Stage Ⅳ non-small cell lung cancer with multiple metastases to the small intestine leading to intussusception: A case report

BACKGROUND Gastrointestinal tract metastasis from lung cancer is rare and compared to small cell lung cancer(SCLC),non-SCLC(NSCLC)is even less likely to metastasize in this manner.Additionally,small intestinal tumors can also present with diverse complications,some of which require urgent intervention.CASE SUMMARY In this report,we detail a unique case of stage IV lung cancer,where the presence of small intestine tumors led to intussusception.Subsequent to a small intestine resection,pathology confirmed that all three tumors within the small intestine were metastases from adenocarcinoma of the lung.The postoperative follow-up period extended beyond 14 mo.CONCLUSION In patients with stage IV NSCLC,local tumor control can be achieved with various treatments.However,if small intestinal metastasis occurs,surgical intervention remains necessary,as it may improve survival.

基于MobileNetV3Small-ECA的水稻病害轻量级识别研究

为实现水稻病害的轻量化识别与检测,使用ECA注意力机制改进MobileNetV3Small模型,并使用共享参数迁移学习对水稻病害进行智能化轻量级识别和检测。在PlantVillage数据集上进行预训练,将预训练得到的共享参数迁移到对水稻病害识别模型上微调优化。在开源水稻病害数据集上进行试验测试,试验结果表明,在非迁移学习下,识别准确率达到97.47%,在迁移学习下识别准确率达到99.92%,同时参数量减少26.69%。其次,通过Grad-CAM进行可视化,本文方法与其他注意力机制CBAM和SENET相比,ECA模块生成的结果与图像中病斑的位置和颜色更加一致,表明网络可以更好地聚焦水稻病害的特征,并且通过可视化和各水稻病害分析了误分类原因。本文方法实现了水稻病害识别模型的轻量化,使其能够在移动设备等资源受限的场景中部署,达到快速、高效、便携的目的。同时开发了基于Android的水稻病害识别系统,方便于在边缘端进行水稻病害识别分析。

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

Blood-brain barrier pathology in cerebral small vessel disease

Cerebral small vessel disease is a neurological disease that affects the brain microvasculature and which is commonly observed among the elderly.Although at first it was considered innocuous,small vessel disease is nowadays regarded as one of the major vascular causes of dementia.Radiological signs of small vessel disease include small subcortical infarcts,white matter magnetic resonance imaging hyperintensities,lacunes,enlarged perivascular spaces,cerebral microbleeds,and brain atrophy;however,great heterogeneity in clinical symptoms is observed in small vessel disease patients.The pathophysiology of these lesions has been linked to multiple processes,such as hypoperfusion,defective cerebrovascular reactivity,and blood-brain barrier dysfunction.Notably,studies on small vessel disease suggest that blood-brain barrier dysfunction is among the earliest mechanisms in small vessel disease and might contribute to the development of the hallmarks of small vessel disease.Therefore,the purpose of this review is to provide a new foundation in the study of small vessel disease pathology.First,we discuss the main structural domains and functions of the blood-brain barrier.Secondly,we review the most recent evidence on blood-brain barrier dysfunction linked to small vessel disease.Finally,we conclude with a discussion on future perspectives and propose potential treatment targets and interventions.

Cognitive impairment in cerebral small vessel disease induced by hypertension

Hypertension is a primary risk factor for the progression of cognitive impairment caused by cerebral small vessel disease,the most common cerebrovascular disease.Howeve r,the causal relationship between hypertension and cerebral small vessel disease remains unclear.Hypertension has substantial negative impacts on brain health and is recognized as a risk factor for cerebrovascular disease.Chronic hypertension and lifestyle factors are associated with risks for stro ke and dementia,and cerebral small vessel disease can cause dementia and stroke.Hypertension is the main driver of cerebral small vessel disease,which changes the structure and function of cerebral vessels via various mechanisms and leads to lacunar infarction,leukoaraiosis,white matter lesions,and intracerebral hemorrhage,ultimately res ulting in cognitive decline and demonstrating that the brain is the to rget organ of hypertension.This review updates our understanding of the pathogenesis of hypertensioninduced cerebral small vessel disease and the res ulting changes in brain structure and function and declines in cognitive ability.We also discuss drugs to treat cerebral small vessel disease and cognitive impairment.

Small nucleolar RNA and its potential role in the oncogenesis and development of colorectal cancer

Small nucleolar RNAs(snoRNAs)represent a class of non-coding RNAs that play pivotal roles in post-transcriptional RNA processing and modification,thereby contributing significantly to the maintenance of cellular functions related to protein synthesis.SnoRNAs have been discovered to possess the ability to influence cell fate and alter disease progression,holding immense potential in controlling human diseases.It is suggested that the dysregulation of snoRNAs in cancer exhibits differential expression across various cancer types,stages,metastasis,treatment response and/or prognosis in patients.On the other hand,colorectal cancer(CRC),a prevalent malignancy of the digestive system,is characterized by high incidence and mortality rates,ranking as the third most common cancer type.Recent research indicates that snoRNA dysregulation is associated with CRC,as snoRNA expression significantly differs between normal and cancerous conditions.Consequently,assessing snoRNA expression level and function holds promise for the prognosis and diagnosis of CRC.Nevertheless,current comprehension of the potential roles of snoRNAs in CRC remains limited.This review offers a comprehensive survey of the aberrant regulation of snoRNAs in CRC,providing valuable insights into the discovery of novel biomarkers,therapeutic targets,and potential tools for the diagnosis and treatment of CRC and furnishing critical cues for advancing research into CRC and the judicious selection of therapeutic targets.

Development of small molecule drugs targeting immune checkpoints

Immune checkpoint inhibitors(ICIs)are used to relieve and refuel anti-tumor immunity by blocking the interaction,transcription,and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune checkpoints.Thousands of small molecule drugs or biological materials,especially antibody-based ICIs,are actively being studied and antibodies are currently widely used.Limitations,such as anti-tumor efficacy,poor membrane permeability,and unneglected tolerance issues of antibody-based ICIs,remain evident but are thought to be overcome by small molecule drugs.Recent structural studies have broadened the scope of candidate immune checkpoint molecules,as well as innovative chemical inhibitors.By way of comparison,small molecule drug-based ICIs represent superior oral bioavailability and favorable pharmacokinetic features.Several ongoing clinical trials are exploring the synergetic effect of ICIs and other therapeutic strategies based on multiple ICI functions,including immune regulation,anti-angiogenesis,and cell cycle regulation.In this review we summarized the current progression of small molecule ICIs and the mechanism underlying immune checkpoint proteins,which will lay the foundation for further exploration.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

H-and J-aggregation of conjugated small molecules in organic solar cells

As H-and J-aggregation receive more and more attention in the research of organic solar cells(OSCs),especially in small molecular systems,deep understanding of aggregation behavior is needed to guide the design of conjugated small molecular structure and the fabrication process of OSC device.For this end,this review is written.Here,the review firstly introduced the basic information about H-and J-aggregation of conjugated small molecules in OSCs.Then,the characteristics of H-and J-aggregation and the methods to identify them were summarized.Next,it reviewed the research progress of H-and J-aggregation of conjugated small molecules in OSCs,including the factors influencing H-and J-aggregation in thin film and the effects of H-and J-aggregation on OPV performance.

Responses of Nutrients to the Precipitation Variation and Land Use in Subtropical Monsoonal Small Mountainous Rivers:A Case Study of Baixi Watershed

Small mountainous rivers are characterized by large instantaneous fluxes and susceptible to extreme weather events,which can rapidly transport materials into the sea and have a significant impact on the ecological environment of estuaries and bays.In order to investigate the seasonal characteristics of nutrients in small mountainous rivers in the subtropical monsoon region and the output pattern to the sea during heavy precipitation,surveys on the mountainous rivers were carried out in Baixi watershed in August 2020(wet season),March 2021(dry season)and June 2021(Meiyu period).The results showed that the dissolved inorganic nitrogen(DIN)of the rivers has an average concentration of 752μg L^(−1)in the wet season and 1472μg L^(−1)in the dry season.The concentrations of dissolved inorganic phosphorus(DIP)in wet season and dry season were 63μg L^(−1)and 51μg L^(−1),respectively.Influenced by the changes of land use in sub-watersheds,DIN concentrations in the mainstream increased from 701μg L^(−1)in the upper reaches to 1284μg L^(−1)in the middle reaches.Two rainstorms during the Meiyu period in the watershed caused the pulse runoff in the river.The maximum daily runoff reached 70 times that before rains.The maximum daily fluxes of DIN and DIP were 109 and 247 times that before rains,respectively.In view that the watershed experienced several rainstorms in the wet season,the river,with pulse runoff,carries a large amount of nutrients into the sea in a short time,which will have a significant impact on the environment of Sanmen bay and its adjacent sea.

Socio-environmental factors associated with small mammal assemblage and Leptospira prevalence in Suburban Areas of Terengganu,Malaysia

Objective:To examine the socio-environmental factors associated with the assemblage of small mammals and the prevalence of Leptospira pathogen in poor suburban communities of Terengganu,Malaysia.Methods:We trapped small mammals from 119 trapping points scattered around three suburban communities of Terengganu using sausage-baited live traps.On the average,we set up five traps for three nights at each sampling point during the trapping period.Kidneys of captured animals were harvested and processed for Leptospira investigation.Additionally,environmental survey was conducted at each trapping point to obtain information about possible variables supporting small mammal assemblage.We used a generalized linear model to evaluate the effect of different socio-environmental variables on small mammals’assemblage.Results:A total of 89 small mammals,specifically,Rattus norvegicus(n=39),Rattus rattus(n=27),Rattus exulans(n=10),Suncus murinus(n=11),and Tupaia glis(n=2)were captured from 1385 trap nights.Fourteen individuals(15.7%)of the captured animals tested positive for Leptospira bacteria using PCR detection.Results of our generalized linear model showed only residences bordering vacant lots as the variable positively associated with small mammal occurrence in the three study sites.Conclusions:Small mammal community,especially the often neglected species,could harbour and potentially contribute towards pathogenic Leptospira maintenance in the study sites.To adequately control small mammals’population and subsequent human zoonoses transmission,it is critical to advocate and promote appropriate infrastructure and suburban services,together with good hygiene practices that can reduce the animals’access to food and harborage.

Small extracellular vesicles derived from cerebral endothelial cells with elevated microRNA 27a promote ischemic stroke recovery

Axonal remodeling is a critical aspect of ischemic brain repair processes and contributes to spontaneous functional recovery.Our previous in vitro study demonstrated that exosomes/small extracellular vesicles(sEVs)isolated from cerebral endothelial cells(CEC-sEVs)of ischemic brain promote axonal growth of embryonic cortical neurons and that microRNA 27a(miR-27a)is an elevated miRNA in ischemic CEC-sEVs.In the present study,we investigated whether normal CEC-sEVs engineered to enrich their levels of miR-27a(27a-sEVs)further enhance axonal growth and improve neurological outcomes after ischemic stroke when compared with treatment with non-engineered CEC-sEVs.27a-sEVs were isolated from the conditioned medium of healthy mouse CECs transfected with a lentiviral miR-27a expression vector.Small EVs isolated from CECs transfected with a scramble vector(Scra-sEVs)were used as a control.Adult male mice were subjected to permanent middle cerebral artery occlusion and then were randomly treated with 27a-sEVs or Scra-sEVs.An array of behavior assays was used to measure neurological function.Compared with treatment of ischemic stroke with Scra-sEVs,treatment with 27a-sEVs significantly augmented axons and spines in the peri-infarct zone and in the corticospinal tract of the spinal grey matter of the denervated side,and significantly improved neurological outcomes.In vitro studies demonstrated that CEC-sEVs carrying reduced miR-27a abolished 27a-sEV-augmented axonal growth.Ultrastructural analysis revealed that 27a-sEVs systemically administered preferentially localized to the pre-synaptic active zone,while quantitative reverse transcription-polymerase chain reaction and Western Blot analysis showed elevated miR-27a,and reduced axonal inhibitory proteins Semaphorin 6A and Ras Homolog Family Member A in the peri-infarct zone.Blockage of the Clathrin-dependent endocytosis pathway substantially reduced neuronal internalization of 27a-sEVs.Our data provide evidence that 27a-sEVs have a therapeutic effect on stroke recovery by promoting axonal remodeling and improving neurological outcomes.Our findings also suggest that suppression of axonal inhibitory proteins such as Semaphorin 6A may contribute to the beneficial effect of 27a-sEVs on axonal remodeling.

Artificial intelligence in detection of small bowel lesions and their bleeding risk:A new step forward

The present letter to the editor is related to the study with the title“Automatic detection of small bowel(SB)lesions with different bleeding risk based on deep learning models”.Capsule endoscopy(CE)is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate.The development of artificial intelligence(AI)in CE could simplify physicians’tasks.The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk,and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.

Research on fast detection method of infrared small targets under resourceconstrained conditions

Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy.A fast infrared small target detection method tailored for resource-constrained conditions is pro⁃posed for the YOLOv5s model.This method introduces an additional small target detection head and replaces the original Intersection over Union(IoU)metric with Normalized Wasserstein Distance(NWD),while considering both the detection accuracy and the detection speed of infrared small targets.Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU,while reach⁃ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

THE EXISTENCE OF PSEUDOHARMONIC MAPS FOR SMALL HORIZONTAL ENERGY

In this paper,we consider pseudoharmonic heat flow with small initial horizontal energy and give the existence of pseudoharmonic maps from closed pseudo-Hermitian manifolds into closed Riemannian manifolds.

Gastric metastasis of small cell lung carcinoma:Three case reports and review of literature

BACKGROUND Small cell lung carcinoma(SCLC)is highly susceptible to metastasis in the early stages of the disease.However,the stomach is an uncommon site of metastasis in SCLC,and only a few cases of this type of metastasis have been reported.Therefore,SCLC gastric metastases have not been systematically characterized and are easily missed and misdiagnosed.CASE SUMMARY We report three cases of gastric metastasis from SCLC in this article.The first patient presented primarily with cough,hemoptysis,and epigastric fullness.The other two patients presented primarily with abdominal discomfort,epigastric distension,and pain.All patients underwent gastroscopy and imaging examinations.Meanwhile,the immunohistochemical results of the lesions in three patients were suggestive of small cell carcinoma.Finally,the three patients were diagnosed with gastric metastasis of SCLC through a comprehensive analysis.The three patients did not receive appropriate treatment and died within a short time.CONCLUSION Here,we focused on summarizing the characteristics of gastric metastasis of SCLC to enhance clinicians'understanding of this disease.

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.