Follow-up analysis and research of very low-risk and low-risk gastrointestinal stromal tumors after endoscopic resection.

Currently, due to limited long-term evidence, there remains some controversy surrounding the recommended postoperative monitoring strategy for primary low-risk gastrointestinal stromal tumors (GISTs). This study recruited a total of 532 patients diagnosed with very low-risk and low-risk GISTs who underwent endoscopic resection from 2015 to 2021, including 460 very low-risk patients and 72 low-risk patients. Descriptive statistical analysis was used to evaluate the clinical and pathological characteristics of GIST patients, and Kaplan-Meier methods were employed for survival analysis. The results showed that the 5-year recurrence-free survival rates for very low-risk and low-risk patients were 98.5% and 95.9%, respectively. The 5-year disease-specific survival rates for both groups were 100%. Additionally, the 5-year overall survival rates were 99.7% for very low-risk patients and 100% for low-risk patients (P = 0.69). Therefore, it is suggested that routine follow-up monitoring, including endoscopic surveillance and imaging, may not be necessary for very low-risk and low-risk GISTs after endoscopic resection.

A Dual-Stream-Modulated Learning Framework for Illuminating and Super-Resolving Ultra-Dark Images.

Enhancement of image resolution for scenes captured under extremely dim conditions represents a practical yet challenging problem that has received little attention. In such low-light scenarios, the limited lighting and minimal signal clarity tend to intensify issues such as diminished detail visibility and altered color accuracy, which are often more severe during the image enhancement process than in scenarios with adequate lighting. Consequently, standard methods for enhancing low-light images or improving their resolution, whether implemented independently or through a combined approach, generally face challenges in effectively restoring luminance, preserving color integrity, and detailing intricate features. To conquer these issues, this article introduces an innovative dual-stream (DS) modulated learning framework designed to tackle the real-world coupled degradation issues in super-resolution (SR) under low-light conditions. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint to specifically target uneven illumination. We develop illumination-semantic dual modulator (ISDM), a refinement middleware embedded in the decoding stage to bridge illumination and semantic features concurrently, aimed at safeguarding the integrity of lighting and color details at the feature level. Our approach replaces simple upsampling methods with the resolution-sensitive merging upsampler (RSMU) module, which integrates diverse sampling techniques to effectively reduce artifacts and halo effects. Comprehensive experiments on three benchmarks showcase the applicability and generalizability of our approach to diverse and challenging ultra-poorly lit settings, outperforming state-of-the-art methods with a notable improvement. The code and benchmark are publicly available at https://github.com/moriyaya/UltraIS.

Survey on Perkinsus species in two economic mussels (Mytilus coruscus and M. galloprovincialis) along the coast of the East China Sea and the Yellow Sea.

Perkinsus, a parasitic pathogen of marine bivalves, is widely distributed among various mollusks in numerous countries. However, the prevalence and diversity of Perkinsus species in the two economically important mussels, Mytilus coruscus and M. galloprovincialis, in China remain unknown. The presence of the Perkinsus species was identified in the two mussels sampled along the coast of the East China Sea and the Yellow Sea, using both the alternative Ray's fluid thioglycolate medium (ARFTM) and conventional polymerase chain reaction (PCR). The ARFTM test indicated the presence of Perkinsus-like hypnospores in the two mussels. The diameter of the hypnospores in M. coruscus was significantly smaller than that in M. galloprovincialis. The prevalence of Perkinsus in M. galloprovincialis and M. coruscus ranged from 0 to 37.5% and 0 to 25%, respectively. The mean intensity of Perkinsus in M. galloprovincialis and M. coruscus ranged from 0 to 5.14 and 0 to 4.92, respectively. The PCR assay showed that the prevalence of Perkinsus spp. in M. galloprovincialis and M. coruscus was 0 to 25.0% and 0 to 12.5%, respectively. The homology analysis of the newly obtained internal transcribed spacer (ITS) sequences of Perkinsus revealed the highest identity of 100% with P. beihaiensis. The phylogenetic analysis indicated that the Perkinsus isolates from the two mussels were clustered with P. beihaiensis. The results of the molecular biology indicated that only P. beihaiensis was detected in the two mussels. The highest prevalence of P. beihaiensis was observed in Liaoning province (Dalian, 20.83%), followed by Shandong province, Zhejiang province and Fujian province. Consequently, it is recommended that surveillance should be conducted in Dalian, where the prevalence and mean intensity of P. beihaiensis in M. galloprovincialis are the highest.

Helical-sampled fiber Bragg grating inscribed by a femtosecond laser in a ring core fiber.

The inscription of a helical-sampled fiber Bragg grating (HSFBG) in a ring core fiber (RCF) using a low repetition rate femtosecond laser point-by-point technique is demonstrated. The reflection spectrum exhibits several peak groups attributed to the helical-sampled structure, with the wavelength interval between different groups determined by the helical pitch. Meanwhile, the number and spacing of the peaks within each group are dictated by the RCF. An investigation into the effects of helical pitch, helical radius, and grating length of the HSFBG on the reflection spectra is conducted. Furthermore, thermal annealing experiments demonstrate that this HSFBG can survive at the temperatures up to 800°C.

HucMSC extracellular vesicles increasing SATB 1 to activate the Wnt/ß-catenin pathway in 6-OHDA-induced Parkinson's disease model.

Parkinson's disease (PD) is a degenerative disorder of the nervous system characterized by the loss of dopaminergic neurons and damage of neurons in the substantia nigra (SN) and striatum, resulting in impaired motor functions. This study aims to investigate how extracellular vesicles (EVs) derived from human umbilical cord mesenchymal stem cells (HucMSC) regulate Special AT-rich sequence-binding protein-1 (SATB 1) and influence Wnt/ß-catenin pathway and autophagy in PD model. The PD model was induced by damaging SH-SY5Y cells and mice using 6-OHDA. According to the study, administering EVs every other day for 14 days improved the motor behavior of 6-OHDA-induced PD mice and reduced neuronal damage, including dopaminergic neurons. Treatment with EVs for 12 hours increased the viability of 6-OHDA-induced SH-SY5Y cells. The upregulation of SATB 1 expression with EV treatment resulted in the activation of the Wnt/ß-catenin pathway in PD model and led to overexpression of ß-catenin. Meanwhile, the expression of LC3 II was decreased, indicating alterations in autophagy. In conclusion, EVs could mitigate neuronal damage in the 6-OHDA-induced PD model by upregulating SATB 1 and activating Wnt/ß-catenin pathway while also regulating autophagy. Further studies on the potential therapeutic applications of EVs for PD could offer new insights and strategies.

Children with developmental coordination disorders: a review of approaches to assessment and intervention.

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by deficits in motor skills, with gross and fine motor dysfunction being the main symptom. This condition greatly impairs children's daily life, learning, and social interaction. Symptoms typically appear during preschool or school age, and if left untreated, they can persist into adulthood. Thus, early assessment and intervention are crucial to improve the prognosis. This study aims to review the existing literature on DCD, providing a comprehensive overview of the assessment for children with DCD in terms of body functions and structures, activities and participation, and environmental factors within the framework of the International Classification of Functioning, Disability, and Health - Children and Youth (ICF-CY). Additionally, specific rehabilitation interventions will be described, offering valuable insights for the clinical assessment and intervention of children with DCD.

The role of the "gut microbiota-mitochondria" crosstalk in the pathogenesis of multiple sclerosis.

Multiple Sclerosis (MS) is a neurologic autoimmune disease whose exact pathophysiologic mechanisms remain to be elucidated. Recent studies have shown that the onset and progression of MS are associated with dysbiosis of the gut microbiota. Similarly, a large body of evidence suggests that mitochondrial dysfunction may also have a significant impact on the development of MS. Endosymbiotic theory has found that human mitochondria are microbial in origin and share similar biological characteristics with the gut microbiota. Therefore, gut microbiota and mitochondrial function crosstalk are relevant in the development of MS. However, the relationship between gut microbiota and mitochondrial function in the development of MS is not fully understood. Therefore, by synthesizing previous relevant literature, this paper focuses on the changes in gut microbiota and metabolite composition in the development of MS and the possible mechanisms of the crosstalk between gut microbiota and mitochondrial function in the progression of MS, to provide new therapeutic approaches for the prevention or reduction of MS based on this crosstalk.

Local symmetry-driven interfacial magnetization and electronic states in (ZnO)n/(w-FeO)n superlattices.

Superlattices constructed with the wide-band-gap semiconductor ZnO and magnetic oxide FeO, both in the wurtzite structure, have been investigated using spin-polarized first-principles calculations. The structural, electronic and magnetic properties of the (ZnO)n/(w-FeO)n superlattices were studied in great detail. Two different interfaces in the (ZnO)n/(w-FeO)n superlattices were identified and they showed very different magnetic and electronic properties. Local symmetry-driven interfacial magnetization and electronic states can arise from different Fe/Zn distributions at different interfaces or spin ordering of Fe in the superlattice. The local symmetry-driven interfacial magnetization and electronic states, originating either from different Fe/Zn distribution across interfaces I and II, or by spin ordering of Fe in the superlattice, can be identified. It was also found that, in the case of the ferromagnetic phase, the electrons are more delocalized for the majority spin but strongly localized for the minority spin, which resulted in interesting spin-dependent transport properties. Our results will pave the way for designing novel spin-dependent electronic devices through the construction of superlattices from semiconductors and multiferroics.

MSR405: Inhibiting Neuroinflammation after Spinal Cord Injury in Rats.

The treatment of spinal cord injury (SCI) is often ineffective. Additionally, SCI-induced inflammation leads to secondary injury. Current anti-inflammatory hydrophilic drugs fail to reach the nerve injury site due to the blood-brain barrier. Here, we synthesized MSR405, a new lipophilic unsaturated fatty acid derivative of Radix Isatidis and investigated its therapeutic effect in SCI model rats. Furthermore, we systematically investigated its structure, toxicity, anti-inflammatory effect, and the underlying mechanism. MSR405 was injected into the abdominal cavity of the Sprague Dawley SCI model rats, and the effect on their behavioral scores and pathology was estimated to assess the status of neurological inflammation. Our data show that MSR405 treatment significantly improved the motor function of SCI rats, and markedly suppressed the associated neuroinflammation. Moreover, MSR405 could attenuate LPS-induced inflammatory response in BV2 cells (Mouse microglia cells) in vitro. Mechanistically, MSR405 inhibits proinflammatory cytokines, supporting the anti-inflammatory response. Additionally, MSR405 can significantly block the TLR4/NF-κB signaling pathway and nitric oxide production. In summary, MSR405 reduces inflammation in SCI rats through the TLR4/NF-κB signal cascade and can inhibit neuroinflammation after spinal cord injury.

LiDAR Dynamic Target Detection Based on Multidimensional Features.

To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman's rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer-Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection.

Non-halogenated Solvent-Processed Organic Solar Cells with Approaching 20 % Efficiency and Improved Photostability.

The development of high-efficiency organic solar cells (OSCs) processed from non-halogenated solvents is crucially important for their scale-up industry production. However, owing to the difficulty of regulating molecular aggregation, there is a huge efficiency gap between non-halogenated and halogenated solvent processed OSCs. Herein, we fabricate o-xylene processed OSCs with approaching 20 % efficiency by incorporating a trimeric guest acceptor named Tri-V into the PM6:L8-BO-X host blend. The incorporation of Tri-V effectively restricts the excessive aggregation of L8-BO-X, regulates the molecular packing and optimizes the phase-separation morphology, which leads to mitigated trap density states, reduced energy loss and suppressed charge recombination. Consequently, the PM6:L8-BO-X:Tri-V-based device achieves an efficiency of 19.82 %, representing the highest efficiency for non-halogenated solvent-processed OSCs reported to date. Noticeably, with the addition of Tri-V, the ternary device shows an improved photostability than binary PM6:L8-BO-X-based device, and maintains 80 % of the initial efficiency after continuous illumination for 1380 h. This work provides a feasible approach for fabricating high-efficiency, stable, eco-friendly OSCs, and sheds new light on the large-scale industrial production of OSCs.

Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumors.

BACKGROUND AND STUDY AIMS: Gastrointestinal stromal tumors (GIST) carry a potential risk of malignancy, and the treatment of GIST varies for different risk levels. However, there is no systematic preoperative assessment protocol to predict the malignant potential of GIST. The aim of this study was to develop a reliable and clinically applicable preoperative nomogram prediction model to predict the malignant potential of gastric GIST. PATIENTS AND METHODS: Patients with a pathological diagnosis of gastric GIST from January 2015 to December 2021 were screened retrospectively. Univariate and multivariate logistic analyses were used to identify independent risk factors for gastric GIST with high malignancy potential. Based on these independent risk factors, a nomogram model predicting the malignant potential of gastric GIST was developed and the model was validated in the validation group. RESULTS: A total of 494 gastric GIST patients were included in this study and allocated to a development group (n = 345) and a validation group (n = 149). In the development group, multivariate logistic regression analysis revealed that tumor size, tumor ulceration, CT growth pattern and monocyte-to- lymphocyte ratio (MLR) were independent risk factors for gastric GIST with high malignancy potential. The AUC of the model were 0.932 (95% CI 0.890-0.974) and 0.922 (95% CI 0.868-0.977) in the development and validation groups, respectively. The best cutoff value for the development group was 0.184, and the sensitivity and specificity at this value were 0.895 and 0.875, respectively. The calibration curves indicated good agreement between predicted and actual observed outcomes, while the DCA indicated that the nomogram model had clinical application. CONCLUSIONS: Tumor size, tumor ulceration, CT growth pattern and MLR are independent risk factors for high malignancy potential gastric GIST, and a nomogram model developed based on these factors has a high ability to predict the malignant potential of gastric GIST.

Original speech and its echo are segregated and separately processed in the human brain.

Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.

Learning With Constraint Learning: New Perspective, Solution Strategy and Various Applications.

The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms. Existing approaches often address these problems in isolation, lacking a unified perspective that can reveal commonalities and enable effective solutions. Therefore, in this work, we proposed a new framework, named Learning with Constraint Learning (LwCL), that can holistically examine challenges and provide a unified methodology to tackle all the above-mentioned complex learning and vision problems. Specifically, LwCL is designed as a general hierarchical optimization model that captures the essence of these diverse learning and vision problems. Furthermore, we develop a gradient-response based fast solution strategy to overcome optimization challenges of the LwCL framework. Our proposed framework efficiently addresses a wide range of applications in learning and vision, encompassing three categories and nine different problem types. Extensive experiments on synthetic tasks and real-world applications verify the effectiveness of our approach. The LwCL framework offers a comprehensive solution for tackling complex machine learning and computer vision problems, bridging the gap between theory and practice.

Solid Additive Dual-Regulates Spectral Response Enabling High-Performance Semitransparent Organic Solar Cells.

Semi-transparent organic solar cells (ST-OSCs) possess significant potential for applications in vehicles and buildings due to their distinctive visual transparency. Conventional device engineering strategies are typically used to optimize photon selection and utilization at the expense of power conversion efficiency (PCE); moreover, the fixed spectral utilization range always imposes an unsatisfactory upper limit to its light utilization efficiency (LUE). Herein, a novel solid additive named 1,3-diphenoxybenzene (DB) is employed to dual-regulate donor/acceptor molecular aggregation and crystallinity, which effectively broadens the spectral response of ST-OSCs in near-infrared region. Besides, more visible light is allowed to pass through the devices, which enables ST-OSCs to possess satisfactory photocurrent and high average visible transmittance (AVT) simultaneously. Consequently, the optimal ST-OSC based on PP2+DB/BTP-eC9+DB achieves a superior LUE of 4.77%, representing the highest value within AVT range of 40-50%, which also correlates with the formation of multi-scale phase-separated morphology. Such results indicate that the ST-OSCs can simultaneously meet the requirements for minimum commercial efficiency and plant photosynthesis when integrated with the roofs of agricultural greenhouses. This work emphasizes the significance of additives to tune the spectral response in ST-OSCs, and charts the way for organic photovoltaics in economically sustainable agricultural development.

Estimating low concentration heavy metals in water through hyperspectral analysis and genetic algorithm-partial least squares regression.

Hyperspectral spectrum enables assessment of heavy metal content, but research on low concentration in water is limited. This study employed in situ hyperspectral data from Dalian Lake, Shanghai to develop a machine learning model for accurately determining heavy metal concentrations. Initially, we employed a combination of empirical analysis and algorithm-based analysis to identify the optimal features for retrieving Cu and Fe ions. Based on the correlation coefficients between heavy metals and water quality, the feature bands for TOC, Chl-a and TP were selected as empirical features. Algorithm-based feature selection was conducted by employing the random forest (RF) approach with the original spectrum (OR), first-order derivative reflectance (FDR), and second-order derivative reflectance (SDR). For the development of a prediction model, we utilized the Genetic Algorithm-Partial Least Squares Regression (GA-PLSR) approach for Cu and Fe ions inversion. Our findings demonstrated that the integration of both empirical features and algorithm-selected features resulted in superior performance compared to using algorithm-selected features alone. Importantly, the crucial wavelength data primarily located at 497, 665, 686, 831 and 935 nm showed superior results for Cu retrieval, while wavelengths of 700, 746, 801, 948, and 993 nm demonstrated better results for Fe retrieval. These results also displayed that the GA-PLSR model outperformed both the PLSR and RF models, exhibiting an R2 of 0.75, RMSE of 0.004, and MRE of 0.382 for Cu inversion. For Fe inversion, the GA-PLSR model outperformed other models with an R2 of 0.73, RMSE of 0.036, and MRE of 0.464. This research provides a scientific basis and data support for monitoring low concentrations of heavy metals in water bodies using hyperspectral remote sensing techniques.

Multifunctional Ni-NPC Single-Atom Nanozyme for Removal and Smartphone-Assisted Visualization Monitoring of Carbamate Pesticides.

A multifunctional single-atom nanozyme, denoted as 3D Ni,N-codoped porous carbon (Ni-NPC), was devised that exhibits remarkable adsorption capabilities and a repertoire of enzyme mimetic functions (oxidase- and peroxidase-like). These attributes stem from the distinctive mesoporous thin-shell structure and well-dispersed Ni sites. The efficient adsorption capacity of Ni-NPC was assessed with respect to three carbamate pesticides (CMPs): metolcarb, carbaryl, and isoprocarb. Moreover, a colorimetric detection method for CMP was established based on its robust peroxidase-like catalytic activity and sequential catalytic interactions with acetylcholinesterase. Furthermore, a portable colorimetric sensor based on a hydrogel sphere integrated with a smartphone platform was devised. This sensor enables rapid, on-site, and quantitative assessment of CMP, boasting an extraordinarily low detection limit of 1.5 ng mL-1. Notably, this sensor was successfully applied to the analysis of CMP levels in lake water and vegetable samples (pakchoi and rape), propelling the progress of real-time detection technologies in food and environment monitoring.

Genome-wide profiling of piggyBac transposon insertion mutants reveals loss of the F1F0 ATPase complex causes fluconazole resistance in Candida glabrata.

Invasive candidiasis caused by non-albicans species has been on the rise, with Candida glabrata emerging as the second most common etiological agent. Candida glabrata possesses an intrinsically lower susceptibility to azoles and an alarming propensity to rapidly develop high-level azole resistance during treatment. In this study, we have developed an efficient piggyBac (PB) transposon-mediated mutagenesis system in C. glabrata to conduct genome-wide genetic screens and applied it to profile genes that contribute to azole resistance. When challenged with the antifungal drug fluconazole, PB insertion into 270 genes led to significant resistance. A large subset of these genes has a role in the mitochondria, including almost all genes encoding the subunits of the F1F0 ATPase complex. We show that deleting ATP3 or ATP22 results in increased azole resistance but does not affect susceptibility to polyenes and echinocandins. The increased azole resistance is due to increased expression of PDR1 that encodes a transcription factor known to promote drug efflux pump expression. Deleting PDR1 in the atp3Δ or atp22Δ mutant resulted in hypersensitivity to fluconazole. Our results shed light on the mechanisms contributing to azole resistance in C. glabrata. This PB transposon-mediated mutagenesis system can significantly facilitate future genome-wide genetic screens.

Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study.

The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.