The number of older people in Germany has risen steadily in recent decades. One in four people is now aged 65 or over. As people age, their health problems tend to increase, as do their fundamental care needs. Nurses play a key role in addressing these needs through a holistic approach. To fulfil this responsibility effectively, it is necessary to examine existing nursing research on the fundamentals of care for older people and to identify gaps in the current evidence base. Therefore, we plan to conduct a mapping review with the aim of mapping the extent, range and nature of nursing research activities on the fundamentals of care, as defined in the physical, psychosocial and relational components of the Integration of Care dimension of the Fundamentals of Care Framework for older people in Germany. We will search the electronic databases PubMed/MEDLINE, CINAHL, CareLit and GeroLit, the catalogue of the German Federal Ministry of Research, Technology and Space and the German National Library for publications on nursing research based on the Integration of Care dimension of the Fundamentals of Care Framework among older people (≥ 65 years). There will be no time limit. We will include studies published in English and German. Initial screening of the first ten per cent of titles and abstracts and other stages will be carried out by two independent researchers. This process will be repeated until full agreement between the researchers. Any discrepancies will be resolved with consultation of a third reviewer. Results will be reported in a narrative synthesis and complemented by tabular and numerical presentations. To the best of our knowledge, this mapping review will be the first to provide an overview of current nursing research on the fundamentals of care for older people in Germany. The inclusion of German-language texts and the absence of time limits in this review are intended to complement previous reviews. The planned mapping review will also identify the evidence gap in research in this area and contribute to the determination of future scientific research in Germany. Consequently, the findings of the mapping review could be of great interest to nurses and other health professionals for evidence-based practice, research and educational programmes. In addition, the data can be used to develop a programme for the provision of age-friendly and caring living conditions for older people in the future. The protocol was registered with Open Science Framework (osf.io/9e3uv).
PurposeTo examine the relationship between fall-related mortality, disability-adjusted life years (DALY), healthcare expenditures, and research funding and determine whether fall prevention funding is proportional to fall-related public health impact.DesignCross-sectional.SettingUnited States.SampleNot applicable.MeasuresMortality rates (2018-2022) for leading causes of death were obtained from CDC WONDER. Disability-adjusted life-year (DALY) rates (2021) were obtained from the World Health Organization. Healthcare expenditures (2016) were obtained from the Institute for Health Metrics and Evaluation. Research funding data (2018-2022) were obtained from NIH ExPORTER and linked to causes of death using MeSH term searches.AnalysisLinear regression models were used with log-transformed research funding as the dependent variable and log-transformed mortality rates, DALY rates, and healthcare expenditures as predictors.ResultsFall mortality rate was 13.1 deaths per 100 000 individuals, fall-related DALY rate was 713.2 per 100 000, and fall-related healthcare expenditures were $106.6 billion. Falls ranked 12th in mortality, 8th in DALY, and 5th in healthcare costs but 20th in research funding, receiving $489 million over 5 years. Falls received significantly less funding than expected based on mortality rates (predicted $1.95 billion), DALY rates (predicted $3.27 billion) and healthcare expenditures (predicted $5.63 billion).ConclusionAlthough falls have a significant impact on older adults' health and mortality, fall research funding is disproportionately low. To reduce mortality and mitigate rising healthcare costs associated with falls, federal investment in fall prevention research should be a higher priority.
Patient and Public Involvement (PPI) in health research, including clinical trials, enhances research relevance and quality. However, data on PPI prevalence and characteristics in trials involving older adults remain scarce. We aimed to describe the prevalence and nature of PPI in trials with older adults and identify the main benefits and challenges associated with PPI in such trials. We conducted a multi-methods study, embedded within a survey of 3,163 corresponding authors of pragmatic trials published between 2014 and 2019. We used authors' self-reports and an electronic search filter to identify the subset involving the older adult population (≥65 years). We approached interested respondents who indicated that they had conducted PPI to participate in a semi-structured interview. Survey results were summarized using descriptive statistics, and interview transcripts were analyzed using thematic analysis. One hundred authors met the eligibility criteria, having completed the survey and been involved in a trial involving older adults. Most respondents were women (64.8%). PPI was reported in 46.0% of trials, primarily involving in-person discussions. Most respondents (90.7%) perceived PPI as beneficial, citing improved interventions, increased applicability of findings, higher research quality, and enhanced recruitment/retention. Challenges included communicating trial design, methods, and results (62.5%), identifying or recruiting PPI partners (50%), scheduling meetings (45.8%), and sustaining involvement (45.8%). Thematic analysis of N=8 interviews revealed five main themes related to challenges, some specific to older adults: recruitment and retention of PPI partners, importance of a good PPI chair, training for PPI partners, workload for researchers and burden for PPI partners, and procedural barriers. PPI partners influenced various research aspects, sometimes described as exceeding expectations, by influencing aims and outcomes to measure, developing interventions, refining patient-facing materials, aiding recruitment and retention, and contributing to analysis and interpretation of results. Despite being implemented in fewer than half of the trials, PPI had a significant perceived impact. Addressing identified challenges, both general and specific to older adults, could enhance PPI uptake, as well as the quality and relevance of research.
Understanding reaction kinetics is fundamental to organic synthesis, yet traditional quantum chemistry-based transition state searches are computationally expensive. Here we present DeePEST-OS, a reactive machine learning potential designed for rapid and accurate transition state optimization and energy barrier prediction spanning ten chemical elements. Trained on approximately 75,000 reactions generated by a low-cost data preparation strategy, this model integrates physical priors from semi-empirical quantum chemistry with equivariant message passing networks to predict potential energy surfaces nearly 10,000 times faster than quantum chemistry methods, while achieving high accuracy for transition state geometry (averaged root mean square deviation of 0.12 Å) and energy barriers (mean absolute error of 0.60 kcal/mol) on unseen reactions. DeePEST-OS enables practical applications including transition state conformer screening, barrier prediction for retrosynthesis of complex pharmaceuticals, and experimentally validated diastereoselectivity prediction in Diels-Alder reactions. Collectively, these results establish DeePEST-OS as a powerful tool for accelerating reaction kinetics studies in multi-element organic synthesis.
Selenoproteins represent a structurally and functionally distinct class of proteins that contribute to cellular antioxidant defense and a wide range of other essential biological processes in specific organisms. They contain the 21st amino acid selenocysteine (Sec) in their active sites, which is encoded by an in-frame UGA stop codon through a translational reprogramming mechanism. Due to the dual functionality of the UGA codon, selenoprotein genes are frequently misidentified and misannotated in genomic databases, especially in bacteria where selenoproteins exhibit greater complexity and diversity compared to their eukaryotic counterparts. Thus, a comprehensive resource is urgently required to enable accurate identification of selenoprotein genes across diverse bacterial genomes. We have developed BSepDB, a specialized database dedicated to systematic curation of bacterial selenoprotein genes and proteins, providing an exhaustive resource for the research community. The current version (BSepDB v. 1.0) encompasses over 57,000 selenoprotein entries derived from 16,621 bacterial species, representing the largest and most taxonomically diverse repository of bacterial selenoproteins to date. To facilitate intuitive data exploration, BSepDB offers multiple user-friendly interfaces, such as interactive browsing and search tools, an integrated BLAST search function, and options for bulk data download. Additionally, the curated entries in BSepDB are cross-referenced with established genomic databases (e.g., GenBank and RefSeq) to improve the accuracy of selenoprotein annotations in large-scale genomic projects. BSepDB serves as a valuable resource for researchers investigating selenium utilization and the functional diversity of selenoproteins in bacteria. The database is freely available at https://bsepdb.metalbioinfolab.net.
This research examined the performance of a machine learning algorithm when predicting the surface roughness of tempered steel AISI 1060. Different machine learning algorithms, such as decision tree (DT), random forest (RF), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB), were optimized by using 10-fold cross-validation and the grid search method. From these optimized models, the decision tree, adaptive boosting, gradient boosting, and extreme gradient boosting were used as base models to develop a more powerful machine learning model called super learner machine learning. The linear regression (LR) was used as a meta-model in developing super learner machine learning. The developed super learner model performance was then validated against all machine learning models used in this research. For performance measurement metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) has been used. The developed super learner model achieved the highest R² of 99.2% and the lowest MAPE of 2.6% on the test data set when compared with other machine learning models. Further, the SHAP method shows that hardness has the highest effect, followed by feed rate and cutting speed, respectively. Most machine learning approaches are not used practically for user applications, but in this research, a graphic user interface framework called fast, accurate, and intelligent (FAI) frame was developed to predict the surface roughness of tempered steel AISI 1060. This research is used for practical application for any user in industry and for research purposes.
To review the literature on psychosocial care and experiences of young adults with early-onset type 2 diabetes (EOT2D), to identify what is known, current gaps and to develop recommendations to help advance psychosocial care and support for the population. We searched Medline (Ovid), Google Scholar and diabetes-specific journals for English-language articles focused on psychosocial aspects in young adults (aged 18-45 years) with EOT2D. Two people with lived experience reviewed and commented on the review findings. Growing evidence indicates that a diagnosis of EOT2D is associated with an increased risk of developing diabetes-related psychological comorbidities. Experiences of diabetes-related stigma, compounded by age-related negative preconceptions, contribute to heightening the psychosocial impact of EOT2D. Some population sub-groups appear to be more likely to experience adverse psychological effects. However, the evidence base is limited by a dearth of diverse research specifically focused on the psychosocial experiences and needs of this population (e.g., longitudinal and qualitative studies). Adults with EOT2D also experience unmet education, care and support needs relevant to optimising their psychosocial well-being and diabetes management. Overall, they require enhanced, tailored care and support that is age-appropriate, person-centred and responsive to their psychosocial needs. Digital technology and support-based strategies may help to address current gaps and improve the psychological well-being of this group, but these require further exploration. Despite the importance of psychosocial factors in young adults' diabetes management and outcomes, there remain gaps in research and practice and the need for further research, alongside changes in practice.
The integration of neurocognitive challenges into biomechanical movement tasks has gained attention due to its potential relevance for assessments of physical function, injury risk, and performance. However, a comprehensive mapping of the chosen methodological approaches, targeted populations, and applied outcome measures is still lacking. This scoping review hence aimed to synthesize the current literature on unplanned movement tasks combining cognitive decision-making and biomechanical outcome measurements. A systematic literature search following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-extension for scoping reviews) guidelines was performed in Web of Science (Core Collection), MEDLINE (PubMed), Cochrane Library, and Google Scholar. Included studies combined unplanned movement tasks (e.g., change-of-direction or stopping) with biomechanical assessments. Eligible articles were analysed in terms of participant characteristics, movement type, unplanned task type, reactive stimulus, and biomechanical outcome variables. From the total of 167 studies, the majority focused on change-of-direction tasks (82%), mostly using standardized angles of 45° and moderate approach speeds (3.9 ± 0.9 m/s). Jump (7%), land (12%), and/or stop tasks (3%) were less frequent. Most studies (83%) relied on simple visual cues (e.g., lights or symbols), whereas more ecologically valid stimuli (e.g., videos or real opponents) were rarely applied. Biomechanical analyses predominantly focused on knee angles and moments as well as ground reaction forces, while only 23% of studies included electromyography measurements. Older adults (50+ years) were not represented. Although research on unplanned biomechanical tasks is growing, significant methodological heterogeneity and limited ecological validity may constrain the interpretability and applicability of findings. Future research should aim for task designs that better reflect real-world conditions and include diverse populations and comprehensive neuromuscular assessments.
Timely palliative care can reduce the disease burden and improve quality of life in patients with cancer. Although several studies have developed assessment models for palliative care needs in cancer patients, the quality and clinical applicability of these models remain unclear. To systematically review existing assessment models for palliative care needs in patients with cancer, with a focus on their characteristics, predictors, risk of bias, and applicability. A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI) through September 12, 2025. Data extraction and evaluation were rigorously performed by two researchers based on the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 5714 articles were identified, and eight studies were included, which covered 24 models for assessing palliative care needs. The sample size of the included studies ranged from 179 to 54,628, with areas under the curve ranging from 0.724 to 0.998. The models in all the included studies encompassed four categories of predictive factors: general demographic data, symptom/functional assessments, laboratory indicators, and treatment status. Five studies were rated as having a high risk of bias, primarily due to high risks associated with participants and conclusions, with generally low applicability. Existing models demonstrate potential for identifying patients with cancer who have increased palliative care needs using routinely collected clinical data. Commonly included predictors were symptom burden, functional status, laboratory parameters, treatment-related factors, and demographic characteristics. However, the overall body of evidence is constrained by a substantial risk of bias, particularly arising from inappropriate data sources, limited sample sizes, suboptimal handling of continuous variables, insufficient reporting of missing data, and the lack of robust internal or external validation. In addition, many models adopted mortality-based surrogate outcomes rather than clinically meaningful indicators of palliative care needs. Therefore, the currently available models should be interpreted with caution, and further high-quality model development and external validation are required before they can support broader routine clinical implementation. Future research should prioritize clinically actionable outcomes and incorporate patient-, caregiver-, and family-level factors to improve the relevance of these models for referral decisions and care planning.
This study aimed to standardize the global prevalence of metabolic syndrome (MetS) in patients with epilepsy. Several databases including PubMed, Scopus, Web of Science, Embase, Science Direct, and Google Scholar were thoroughly searched. Studies up to 2025 on MetS prevalence in people with epilepsy were included. The search covered cross-sectional, cohort, and case-control studies in English that reported MetS prevalence in this group. Statistical analysis was performed using Comprehensive Meta-Analysis software (Version 2). The I² statistic was used to assess heterogeneity across studies, and the random-effects model was applied for data analysis. Analysis of 24 findings from 18 studies estimated that the overall prevalence of (MetS) in people with epilepsy was 29.6% (95% CI: 25.7%-33.9%). Further analysis showed that the highest prevalence of MetS in epilepsy patients was in studies conducted in South America, with a prevalence of 42.4% (95% CI: 37.8%-47.0%). The only study that used the Harmonized Criteria diagnostic tool reported the prevalence equal to 49.4% (95% CI: 39.2%-59.7%). Furthermore, a meta-regression analysis found no significant association between the year of publication or the number of participants and the prevalence of MetS in epilepsy patients (P > 0.01). The results highlighted a high prevalence of MetS among patients with epilepsy. Therefore, healthcare professionals should not only focus on epilepsy but also regularly monitor risk factors associated with MetS and identify them early in patients with epilepsy.
Self-harm in young people is a pressing public health issue, with family support playing a crucial role in the young person's prognosis. Concurrently, the impact extends to families themselves, who must navigate caregiving responsibilities while also requiring support. Understanding these experiences is key to providing more effective assistance in their caregiving roles. With this mixed-methods systematic review we investigated the experiences, barriers, and needs of families of young people who self-harm. Following PRISMA guidelines, a search was conducted within PsycINFO, PubMed, Web of Science, and Scopus databases in July 2024 and April 2025. Thirty-one studies were included in this review, and quality was assessed with the Mixed Methods Appraisal Tool (MMAT). A narrative synthesis was employed for the quantitative data, while qualitative data were analysed with thematic synthesis. The quantitative findings revealed two themes: (1) the repercussions of self-harm in the family and (2) family support needs. Qualitative analysis identified four themes: (1) parents' emotional and psychological impact; (2) impact on parenting and the bond with the young person; (3) disruption of family dynamics; and (4) barriers to and pathways for family support. We discuss the implications of these findings, offering recommendations for future research and improvements in family support services to alleviate caregiver burden and foster supportive environments for recovery. Siblings’ experiences can vary with age, and many of their needs remain unaddressedAccessible care, family involvement, trained staff, and follow-up are neededFindings call for a systemic, family-centered approach to self-harm recovery.
Growth differentiation factor-15 (GDF15), a stress-responsive cytokine of the transforming growth factor-β superfamily, is elevated in cancer cachexia, chemotherapy-induced nausea, and hyperemesis gravidarum, making it both a biomarker and a therapeutic target. Here, we developed high-affinity GDF15 binders using an artificial intelligence-driven protein design framework. To achieve this, we systematically explored three complementary scaffold-generation strategies: scaffold grafting, diffusion-based de novo design, and scaffold-search and grafting, identifying distinct advantages - scaffold grafting rapidly optimized receptor-derived motifs to sub-nanomolar affinity; de novo diffusion produced topologically novel binders; and scaffold-search and grafting enabled access to concave site B of GDF15 by repurposing evolutionary structural analogs from natural complexes. The designed GDF15 binders were translated into two functional modalities. First, a one-step, wash-free luminescent biosensor was created by coupling a de novo binder to split-luciferase fragments, enabling the rapid and sensitive quantification of GDF15. Second, the highest-affinity binder was engineered as an Fc-fusion decoy receptor, thereby effectively neutralizing GDF15 signaling in cell-based assays (IC50 = 7.2 nM), demonstrating comparable in vitro potency to ponsegromab, a monoclonal antibody currently undergoing phase II clinical trials. Together, this work establishes a versatile artificial intelligence-driven binder design pipeline with broad potential for next-generation diagnostics and therapeutics in cancer cachexia and other GDF15-mediated diseases.
Gastrointestinal (GI) tumours are one of the most prevalent cancers globally. Even with normal GI function, individuals may develop neoplasms, highlighting the need to better understand the underlying causes of tumorigenesis. The emergence of tumour drug resistance represents the main reason for the failure of drug treatment; thus, it is imperative to investigate all the potential mechanisms of this very complex phenomenon. It is significant that exosomes and noncoding RNAs, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), originating from tumour cells, are linked to both GI drug resistance and the carcinogenesis and development of GI disease. Thus, we propose a systematic review of the literature to provide an overview of the current landscape concerning exosomal ncRNAs as diagnostic and prognostic biomarker resources in resistant GI cancer. We performed the current systematic review according to PRISMA guidelines and comprehensively explored PubMed, Web of Science, Scopus and Google Scholar databases to achieve the article search. Seventy-six studies were included in the investigation, comprising 50 cohort studies and 26 nested case-control studies (between 1994 and 2025). Among all systematically reviewed exosomal ncRNAs, we primarily found molecules with prognostic significance and predictive relevance, and those that serve both purposes have been identified. Furthermore, among all analysed ncRNAs isolated from exosomes, miRNAs, lncRNAs and circRNAs have emerged as the most extensively studied and are proposed as potential tools for predicting resistance or sensitivity to GI cancer treatments. Our analysis offers a comprehensive overview of the current landscape concerning exosomal ncRNAs as potential clinical biomarkers for resistant GI tumours. However, despite extensive research efforts, the application of exosomal biomarkers in GI cancers is still in its infancy. None of the identified exosomal biomarkers have advanced beyond preclinical studies, and their applicability in clinical settings remains limited, largely due to challenges in clinical validation, standardisation and biomarker specificity.
Epilepsy is one of the most prevalent chronic neurological disorders worldwide, affecting approximately 70 million people globally and imposing substantial burdens on patients, families, and healthcare systems. Its multifaceted treatment landscape spanning antiepileptic drug (AED) therapy, epilepsy surgery, ketogenic dietary therapy, and neuromodulation makes accurate health information critical for patient decision-making and treatment adherence. Short-video platforms such as TikTok (Douyin) and Bilibili have emerged as primary channels through which the public accesses health-related content, yet the quality and reliability of epilepsy-related content on these platforms remain largely unexamined. A cross-sectional content analysis was conducted. We systematically retrieved videos via keyword search on TikTok (Douyin) and Bilibili, using the terms "dianxian" (epilepsy) and "jingfeng" (seizure/convulsion). For each platform, we collected the top 100 unique videos ranked by the platform's default relevance algorithm, with duplicate results from the two search terms removed. After applying pre-specified inclusion and exclusion criteria, 182 videos were included in the final analysis. Two physicians independently assessed the videos using a multi-instrument framework with clear applicable boundaries: Global Quality Score (GQS, for overall educational quality across all content types), modified DISCERN (mDISCERN, exclusively for treatment information reliability), JAMA benchmark criteria (for source transparency, not direct clinical accuracy), and a novel Treatment Misinformation Risk Scale (TMRS, specifically for epilepsy treatment-related content). Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Engagement metrics and uploader characteristics were also recorded, with sensitivity analyses performed to control for confounding from uneven content theme distribution between platforms. A total of 182 videos were analyzed (96 from TikTok, 86 from Bilibili). The overall educational quality was suboptimal (mean GQS: 2.65 ± 0.93; mDISCERN: 2.12 ± 0.89 for treatment-containing videos). Bilibili videos demonstrated significantly higher performance across all instruments: overall educational quality (GQS: 3.11 ± 0.87 vs. 2.24 ± 0.84, P < 0.001), treatment information reliability (mDISCERN: 2.56 ± 0.81 vs. 1.74 ± 0.76, P < 0.001), and source transparency (JAMA: 2.18 ± 0.72 vs. 1.42 ± 0.68, P < 0.001). The mean normalized TMRS score was 1.15 ± 0.62, with TikTok showing significantly higher treatment misinformation risk (1.41 ± 0.54) than Bilibili (0.86 ± 0.53, P < 0.001). TMRS scores were positively correlated with likes (rho = 0.46, P < 0.001), shares (rho = 0.43, P < 0.001), and comments (rho = 0.39, P < 0.001), while quality scores showed no significant correlation with engagement. Sensitivity analyses confirmed that the observed platform differences were not confounded by differences in content theme distribution. Epilepsy-related content on China's major short-video platforms is of concerningly poor quality, with treatment misinformation receiving disproportionately higher user engagement. These findings highlight the urgent need for collaborative efforts among neurologists, platform operators, and health authorities to improve the quality of epilepsy health information in the digital environment.
Breastfeeding has been shown to provide numerous benefits for mothers and babies in the short and long term. During the COVID-19 pandemic, breastfeeding support, which was traditionally provided offline, shifted to online platforms. Although these remote services were available before the pandemic began, online interventions emerged as an alternative and proved effective in helping mothers breastfeed during that period. We aimed to explore the existing literature on the experiences of mothers and health care professionals with remote one-to-one synchronous breastfeeding support and to identify the unmet support needs of mothers regarding this type of support. We systematically searched seven literature databases: MEDLINE, CINAHL Plus, MIDIRS, Web of Science, ASSIA, WHO Global Index, and Google Search. Articles published before 2010 and in languages other than English and Bahasa were excluded. A thematic approach was used to synthesise the data. Twenty-one studies were included in this review. Three themes generated from the synthesis: (1) mothers' acceptance of one-to-one synchronous telelactation, (2) benefit of one-to-one synchronous telelactation, and (3) challenges faced in one-to-one synchronous telelactation. In conclusion, mothers generally accepted one-to-one synchronous breastfeeding support as an alternative to in-person sessions, although some challenges remain. Further improvements are needed to address accessibility and scheduling issues.
Tuberculosis (TB) is one of the deadliest bacterial infectious diseases worldwide, with rising cases of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains. Bedaquiline (BDQ)-containing regimens have become important for the treatment of MDR/XDR-TB, and resistance to BDQ is increasing. Understanding genetic mutations is crucial for early detection of BDQ-resistant strains and thus maintaining the effectiveness of these drugs. This study aimed to review mutations associated with BDQ-resistant TB globally. This study systematically searched the keywords TB, XDR, MDR, BDQ, and genes in the PubMed, Scopus, Web of Science, and Embase databases for studies reporting BDQ-resistant TB and their associated genes globally from 2014 to 2025. This systematic review included 40 studies and 25,234 patient samples with MDR and XDR-TB from around the world. Results showed significant variation in BDQ resistance across the World Health Organization (WHO) regions, with the highest in the Eastern Mediterranean and the lowest in the Western Pacific. Furthermore, the data collected showed that, among the continents studied, resistance was highest in Africa and lowest in the Americas. The country distribution showed that resistance rates were higher in Iran (n = 24), Moldova (n = 26), and Armenia (n = 35), and lower in Italy (n = 1001) and the Philippines (n = 724) than in other countries in the analysis. Genetically, the most resistance-associated mutations were observed in the Rv0678, atpE, and pepQ genes, respectively. Given the increasing BDQ resistance and regional variability, it is essential to develop early detection systems, genomic surveillance, robust drug policy enforcement, and rapid diagnostics to maintain treatment effectiveness and curb the spread of resistance. Future research should focus on elucidating resistance mechanisms and developing novel therapeutic strategies.
Tobacco smoking is a major modifiable risk for poor pregnancy outcomes. Pregnant women whose partner smokes are six times more likely to continue smoking. Typically, interventions target pregnant mothers or smoking fathers to reduce second-hand smoke exposure. Evidence reviews advocate for couple-targeted smoking cessation interventions, however no recent review has evaluated the effectiveness of interventions targeting both pregnant women and their partner/household member. We aimed to systematically review and evaluate the effectiveness of smoking cessation interventions targeting the couple/household where both parties smoke for antenatal smoking cessation in pregnant women. We searched 8 databases (MEDLINE, Embase, Emcare, AMED, BNI, CINAHL, PsycINFO, Cochrane Register of Controlled Trials) for randomised controlled trials (RCTs), controlled non-randomised studies and before-and-after studies, meeting PICO criteria: Population: pregnant woman and her partner/household member/s who are tobacco smokers antenatally. Tobacco smoking cessation intervention targeting a couple/household. Intervention targeting woman only, usual care, historical control group. Outcome/s: Objectively assessed or self-reported antenatal maternal smoking cessation. Studies were systematically selected for inclusion and data were extracted by two researchers. Meta-analysis was not undertaken due to clinical and methodological heterogeneity, including differing intervention types, outcome measures, cut-offs used to define cessation and follow-up timepoints. Data were narratively synthesised. Six studies were included: 4 RCTs, 1 non-randomised comparative study and 1 before-and-after study. Interventions were poorly described, variable in content across studies, and differed in content between women and partners/household members within studies. They included behavioural support (n = 6 women, n = 4 partners), written or self-help materials (n = 4 women, n = 6 partners), nicotine replacement therapy (n = 1 women, n = 1 partners), demonstration of smoking effects on the fetal heart (n = 1 women) and incentives (n = 1 women). Three studies directly targeted partners and three targeted them indirectly via the woman. Only one study compared targeting the couple versus the woman only. Varied subjective and objective cessation measures were assessed. Quit definition, measurement timepoint, and whether partner cessation was evaluated varied. Two RCTs, one non-randomised controlled and one before-and-after study reported that their intervention positively impacted smoking cessation for women compared with usual care. One RCT reported increased cessation for intervention versus control for partners. Few up-to-date studies have evaluated smoking cessation interventions targeting couples/households who smoke during pregnancy. Interventions rarely directly target partners. Interventions directly targeting the woman and her partner or smoking household members, using up-to-date interventions (e.g., nicotine replacement therapy, vapes, behavioural support) are yet to be assessed in high quality studies and more evidence in this field is needed. Standardised outcomes for both women and partners are needed to evaluate efficacy, identify the active components of interventions and facilitate evidence syntheses.
Parkinsonian disorders, including Parkinson's disease, Lewy body dementia, multiple system atrophy, and progressive supranuclear palsy, are progressive neurodegenerative conditions with no treatment options to slow disease progression. This systematic review provides an overview of evidence of disease-modifying therapies that have been evaluated in clinical studies across these disorders, based on a comprehensive literature search up to May 2025. Eligible studies included clinical trials investigating pharmacological interventions aimed at slowing disease progression. Most clinical development has focused on Parkinson's disease, with limited progress in other Parkinsonian disorders. Therapies targeting alpha-synuclein, such as monoclonal antibodies and small molecules, have shown target engagement but limited clinical efficacy. Glucocerebrosidase-enhancing agents, particularly ambroxol, demonstrated promising biomarker and clinical signals in early-phase trials. Glucagon-like peptide-1 receptor agonists and kinase inhibitors have yielded mixed results, with some agents progressing to phase 3 trials. Neurotrophic factors, cell survival and neuroprotective therapies, stem cell therapies, and anti-inflammatory agents remain largely investigational, with limited evidence of efficacy. Repurposed drugs, including memantine and riluzole, have shown preliminary signals of benefit, though confirmatory trials are lacking. Despite substantial research efforts, no disease-modifying therapy has been approved for any Parkinsonian disorder. The heterogeneity of disease mechanisms and the limitations of current clinical endpoints, such as the Unified Parkinson's Disease Rating Scale, underscore the need for biomarker-driven approaches and stratified trial designs. Future success will likely depend on improved patient selection, mechanistic targeting, and the integration of fluid and imaging biomarkers to demonstrate disease modification.
Physician associations play a significant role in shaping health policy at national and sub-national levels. However, the influence of such associations in low- and middle-income countries has not been synthesized or assessed. The Indian Medical Association (IMA), one of the largest physician associations in the world, has a long history of policy engagement at national and state levels across multiple issues. This review aims to assess - for the first time - the empirical literature available on the IMA as a political actor. Adopting a scoping review methodology, the paper sought to identify the policy stances, strategies and influence of the IMA over India's health policy. Nine health, social science, and policy research databases were searched for English-language studies published between 1974 and 2024. Reviewing 37 papers, it finds that the IMA has been active in seven main policy domains: violence against doctors; regulation of the private healthcare sector; restriction of traditional medicine; professional authority or autonomy for physicians; publicly funded health insurance; medical ethics; and partnership in public health programs. It has been reactive against new legislation, reform or regulation in all domains except for violence against doctors. Through interrelated interior and exterior strategies, the organization has been successful in influencing, stalling or limiting legislation. While the IMA holds influence through the size of its membership and its embeddedness in health administration and corporate interests, the tactics of the organization often lack coherence and consistency. Situating these findings in the broader landscape of health governance, our review contributes further evidence for the need to develop more inclusive and transparent pathways for participation in decision-making.
Digital Twin (DT) technology creates a dynamic virtual representation of a physical system using real-time data and computational modeling. While DTs have demonstrated profound impact in several medical disciplines, their translation into dentistry is still emerging and has not been comprehensively mapped. To systematically review and delineate the current applications, technological advancements, and prospective opportunities of digital twin (DT) technology in dentistry. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of MEDLINE (PubMed), EMBASE, Scopus, and Web of Science identified English-language publications from January 2000 to April 2025. All empirical and conceptual studies describing DT development, validation, and/or application in dental contexts were eligible. Two reviewers independently conducted screening and study selection, with a third reviewer resolving discrepancies. No automation tools were used. A total of 5989 records were retrieved, and 7 studies met the inclusion criteria. Included studies represented orthodontics, prosthodontics, endodontics, and dental education. DT applications primarily involved: patient-specific virtual modeling for diagnosis and treatment simulation, predictive or performance-monitoring frameworks using biomechanical/algorithmic analysis, and simulation-based skill training. Most were conceptual or prototype studies with small samples and limited clinical validation. DT technology has substantial potential to enhance precision, simulation, monitoring, and personalization in dentistry. However, current evidence remains constrained by fragmented research, methodological inconsistency and insufficient clinical validation. Future adoption of DT requires standardized data pipelines, robust ethical and regulatory frameworks and interdisciplinary collaboration to achieve clinically meaningful and widely adoptable DT integration in dental care.