Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst(OKC)is a common jaw cyst with a high recurrence rate.OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome.Moreover,OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts.Because of the different prognosis,differential diagnosis of several cysts can contribute to clinical management.We collected 519 cases,comprising a total of 2157 hematoxylin and eosinstained images,to develop digital pathology-based artificial intelligence(AI)models for the diagnosis and prognosis of OKC.The Inception_v3 neural network was utilized to train and test models developed from patch-level images.Finally,whole slide imagelevel AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms.The AI models showed great performance in the diagnosis(AUC=0.935,95%CI:0.898–0.973)and prognosis(AUC=0.840,95%CI:0.751–0.930)of OKC.The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model.Furthermore,the study investigates the correlation between AI features generated by deep learning and pathological findings,highlighting the interpretative potential of AI models in the pathology.Here,we have developed the robust diagnostic and prognostic models for OKC.The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

Going straight for the gut:gut-brain axis pathology and treatment of Parkinson's disease

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.

Amyloid-beta pathology-induced nanoscale synaptic disruption:the case of the GABA_B-GIRK assembly

Alzheimer's disease (AD) is characterized by an imbalance between excitatory and inhibitory brain networks,leading to aberrant homeostatic synaptic plasticity.AD has progressively been recognized as syna ptopathy and syna ptic dysfunction has been identified as a key component of its pathogenesis (Schirinzi et al.,2020).Syna ptic dysfunction is believed to precede synapse loss,a primary biological correlate of cognitive decline in AD,inevita bly associated with neuronal death.

Clinicopathological features,psychological status,and prognosis of 33 patients with occult breast cancer

BACKGROUND Occult breast cancer(OBC)has traditionally been considered to be a carcinoma of unknown primary origin with a favorable prognosis and can be treated as stage II-III breast cancer.Due to the small number of cases and limited clinical experience,treatments vary greatly around the world and no standardized treatment has yet been established.AIM To investigate the clinicopathological features,psychological status and prognostic features of patients with OBC.METHODS The clinicopathological data of 33 OBC patients diagnosed and treated in the Affiliated Hospital of Xuzhou Medical University and Xuzhou Central Hospital from November 2015 to November 2022 were retrospectively analyzed.The psychological status of OBC patients was evaluated by the Self-rating Anxiety Scale and Self-rating Depression Scale.Patients’emotions,stress perception and psychological resilience were evaluated by the Positive and Negative Affect Schedule,the Chinese Perceived Stress Scale,and the Connor-Davidson Resilience Scale(CD-RISC),respectively.Patient survival was calculated using the Kaplan-Meier method,and survival curves were plotted for analysis with the log-rank test.Univariate and multivariate survival analyses were performed using the Cox regression model.RESULTS The 33 OBC patients included 32 females and 1 male.Of the 33 patients,30(91%)had axillary tumors,3(9%)had a neck mass as the primary symptom;18(54.5%)had estrogen receptor-positive tumors,17(51.5%)had progesterone receptor-positive tumors,and 18(54.5%)had Her-2-positive tumors;24(72.7%)received surgical treatment,including 18 patients who underwent modified radical mastectomy,1 patient who underwent breast-conserving surgery plus axillary lymph node dissection(ALND),and 5 patients who underwent ALND alone;12 patients received preoperative neoadjuvant therapy.All 30 patients developed anxiety and depression,with low positive affect scores and high negative affect scores,accompanied by a high stress level and poor psychological resilience.There were no differences in the psychological status of patients according to age,body mass index,or menopausal status.The overall survival and disease-free survival(DFS)of all the patients were 83.3%and 55.7%,respectively.Univariate analysis demonstrated that the initial tumor site(P=0.021)and node stage(P=0.020)were factors that may affect patient prognosis.The 5-year DFS rate of OBC patients who received radiotherapy was greater(P<0.001),while the use of different surgical methods(P=0.687)had no statistically significant effect on patient outcomes.Multivariate analysis revealed that radiotherapy(P=0.031)was an independent prognostic factor.Receiving radiotherapy had a significant effect on the CD-RISC score(P=0.02).CONCLUSION OBC is a rare breast disease whose diagnosis and treatment are currently controversial.There was no significant difference in the efficacy of other less invasive surgical procedures compared to those of modified radical mastectomy.In addition,radiotherapy can significantly improve patient outcomes.We should pay attention to the psychological state of patients while they receive antitumor therapy.

Using MsfNet to Predict the ISUP Grade of Renal Clear Cell Carcinoma in Digital Pathology Images

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.

NLRP3-mediated autophagy dysfunction links gut microbiota dysbiosis to tau pathology in chronic sleep deprivation

Emerging evidence indicates that sleep deprivation(SD)can lead to Alzheimer’s disease(AD)-related pathological changes and cognitive decline.However,the underlying mechanisms remain obscure.In the present study,we identified the existence of a microbiota-gut-brain axis in cognitive deficits resulting from chronic SD and revealed a potential pathway by which gut microbiota affects cognitive functioning in chronic SD.Our findings demonstrated that chronic SD in mice not only led to cognitive decline but also induced gut microbiota dysbiosis,elevated NLRP3 inflammasome expression,GSK-3βactivation,autophagy dysfunction,and tau hyperphosphorylation in the hippocampus.Colonization with the“SD microbiota”replicated the pathological and behavioral abnormalities observed in chronic sleep-deprived mice.Remarkably,both the deletion of NLRP3 in NLRP3-/-mice and specific knockdown of NLRP3 in the hippocampus restored autophagic flux,suppressed tau hyperphosphorylation,and ameliorated cognitive deficits induced by chronic SD,while GSK-3βactivity was not regulated by the NLRP3 inflammasome in chronic SD.Notably,deletion of NLRP3 reversed NLRP3 inflammasome activation,autophagy deficits,and tau hyperphosphorylation induced by GSK-3βactivation in primary hippocampal neurons,suggesting that GSK-3β,as a regulator of NLRP3-mediated autophagy dysfunction,plays a significant role in promoting tau hyperphosphorylation.Thus,gut microbiota dysbiosis was identified as a contributor to chronic SD-induced tau pathology via NLRP3-mediated autophagy dysfunction,ultimately leading to cognitive deficits.Overall,these findings highlight GSK-3βas a regulator of NLRP3-mediated autophagy dysfunction,playing a critical role in promoting tau hyperphosphorylation.

Clinicopathological Features and Long-Term Prognostic Role of Human Epidermal Growth Factor Receptor-2 Low Expression in Chinese Patients with Early Breast Cancer:A Single-Institution Study

Objective This study aimed to comprehensively analyze and compare the clinicopathological features and prognosis of Chinese patients with human epidermal growth factor receptor 2(HER2)-low early breast cancer(BC)and HER2-IHC0 BC.Methods Patients diagnosed with HER2-negative BC(N=999)at our institution between January2011 and December 2015 formed our study population.Clinicopathological characteristics,association between estrogen receptor(ER)expression and HER2-low,and evolution of HER2 immunohistochemical(IHC)score were assessed.Kaplan-Meier method and log-rank test were used to compare the long-term survival outcomes(5-year follow-up)between the HER2-IHC0 and HER2-low groups.Results HER2-low BC group tended to demonstrate high expression of ER and more progesterone receptor(PgR)positivity than HER2-IHC0 BC group(P<0.001).The rate of HER2-low status increased with increasing ER expression levels(Mantel-Haenszelχ^(2)test,P<0.001,Pearson’s R=0.159,P<0.001).Survival analysis revealed a significantly longer overall survival(OS)in HER2-low BC group than in HER2-IHC0 group(P=0.007)in the whole cohort and the hormone receptor(HR)-negative group.There were no significant differences between the two groups in terms of disease-free survival(DFS).The discordance rate of HER2 IHC scores between primary and metastatic sites was 36.84%.Conclusion HER2-low BC may not be regarded as a unique BC group in this population-based study due to similar clinicopathological features and prognostic roles.

Analysis of the Clinical Value of Immunohistochemical Testing in the Pathological Diagnosis of Breast Cancer

Objective:To investigate the clinical value of immunohistochemistry(IHC)detection in the pathological diagnosis of breast cancer.Methods:Eighty breast cancer patients admitted to Baoding No.1 Central Hospital from June 2022 to June 2023 were selected as study subjects.The samples were divided into a positive group(40 cases)and a negative group(40 cases)according to ER and PR test results.Immunohistochemistry was performed on all patients to compare the differences between the two groups in C-erbB-2 positive expression and axillary lymph node metastasis.Results:The positive expression rate of C-erbB-2 in the positive group(35.00%)was significantly lower than that in the negative group(80.00%),with a highly significant difference(P<0.001).The axillary lymph node metastasis rate in the positive group(40.00%)was significantly lower than that in the negative group(75.00%),with a significant difference(P<0.05).Conclusion:Immunohistochemical detection in breast cancer pathology enhances diagnostic accuracy,predicts prognosis,and supports personalized treatment by identifying ER,PR,and C-erbB-2.It is worth being widely adopted in clinical practice.

A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.

Microvascular structural changes in esophageal squamous cell carcinoma pathology according to intrapapillary capillary loop types under magnifying endoscopy

BACKGROUND The intrapapillary capillary loop(IPCL)characteristics,visualized using magnifying endoscopy,are commonly assessed for preoperative evaluation of the infiltration depth of esophageal squamous cell carcinoma(ESCC).Japan Esophageal Society(JES)classification is the most widely used classification.Microvascular structural changes are evaluated by magnifying endoscopy for the presence or absence of each morphological factor:tortuosity,dilatation,irregular caliber,and different shapes.However,the pathological characteristics of IPCLs have not been thoroughly investigated,especially the microvascular structures corresponding to the deepest parts of the lesions'infiltration.AIM To investigate differences in pathological microvascular structures of ESCC,which correspond to the deepest parts of the lesions'infiltration.METHODS Patients with ESCC and precancerous lesions diagnosed at Peking University Third Hospital were enrolled between January 2019 and April 2023.Patients first underwent magnified endoscopic examination,followed by endoscopic submucosal dissection or surgical treatment.Pathological images were scanned using a threedimensional slice scanner,and the pathological structural differences in different types,according to the JES classification,were analyzed using nonparametric tests and t-tests.RESULTS The 35 lesions were divided into four groups according to the JES classification:A,B1,B2,and B3.Statistical analyses revealed significant differences(aP<0.05)in the short and long calibers,area,location,and density between types A and B.Notably,there were no significant differences in these parameters between types B1 and B2 and between types B2 and B3(P>0.05).However,significant differences in the short calibers,long calibers,and area of IPCL were observed between types B1 and B3(aP<0.05);no significant differences were found in the density or location(P>0.05).CONCLUSION Pathological structures of IPCLs in the deepest infiltrating regions differ among various IPCL types classified by the JES classification under magnifying endoscopy,especially between the types A and B.

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

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.

Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology

Digital pathology(DP)and its subsidiaries including artificial intelligence(AI)are rapidly making inroads into the area of diagnostic anatomic pathology(AP)including gastrointestinal(GI)pathology.It is poised to revolutionize the field of diagnostic AP.Historically,AP has been slow to adopt digital technology,but this is changing rapidly,with many centers worldwide transitioning to DP.Coupled with advanced techniques of AI such as deep learning and machine learning,DP is likely to transform histopathology from a subjective field to an objective,efficient,and transparent discipline.AI is increasingly integrated into GI pathology,offering numerous advancements and improvements in overall diagnostic accuracy,efficiency,and patient care.Specifically,AI in GI pathology enhances diagnostic accuracy,streamlines workflows,provides predictive insights,integrates multimodal data,supports research,and aids in education and training,ultimately improving patient care and outcomes.This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology.The main aim was to provide updates and create awareness among the pathology community.

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

A multicenter study on efficacy of dual-target neoadjuvant therapy for HER2-positive breast cancer and a consistent analysis of efficacy evaluation of neoadjuvant therapy by Miller-Payne and RCB pathological evaluation systems(CSBrS-026)

Objective: The aim of this study was to investigate the factors influencing pathological complete response(pCR)rate in early breast cancer patients receiving neoadjuvant dual-target [trastuzumab(H) + pertuzumab(P)] therapy combined with chemotherapy. Additionally, the consistency of the Miller-Payne and residual cancer burden(RCB)systems in evaluating the efficacy of neoadjuvant therapy for early human epidermal growth factor receptor-2(HER2)+ breast cancer was analyzed.Methods: The clinicopathological data of female patients with early-stage HER2+ breast cancer who received dual-target neoadjuvant therapy at 26 hospitals of the Chinese Society of Breast Surgery(CSBrS) from March 2019 to December 2021 were collected. Patients were allocated to four groups: the HER2 immunohistochemistry(IHC)3+/hormone receptor(HR)-, IHC3+/HR+, IHC2+ in situ hybridization(ISH)+/HR-and IHC2+ ISH+/HR+groups. The overall pCR rate for patients, the pCR rate in each group and the factors affecting the pCR rate were analyzed. The consistency between the Miller-Payne and RCB systems in assessing the efficacy of neoadjuvant therapy was analyzed.Results: From March 1, 2019, to December 31, 2021, 77,376 female patients with early-stage breast cancer were treated at 26 hospitals;18,853(24.4%) of these patients were HER2+. After exclusion of unqualified patients, 2,395 patients who received neoadjuvant dual-target(H+P) therapy combined with chemotherapy were included in this study. The overall pCR rate was 53.0%, and the patients' HR statuses and different HER2+ statuses were significantly correlated with the pCR rate(P<0.05). The consistency of the pathological efficacy assessed by the Miller-Payne and RCB systems was 88.0%(κ=0.717, P<0.001).Conclusions: Different HER2 expression statuses and HR expression statuses are correlated with the pCR rate after dual-target neoadjuvant therapy in HER2+ breast cancer patients. There is a relatively good consistency between Miller-Payne and RCB systems in evaluating the pathologic efficacy of neoadjuvant therapy for HER2+breast cancer.

Axial length,vitreoretinal pathology,and anterior chamber depth can predict postoperative refractive outcomes in phacovitrectomy/silicone oil removal

AIM:To evaluate the postoperative refractive prediction error(PE)and determine the factors that af fect the refractive outcomes of combined pars plana vitrectomy(PPV)or silicone oil removal(SOR)with cataract surgery.METHODS:The study is a retrospective,case-series study.Totally 301 eyes of 301 patients undergoing combined PPV/SOR with cataract surgery were enrolled.Eligible individuals were separated into four groups according to their preoperative diagnoses:silicone oil-filled eyes after PPV(group 1),epiretinal membrane(group 2),macular hole(group 3),and primary retinal detachment(RD;group 4).The variables af fecting postoperative refractive outcomes were analyzed,including age,gender,preoperative best-corrected visual acuity(BCVA),axial length(AL),keratometry average,anterior chamber depth(ACD),intraocular tamponade,and vitreoretinal pathology.The outcome measurements include the mean refractive PE and the proportions of eyes with a PE within±0.50 diopter(D)and±1.00 D.RESULTS:For all patients,the mean PE was-0.04±1.17 D,and 50.17%of patients(eyes)had a PE within±0.50 D.There was a significant difference in refractive outcomes among the four groups(P=0.028),with RD(group 4)showing the least favorable refractive outcome.In multivariate regression analysis,only AL,vitreoretinal pathology,and ACD were strongly associated with PE(all P<0.01).Univariate analysis revealed that longer eyes(AL>26 mm)and a deeper ACD were correlated with hyperopic PE,and shorter eyes(AL<26 mm)and a shallower ACD were correlated with myopic PE.CONCLUSION:RD patients have the least favorable refractive outcome.AL,vitreoretinal pathology,and ACD are strongly associated with PE in the combined surgery.These three factors affect refractive outcomes and thus can be used to predict a better postoperative refractive outcome in clinical practice.

Analysis of clinicopathological features and prognostic factors of breast cancer brain metastasis

BACKGROUND Breast cancer(BC)has become the most common malignancy in women.The incidence and detection rates of BC brain metastasis(BCBM)have increased with the progress of imaging,multidisciplinary treatment techniques and the extension of survival time of BC patients.BM seriously affects the quality of life and survival prognosis of BC patients.Therefore,clinical research on the clinicopathological features and prognostic factors of BCBM is valuable.By analyzing the clinicopathological parameters of BCBM patients,and assessing the risk factors and prognostic indicators,we can perform hierarchical diagnosis and treatment on the high-risk population of BCBM,and achieve clinical benefits of early diagnosis and treatment.AIM To explore the clinicopathological features and prognostic factors of BCBM,and provide references for diagnosis,treatment and management of BCBM.METHODS The clinicopathological data of 68 BCBM patients admitted to the Air Force Medical Center,Chinese People’s Liberation Army(formerly Air Force General Hospital)from 2000 to 2022 were collected.Another 136 BC patients without BM were matched at a ratio of 1:2 based on the age and site of onset for retrospective analysis.Categorical data were subjected to χ^(2) test or Fisher’s exact probability test,and the variables with P<0.05 in the univariate Cox proportional hazards model were incorporated into the multivariate model to identify high-risk factors and independent prognostic factors of BCBM,with a hazard ratio(HR)>1 suggesting poor prognostic factors.The survival time of patients was estimated by the Kaplan-Meier method,and overall survival was compared between groups by log-rank test.RESULTS Multivariate Cox regression analysis showed that patients with stage Ⅲ/Ⅳ tumor at initial diagnosis[HR:5.58,95% confidence interval(CI):1.99–15.68],lung metastasis(HR:24.18,95%CI:6.40-91.43),human epidermal growth factor receptor 2(HER2)-overexpressing BC and triple-negative BC were more prone to BM.As can be seen from the prognostic data,52 of the 68 BCBM patients had died by the end of follow-up,and the median time from diagnosis of BC to the occurrence of BM and from the occurrence of BM to death or last follow-up was 33.5 and 14 mo,respectively.It was confirmed by multivariate Cox regression analysis that patients with neurological symptoms(HR:1.923,95%CI:1.005-3.680),with bone metastasis(HR:2.011,95%CI:1.056-3.831),and BM of HER2-overexpressing and triple-negative BC had shorter survival time.CONCLUSION HER2-overexpressing,triple-negative BC,late tumor stage and lung metastasis are risk factors of BM.The presence of neurological symptoms,bone metastasis,and molecular type are influencing prognosis factors of BCBM.

Comparative Study of the Effect of Shugan Shuru Granule (疏肝舒乳颗粒) on Pathology and p53 Gene Expression in Patients with Hyperplastic Disease of Breast

Objective: To study the effect of Shugan Shuru Granule (疏肝舒乳颗粒,SSG) on the p53 gene expression in patients with hyperplastic disease of breast (HDB) to indirectly explore the mechanism of SSG’s effect on HDB on the molecular pathological level. Methods: Sixty-six patients with HDB were allocated in the treated group and the control group,with the former treated with SSG and the latter not. All patients underwent breast operation and their diseased mammary tissues were cut out, sectioned, and observed under microscopy with HE staining and immunohistochemical staining, with ABC method adopted to estimate the degree of hyperplasia and p53 gene expression. The severity of HDB was classified into normal, mild, moderate and severe grades (marked as 0 to Ⅲ), according to the degree of hyperplasia in the mammary gland. Results: Hyperplasia in the control group mostly belonged to grade Ⅰ-Ⅲ before treatment, showing overgrowth of gland and proliferation of glandular epithelial cells, which were high columnar shaped, more stratified, with papillary or substantive dysplasia. While in the treated group, most belonged to grade 0-Ⅰ after SSG treatment, with proliferated gland and dysplasia recovered to normal or disappeared. The positive rate of p53 gene expression in the treated group was 9.09%, and in the control group 39.39%, comparison between the two groups showing significant difference (P<0.01), the intensity in the former was significantly weaker than that in the latter.Conclusion: SSG could not only inhibit the proliferation of mammary duct epithelia and lobuli, but also inhibit the over-expression of P53. Therefore, it could be regarded as an effective remedy for treatment of HDB and prevention of mammary cancer genesis.

Aquaporin 5 in Alzheimer’s disease:a link between oral and brain pathology?

The involvement of aquaporins(AQPs)in the development of diseases has been widely described(Azad et al.,2021).AQP5 has been described in astrocytes changing after traumatic brain injuries(Chai et al.,2013),but the precise role of AQP5 in Alzheimer’s disease(AD)pathology is yet to be understood.We have recently reported that AQP5 expression changes during the development of AD(Antequera et al.,2022).The AQP5 expression in salivary glands is decreased in 6-month-old APP/PS1 mice and AD patients.This decrease in AQP5 expression could be involved in the mechanism of salivary gland dysfunction described in a previous study(Antequera et al.,2021).Now,we propose a new indirect role of AQP5 in the connection between infection-induced oral dysbiosis and AD(Sureda et al.,2020).Here,we suggest that the proinflammatory response induced by oral pathogen infection results in the downregulation of AQP5 contributing to the salivary gland secretory dysfunction.All these alterations destabilize the peripheral immune-inflammatory balance and exacerbate neuroinflammation and neurodegeneration leading to AD pathology.

Circular RNAs in breast cancer diagnosis,treatment and prognosis

Breast cancer has surpassed lung cancer to become the most common malignancy worldwide.The incidence rate and mortality rate of breast cancer continue to rise,which leads to a great burden on public health.Circular RNAs(circRNAs),a new class of noncoding RNAs(ncRNAs),have been recognized as important oncogenes or suppressors in regulating cancer initiation and progression.In breast cancer,circRNAs have significant roles in tumorigenesis,recurrence and multidrug resistance that are mediated by various mechanisms.Therefore,circRNAs may serve as promising targets of therapeutic strategies for breast cancer management.This study reviews the most recent studies about the biosynthesis and characteristics of circRNAs in diagnosis,treatment and prognosis evaluation,as well as the value of circRNAs in clinical applications as biomarkers or therapeutic targets in breast cancer.Understanding the mechanisms by which circRNAs function could help transform basic research into clinical applications and facilitate the development of novel circRNA-based therapeutic strategies for breast cancer treatment.