Although autistic individuals can require more health care services than the general population, the care they receive is often suboptimal. During young adulthood, autistic patients face additional developmental barriers and achieve poorer medical outcomes as they transition between pediatric and adult health systems. However, little is known about their transition needs, perceptions, and experiences. This study examined the health care experiences and transition perceptions of 213 autistic young adults aged 18-26 years (mean age 22.72). Both formally diagnosed and self-identified autistic individuals participated in the study. Our research team used mixed methods online survey, including an original health care experience questionnaire, an adapted measure of health care transition readiness, and short-answer questions. We used nonparametric statistical tests to examine relationships between quantitative variables, and qualitative responses were analyzed using an inductive, open-coding approach. Quantitative analyses revealed health care environments to be least accessible for individuals who are female or gender nonconforming, nonspeaking, and/or in their late teenage years. These individuals also showed lower levels of involvement in their own care compared with other subgroups. Conversely, those with a history of regular medication management reported higher levels of readiness for health care transition. Open-ended survey responses clustered around the following five themes: (1) deciding whether to disclose an autism diagnosis, (2) medical staff's current understanding of autism, (3) discrimination, (4) communication challenges, and (5) unique needs. Participants in this study preferred to be actively involved in their health care and required supportive, knowledgeable providers and inclusive environments to accomplish this goal. Noting this, it is important for health care providers to assess individual needs and preferences and design focused supports for autistic patients transitioning from pediatric to adult care. Community Brief Why is this an important issue? After young people reach a certain age, they can no longer be seen by a pediatrician. Therefore, part of being a young adult is finding a doctor specializing in adult medicine. Autistic adults are not well served in the health care system, and many receive less optimal care than neurotypical adults. This may be because doctors in adult medicine expect their patients to be active participants who are comfortable in and knowledgeable about health care settings yet do not take steps to make the setting accessible for autistic people. What was the purpose of this study? The purpose of this study was to explore the health care transition experiences, needs, and perceptions of autistic young adults. What did the researchers do? In this study, we surveyed 213 autistic young adults about their health care experiences. The online survey included multiple-choice, yes/no, and write-in questions. We used the multiple-choice and yes/no answers to see what type of health care experiences people had, how accessible health care was to them, and how involved they were in their own care. We read the write-in answers and sorted them into themes by grouping similar responses together. What were the results of the study? Autistic young adults in this study described several challenges, including deciding whether to tell their doctor about their autism diagnosis, interacting with medical staff who do not understand autism (or discriminate against autistic people), communicating their needs to medical staff, and wanting their needs to be accommodated while also being treated as an adult. The people in this study who had an especially hard time transitioning to adult health care were (1) women and gender nonconforming, (2) nonspeaking, (3) teenaged, and (4) not regularly taking medications. A person who belongs to none of these groups might still have a hard time transitioning to adult health care, and someone who belongs to all these groups might find transition very easy. However, it is important to know who is most at risk so that we can identify ways to help make transition easier for autistic young adults. What do these findings add to what was already known? These findings include new information on how accessible health care is to autistic young adults, how involved they are in their care, and how ready they are to transition to adult health care. This study explored different factors that affect all these outcomes. We used that information to draw conclusions about different ways to support autistic young adults who are going through this important time of growth and change. What are potential weaknesses in the study? This study was an online survey, which may not have been accessible to some autistic adults with certain language-related disabilities or intellectual disabilities. In addition, most of our participants were able to use spoken language. The experiences of other autistic people, particularly those who are nonspeaking, may be different from these results. How will these findings help autistic adults now or in the future? By knowing what factors make it difficult for autistic young adults to transition to adult health care, we can understand how to make the transition easier. For example, medical staff can help by making the environment more sensory-friendly, using the autistic patient’s preferred method of communication, and seeking out up-to-date knowledge on autism.
Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency ("black-box" models), impacting clinicians' trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide.
The digitalization of society has transformed daily life and health care, offering opportunities for accessibility and independence for individuals with complex care needs. However, users with limited digital skills still experience challenges because the technologies do not to align with their needs. Inclusive research and design approaches can improve technology by actively involving end users and stakeholders. This study investigated the experiences of co-researchers with a mild intellectual disability or autism spectrum disorder and other key stakeholders over time regarding the inclusive design process for a digital tool for individuals with complex care needs that was developed in a transdisciplinary consortium. The project that was examined applied an inclusive design process to develop a sensitive virtual assistant using the Vision in Product Design method and the design thinking approach. Nine consortium members, including 3 co-researchers, participated in semistructured interviews and a group discussion about the inclusive design process after each of the project's 5 work packages (WPs). This resulted in 31 interviews and 5 group discussions in total. Individual experiences were gathered during interviews, and group discussions facilitated collective reflection. During the interviews, an adapted questionnaire was used for each WP with Likert scales and open-ended questions. The data analysis was conducted using a thematic approach and descriptive statistics for the questionnaire data. Quantitative findings from questionnaires were complemented with qualitative insights from interviews and group discussions, with results presented chronologically per WP. The qualitative analysis resulted in 3 main themes: project approach, collaborative dynamics, and co-design in practice. Project approach showed how the team adapted its inclusive collaboration through expectation management, structured processes, and accessible materials. Collaborative dynamics described how communication and support evolved and how inclusive design principles were applied in practice. Co-design in practice outlined co-researcher involvement and content adaptations across the 5 WPs, highlighting how experiential knowledge directly informed design decisions. These findings show that inclusive collaboration developed over time and contributed meaningfully to both process and content. This study shows that, to accommodate an inclusive research and design process, tensions between project efficiency and meaningful inclusion need to be addressed, underlining the importance of continuous coordination, collaboration, and flexibility in transdisciplinary settings. Further, applying a stepwise approach in inclusive collaborations supports coordination, continuous evaluation, and flexibility. Inclusive methods, like preparatory activities, clear role division, accessible materials, and iterative feedback, enabled active co-researcher participation. These methods contributed to a shift in ownership, allowing co-researchers to gain greater influence and co-shape both the development process and the content. The findings provide insights into how to enhance equity and relevance in inclusive technology design for individuals with complex care needs, such as individuals with a mild intellectual disability or autism spectrum disorder.
Motor impairments are common in children with autism spectrum disorder (ASD) and contribute to reduced functional independence, participation, and quality of life. Although motor-rehabilitation interventions can improve motor and adaptive outcomes, services are often fragmented, inconsistently delivered, and difficult to scale beyond single clinical settings. Multisite implementation of ASD motor-rehabilitation programs remains limited, underscoring the need for coordinated approaches that address both clinical and organizational challenges. This integrative narrative review synthesizes representative literature on motor-rehabilitation interventions for children with ASD alongside implementation-science and project-management frameworks relevant to multisite healthcare delivery in the United States. A broad search of peer-reviewed literature and policy sources published between 2000 and 2025 was conducted. Evidence was synthesized qualitatively with attention to implementation-relevant factors, governance structures, and scalability considerations; no quantitative synthesis or statistical analysis was performed. The reviewed literature indicates that motor-rehabilitation interventions can improve motor proficiency, adaptive behavior, and participation in children with ASD. However, the evidence base is dominated by small, single-site studies with substantial heterogeneity in intervention design and outcome reporting. Key barriers to multisite implementation include variability in workforce training, infrastructure, reimbursement models, data systems, and family engagement across settings. Implementation-science frameworks (e.g., the Consolidated Framework for Implementation Research (CFIR), Reach-Effectiveness-Adoption-Implementation-Maintenance (RE-AIM), and Normalization Process Theory) and project-management methodologies offer complementary strategies to address these barriers but are rarely integrated within ASD motor-rehabilitation research. Drawing on these domains, this review proposes a Coordinated Project-Management Framework to guide multisite implementation through iterative phases of initiation, planning, delivery, monitoring and evaluation, and sustainability. Scaling effective motor-rehabilitation services for children with ASD will require coordinated, multisite implementation rather than isolated clinic-based efforts. Future research should prioritize piloting and evaluating this framework in real-world healthcare networks using hybrid effectiveness-implementation designs to improve access, consistency, and equity in ASD motor rehabilitation.
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by high clinical and biological heterogeneity. Identifying discrete ASD subtypes is crucial for understanding the neurobiological substrates and developing individualized treatments. However, most existing approaches focus solely on features from single modality, ignoring the valuable interaction information between multiple imaging modalities. In this study, we propose a novel approach that combines structural and functional neuroimaging data with semi-supervised learning techniques to cluster individuals with ASD into distinct subtypes. We aim to reveal quantitative biomarkers and elucidate the biological basis of ASD subgroups, potentially leading to improved diagnosis and targeted interventions. Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data from 92 individuals with ASD and 65 neurotypical controls were collected from four independent sites within the Autism Brain Imaging Data Exchange (ABIDE) database. We initially integrated structural and functional MRI data through a skeleton-based white matter (WM) functional analysis, enabling voxel-wise function-structure coupling by projecting fMRI signals onto a WM skeleton. Subsequently, we employed WM low-frequency oscillations (LFOs) as input features for a clustering algorithm, aiming to categorize individuals with Autism Spectrum Disorder (ASD) into distinct neurological subgroups. Statistical analyses were performed to identify significant disparities in fractional anisotropy (FA), mean diffusivity (MD), and various clinical measures between these ASD subgroups and the control group. Additionally, we employed a support vector machine (SVM) to evaluate the potential of these subgroups to enhance diagnostic accuracy for ASD. Two neurosubtypes of ASD were identified. Subtype 1 displayed significantly lower FA in the posterior cingulate cortex (PCC) compared to neurotypical controls, with no significant differences observed for Subtype 2 in this region. Conversely, Subtype 2 exhibited reduced FA in the anterior cingulate cortex, middle temporal gyrus, parahippocampus, and thalamus relative to neurotypical controls, whereas Subtype 1 showed no significant alterations in these areas. Additionally, Subtype 2 had markedly higher mean diffusivity in the middle temporal gyrus, parahippocampus and thalamus than the control group, a pattern not seen in Subtype 1. The full-scale intelligence quotient (FIQ) and performance IQ (PIQ) scores were also lower for Subtype 2 compared to Subtype 1. Moreover, diagnostic prediction accuracy was enhanced when distinguishing between these subtypes compared to the general ASD classification. Our study identified two distinct neurosubtypes of ASD, shedding light on the biological underpinnings of the disorder's heterogeneity. The unique biomarkers associated with each subgroup reveal potential neurological signatures specific to individuals with autism, which could facilitate tailored therapeutic strategies and early interventions. This differentiation enhances the understanding of ASD and underscores the importance of personalized approaches in managing the spectrum of autism disorders.
Porcine circovirus type 2 (PCV2) remains one of the most important pathogens in swine production, associated with a spectrum of clinical conditions collectively termed PCV2-associated diseases. Despite the remarkable success of vaccination programs, which have drastically reduced the incidence of systemic disease and reproductive disorders, PCV2 continues to circulate globally in both domestic and wild swine populations. Its high evolutionary rate, capacity for recombination, and broad host plasticity raise ongoing concerns regarding viral persistence and long-term control. This review synthesizes current knowledge and identifies critical research gaps that hinder the development of sustainable PCV2 control strategies. While vaccines effectively mitigate clinical disease, they do not fully prevent infection or virus shedding, thereby allowing continued circulation and genetic diversification. The biological consequences of this viral evolution—including potential impacts on cross-protection, virulence, and vaccine escape—remain insufficiently understood. Similarly, the role of host immunity, co-infections, and environmental or management factors in modulating disease outcomes is incompletely characterized. A deeper understanding of the mechanisms underlying PCV2 pathogenesis, including immune modulation and determinants of subclinical versus clinical outcomes, is urgently needed. Diagnostic approaches have also evolved, with molecular techniques such as quantitative PCR largely replacing histopathology and immunohistochemistry. While highly sensitive, these methods cannot establish causal relationships between viral presence and disease, underscoring the need for integrated diagnostic frameworks. In addition, harmonized thresholds for viral load quantification and standardized serological assays to assess protective immunity are lacking, limiting comparability across studies and surveillance systems. Future priorities should include the development of next-generation vaccines capable of inducing sterilizing immunity, investigation of optimal vaccination schedules in the context of maternally derived antibodies, and exploration of innovative vaccine delivery platforms. Furthermore, integrated surveillance strategies combining molecular epidemiology, wildlife monitoring, and international data sharing will be essential to track viral evolution and detect potential vaccine breakthroughs. Addressing these knowledge gaps will require coordinated efforts across fundamental, applied, and translational research, aligned with the needs of veterinarians and the swine industry. Only through such an integrated agenda can the sector advance from disease control towards the long-term goal of PCV2 elimination.
To synthesize evidence on deep learning applications for diagnosing central serous chorioretinopathy (CSCR), a macular disorder associated with vision loss, this systematic review categorized studies by diagnostic task and imaging modality. The study evaluates advances in deep learning performance, clinical integration potential, dataset limitations, and the contributions of multimodal imaging and Explainable AI (XAI) to diagnostic accuracy and clinical decision-making. We conducted a PRISMA-compliant systematic review of PubMed, Scopus, and IEEE Xplore, including peer-reviewed English-language studies published from January 1990 to February 2024 that reported quantitative deep learning metrics for CSCR diagnosis. A two-stage selection process was applied (Cohen's κ = 0.84), resulting in 96 studies for analysis. Risk of bias was evaluated using the QUADAS-2 tool, and data were synthesized by imaging modality, model architecture, and diagnostic task. Deep learning models demonstrate exceptional performance in CSCR diagnosis. DenseNet architectures applied to optical coherence tomography (OCT) images achieved peak Metrics, including 99.78% accuracy, 99.68% sensitivity, and 100% specificity. Segmentation models for subretinal fluid (SRF) reported Dice scores of up to 0.965, while multimodal models for differential diagnosis achieved an area under the curve (AUC) of 0.999. Despite these advances, clinical adoption remains limited by several challenges: scarce and imbalanced datasets (e.g., SRF/non-SRF ratio of 1:8), lack of open-access datasets and models, risks of overfitting, and insufficient external validation. Emerging approaches, such as few-shot learning and diffusion models, are promising for mitigating data constraints; however, improvements in dataset quality and the implementation of rigorous cross-institutional validation are essential for real-world deployment. By leveraging OCT and multimodal imaging data, deep learning has the potential to transform CSCR diagnosis through enhanced accuracy and automation. However, translating these advances into routine clinical practice necessitates overcoming key challenges, including limited and heterogeneous datasets and models with restricted generalizability. Future research should prioritize standardized reporting frameworks, transparent model interpretability through XAI, and rigorous large-scale validation. Essential strategies include employing federated learning to leverage distributed data, implementing effective multimodal fusion techniques, and fostering collaborative frameworks to improve diagnostic accuracy, ensure algorithmic fairness, and enable real-world clinical applicability.
Artificial intelligence, particularly machine learning, has profoundly reshaped drug discovery, addressing longstanding challenges such as exorbitant costs and protracted timelines. Conventional approaches often exceed $2.6 billion per drug over 12-15 years, with attrition rates nearing 90%; AI mitigates these through advanced target identification, high-throughput virtual screening, and generative molecule design. The present review synthesizes pivotal studies of several years drawn from PubMed, Scopus, and leading journals, including ACS Omega and Nature Reviews. It encompasses supervised quantitative structure-activity relationship models, neural networks, graph convolutional networks, and generative adversarial networks for de novo drug design. Emphasis is placed on machine learning's capacity to process vast omics and cheminformatics datasets, with critical attention to how data quality, measurement uncertainty, and analytical method variability fundamentally constrain predictive accuracy. In practice, AI empowers scientists by automating hypothesis generation, exemplified by AlphaFold's structural predictions and enabling early toxicity forecasting or drug repurposing, yet these computational advances remain dependent on rigorous experimental validation through orthogonal analytical techniques. A distinctive contribution of this review lies in its systematic integration of analytical chemistry as the foundational discipline underpinning reliable AI predictions. We present a conceptual framework, the Analytical Integrity Spectrum, that traces the bidirectional relationship between analytical measurements and computational models, emphasizing how measurement uncertainty, data quality, and experimental validation collectively determine the trustworthiness of AI-driven discoveries. The chemistry-focused synthesis distinguishes the present work from computational reviews by critically examining representative case studies of AI-discovered compounds, including their molecular structures, scaffolds, and experimental outcomes. This review provides a tangible assessment of AI's impact on medicinal chemistry. The review further examines AI's emerging application to climate-resilient supply chains, forecasting disruptions from environmental events while emphasizing the analytical monitoring essential for maintaining pharmaceutical quality during transport. Persistent challenges, including dataset biases, activity cliff insensitivity, and validation uncertainty, are traced to their analytical origins. Future prospects encompass federated learning, quantum-accelerated simulations, and standardized analytical data formats that preserve measurement integrity for machine learning. Ultimately, AI equips researchers with transformative tools for accelerated, equitable therapeutic innovation, provided that computational predictions remain grounded in the experimental reality that analytical chemistry provides.
Wheezing in the pre-school aged group (under 5 years) is a common presentation and significant healthcare burden. It is a heterogenous presentation representing a spectrum of phenotypes, and although the causes may be multifactorial, viral infection is the most common trigger, with rhinovirus and Respiratory Syncytial Virus (RSV) being the most commonly detected. Rigorous evidence-based guidance for the acute management of preschool wheeze (PW) with respect to which children are likely to benefit from oral corticosteroid therapy (OCS) is lacking. RCTs of OCS use in PW have not adequately assessed the impact of respiratory virus testing in the management of PW. In order to address the hypothesis that OCS response may be determined by the specific virus in a future definitive trial, the feasibility of performing POC respiratory virus tests prior to randomisation in an acute paediatric ED setting needs to be ascertained. The PRECISE Study will be a single centre, randomised, open-label, feasibility trial. Children aged 24-59 months with acute wheeze will be eligible if the clinician is uncertain if there is a role for oral corticosteroid therapy or not. At enrolment, participants will undergo a nasal swab for rapid respiratory virus testing. Children will be randomised in a 1:1 ratio to receive oral dexamethasone or not, based on their RSV result. Participants will continue to be managed by the clinician according to local guidance. They will be invited for clinical review at 72 h where a repeat nasal swab may be performed. There will be a telephone follow-up at 1 month and parents will be invited for extended telephone interviews within a further month. Comprehensive screening logs will address the primary outcome of recruitment and timeliness until enrolment. Remaining timeliness and adherence outcomes will be recorded in individual participant records and described using CONSORT diagrams. Acceptability will be measured using both qualitative and quantitative approaches based on the theory of acceptability framework. This pragmatically designed trial will address key feasibility points needed to inform a future, definitive multi-centre RCT prospectively testing the role of respiratory virus testing to randomise children with PW to receive oral corticosteroids or not. Clinicaltrials.gov ID NCT06580600.
Chemotherapy-induced gastrointestinal toxicity (CIGT) is a common and distressing adverse effect in cancer care, manifesting as nausea, vomiting, appetite loss, oral mucositis, constipation, and diarrhea. These symptoms severely impair patients' quality of life, reduce treatment adherence, and may lead to premature therapy discontinuation. Aromatherapy, a complementary therapy using plant-derived essential oils, has shown potential benefits for alleviating CIGT symptoms; however, most existing systematic reviews focus solely on nausea and vomiting, leaving its effects on other CIGT symptoms under-studied. Moreover, the influence of essential oil types, intervention forms, and intervention durations on therapeutic outcomes remains unclear. This systematic review and meta-analysis aims to comprehensively evaluate the efficacy and safety of aromatherapy for the full spectrum of CIGT symptoms in patients with cancer and to clarify how essential oil types, intervention forms, and intervention durations influence treatment outcomes. Nine databases (PubMed, Cochrane Library, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature, Chinese National Knowledge Infrastructure, Wanfang, Chinese Science and Technology Journal Database, and SinoMed); the World Health Organization (WHO) Trials Portal; and the Chinese Clinical Trial Registry will be searched from inception to August 2025 to identify randomized controlled trials focusing on aromatherapy for CIGT management in patients with cancer. Data on participant characteristics, interventions, comparisons, outcomes, and adverse effects will be extracted from included studies. Continuous outcomes will be synthesized using standardized mean differences with 95% CIs, and categorical outcomes will be summarized as odds ratios with 95% CIs. All analyses will adopt a random-effects model to account for expected clinical and methodological heterogeneity. Subgroup and meta-regression analyses will be conducted to examine differences across essential oil types, intervention forms, and intervention durations. The Hartung-Knapp-Sidik-Jonkman method will be used for random-effects estimation, and prediction intervals will be calculated where applicable to reflect real-world variation. Risk of bias will be assessed using the Cochrane Risk of Bias 2 tool, and evidence certainty will be graded using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. This study was funded in February 2024. As of August 2025, the literature search and study selection have been completed, and 20 eligible randomized controlled trials have been identified. Data extraction and quantitative synthesis are expected to be completed in December 2025, and the final results are anticipated to be submitted for publication in March 2026. The anticipated findings will address key evidence gaps by evaluating aromatherapy's therapeutic potential for CIGT beyond nausea and vomiting and clarifying parameter-specific effects on CIGT management. These findings will support the development of evidence-based, standardized aromatherapy interventions, guide future mechanism-based research, and inform clinical decision-making in supportive cancer care.
Upper respiratory tract disorders are seriously prevalent disorders that impact people daily all over the world. Antipyretics, analgesics, bronchodilators, antitussives, expectorants, mucolytics, antihistamines, and antibiotics are among the pharmacological classes that are often administered either alone or in combination to treat and manage the symptoms attributed to upper respiratory tract disorders. In the rising era of green analytical chemistry, the development of broad, multi-analyte analytical techniques that can separate and quantify numerous pharmacologically related substances simultaneously is becoming of increasing significance. This work introduces a new, concise, accurate, and robust high performance liquid chromatography method with diode array detection approach for the simultaneous assay of eight substances, namely; Albuterol, Erdosteine, Paracetamol, Amoxicillin, Chlorpheniramine, Guaifenesin and two of the most frequently encountered preservatives that are incorporated in upper respiratory tract medications; Methyl and Propyl Parabens in their bulk and pharmaceutical formulations. An Inertsil octadecyl silane-3 (4.6 × 250 mm, 5 μm) column and a gradient mobile phase system consisting of methanol and 0.025 M potassium dihydrogen orthophosphate buffer (pH 4) were employed to achieve the separation. Chromatograms were detected at 225 nm for Albuterol and Chlorpheniramine, 230 nm for Guaifenesin and Amoxicillin, 236 nm for Erdosteine, 250 nm for Paracetamol, and 256 nm for Methyl and Propyl Parabens using the diode array detector. The suggested approach was successfully verified in compliance with the International Council for Harmonization guidelines with low values of percentage relative error and percentage relative standard deviation (< 2%) and strong correlation coefficients (> 0.9991), demonstrating its good accuracy, precision and linearity. Additionally, an in-depth five-sided examination was conducted to demonstrate the suggested method’s greenness, blueness, violet innovation and sustainability profiles using the analytical GREEnness metric, analytical eco-scale, blue applicability grade index, click analytical chemistry index, violet innovation grade index and white analytical chemistry assessment metrics. This work represents the establishment of a broad spectrum, multianalyte, versatile methodology that would provide an indispensable asset for quality control laboratories in terms of its affordability, speed, sensitivity, and sustainability. The online version contains supplementary material available at 10.1038/s41598-026-45971-7.
Narrative review. To explore the intersection of osteoporosis, sarcopenia, radiomics, and machine learning in spine surgery, with a focus on clinical applications and opportunities for advancing assessment and predictive modeling methods. Osteoporosis and sarcopenia are significant contributors to negative outcomes in the aging adult spine. Current methodologies for evaluating these disease states remain limited, with significant variability and poor standardization. Advances in computational medicine provide a novel opportunity to improve quantitative assessment of osteosarcopenia, as demonstrated in other areas of medicine. Using radiomic approaches for predictive outcome modeling in spine surgery remains largely untapped. A comprehensive literature search was performed. Articles were identified using the search terms "osteoporosis," "sarcopenia," "osteosarcopenia," "radiomics," "spine surgery," and "machine learning." Relevant studies were selected based on their focus on the intersection of these topics, emphasizing clinical, imaging, and computational methodologies in spine surgery. This review highlights the existing conventional and research methods of assessing both osteoporosis and sarcopenia, particularly regarding their clinical application in spine surgery. Areas of research within the radiomic space for both conditions are also discussed to describe opportunities for growth of future research and areas of focus needed to advance the field of spine surgery alongside the rapid growth of artificial intelligence. Understanding the relationship between osteoporosis, sarcopenia, and frailty is essential to improving outcomes in spine surgery. Advanced imaging and machine learning approaches offer the potential for more precise assessments and tailored interventions. The Scoliosis Research Society Adult Spinal Deformity Task Force on Senescence has identified this as an area of maximal importance for strategic growth and development of the field.
Failure Modes and Effects Analysis (FMEA) is crucial for complex system reliability. However, traditional FMEA and its existing enhancements face significant limitations. These notably include difficulties in handling diverse heterogeneous data, effectively coordinating large expert groups, and robustly propagating inherent uncertainties. To bridge these critical gaps, this paper proposes an innovative and robust FMEA framework, specifically designed for Large Group Decision Making (LGDM) under uncertainty, leveraging the Normal Cloud Model (NCM). First, LGDM is genuinely integrated into FMEA by involving an unprecedented number of experts (>50). Second, a broad spectrum of heterogeneous data, including exact numbers, interval numbers, NCMs, linguistic terms, and linguistic expressions, is utilized to effectively model and manage diverse uncertainties. Third, a four-step data preprocessing method is incorporated to efficiently screen invalid and low-quality inputs, significantly enhancing the reliability of aggregated results. Fourth, an innovative and comprehensive expert weight determination method that judiciously combines subjective factors with objective data quality is proposed, ensuring more trustworthy and equitable aggregation of judgments. Distinctively, our method explicitly preserves and propagates uncertainty information across the entire computational process, yielding more insightful and informative results beyond simple rankings, encompassing detailed quantitative uncertainty analysis. A practical case study, alongside detailed result analysis, sensitivity analysis, both qualitative and quantitative comparative analysis, and advantages and limitations analysis, collectively confirms the effectiveness, practicality, rationality, and robustness of the proposed method. The sensitivity analyses demonstrate that the final risk rankings are highly stable even under varying trade-off coefficients, confirming the method's strong robustness and insensitivity to parameter fluctuations. Our framework provides a scientifically advanced and robust approach for FMEA in complex decision-making environments, particularly applicable to high-stakes industries such as modern aviation, thereby enabling more informed risk management decisions.
Cortisol is a key glucocorticoid hormone involved in the regulation of stress response, metabolism, and immune function, and abnormal cortisol levels are closely associated with a range of endocrine and psychiatric disorders. Accurate monitoring of cortisol is therefore important for both clinical assessment and disease management. Lateral flow immunoassays (LFIAs) are widely used in point-of-care testing (POCT) because of their simplicity and rapid response. However, conventional colloidal gold-based LFIAs generally lack sufficient sensitivity for the detection of low-abundance small-molecule hormones. As a result, there remains a clear need for a rapid and highly sensitive POCT platform for cortisol detection. Here, we report a triple-mode surface-enhanced Raman scattering (SERS)-based LFIA platform for the ultrasensitive detection of cortisol, employing Au@4-MBA@Ag core-shell nanorods as signal probes. The platform enables visual readout, portable strip reader quantification, and SERS-based precise analysis within a single assay. Anti-cortisol antibodies were conjugated to the probe surface, allowing the assay to be completed within 8 min. The limits of detection were 250 pg/mL for visual inspection, 39.51 pg/mL for optical quantification, and 3.39 pg/mL for SERS detection, corresponding to an approximately three-order-of-magnitude improvement over conventional colloidal gold LFIAs. The assay exhibited high specificity, good stability, and satisfactory reproducibility. Recovery tests in artificial saliva samples ranged from 100.67% to 107.74%. In addition, cortisol concentrations determined by the SERS-LFIA showed strong agreement with ELISA results, with a linear correlation described by y = 0.96 + 0.89x (R2 = 0.97). This work presents a rapid, sensitive, and quantitative SERS-LFIA platform that overcomes the sensitivity limitations of traditional LFIA-based cortisol assays. The entire detection process can be completed within 8 min, making the platform well suited for time-critical point-of-care testing. The triple-mode readout strategy further enhances flexibility and reliability. Overall, this approach provides a practical and extendable solution for the detection of low-abundance small-molecule biomarkers in POCT settings.
To evaluate the implementation process of a community-based, multidisciplinary intervention (CONNACT) through a randomized controlled trial (RCT) in order to contextualize the RCT outcomes and inform implementation opportunities. This study is an embedded qualitative process evaluation of the CONNACT effective-implementation hybrid RCT. Semi-structured interviews with 22 intervention patients and 14 healthcare professionals were conducted. Interviews were audio-recorded, transcribed and translated using framework analysis. Data was analysed thematically and the emergent themes were organised into the conceptual domains of RE-AIM. An explanatory sequential methods approach was used to discuss the Reach and Effectiveness domains of RE-AIM, whereby quantitative data (i.e. recruitment logs and published quantitative results) was discussed in relation to this study's qualitative data. Reach: 55.4% of the patients who met the inclusion criteria participated, while work or family commitments and disinterest in physiotherapy are the primary reasons for declining participation. CONNACT intervention is not superior to control hospital-based usual care in terms of pain, function, and quality of life, but superior in physical performance, knee satisfaction, global perceived effect and positive dietary change. The results and effectiveness of CONNACT are presented and discussed in a related publication. Adoption: Healthcare professionals proposed changes for long-term sustainability (transdisciplinary approach, expert patients) despite their strong support for CONNACT. Implementation (context): A spectrum of passive-resigned and impatient-unrealistic mindsets, pain beliefs, and expectations was elucidated. Implementation (mechanism of impact): Focus on patient education and empowerment encouraged patients to actively accept their condition and practice self-efficacy. Although group classes are a source of support, motivation, and positive peer pressure, several patients preferred personalized treatments. CONNACT's synergistic nature benefitted patients who are more complex. Maintenance (patient-level): Patients highlighted the importance of incorporating exercise into their regular routines, but lack of time and inertia remain as significant barriers. The themes have allowed a better understanding of the RCT primary and secondary outcomes and informed the next phase of implementation. CONNACT and similar interventions should identify and address reasons for refusing participation [Reach]; improve group classes with initial evaluations, equal attention paid to patients, tailored exercises, and acknowledge progress [Effectiveness, Implementation]; and adopt a streamlined resource-efficient transdisciplinary design [Maintenance]. This study, which primarily employs qualitative methods for data collection, is not a clinical trial (clinical trial number: not applicable). Ethics approval (NHG DSRB ref no: 2020/00067;) was obtained before commencement of this study.
Given the inherent complexity of metabolic pathways and disease-associated agents, next-generation healthcare necessitates wearable, non-invasive, and customized approaches to continuously monitor a broad spectrum of physiologically relevant biomarkers for personalized health management. Moreover, existing data-based analytical strategies remain inadequate for delivering quantitative and predictive evaluations of health status in real-life settings. Here, we report an electronic multiplexed microneedle-based biosensor patch (eMPatch) that enables real-time, minimally invasive monitoring of key metabolic biomarkers in interstitial fluid, including glucose, uric acid, cholesterol, sodium, potassium, and pH. By integrating modular microneedle (MN) sensors into a skin-interfaced flexible platform, the eMPatch achieves robust mechanical stability and seamless skin conformity, thereby ensuring reliable and continuous sensing within the dermal space. In vivo validation in animal models under metabolic intervention highlights the strong capability of the eMPatch for real-time physiological tracking across diverse daily activities. Implemented with a machine learning algorithm, the eMPatch enables automatic feature extraction and multi-task health assessment, achieving a classification accuracy of 0.996 in distinguishing normal and diet-induced metabolic disorder for health condition identification and an R2 score of 0.977 for the corresponding degree evaluation. This study highlights the potential of the MN-integrated, machine learning-enhanced biosensing platform toward personalized health management.
There is an absence of investigations and knowledge of the pathways from suicidal ideation to attempt among individuals with schizophrenia spectrum disorders (SSDs). This study explored suicide attempt experiences and identified pathway steps and timeframes among adults with SSDs. Quantitative and qualitative data were collected from adults with SSDs in a community mental health setting of the United States who had a history of suicide attempt and 6-month history of suicidal ideation (n = 19). The Pathway to Suicidal Action Interview (PSAI) was administered and data from qualitative questions were analyzed using a hybrid analytic approach with inductive and deductive coding. Transition toward suicide attempt occurred often within minutes or seconds of the decision point (mode of 30 minutes) and first step of attempt action (mode of 30 seconds). Most participants did not engage in preparatory behavior (68%) or mull in contemplation (53%) prior to their attempt. Symptoms of depression (89%) and psychosis symptoms (63%) were endorsed by most participants at the time of attempt, with 47% reporting hearing auditory hallucinations to kill themselves or die. Suicide attempt experience themes pertained to impulsivity, access to means, psychosis symptoms, survival ambivalence, attempt antecedents, attempt location, travel, and role of others. Findings underscore the heterogeneity of suicide attempt pathways and reveal opportunities for prevention efforts related to lethal means safety, management of acute emotional distress, and targeting psychosis symptoms that can impact suicide risk for those with SSDs. Future research is needed among larger and more diverse samples. Transition from suicidal ideation to attempt among individuals with schizophrenia spectrum disorders was often rapid, occurring within minutes or seconds and often without preparatory behaviors.Depressive symptoms (including low mood and anhedonia), acute emotional distress, and psychosis symptoms (particularly auditory hallucinations) were frequently present at the time of suicide attempt.Findings highlight opportunities for suicide prevention in SSD populations focused on lethal means safety, acute distress intervention, and targeted management of psychosis symptoms contributing to suicide risk.
Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field 1H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H2O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by 1H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the 1H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. 1H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the 1H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The 1H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis.
Chronic airway diseases (CAD), including asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, and cystic fibrosis, are increasingly recognized as heterogeneous and overlapping syndromes that share treatable biological and clinical characteristics. The Treatable Traits (TT) approach is a precision medicine framework that transcends diagnostic labels. It identifies and targets modifiable pulmonary, extrapulmonary, and behavioral characteristics in each patient. Biomarkers are central to this paradigm, translating latent endotypes into measurable traits that inform diagnosis, treatment selection, and longitudinal monitoring. This review synthesizes contemporary evidence on the role of biomarkers in implementing the TT model across CAD. Peripheral and airway biomarkers, including blood eosinophil count (BEC), fractional exhaled nitric oxide (FeNO), and sputum cell profiles, enable the identification of type 2 inflammatory traits and the prediction of corticosteroid or biologic responsiveness. Imaging and quantitative computed tomography metrics extend trait definition to structural and functional domains. Meanwhile, multi-omic and microbiome signatures reveal the molecular endotypes that underpin disease heterogeneity. Canonical examples include BEC predicting the benefit of inhaled corticosteroids in COPD and FeNO indicating steroid responsiveness in asthma. Additionally, emerging data suggest that rapid trait identification during acute exacerbations may facilitate targeted biologic therapy, extending precision care into acute management contexts. Integrating biomarker-guided assessment with individualized therapy redefines the management of CAD by offering a pathway toward biologically precise, dynamically adaptive care. Continued research should focus on standardizing biomarker thresholds, validating composite panels, and translating omic and imaging discoveries into routine clinical tools to optimize outcomes across the chronic airway disease spectrum.
The human brain is the most intricate organ, comprising trillions of synaptic connections and governing every thought, feeling, and action. However, abnormalities in its structure or dysfunction in neural connections can often underpin the development of mental disorders. Mental health conditions affect nearly 1 in 8 people globally, creating a significant challenge for healthcare systems to manage. Advances in neuroimaging and artificial intelligence (AI) hold the potential to transform mental health diagnosis and treatment by enabling the timely detection of these disorders. Radiomics, a technique that extracts quantitative features, has emerged as a promising approach for improving diagnostic accuracy and predicting treatment response. This review explores the current status of radiomics-based applications derived from neuroimaging and AI in addressing various mental disorders categorized under the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM). These include bipolar and anxiety disorders, depressive and neurodevelopmental disorders, schizophrenia spectrum and other psychosis, Post-traumatic stress disorder (PTSD) and Internet Gaming Disorder. The findings highlight the critical role of radiomic features and identify the brain regions associated with each disorder, alongside the tools, algorithms, and methodologies used. While the review also discusses limitations and challenges in radiomics research, it underscores the potential of radiomics and AI to identify significant biomarkers for the precise diagnosis of mental health conditions, as well as to enhance precision in treatment response. The potential of this technology could offer new approaches for the diagnosis and personalized treatment of mental disorders, ultimately improving the well-being of millions people worldwide.