A vision-language foundation model for precision oncology.

Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models.

Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment.

Background: Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed to identify novel digital biomarkers of disease progression and assess the clinical relevance of the interplay between a variety of PTC characteristics and their microenvironment in glomerular diseases.

Methods: A total of 344 NEPTUNE/CureGN participants were included: 112 minimal change disease, 134 focal segmental glomerulosclerosis, 61 membranous nephropathy, and 37 IgA nephropathy.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis.

Methods: Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM).

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning.

Carbon sinks/sources' spatiotemporal heterogeneity and their spatial response to economic-social factors in relatively developed regions: A case of China's coastal area.

With the global climate change, carbon reduction in economically active regions has gradually become a focus of attention and its underlying drivers were essential for understanding alterations in ecosystems in response to human behavior. However, the exploration of Carbon Sinks/Sources Patterns (CSSP) in an Economic-Social context was lacking. Distinguished from traditional Net Ecosystem Productivity (NEP) estimation methods, we optimized model parameters, adjusted estimation logic, and revealed CSSP more reasonably.

Highly sensitive and rapid determination of Mycobacterium leprae based on real-time multiple cross displacement amplification.

Background: Mycobacterium leprae (ML) is the pathogen that causes leprosy, which has a long history and still exists today. ML is an intracellular mycobacterium that dominantly induces leprosy by causing permanent damage to the skin, nerves, limbs and eyes as well as deformities and disabilities. Moreover, ML grows slowly and is nonculturable in vitro.

Biosensor-Based Multiple Cross Displacement Amplification for the Rapid Detection of .

Leprosy is an ancient disease caused by (ML) that remains a public health problem in poverty-stricken areas worldwide. Although many ML detection techniques have been used, a rapid and sensitive tool is essential for the early detection and treatment of leprosy. Herein, we developed a rapid ML detection technique by combining multiple cross displacement amplification (MCDA) with a nanoparticle-based lateral flow biosensor (LFB), termed ML-MCDA-LFB.

STK24 Promotes Progression of LUAD and Modulates the Immune Microenvironment.

Objective: Recent studies have shown that serine/threonine-protein kinase 24 (STK24) plays an important role in cancer development. However, the significance of STK24 in lung adenocarcinoma (LUAD) remains to be determined. This study is aimed at investigating the significance of STK24 in LUAD.

Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.

Key Points: Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when measured in interstitial fractional space. PTC shape (aspect ratio) is associated with clinical outcome in glomerular diseases.

Development of CRISPR/Cas12b-Based Multiple Cross Displacement Amplification Technique for the Detection of Complex in Clinical Settings.

Tuberculosis (TB) is a chronic infectious disease with high mortality caused by the Mycobacterium tuberculosis complex (MTC). Its clinical symptoms include a prolonged cough with mucus, pleuritic chest pain, hemoptysis, etc., and predominant complications such as tuberculous meningitis and pleural effusion.

Ultrafast, One-Step, Label-Based Biosensor Diagnosis Platform for the Detection of in Clinical Applications.

Tuberculosis (TB) is a chronic infectious disease caused by the etiological agent (MTB). Because the majority of TB patients come from poor economic backgrounds, the development of a simple, specific, low-cost, and highly sensitive detection method for the pathogen is extremely important for the prevention and treatment of this disease. In the current study, an efficient detection method for visual, rapid, and highly sensitive detection of MTB utilizing multiplex loop-mediated isothermal amplification combined with a label-based lateral flow immunoassay biosensor (mLAMP-LFIA) was developed.

Improving the elicitation of critical customer requirements through an understanding of their sensitivity.

Customer requirements (CRs) are the essential driven forces of product development. Constrained by the rigid budget and time allocated to product development, much attentions and resources should be paid on critical customer requirements (CCRs). Product design occurs with an increasingly frenetic pace of change in today's competitive market, and the changes of external environment will lead to the changes of CRs.

HEY1-mediated cisplatin resistance in lung adenocarcinoma via epithelial-mesenchymal transition.

Lung cancer is one of the most common malignancies and the leading cause of cancer-related death in the world. In patients with advanced lung adenocarcinoma who are negative for driver gene mutations, platinum-based chemotherapy represented by cisplatin remain the standard of care. Therefore, studying the mechanism behind inevitable cisplatin resistance in lung adenocarcinoma is still important.

Why Do We "Like" on WeChat Moments: The Effects of Personality Traits and Content Characteristics.

To probe the motivational roles of hedonic gratification and social gratification in giving "Like" feedback on social media, we developed a set of novel pictures to simulate WeChat Moments. We subsequently examined how the personality trait of extraversion and stimulus content characteristics (e.g.

Clinical significance of the diverse interlobar veins hidden in the upper oblique fissure.

Background: The interlobar veins hidden in the upper oblique fissure (UOF) of the right lung are usually mismanaged cursorily according to the target lobe, which results in accidental injury of the interlobar veins and complications. The detailed classification of interlobar veins based on surgical anatomical analysis is of great clinical significance.

Methods: Three-dimensional computed tomography bronchography and angiography (3D-CTBA) reconstructed images of 398 patients from January 2019 to June 2020 were retrospectively analyzed.

Two target genes based multiple cross displacement amplification combined with a lateral flow biosensor for the detection of Mycobacterium tuberculosis complex.

Background: Tuberculosis (TB) is a serious chronic infectious disease caused by Mycobacterium tuberculosis complex (MTBC). Hence, the development of a novel, simple, rapid and sensitive method to detect MTBC is of great significance for the prevention and treatment of TB.

Results: In this study, multiple cross displacement amplification (MCDA) combined with a nanoparticle-based lateral flow biosensor (LFB) was developed to simultaneously detect two target genes (IS6110 and mpb64) of MTBC (MCDA-LFB).

Tumor-derived exosomal miR-3157-3p promotes angiogenesis, vascular permeability and metastasis by targeting TIMP/KLF2 in non-small cell lung cancer.

Metastasis is the main cause of death in patients with advanced lung cancer. The exosomes released by cancer cells create tumor microenvironment, and then accelerate tumor metastasis. Cancer-derived exosomes are considered to be the main driving force for metastasis niche formation at foreign sites, but the mechanism in Non-small cell lung carcinoma (NSCLC) is unclear.

Highly specific and sensitive detection of the Mycobacterium tuberculosis complex using multiplex loop-mediated isothermal amplification combined with a nanoparticle-based lateral flow biosensor.

Tuberculosis (TB) is the deadliest infectious caused by Mycobacterium tuberculosis complex (MTBC). Because most TB cases occur within low-income populations, developing a specific, sensitive, cost-saving, and rapid point-of-care test for the early diagnosis of TB is important for achieving the WHO's End Tuberculosis Strategy. In the current study, a novel nucleic acid detection strategy that includes multiplex loop-mediated isothermal amplification combined with a nanoparticle-based lateral flow biosensor (mLAMP-LFB) was used to detect MTBC.

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies.

Inconsistencies in the preparation of histology slides and whole-slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e.

miR-550a-5p Functions as a Tumor Promoter by Targeting LIMD1 in Lung Adenocarcinoma.

Lung adenocarcinoma accounts for half of all lung cancer cases in most countries. Mounting evidence has demonstrated that microRNAs play important roles in cancer progression, and some of them can be identified as potential biomarkers. This study aimed to explore the role of miR-550a-5p, a lung adenocarcinoma-associated mature microRNA screened out from the TCGA database via R-studio and Perl, with abundant expression in samples and with 5-year survival prognosis difference, as well as having not been studied in lung cancer yet.

Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3).

Circular RNA LPAR3 sponges microRNA-198 to facilitate esophageal cancer migration, invasion, and metastasis.

In this study, we explored expression and functions of circular RNA LPAR3 (circLPAR3) in esophageal squamous cell carcinoma (ESCC). The differential expression of circular RNAs (circRNAs) in 10 ESCC and corresponding paracarcinoma tissues was analyzed through circRNA microarray, then the candidate circRNAs were detected and verified through quantitative RT-PCR, and a novel circRNA was screened, which was circLPAR3. Circular RNA LPAR3 showed apparently high expression in ESCC tissues and cells, which was closely correlated with the clinical stage and lymph node metastasis of ESCC patients.