Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and interv...Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and intervention pathways,which make it possible to develop effective skin regeneration and repair ingredients.With the rapid development of computational biology,bioinformatics as well as artificial intelligence(A.I.),the development of new ingredients for regenerative medicine has been greatly accelerated,and the success rate has been improved.Some application cases have appeared in topical skin regeneration and repair scenarios.This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I.based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios.Typically,two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.展开更多
Hepatitis C virus(HCV)helicase is a molecular motor that splits nucleic acid duplex structures during viral replication,therefore representing a promising target for antiviral treatment.Hence,a detailed understanding ...Hepatitis C virus(HCV)helicase is a molecular motor that splits nucleic acid duplex structures during viral replication,therefore representing a promising target for antiviral treatment.Hence,a detailed understanding of the mechanism by which it operates would facilitate the development of efficient drug-assisted therapies aiming to inhibit helicase activity.Despite extensive investigations performed in the past,a thorough understanding of the activity of this important protein was lacking since the underlying internal conformational motions could not be resolved.Here we review investigations that have been previously performed by us for HCV helicase.Using methods of structure-based computational modelling it became possible to follow entire operation cycles of this motor protein in structurally resolved simulations and uncover the mechanism by which it moves along the nucleic acid and accomplishes strand separation.We also discuss observations from that study in the light of recent experimental studies that confirm our findings.展开更多
Computational biology plays a significant role in the discovery of new biomarkers, the analyses of disease states and the validation of potential biomarkers. Biomarkers are used to measure the progress of disease or t...Computational biology plays a significant role in the discovery of new biomarkers, the analyses of disease states and the validation of potential biomarkers. Biomarkers are used to measure the progress of disease or the physiological effects of therapeutic intervention in the treatment of disease. They are also used as early warning signs for various diseases such as cancer and inflammatory diseases. In this review, we outline recent progresses of computational biology application in research on biomarkers discovery. A brief discussion of some necessary preliminaries on machine learning techniques (e.g., clustering and support vector machines—SVM) which are commonly used in many applications to biomarkers discovery is given and followed by a description of biological background on biomarkers. We further examine the integration of computational biology approaches and biomarkers. Finally, we conclude with a discussion of key challenges for computational biology to biomarkers discovery.展开更多
<strong>Background:</strong> Worldwide, prostatic adenocarcinoma is the most common tumour type among men. <strong>Aim:</strong> The aim of the present investigation was to develop a computer p...<strong>Background:</strong> Worldwide, prostatic adenocarcinoma is the most common tumour type among men. <strong>Aim:</strong> The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma. <strong>Method:</strong> Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5). Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (CelldaTM), intended to identify the morphological alterations. Visual microscopic finding was used as a reference. <strong>Result:</strong> Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%) as having no malignancy on the basis of the visual microscopy. In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease). Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common. Thus, it was uncertain whether these cases should be regarded as malignant or not. The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80). <strong>Conclusion:</strong> It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.展开更多
Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal cha...Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.展开更多
Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study lever...Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.展开更多
目的探讨抗核抗体(ANA)与低分子肝素(LMWH)治疗不明原因复发性流产(URSA)孕妇子宫胎盘血流指数的关系。方法2020年1月至2022年12月期间,共纳入80例URSA孕妇,其中40例接受LMWH治疗,另外40例未接受LMWH治疗,2组ANA阴性(-)和ANA阳性(+)各2...目的探讨抗核抗体(ANA)与低分子肝素(LMWH)治疗不明原因复发性流产(URSA)孕妇子宫胎盘血流指数的关系。方法2020年1月至2022年12月期间,共纳入80例URSA孕妇,其中40例接受LMWH治疗,另外40例未接受LMWH治疗,2组ANA阴性(-)和ANA阳性(+)各20例。借助虚拟器官计算机辅助分析技术进行2D多普勒测量子宫动脉搏动指数(PI)和3D超声测定血管化指数(VI)、血流指数(FI)和血管化血流指数(VFI),并对所有女性进行血清ANA测定。结果未接受LMWH治疗和接受LMWH治疗的ANA(-)URSA孕妇分娩孕周、新生儿结局比较,差异具有统计学意义(P<0.05)。无论ANA状态如何,未接受LMWH治疗和接受LMWH治疗的URSA孕妇间PI、VFI、FI值差异均无统计学意义(P>0.05)。而接受LMWH治疗的ANA(-)URSA孕妇VI值显著高于未接受LMWH治疗的ANA(-)URSA孕妇(20.02±6.06 vs 11.60±3.04,P<0.001)。仅考虑ANA(-)URSA患者,VI用于区分接受和未接受LMWH治疗孕妇的ROC曲线下面积为0.889(标准误=0.053,P<0.001,95%置信区间=0.785~0.993),VI临界值为16.05,灵敏度为75.0%,特异性为100.0%。结论LMWH可能对于恢复ANA(-)状态下URSA女性VI的生理血流供应具有潜在的有益作用,但是仍需要更进一步的研究来解释彼此之间的关系。展开更多
Although the detection of viral particles by reverse transcription polymerase chain reaction(RT-PCR)is the gold standard diagnostic test for coronavirus disease 2019(COVID-19),the false-negative results constitute a b...Although the detection of viral particles by reverse transcription polymerase chain reaction(RT-PCR)is the gold standard diagnostic test for coronavirus disease 2019(COVID-19),the false-negative results constitute a big challenge.AIM To examine a group of patients diagnosed and treated as possible COVID-19 pneumonia whose multiple nasopharyngeal swab samples were negative for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)by RT-PCR but then serological immunoglobulin M/immunoglobulin G(IgM/IgG)antibody against SARS-CoV-2 were detected by rapid antibody test.METHODS Eighty possible COVID-19 patients who had at least two negative consecutive COVID-19 RT-PCR test and were subjected to serological rapid antibody test were evaluated in this study.RESULTS The specific serological total IgM/IgG antibody against SARS-CoV-2 was detected in twenty-two patients.The mean age of this patient group was 63.2±13.1-yearsold with a male/female ratio of 11/11.Cough was the most common symptom(90.9%).The most common presenting chest computed tomography findings were bilateral ground glass opacities(77.2%)and alveolar consolidations(50.1%).The mean duration of time from appearance of first symptoms to hospital admission,to hospital admission,to treatment duration and to serological positivity were 8.6 d,11.2 d,7.9 d,and 24 d,respectively.Compared with reference laboratory values,serologically positive patients have shown increased levels of acute phase reactants,such as C-reactive protein,ferritin,and procalcitonin and higher inflammatory markers,such as erythrocyte sedimentation rate,lactate dehydrogenase enzyme,and fibrin end-products,such as D-dimer.A left shift on white blood cell differential was observed with increased neutrophil counts and decreased lymphocytes.CONCLUSION Our study demonstrated the feasibility of a COVID-19 diagnosis based on rapid antibody test in the cases of patients whose RT-PCR samples were negative.Detection of antibodies against SARS-CoV-2 with rapid antibody test should be included in the diagnostic algorithm in patients with possible COVID-19 pneumonia.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030047)Zhejiang Provincial Department of Agriculture and Rural Affairs(2022SNJF078).
文摘Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and intervention pathways,which make it possible to develop effective skin regeneration and repair ingredients.With the rapid development of computational biology,bioinformatics as well as artificial intelligence(A.I.),the development of new ingredients for regenerative medicine has been greatly accelerated,and the success rate has been improved.Some application cases have appeared in topical skin regeneration and repair scenarios.This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I.based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios.Typically,two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.
文摘Hepatitis C virus(HCV)helicase is a molecular motor that splits nucleic acid duplex structures during viral replication,therefore representing a promising target for antiviral treatment.Hence,a detailed understanding of the mechanism by which it operates would facilitate the development of efficient drug-assisted therapies aiming to inhibit helicase activity.Despite extensive investigations performed in the past,a thorough understanding of the activity of this important protein was lacking since the underlying internal conformational motions could not be resolved.Here we review investigations that have been previously performed by us for HCV helicase.Using methods of structure-based computational modelling it became possible to follow entire operation cycles of this motor protein in structurally resolved simulations and uncover the mechanism by which it moves along the nucleic acid and accomplishes strand separation.We also discuss observations from that study in the light of recent experimental studies that confirm our findings.
文摘Computational biology plays a significant role in the discovery of new biomarkers, the analyses of disease states and the validation of potential biomarkers. Biomarkers are used to measure the progress of disease or the physiological effects of therapeutic intervention in the treatment of disease. They are also used as early warning signs for various diseases such as cancer and inflammatory diseases. In this review, we outline recent progresses of computational biology application in research on biomarkers discovery. A brief discussion of some necessary preliminaries on machine learning techniques (e.g., clustering and support vector machines—SVM) which are commonly used in many applications to biomarkers discovery is given and followed by a description of biological background on biomarkers. We further examine the integration of computational biology approaches and biomarkers. Finally, we conclude with a discussion of key challenges for computational biology to biomarkers discovery.
文摘<strong>Background:</strong> Worldwide, prostatic adenocarcinoma is the most common tumour type among men. <strong>Aim:</strong> The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma. <strong>Method:</strong> Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5). Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (CelldaTM), intended to identify the morphological alterations. Visual microscopic finding was used as a reference. <strong>Result:</strong> Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%) as having no malignancy on the basis of the visual microscopy. In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease). Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common. Thus, it was uncertain whether these cases should be regarded as malignant or not. The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80). <strong>Conclusion:</strong> It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.
基金Taishan Young Scholars Program of Shandong Province,Key Development Program for Basic Research of Shandong Province(ZR2020ZD44).
文摘Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.
文摘Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.
文摘目的探讨抗核抗体(ANA)与低分子肝素(LMWH)治疗不明原因复发性流产(URSA)孕妇子宫胎盘血流指数的关系。方法2020年1月至2022年12月期间,共纳入80例URSA孕妇,其中40例接受LMWH治疗,另外40例未接受LMWH治疗,2组ANA阴性(-)和ANA阳性(+)各20例。借助虚拟器官计算机辅助分析技术进行2D多普勒测量子宫动脉搏动指数(PI)和3D超声测定血管化指数(VI)、血流指数(FI)和血管化血流指数(VFI),并对所有女性进行血清ANA测定。结果未接受LMWH治疗和接受LMWH治疗的ANA(-)URSA孕妇分娩孕周、新生儿结局比较,差异具有统计学意义(P<0.05)。无论ANA状态如何,未接受LMWH治疗和接受LMWH治疗的URSA孕妇间PI、VFI、FI值差异均无统计学意义(P>0.05)。而接受LMWH治疗的ANA(-)URSA孕妇VI值显著高于未接受LMWH治疗的ANA(-)URSA孕妇(20.02±6.06 vs 11.60±3.04,P<0.001)。仅考虑ANA(-)URSA患者,VI用于区分接受和未接受LMWH治疗孕妇的ROC曲线下面积为0.889(标准误=0.053,P<0.001,95%置信区间=0.785~0.993),VI临界值为16.05,灵敏度为75.0%,特异性为100.0%。结论LMWH可能对于恢复ANA(-)状态下URSA女性VI的生理血流供应具有潜在的有益作用,但是仍需要更进一步的研究来解释彼此之间的关系。
文摘Although the detection of viral particles by reverse transcription polymerase chain reaction(RT-PCR)is the gold standard diagnostic test for coronavirus disease 2019(COVID-19),the false-negative results constitute a big challenge.AIM To examine a group of patients diagnosed and treated as possible COVID-19 pneumonia whose multiple nasopharyngeal swab samples were negative for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)by RT-PCR but then serological immunoglobulin M/immunoglobulin G(IgM/IgG)antibody against SARS-CoV-2 were detected by rapid antibody test.METHODS Eighty possible COVID-19 patients who had at least two negative consecutive COVID-19 RT-PCR test and were subjected to serological rapid antibody test were evaluated in this study.RESULTS The specific serological total IgM/IgG antibody against SARS-CoV-2 was detected in twenty-two patients.The mean age of this patient group was 63.2±13.1-yearsold with a male/female ratio of 11/11.Cough was the most common symptom(90.9%).The most common presenting chest computed tomography findings were bilateral ground glass opacities(77.2%)and alveolar consolidations(50.1%).The mean duration of time from appearance of first symptoms to hospital admission,to hospital admission,to treatment duration and to serological positivity were 8.6 d,11.2 d,7.9 d,and 24 d,respectively.Compared with reference laboratory values,serologically positive patients have shown increased levels of acute phase reactants,such as C-reactive protein,ferritin,and procalcitonin and higher inflammatory markers,such as erythrocyte sedimentation rate,lactate dehydrogenase enzyme,and fibrin end-products,such as D-dimer.A left shift on white blood cell differential was observed with increased neutrophil counts and decreased lymphocytes.CONCLUSION Our study demonstrated the feasibility of a COVID-19 diagnosis based on rapid antibody test in the cases of patients whose RT-PCR samples were negative.Detection of antibodies against SARS-CoV-2 with rapid antibody test should be included in the diagnostic algorithm in patients with possible COVID-19 pneumonia.
