Clinicopathological features and surgical treatments of intraductal papillary neoplasm of the bile duct: a case report and literature review.

Intraductal papillary neoplasm of bile duct (IPNB), as a precancerous lesion of cholangiocarcinoma, is a rare biliary tract tumor. A 66-year-old female patient was found to have a bile duct mass by routine examination. The liver function tests and tumor markers were normal. Imaging findings revealed a 2.6 cm mass in the common hepatic duct, accompanied by dilatation of both intrahepatic and extrahepatic bile ducts. The patient underwent open extrahepatic bile duct resection, cholecystectomy and Roux-en-Y hepaticojejunostomy. We also conducted a literature review to summarize the clinicopathological features and surgical treatments of IPNB.

A two-stage recommendation optimization algorithm based on item popularity and user features.

Financial product recommendation algorithms are mainly product-centered. This article proposes a two-stage recommendation optimization algorithm based on item popularity and user features, named CPCF-TSP, that can make full use of the demographic characteristics of users and mitigate the problem of users being more inclined to choose "hot" financial products. A popularity weight factor is introduced to normalize popularity and modify Pearson's similarity function. The modified Pearson's similarity function is combined with popularity normalization and user features to improve modeling performance. The two-stage recommendation optimization procedure was combined with a collaborative filtering algorithm to improve recommendation precision. CPCF-TSP fully considers user features in building a hybrid recommendation model and solves the problem of user cold-start. It can also mitigate popularity deviations and improve recommendation precision. MovieLens data and Santander Bank client trading data were used in a case study. The results show that the algorithm reduces inaccuracy in the calculation of the weights for recommendation popularity and similarity and is especially suitable for recommending financial products in which user information can be easily collected and the number of users is far greater than the number of products considered.

Human Leukocyte Antigen Class I Expression and Natural Killer Cell Infiltration and Its Correlation with Prognostic Features in Luminal Breast Cancers.

Purpose: The aim of this study was to determine whether low HLA-I expression and NK cells infiltration are related to prognostic features in breast cancer, as observed in cancers in other locations and non-hormone dependent breast cancers. Particularly, we explored their relation to infiltrated axillary lymph nodes (ALNs), with the aim of finding new predictors helping to decide the extent of axillary surgery. Patients and Methods: We conducted a retrospective correlational analysis of 35 breast cancers from 35 breast cancer patients showing axillary infiltration at diagnosis and with upfront surgery. HLA-I H-score and the number of NK cells x 50 high power fields (HPF) in the biopsy specimen were correlated with pathological variables of the surgical specimen: number of infiltrated ALNs, tumor size, histological type, the presence of ductal carcinoma in situ, focality, histological grade, necrosis, lymphovascular and perineural invasion, Her2Neu status, and the percentages of tumor-infiltrating lymphocytes (TILs), estrogen receptor, progesterone receptor, ki67, and p53. Results: All tumors showed hormone receptor expression and three of them Her2Neu positivity. A positive correlation (p=0.001**) was found between HLA-I H-score and TILs and Ki67 expression. HLA H-score increased with histological grade and was higher in unifocal than in multifocal disease (p=0.044 and p=0.011, respectively). No other correlations were found. Conclusion: High HLA-I H-score values correlated with features of poor prognosis in this cohort of luminal breast tumors, but not with infiltrated ALNs. This finding highlights the differences between luminal breast cancer, and cancers in other locations and non-hormone dependent breast cancers, in which low HLA-I expression tends to be associated with poor prognostic features.

Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.

Background: Previous studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep learning methods to construct classification and prediction models for major depression and healthy control groups. Methods: 120 participants aged 16-25 participated in this study, included 64 MDD group and 56 HC group. We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model. Results: The classification modelling of the major depression and the healthy control groups by relevant and significant vocal acoustic features was 0.90, and the Receiver Operating Characteristic (ROC) curves analysis results showed that the classification accuracy was 84.16%, the sensitivity was 95.38%, and the specificity was 70.9%. The depression prediction model of speech characteristics showed that the predicted score was closely related to the total score of 17 items of the Hamilton Depression Scale(HAMD-17) (r=0.687, P<0.01); and the Mean Absolute Error(MAE) between the model's predicted score and total HAMD-17 score was 4.51. Limitation: This study's results may have been influenced by anxiety comorbidities. Conclusion: The vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms.

Anti-interferon gamma-inducible protein 16 antibodies: Identification of a novel autoantigen in idiopathic interstitial pneumonia and its clinical characteristics based on a multicenter cohort study.

Autoantibodies are detected in idiopathic interstitial pneumonias (IIPs) without a clear connective tissue disease diagnosis, and their clinical significance is unclear. This study aimed to identify a novel autoantibody in IIPs. We screened 295 IIP patients using a 35S-methionine labeled protein immunoprecipitation assay. Candidate autoantigens were identified via protein array and confirmed by immunoprecipitation. Six sera from 295 IIP patients immunoprecipitated common tetrameric proteins (100 kDa). The protein array identified interferon gamma-inducible protein 16 (IFI16) as the candidate autoantigen. Patients with anti-IFI16 antibodies received immunosuppressants less frequently. Five-year survival rates were 50 %, 69 %, and 63 % (P = 0.60), and acute exacerbation-free rates were 50 %, 96 %, and 84 % (P = 0.15) for patients with anti-IFI16, anti-aminoacyl tRNA antibodies, and others. Anti-IFI16 is a novel autoantibody in IIPs. Patients with this antibody often receive less immunosuppressive therapy and could have a poor prognosis. Further research is needed to refine patient stratification and management.

Comparison of clinicopathological characteristics, efficacy of neoadjuvant therapy, and prognosis in HER2-low and HER2-ultralow breast cancer.

BACKGROUND: This study aims to analyze potential differences in clinicopathology, efficacy of neoadjuvant therapy (NAT), and clinical outcome among HER2-null, HER2-ultralow and HER2-low breast cancers. METHODS: Consecutive cases of HER2-negative breast cancer that received NAT were included. They were classified as HER2-null (no staining), HER2-ultralow (incomplete faint staining in ≤ 10% of tumour cells) and HER2-low (HER2-1 + or HER2-2+, in situ hybridisation negative). Subgroup analysis was performed based on the HER2 expression level. RESULTS: Out of 302 patients, 215 (71.19%) were HER2-low, 59 (19.54%) were HER2-ultralow, and 28 (9.27%) were HER2-null. In comparison to the HER2-ultralow group, the HER2-low group exhibited higher expression frequencies of ER (p < 0.001), PR (p < 0.001), and AR (p = 0.004), along with a greater prevalence of the luminal subtype (p < 0.001). The HER2-ultralow group also demonstrated a higher prevalence of lymph node metastasis compared to the HER2-null group (p = 0.026). Varied rates of pathologic complete response (pCR) were observed among the three subgroups: HER2-null, HER2-ultralow, and HER2-low, with rates of 35.71%, 22.03%, and 12.56%, respectively. Only the HER2-low subgroup exhibited a significant difference compared to HER2-null (p = 0.001). Despite variations in pCR rates, the three subgroups exhibited comparable disease-free survival (DFS) (p = 0.571). Importantly, we found HER2-low patients with better treatment response (RCB-0/I) exhibited significantly better DFS than those with significant residual disease (RCB-II/III) (P = 0.036). The overall rate of HER2 immunohistochemical score discordance was 45.24%, mostly driven by the conversion between HER2-0 and HER2-low phenotype. Notably, 32.19% of cases initially classified as HER2-0 phenotype on baseline biopsy were later reclassified as HER2-low after neoadjuvant therapy, and it is noteworthy that 22 out of these cases (78.57%) originally had an HER2-ultralow status in the pretreatment biopsy sample. CONCLUSIONS: Our results demonstrate the distinct clinicopathological features of HER2-low and HER2-ultralow breast tumors and confirm that RCB is an effective predictor of prognosis in HER2-low populations for the first time. Notably, our findings demonstrate high instability in both HER2-low and HER2-ultralow expression from the primary baseline biopsy to residual disease after NAT. Furthermore, this study is the first to investigate the clinicopathological feature and the effectiveness of NAT for HER2-ultralow breast cancer.

Advanced CNN models in gastric cancer diagnosis: enhancing endoscopic image analysis with deep transfer learning.

Introduction: The rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer. Methods: This research aims to utilize endoscopic images to identify various gastric disorders using an advanced Convolutional Neural Network (CNN) model. The Kvasir dataset, comprising images of normal Z-line, normal pylorus, ulcerative colitis, stool, and polyps, was used. Images were pre-processed and graphically analyzed to understand pixel intensity patterns, followed by feature extraction using adaptive thresholding and contour analysis for morphological values. Five deep transfer learning models-NASNetMobile, EfficientNetB5, EfficientNetB6, InceptionV3, DenseNet169-and a hybrid model combining EfficientNetB6 and DenseNet169 were evaluated using various performance metrics. Results & discussion: For the complete images of gastric cancer, EfficientNetB6 computed the top performance with 99.88% accuracy on a loss of 0.049. Additionally, InceptionV3 achieved the highest testing accuracy of 97.94% for detecting normal pylorus, while EfficientNetB6 excelled in detecting ulcerative colitis and normal Z-line with accuracies of 98.8% and 97.85%, respectively. EfficientNetB5 performed best for polyps and stool with accuracies of 98.40% and 96.86%, respectively.The study demonstrates that deep transfer learning techniques can effectively predict and classify different types of gastric cancer at early stages, aiding experts in diagnosis and detection.

Classification of cyclic alternating patterns of sleep using EEG signals.

Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP cycles. In this work, we develop an easy-to-implement accurate system to differentiate between CAP A and CAP B. Small segments of the EEG signal are processed using Gaussian filters to obtain sub-band components. Features are extracted using some statistical characteristics of these signal components. Minimum redundancy maximum relevance test is employed to identify the more significant features. Three different machine learning classifiers are considered and their performance is compared. The results are analyzed for both the balanced and unbalanced datasets. The k-nearest neighbour (kNN) classifier achieves 79.14 % accuracy and F-1 score of 79.24 % for the balanced dataset. The proposed method outperforms the existing methods for CAP classification. It is easy-to-implement and can be considered as a candidate for real-time deployment.

Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: Utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information.

BACKGROUND: The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy. Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners. Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion. Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model. Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.

Lateral lymph node metastasis without mesenteric lymph node involvement in middle-low rectal cancer: Results of a multicentre lateral node collaborative group study in China.

BACKGROUND: Characteristics and prognoses of lateral lymph node (LLN) metastasis but not mesenteric lymph node (LN) metastasis are poorly understood. This study explored patterns of mesenteric and LLN metastases in rectal cancer patients. METHOD: This retrospective, multicentre study was conducted at three institutions and included patients who underwent total mesorectal excision (TME) with lateral lymph node dissection (LLND) for rectal cancer (n = 271). RESULTS: Among the patients with LLN metastases, 210 patients (77.5 %) with clinical stage T3-4 disease and 157 patients (57.9 %) with clinical stage N1-N2 disease underwent TME as well as LLND. The prognoses of patients with metastasis confined to LLNs were significantly better than those of patients with both mesenteric and LLN metastases (3-year overall survival: 85.0 % vs. 51.0 %, p = 0.005; 3-year disease-free survival: 75.0 % vs. 26.5 %, p = 0.003) and were similar to those of patients with metastasis confined to mesenteric LNs (3-year overall survival: 85.0 % vs. 83.8 %, p = 0.607; 3-year disease-free survival 75.0 % vs. 68.8 %, p = 0.717). Patients with metastases confined to LLN had a lower proportion of poor histological types (20.0 % vs. 65.3 %, p = 0.002), lymphatic invasion (20.0 % vs. 59.2 %, p = 0.036) and number of LLN metastases (1.6 vs 2.7, p = 0.004), and all metastases were confined to the internal iliac or obturator region (100.0 % vs. 77.6 %, p = 0.008) compared to patients with both mesenteric and LLN metastasis. CONCLUSIONS: Approximately a quarter of patients with rectal cancer have LLN metastases but no mesenteric LN metastases. These patients have favourable pathological features and prognoses and can be managed and treated for mesenteric LN metastasis.

Spin-dependent electrocatalysis.

The shift towards sustainable energy requires efficient electrochemical conversion technologies, emphasizing the crucial need for robust electrocatalyst design. Recent findings reveal that the efficiency of some electrocatalytic reactions is spin-dependent, with spin configuration dictating performance. Consequently, understanding the spin's role and controlling it in electrocatalysts is important. This review succinctly outlines recent investigations into spin-dependent electrocatalysis, stressing its importance in energy conversion. It begins with an introduction to spin-related features, discusses characterization techniques for identifying spin configurations, and explores strategies for fine-tuning them. At the end, the article provides insights into future research directions, aiming to reveal more unknown fundamentals of spin-dependent electrocatalysis and encourage further exploration in spin-related research and applications.

Adult diffuse hepatic hemangiomatosis lesion occupying the entire abdominal and pelvic cavities: a case report.

Introduction: Adult diffuse hepatic hemangiomatosis (DHH) is an extremely rare disease. Consequently, its characteristics are poorly understood. Herein, we report a case of adult DHH involving both liver lobes but without extrahepatic involvement. To the best of our knowledge, this the largest reported adult DHH to date. Case presentation: A 51-year-old man was admitted due to abdominal distension and dyspnea. Physical examination revealed marked liver enlargement. Color Doppler, plain and contrast-enhanced computed tomography, and contrast-enhanced magnetic resonance imaging revealed a hepatic lesion sized 35.1 × 32.1 × 14.1 cm occupying nearly the entire abdominal and pelvic cavities. Diagnosis was established by liver puncture biopsy. The patient exhibited clinical signs of portal hypertension and hypersplenism, but remains free of serious DHH-related complications. He is followed up regularly, with proactive evaluation for future liver transplantation. Conclusion: This case will contribute to the current knowledge on the clinical and imaging features of this rare entity.

A Nomogram Based on a Non-Invasive Method to Distinguish Between Gram-Positive and Gram-Negative Bacterial Infections of Liver Abscess.

Purpose: The diagnosis of liver abscess (LA) caused by Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB) depends on ultrasonography, but it is difficult to distinguish the overlapping features. Valuable ultrasonic (US) features were extracted to distinguish GPB-LA and GNB-LA and establish the relevant prediction model. Materials and Methods: We retrospectively analyzed seven clinical features, three laboratory indicators and 11 US features of consecutive patients with LA from April 2013 to December 2023. Patients with LA were randomly divided into training group (n=262) and validation group (n=174) according to a ratio of 6:4. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group. Results: A total of 436 participants (median age: 55 years; range: 42-68 years; 144 women) were evaluated, including 369 participants with GNB-LA and 67 with GPB-LA, respectively. A total of 11 predictors by LASSO regression analysis, which included gender, age, the liver background, internal gas bubble, echogenic debris, wall thickening, whether the inner wall is worm-eaten, temperature, diabetes mellitus, hepatobiliary surgery and neutrophil(NEUT). The performance of the Nomogram prediction model distinguished between GNB-LA and GPB-LA was 0.80, 95% confidence interval [CI] (0.73-0.87). In the validation group, the AUC of GNB was 0.79, 95% CI (0.69-0.89). Conclusion: A model for predicting the risk of GPB-LA was established to help diagnose pathogenic organism of LA earlier, which could help select sensitive antibiotics before the results of drug-sensitive culture available, thereby shorten the treatment time of patients.

Clinical Profile and Outcomes of Patients With Systemic Lupus Erythematosus.

Background Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by relapsing-remitting immune system activation, affecting multiple organ systems. Despite significant advances in understanding SLE's pathogenesis, there remains a need for comprehensive clinical profiling at the time of diagnosis to improve early detection and management. This study addresses this gap by providing a detailed analysis of the clinical presentation, disease activity, and patient outcomes using the Systemic Lupus International Collaborating Clinics (SLICC) criteria and Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) index. Methodology This cross-sectional observational study included 80 patients diagnosed with SLE using the 2012 SLICC criteria. Patients were recruited from the Rheumatology department and other wards of Byramjee Jeejeebhoy Government Medical College and Sassoon General Hospital, Pune, India. All participants provided informed consent and institutional ethical approval was obtained. Data were collected through detailed clinical history, physical examinations, and standard tests such as chest X-rays, CBC, RFT, LFT, urine microscopy, creatine phosphokinase, ANA, AntiDsDNA, complement consumption, and Coombs' tests, with 2D echocardiography performed as needed. Follow-ups every three months over 1.5 years assessed disease activity using SLEDAI criteria. Patients aged 12 and above who met the SLICC criteria were included and those with other connective tissue disorders were excluded. Associations between clinical symptoms and organ involvement were analyzed using the chi-square test with a p-value of <0.05 considered significant. Results The study evaluated 80 patients with SLE, revealing a predominantly female cohort (80%) with a mean age of 29.4 years and a standard deviation of 8.3 years, skewed towards younger age groups. Clinical manifestations were diverse; the most common symptoms were (83.75%), oral ulcers (98.75%), and alopecia (95%). Anemia (66.25%) was the most prevalent abnormality, followed by albuminuria and renal abnormalities. Organ involvement was highest in the renal system (50%) and mucocutaneous features, with lower incidences in cardiac, gastrointestinal, and vascular systems. Gender-specific analyses indicated significant differences in SLE nephritis (p=0.048) and autoimmune hemolytic anemia (p=0.046). Autoantibody profiles showed high positivity for ANA (98.8%) and DsDNA (61.3%). Clinical outcomes demonstrated that 68.8% of patients achieved remission and 16.3% experienced organ damage. The SLEDAI scores significantly improved over time, with substantial reductions from baseline to nine months (p<0.001). Conclusion In conclusion, this study provides a detailed examination of SLE, revealing that it predominantly affects young adults and is characterized by diverse manifestations including mucocutaneous symptoms, significant renal involvement, and notable autoantibody profiles. The high prevalence of anti-nucleosome and anti-dsDNA antibodies underscores their diagnostic and prognostic value. Clinically, the findings highlight the necessity for early detection and targeted management of SLE, particularly in addressing renal and mucocutaneous symptoms. Future research should focus on longitudinal studies to track disease progression, explore genetic and environmental influences, and investigate regional variations to enhance treatment strategies and patient outcomes.

Multimodal and multiscale feature fusion for weakly supervised video anomaly detection.

Weakly supervised video anomaly detection aims to detect anomalous events with only video-level labels. In the absence of boundary information for anomaly segments, most existing methods rely on multiple instance learning. In these approaches, the predictions for unlabeled video snippets are guided by the classification of labeled untrimmed videos. However, these methods do not account for issues such as video blur and visual occlusion, which can hinder accurate anomaly detection. To address these issues, we propose a novel weakly supervised video anomaly detection method that fuses multimodal and multiscale features. Firstly, RGB and optical flow snippets are input into pre-trained I3D to extract appearance and motion features. Then, we introduce an Attention De-redundancy (AD) module, which employs an attention mechanism to filter out task-irrelevant redundancy in these appearance and motion features. Next, to mitigate the effects of video blurring and visual occlusion, we propose a Multi-scale Feature Learning module. This module captures long-term and short-term temporal dependencies among video snippets to provide global and local guidance for blurred or occluded video snippets. Finally, to effectively utilize the discriminative features of different modalities, we propose an Adaptive Feature Fusion module. This module adaptively fuses appearance and motion features based on their respective feature weights. Extensive experimental results demonstrate that our proposed method outperforms mainstream unsupervised and weakly supervised methods in terms of AUC. Specifically, our proposed method achieves 97.00% AUC and 85.31% AUC on two benchmark datasets, i.e., ShanghaiTech and UCF-Crime, respectively.

Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data.

Polycystic Ovarian Disease or Polycystic Ovary Syndrome (PCOS) is becoming increasingly communal among women, owing to poor lifestyle choices. According to the research conducted by National Institutes of Health, it has been observe that PCOS, an endocrine condition common in women of childbearing age, has become a significant contributing factor to infertility. Ovarian abnormalities brought on by PCOS carry a high risk of miscarriage, infertility, cardiac problems, diabetes, uterine cancer, etc. Ovarian cysts, obesity, menstrual irregularities, elevated amounts of male hormones, acne vulgaris, hair loss, and hirsutism are some of the symptoms of PCOS. It is not easy to determine PCOS because of its different combinations of symptoms in different women and various criteria needed for diagnosis. Taking biochemical tests and ovary scanning is a time-consuming process and the financial expenses have become a hardship to the patients. Thus, early prognosis of PCOS is crucial to avoid infertility. The goal of the proposed work is to analyse PCOS symptoms based on clinical data for early diagnosis and to classify into PCOS affected or not. To achieve this objective, clinical features dataset and ultrasound imaging dataset from Kaggle is utilized. Initially 541 instances of 45 clinical features such as testosterone, hirsutism, family history, BMI, fast food, menstrual disorder, risk etc. are considered and correlation-based feature extraction method is applied to this dataset which results in 17 features. The extracted features are applied to various machine learning algorithms such as Logistic Regression, Naïve Bayes and Support Vector Machine. The performance of each method is evaluated based on accuracy, precision, recall, F1-score and the result shows that among three models, Support Vector Machine model achieved high accuracy of 94.44%. In addition to this, 3856 ultrasound images are analysed by CNN based deep learning algorithm and VGG16 transfer learning algorithm. The performance of these models is evaluated using training accuracy, loss and validation accuracy, loss and the result depicts that VGG16 outperforms than CNN model with validation accuracy of 98.29%.

Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors. PURPOSE: To identify high-risk subregions associated with synchronous distant metastasis. METHODS: This study enrolled a total of 277 patients with ccRCC. Voxel intensity and local entropy values were compiled within the region of interest for all patients. Unsupervised k-means clustering yielded three subregions per tumor. Radiomic features were extracted, and random forest-based feature selection was conducted. The selected features were used in a multi-instance support vector machine (mi-SVM) model for training, and predictions were made on the validation cohort. Model performance was evaluated using five-fold cross-validation. The subregion with the highest score for patients with synchronous distant metastasis was identified across all cohorts. RESULTS: The mi-SVM model yielded an average area under the curve (AUC) of 0.812 in the training cohort and 0.805 in the validation cohort. In the entire cohort of patients with synchronous distant metastasis, subregion 2, characterized by tumor periphery and intratumoral transitional components, accounted for the highest proportion (48.57%, 30.6/63) among all subregions. It represents a high-risk subregion for synchronous distant metastasis of clear cell renal cell carcinoma. CONCLUSION: The peripheral and intratumoral transition zones of clear cell renal cell carcinoma are high-risk subregions associated with synchronous distant metastasis.

Enhancing Sensitivity in Detecting Severe Fever With Thrombocytopenia Syndrome Virus: Development of a Reverse Transcription-Droplet Digital Polymerase Chain Reaction.

Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal disease. Droplet digital polymerase chain reaction (ddPCR) presents unparalleled sensitivity and enables absolute quantification of viral load. In this prospective study, we enrolled 111 patients with SFTS and collected 259 continuous samples. Our findings unveil a robust reverse transcription (RT)-ddPCR method for SFTS with a limit of detection of 2.46 copies/µL (95% CI, 1.50-11.05), surpassing the sensitivity of RT-quantitative polymerase chain reaction at 103.29 copies/µL (95% CI, 79.69-216.35). Longitudinal cohort analysis revealed significantly higher RT-ddPCR detection rates at days 10 to 11, 13 to 14, and ≥15 of the disease course as compared with RT-quantitative polymerase chain reaction (P < .05). Positive RT-ddPCR results were associated with declined platelet and elevated aspartate aminotransferase and lactate dehydrogenase on the same day vs negative RT-ddPCR samples. RT-ddPCR exhibits commendable diagnostic efficacy in SFTS, and it remains detectable in blood samples from patients with an extended disease course. Furthermore, RT-ddPCR correlates with clinical laboratory tests, furnishing valuable reference data for clinical diagnosis.

Clinical characteristics of Behçet's syndrome in Shanghai database: Baseline data of a cross-sectional cohort study.

OBJECTIVE: Behçet's syndrome (BS) is a variant vessel vasculitis that can involve multiple organs, with highly heterogeneous clinical manifestations. This study aims to analyze baseline data of BS patients to enhance the comprehension of its clinical features. METHODS: This study included 1216 registered cases of BS patients referred to Huadong Hospital affiliated with Fudan University. Each patient was thoroughly assessed and recorded for demographic data, clinical manifestations, gastrointestinal endoscope, imaging, etc. RESULTS: Significant gender differences were observed in clinical manifestations. Pseudofolliculitis (p < .001), uveitis (p = .003), vascular (p < .001), and cardiovascular involvement (p < .001) were significantly more prevalent in male BS patients, while genital ulcers (p = .011) and erythema nodosum (p = .009) were more common among the female. Furthermore, pseudofolliculitis (44.3%, 37.4% vs. 25.0%, p < .001), pathergy test positivity (37.0%, 24.5% vs. 12.6%, p < .001), and uveitis (18.8%, 18.4% vs. 11.2%, p < .001) showed higher incidence rates in the 16-35 years age group. Vascular involvement (11.1%, 18.0% vs. 15.8%, p < .001) notably increased in the 36-50 years age group. Additionally, the ISG diagnostic criteria were more likely to be met in the 16-35 age group (OR: 2.039, 95% CI: 1.581-2.631, p < .001), whereas the ICBD criteria were less likely to be met in the 16-35 age group (OR: 0.266, 95% CI: 0.150-0.474, p < .001). CONCLUSIONS: This study provided data on the baseline of clinical features of BS in a single center, BS patients presented significant heterogeneity, showing different manifestations across various genders and age groups. This diversity might contribute to a better understanding of BS clinical features and pave the way for future multi-center studies.

Soft-label recover based label-specific features learning.

Presently, multi-label classification algorithms are mainly based on positive and negative logical labels, which have achieved good results. However, logical labeling inevitably leads to the label misclassification problem. In addition, missing labels are common in multi-label datasets. Recovering missing labels and constructing soft labels that reflect the mapping relationship between instances and labels is a difficult task. Most existing algorithms can only solve one of these problems. Based on this, this paper proposes a soft-label recover based label-specific features learning (SLR-LSF) to solve the above problems simultaneously. Firstly, the information entropy is used to calculate the confidence matrix between labels, and the membership degree of soft labels is obtained by combining the label density information. Secondly, the membership degree and confidence matrix are combined to construct soft labels, and this process not only solves the problem of missing labels but also obtains soft labels with richer semantic information. Finally, in the process of learning specific label features for soft labels. The local smoothness of the labels learned through stream regularization is complemented by the global label correlation, thus improving the classification performance of the algorithm. To demonstrate the effectiveness of the proposed algorithm, we conduct comprehensive experiments on several datasets.