[Clinical study on hemodynamics and analgesic effect of local infiltration anesthesia in the treatment of severe early childhood caries under general anesthesia].

Objectives: This study aimed to explore the clinical efficacy of severe early childhood caries (SECC) treatment combined with local anesthesia under general anesthesia.

Methods: A total of 108 children under 6 years old who underwent SECC dental treatment under general anesthesia at the Department of Pediatric Dentistry, Third Affiliated Hospital of Air Force Medical University from March to December 2023 were selected as the study subjects, with American Society of Anesthesiologists (ASA) classification of classⅠor Ⅱ. The study subjects were divided into a control group (=54) and an experimental group (=54) by retrieving intraoperative cases and postoperative follow-up records.

Spatiotemporal subtypes of brain and spinal cord atrophy in neuromyelitis optica spectrum disorders and multiple sclerosis.

Background: Neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) are autoimmune demyelinating diseases with overlapping clinical features but distinct patterns of brain and spinal cord atrophy. The precise atrophy subtypes specific to each disease remain elusive. This study aimed to identify shared and distinct atrophy subtypes in NMOSD and MS, using neuroimaging to explore their clinical significance and potential implications for tailored treatment strategies.

The pharmacy graduate global competence evaluation scale (PGGCES): a nationwide study in China.

Background: Research on global competence (GC) among postgraduate pharmacy students remains limited, with most studies focusing on undergraduates or general student populations. To address this gap, this study investigates the GC of Chinese pharmacy graduate students using the OECD's 2018 PISA framework and established GC assessment tools. By analyzing personal attributes, family background, and academic environment, this study identifies key influencing factors and evaluates the effectiveness of current educational practices.

A Quality Improvement Initiative to Reduce Underdosing of Rectal Diazepam as Home Seizure Rescue Medication After Hospitalization.

Background And Objectives: Seizure rescue medications are commonly prescribed to patients with epilepsy to treat and prevent clusters of seizures or status epilepticus. Underdosing of rescue medications decreases their efficacy, which may lead to status epilepticus and potentially avoidable emergency department (ED) visits or hospitalizations. In this quality improvement initiative, we aimed to reduce the rate of underdosed rectal diazepam prescriptions for children discharged from the inpatient neurology service at our institution from a baseline of 6% to 3% by July 2023.

Genetic determinants of testicular sperm extraction outcomes: insights from a large multicentre study of men with non-obstructive azoospermia.

Study Question: What is the diagnostic yield and the pre-testicular sperm extraction (TESE) prognostic value of a non-obstructive azoospermia (NOA)-specific virtual gene panel?

Summary Answer: The diagnostic yield in our cohort was 6.1%, and by combining our data with published literature, we identified 11 genes compatible with testicular sperm production and 19 genes associated with no sperm retrieval in carriers of pathogenic (P) or likely pathogenic (LP) mutations.

What Is Known Already: Azoospermia, the most severe form of male infertility, affects ∼1% of the male population, with TESE being the primary treatment option.

Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.

Background: Currently, there is no fully automated tool available for evaluating the degree of cervical spinal stenosis. The aim of this study was to develop and validate the use of artificial intelligence (AI) algorithms for the assessment of cervical spinal stenosis.

Methods: In this retrospective multi-center study, cervical spine magnetic resonance imaging (MRI) scans obtained from July 2020 to June 2023 were included.

Efficient Fracture Propagation Solver Based on the Displacement Discontinuity Method with Application to Fiber Optic Sensing.

Hydraulic fracturing is essential for developing not only unconventional oil and gas reservoirs but also clean-energy resources, such as enhanced geothermal systems. Accurate simulation of fracture propagation is crucial for estimating poststimulation production. However, current approaches to calculating fracture physical parameters are often computationally inefficient.

Scafari: Exploring scDNA-seq data.

Summary: Recent advances in single-cell sequencing made it possible to not just analyze a cell's individual expression pattern, but to gain insights into a single cell's genome using the cutting-edge technology single-cell DNA sequencing. Mission Bio is, with the Tapestri platform, one of the few providers of this technology. So far, however, there is only little open-source software available for user-friendly processing and quality analysis of this data type.

Bio-Transport Regulation Inspired 5-Fluorouracil Keto to Enol Tautomerism Achieving High Proton Conductivity.

The strategic modulation of proton transport kinetics and precise control of migration energy barriers in metal-organic frameworks (MOFs) are essential for developing next-generation proton conductors. Inspired by biological proton channels, this study introduces a dynamic regulation strategy by keto-enol tautomerism to reconcile the intrinsic trade-off between low activation energy (E) and sustained proton mobility. We successfully construct a hierarchical proton conductive system (denoted as FU@MOF-808-SOH) by integrating 5-fluorouracil (5-FU) molecules into sulfonic-functionalized MOF-808 through a two-step post-synthetic modification.

Supramolecular Microspheres Fabricated from Macrocycle-Incorporated Polymers and Their Biomedical Applications Based on Host-Guest Recognition.

Microspheres have emerged as a pivotal platform for micron-scale drug delivery, yet their utility has been greatly hindered by limitations in biodegradability, drug loading efficiency, and release kinetics, underscoring the urgent need for a next-generation microsphere platform that integrates high performance, scalability, and multifunctionality. Leveraging host-guest recognition, a series of macrocycle-incorporated polymers is synthesized and engineered a new class of supramolecular microspheres, which feature precisely tunable components, including host molecules, guest cargoes, and polymer components, as well as customizable morphologies, while enabling cost-effective, large-scale production. Following systematic validation of the host-guest recognition between β-cyclodextrin (β-CD) and lanreotide, we developed supramolecular microspheres (LAN@S-CPMs) that achieve a drug loading capacity and release duration approximately twice that of conventional microspheres, effectively curbing the disease progression of acromegaly.

Overcoming ACQ: DPA-modified TICT-type AIEgens for high-resolution and multilevel latent fingerprint visualization.

Latent fingerprints (LFPs), as critical carriers of personal identification information, present a long-standing challenge for high-resolution imaging in forensic science. Aggregation-induced emission luminogens (AIEgens), known for their superior luminescence in aggregated or high-viscosity environments, have emerged as ideal candidates for high-contrast fingerprint visualization. In this study, we designed a series of novel AIEgens by introducing diphenylamine (DPA) donor groups at the 3- and 11-positions of a quinazolinone core, effectively constructing twisted intramolecular charge transfer (TICT) systems.

A Room-Temperature Diradical-Based High-Spin Qubit.

Organic luminescent diradicals have recently attracted more attention for their potential applications in quantum science. Here, we have investigated the spin dynamic properties of two solid-state Müller- and Chichibabin-type Kekulé diradicals featuring different distances between two radical centers. The continuous-wave EPR (cw-EPR) together with echo-detected field-swept spectrum (EDFS) prove the existence of a thermally accessible triplet state at room temperature, promising a potential candidate for quantum manipulation as a high-spin state.

Sarcopenia index based on serum creatinine and cystatin C is associated with the risk of stroke in middle-aged and older adults in Chinese: a prospective cohort study from the China health and retirement longitudinal study.

Objective: The sarcopenia index (SI), calculated as the serum creatinine divided by the serum cystatin C, multiplied by 100, is recommended for predicting sarcopenia. However, limited evidence exists regarding its association with incident stroke. The aim of this study was to assess the relationship between SI and the risk of stroke in middle-aged and older adults.

Continental maize mapping and distribution in Africa by integrating radar and optical imagery.

High-resolution, accurate mapping of crops is critical to enhance food security, resource efficiency, and policy effectiveness in agriculture across Africa, where maize remains a crucial staple crop. However, mosaic landscapes, common cloud cover, and scarce ground information have hindered large-area and field-level maize monitoring. This study presents a novel continent-wide framework for mapping maize cultivation across Africa for the 2023-2024 growing season at 10-m resolution using multi-temporal and multi-sensor remote sensing data.

Feature-guided multilayer encoding-decoding network for segmentation for 3D intraoral scan data.

Accurate segmentation of malocclusion is crucial in orthodontic diagnosis and treatment planning, but existing deep learning methods seriously affect the reliability of clinical applications due to poor robustness and feature confusion between neighboring tooth classes when dealing with malocclusion. To address this problem, a U-shaped 3D dental model segmentation method based on hierarchical feature guidance is proposed. First, a feature-guided deep encoder architecture is constructed, which introduces a normalization method that combines the local mean with the global standard deviation.

Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.

Aims: We aim to develop a fully automated deep-learning model to detect and classify benign/malignant bone tumours in full-field limb radiographs using an object detection model. The secondary aim is to identify differences in classification characteristics between the developed automated model, three orthopaedic oncologists, and three general orthopaedic surgeons.

Methods: This retrospective analysis included 642 limb bone tumours with 40 diagnoses confirmed pathologically from three institutions (378 benign, 264 malignant including intermediate types).

Kidney transcriptomics signature of prospective rapid diabetic kidney disease progression.

Background: Previous cross-sectional transcriptomics studies on diabetic kidney disease (DKD) kidney tissue have shown correlations between gene expression and both disease status and kidney function at the time of biopsy; however, longitudinal data are scarce.

Methods: We utilized clinical follow-up data up to five years post-biopsy, linking the transcriptomes of diagnostic kidney biopsies to progression rates and outcomes in 19 patients with DKD. Patients were stratified into “rapid progressors” and “non-rapid progressors” based on clinical parameters (eGFR slope, CKD stage advancement, degree of albuminuria, composite outcome of kidney failure or 40% eGFR decline).

Major pathophysiological changes in pulmonary disease provided a molecular insight based on deep learning approach.

The outburst of pulmonary disorders among the society has shown the devastating effect of undergoing a delay in diagnosis and treatment. Sometimes the traditional methods in detecting and treating the airway disease fail to cure efficiently due to a lack of pathophysiological descriptions along with the molecular expression. The studies published so far are missing the collective information of the pathways and the role of signature molecules during the disease that constricts the use of therapeutics like nitric oxide(NO) and hydrogen sulfide(HS).

Lanthanum strontium cobalt ferrite [LaSrCoFeO] alloyed impedimetric sensing platform for human papillomavirus type 16 mutant DNA detection.

Human papillomavirus type 16 (HPV-16) is a leading cause of cervical cancer among women worldwide. This study presents an electrochemical impedance spectroscopy (EIS)-based biosensor for the sensitive and selective detection of HPV-16 mutant DNA. A single-stranded DNA probe complementary to the target sequence was immobilized on a gold interdigitated electrode (G-IDE) modified with lanthanum strontium cobalt ferrite LaSrCoFeO (LSCF) nanopowder to enhance charge transfer.

Hierarchical extreme learning machine for dimensionality reduction in near-infrared spectral analysis.

Dimensionality reduction (DR) is a crucial step in the analysis of high-dimensional near-infrared (NIR) spectroscopy. However, traditional spectral DR techniques are often challenged by selection difficulties and limited effectiveness in improving model accuracy. A novel Hierarchical Extreme Learning Machine (HELM) for DR was proposed in this paper.

International Trends in Opioid Prescribing by Age and Sex from 2001 to 2019: An Observational Study Using Population-Based Databases from 18 Countries and One Special Administrative Region.

Objective: To characterize multinational trends and patterns of opioid analgesic prescribing by sex and age.

Design, Setting, And Participants: We studied opioid analgesic prescribing from 2001 to 2019 with common protocol using population-based databases from eighteen countries and one special administrative region.

Main Outcome Measures: We measured opioid prescribing by geographical region, sex and age, estimating annual prevalent, incident, and nonincident opioid prescribing per 100 population with a 95% confidence interval (CI) and meta-analyzed the multinational and regional opioid prescribing with a random-effects model.

Morphological changes in microglia in the mouse brain during postnatal development and obesity.

Microglia are innate immune cells in the central nervous system (CNS) and play critical roles in proper brain development and function. During postnatal development, microglia have a highly plastic morphology and change rapidly in response to the temporal brain environment. However, their dynamics and phenotypes during this period are still not fully elucidated.

A Robust Immobilization Method for Membrane Protein SPR Assays Using SpyCatcher-SpyTag.

Membrane proteins are essential for numerous biological processes and constitute a significant portion of drug targets, making the study of their interactions with ligands crucial for drug discovery. Surface plasmon resonance (SPR) is a powerful, label-free technique for real-time biomolecular interaction analysis, but its application to membrane proteins is limited by inherent difficulties in immobilizing these proteins on sensor surfaces while preserving their native conformation and functionality. Lipid nanodiscs offer a promising solution by providing a native-like membrane environment.

Inversion of soil salinity and pH in farmland of the Hetao Plain based on Sentinel-2 and explainable machine learning.

The escalating salinization and alkalization of arable soils represents a significant threat to the sustainable development of agriculture and environment. The assessment of salinization and alkalization can be facilitated by measuring crucial indicators including soil salinity content (SSC) and pH. The utilization of remote sensing technology could facilitate the effective and large-scale monitoring of soil salinity and alkalinity conditions.

Unconstrained deep learning-based sleep stage classification using cardiorespiratory and body movement activities in adults with suspected sleep apnea.

This study assessed the feasibility of unconstrained deep-learning-based sleep stage classification using cardiorespiratory and body movement activities derived from piezoelectric sensors installed under a bed mattress. Heart and respiratory rates and their respective variabilities, cardiorespiratory coupling index, and body movement were simultaneously acquired through polysomnography (PSG) for 106 untreated participants with suspected sleep apnea. We used a bidirectional long short-term memory network to predict the five sleep stages using five different input feature combinations.