A Universal Unbiased Method for Classification from Aggregate Observations

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary losses -- previous research failed to achieve this goal. Practically, our method works by weighing the importance of each label for each instance in the group, which provides purified supervision for the classifier to learn. Theoretically, our proposed method not only guarantees the risk consistency due to the unbiased risk estimator but also can be compatible with arbitrary losses. Extensive experiments on various problems of CFAO demonstrate the superiority of our proposed method

Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility

Accelerating the Drug Delivery Pipeline for Acute and Chronic Pancreatitis: Summary of the Working Group on Drug Development and Trials in Chronic Pancreatitis at the National Institute of Diabetes and Digestive and Kidney Diseases Workshop

The lack of effective therapeutic agents specifically tailored for chronic pancreatitis (CP) has hampered clinical care and negatively impacted patients' lives. New mechanistic insights now point to novel therapies, which involve both recently developed and/or repurposed agents. This working group focused on 2 main outcomes for CP: pain and progression of disease. The goal is to frame the essential aspects of trial design including patient-centered outcomes, proposed methods to measure the outcomes of pain and progression, and study design considerations for future trials to facilitate rapid drug development for patients with CP

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis

Unilateral retinocytoma associated with a variant in the RB1 gene

Abstract Background Retinocytoma is a rare benign retinal tumor associated with variants in the RB1 gene. Ophthalmoscopic features can include a translucent retinal mass, calcification, retinal pigment epithelial alteration and chorioretinal atrophy. Methods Detailed ophthalmological examinations were performed in a Chinese patient with retinocytoma and his daughter with bilateral retinoblastoma. Sanger sequencing was performed to detect RB1 genetic variants in the patient, his daughter and tumor tissue from his daughter. Results A 33‐year‐old man presented with poor vision and strabismus in the right eye since childhood. Fundus examination revealed a round yellow‐white lesion stretching from the nasal side of the optic disc to the temporal periphery of the right eye. Sequencing result identified a reported variant (c.658C>G, p.Leu220Val) in the RB1 gene (NM_000321.2) of DNA extracted from peripheral blood of the patient and his daughter. The missense variant was also found in the tumor tissue from his daughter. Conclusions We report detailed clinical features and genetic analysis of a case with unilateral retinocytoma. Retinocytoma has a wide range of clinical phenotypes; genetic testing is therefore a useful tool for the diagnosis of atypical cases

Baicalin Relieves LPS-Induced Lung Inflammation via the NF-κB and MAPK Pathways

Baicalin is an active ingredient extracted from the Chinese medicine Scutellaria and has many beneficial effects. Pulmonary interstitial and alveolar edema are common symptoms of an acute lung injury (ALI). We investigated the effects of baicalin on LPS-induced inflammation and the underlying mechanisms in mice and cells. The protein contents and mRNA expression of TNF-α, IL-1β, and IL-6 in RAW264.7 cells and mice were detected using ELISA and qRT-PCR. Baicalin significantly suppressed TNF-α, IL-1β, and IL-6 levels and expression, both in vitro and in vivo, compared with the LPS group. Baicalin inhibits the expression of TLR4 and MyD88, resulting in significant decreases in p-p65, p-p38, p-ERK, and p-JNK, as measured by the Western blotting of RAW264.7 cells. A baicalin treatment for 12 h resulted in a rapid increasing of the white blood cell number and significantly improved the pathological changes in the lung. We also found that the baicalin pretreatment for 12 h could decrease the MPO content and wet/dry (W/D) weight ratio, which indicates that baicalin can significantly reduce pulmonary edema. Furthermore, the baicalin pretreatment also resulted in the recovery of TGF-β protein levels and decreased iNOS. Baicalin inhibits ALI inflammation in mice and cells and is a potential candidate for the treatment of ALI

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified access to medical data. In order to improve the retrieval speed, the recognition model S-TCR and the dynamic management algorithm SL-TCR, based on the behavior characteristics of access, were proposed to identify the frequently accessed hot data and dynamically manage the data storage medium as to maximize the system access performance. In order to improve the search performance of keys, an optimized secondary index strategy was proposed to reduce I/O overhead and optimize the search performance of non-primary key indexes. Comparative experiments were conducted on real medical data sets. The experimental results show that the optimized retrieval model can meet the needs of hot data access and diversified medical data retrieval

Development of a DNA aptamer targeting IDO1 with anti-tumor effects

Summary: Immune checkpoint blockade has become an effective approach to reverse the immune tolerance of tumor cells. Indoleamine 2,3-dioxygenase 1 (IDO1) is frequently upregulated in many types of cancers and contributes to the establishment of an immunosuppressive cancer microenvironment, which has been thought to be a potential target for cancer therapy. However, the development of IDO1 inhibitors for clinical application is still limited. Here, we isolated a DNA aptamer with a strong affinity and inhibitory activity against IDO1, designated as IDO-APT. By conjugating with nanoparticles, in situ injection of IDO-APT to CT26 tumor-bearing mice significantly suppresses the activity of regulatory T cells and promotes the function of CD8+ T cells, leading to tumor suppression and prolonged survival. Therefore, this functional IDO1-specific aptamer with potent anti-tumor effects may serve as a potential therapeutic strategy in cancer immunotherapy. Our data provide an alternative way to target IDO1 in addition to small molecule inhibitors

Ventricular tachyarrhythmias in rats with acute myocardial infarction involves activation of small-conductance Ca\u3csup\u3e2+\u3c/sup\u3e-activated K\u3csup\u3e+\u3c/sup\u3e channels

In vitro experiments have shown that the upregulation of small-conductance Ca2+-activated K+ (SK) channels in ventricular epicardial myocytes is responsible for spontaneous ventricular fibrillation (VF) in failing ventricles. However, the role of SK channels in regulating VF has not yet been described in in vivo acute myocardial infarction (AMI) animals. The present study determined the role of SK channels in regulating spontaneous sustained ventricular tachycardia (SVT) and VF, the inducibility of ventricular tachyarrhythmias, and the effect of inhibition of SK channels on spontaneous SVT/VF and electrical ventricular instability in AMI rats. AMI was induced by ligation of the left anterior descending coronary artery in anesthetized rats. Spontaneous SVT/VF was analyzed, and programmed electrical stimulation was performed to evaluate the inducibility of ventricular tachyarrhythmias, ventricular effective refractory period (VERP), and VF threshold (VFT). In AMI, the duration and episodes of spontaneous SVT/VF were increased, and the inducibility of ventricular tachyarrhythmias was elevated. Pretreatment in the AMI group with the SK channel blocker apamin or UCL-1684 significantly reduced SVT/VF and inducibility of ventricular tachyarrhythmias (P \u3c 0.05). Various doses of apamin (7.5, 22.5, 37.5, and 75.0 μg/kg iv) inhibited SVT/VF and the inducibility of ventricular tachyarrhythmias in a dose-dependent manner. Notably, no effects were observed in sham-operated controls. Additionally, VERP was shortened in AMI animals. Pretreatment in AMI animals with the SK channel blocker significantly prolonged VERP (P \u3c 0.05). No effects were observed in sham-operated controls. Furthermore, VFT was reduced in AMI animals, and block of SK channels increased VFT in AMI animals, but, again, this was without effect in sham-operated controls. Finally, the monophasic action potential duration at 90% repolarization (MAPD90) was examined in the myocardial infarcted (MI) and nonmyocardial infarcted areas (NMI) of the left ventricular epicardium. Electrophysiology recordings showed that MAPD90 in the MI area was shortened in AMI animals, and pretreatment with SK channel blocker apamin or UCL-1684 significantly prolonged MAPD90 (P \u3c 0.05) in the MI area but was without effect in the NMI area or in sham-operated controls. We conclude that the activation of SK channels may underlie the mechanisms of spontaneous SVT/VF and susceptibility to ventricular tachyarrhythmias in AMI. Inhibition of SK channels normalized the shortening of MAPD90 in the MI area, which may contribute to the inhibitory effect on spontaneous SVT/VF and inducibility of ventricular tachyarrhythmias in AMI