Shared Paths to Well-Being: The Impact of Group Therapy.

This study explored the impact of an art of living intervention within group psychotherapy for depression, focusing on constructs like life satisfaction, self-efficacy, and depression. Mental illness prevalence often exceeds available treatment options, particularly in Germany, where group psychotherapy is a viable alternative. While less researched, group therapy effectively improves well-being, especially through interpersonal exchange.

Current Status and Future Directions of Artificial Intelligence in Post-Traumatic Stress Disorder: A Literature Measurement Analysis.

This study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such as Bibliometrix (version 4.1), VOSviewer (version 1.

From Acceptance to Dependence: Exploring Influences of Smart Healthcare on Continuous Use Intention of Mobile Health Services Among Older Adults with Chronic Illnesses in China.

With the acceleration of the aging process in China, chronic diseases have become one of the main health threats for older adults, creating significant pressure on society and the healthcare system. As information technology and artificial intelligence advance rapidly, smart health services have become readily accessible. However, utilization rates among the older adults, especially those with chronic illnesses, remain low, preventing them from fully benefiting from these advanced technologies.

Design of a Wearable Exoskeleton Piano Practice Aid Based on Multi-Domain Mapping and Top-Down Process Model.

This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model.

Multi-Condition Remaining Useful Life Prediction Based on Mixture of Encoders.

Accurate Remaining Useful Life (RUL) prediction is vital for effective prognostics in and the health management of industrial equipment, particularly under varying operational conditions. Existing approaches to multi-condition RUL prediction often treat each working condition independently, failing to effectively exploit cross-condition knowledge. To address this limitation, this paper introduces MoEFormer, a novel framework that combines a Mixture of Encoders (MoE) with a Transformer-based architecture to achieve precise multi-condition RUL prediction.

Self-Normalized Moderate Deviations for Degenerate -Statistics.

In this paper, we study self-normalized moderate deviations for degenerate -statistics of order 2. Let {Xi,i≥1} be i.i.

Long-Term Comparative Outcomes of Short Implants Versus Maxillary Sinus Elevation in Posterior Maxilla Rehabilitation.

: Vertical atrophy of the maxilla has traditionally been treated with sinus lift procedures and implant placement, performed in one or two surgical stages. Subsequently, the transcrestal sinus lift technique was introduced, offering distinct advantages in terms of indications and reduced morbidity. Most recently, short implants have emerged as a valid alternative to these procedures, even in cases of severe horizontal resorption, allowing for direct placement in many cases.

The Neurodevelopmental Dynamics of Multilingual Experience During Childhood: A Longitudinal Behavioral, Structural, and Functional MRI Study.

Background/objectives: A neurobiological framework of bi- or multilingual neurocognitive development must consider the following: (i) longitudinal behavioral and neural measures; (ii) brain developmental constraints across structure and function; and (iii) the development of global multilingual competence in a homogeneous social environment. In this study, we investigated whether multilingual competence yields early changes in executive attention control mechanisms and their underlying neural structures in the frontal-striatal system, such as the dorsal anterior cingulate cortex/pre-supplemental area and the left caudate.

Methods: We employed longitudinal neuroimaging and functional connectivity methods in a small group of multilingual children over two years.

Narrative Review and Guide: State of the Art and Emerging Opportunities of Bioprinting in Tissue Regeneration and Medical Instrumentation.

Three-dimensional printing was introduced in the 1980s, though bioprinting started developing a few years later. Today, 3D bioprinting is making inroads in medical fields, including the production of biomedical supplies intended for internal use, such as biodegradable staples. Medical bioprinting enables versatility and flexibility on demand and is able to modify and individualize production using several established printing methods.

Polycaprolactone for Hard Tissue Regeneration: Scaffold Design and In Vivo Implications.

In the last thirty years, tissue engineering (TI) has emerged as an alternative method to regenerate tissues and organs and restore their function by implanting specific lineage cells, growth factors, or biomolecules functionalizing a matrix scaffold. Recently, several pathologies have led to bone loss or damage, such as malformations, bone resorption associated with benign or malignant tumors, periodontal disease, traumas, and others in which a discontinuity in tissue integrity is observed. Bone tissue is characterized by different stiffness, mechanical traction, and compression resistance as a function of the different compartments, which can influence susceptibility to injury or destruction.

The Potential Clinical Utility of the Customized Large Language Model in Gastroenterology: A Pilot Study.

The large language model (LLM) has the potential to be applied to clinical practice. However, there has been scarce study on this in the field of gastroenterology. Aim: This study explores the potential clinical utility of two LLMs in the field of gastroenterology: a customized GPT model and a conventional GPT-4o, an advanced LLM capable of retrieval-augmented generation (RAG).

Risks of neonatal anomalies and obstetric complications in 7378 singleton births after frozen-thawed and fresh embryo transfers in Japan: An analysis using doubly robust estimation.

Purpose: To compare risks of neonatal anomalies and obstetric complications among frozen-thawed embryo transfer (FET), fresh embryo transfer (FreshET), and non-assisted reproductive technology (non-ART) treatments in infertile women.

Methods: This retrospective cohort study analyzed 7378 singleton births (2643 non-ART, 4219 FET, 516 FreshET) from 2013 to 2022. Outcomes were compared using inverse probability weighting regression adjustment, with adjustment for maternal factors.

Color fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features.

Introduction: Diabetic retinopathy (DR) has long been recognized as a common complication of diabetes, making accurate automated grading of its severity essential. Color fundus photographs play a crucial role in the grading of DR. With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.

ClinClip: a Multimodal Language Pre-training model integrating EEG data for enhanced English medical listening assessment.

Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.

Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process.

Unlabelled: This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn.

Objectives: To investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes.

Methods: A comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems.

Machine learning for antimicrobial peptide identification and design.

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues.

Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery.

Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.

Rapid gram-scale microwave-assisted synthesis of organic anodes for sodium-ion batteries with environmental impact assessment.

Development of sustainable synthesis methods of organic electrode materials (OEMs) for sodium (Na)-ion batteries must take hold rapidly in large scale-synthesis if subsequent commercialisation is to occur. We report a facile and rapid gram-scale synthesis method based on microwave irradiation for disodium naphthalene-2,6-dicarboxylate (Na-NDC) and mono/disodium benzene-1,4-dicarboxylate (Na-BDC) as model compounds. Phase purity and formation of materials was confirmed by various characterisation techniques.

'Striving to achieve control'. Registered nurses' experiences of palliative care quality during the COVID-19 pandemic - a qualitative study.

Background: Providing quality palliative care during a pandemic was challenging. Both specialist and community healthcare services cared for patients that faced life-threatening illness and who were influenced by the restrictions of the COVID-19 pandemic. Little knowledge has yet been provided on how registered nurses (RNs) experienced the palliative care quality during the COVID-19 pandemic.

A Dual-Pathway Responsive Mechanophore for Intelligent Luminescent Polymer Materials.

Mechanoluminescent units, when integrated into polymer matrices, undergo structural transformations in response to mechanical force, resulting in changes in fluorescence. This phenomenon holds considerable promise for the development of stress-sensing materials. Despite the high demand for robust, tunable mechanoluminescent mechanophores for force assessment and smart force-responsive materials, strategies for their design and synthesis remain underdeveloped.

Wound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning.

Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area.

Unleashing creativity in people with Parkinson's disease: a pilot study of a co-designed creative arts therapy.

Background: Conventional medical management, while essential, cannot address all multifaceted consequences of Parkinson's disease (PD). This pilot study explores the potential of a co-designed creative arts therapy on health-related quality of life, well-being, and pertinent non-motor symptoms.

Methods: We conducted an exploratory pilot study with a pre-post design using validated questionnaires.

Simultaneous multimodal measures for aesthetic evaluation of furniture color and form.

The tendency toward the aesthetic preference affects an individual's intention to purchase furniture. Color and form are two fundamental elements of furniture appearance. However, there is a significant lack of human-computer interaction research on the aesthetic evaluation of furniture with various colors and forms, necessitating a comprehensive study to provide theoretical and empirical support to furniture designers and businesses.

Lead-Grouped Multi-Stage Learning for Myocardial Infarction Localization.

The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to localize myocardial infarction (MI) that is characterized by abnormal waveform patterns on the ECG. MI is a serious cardiovascular disease, and accurate, timely diagnosis is crucial for preventing severe outcomes. Current ECG analysis methods mainly rely on intra- and inter-lead feature extraction, but most models overlook the medical knowledge relevant to disease diagnosis.