Comprehensive bioinformatics analysis reveals key hub genes linked to prognosis in multiple myeloma with drug resistance.

Multiple myeloma (MM) is an incurable hematologic malignancy, with chemotherapy being the primary treatment. However, the development of drug resistance remains a major challenge. This study aimed to identify therapeutic targets associated with drug resistance in MM and assess their prognostic significance.

CD4+ Effector Memory T Cells Related Marker Gene Signatures in Osteoporosis and Aging: Insight From Single-Cell Analysis and Mendelian Randomization.

Objective: With the accelerated aging of the population, aging has emerged as a major risk factor for osteoporosis (OP). This study aims to investigate the relationship and shared molecular mechanisms between OP and aging through various genetic approaches.

Methods: Single-cell data from the peripheral blood of osteoporosis patients, aging individuals, and healthy controls were integrated to analyze characteristic changes in cell subpopulations.

Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment.

Monitoring the microparticle transfer process in wastewater treatment systems is crucial for improving treatment performance. Supervised deep learning methods show high performance to automatically detect particles, but they rely on vast amounts of labeled data for training. To overcome this issue, we proposed a semi-supervised learning (SSL) method based on the Simple framework for Contrastive Learning of visual Representations (SimCLR), to detect microparticles free from sludge and attached to sludge.

Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation.

Osteoarthritis (OA) is a degenerative bone disease characterized by the destruction of joint cartilage and synovial inflammation, involving intricate immune regulation processes. Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and roles of disulfidptosis-related genes (DR-DEGs) in OA remain unclear. We obtained six OA datasets from the GEO database, using four as training sets and two as validation sets.

Self-Adaptive Smart Thermochromic Film with Quick Response for All-Year Radiative Cooling and Solar Heating.

The advancement of energy-saving buildings requires both high-performance passive radiative cooling (PRC) and solar absorption heating (SAH) materials. Although many materials with PRC or SAH functions have been developed, they cannot adapt to the large fluctuations of ambient temperature in different seasons. Herein, we report the design and fabrication of a new thermochromic porous film (TMRC) with combined temperature-adaptive SAH and PRC performance to achieve "warm in winter and cool in summer" for all-year radiative cooling and solar heating.

Predicting steroid-induced osteonecrosis of the femoral head: role of lipid metabolism biomarkers and radiomics in young and middle-aged adults.

Background: Femoral head necrosis is a common orthopedic disease that results in significant physical disability in patients. Early prediction and diagnosis of steroid-induced osteonecrosis of the femoral head (SONFH) are crucial for the prevention and treatment of this condition.

Methods: In this study, initial CT images and clinical data of patients with SONFH, admitted from January 2019 to December 2022, were collected.

Addressing data limitations in leakage detection of water distribution systems: Data creation, data requirement reduction, and knowledge transfer.

Leakage in water distribution systems is a significant problem worldwide, leading to wastage of water resources, compromised water quality and excess energy consumption. Leakage detection is essential to reduce the duration of leaks and data-driven methods are increasingly being used for this purpose. However, these models are data hungry and available observed data, especially leakage data, is limited in most cases.

Characteristic Study of a Typical Satellite Solar Panel under Mechanical Vibrations.

As the most common energy source of spacecraft, photovoltaic (PV) power generation has become one of the hottest research fields. During the on-orbit operation of spacecraft, the influence of various uncertain factors and the unbalanced inertial force will make the solar PV wing vibrate and degrade its performance. In this study, we investigated the influence of mechanical vibration on the output characteristics of PV array systems.

Bidirectional Cascaded Superfluorescent Lasing in Air Enabled by Resonant Third Harmonic Photon Exchange from Nitrogen to Argon.

Cavity-free lasing in atmospheric air has stimulated intense research toward a fundamental understanding of underlying physical mechanisms. In this Letter, we identify a new mechanism-a third-harmonic photon mediated resonant energy transfer pathway leading to population inversion in argon via an initial three-photon excitation of nitrogen molecules irradiated by intense 261 nm pulses-that enables bidirectional two-color cascaded lasing in atmospheric air. By making pump-probe measurements, we conclusively show that such cascaded lasing results from superfluorescence rather than amplified spontaneous emission.

The heterogeneous impact of population mobility on the influent characteristics of wastewater treatment facilities.

Improving the resilience of wastewater treatment facilities (WWTFs) has never been more important with rising risks of disasters under climate change. Beyond physical damages, non-physical shocks induced by disasters warrant attention. Human mobility is a vital mediator in transferring the stresses from extreme events into tangible challenges for urban sewage systems by reshaping influent characteristics.

Integrative gene expression analysis and animal model reveal immune- and autophagy-related biomarkers in osteomyelitis.

Background: Osteomyelitis (OM) is recognized as a significant challenge in orthopedics due to its complex immune and inflammatory responses. The prognosis heavily depends on timely diagnosis, accurate classification, and assessment of severity. Thus, the identification of diagnostic and classification-related genes from an immunological standpoint is crucial for the early detection and tailored treatment of OM.

Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective.

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods.

A Network Pharmacology Approach and Validation Experiments to Investigate the Mechanism of Wen-Dan Decoction in the Treatment of SINFH.

Introduction: Steroid-induced necrosis of the femoral head (SINFH) is a femoral head necrotic disease caused by prolonged use of hormones. Wen-Dan decoction is used in Chinese clinical practice for the treatment of steroid-induced necrosis of the femoral head (SINFH). However, the mechanism and active compounds of Wen-Dan decoction used to treat SINFH are not well understood.

Antipyretic and anti-inflammatory effects of inosine, an active component of Kangfuxin.

Kangfuxin has been widely recognized for its use in treating ulcerative conditions and mucositis, primarily due to its anti-inflammatory properties, which promote cell proliferation, granulation tissue growth, and angiogenesis. However, the exact mechanisms underlying these effects remain poorly understood. In this study, we employed high-throughput mass spectrometry to identify 11 compounds in Kangfuxin, including uracil, hypoxanthine, xanthine, inosine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, and lysine.

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects.

Graph theory (GT) and complex network theory play an increasingly important role in the design, operation, and management of water distribution networks (WDNs) and these tasks were originally often heavily dependent on hydraulic models. Facing the general reality of the lack of high-precision hydraulic models in water utilities, GT has become a promising surrogate or assistive technology. However, there is a lack of a systematic review of how and where the GT techniques are applied to the field of WDNs, along with an examination of potential directions that GT can contribute to addressing WDNs' challenges.

Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations.

Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters.

Efficacy and Safety of Different Antibiotic Therapies for Bone and Joint Infections: A Network Meta-analysis of Randomized Controlled Trials.

Background: Although an increasing number of antibiotics are being used to treat bone and joint infections, their specific efficacy remains controversial. Thus, we aimed to systematically compare the efficacy and safety of antibiotic therapies for orthopedic infections.

Methods: PubMed, Embase, The Cochrane Library, and Web of Science databases were searched from inception to April 2022.

MCM6 is a Poor Prognostic Biomarker and Promotes Progression in Breast Cancer.

Background: Breast cancer is the commonest global malignancy and the primary cause of carcinoma death. MCM6 is vital to carcinogenesis, but the pathogenesis of MCM6 remains unclear.

Methods: MCM6 expression in patients with breast cancer was examined through The Cancer Genome Atlas (TCGA) database, immunohistochemistry, Quantitative Real-Time PCR (qRT‒PCR) and Western blotting.

Pmepa1 knockdown alleviates SpA-induced pyroptosis and osteogenic differentiation inhibition of hBMSCs via p38MAPK/NLRP3 axis.

Background: Osteomyelitis is a refractory bone infectious disease, which usually results in progressive bone destruction and bone loss. The invasion of pathogens and subsequent inflammatory response could damage bone marrow mesenchymal stem cells (BMSCs) and inhibit osteogenic differentiation, and finally aggravate uncontrolled bone remodeling in osteomyelitis by affecting bone formation. Exploring the mechanisms of BMSCs injury and osteogenic differentiation inhibition may would help us to find potential therapeutic targets.

Identification of molecular subgroups in osteomyelitis induced by staphylococcus aureus infection through gene expression profiles.

Background: Staphylococcus aureus (S. aureus) infection-induced osteomyelitis (OM) is an inflammatory bone disease accompanied by persistent bone destruction, and the treatment is challenging because of its tendency to recur. Present study was aimed to explore the molecular subgroups of S.

Resilience evaluation for water distribution system based on partial nodes' hydraulic information.

Accurate resilience evaluation for water distribution systems generally requires all nodes' hydraulic data which are usually obtained from a well-calibrated hydraulic model. However, in reality, few utilities maintain a workable hydraulic model, making the resilience evaluation far more from practicability. Under this condition, whether resilience evaluation can be realized based on a small amount of monitoring nodes is still a research gap.