This perspective focuses on the recent literature regarding the role of the gut-brain axis(GBA) in fecal microbiota transplantation(FMT) and stem cell therapy(SCT) in Parkinson's disease(PD).PD is the second most ...This perspective focuses on the recent literature regarding the role of the gut-brain axis(GBA) in fecal microbiota transplantation(FMT) and stem cell therapy(SCT) in Parkinson's disease(PD).PD is the second most common neurodegenerative disease in the United States,yet therapies remain limited.Current research suggests that the GBA may play a role in the pathogenesis of PD.GBAbased FMT as well as SCT offer promising new avenues for PD treatment.Pro bing the interactions between FMT and SCT with the GBA may reveal novel therapeutics for PD.展开更多
Clear cell renal cell carcinoma(ccRCC)represents the most frequent form of renal cell carcinoma(RCC),and accurate International Society of Urological Pathology(ISUP)grading is crucial for prognosis and treatment selec...Clear cell renal cell carcinoma(ccRCC)represents the most frequent form of renal cell carcinoma(RCC),and accurate International Society of Urological Pathology(ISUP)grading is crucial for prognosis and treatment selection.This study presents a new deep network called Multi-scale Fusion Network(MsfNet),which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images.The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity.The model was trained and tested using 90 Hematoxylin and Eosin(H&E)stained whole slide images(WSIs),which were all cropped into 320×320-pixel patches at 40×magnification.MsfNet achieved a micro-averaged area under the curve(AUC)of 0.9807,a macro-averaged AUC of 0.9778 on the test dataset.The Gradient-weighted Class Activation Mapping(Grad-CAM)visually demonstrated MsfNet’s ability to distinguish and highlight abnormal areas more effectively than ResNet50.The t-Distributed Stochastic Neighbor Embedding(t-SNE)plot indicates our model can efficiently extract critical features from images,reducing the impact of noise and redundant information.The results suggest that MsfNet offers an accurate ISUP grade of ccRCC in digital images,emphasizing the potential of AI-assisted histopathological systems in clinical practice.展开更多
Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’...Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.展开更多
目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的s...目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的shRNA(shRNA-METTL5)转染TNBC细胞后,用CCK-8、集落形成、伤口愈合以及Transwell实验分别检测细胞增殖活性、迁移与侵袭,Western blot检测Wnt/β-catenin信号关键蛋白的表达。构建异种移植瘤模型,验证敲降METTL5对TNBC细胞在体内生长以及Wnt/β-catenin信号活性的影响。结果METTL5在TNBC肿瘤组织和细胞系中表达上调(P<0.01)。敲降METTL5可抑制TNBC细胞的增殖、迁移和侵袭并降低了Wnt/β-catenin信号分子β-catenin、细胞周期蛋白(Cyclin)D1、基质金属蛋白酶(MMP)-2和MMP-7的表达(均P<0.01)。体内实验显示,敲降METTL5减缓了移植瘤生长和Wnt/β-catenin信号活性。结论敲降METTL5能抑制TNBC细胞的增殖、迁移与侵袭,其作用可能与抑制Wnt/β-catenin信号通路有关。展开更多
目的观察β-catenin/Slug信号特异性抑制剂FH535与EMT的关系,探讨LPCAT1在调节子宫颈癌细胞侵袭、转移和生长中的作用。方法采用sh-NC和sh-LPCAT1转染Hela细胞,利用载体(Vector)组和LPCAT1过表达质粒转染SiHa细胞,将SiHa细胞分为对照组(...目的观察β-catenin/Slug信号特异性抑制剂FH535与EMT的关系,探讨LPCAT1在调节子宫颈癌细胞侵袭、转移和生长中的作用。方法采用sh-NC和sh-LPCAT1转染Hela细胞,利用载体(Vector)组和LPCAT1过表达质粒转染SiHa细胞,将SiHa细胞分为对照组(Con)、LPCAT1组、LPCAT1+FH535组和FH535组。运用CCK-8法和集落形成试验检测子宫颈癌细胞的增殖。通过伤口愈合试验和Transwell实验检测子宫颈癌细胞的转移、侵袭能力。应用Western blot分析细胞中LPCAT1、β-catenin/Slug信号通路和EMT相关蛋白的表达。结果与Vector组相比,LPCAT1组SiHa细胞的活力、集落数、迁移和侵袭细胞数均显著增加(P<0.05);与sh-NC组相比,sh-LPCAT1组Hela细胞的活力、集落数、迁移和侵袭细胞数均显著降低(P<0.05)。与LPCAT1组相比,LPCAT1+FH535组SiHa细胞中Wnt4(1.18±0.05 vs 0.80±0.06)、β-catenin(1.05±0.08 vs 0.77±0.05)、Slug(1.13±0.06 vs 0.28±0.02)、Cyclin D1(0.99±0.06 vs 0.44±0.02)、N-cadherin(0.91±0.07 vs 0.46±0.03)和vimentin(0.95±0.06 vs 0.49±0.03)表达降低(P<0.05),E-cadherin(0.44±0.03 vs 0.58±0.03)表达增加(P<0.05)。此外,与LPCAT1组相比,LPCAT1+FH535组SiHa细胞的集落数(224±15 vs 146±11)、迁移数(85±3 vs 51±4)和侵袭数(166±10 vs 90±5)均降低(P<0.05)。结论LPCAT1表达增加可能通过激活β-catenin/Slug信号通路促进子宫颈癌的转移和进展,LPCAT1的靶向治疗有望提高子宫颈癌患者的预后。展开更多
文摘This perspective focuses on the recent literature regarding the role of the gut-brain axis(GBA) in fecal microbiota transplantation(FMT) and stem cell therapy(SCT) in Parkinson's disease(PD).PD is the second most common neurodegenerative disease in the United States,yet therapies remain limited.Current research suggests that the GBA may play a role in the pathogenesis of PD.GBAbased FMT as well as SCT offer promising new avenues for PD treatment.Pro bing the interactions between FMT and SCT with the GBA may reveal novel therapeutics for PD.
基金supported by the Scientific Research and Innovation Team of Hebei University(IT2023B07)the Natural Science Foundation of Hebei Province(F2023201069)the Postgraduate’s Innovation Fund Project of Hebei University(HBU2024BS021).
文摘Clear cell renal cell carcinoma(ccRCC)represents the most frequent form of renal cell carcinoma(RCC),and accurate International Society of Urological Pathology(ISUP)grading is crucial for prognosis and treatment selection.This study presents a new deep network called Multi-scale Fusion Network(MsfNet),which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images.The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity.The model was trained and tested using 90 Hematoxylin and Eosin(H&E)stained whole slide images(WSIs),which were all cropped into 320×320-pixel patches at 40×magnification.MsfNet achieved a micro-averaged area under the curve(AUC)of 0.9807,a macro-averaged AUC of 0.9778 on the test dataset.The Gradient-weighted Class Activation Mapping(Grad-CAM)visually demonstrated MsfNet’s ability to distinguish and highlight abnormal areas more effectively than ResNet50.The t-Distributed Stochastic Neighbor Embedding(t-SNE)plot indicates our model can efficiently extract critical features from images,reducing the impact of noise and redundant information.The results suggest that MsfNet offers an accurate ISUP grade of ccRCC in digital images,emphasizing the potential of AI-assisted histopathological systems in clinical practice.
文摘Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.
文摘目的观察β-catenin/Slug信号特异性抑制剂FH535与EMT的关系,探讨LPCAT1在调节子宫颈癌细胞侵袭、转移和生长中的作用。方法采用sh-NC和sh-LPCAT1转染Hela细胞,利用载体(Vector)组和LPCAT1过表达质粒转染SiHa细胞,将SiHa细胞分为对照组(Con)、LPCAT1组、LPCAT1+FH535组和FH535组。运用CCK-8法和集落形成试验检测子宫颈癌细胞的增殖。通过伤口愈合试验和Transwell实验检测子宫颈癌细胞的转移、侵袭能力。应用Western blot分析细胞中LPCAT1、β-catenin/Slug信号通路和EMT相关蛋白的表达。结果与Vector组相比,LPCAT1组SiHa细胞的活力、集落数、迁移和侵袭细胞数均显著增加(P<0.05);与sh-NC组相比,sh-LPCAT1组Hela细胞的活力、集落数、迁移和侵袭细胞数均显著降低(P<0.05)。与LPCAT1组相比,LPCAT1+FH535组SiHa细胞中Wnt4(1.18±0.05 vs 0.80±0.06)、β-catenin(1.05±0.08 vs 0.77±0.05)、Slug(1.13±0.06 vs 0.28±0.02)、Cyclin D1(0.99±0.06 vs 0.44±0.02)、N-cadherin(0.91±0.07 vs 0.46±0.03)和vimentin(0.95±0.06 vs 0.49±0.03)表达降低(P<0.05),E-cadherin(0.44±0.03 vs 0.58±0.03)表达增加(P<0.05)。此外,与LPCAT1组相比,LPCAT1+FH535组SiHa细胞的集落数(224±15 vs 146±11)、迁移数(85±3 vs 51±4)和侵袭数(166±10 vs 90±5)均降低(P<0.05)。结论LPCAT1表达增加可能通过激活β-catenin/Slug信号通路促进子宫颈癌的转移和进展,LPCAT1的靶向治疗有望提高子宫颈癌患者的预后。
