In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOL...In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOLO)v8n network is proposed.First,a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual(DWR)module and a dilated reparameterization block(DRB)to replace the C2f module at the high level of the backbone network,enriching the gradient flow information and increasing the effective receptive field(ERF).Second,an efficient local attention(ELA)mechanism is fused with the high-level screening-feature pyramid networks(HS-FPN)module,and an ELA_HSFPN is designed to replace the original feature fusion module,enhancing the ability of the network to cope with multiscale detection tasks.Moreover,a lightweight shared convolutional detection head(SCDH)is introduced to reduce the number of parameters and the computational complexity of the module while enhancing the performance and generalizability of the model.Finally,the soft intersection over union(SIoU)replaces the original loss function to improve the convergence speed and prediction accuracy of the model.Experimental results show that compared with that of the original YOLOv8n model,the mAP@0.5 of the improved algorithm is increased by 5.1%,the number of parameters and computational complexity are reduced by 33.3%and 32.1%,respectively,and the FPS is increased by 4.9%.Compared with other mainstream object detection algorithms,the improved algorithm still leads in terms of core indicators and has good generalizability for surface defects encountered in other industrial scenarios.展开更多
Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges ...Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.展开更多
Aromatic nitro compounds present substantial health and environmental concerns due to their toxic nature and potential explosive properties.Consequently,the development of host–vip molecular recognition systems for...Aromatic nitro compounds present substantial health and environmental concerns due to their toxic nature and potential explosive properties.Consequently,the development of host–vip molecular recognition systems for these compounds serves a dual-purpose:enabling the fabrication of high-performance sensors for detection and guiding the design of efficient adsorbents for environmental remediation.This study investigated the host–vip recognition behavior of perethylated pillar[n]arenes toward two aromatic nitro molecules,1-chloro-2,4-dinitrobenzene and picric acid.Various techniques including^(1)H NMR,2D NOESY NMR,and UV-vis spectroscopy were employed to explore the binding behavior between pillararenes and aromatic nitro vips in solution.Moreover,valuable single crystal structures were obtained to elucidate the distinct solid-state assembly behaviors of these vips with different pillararenes.The assembled solid-state supramolecular structures observed encompassed a 1:1 host–vip inclusion complex,an external binding complex,and an exo-wall tessellation complex.Furthermore,based on the findings from these systems,a pillararene-based test paper was developed for efficient picric acid detection,and the removal of picric acid from solution was also achieved using pillararenes powder.This research provides novel insights into the development of diverse host–vip systems toward hazardous compounds,offering potential applications in environmental protection and explosive detection domains.展开更多
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 arc...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.展开更多
In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,wh...In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.展开更多
[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were ...[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were obtained in Escherichia coli prokaryotic expression system by optimizing codons and expression conditions of E.coli.Furthermore,based on the purified soluble N protein and NH fusion protein,a high-sensitivity fluorescence immunoassay kit for detecting the antibody against PPR V was established.[Results]The method could quickly and quantitatively detect PPR V antibody in sheep serum,with high sensitivity and specificity,without any cross reaction to other related sheep pathogens.The intra-batch and inter-batch coefficients of variation were less than 10%and 15%,respectively,and the method had good repeatability.Through detection on 292 clinical serum samples,it was compared with the French IDVET competitive ELISA kit,and the coincidence rate of the two methods reached 93.84%.Compared with the serum neutralization test,the detected titer value of the high-sensitivity rapid fluorescence quantitative detection method was basically consistent with the tilter value obtained by the neutralization test on the standard positive serum(provided by the WOAH Brucellosis Reference Laboratory of France).[Conclusions]This method can realize rapid quantitative detection of PPR V antibody on site,and has high practical value and popularization value.展开更多
目的:基于SEER数据库的宫颈癌患者影响因素分析,分析影响宫颈癌预后的相关因素,为宫颈癌患者预后恢复及治疗提供科学参考依据。方法:利用SEER数据库初步筛查宫颈癌相关数据,从中下载所有符合研究的宫颈癌患者的不同病理分型的数据,选用...目的:基于SEER数据库的宫颈癌患者影响因素分析,分析影响宫颈癌预后的相关因素,为宫颈癌患者预后恢复及治疗提供科学参考依据。方法:利用SEER数据库初步筛查宫颈癌相关数据,从中下载所有符合研究的宫颈癌患者的不同病理分型的数据,选用第八期T,N,M分期对数据的T,N,M分期进行整理,标准化,筛选,筛选条件为患者第八期T,N,M分期明确,有符合要求的观察结局,如死亡或存活。通过筛查出的患者的不同病理分型的数据,运用SPSS26.0统计软件进行统计描述,采用Kaplan-Meier法。描绘生存曲线并估计患者的生存率,死亡率。生存时间分布的组间比较采用Log-Rank检验,对分析结果中P α = 0.05,以P Objective: Study on the prognostic risk model of cervical cancer patients based on SEER database, analyze the relevant factors affecting the prognosis of cervical cancer, and provide scientific reference for the prognosis recovery and treatment of cervical cancer patients. Methods: The SEER database was used to preliminarily screen the data related to cervical cancer, download the data of different pathological types of cervical cancer patients in line with the study, and select the eighth stage T, N, M to sort out, standardize and screen the T, N and M stages of the data. The screening conditions were that the eighth stage T, N and M stages of patients were clear, and there were satisfactory observed outcomes, such as death or survival. Through the data of different pathological types of patients screened, spss26 0 statistical software for statistical description, using Kaplan Meier method. Draw the survival curve and estimate the survival rate and mortality of patients. The inter group comparison of survival time distribution adopts log rank test to conduct multi factor Cox analysis on the variables with statistical significance of P α = 0.05, P < 0.05. Result: (1) Kaplan Meier univariate analysis: different pathological stages of T stage, N stage and M stage have an impact on the survival of patients, which is statistically significant (P < 0.05), and can affect the survival of patients to varying degrees. (2) Cox multivariate analysis: T stage, N stage and M stage were independent prognostic factors affecting the survival time of patients. Conclusion: Different T, N and M pathological stages of different cervical cancer patients can affect the prognosis of cervical cancer patients to varying degrees. They have a significant impact on the mortality and survival rate of cervical cancer patients, and can provide relevant basis for the development and improvement of the treatment scheme of cervical cancer.展开更多
RNA contains diverse post-transcriptional modifications,and its catabolic breakdown yields numerous modified nucleosides requiring correct processing,but the mechanisms remain unknown.Here,we demonstrate that three RN...RNA contains diverse post-transcriptional modifications,and its catabolic breakdown yields numerous modified nucleosides requiring correct processing,but the mechanisms remain unknown.Here,we demonstrate that three RNA-derived modified adenosines,N6-methyladenosine(m6A),N6,N6-dimethyladenosine(m6,6A),and N6-isopentenyladenosine(i6A),are sequentially metabolized into inosine monophosphate(IMP)to mitigate their intrinsic cytotoxicity.展开更多
Chirality,a common phenomenon in nature,appears in structures ranging from galaxies and condensed matter to atomic nuclei.There is a persistent demand for new,high-precision methods to detect chiral structures,particu...Chirality,a common phenomenon in nature,appears in structures ranging from galaxies and condensed matter to atomic nuclei.There is a persistent demand for new,high-precision methods to detect chiral structures,particularly at the microscale.Here,we propose a novel method,vortex Mössbauer spectroscopy,for probing chiral structures.By leveraging the orbital angular momentum carried by vortex beams,this approach achieves high precision in detecting chiral structures at scales ranging from nanometers to hundreds of nanometers.Our simulation shows the ratio of characteristic lines in the Mössbauer spectra of ^(57)Fe under vortex beams exhibits differences of up to four orders of magnitude for atomic structures with different arrangements.Additionally,simulations reveal the response of ^(229m)Th chiral structures to vortex beams with opposite angular momenta differs by approximately 49-fold.These significant spectral variations indicate that this new vortex Mössbauer probe holds great potential for investigating the microscopic chiral structures and interactions of matter.展开更多
Ménétrier disease(MD)is a rare gastric disorder characterized by hypertrophy of the gastric mucosa,resulting in giant gastric folds,excessive mucus secretion,and significant protein loss.It is most common in...Ménétrier disease(MD)is a rare gastric disorder characterized by hypertrophy of the gastric mucosa,resulting in giant gastric folds,excessive mucus secretion,and significant protein loss.It is most common in middle-aged males,although cases have also been reported in children.This condition,also known as hyperplastic hypersecretory gastropathy,primarily affects the fundus and body of the stomach,leading to protein-losing gastropathy due to increased mucosal permeability.The exact pathogenesis of MD remains unclear;however,it has been linked to excessive transforming growth factor-alpha signaling via the epidermal growth factor receptor(EGFR),which promotes mucosal hyperplasia.Clinical manifestations include epigastric pain,nausea,vomiting,anorexia,weight loss,and peripheral edema due to protein-losing enteropathy.Diagnosis is based on clinical presentation,endoscopic findings,and histopathology revealing foveolar hyperplasia and glandular atrophy.Treatment options vary from symptomatic management with proton pump inhibitors and nutritional support to monoclonal antibodies targeting EGFR(e.g.,cetuximab)in severe cases.In refractory situations,gastrectomy may be required.This review highlights the current understanding,diagnostic approaches,and therapeutic strategies for MD.展开更多
基金supported by the Jiangsu Province Science and Technology Policy Guidance Program(Industry-University-Research Cooperation)/Forward-Looking Joint Research Project(BY2016005-05).
文摘In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOLO)v8n network is proposed.First,a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual(DWR)module and a dilated reparameterization block(DRB)to replace the C2f module at the high level of the backbone network,enriching the gradient flow information and increasing the effective receptive field(ERF).Second,an efficient local attention(ELA)mechanism is fused with the high-level screening-feature pyramid networks(HS-FPN)module,and an ELA_HSFPN is designed to replace the original feature fusion module,enhancing the ability of the network to cope with multiscale detection tasks.Moreover,a lightweight shared convolutional detection head(SCDH)is introduced to reduce the number of parameters and the computational complexity of the module while enhancing the performance and generalizability of the model.Finally,the soft intersection over union(SIoU)replaces the original loss function to improve the convergence speed and prediction accuracy of the model.Experimental results show that compared with that of the original YOLOv8n model,the mAP@0.5 of the improved algorithm is increased by 5.1%,the number of parameters and computational complexity are reduced by 33.3%and 32.1%,respectively,and the FPS is increased by 4.9%.Compared with other mainstream object detection algorithms,the improved algorithm still leads in terms of core indicators and has good generalizability for surface defects encountered in other industrial scenarios.
文摘Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.
基金supported by the fundamental research funds of Zhejiang Sci-Tech University(No.22212286-Y)the Natural Science Foundation of Zhejiang Province(No.LQ24B040003)。
文摘Aromatic nitro compounds present substantial health and environmental concerns due to their toxic nature and potential explosive properties.Consequently,the development of host–vip molecular recognition systems for these compounds serves a dual-purpose:enabling the fabrication of high-performance sensors for detection and guiding the design of efficient adsorbents for environmental remediation.This study investigated the host–vip recognition behavior of perethylated pillar[n]arenes toward two aromatic nitro molecules,1-chloro-2,4-dinitrobenzene and picric acid.Various techniques including^(1)H NMR,2D NOESY NMR,and UV-vis spectroscopy were employed to explore the binding behavior between pillararenes and aromatic nitro vips in solution.Moreover,valuable single crystal structures were obtained to elucidate the distinct solid-state assembly behaviors of these vips with different pillararenes.The assembled solid-state supramolecular structures observed encompassed a 1:1 host–vip inclusion complex,an external binding complex,and an exo-wall tessellation complex.Furthermore,based on the findings from these systems,a pillararene-based test paper was developed for efficient picric acid detection,and the removal of picric acid from solution was also achieved using pillararenes powder.This research provides novel insights into the development of diverse host–vip systems toward hazardous compounds,offering potential applications in environmental protection and explosive detection domains.
基金funded by the General Project of Key Research and Develop-ment Plan of Shaanxi Province(No.2022NY-087).
文摘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.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003).
文摘In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.
基金Supported by The National Project for the Prevention and Control of Major Exotic Animal Diseases(2022YFD1800500)National Mutton Sheep Industrial Technology System(CARS39).
文摘[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were obtained in Escherichia coli prokaryotic expression system by optimizing codons and expression conditions of E.coli.Furthermore,based on the purified soluble N protein and NH fusion protein,a high-sensitivity fluorescence immunoassay kit for detecting the antibody against PPR V was established.[Results]The method could quickly and quantitatively detect PPR V antibody in sheep serum,with high sensitivity and specificity,without any cross reaction to other related sheep pathogens.The intra-batch and inter-batch coefficients of variation were less than 10%and 15%,respectively,and the method had good repeatability.Through detection on 292 clinical serum samples,it was compared with the French IDVET competitive ELISA kit,and the coincidence rate of the two methods reached 93.84%.Compared with the serum neutralization test,the detected titer value of the high-sensitivity rapid fluorescence quantitative detection method was basically consistent with the tilter value obtained by the neutralization test on the standard positive serum(provided by the WOAH Brucellosis Reference Laboratory of France).[Conclusions]This method can realize rapid quantitative detection of PPR V antibody on site,and has high practical value and popularization value.
文摘目的:基于SEER数据库的宫颈癌患者影响因素分析,分析影响宫颈癌预后的相关因素,为宫颈癌患者预后恢复及治疗提供科学参考依据。方法:利用SEER数据库初步筛查宫颈癌相关数据,从中下载所有符合研究的宫颈癌患者的不同病理分型的数据,选用第八期T,N,M分期对数据的T,N,M分期进行整理,标准化,筛选,筛选条件为患者第八期T,N,M分期明确,有符合要求的观察结局,如死亡或存活。通过筛查出的患者的不同病理分型的数据,运用SPSS26.0统计软件进行统计描述,采用Kaplan-Meier法。描绘生存曲线并估计患者的生存率,死亡率。生存时间分布的组间比较采用Log-Rank检验,对分析结果中P α = 0.05,以P Objective: Study on the prognostic risk model of cervical cancer patients based on SEER database, analyze the relevant factors affecting the prognosis of cervical cancer, and provide scientific reference for the prognosis recovery and treatment of cervical cancer patients. Methods: The SEER database was used to preliminarily screen the data related to cervical cancer, download the data of different pathological types of cervical cancer patients in line with the study, and select the eighth stage T, N, M to sort out, standardize and screen the T, N and M stages of the data. The screening conditions were that the eighth stage T, N and M stages of patients were clear, and there were satisfactory observed outcomes, such as death or survival. Through the data of different pathological types of patients screened, spss26 0 statistical software for statistical description, using Kaplan Meier method. Draw the survival curve and estimate the survival rate and mortality of patients. The inter group comparison of survival time distribution adopts log rank test to conduct multi factor Cox analysis on the variables with statistical significance of P α = 0.05, P < 0.05. Result: (1) Kaplan Meier univariate analysis: different pathological stages of T stage, N stage and M stage have an impact on the survival of patients, which is statistically significant (P < 0.05), and can affect the survival of patients to varying degrees. (2) Cox multivariate analysis: T stage, N stage and M stage were independent prognostic factors affecting the survival time of patients. Conclusion: Different T, N and M pathological stages of different cervical cancer patients can affect the prognosis of cervical cancer patients to varying degrees. They have a significant impact on the mortality and survival rate of cervical cancer patients, and can provide relevant basis for the development and improvement of the treatment scheme of cervical cancer.
文摘RNA contains diverse post-transcriptional modifications,and its catabolic breakdown yields numerous modified nucleosides requiring correct processing,but the mechanisms remain unknown.Here,we demonstrate that three RNA-derived modified adenosines,N6-methyladenosine(m6A),N6,N6-dimethyladenosine(m6,6A),and N6-isopentenyladenosine(i6A),are sequentially metabolized into inosine monophosphate(IMP)to mitigate their intrinsic cytotoxicity.
基金supported in part by the National Key R&D Program(Grant No.2023YFA1606900)the National Natural Science Foundation of China(Grant No.12235003)。
文摘Chirality,a common phenomenon in nature,appears in structures ranging from galaxies and condensed matter to atomic nuclei.There is a persistent demand for new,high-precision methods to detect chiral structures,particularly at the microscale.Here,we propose a novel method,vortex Mössbauer spectroscopy,for probing chiral structures.By leveraging the orbital angular momentum carried by vortex beams,this approach achieves high precision in detecting chiral structures at scales ranging from nanometers to hundreds of nanometers.Our simulation shows the ratio of characteristic lines in the Mössbauer spectra of ^(57)Fe under vortex beams exhibits differences of up to four orders of magnitude for atomic structures with different arrangements.Additionally,simulations reveal the response of ^(229m)Th chiral structures to vortex beams with opposite angular momenta differs by approximately 49-fold.These significant spectral variations indicate that this new vortex Mössbauer probe holds great potential for investigating the microscopic chiral structures and interactions of matter.
文摘Ménétrier disease(MD)is a rare gastric disorder characterized by hypertrophy of the gastric mucosa,resulting in giant gastric folds,excessive mucus secretion,and significant protein loss.It is most common in middle-aged males,although cases have also been reported in children.This condition,also known as hyperplastic hypersecretory gastropathy,primarily affects the fundus and body of the stomach,leading to protein-losing gastropathy due to increased mucosal permeability.The exact pathogenesis of MD remains unclear;however,it has been linked to excessive transforming growth factor-alpha signaling via the epidermal growth factor receptor(EGFR),which promotes mucosal hyperplasia.Clinical manifestations include epigastric pain,nausea,vomiting,anorexia,weight loss,and peripheral edema due to protein-losing enteropathy.Diagnosis is based on clinical presentation,endoscopic findings,and histopathology revealing foveolar hyperplasia and glandular atrophy.Treatment options vary from symptomatic management with proton pump inhibitors and nutritional support to monoclonal antibodies targeting EGFR(e.g.,cetuximab)in severe cases.In refractory situations,gastrectomy may be required.This review highlights the current understanding,diagnostic approaches,and therapeutic strategies for MD.
