Perplexity and proximity: Large language model perplexity complements semantic distance metrics for the detection of incoherent speech.

Objective: Semantic coherence in speech is characterized by a logical, connected flow of ideas. A lack of coherence in speech may reflect disorganized thinking, a core feature of psychosis in schizophrenia spectrum disorders (SSDs). Developing tools that could help with automated assessment of semantic coherence in language could facilitate early detection of SSDs and improved monitoring of symptoms, enabling more timely intervention.

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification.

Objective: Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspiration Cytology (FNAC) can be subjective and operator-dependent. Indeterminate thyroid nodules (ITNs) complicate diagnosis, coming at the expense of time, money, and potentially additional FNA samplings, causing more discomfort for the patients. Recent advancements in artificial intelligence (AI) assisted ultrasound diagnosis system have demonstrated excellent diagnostic performance and the potential to aid in the differentiation of ITNs.

A minimalist self-assembly nanosystem for cancer immunotherapy via multiple immune activation.

In recent years, anti-tumor immunity has emerged as a central focus in cancer research, with the rapid advancement of immunotherapy heralding a new era in cancer treatment. Despite the significant potential of immunotherapy, the use of single-agent approaches or limited combination therapies has not consistently yielded optimal therapeutic outcomes. The strategic and controlled integration of diverse immune activation techniques within a single nanoparticle, utilizing a straightforward and universal methodology, continues to present a substantial challenge.

Heat shock protein 70 mitigates black carbon particles-induced cardiac damage.

Studies have demonstrated the association between black carbon (BC) particles and elevated risk of cardiovascular disease. However, the mechanisms underlying this relationship remain unclear. This study aims to investigate the effects of BC exposure on gene expressions in mice myocardium.

Validation of a Sensitive, Simple and High-Throughput UPLC-MS/MS Method for Quantification of Catecholamines and Their Metabolites in Serum and Urine: Application in Clinical Analysis.

A sensitive, simple and high-throughput UPLC-MS/MS method has been validated for the simultaneous quantification of catecholamines and their metabolite levels in serum and urine for clinical applications. The analytes and their isotope-labeled internal standards were extracted using a 96-well solid-phase extraction cartridge and then separated on an HSS PFP column with a 4-min gradient elution. The linear ranges were 10 ~ 5000 pg/mL for dopamine (DA), epinephrine (E), metanephrine (MN) and normetanephrine (NMN), 2 ~ 5000 pg/mL for norepinephrine (NE), and 2 ~ 2000 pg/mL for 3-methoxytyramine (3-MT).

Complete biosynthetic pathway of furochromones in Saposhnikovia divaricata and its evolutionary mechanism in Apiaceae plants.

Furochromones are specific bioactive secondary metabolites of many Apiaceae plants. Their biosynthesis remains largely unexplored. In this work, we dissect the complete biosynthetic pathway of major furochromones in the medicinal plant Saposhnikovia divaricata by characterizing prenyltransferase, peucenin cyclase, methyltransferase, hydroxylase, and glycosyltransferases.

Passivity and robust passivity of inertial memristive neural networks with time-varying delays via non-reduced order method.

This study examines the concepts of passivity and robust passivity in inertial memristive neural networks (IMNNs) that feature time-varying delays. By using non-smooth analysis and the passivity theorem, algebraic criteria for both passivity and robust passivity are derived by using the non-reduced order method. The proposed criteria, based on the non-reduced order method, effectively reduce the complexity of derivation and computation, thereby simplifying the verification process.

A Spatiotemporally Controlled Gene-Regulation Strategy for Combined Tumor Therapy Based on Upconversion Hybrid Nanosystem.

The lack of precise spatiotemporal gene modulation and therapy impedes progress in medical applications. Herein, a 980 nm near-infrared (NIR) light-controlled nanoplatform, namely URMT, is developed, which can allow spatiotemporally controlled photodynamic therapy and trigger the enzyme-activated gene expression regulation in tumors. URMT is constructed by engineering an enzyme-activatable antisense oligonucleotide, which combined with an upconversion nanoparticle (UCNP)-based photodynamic nanosystem, followed by the surface functionalization of triphenylphosphine (TPP), a mitochondria-targeting ligand.

Quantifying abnormal emotion processing: A novel computational assessment method and application in schizophrenia.

Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n = 37) and healthy controls (n = 51).

Useful blunders: Can automated speech recognition errors improve downstream dementia classification?

Objectives: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the "Cookie Theft" picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer's disease (AD).

Methods: We conducted experiments using various ASR models, refining their transcripts with post-editing techniques.

Recent advances in the nanoarchitectonics of metal-organic frameworks for light-activated tumor therapy.

Metal-organic frameworks (MOFs) have received extensive attention in tumor therapy because of their advantages, including large specific surface area, regular pore size, adjustable shape, and facile functionalization. MOFs are porous materials formed by the coordination bonding of metal clusters and organic ligands. This review summarized the most recent advancements in tumor treatment based on nMOFs.

TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments.

The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals. Valuable public data repositories such as TalkBank have made it possible for researchers in the computational community to join forces and learn from each other to make significant advances in this area. However, due to variability in approaches and data selection strategies used by various researchers, results obtained by different groups have been difficult to compare directly.

UPLC-MS/MS simultaneous quantification of urinary circadian rhythm hormones and related metabolites: Application to air traffic controllers.

Civil aviation flight crew and civil aviation air traffic controllers are prone to circadian rhythm abnormalities, which can lead to a slew of other maladies. It could endanger people's health and provide a serious threat to the safety of civil aviation flights if it is not appropriately evaluated and addressed. Early detection of rhythm irregularities and prompt treatment for particular populations that are vulnerable to rhythm disorders are crucial for enhancing civil aviation safety.

Amelioration of Hepatic Steatosis by the Androgen Receptor Inhibitor EPI-001 in Mice and Human Hepatic Cells Is Associated with the Inhibition of CYP2E1.

Nonalcoholic fatty liver disease (NAFLD) is recognized as a metabolic disease characterized by hepatic steatosis. Despite the growing burden of NAFLD, approved pharmacological treatment is lacking. As an inhibitor of androgen receptor (AR), EPI-001 is being explored for the treatment of prostate cancer.

Oncolytic Vaccinia Virus Carrying Lectin (oncoVV-AVL) Enhances Inflammatory Response in Hepatocellular Carcinoma Cells.

lectin (AVL) is a C-type marine lectin derived from sponges. Our previous study demonstrated that oncolytic vaccinia virus carrying AVL (oncoVV-AVL) significantly enhanced the cytotoxicity of oncoVV in cervical cancer, colorectal cancer and hepatocellular carcinoma through the activation of Ras/ERK, MAPK/ERK and PI3K/Akt signaling pathways. In this study, the inflammatory response induced by oncoVV-AVL in a hepatocellular carcinoma cell (HCC) model was investigated.

Light-activated nanomaterials for tumor immunotherapy.

Tumor immunotherapy mainly relies on activating the immune system to achieve antitumor treatment. However, the present tumor immunotherapy used in the clinic showed low treatment efficacy with high systematic toxicity. To overcome the shortcomings of traditional drugs for immunotherapy, a series of antitumor immunotherapies based on nanomaterials have been developed to enhance the body's antitumor immune response and reduce systematic toxicity.

Inflammation down regulates stromal cell-derived factor 1α in the early phase of pulpitis.

The key to prevent pulp necrosis in the early stage of pulpitis is to promote tissue repair, which begins with cell migration. Stromal cell-derived factor 1α (SDF-1α) has been proven to promote cell migration. Related research has so far concentrated on the biological effects of SDF-1α while its expression in pulpitis is still unclear.

GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models.

Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD). However, questions remain about their ability to generalize beyond the small reference sets that are publicly available for research. As an alternative to fitting model parameters directly, we propose a novel method by which a Transformer DL model (GPT-2) pre-trained on general English text is paired with an artificially degraded version of itself (GPT-D), to compute the ratio between these two models' on language from cognitively healthy and impaired individuals.

Deep Scale-Variant Network for Femur Trochanteric Fracture Classification with HP Loss.

Achieving automatic classification of femur trochanteric fracture from the edge computing device is of great importance and value for remote diagnosis and treatment. Nevertheless, designing a highly accurate classification model on 31A1/31A2/31A3 fractures from the X-ray is still limited due to the failure of capturing the scale-variant and contextual information. As a result, this paper proposes a deep scale-variant (DSV) network with a hybrid and progressive (HP) loss function to aggregate more influential representations of the fracture regions.

Wnt4 regulates bone metabolism through IKK-NF-κB and ROCK signaling under occlusal traumatic periodontitis.

Background And Objective: Occlusal trauma is one of the most important local contributing factors of periodontitis. It has been reported that Wnt4, a noncanonical Wnt ligand, can inhibit osteoclast formation and inflammation and promote bone formation in vivo. However, the prospects of Wnt4 application in occlusal trauma and periodontitis have not yet been described.

The Centroid Cannot Hold: Comparing Sequential and Global Estimates of Coherence as Indicators of Formal Thought Disorder.

Thought disorder (TD) as reflected in incoherent speech is a cardinal symptom of schizophrenia and related disorders. Quantification of the degree ofTD can inform diagnosis, monitoring, and timely intervention. Consequently, there has been an interest in applying methods ofdistributional semantics to quantify incoherence ofspoken language.

Multi-Dimensional Printing for Bone Tissue Engineering.

The development of 3D printing has significantly advanced the field of bone tissue engineering by enabling the fabrication of scaffolds that faithfully recapitulate desired mechanical properties and architectures. In addition, computer-based manufacturing relying on patient-derived medical images permits the fabrication of customized modules in a patient-specific manner. In addition to conventional 3D fabrication, progress in materials engineering has led to the development of 4D printing, allowing time-sensitive interventions such as programed therapeutics delivery and modulable mechanical features.