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Cholesteric liquid crystal elastomers (CLCEs) are a vibrant subset of photonic materials exhibiting extraordinarily mechanochromic properties that make them an ideal platform for the development of displays, sensing, anti-counterfeiting, and camouflage technologies. In recent years, researchers have increasingly extended the structure and morphology of CLCEs to uncover their fascinating functionalities and promising applications. In this review, the historical development of CLCEs is retrospect, recent advances in the preparation methods are highlighted, and the well-ordered CLCEs with extended functionality are introduced. Within this scope, we highlight the unique advantages of CLCEs and present recent progress in expanding their structural, morphological, and functional versatility. We conclude with an outlook on current challenges and near-term application opportunities.
In the context of novel view synthesis, 3D Gaussian Splatting (3DGS) has recently emerged as an efficient and competitive counterpart to Neural Radiance Field (NeRF), enabling high-fidelity photorealistic rendering in real time. Beyond novel view synthesis, the explicit and compact nature of 3DGS enables a wide range of downstream applications that require geometric and semantic understanding. This survey provides a comprehensive overview of recent progress in 3DGS applications. It first reviews the reconstruction preliminaries of 3DGS, followed by the problem formulation, 2D foundation models, and related NeRF-based research areas that inform downstream 3DGS applications. We then categorize 3DGS applications into three foundational tasks: segmentation, editing, and generation, alongside additional functional applications built upon or tightly coupled with these foundational capabilities. For each, we summarize representative methods, supervision strategies, and learning paradigms, highlighting shared design principles and emerging trends. Commonly used datasets and evaluation protocols are also summarized, along with a comparative analysis of recent methods across public benchmarks. To support ongoing research and development, a continually updated repository of papers, code, and resources is maintained at https://github.com/heshuting555/Awesome-3DGS-Applications.
Obesity is a highly heritable trait, but rising obesity rates suggest environmental change is also of profound importance. We conducted a cross-cohort analysis to examine how associations between genetic risk for high BMI and observed BMI differed in four British birth cohorts born before and amidst the obesity epidemic (1946, 1958, 1970 and ~2001; N = 19,379). BMI (kg/m2) was measured at multiple time points between ages 3 and 69 years. We used polygenic indices (PGI) derived from GWAS of adulthood and childhood BMI, respectively, with mixed effects models used to estimate associations with mean BMI and quantile regression used to assess associations across the distribution of BMI. We further used linear regression to estimate PGI-heritability (PGI-h2; incremental variance explained by the PGI) and Genomic Relatedness Restricted Maximum Likelihood (GREML) to calculate SNP-heritability (SNP-h2) by cohort and age. Adulthood BMI PGI was associated with BMI in all cohorts and ages but was more strongly associated with BMI in more recently born generations, e.g., at age 16y, a 1 SD increase in the adulthood PGI was associated with 0.46 kg/m2 (0.37, 0.55) higher BMI in the 1946c and 0.90 kg/m2 (0.83, 0.97) higher BMI in the 2001c. Cross-cohort differences widened with age and were larger at the upper end of the BMI distribution, indicating disproportionate increases in obesity in more recent generations for those with higher PGIs. Differences were also observed when using the childhood PGI. There were no clear, consistent differences in PGI-h2 or SNP-h2, possibly due to limited statistical power, except that PGI-h2 was highest in the most recently born cohort (2001c) when using the most predictive PGI for adulthood BMI. Findings highlight how the environment can modify genetic associations; genetic associations with BMI differed by birth cohort, age, and outcome centile.
Salmonella Rissen (6,7,14:f,g:-) has recently emerged in Thailand; a high prevalence of multidrug resistance (MDR) has been reported. S. Rissen is a monophasic serotype, lacking phase 1 or phase 2 flagellin, and its biphasic ancestor remains unknown. In this study, pangenome SNP analysis was performed on 119 Salmonella serotypes (i.e., 325 genomes) that share antigens with S. Rissen, including those with the 6,7,14 O antigen, f,g phase 1 flagellin antigen, or the absence of phase 2 antigen, as well as those in closely related clades. Although the biphasic ancestor of S. Rissen was not determined, all 109 Thai S. Rissen isolates belong to S. Rissen lineage A, which shares a most recent common ancestor (MRCA) with another monophasic serotype, S. Oranienburg lineage G; the antigenic formula (6,7,14:m,t:[z57]; [z57]), however, indicates that some rare S. Oranienburg isolates are biphasic. Thai S. Rissen isolates not only lack the phase 2 flagellin gene, fljB, but the entire fljAB-hin region is replaced by a SEN8-like prophage. However, unlike MDR Salmonella Typhimurium monophasic variant, 4,[5],12:i:- in which AMR genes replace the fljAB-hin region, AMR genes in S. Rissen were detected within the tnsD-silE region in the chromosome, suggesting an alternative AMR hotspot in S. Rissen. This tnsD-silE region was reported as a part of the Tn6777 transposon that can translocate from the S. Rissen chromosome to a plasmid, suggesting mobility of AMR genes in S. Rissen genomes. This study highlights the single emergence and clonal expansion of Thai MDR S. Rissen isolates and the plasticity of S. Rissen genomes.
Gastroesophageal reflux disease (GERD) is a common chronic disease of the gastrointestinal tract and is a problem in developed countries. In particular, clinicians are interested in the extraesophageal manifestations of this disease, and recently, dentists have been studying the erosion of hard dental tissues. This article provides a literature review of scientific papers published over the past 8 years that provide evidence-based experimental and clinical data on the most recent issues related to the prevention of hard dental tissue erosion in patients with gastroesophageal reflux disease. The meta-analysis used the resources of the eLibrary search engines, the PubMed website, and the data from the disserCat dissertation abstracts. The purpose of the study was to comprehensively examine the prevention of erosive lesions of hard dental tissues in patients with GERD. It was important to consider this issue from a preventive perspective, as it is often overlooked. Гастроэзофагеальная рефлюксная болезнь (ГЭРБ) — распространенное хроническое заболевание желудочно-кишечного тракта, представляющее собой проблему в экономически развитых странах. В наибольшей степени клиницистов привлекают внепищеводные проявления этого заболевания, а в недавнее время для стоматологов областью исследования по теме стали эрозии твердых тканей зубов. Представлен обзор научных трудов за последние 8 лет, содержащих доказательную экспериментальную и клиническую базу по наиболее современным вопросам профилактики эрозии твердых тканей зубов у пациентов с ГЭРБ. Для метаанализа использованы ресурсы поисковых систем eLibrary, сайта PubMed и данные авторефератов диссертаций disserCat. Эффективная профилактика эрозивного поражения зубов при ГЭРБ не может быть достигнута усилиями лишь одной клинической дисциплины и требует мультифакторного подхода. В целом можно выделить такие ключевые направления, как медикаментозная терапия с учетом коррекции диеты и образа жизни; специализированные стоматологические мероприятия, включающие обучение гигиене рта, повышение резистентности эмали и стимуляцию защитных свойств слюны, а также частые профессиональные осмотры у стоматолога.
REM sleep behavior disorder (RBD) is characterized by loss of REM sleep atonia leading to dream enactment behavior. Isolated RBD (iRBD) is a known prodrome of alpha-synuclein pathology including Parkinson disease (PD) and dementia with Lewy bodies (DLB). Depression is also a common prodromal symptom of PD/DLB, and death by suicide is greater among individuals with PD. Patients with RBD are at greater risk of comorbid depression, but the prevalence of suicidal ideation in this population is unknown. We aim to assess the frequency of suicidal ideation and whether it is associated with autonomic, motor, and cognitive dysfunction in the North American Prodromal Synucleinopathy 2 (NAPS2) consortium. NAPS2 is a longitudinal study that includes measurements of autonomic function (SCOPA-AUT, orthostatic blood pressure), motor function (UPDRS), cognition (MoCA), and suicidal ideation (PHQ-9). We used a linear mixed-effects model, adjusting for age, sex, impulsivity, current carbidopa/levodopa use, and PHQ-9 score, to compare those who endorsed recent suicidal ideation with those who did not. Of 489 total participants, 40 endorsed suicidal ideation at the first visit (22.5% female, mean age 61.5) and 449 denied suicidal ideation (19.4% female, mean age 64.6). Higher PHQ-9 scores were significantly associated with higher SCOPA-AUT scores (+0.566; SE = 0.045; p < 2 × 10-16), higher UPDRS II scores (+0.372; SE = 0.03; p < 2 × 10-16), higher UPDRS III scores (+0.265; SE = 0.057; p = 3.80 × 10-6), and lower MoCA scores (-0.078; SE = 0.021; p = 3 × 10-4). The PHQ-9 score was not significantly associated with orthostatic blood pressure drops. Recent suicidal ideation occurred in approximately 1 of 12 patients with iRBD, a prodromal syndrome of PD with an increased risk of death by suicide. Individuals with iRBD who scored higher on PHQ-9 had on average greater autonomic dysfunction on SCOPA-AUT, more motor findings, and greater cognitive deficits. This study establishes that suicidal ideation is common in iRBD and suggests an iRBD phenotype that identifies individuals at high risk of depression and suicide. Future studies should expand on these findings and target interventions to decrease mortality and self-harm.
GPR3, GPR6, and GPR12 form a subfamily of Class A orphan G protein-coupled receptors (oGPCRs), for which endogenous ligands have not been identified. Despite their high sequence similarity, each receptor exhibits unique expression profiles in human tissues. Their physiological roles and therapeutic potential are gradually being understood, indicating their critical involvement in various diseases, including central nervous system (CNS) disorders, metabolic diseases, and cancer. Notably, the GPR6 inverse agonist CVN424 is currently in Phase III clinical trials for the treatment of Parkinson's disease (PD). The recent determination of high-resolution structures of GPR3, GPR6, and GPR12 has significantly enhanced their attractiveness as emerging therapeutic targets for drug discovery. Herein, we summarize the current understanding of the structural and functional characteristics of GPR3, GPR6, and GPR12. We further highlight recent progress in relevant ligand discovery and discuss the key challenges and opportunities in developing potent and selective modulators targeting these orphan receptors.
In recent years, there has been growing concern over the wellbeing and mental health of medical students in the United States, driven by the academic, personal, and professional challenges inherent in medical school. Recent data indicates that medical students experience higher rates of psychological stress, anxiety, and depression compared to the general population, with the COVID-19 pandemic exacerbating these challenges. Medical student suicide, linked to burnout and depression, highlights the urgent need for effective wellbeing support. Despite the documented barriers to mental wellbeing, such as self-imposed pressures, imposter syndrome, stigma around help-seeking, and financial difficulties, medical student wellbeing programs remain understudied at the structural and design level. This is a multi‑phase qualitative study (sequential-exploratory) that combines a web-based environmental scan and content analysis with key informant interviews and focus groups, using methodological triangulation to develop a framework for evaluating wellbeing programs. First, we will conduct a web-based content analysis of publicly available resources across medical school websites. We will identify key characteristics of wellbeing programs, such as mental health resources, structural well-being components, and culturally integrated approaches. Then, we will conduct key informant interviews with medical school administrative staff to discuss wellbeing programs in detail and hold focus group interviews with medical students to gather their perspectives on how to improve their health and wellbeing. Based on the findings from these three components, we will develop a comprehensive and standardized framework for evaluating medical school wellbeing programs that can be used across institutions. Human Research Ethics Approval was obtained from the NYU Langone Health Institutional Review Board (IRB ID: i25-00965). The content analysis results and qualitative themes extracted from key informant and focus group interviews will be made available to all study participants. They will also be disseminated in a peer-reviewed journal.
Modeling genomic sequence data is crucial for identifying functional genomic elements, predicting mutation effects, and driving advancements in precision medicine and bioengineering. However, the inherent complexity of genomic information poses significant challenges for functional analysis and structural prediction of genomic sequences. Recent developments in large language models (LLMs) have introduced powerful new paradigms for modeling biological sequences. The genomic foundation model Evo, trained on vast multi-species DNA sequence data, has demonstrated remarkable capabilities in generative tasks across molecular to genomic scales. However, Evo cannot be directly applied to specific supervised functional genomics prediction tasks, such as core promoter detection. To address this limitation, we propose Evo-TSFT, a novel progressive two-stage fine-tuning strategy that adapts Evo for DNA functional genomics classification tasks. Evo-TSFT integrates LoRA-based fine-tuning and selective layer unfreezing with the pre-trained Evo model, achieving strong overall performance across 7 DNA functional genomics classification tasks spanning 24 datasets. Experimental results show that Evo-TSFT is an effective and competitive strategy for adapting Evo to downstream DNA functional genomics prediction tasks.
Female orgasmic disorder represents a significant, yet underrecognized aspect of women's sexual health that affects 10-28% of women and is characterized by persistent difficulty or absence of orgasm after adequate sexual stimulation. It remains poorly understood and infrequently addressed in clinical practice. The female orgasm involves complex neurobiological, vascular, hormonal, and psychosocial mechanisms, with the clitoris serving as the primary anatomical structure for orgasmic response. The orgasm gap, which refers to disparities in orgasm frequency between men and women, signifies biological and sociocultural factors that contribute to this condition. Recent research has identified interoceptive awareness, cognitive-affective factors, and the partner's gender expectations as important contributors to orgasmic function. Evaluation requires comprehensive biopsychosocial assessment that includes detailed sexual history, physical examination with attention to clitoral anatomy and pelvic floor function, and selective laboratory testing. Although no pharmacologic treatments approved by the U.S. Food and Drug Administration exist specifically for female orgasmic disorder, there are effective treatment options and management of these treatment options should be individualized. Future research directions include the standardization of diagnostic tools such as clitoral blood flow assessment and quantitative sensory testing, development of targeted pharmacologic interventions, and improvement of clinical education on female sexual anatomy and function. This review synthesizes current evidence on female orgasmic disorder to provide clinicians with practical strategies for diagnosis and management.
The alignment among mutational variance (M), standing genetic variance (G), and macroevolutionary divergence (R) in Drosophila wing shape poses a rate paradox under a simple constraint hypothesis: Evolution follows mutational lines of least resistance, yet proceeds orders of magnitude slower than the abundant genetic variation would permit. This is difficult to reconcile with a simple constraint view in which long-term evolution merely tracks the amount of available variation in each direction. Previous explanations invoke deleterious pleiotropy on unmeasured traits or correlational selection on trait combinations, but recent empirical work finds little evidence of fitness costs beyond flight performance. Here, by reanalyzing published data, I show that wing size shows the hallmark of the primary selection target: Among all wing traits, size exhibits the lowest ratio of standing genetic to mutational variance, indicating the strongest selective depletion. Based on this empirical observation, I develop a single-axis selection model in which natural selection targets only a single trait while all other traits evolve as correlated byproducts via within-module pleiotropy. This minimal model reproduces both the observed M-G-R alignment and slower-than-neutral divergence rates, explaining micro- and macroevolutionary patterns in fly wings without invoking complex adaptive landscapes.
Multi-modal image segmentation has recently attracted considerable attention due to its ability to integrate complementary information from diverse sensors, thereby enabling more accurate semantic predictions in complex or specialized scenarios. However, as data volume and model capacity continue to grow, many existing methods suffer substantial increases in parameters and computational costs, particularly with the widespread adoption of Vision Foundation Models (VFMs). To address these challenges, we introduce a Lightweight Multi-modal Fine-Tuning framework (LiteMFT) designed for efficient and generalizable adaptation of RGB-pretrained VFMs to multi-modal semantic segmentation. By incorporating only a small number of trainable parameters, LiteMFT enables effective extension of existing models to handle multi-modal image fusion tasks. The framework centers around two key components: the Modality Local Competition (MLC) module, which dynamically and efficiently fuses complementary features across modalities, and the Gated Low-Rank Adapter (GLR), which improves the backbone's adaptability to multi-modal data through content-aware low-rank transformation. Extensive experiments on both bi-modal and tri-modal segmentation tasks demonstrate that LiteMFT not only achieves competitive or superior performance but also exhibits strong scalability for additional modalities, underscoring its practicality and broad applicability in multi-modal semantic segmentation.
Conjunctival ultraviolet autofluorescence (CUVAF) is a marker for ocular sun damage. While its potential is recognised in research, factors influencing its development and temporal dynamics remain poorly documented. This systematic review evaluates whether the size of CUVAF reflects recent ultraviolet (UV) exposure, lifetime cumulative damage or both. Google Scholar, PubMed, Embase, Scopus and ProQuest Global databases were searched for papers using defined search terms. Ninety-one articles were assessed and 35 studies using CUVAF as an indicator of sun exposure were analysed. Extracted data included study location, CUVAF area (mean/median, mm²)/presence or pattern, participant's age, skin type, occupation, time spent outdoors, UV protection behaviour, the presence of UV eye disease and seasonal variations if assessed. Study quality was evaluated using the Newcastle-Ottawa-Scale. A locally weighted scatterplot smoothing (LOWESS) and polynomial curve were generated to visualise age-related trends. CUVAF area peaked in young adults and followed nonlinear, oscillating pattern with age. High ambient UV regions like Australia showed greater weighted average CUVAF (28.8 mm², range: 0.6-45.4 mm2) compared to moderate or lower UV regions such as India (6.8 mm², range: 4.3-11.1 mm2), the USA (7.5 mm², range: 5.5-9.2 mm2) and Europe (5.3 mm², range 0.4-6.4 mm2). Outdoor work and fair pigmentation were associated with larger CUVAF area. CUVAF size did not vary with season. Myopes had smaller average CUVAF areas than non-myopes (14.5 vs. 21.5 mm²). Eyes with UV-related eye disease exhibited larger average areas (43.4 vs. 20.7 mm2) compared to healthy eyes. There was no reported correlation between sunglass use and CUVAF. Artificial UV irradiation temporarily increased CUVAF area. Age-related oscillations in CUVAF, larger values in high-UV regions and transient increases following artificial UV exposure suggest that CUVAF may reflect both acute conjunctival responses and cumulative UV exposure. However, its ability to capture dynamic changes under natural environmental UV remains uncertain without longitudinal data.
Many reports in the literature have proposed the use of percentile curves for tracking ocular growth and monitoring myopic development. Recently, this practice has been criticised, particularly its inability to accurately track myopia onset and progression due to the inclusion of multiple refractive groups. This work assesses the validity of this criticism and proposes corrected curves tailored to specific refractive development trajectories. The longitudinal biometric data of 1999 Chinese schoolchildren (10,766 measurements) in the Anyang Childhood Eye Study were analysed. Children were categorised into emmetropic and myopic subgroups based on the progression of their cycloplegic spherical equivalent (SE) refractive error. Percentile curves were generated for the axial length (AL), axial growth (dAL), axial length/corneal radius (AL/CR) ratio and cycloplegic SE using the Lambda-Mu-Sigma (LMS) method, stratified by sex and refractive group. Distinct percentile curves for emmetropic and myopising eyes revealed significant differences compared with traditional population-based curves, confirming that whole-population curves underestimate myopia risk and overestimate treatment effects. Girls demonstrated greater myopic progression and axial elongation than boys. SE percentile curves, stratified by age of myopia onset, were presented to estimate progression trajectories. This study presents percentile curves for ocular biometry and refractive error to enhance the ability to detect early myopic changes and monitor myopia control interventions. Recommendations include using SE curves based on cycloplegic refraction as the primary reference, developing sex- and region-specific models and avoiding reliance on AL alone.
PC1 (polycystin-1), traditionally viewed through the lens of renal pathophysiology in autosomal dominant polycystic kidney disease, has emerged as a central regulator of cardiovascular mechanobiology. Recent structural elucidation of the PC1/PC2 (polycystin-2) complex provides a molecular framework emphasizing its mechanically sensitive ectodomain, regulated proteolytic cleavage, and functional coupling with PC2, framing PC1 as a versatile integrator of biomechanical cues, extracellular matrix interactions, and Ca2+ signaling across cardiovascular cell types. This review synthesizes evidence demonstrating that PC1 plays a direct and primary role in the cardiovascular system, independent of renal decline, regulating vascular homeostasis, endothelial shear stress responsiveness, smooth muscle phenotype, and myocardial mechanotransduction. We describe the molecular mechanisms whereby PC1 dysfunction perturbs nitric oxide signaling, cytoskeletal remodeling, excitation-contraction coupling, and hypertrophic transcriptional programs, and highlight tissue-specific roles in cardiac morphogenesis and adult myocardial integrity. By integrating structural biology with cardiovascular physiology, this review provides a unified framework for understanding PC1 as a master mechanosensor linking biomechanical forces to pathological remodeling. Critical knowledge gaps, emerging therapeutic opportunities, and the potential role of artificial intelligence in PC1-targeted drug discovery are also discussed.
Visible and infrared image fusion (VIF) has gained significant attention in recent years due to its wide application in tasks such as scene segmentation and object detection. VIF methods can be broadly classified into traditional VIF methods and application-oriented VIF methods. Traditional methods focus solely on improving the quality of fused images, while applicationoriented VIF methods additionally consider the performance of downstream tasks on fused images by introducing task-specific loss terms during training. However, compared to traditional methods, application-oriented VIF methods require datasets labeled for downstream tasks (e.g., semantic segmentation or object detection), making data acquisition labor-intensive and time-consuming. To address this issue, we propose a self-supervised training framework for segmentation-oriented VIF methods (SSVIF). Leveraging the consistency between feature-level fusion-based segmentation and pixel-level fusion-based segmentation, we introduce a novel self-supervised task, i.e., cross-segmentation consistency, that enables the fusion model to learn high-level semantic features without the supervision of segmentation labels. Additionally, we design a two-stage training strategy and a dynamic weight adjustment method for effective joint learning within our self-supervised framework. Extensive experiments on public datasets demonstrate the effectiveness of our proposed SSVIF. Remarkably, although trained only on unlabeled visible-infrared image pairs, our SSVIF outperforms traditional VIF methods and rivals supervised segmentation-oriented ones. Our code will be released upon acceptance.
Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with a steadily rising global incidence. The Cancer Genome Atlas (TCGA) study identified four molecular subtypes- POLE (ultramutated), MSI (hypermutated), copy number low, and copy number high-each with distinct prognostic and therapeutic implications. Integration of molecular classification into the WHO 2020 classification and the FIGO 2023 staging system has transformed clinical practice. Although major European ESGO/ESTRO/ESP guidelines now incorporate molecular subtyping into treatment algorithms, the Japanese JSGO 2023 guidelines have not yet formally integrated molecular classification into adjuvant treatment decisions. Notable differences exist between Japanese and Western approaches, particularly regarding the role of adjuvant radiation therapy versus chemotherapy. Recent landmark trials have provided compelling evidence for biomarker-driven treatment based on molecular classification. Furthermore, some immune checkpoint inhibitors (ICIs) and antibody-drug conjugates (ADCs) are emerging as promising therapeutic options. This review provides an updated overview of the molecular classification of EC, its impact on current guidelines with emphasis on radiation therapy, and the latest therapeutic advances including immunotherapy and ADCs.
Artificial intelligence (AI) has evolved over several decades and is increasingly recognized as a transformative tool for improving agricultural productivity, resilience, and access to information, particularly in smallholder farming systems such as those in East Africa. This systematic literature review synthesizes existing evidence on the applications, adoption dynamics, implications, and policy considerations of AI in East African agriculture over the period 1985-2025. The study follows PRISMA guidelines and draws on peer-reviewed articles, conference papers, and institutional reports retrieved from major academic databases, including Scopus, Web of Science, and Google Scholar. A thematic analysis approach was used to organize and interpret the findings. The review shows that early developments in AI-related agricultural technologies were limited and largely experimental, but advancements in digital technologies, mobile connectivity, remote sensing, and data analytics have significantly expanded AI applications in recent years. Key application areas identified include AI-powered advisory services, precision agriculture, crop and pest monitoring, financial and market intelligence, and climate-smart agriculture. These technologies support farmers by enabling real-time, data-driven decision-making, improving resource use efficiency, and enhancing access to agricultural information and markets. Despite these advancements, the adoption of AI among smallholder farmers in East Africa remains relatively low and uneven. The review identifies several factors influencing adoption, including education, digital literacy, access to extension services, infrastructure availability, income levels, and institutional support. Major barriers include limited rural infrastructure, high costs, inadequate digital skills, weak integration with extension systems, and data-related constraints. Although AI offers promising benefits in terms of productivity, information access, and inclusivity, concerns remain regarding digital inequality, affordability, data privacy, and potential exclusion of marginalized groups. From a policy perspective, the study underscores the importance of strengthening digital infrastructure, investing in capacity building, enhancing extension services, and promoting inclusive public-private partnerships to support the effective deployment of AI technologies. Overall, the review concludes that although AI has significant potential to transform East African agriculture, its impact depends on addressing systemic constraints and ensuring that technologies are accessible, affordable, and aligned with the needs of smallholder farmers. The study also identifies research gaps and suggests future directions for advancing AI integration in the region.
Anxiety and depression are common conditions among medical students. In recent years, numerous factors associated with these disorders have been investigated; however, evidence regarding the role of dietary habits remains limited. To evaluate the association between dietary habits and the presence of anxiety and depressive symptoms among medical students at a private university in Lima, Peru. An analytical cross-sectional study was conducted in 2025 among medical students aged 18 years and older. Dietary habits were assessed using the Healthy Eating Index, while anxiety and depression were measured using the GAD-7 and PHQ-9 scales, respectively. Poisson regression with robust variance was used to calculate prevalence ratios (PR). A total of 264 students were included. Only a minority had healthy dietary habits (1.1%), and the prevalences of anxiety (34.9%) and depression (45.1%) were high. For each additional point in the Healthy Eating Index, the prevalence of anxiety decreased by 3% (aPR: 0.97; 95% CI: 0.95 to 0.98), while the prevalence of depression decreased by 2% (aPR: 0.98; 95% CI: 0.96 to 0.99). Dietary habits were inversely associated with the prevalence of anxiety and depression among medical students. These findings highlight the importance of promoting healthy dietary patterns as part of comprehensive strategies for the prevention and promotion of mental health in this population.
Recent research has increasingly focused on childhood apraxia of speech (CAS) in non-English-speaking populations, with Cantonese emerging as a significant language of study. This tutorial seeks to consolidate current knowledge regarding CAS in Cantonese speakers and proposes an assessment battery accompanied by observational criteria to support both clinical practice and research applications. The proposed assessment battery includes five tasks: the Hong Kong Cantonese Articulation Test, a connected speech sample derived from a story retelling task, the Cantonese Oral and Speech Motor Assessment, the Imitation of Polysyllables task, and the Tone Sequencing Task (Version 3.2). Diagnostic criteria incorporate five key features observed across these tasks, supported by quantitative thresholds, reference values, and cutoff scores drawn from prior research. A modified version of the Profile of Childhood Apraxia of Speech and Childhood Dysarthria, tailored for Cantonese speakers, is also included to differentiate CAS from childhood dysarthria. This tutorial offers a comprehensive overview of CAS in Cantonese speakers and provides an evidence-based assessment battery alongside diagnostic criteria to support both clinical and research applications.