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Abstract Background Risk-reducing salpingo-oophorectomy (RRSO) is recommended for women at increased risk of breast and ovarian cancer. We launched a prospective study of women receiving RRSO, including those with mutations in genes beyond BRCA1/2. Patients and methods 80 women were enrolled for RRSO with sectioning and extensively examining the fimbriae (SEE-FIM) protocol between October 2016 and June 2022. The majority of participants had inherited susceptibility gene mutations or a family history suggesting ovarian cancer risk, while patients with isolated metastatic high-grade serous cancer of unknown origin were also included. Results Overall, two patients had isolated metastatic high-grade serous cancer with unknown origin, and four patients had family histories but refused to take genetic tests. The rest 74 patients harbored deleterious susceptible gene, including 43 (58.1%) with BRCA1 mutation, and 26 (35.1%) with BRCA2 mutation, respectively. Other mutated genes included ATM (1), BRIP1(1), PALB2(1), MLH1(1) and TP53 (1) in each patient. Among the 74 mutation carriers, three (4.1%) cancers were recognized, one (1.4%) was found to have serous tubal intraepithelial carcinoma (STIC), and five patients (6.8%) was diagnosed with serous tubal intraepithelial lesions (STILs). P53 signature was recognized in 24 patients (32.4%). For other genes, MLH1 mutation carrier had endometrial atypical hyperplasia and p53 signature in fallopian tubes. The germline TP53 mutation carrier had STIC in the surgical specimens. Evidence for precursor escape was also recognized in our cohort. Conclusion Our study demonstrated clinic-pathological findings of patients at increased risk of breast and ovarian cancer, and expand the clinical application of SEE-FIM protocol.

AbstractBackground Anemia is a common complication in patients with progressive chronic kidney disease. This cohort study evaluated the prevalence, clinical features and prognosis of membranous nephropathy (MN) with anemia.Methods We retrospectively analyzed a cohort of MN patients diagnosed using renal biopsy between February 2012 and February 2018. The clinical and pathological characteristics at baseline were recorded, and the outcomes (hemoglobin, proteinuria and renal function) during follow-ups were also evaluated. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for anemia in MN patients. The MN patients were divided according to the therapeutic effect they experienced as follows: without-anemia, completely corrected anemia, standard anemia treatment and nonstandard anemia treatment groups. We compared the rate of complete remission of MN and renal end-point events among the four groups.Results The median age of 483 patients was 42.43 (26.59, 50.20) years at the time of MN diagnosis. The prevalence of anemia at baseline was 23.81%, and the cumulative prevalence was 50.72%. There were 133 cases of mild anemia, 103 cases of moderate anemia and 9 cases of severe anemia; in addition, there were 228 cases of normocytic anemia and 17 cases of microcytic hypochromic anemia. Multivariate logistic regression indicated that acute renal tubule injury >5% (OR = 1.634, 95% CI 1.034, 2.581; p = 0.035), total protein level (OR = 0.949, 95% CI 0.923, 0.975; p

Abstract In recent years, deep learning has been applied in the field of clinical medicine to process large-scale medical images, for large-scale data screening, and in the diagnosis and efficacy evaluation of various major diseases. Multi-modal medical data fusion based on deep learning can effectively extract and integrate characteristic information of different modes, improve clinical applicability in diagnosis and medical evaluation, and provide quantitative analysis, real-time monitoring, and treatment planning. This study investigates the performance of existing multi-modal fusion pre-training algorithms and medical multi-modal fusion methods and compares their key characteristics, such as supported medical data, diseases, target samples, and implementation performance. Additionally, we present the main challenges and goals of the latest trends in multi-modal medical convergence. To provide a clearer perspective on new trends, we also analyzed relevant papers on the Web of Science. We obtain some meaningful results based on the annual development trends, country, institution, and journal-level research, highly cited papers, and research directions. Finally, we perform co-authorship analysis, co-citation analysis, co-occurrence analysis, and bibliographic coupling analysis using the VOSviewer software.

This study aims to analyze the characteristics of idiopathic membranous nephropathy (iMN) with nondiabetic urine glucose during the follow-up. We retrospectively analyzed the data of 1313 patients who were diagnosed iMN. The prevalence of nondiabetic urine glucose during follow-up was 10.89%. There were significant differences between the patients with nondiabetic urine glucose and those without urine glucose in gender, hypertension ratio, proteinuria, N-acetyl-β-glucosaminidase, retinol binding protein, serum albumin, serum creatinine (Scr), cholesterol, triglyceride and positive anti-phospholipase A2 receptor antibody ratio, glomerular sclerosis ratio, acute and chronic tubular injury lesion at baseline. To exclude the influence of the baseline proteinuria and Scr, case control sampling of urine glucose negative patients was applied according to gender, baseline proteinuria and Scr. The proteinuria nonremission (NR) ratio was 45.83 versus 12.50% of the urine glucose positive group and case control group. Partial remission (PR) ratio of the two groups was 36.46 versus 23.96% and complete remission (CR) ratio was 19.79% versus 63.54%, respectively. Patients with urine glucose had higher risk of 50% estimated glomerular filtration rate (eGFR) reduction. Cox regression showed that urine glucose and baseline Scr were risk factors of 50% reduction of eGFR. Urine glucose remission ratio of the patients with proteinuria NR, PR, and CR was 13.33, 56.25, and 94.73% (p

Abstract Background Building biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging. Results In this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling. Conclusion We have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.

Marjault et al. report two crystal structures of a drug molecule bound to NEET proteins mNT and NAF-1 and the effects of this molecule on diabetic mice, suggesting an approach for reducing mitochondrial iron and ROS levels in diabetic patients.

The heterogeneity of cell-of-origin for papillary renal cell carcinoma (pRCC) remains unknown. Here, with single-cell ATAC-seq from normal human kidney cells and ATACseq profiles from pRCC samples, the authors show that pRCC can originate from kidney collecting duct principal cells and this subtype is associated with advanced pRCC.

Human NEET proteins, such as NAF-1 and mitoNEET, are homodimeric, redox iron-sulfur proteins characterized by triple cysteine and one histidine-coordinated [2Fe-2S] cluster. They exist in an oxidized and reduced state. Abnormal release of the cluster is implicated in a variety of diseases, including cancer and neurodegeneration. The computer-aided and structure-based design of ligands affecting cluster release is of paramount importance from a pharmaceutical perspective. Unfortunately, experimental structural information so far is limited to only one ligand/protein complex. This is the X-ray structure of furosemide bound to oxidized mitoNEET. Here we employ an enhanced sampling approach, Localized Volume-based Metadynamics, developed by some of us, to identify binding poses of furosemide to human mitoNEET protein in solution. The binding modes show a high variability within the same shallow binding pocket on the protein surface identified in the X-ray structure. Among the different binding conformations, one of them is in agreement with the crystal structure’s one. This conformation might have been overstabilized in the latter because of the presence of crystal packing interactions, absent in solution. The calculated binding affinity is compatible with experimental data. Our protocol can be used in a straightforward manner in drug design campaigns targeting this pharmaceutically important family of proteins.

The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.

BackgroundSkin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited. ObjectiveThis study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases. MethodsEach image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences. ResultsXiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%). ConclusionsXiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.

BackgroundThe rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable on open excision. The purpose of the present study was to develop and validate a simple-to-use nomogram for predicting the upgrade of ADH diagnosed with ultrasound (US)-guided core needle biopsy in patients with US-detected breast lesions.MethodsTwo retrospective sets, the training set (n = 401) and the validation set (n = 186), from Fudan University Shanghai Cancer Center between January 2014 and December 2019 were retrospectively analyzed. Clinicopathological and US features were selected using univariate and multivariable logistic regression, and the significant features were incorporated to build a nomogram model. Model discrimination and calibration were assessed in the training set and validation set.ResultsOf the 587 ADH biopsies, 67.7% (training set: 267/401, 66.6%; validation set: 128/186, 68.8%) were upgraded to cancers. In the multivariable analysis, the risk factors were age [odds ratio (OR) 2.739, 95% confidence interval (CI): 1.525–5.672], mass palpation (OR 3.008, 95% CI: 1.624–5.672), calcifications on US (OR 4.752, 95% CI: 2.569–9.276), ADH extent (OR 3.150, 95% CI: 1.951–5.155), and suspected malignancy (OR 4.162, CI: 2.289–7.980). The model showed good discrimination, with an area under curve (AUC) of 0.783 (95% CI: 0.736–0.831), and good calibration (p = 0.543). The application of the nomogram in the validation set still had good discrimination (AUC = 0.753, 95% CI: 0.666–0.841) and calibration (p = 0.565). Instead of surgical excision of all ADHs, if those categorized with the model to be at low risk for upgrade were surveillanced and the remainder were excised, then 63.7% (37/58) of surgeries of benign lesions could have been avoided and 78.1% (100/128) malignant lesions could be treated in time.ConclusionsThis study developed a simple-to-use nomogram by incorporating clinicopathological and US features with the overarching goal of predicting the probability of upgrade in women with ADH. The nomogram could be expected to decrease unnecessary surgery by nearly two-third and to identify most of the malignant lesions, helping guide clinical decision making with regard to surveillance versus surgical excision of ADH lesions.

Aims: To describe the characteristics and prognosis of 19 patients with fibronectin glomerulopathy (FNG) and evaluate prognostic factors associated with poor renal outcomes.Methods: Included in this retrospective study was 19 FNG patients in Nanjing Glomerulonephritis Registry system. Associations between the clinical parameters, pathological features, and renal outcomes were evaluated by Kaplan-Meier survival analysis.Results: Of the 19 FNG patients included in this study, 8 (42.1%) were women. The median age of the 19 FNG patients was 31 (17–71) years, and the median disease duration 48 (1–175) months at diagnosis. At the time of renal biopsy, the mean serum creatinine (Scr) was 1.22 ± 0.16 mg/dl and urinary protein was 6.24 ± 0.97 mg/24 h. Renal biopsy showed a lobular appearance with cellular mesangial nodules expanded by matrix in 14 cases. After a median follow-up period of 87 months (interquartile range 34–114.5 months), 8 FNG patients developed renal function decline, including 7 progressing into end-stage renal disease (ESRD) and 1 presenting with by a 2-fold-increase in Scr. Scr and proteinuria remained stable in the remaining 11 patients. Kaplan-Meier survival analysis showed that nephrotic range proteinuria (P = 0.022) and focal glomerular sclerosis (P = 0.028) were associated with renal function decline.Conclusions: Nephrotic range proteinuria and focal glomerular sclerosis were associated with renal function decline during the follow-up period of the FNG patients in our series. FNG Patients at risk of renal function decline should be identified preferentially and given more progressive and effective therapies to prevent further disease progression.

This study aimed to determine the impact of HLA-B27 on clinical phenotype and renal function during follow-up periods in patients with ankylosing spondylitis (AS) and secondary IgA nephropathy (IgAN). This single-center retrospective study included 71 AS patients with secondary IgAN. Renal function decline was defined as a mean eGFR decline of more than 5 mL/min/1.73 m2 per year or progression into the dialysis stage. The association between HLA-B27 status and renal function decline was evaluated by univariable and multivariable Cox regression analyses. The results showed that seven (9.85%) of the 71 included patients were HLA-B27-negative. The median follow-up period was 4.0 years. HLA-B27-negative patients showed higher levels of uric acid (UA) than those who were HLA-B27-positive. Pathologically, a higher percentage of globally sclerotic glomeruli was observed in HLA-B27-negative patients. Survival analysis indicated that HLA-B27 negativity was associated with a significantly higher probability of renal function decline than HLA-B27 positivity. This significant association was also found in subgroup analyses of patients with either substantial proteinuria (more than 1.0 g per day) or interstitial fibrosis and tubular atrophy. Multivariable analysis showed that HLA-27 negativity was independently associated with renal function decline (HR 6.58; 95% CI 1.65 to 26.21; p = 0.008). In conclusion, HLA-B27 negativity is associated not only with a higher level of UA and a higher percentage of globally sclerotic glomeruli in AS patients with secondary IgAN but with renal function decline during follow-up periods.

Abstract Background Invasive pleomorphic lobular carcinoma (PLC) is an aggressive subtype of invasive lobular carcinoma of the breast, which has its own histopathological and biological features. The metastatic patterns for PLC are distinct from those of invasive ductal carcinoma. In addition, pancreatic metastasis from PLC is extremely rare. Case presentation We report a rare case of a 48-year-old woman presenting with clinical gastrointestinal symptoms and pancreatic metastasis of PLC. The pancreatic tumor was composed of pleomorphic tumor cells arranged in the form of solid sheets and nests and as single files, with frequent mitotic figures, nucleolar prominence, high nuclear to cytoplasmic ratio and loss of cohesion. The malignant cells were positive for p120 (cytoplasmic) and GATA3 and negative for estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, E-cadherin, gross cystic disease fluid protein 15 and mammaglobin, which indicated a lobular carcinoma phenotype of the breast. Conclusions To the best of our knowledge, this is one of the few reported cases in the literature of pancreatic metastasis of invasive lobular carcinoma of the breast, of which the definitive diagnosis was obtained only after surgery. Rare metastasis sites should be considered, particularly, when a patient has a medical history of PLC.

Categorical data are ubiquitous in machine learning tasks, and the representation of categorical data plays an important role in the learning performance. The heterogeneous coupling relationships between features and feature values reflect the characteristics of the real-world categorical data which need to be captured in the representations. The paper proposes an enhanced categorical data embedding method, i.e., CDE++, which captures the heterogeneous feature value coupling relationships into the representations. Based on information theory and the hierarchical couplings defined in our previous work CDE (Categorical Data Embedding by learning hierarchical value coupling), CDE++ adopts mutual information and margin entropy to capture feature couplings and designs a hybrid clustering strategy to capture multiple types of feature value clusters. Moreover, Autoencoder is used to learn non-linear couplings between features and value clusters. The categorical data embeddings generated by CDE++ are low-dimensional numerical vectors which are directly applied to clustering and classification and achieve the best performance comparing with other categorical representation learning methods. Parameter sensitivity and scalability tests are also conducted to demonstrate the superiority of CDE++.

The first complete mitogenome of Hong Kong paradise fish (Macropodus hongkongensis) was determined in this study. The assembled mitogenome is 16,494 bp and consisted of 13 protein-coding genes, 22 tRNAs, 2 rRNAs, and a control region. Nucleotide composition of the complete mitogenome is 30.6% A, 24.8% C, 14.8% G, and 29.8% T, with an A + T bias of 60.4%. The maximum-likelihood tree based on 13 protein-coding genes showed that M. erythropterus was the closest related species to M. hongkongensis.

The anti-PD-L1 monoclonal antibody (mAb) targeting PD-1/PD-L1 immune checkpoint has achieved outstanding results in clinical application and has become one of the most popular anti-cancer drugs. The mechanism of molecular recognition and inhibition of PD-L1 mAbs is not yet clear, which hinders the subsequent antibody design and modification. In this work, the trajectories of PD-1/PD-L1 and nanobody/PD-L1 complexes were obtained via comparative molecular dynamics simulations. Then, a series of physicochemical parameters including hydrogen bond, dihedral angle distribution, pKa value and binding free energy, and so forth, were all comparatively analyzed to investigate the recognition difference between PD-L1 and PD-1 and nanobody. Both LR113 (the amino acid residues in PD-L1 are represented by the lower left sign of L) and LR125 residues of PD-L1 undergo significant conformational change after association with mAbs, which dominates a strong electrostatic interaction. Solvation effect analysis revealed that solvent-water enhanced molecular recognition between PD-L1 and nanobody. By combining the analyses of the time-dependent root mean squared fluctuation (RMSF), free energy landscape, clustering and energy decomposition, the potential inhibition mechanism was proposed that the nanobody competitively and specifically bound to the β-sheet groups of PD-L1, reduced the PD-L1’s flexibility and finally blocked the formation of PD-1/PD-L1 complex. Based on the simulation results, site-directed mutagenesis of ND99 (the amino acid residues in Nano are displayed by the lower left sign of N) and NQ116 in the nanobody may be beneficial for improving antibody activity. This work offers some structural guidance for the design and modification of anticancer mAbs based on the structure of the PD-1/PD-L1 complex.