Fine mapping and candidate gene analysis of qSRC3 controlling the silk color in maize (Zea mays L.).

KEY MESSAGE: Fine mapping of the maize QTL qSRC3, responsible for red silk, uncovered the candidate gene ZmMYB20, which encodes an R2R3-MYB transcription factor, has light-sensitive expression, and putatively regulates genes expression associated with anthocyanin biosynthesis. Colorless silk is a key characteristic contributing to the visual quality of fresh corn intended for market distribution. Nonetheless, the identification of Mendelian trait loci and associated genes that control silk color has been scarce. In this study, a F2 population arising from the hybridization of the single-segment substitution line qSRC3MT1 with red silk, carrying an introgressed allele from teosinte (Zea mays ssp. mexicana), and the recurrent maize inbred line Mo17, characterized by light green silk, was utilized for fine mapping. We found that the red silk trait is controlled by a semi-dominant genetic locus known as qSRC3, and its expression is susceptible to light-mediated inhibition. Moreover, qSRC3 explained 68.78% of the phenotypic variance and was delimited to a 133.2 kb region, which includes three genes. Subsequent expression analyses revealed that ZmMYB20 (Zm00001d039700), which encodes an R2R3-MYB transcription factor, was the key candidate gene within qSRC3. Yeast one-hybrid and dual-luciferase reporter assays provided evidence that ZmMYB20 suppresses the expression of two crucial anthocyanin biosynthesis genes, namely ZmF3H and ZmUFGT, by directly binding to their respective promoter regions. Our findings underscore the significance of light-inhibited ZmMYB20 in orchestrating the spatial and temporal regulation of anthocyanin biosynthesis. These results advance the production of colorless silk in fresh corn, responding to the misconception that fresh corn with withered colored silk is not fresh and providing valuable genetic resources for the improvement of sweet and waxy maize.

Integrated genetic analysis of diabetic complications: Bioinformatics insights into foot ulcers, neuropathy and peripheral artery disease.

Diabetic foot ulcers (DFU), diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD) are common complications of diabetes mellitus, while diabetic peripheral neuropathy and peripheral arterial disease contribute to the pathogenesis of diabetic foot ulcers, and the pathogenic mechanisms between these three diseases still need further investigation. The keywords 'diabetic foot ulcer', 'diabetic peripheral neuropathy' and 'atherosclerosis' were used to search for related gene sets in the GEO database. Differentially expressed genes (DEGs) were screened and analysed for GO, KEGG and enrichR functional enrichment. Potential three disease biomarkers were identified by SVM-SVM-RFE and LASSO regression analysis. The results were also validated using external datasets and discriminability was measured by area under the ROC curve (AUC). Finally, biomarkers and co-upregulated genes were analysed through the GSEA and Attie Laboratories diabetes databases. A total of 11 shared genes (KRT16, CD24, SAMD9L, SRGAP2, FGL2, GPR34, DDIT4, NFE2L3, FBLN5, ANXA3 and CPA3), two biomarkers (SAMD9L and FGL2) and one co-upregulated gene (CD24) were screened. GO and KEGG pathway analysis of DEGs, enrichr enrichment analysis of shared differential genes and GSEA analysis of biomarkers showed that these significant genes were mainly focused on vasoregulatory, inflammatory-oxidative stress and immunomodulatory pathways. In this study, we used bioinformatics to investigate the intrinsic relationship and potential mechanisms of three common lower extremity complications of diabetes and identified two pivotal genes using the LASSO model and the SVM-RFE algorithm, which will further help clinicians to understand the relationship between diabetic complications, improve the diagnosis and treatment of diabetic foot problems and help doctors to identify the potential risk factors of diabetic foot.

Deploying QTL-seq rapid identification and separation of the major QTLs of tassel branch number for fine-mapping in advanced maize populations.

The tassel competes with the ear for nutrients and shields the upper leaves, thereby reducing the yield of grain. The tassel branch number (TBN) is a pivotal determinant of tassel size, wherein the reduced TBN has the potential to enhance the transmission of light and reduce the consumption of nutrients, which should ultimately result in increased yield. Consequently, the TBN has emerged as a vital target trait in contemporary breeding programs that focus on compact maize varieties. In this study, QTL-seq technology and advanced population mapping were used to rapidly identify and dissect the major effects of the TBN on QTL. Advanced mapping populations (BC4F2 and BC4F3) were derived from the inbred lines 18-599 (8-11 TBN) and 3237 (0-1 TBN) through phenotypic recurrent selection. First, 13 genomic regions associated with the TBN were detected using quantitative trait locus (QTL)-seq and were located on chromosomes 2 and 5. Subsequently, validated loci within these regions were identified by QTL-seq. Three QTLs for TBN were identified in the BC4F2 populations by traditional QTL mapping, with each QTL explaining the phenotypic variation of 6.13-18.17%. In addition, for the major QTL (qTBN2-2 and qTBN5-1), residual heterozygous lines (RHLs) were developed from the BC4F2 population. These two major QTLs were verified in the RHLs by QTL mapping, with the phenotypic variation explained (PVE) of 21.57% and 30.75%, respectively. Near-isogenic lines (NILs) of qTBN2-2 and qTBN5-1 were constructed. There were significant differences between the NILs in TBN. These results will enhance our understanding of the genetic basis of TBN and provide a solid foundation for the fine-mapping of TBN. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01431-y.

Long-term survival benefit of anti-PD-1 therapy in patients with relapsed or refractory classical Hodgkin lymphoma.

Anti-programmed cell death-1 (anti-PD-1) therapies have shown a favorable efficacy and good tolerance for relapsed or refractory (r/r) classical Hodgkin lymphoma (cHL). However, there are limited data on long-term outcomes among patients with r/r cHL who achieve an objective response to anti-PD-1 therapies. A total of 260 responders from four, phase 2 clinical trials were included in this study. The median age was 32 years with a male/female ratio of 1.3:1. After a median follow-up period of 31.1 months, 116 (44.6%) responders experienced disease progression and 18 (6.9%) died. The 3-year progression-free survival (PFS) and overall survival (OS) rates were 55.1% and 89.7% overall. Patients with partial remission (PR) had inferior outcomes compared with those who achieved complete remission (3-year PFS, 29.5% vs. 72.3%, P < 0.001; 3-year OS, 81.5% vs. 94.4%, P = 0.017). Moreover, the survival outcome was inferior for patients with refractory disease compared with those with relapsed disease. Multivariate Cox regression analysis showed PR and refractory disease were independent risk factors for PFS. In conclusion, PR and refractory disease have a negative impact on the survival benefit of anti-PD-1 therapeutics in patients with r/r cHL, which highlights the need for multimodal treatment strategies.

[Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study]. / æ¹–åŒ—æ©æ–½åœŸå®¶æ—è‹—æ—è‡ªæ²»å·žæ–°ç”Ÿå„¿çª’æ¯å±é™©å› ç´ åˆ†æžåŠåˆ—çº¿å›¾é¢„æµ‹æ¨¡åž‹çš„æž„å»ºï¼šä¸€é¡¹å¤šä¸­å¿ƒç ”ç©¶.

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS: The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.

Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. METHODS: Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. RESULTS: We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. CONCLUSIONS: The CRRS model has reliable predictive value for the prognosis of HCC patients.

Attention-deficit/hyperactivity disorder and ischemic stroke: A Mendelian randomization study.

BACKGROUND: Observational studies have found an association between attention-deficit/hyperactivity disorder (ADHD) and ischemic stroke. AIMS: The purpose of this study was to investigate whether genetic liability to ADHD has a causal effect on ischemic stroke and its subtypes. METHODS: In this two-sample Mendelian randomization (MR) study, genetic variants (nine single-nucleotide polymorphisms; P < 5 × 10-8) using as instrumental variables for the analysis was obtained from a genome-wide association study of ADHD with 19,099 cases and 34,194 controls. The outcome datasets for stroke and its subtypes were obtained from the MEGASTROKE consortium, with 40,585 cases and 406,111 controls. MR inverse variance-weighted method was conducted to investigate the effect of genetic liability to ADHD on ischemic stroke and its subtypes. Sensitivity analyses (median-based methods, MR-Egger, MR-robust adjusted profile scores, MR-pleiotropy residual sum and outlier) were also utilized to assess horizontal pleiotropy and remove outliers. Multivariable MR (MVMR) analyses were conducted to explore potential mediators. RESULTS: Genetically determined ADHD (per 1 SD) was significantly associated with a higher risk of any ischemic stroke (AIS) (odds ratio (OR) = 1.15, 95% confidence interval (CI) = 1.05-1.25, P = 0.002) and large-artery atherosclerotic stroke (LAS) (OR = 1.40, 95% CI = 1.10-1.76, P = 0.005). The significant association was also found in sensitivity analyses and MVMR analyses. CONCLUSIONS: Genetic liability to ADHD was significantly associated with an increased risk of AIS, especially LAS. The association between ADHD and LAS was independent of age of smoking initiation but mediated by coronary artery disease.

AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors.

As important drug targets, G protein-coupled receptors (GPCRs) play pivotal roles in a wide range of physiological processes. Extensive efforts of structural biology have been made on the study of GPCRs. However, a large portion of GPCR structures remain unsolved due to structural instability. Recently, AlphaFold2 has been developed to predict structure models of many functionally important proteins including all members of the GPCR family. Herein we evaluated the accuracy of GPCR structure models predicted by AlphaFold2. We revealed that AlphaFold2 could capture the overall backbone features of the receptors. However, the predicted models and experimental structures were different in many aspects including the assembly of the extracellular and transmembrane domains, the shape of the ligand-binding pockets, and the conformation of the transducer-binding interfaces. These differences impeded the use of predicted structure models in the functional study and structure-based drug design of GPCRs, which required reliable high-resolution structural information.

Lipoxin A4 attenuates MSU-crystal-induced NLRP3 inflammasome activation through suppressing Nrf2 thereby increasing TXNRD2.

Gout is a common inflammatory disease. The activation of NLRP3 inflammasome induced by monosodium urate (MSU) crystals has a critical role in gout, and its prevention is beneficial for patients. Lipoxin A4 (LXA4) is an endogenous lipoxygenase-derived eicosanoid mediator with powerful anti-inflammatory properties. However, whether LXA4 can suppress NLRP3 inflammasome activation induced by MSU crystals remains unclear. This study aimed to investigate the protective effect of LXA4 on MSU-crystal-induced NLRP3 inflammasome activation and its underlying molecular mechanisms. We found that LXA4 inhibited MSU-crystal-induced NLRP3 inflammasome activation, interleukin (IL)-1ß maturation, and pyroptosis. More specifically, LXA4 suppressed the assembly of the NLRP3 inflammasome, including oligomerization and speck formation of ASC, and ASC-NLRP3 interaction. Furthermore, LXA4 suppressed oxidative stress, the upstream events for NLRP3 inflammasome activation, as evidenced by the fact that LXA4 eliminated total reactive oxygen species (ROS) generation and alleviated nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation and mitochondrial dysfunction. However, LXA4 also depressed the Nrf2 activation, a critical molecule in the antioxidant pathway, and then exerted an inhibitory impact on Klf9 expression and promotional impact on TXNRD2 expression, two molecules located downstream of Nrf2 in sequence. Knockdown of TXNRD2 reversed the LXA4-induced depression of ROS and NLRP3 inflammasome. Moreover, LXA4 alleviated joint inflammation and decreased the production of cleaved caspase-1 and matured IL-1ß in gouty arthritis rats. Taken together, our findings demonstrate that LXA4 can attenuate MSU-crystal-induced NLRP3 inflammasome activation, probably through suppressing Nrf2 activation to increase TXNRD2 expression. The present study highlights the potential of LXA4 as an attractive new gout treatment candidate.

Comparison of ischemic stroke diagnosis models based on machine learning.

Background: The incidence, prevalence, and mortality of ischemic stroke (IS) continue to rise, resulting in a serious global disease burden. The prediction models have a great value in the early prediction and diagnosis of IS. Methods: The R software was used to screen the differentially expressed genes (DEGs) of IS and control samples in the datasets GSE16561, GSE58294, and GSE37587 and analyze DEGs for enrichment analysis. The feature genes of IS were obtained by several machine learning algorithms, including the least absolute shrinkage and selector operation (LASSO) logistic regression, the support vector machine-recursive feature elimination (SVM-RFE), and the Random Forest (RF). The IS diagnostic models were constructed based on transcriptomics by machine learning and artificial neural network (ANN). Results: A total of 69 DEGs, mainly involved in immune and inflammatory responses, were identified. The pathways enriched in the IS group were complement and coagulation cascades, lysosome, PPAR signaling pathway, regulation of autophagy, and toll-like receptor signaling pathway. The feature genes selected by LASSO, SVM-RFE, and RF were 17, 10, and 12, respectively. The area under the curve (AUC) of the LASSO model in the training dataset, GSE22255, and GSE195442 was 0.969, 0.890, and 1.000. The AUC of the SVM-RFE model was 0.957, 0.805, and 1.000, respectively. The AUC of the RF model was 0.947, 0.935, and 1.000, respectively. The models have good sensitivity, specificity, and accuracy. The AUC of the LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 1.000, 0.995, and 0.997, respectively, in the training dataset. However, the AUC of LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 0.688, 0.605, and 0.619, respectively, in the GSE22255 dataset. The AUC of the LASSO+ANN and RF+ANN models was 0.740 and 0.630, respectively, in the GSE195442 dataset. In the training dataset, the sensitivity, specificity, and accuracy of the LASSO+ANN model were 1.000, 1.000, and 1.000, respectively; of the SVM-RFE+ANN model were 0.946, 0.982, and 0.964, respectively; and of the RF+ANN model were 0.964, 1.000, and 0.982, respectively. In the test datasets, the sensitivity was very satisfactory; however, the specificity and accuracy were not good. Conclusion: The LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.

AR Expression Correlates with Distinctive Clinicopathological and Genomic Features in Breast Cancer Regardless of ESR1 Expression Status.

Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with AR negativity in both ER-positive and ER-negative breast cancer, trying to elucidate the molecular correlation between AR and ER. Our results showed that AR negativity was associated with different clinicopathological characteristics and molecular features in ER-positive and ER-negative breast cancer. Moreover, AR-positive breast cancer has better clinicopathological features than AR-negative breast cancer, especially in the ER-negative subtype. These results suggest that the role of AR in ER-negative breast cancer is distinctive from that in ER-positive breast cancer.

Susceptibility genes of hyperuricemia and gout.

Gout is a chronic metabolic disease that seriously affects human health. It is also a major challenge facing the world, which has brought a heavy burden to patients and society. Hyperuricemia (HUA) is the most important risk factor for gout. In recent years, with the improvement of living standards and the change of dietary habits, the incidence of gout in the world has increased dramatically, and gradually tends to be younger. An increasing number of studies have shown that gene mutations may play an important role in the development of HUA and gout. Therefore, we reviewed the existing literature and summarized the susceptibility genes and research status of HUA and gout, in order to provide reference for the early diagnosis, individualized treatment and the development of new targeted drugs of HUA and gout.

Lipoxin alleviates oxidative stress: a state-of-the-art review.

OBJECTIVE: This review aims to summarize the capability of lipoxin in regulating oxidative stress. BACKGROUND: Oxidative stress is defined as an imbalance between the production of free radicals and the antioxidant system, and it is associated with the existence of a large number of oxidation products, such as reactive oxygen species (ROS) and reaction nitrogen species (RNS), causing damage to human tissues through immunoinflammatory responses. Therefore, reducing oxidative stress is vital to alleviate pathological damage. Lipoxin, an acronym for lipoxygenase interaction product, is a bioactive autacoid metabolite of arachidonic acid made by various cell types. Previous studies have shown that lipoxin is associated with a variety of biological functions, including anti-inflammatory, regulating immune responses, promoting the repair of damaged cells, etc. The deficiency of lipoxin is a critical pathological mechanism in different diseases. Moreover, the ability of lipoxin to attenuate oxidative stress is noteworthy, thereby protecting the human body from diverse diseases. METHODS: We searched papers from PubMed database using search terms, such as lipoxin, lipoxin A4, oxidative stress, and other relevant terms. RESULTS: A total of 103 articles published over the past 20 years were identified for inclusion. We summarized the capability of lipoxin in regulating oxidative stress and mechanism. CONCLUSION: Lipoxin is provided with a protective role in attenuating oxidative stress.

Equivalence tests for ratio of means in bioequivalence studies under crossover design.

There are several problems concerning the statistical definition of average bioequivalence provided by the U.S. Food and Drug Administration. We proposed a ratio of means based on the original bioavailability measure as the definition for average bioequivalence. Under the log-normal distribution assumption, we proposed a hypothesis testing-based method and a confidence interval-based method to answer the question of whether the ratio of means falls into a predetermined interval. For the hypothesis testing-based method, we decomposed the null two-sided hypothesis of the ratio of means into two one-sided hypotheses. With the intersection-union theorem for asymptotic tests, we constructed two asymptotic size-α tests for the original null hypothesis. The method of variance estimation recovery was adopted to develop the confidence interval-based method. Simulation studies showed that the proposed methods can maintain the empirical type I error rate closely at the nominal level and is as powerful as the two one-sided t-test for testing the ratio of means under different settings. The application of the proposed methods was illustrated through six datasets in real-world examples.

Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces.

Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as k-medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the l1 norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points.

Compositions of sequestrated soil carbon in constructed wetlands of Taiwan.

Constructed wetlands are an ecological engineering technology that has been widely applied to treat anthropogenic wastewater. Until now, few studies have focused on soil carbon (C) in the constructed treatment wetlands in tropical regions. Therefore, this study provides insight into the changes in soil C composition of tropically constructed wetlands at different ages. Five constructed wetlands were investigated in northern Kaohsiung, Taiwan. Soil C was analyzed at three different depths using an acid-hydrolysable method. The results showed that soil TOC content was highest on the soil surface (0-2 cm) and decreased at greater soil depths (2-5 and 5-10 cm) in all the studied constructed wetlands. There was more soil acid-hydrolysable C in the older constructed wetlands than in the younger ones at all depths. On the contrary, the soil recalcitrant carbon (RP-C) did not vary much across the wetland soils. In addition, the RP-C to TOC ratios were higher in the younger than older constructed wetlands, implying that the soil bioavailable C sources for microbial growth increased with the wetland's age. As a result, the compositions of organotrophic microbes, such as methanogens (mcrA copies), appeared to increase with wetlands' ages (i.e., negatively correlated with RP-C/TOC), while the total microbial abundance (16S rDNA) and abundance of lithotrophic microbes, such as methanotrophs (pmoA copies), were not correlated with RP-C/TOC or AHPI-C/TOC ratios, based on the results of our canonical correspondence analysis. Furthermore, the constructed wetlands accumulated soil RP-C from 2.33 to 0.08 g C m-2 day-1 in the constructed wetlands 1 to 30 years old, respectively.

Biomarker Alteration after Neoadjuvant Endocrine Therapy or Chemotherapy in Estrogen Receptor-Positive Breast Cancer.

In estrogen receptor (ER)-positive breast cancer, changes in biomarker expression after neoadjuvant therapy indicate the therapeutic response and are prognostic. However, there is limited information about the biomarker alteration caused by neoadjuvant endocrine therapy in ER-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. We recruited ER-positive/HER2-negative breast cancer patients who received neoadjuvant chemotherapy (NCT), neoadjuvant endocrine therapy (NET), or sequential neoadjuvant endocrine-chemotherapy (NECT) at Peking University Cancer Hospital from 2015 to 2021. A total of 579 patients had paired immunohistochemistry information in both diagnostic biopsy samples and post-neoadjuvant therapy surgical samples. Through a paired comparison of the immunohistochemical information in pre-treatment and post-treatment samples, we found that progesterone receptor (PR) expression reductions were more frequent than ER expression reductions (70.8% vs. 35.2%) after neoadjuvant therapy. The percentage of patients who had a decreased Ki-67 index in the post-operative samples was similar in the three groups (79.8% vs. 79.7% vs. 78.4%). Moreover, PR losses caused by NET were related to low baseline PR expression (p = 0.001), while we did not find a significant association between PR losses and Ki-67 reductions (p = 0.428) or ER losses (p = 0.274). All three types of neoadjuvant therapies caused a reduction in ER, PR, and Ki-67 expression. In conclusion, we found that PR loss after NET was only significantly related to low baseline PR expression, and there is no significant difference in the extent of prognostic factor change including Ki-67 and ER between the PR loss and non-loss groups.

Determining the Covertness of COVID-19 - Wuhan, China, 2020.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has been going on for over a year and has reemerged in several regions. Therefore, understanding the covertness of COVID-19 is critical to more precisely estimating the pandemic size, especially the population of hidden carriers (those with very mild or no symptoms). METHODS: A stochastic dynamic model was proposed to capture the transmission mechanism of COVID-19 and to depict the covertness of COVID-19. The proposed model captured unique features of COVID-19, changes in the diagnosis criteria, and escalating containment measures. RESULTS: The model estimated that, for the epidemic in Wuhan, 79.8% (76.7%-82.7%) of the spread was caused by hidden carriers. The overall lab-confirmation rate in Wuhan up until March 8, 2020 was 0.17 (0.15-0.19). The diagnostic rate among patients with significant symptoms went up to 0.82 on March 8, 2020 from 0.43 on January 1, 2020 with escalating containment measures and nationwide medical supports. The probability of resurgence could be as high as 0.72 if containment measures were lifted after zero new reported (lab-confirmed or clinically confirmed) cases in a consecutive period of 14 days. This probability went down to 0.18 and 0.01 for measures lifted after 30 and 60 days, respectively. DISCUSSION: Consistent with the cases detected in Wuhan in mid-May, 2020, this study suggests that much of the COVID-19 pandemic is underreported and highly covert, which suggests that strict measures must be enforced continuously to contain the spread of the pandemic.

A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening.

Early detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. But manual detection requires experienced pathologists and is time-consuming and error prone. Previously, some methods have been proposed for automated abnormal cervical cell detection, whose performance yet remained debatable. Here, we develop an attention feature pyramid network (AttFPN) for automatic abnormal cervical cell detection in cervical cytology images to assist pathologists to make a more accurate diagnosis. Our proposed method consists of two main components. First, an attention module mimicking the way pathologists reading a cervical cytology image. It learns what features to emphasize or suppress by refining extracted features effectively. Second, a multi-scale region-based feature fusion network guided by clinical knowledge to fuse the refined features for detecting abnormal cervical cells at different scales. The region proposals in the multi-scale network are designed according to the clinical knowledge about size and shape distribution of real abnormal cervical cells. Our method, trained and validated with 7030 annotated cervical cytology images, performs better than the state of art deep learning-based methods. The overall sensitivity, specificity, accuracy, and AUC of an independent testing dataset with 3970 cervical cytology images is 95.83%, 94.81%, 95.08% and 0.991, respectively, which is comparable to that of an experienced pathologist with 10 years of experience. Besides, we further validated our method on an external dataset with 110 cases and 35,013 images from a different organization, the case-level sensitivity, specificity, accuracy, and AUC is 91.30%, 90.62%, 90.91% and 0.934, respectively. Average diagnostic time of our method is 0.04s per image, which is much quicker than the average time of pathologists (14.83s per image). Thus, our AttFPN is effective and efficient in cervical cancer screening, and improvement of clinical workflows for the benefit of potential patients. Our code is available at https://github.com/cl2227619761/TCT_Detection.