay, and incidence of severe pain in the first 2 days postoperatively. Results: In total, 697 patients met the inclusion criteria, and 456 were included in the final analysis after propensity score matching. In the GA and SA groups, 27 (11.84%) and 26 (11.40%) patients, respectively, could not care for themselves at discharge. The incidence rates did not differ between the groups (p = 0.884). In contrast, the incidence of postoperative complications (GA: 10.53% and SA: 4.39%; p = 0.013) and the length of hospital stay (GA: 16.92 ± 10.65 days and SA: 12.75 ± 9.15 days; p < 0.001) significantly differed between the groups. Conclusion: The choice of anesthesia is independent of the loss of postoperative self-care ability in older patients (>65 years) and is not a key factor affecting postoperative rehabilitation after lower limb surgery. However, compared with GA, SA reduces the incidence of postoperative complications and a prolonged hospital stay. Thus, SA as the primary anesthetic method is a protective factor against a prolonged hospital stay.

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.

PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). METHODS: This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. RESULTS: 67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001-0.039). For clinically-relevant liver lesions, the detection rates (91.4%-93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%-89.8%, p = 0.230-0.231) but were superior to PI-DWI (82.7%-85.8%, p = 0.015-0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%-97.4% vs 76.9%-79.5% vs 64.1%-69.2%, p = 0.002-0.045) among the three DWI sequences. CONCLUSION: In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.

The Effect of Low-Intensity Pulsed Ultrasound on Bone Regeneration and the Expression of Osterix and Cyclooxygenase-2 during Critical-Size Bone Defect Repair.

Low-intensity pulsed ultrasound (LIPUS) is a form of ultrasound that utilizes low-intensity pulsed waves. Its effect on bones that heal by intramembranous ossification has not been sufficiently investigated. In this study, we examined LIPUS and the autologous bone, to determine their effect on the healing of the critical-size bone defect (CSBD) of the rat calvaria. The bone samples underwent histological, histomorphometric and immunohistochemical analyses. Both LIPUS and autologous bone promoted osteogenesis, leading to almost complete closure of the bone defect. On day 30, the bone volume was the highest in the autologous bone group (20.35%), followed by the LIPUS group (19.12%), and the lowest value was in the control group (5.11%). The autologous bone group exhibited the highest intensities of COX-2 (167.7 ± 1.1) and Osx (177.1 ± 0.9) expression on day 30. In the LIPUS group, the highest intensity of COX-2 expression was found on day 7 (169.7 ±1.6) and day 15 (92.7 ± 2.2), while the highest Osx expression was on day 7 (131.9 ± 0.9). In conclusion, this study suggests that LIPUS could represent a viable alternative to autologous bone grafts in repairing bone defects that are ossified by intramembranous ossification.

Early identification of autism spectrum disorder based on machine learning with eye-tracking data.

BACKGROUND: Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. METHODS: A retrospective cohort of 449 children (ASD: 246, typically developing [TD]: 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD. The best-performing algorithm was selected to build the final model which was further evaluated in a prospective cohort of 80 children. The Shapley values interpreted important eye-tracking features. RESULTS: Random forest outperformed other algorithms during model development and achieved an area under the curve of 0.849 (< 3 years: 0.832, ≥ 3 years: 0.868) on the external validation set. Of the ten most important eye-tracking features, three measured social cognition, and the rest were related to non-social cognition. A deterioration in model performance was observed using only the social or non-social cognition-related eye-tracking features. LIMITATIONS: The sample size of this study, although larger than that of existing studies of ASD based on eye-tracking data, was still relatively small compared to the number of features. CONCLUSIONS: Machine learning models based on eye-tracking data have the potential to be cost- and time-efficient digital tools for the early identification of ASD. Eye-tracking phenotypes related to social and non-social cognition play an important role in distinguishing children with ASD from TD children.

Extracellular vesicles-based point-of-care testing for the diagnosis and monitoring of Alzheimer's disease.

Alzheimer's disease (AD) is a debilitating condition that affects millions of people worldwide. One promising strategy for detecting and monitoring AD early on is using extracellular vesicles (EVs)-based point-of-care testing; however, diagnosing AD using EVs poses a challenge due to the low abundance of EV-biomarkers. Here, we present a fully integrated organic electrochemical transistor (OECT) that enables high accuracy, speed, and convenience in the detection of EVs from AD patients. We incorporated self-aligned acoustoelectric enhancement of EVs on a chip that rapidly propels, enriches, and specifically binds EVs to the OECT detection area. With our enhancement of pre-concentration, we increased the sensitivity to a limit of detection of 500 EV particles/µL and reduced the required detection time to just two minutes. We also tested the sensor on an AD mouse model to monitor AD progression, examined mouse Aß EVs at different time courses, and compared them with intraneuronal Aß cumulation using MRI. This innovative technology has the potential to diagnose Alzheimer's and other neurodegenerative diseases accurately and quickly, enabling monitoring of disease progression and treatment response.

Improved empirical mode decomposition method based on amplitude-frequency characteristic of vortex signals.

The vortex flowmeter occupies a vital position in flow measurement with its unique advantages. It is essentially a fluid vibration instrument, and its measurement process is susceptible to interference, which seriously affects measurement accuracy. In particular, at low flow rates, it is an urgent problem to extract vortex signals from the complex noise. Among many signal processing methods, Empirical Mode Decomposition (EMD) is a time-frequency analysis method suitable for nonlinear, non-stationary signals. EMD can adaptively decompose noisy signals into noise and useful signal components arranged from high frequency to low frequency. For the above problems, an innovative, improved EMD method is proposed in this paper. The digital filter is designed according to the amplitude-frequency characteristic of vortex signals. After filtering, the vortex signal is adjusted to a fixed value, and high-frequency noise is filtered. According to the consistency of the filtered signal's amplitude, we design a decomposition stop criterion for EMD to process the output signal of the vortex sensor. This method not only maintains the characteristic of adaptive decomposition in EMD but also completes the automatic extraction of the vortex signal under complex noise. It provides a new comprehensive method for realizing high-precision and anti-interference vortex flowmeters.

Characterizing Hydroxyl Radical Formation from the Light-Driven Fe(II)-Peracetic Acid Reaction, a Key Process for Aerosol-Cloud Chemistry.

The reaction of peracetic acid (PAA) and Fe(II) has recently gained attention due to its utility in wastewater treatment and its role in cloud chemistry. Aerosol-cloud interactions, partly mediated by aqueous hydroxyl radical (OH) chemistry, represent one of the largest uncertainties in the climate system. Ambiguities remain regarding the sources of OH in the cloud droplets. Our research group recently proposed that the dark and light-driven reaction of Fe(II) with peracids may be a key contributor to OH formation, producing a large burst of OH when aerosol particles take up water as they grow to become cloud droplets, in which reactants are consumed within 2 min. In this work, we quantify the OH production from the reaction of Fe(II) and PAA across a range of physical and chemical conditions. We show a strong dependence of OH formation on ultraviolet (UV) wavelength, with maximum OH formation at λ = 304 ± 5 nm, and demonstrate that the OH burst phenomenon is unique to Fe(II) and peracids. Using kinetics modeling and density functional theory calculations, we suggest the reaction proceeds through the formation of an [Fe(II)-(PAA)2(H2O)2] complex, followed by the formation of a Fe(IV) complex, which can also be photoactivated to produce additional OH. Determining the characteristics of OH production from this reaction advances our knowledge of the sources of OH in cloudwater and provides a framework to optimize this reaction for OH output for wastewater treatment purposes.

Exploring the flavor changes in mung bean flour through lactobacillus fermentation: insights from volatile compounds and non-targeted metabolomics analysis.

BACKGROUND: Mung beans are abundant in nutrition, but their leguminous flavor limits their development. Lactic Acid Bacteria (LAB) fermentation can decrease unwanted bean flavors in legumes and enhance their flavor. This study examined the influence of Lactobacillus fermentation on the flavor characteristics of mung bean flour (MBF) using volatile compounds and non-targeted metabolomics. RESULTS: Lactobacillus plantarum LP90, Lactobacillus casei LC89, and Lactobacillus acidophilus LA85 eliminated 61.37%, 48.29%, and 43.73% of the primary bean odor aldehydes from MBF. The results of relative odor activity values (ROAV) showed that fermented mung bean flour (FMBF) included volatile chemicals that contributed to fruity, flowery, and milky aromas. These compounds included ethyl acetate, hexyl formate, 3-hydroxy-2-butanone, and 2,3-butanedione. The levels of amino acids with a fresh sweet flavor increased significantly by 93.89%, 49.40%, and 35.27% in LP90, LC89, and LA85, respectively. A total of 49 up-regulated and 13 down-regulated significantly differential metabolites were annotated, and 10 metabolic pathways were screened for contributing to the flavor. The correlation between important volatile compounds and non-volatile substances relies on two primary metabolic pathways: the citric acid cycle pathway and the amino acid metabolic system. CONCLUSION: The flavor of MBF was greatly enhanced by the process of Lactobacillus fermentation, with LP90 having the most notable impact. These results serve as a reference for identifying the flavor of FMBF. This article is protected by copyright. All rights reserved.

Association between alcohol consumption and peripheral artery disease: Two de novo prospective cohorts and a systematic review with meta-analysis.

AIMS: The association between alcohol consumption and risk of peripheral artery disease (PAD) is inconclusive. We conducted this study to examine the association between alcohol consumption and PAD risk in two de novo cohort studies and a meta-analysis of observational studies. METHODS AND RESULTS: A systematic review was conducted to identify studies on alcohol consumption in relation to PAD risk. We further used data from two cohorts of 70,116 Swedish and 405,406 British adults and performed a meta-analysis of results from previously published studies and current cohort studies. There was a U-shaped association between alcohol consumption and incident PAD risk in the Swedish and British cohorts. The meta-analysis of results of these two cohorts and previously published studies found that compared with non- or never-drinkers, the relative risk of PAD was 0.83 (95% confidence interval [CI] 0.77-0.89), 0.81 (95% CI 0.74-0.90), and 0.94 (95% CI 0.83-1.07) for light, moderate, and high-to-heavy alcohol drinkers, respectively. The nonlinear meta-analysis revealed a possibly U-shaped association between alcohol consumption and PAD risk (P-nonlinearity <0.001). The risk of PAD was observed to be the lowest for 2 drinks/week and to be pronounced for ≥10 drinks/week. All these associations persisted in a sensitivity meta-analysis including cohort and other type of observational studies. CONCLUSION: Alcohol intake ≤ 2 drinks/week was associated with a reduced risk of PAD and the risk of PAD became pronounced with intake ≥10 drinkers/week.

The association between alcohol consumption and the risk of peripheral artery disease is conflicting between studies and thus remains undetermined. In the two de novo cohort analyses, we found a U-shaped association between alcohol consumption and peripheral artery disease risk in the Swedish and British populations. In the meta-analysis, light-to-moderate consumption of alcohol was associated with a reduced risk of peripheral artery disease. The dose-response meta-analysis showed that the risk of peripheral artery disease became pronounced for alcohol consumption ≥10 drinkers/week. This is an observational study that cannot infer causality between alcohol consumption and peripheral artery disease risk. We are not able to assess the specific associations to different types of alcoholic beverages.

Engineered Exosomes with ATF5-Modified mRNA Loaded in Injectable Thermogels Alleviate Osteoarthritis by Targeting the Mitochondrial Unfolded Protein Response.

Osteoarthritis (OA) progression is highly associated with chondrocyte mitochondrial dysfunction and disorders of catabolism and anabolism of the extracellular matrix (ECM) in the articular cartilage. The mitochondrial unfolded protein response (UPRmt), which is an integral component of the mitochondrial quality control (MQC) system, is essential for maintaining chondrocyte homeostasis. We successfully validated the pivotal role of activating transcription factor 5 (ATF5) in upregulating the UPRmt, mitigating IL-1ß-induced inflammation and mitochondrial dysfunction, and promoting balanced metabolism in articular cartilage ECM, proving its potential as a promising therapeutic target for OA. Modified mRNAs (modRNAs) have emerged as novel and efficient gene delivery vectors for nucleic acid therapeutic approaches. In this study, we combined Atf5-modRNA (modAtf5) with engineered exosomes derived from bone mesenchymal stem cells (ExmodAtf5) to exert cytoprotective effects on chondrocytes in articular cartilage via Atf5. However, the rapid localized metabolization of ExmodAtf5 limits its application. PLGA-PEG-PLGA (Gel), an injectable thermosensitive hydrogel, was used as a carrier of ExmodAtf5 (Gel@ExmodAtf5) to achieve a sustained release of ExmodAtf5. In vitro and in vivo, the use of Gel@ExmodAtf5 was shown to be a highly effective strategy for OA treatment. The in vivo therapeutic effect of Gel@ExmodAtf5 was evidenced by the preservation of the intact cartilage surface, low OARSI scores, fewer osteophytes, and mild subchondral bone sclerosis and cystic degeneration. Consequently, the combination of ExmodAtf5 and PLGA-PEG-PLGA could significantly enhance the therapeutic efficacy and prolong the exosome release. In addition, the mitochondrial protease ClpP enhanced chondrocyte autophagy by modulating the mTOR/Ulk1 pathway. As a result of our research, Gel@ExmodAtf5 can be considered to be effective at alleviating the progression of OA.

An Appearance-Semantic Descriptor with Coarse-to-Fine Matching for Robust VPR.

In recent years, semantic segmentation has made significant progress in visual place recognition (VPR) by using semantic information that is relatively invariant to appearance and viewpoint, demonstrating great potential. However, in some extreme scenarios, there may be semantic occlusion and semantic sparsity, which can lead to confusion when relying solely on semantic information for localization. Therefore, this paper proposes a novel VPR framework that employs a coarse-to-fine image matching strategy, combining semantic and appearance information to improve algorithm performance. First, we construct SemLook global descriptors using semantic contours, which can preliminarily screen images to enhance the accuracy and real-time performance of the algorithm. Based on this, we introduce SemLook local descriptors for fine screening, combining robust appearance information extracted by deep learning with semantic information. These local descriptors can address issues such as semantic overlap and sparsity in urban environments, further improving the accuracy of the algorithm. Through this refined screening process, we can effectively handle the challenges of complex image matching in urban environments and obtain more accurate results. The performance of SemLook descriptors is evaluated on three public datasets (Extended-CMU Season, Robot-Car Seasons v2, and SYNTHIA) and compared with six state-of-the-art VPR algorithms (HOG, CoHOG, AlexNet_VPR, Region VLAD, Patch-NetVLAD, Forest). In the experimental comparison, considering both real-time performance and evaluation metrics, the SemLook descriptors are found to outperform the other six algorithms. Evaluation metrics include the area under the curve (AUC) based on the precision-recall curve, Recall@100%Precision, and Precision@100%Recall. On the Extended-CMU Season dataset, SemLook descriptors achieve a 100% AUC value, and on the SYNTHIA dataset, they achieve a 99% AUC value, demonstrating outstanding performance. The experimental results indicate that introducing global descriptors for initial screening and utilizing local descriptors combining both semantic and appearance information for precise matching can effectively address the issue of location recognition in scenarios with semantic ambiguity or sparsity. This algorithm enhances descriptor performance, making it more accurate and robust in scenes with variations in appearance and viewpoint.

Mex-3 RNA binding family member A (MEX3A)/circMPP6 complex promotes colorectal cancer progression by inhibiting autophagy.

RNA-binding proteins (RBPs)-RNA networks have contributed to cancer development. Circular RNAs (circRNAs) are considered as protein recruiters; nevertheless, the patterns of circRNA-protein interactions in colorectal cancer (CRC) are still lacking. Processing bodies (PBs) formed through liquid-liquid phase separation (LLPS) are membrane-less organelles (MLOs) consisting of RBPs and RNA. Previous evidence suggests a connection between PBs dynamics and cancer progression. Despite the increasingly acknowledged crucial role of RBPs and RNA in the accumulation and maintenance of MLOs, there remains a lack of specific research on the interactions between PBs-related RBPs and circRNAs in CRC. Herein, we identify that MEX-3 RNA binding family member A (MEX3A), frequently upregulated in CRC tissues, predicts poorer patient survival. Elevated MEX3A accelerates malignance and inhibits autophagy of CRC cells. Importantly, MEX3A undergoes intrinsically disordered regions (IDRs)-dependent LLPS in the cytoplasm. Specifically, circMPP6 acts as a scaffold to facilitate the interaction between MEX3A and PBs proteins. The MEX3A/circMPP6 complex modulates PBs dynamic and promotes UPF-mediated phosphodiesterase 5A (PDE5A) mRNA degradation, consequently leading to the aggressive properties of CRC cells. Clinically, CRC patients exhibiting high MEX3A expression and low PDE5A expression have the poorest overall survival. Our findings reveal a collaboration between MEX3A and circMPP6 in the regulation of mRNA decay through triggering the PBs aggregation, which provides prognostic markers and/or therapeutic targets for CRC.

Genotypic and phenotypic features of 39 Chinese patients with glycogen storage diseases type I, VI, and IX.

Glycogen storage diseases (GSDs) are abnormally inherited glycogen metabolism mainly affecting the liver, muscles, and heart. Deficiency of proteins involved in glycogen metabolism caused by genetic mutations are responsible for different subtype of GSDs. However, there are still some challenges in diagnosing GSD. This study includes 39 suspected GSDs patients from unrelated families in China. Next-generation sequencing (NGS) was used to investigate the reason for their diseases at the genetic level. Finally, all 39 patients were diagnosed with GSDs, including 20 GSD-Ia, 4 GSD-VI, and 15 GSD IX (12 GSD-IXa patients and 3 GSD-IXb patients). Thirty-two mutations in G6PC1, PYGL, PHKA2, and PHKB genes were identified, with 14 of them being novel variants. The pathogenicity of novel variants was classified according to ACMG guildlines and predicted by in slico algorithms. Mutations p.L216L and p.R83H in G6PC1 gene may be the hot spot mutation in Chinese. Hearing impairment is a rare clinical feature of GSD Ia, which has also been observed in our cohort. The severity of GSD VI and IX was indicated by our patients. Close follow-up should be applied to GSD VI and IX patients. Our findings provided evidence for building the phenotype-genotype of GSDs and expanded the mutation spectrum of related genes.

Efficacy and safety of mechanochemical ablation versus laser ablation in the treatment of primary great saphenous vein reflux: A randomized, open, parallel controlled clinical trial.

OBJECTIVE: To evaluate the efficacy of a new mechanochemical ablation (MOCA) device versus endovenous laser ablation (EVLA) for primary great saphenous vein (GSV) reflux. MATERIALS AND METHODS: Prospectively analyze the demographics, treatment detail and outcomes data of 57 primary GSV reflux patients. Patients were randomly assigned to MOCA or EVLA group with random envelope method. Primary endpoint was 6-month closure rate of GSV. Secondary endpoint including technical success rate, the venous clinical severity score (VCSS), chronic venous insufficiency questionnaire (CIVIQ-20) score and visual analogue scale (VAS) for pain. RESULTS: The procedures were well tolerated according to the VAS score. The 6-month closure rate was 85.71% in MOCA and 96.55% in EVLA group (p = .194). Significant changes were observed in regard of VCSS and CIVIQ-20 score at 6-month follow-up. Skin paresthesia occurred in 0 in MOCA and 5 in EVLA group. CONCLUSION: The new MOCA device is safe and effective in treating primary great saphenous vein reflux. The 6-month closure rate is non-inferior compared with EVLA. However, the long-term results need further follow-up.

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.