[Prefrontal dysfunction and mismatch negativity in adolescent depression: A multimodal fNIRS-ERP study].

Early identification of adolescent depression requires objective biomarkers. This study investigated the functional near-infrared spectroscopy (fNIRS) activation patterns and mismatch negativity (MMN) characteristics in adolescents with first-episode mild-to-moderate depression. We enrolled 33 patients and 33 matched healthy controls, measuring oxyhemoglobin (Oxy-Hb) concentration in the frontal cortex during verbal fluency tasks via fNIRS, and recording MMN latency/amplitude at Fz/Cz electrodes using event-related potentials (ERP).

Magnetic Ni-N-Ni Centers in N-substituted NiO.

To explore how anion substitution modifies the existing magnetism in strongly correlated oxides, we investigate local electronic states and magnetic ordering in nickel oxide (NiO) induced by substituting oxygen (O) with nitrogen (N). Each N introduces an additional N 2p hole and modifies the magnetic moment of a neighboring nickel (Ni) cation site, as the exchange interaction between this hole and the Ni e_{g} electrons exceeds the Ni-O-Ni superexchange interaction. This leads to the formation of Ni-N-Ni centers consisting of five spins, without perturbing the antiferromagnetic NiO lattice.

Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding.

Medical phrase grounding is crucial for identifying relevant regions in medical images based on phrase queries, facilitating accurate image analysis and diagnosis. However, current methods rely on manual extraction of key phrases from medical reports, reducing efficiency and increasing the workload for clinicians. Additionally, the lack of model confidence estimation limits clinical trust and usability.

Ophthalmological Question Answering and Reasoning Using OpenAI o1 vs Other Large Language Models.

Importance: OpenAI's recent large language model (LLM) o1 has dedicated reasoning capabilities, but it remains untested in specialized medical fields like ophthalmology. Evaluating o1 in ophthalmology is crucial to determine whether its general reasoning can meet specialized needs or if domain-specific LLMs are warranted.

Objective: To assess the performance and reasoning ability of OpenAI's o1 compared with other LLMs on ophthalmological questions.

Developing angiogenesis-related prognostic biomarkers and therapeutic strategies in bladder cancer using deep learning and machine learning.

Bladder cancer (BLCA) is a prevalent urological malignancy that exhibits a high degree of tumor heterogeneity and morbidity. Tumor angiogenesis, a vital hallmark of cancer, greatly influences the tumor microenvironment (TME). The emergence of anti-angiogenic drugs has provided a new turning point in cancer treatment.

Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.

Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language model that incorporates knowledge from over 400 fundus diseases. The model is pre-trained on 341,896 fundus images with accompanying text descriptions gathered from diverse sources across multiple ethnicities and countries.

Blood biochemical landscape and new insights into clinical decision-making for polycystic ovary syndrome in Chinese women: a prospective cohort study.

Introduction: The Polycystic ovary syndrome (PCOS), a prevalent endocrine disorder affecting women's reproductive and metabolic health, faces diagnostic challenges due to heterogeneous clinical presentations and the absence of reliable biomarkers. This study investigates the role of Glucosaminyl (N-acetyl) transferase 2 (GCNT2) in modulating sex hormone-binding globulin (SHBG) and its potential as a therapeutic target in PCOS pathophysiology.

Methods: A prospective cohort of 103 PCOS patients treated with oral contraceptives (2021-2024) was established.

Ultraviolet irradiation penetration depth on TiO.

High-energy ultraviolet (UVC) irradiation of metal oxides (MOs, e.g., TiO) results in photoinduced surface oxygen vacancies (PI-SOVs), which can change the charge carrier (e.

Training-Free Image Style Alignment for Domain Shift on Handheld Ultrasound Devices.

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) to align the style of handheld device data to those of standard devices.

Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.

Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real world. We develop a foundation model with uncertainty estimation (FMUE) to detect 16 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieves a higher F1 score of 95.

Au Ordered Array Substrate for Rapid Detection and Precise Identification of Etomidate in E-Liquid Through Surface-Enhanced Raman Spectroscopy.

Etomidate (ET), a medical anesthetic, is increasingly being incorporated into e-liquids for consumption and abuse as a new psychoactive substance (NPS), leading to significant social issues. In this work, large-area Au micro- and nano-structured ordered arrays were engineered as surface-enhanced Raman spectroscopy (SERS) substrates for fast detection and precise identification of ET and its metabolites. This ordered array, characterized by abundant electromagnetic enhancement hotspots and structural uniformity, imparts unique properties to the SERS substrate, including ultra-sensitivity, spectral signal reproducibility, and precise quantitative capabilities.

Uncertainty-aware regression model to predict post-operative visual acuity in patients with macular holes.

Full-thickness macular holes are a relatively common and visually disabling condition with a prevalence of approximately 0.5% in the over-40-year-old age group. If left untreated, the hole typically enlarges, reducing visual acuity (VA) below the definition of blindness in the eye affected.

Reliable segmentation of multiple lesions from medical images.

Background: Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, and background noise interference, we aim to enhance the reliability of multiple lesions joint segmentation from medical images.

Purpose: Propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment.

Methods: Focusing on enhancing the model's capability to capture intricate pathological features in medical images, this work introduces a novel segmentation backbone.

Confidence-aware multi-modality learning for eye disease screening.

Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evidential fusion pipeline for eye disease screening.

Crucial computed tomography and magnetic resonance imaging findings of fallopian tubal tuberculosis for diagnosis: a retrospective study of 26 cases.

Background: Fallopian tubal tuberculosis (FTTB), which typically presents with non-specific clinical symptoms and mimics ovarian malignancies clinically and radiologically, often affects young reproductive females and can lead to infertility if not promptly managed. Early diagnosis by imaging modalities is crucial for initiating timely anti-tuberculosis (anti-TB) treatment. Currently, comprehensive radiological descriptions of this relatively rare disease are limited.

The Epworth sleepiness scale may have more advantages than the multiple sleep latency test in assessing sleepiness in patients with obstructive sleep apnea.

This study aimed to analyze the brain function of severe obstructive sleep apnea patients with various sleepiness assessment methods and explore the brain imaging basis for the differences between these methods. This study included 30 severe obstructive sleep apnea patients and 19 healthy controls. Obstructive sleep apnea patients were divided into a subjective excessive daytime sleepiness group and a subjective non-excessive daytime sleepiness group according to the Epworth sleepiness scale.

Ellagic acid promotes osteoblasts differentiation via activating SMAD2/3 pathway and alleviates bone mass loss in OVX mice.

Characterized by bone mass loss, osteoporosis is an orthopedic disease typically found in postmenopausal women and aging individuals. Consistent with its pathogenesis summarized as an imbalance in bone formation/resorption, current pharmacologically therapeutic strategies for osteoporosis mainly aim to promote bone formation or/and inhibit bone resorption. However, few effective drugs with mild clinical side effects have been developed, making it a well-concerned issue to seek appropriate drugs for osteoporosis.

Arabidopsis PHYTOALEXIN DEFICIENT 4 promotes the maturation and nuclear accumulation of immune-related cysteine protease RD19.

Arabidopsis PHYTOALEXIN DEFICIENT 4 (PAD4) has an essential role in pathogen resistance as a heterodimer with ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1). Here we investigated an additional PAD4 role in which it associates with and promotes the maturation of the immune-related cysteine protease RESPONSIVE TO DEHYDRATION 19 (RD19). We found that RD19 and its paralog RD19c promoted EDS1- and PAD4-mediated effector-triggered immunity to an avirulent Pseudomonas syringae strain, DC3000, expressing the effector AvrRps4 and basal immunity against the fungal pathogen Golovinomyces cichoracearum.

Uncertainty-inspired open set learning for retinal anomaly identification.

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence.

Comprehensive analysis of transcriptome-wide N6-methyladenosine methylomes in the Barrett's esophagus in rats.

Purpose: As the most abundant RNA modification, N6-methyladenosine (m6A) methylation plays crucial roles in various diseases. The aim of this study is to comprehensively map the landscape of the mRNA m6A modification pattern in Barrett's esophagus (BE) in order to find key genes and potential therapy for BE and even esophageal adenocarcinoma (EAC).

Methods: Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-sequencing (RNA-seq) were performed to compare the difference in mRNA m6A methylation and differentially expressed mRNAs between BE and normal control (NC) tissues.

RNON: image inpainting via repair network and optimization network.

In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information. However, the existing premier convolutional neural networks methods usually causes the problems of excessive color difference and image texture loss and distortion phenomenon.

Effects of a single night of continuous positive airway pressure on spontaneous brain activity in severe obstructive sleep apnea.

This study aimed to investigate the effect of a single night of continuous positive airway pressure (CPAP) treatment on spontaneous brain activity and the underlying neuropathological mechanisms in patients with severe obstructive sleep apnea (OSA). The study involved 30 severe OSA patients and 19 healthy controls (HC). Fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) methods were employed to evaluate spontaneous brain activity in all participants.

Higenamine Promotes Osteogenesis Via IQGAP1/SMAD4 Signaling Pathway and Prevents Age- and Estrogen-Dependent Bone Loss in Mice.

Osteoporosis is a common bone disease caused by an imbalance of bone resorption and formation that results in a loss of total bone density. SMAD2/3 signal transduction is known to play a crucial role in osteogenic differentiation through transforming growth factor-beta (TGF-β). By screening a library of small-molecule compounds, the current study identifies higenamine (HG) as an active osteogenic agent that could be a therapeutic candidate for osteoporosis.