Integrative analysis of autophagy-related genes reveals that CAPNS1 is a novel prognostic biomarker and promotes the malignancy of melanoma via Notch signaling pathway.

Skin cutaneous melanoma (SKCM) is a highly fatal form of skin cancer that develops from the malignant transformation of epidermal melanocytes. There is substantial evidence linking autophagy to cancer etiology and immunotherapy efficacy. This study aimed to conduct a comprehensive analysis of autophagy-related genes (ARGs) using TCGA datasets and further explore the potential function of critical ARGs in SKCM progression.

Development and validation of a Multi-Causal investigation and discovery framework for knowledge harmonization (MINDMerge): A case study with acute kidney injury risk factor discovery using electronic medical records.

Objective: Accurate diagnoses and personalized treatments in medicine rely on identifying causality. However, existing causal discovery algorithms often yield inconsistent results due to distinct learning mechanisms. To address this challenge, we introduce MINDMerge, a multi-causal investigation and discovery framework designed to synthesize causal graphs from various algorithms.

Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.

Background: The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD.

Methods: The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018.

Transparent and Planar CsCuCl Crystals for Micrometer-Resolution X-ray Imaging Screen.

Copper-based (I) halide perovskites have emerged as a promising candidate for scintillation screens in X-ray inspection and imaging areas due to their solution processability and high light yield. Here, a centimeter-sized CsCuCl single crystal was grown by a slow-cooling method. The planar orientation was controlled in a space-confined chamber, generating a planar crystal which is readily used for a scintillation screen without any further shaping.

Afterglow Phosphor Goes Transparent.

Recently, transparent afterglow phosphors have attracted increasing interest due to the mitigated self-absorption and the ensuing improved light output, which have inspired many advanced applications, including volumetric display and three-dimensional optical encryption. To date, the most successful afterglow phosphors remain those traditional oxide, nitride, or sulfide powders which are not transparent due to a severe scattering effect. By reduction of the number of interfaces and engineering the refractive index, the scattering effect could be circumvented effectively.

Long Non-Coding RNA ZSCAN16-AS1 Promotes the Malignant Progression of Melanoma Through Regulating the miR-503-5p/ARL2 Axis.

Background: LncRNA zinc finger and SCAN domain containing 16 antisense RNA 1 (ZSCAN16-AS1), a newly identified lncRNA, has been proven to accelerate hepatocellular carcinoma progression. However, the function and molecular mechanism of ZSCAN16-AS1 in melanoma are still unknown.

Methods: The level of ZSCAN16-AS1 in melanoma tissues was detected and reported in The Cancer Genome Atlas (TCGA) and GEO#GSE15605.

Prediction differences and implications of acute kidney injury with and without urine output criteria in adult critically ill patients.

Background: Due to the convenience of serum creatinine (SCr) monitoring and the relative complexity of urine output (UO) monitoring, most studies have predicted acute kidney injury (AKI) only based on SCr criteria. This study aimed to compare the differences between SCr alone and combined UO criteria in predicting AKI.

Methods: We applied machine learning methods to evaluate the performance of 13 prediction models composed of different feature categories on 16 risk assessment tasks (half used only SCr criteria, half used both SCr and UO criteria).

Mixed traffic flow of human-driven vehicles and connected autonomous vehicles: String stability and fundamental diagram.

The introduction of connected autonomous vehicles (CAVs) gives rise to mixed traffic flow on the roadway, and the coexistence of human-driven vehicles (HVs) and CAVs may last for several decades. CAVs are expected to improve the efficiency of mixed traffic flow. In this paper, the car-following behavior of HVs is modeled by the intelligent driver model (IDM) based on actual trajectory data.

A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data.

Machine learning has demonstrated success in clinical risk prediction modeling with complex electronic health record data. However, the evolving nature of clinical practices can dynamically change the underlying data distribution over time, leading to model performance drift. Adopting an outdated model is potentially risky and may result in unintentional losses.

Analysis of Energy and Density in Treating Hypertrophic Scar After Burn in Children with CO Dot Matrix Laser.

To analyze and compare the effect of the combination of energy and density parameters of CO dot matrix laser in the hyperplastic stage of pediatric burn. A total of 160 pediatric patients with hypertrophic scar after limb burn from 2017 to 2020 were randomly divided into four parameter groups (n = 40). The patients were treated with ablative fraction carbon dioxide laser, once every 10 weeks.

Flexible and Transparent Ceramic Nanocomposite for Laboratory X-ray Imaging of Micrometer Resolution.

Transparent nanocomposites have attracted considerable attention in many areas including X-ray imaging, wearable electronics, and volumetric display. However, both the transparency and the flexibility were largely jeopardized by the loading content of functional nanoparticles (NPs), posing a major challenge to material engineering. Herein, an ultra-high-loading-ceramic nanocomposite film was fabricated by a blade-coating technique.

A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study.

Background: Clinical prediction models suffer from performance drift as the patient population shifts over time. There is a great need for model updating approaches or modeling frameworks that can effectively use the old and new data.

Objective: Based on the paradigm of transfer learning, we aimed to develop a novel modeling framework that transfers old knowledge to the new environment for prediction tasks, and contributes to performance drift correction.

Mn-Activated Afterglow in a Transparent Perovskite Crystal.

Transparent crystals of long afterglow are keenly desired for advanced applications involving three-dimensional information storage and volumetric display. Here, we report the growth of a perovskite crystal, CsNaInCl:Mn, via a facile hydrothermal reaction. The crystal featured a high transparency up to 90% in a broad range from 400 to 750 nm.

hUC-MSCs exosomal miR-451 alleviated acute lung injury by modulating macrophage M2 polarization via regulating MIF-PI3K-AKT signaling pathway.

In the previous study, we have proved that exosomal miR-451 from human umbilical cord mesenchymal stem cells (hUC-MSCs) attenuated burn-induced acute lung injury (ALI). However, the mechanism of exosomal miR-451 in ALI remains unclear. Therefore, this study aimed to study the molecular mechanism of hUC-MSCs-derived exosomal miR-451 on ALI by regulating macrophage polarization.

Nearly-Unity Quantum Yield and 12-Hour Afterglow from a Transparent Perovskite of Cs NaScCl :Tb.

Unlike afterglow phosphors, transparent crystals free of scattering issues hold great potential in applications such as volumetric displays and three-dimensional encryption. Here, a double-perovskite host of ca. 85 % transparency, a Cs NaScCl single crystal, was grown in aqueous solution.

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.

Importance: Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes.

Objective: To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations.

Characterizing the temporal changes in association between modifiable risk factors and acute kidney injury with multi-view analysis.

Background: Acute kidney injury (AKI) is a common life-threatening clinical syndrome in hospitalized patients. Advances in machine learning has demonstrated success in AKI risk prediction using electronic health records (EHRs). However, to prevent AKI, it is critical to identify clinically modifiable factors and understand their impact at different prevention windows.

Analysis of the therapeutic effect of artificial leather embedding combined with fascial sleeve flap transplantation on chronic wounds of lower limbs with bone and plate exposure.

Background: After severe trauma of lower limbs, bone, tendon or plate graft exposure is common. The traditional repair method is to use a variety of skin flap transplantation to cover the exposed part, but the wound often can not heal after operation, or the wound is cracked, ulcer, sinus, bone and steel plate are exposed again after wound healing. The reason for this result is that when the flap is covered, the space around the bone plate is not well closed, forming a dead cavity, blood and exudate accumulation, hematoma formation or infection, and finally the wound ruptures again.

Development of a knowledge mining approach to uncover heterogeneous risk predictors of acute kidney injury across age groups.

Objectives: Acute kidney injury (AKI) risk increases with age and the underlying clinical predictors may be heterogeneous across age strata. This study aims to uncover the AKI risk factor heterogeneity among general inpatients across age groups using electronic medical records (EMR).

Methods: Patient data (n = 179,370 encounters) were collected from an academic hospital between 2007 and 2016, and were stratified into four age groups: 18-35, 36-55, 56-65, and > 65.

Location Sensitive Network for Human Instance Segmentation.

Location is an important distinguishing information for instance segmentation. In this paper, we propose a novel model, called Location Sensitive Network (LSNet), for human instance segmentation. LSNet integrates instance-specific location information into one-stage segmentation framework.