Little is known about how stigma is perceived within psychiatric genetics, a field increasingly central to public discussions about heredity, neurodiversity, and psychiatric risk. Understanding how stigma is perceived and experienced by psychiatric geneticists is important for guiding responsible communication and future stigma-reduction efforts. The International Society of Psychiatric Genetics (ISPG) Stigma Reduction Special Interest Group surveyed members' experiences and perceptions of mental health-related stigma (122 responses; response rate = 11.3%). Two-thirds believed that psychiatric genetics research reduces stigma, and there was a general consensus that researchers should consider stigma-related impacts when communicating research findings. Almost half of respondents perceived public stigma toward the field, and one-third reported avoiding certain research projects due to stigma-related concerns. Among respondents with a mental health diagnosis, 51% described selective disclosure at work shaped by fears of judgment or career impact. Respondents reported the persistence of stigmatizing attitudes within workplaces, despite reporting perceptions of meaningful cultural improvements in recent years. This first empirical assessment of stigma among psychiatric genetics professionals provides several key action points for the field: (1) developing stigma-sensitive research communication guidelines, (2) explicitly include individuals with lived experience, (3) reconsidering mentoring and hiring practices, and (4) evaluating organizational stigma-reducing efforts.
Rupert Riedl did not use the term evolvability, but his writings and teachings in the 1970s and 1980s were important precursors and influences on the research that became associated with the term when it appeared in the 1990s. Riedl served as a motivator for many researchers that later took up questions related to evolvability, and also had an indirect influence in that Riedl-inspired researchers created a research program to combine the systems thinking that Riedl championed with the theoretical population genetics of the modern synthesis. Notably, this was despite a near-total absence of population perspectives in Riedl's personal research. In this essay I review some of Riedl's perspectives on evolvability, speculate on his influence on later evolvability research, and review what we have later learned about the questions and perspectives initiated by him.
Auricular anomalies, including microtia and preauricular tags (PATs), are frequently overlooked findings on prenatal ultrasound, yet they may represent the earliest and sometimes the only clue to a wide spectrum of craniofacial and multisystem disorders. Distinguishing isolated anomalies from syndromic conditions remains a major diagnostic challenge in fetal medicine. In this illustrated, state of the art case-based review, we propose a practical, imaging-driven diagnostic framework for the prenatal evaluation of auricular anomalies. Drawing on a decade of clinical experience and a curated series of representative cases, we integrate high-resolution ultrasound features with a structured anatomical approach and current advances in molecular genetics. Syndromes associated with microtia and PATs are organized into 3 clinically relevant groups: branchial arch disorders, overlapping syndromic entities, and conditions in which auricular findings are non-specific. Our approach is based on systematic assessment of 3 key craniofacial regions: the auricle and pretragal area, the mandible, and the zygomatic-mandibular complex, combined with evaluation of craniofacial symmetry and targeted screening of extra craniofacial structures. This strategy enables pattern recognition that refines differential diagnosis and guides the appropriate use of genetic testing. Although molecular analyses contribute to etiologic characterization, their diagnostic yield remains limited in certain conditions, particularly craniofacial microsomia, reinforcing the pivotal role of detailed morphological assessment. This integrated imaging-genomic approach provides a clinically applicable framework for improving diagnostic orientation, prenatal counseling, and perinatal management, and highlights the central role of ultrasound in navigating the complexity of auricular anomalies.
Major depressive disorder (MDD) is a widespread, recurrent, and severely disabling psychiatric disorder that imposes a heavy global health burden. Although genetic factors contribute to disease risk, growing evidence highlights gene-environment interaction as the core driver of MDD pathogenesis. Epigenetic regulation acts as a precisely molecular interface that translates environmental stressors into stable changes in gene expression and long-term behavioral phenotypes. In this review, we provide a comprehensive and up-to-date overview of epigenetic dysregulation in MDD, covering five major regulatory layers: DNA methylation, histone post-translational modifications, non-coding RNA networks, RNA chemical modifications, and ATP-dependent chromatin remodeling. We emphasize the spatiotemporal specificity, brain regional selectivity, and cell-type. dependency of these epigenetic events, and their roles in disrupting neuroplasticity, hypothalamic-pituitary-adrenal (HPA) axis function, neurotransmitter homeostasis, and neuroinflammation. We further evaluate the translational value of peripheral epigenetic markers for early diagnosis, severity monitoring, and prediction of antidepressant treatment responses. We also discuss emerging epigenetic-targeted therapeutic strategies, including small-molecule inhibitors, RNA-based modulators, and brain-targeted delivery systems. Finally, we address key obstacles to clinical translation, such as tissue heterogeneity, unclear causality, limited reproducibility, and lack of standardized protocols. We propose future directions centered on single-cell multi-omics, longitudinal clinical validation, and sex-and ethnicity-stratified research. This review aims to establish an integrated framework for understanding MDD epigenetics and accelerating the development of precision diagnostic and therapeutic approaches.
Mycoplasma gallisepticum is a major poultry pathogen responsible for chronic respiratory disease and substantial global economic losses. Its ability to establish chronic infections reflects its effective immune evasion strategies, but the mechanisms underlying this remain poorly understood. Some pathogenic mammalian mycoplasmas use the Mycoplasma Immunoglobulin Binding-Protease (MIB-MIP) system to capture and cleave host immunoglobulins (Ig), but the functionality and host-specificity of this system in M. gallisepticum have not been examined. We aimed to functionally characterise all the MIB-MIP homologues in M. gallisepticum and examine their host specificity. Five putative MIB and five putative MIP genes of M. gallisepticum were cloned, expressed as recombinant GST-fusion proteins, and purified for functional analysis. Immunoglobulin-binding assays showed that all MIB proteins bound both avian and mammalian immunoglobulins, forming stable MIB-Ig complexes, with distinct binding capacities. In contrast, proteolytic assays revealed that only three of the five MIP proteins could cleave avian immunoglobulins, when complexed with any of the five MIBs, generating characteristic Ig heavy-chain fragments. Only one MIP protease showed detectable interaction with mammalian immunoglobulins, indicating strong host specificity of these MIPs and functional specialisation for avian immunoglobulin-cleavage. These results revealed that MIB and MIP proteins of M. gallisepticum are adapted to cleavage of avian immunoglobins, thereby interfering with antibody-mediated host immune responses. Bioinformatic analysis suggested that MIB-MIP homologues are widespread among avian mycoplasmas that share similar hosts, tissue tropisms and transmission patterns, and detected evidence of horizontal gene transfer and recombination, indicating that there have been MIB-MIP evolutionary adaptations among the avian mycoplasmas.
Genome stability is shaped by DNA sequence and chromatin context, but their relative contributions to double-strand break (DSB) sensitivity remain unclear. We show that the DNA language model, GROVER, can infer DSB location based on sequence. DSB hotspots tend to contain GC-rich sequences that belong to promoters, genes and short interspersed nuclear elements (SINEs). Additionally, we identified several specific short sequences (tokens) that are associated with modulating DSB sensitivity. Another model using chromatin and genome regulatory features outperforms the sequence-only model, highlighting complementary and cell-type specific information. Integrating sequence and genome biological features yields the best performance, demonstrating their synergy. Analyzing this model revealed that, dependent on the sample, genome stability information encoded in H3K36me3 and DNase-seq can be learned from the sequence, but not H3K27ac or H3K9me3. Embedding chromatin data directly into the GROVER architecture enabled cell-type specific modeling with performance matching the full chromatin feature model. Our results suggest that while chromatin and regulatory context provides important information, such as cell-type specificity, much of the information shaping DSB patterns is already encoded in the DNA sequence itself. Our integrative modeling approach not only reveals DSB patterns but also provides a generalizable strategy for tracing predictions in genomic data. Data, models, and a tutorial are available on Zenodo. doi:10.5281/zenodo.16375950. doi:10.5281/zenodo.21131569. Supplementary data are available at Bioinformatics online.
This study presents a comprehensive taxonomic adjustment of Zygophyllum simplex (L.) four constituent varieties in Egypt, integrating morphological, anatomical, palynological, molecular, and phytochemical analyses to evaluate their infraspecific differentiation. Samples were collected from the eastern part of Egypt and subjected to detailed morphological, anatomical, and palynological analyses with SCoT molecular genotyping and HPLC phytochemical profiling. Four distinct morphological groups (informally designated as "cylindrica", "retusa", "orbicularis", and "lanceolata") were distinguished by significant differences in growth habit, leaf and stem anatomy, floral morphology, and fruit architecture. SCoT molecular marker analysis revealed a genetic similarity range of 0.825-0.881, with phylogenetic clustering strongly compatible with the morpho-anatomical groupings. HPLC profiling further identified group-specific accumulation patterns of key phenolic and flavonoid compounds, such as chlorogenic acid and catechin. While palynological analysis showed limited diagnostic value, the collective data from multiple disciplines provide robust evidence for the recognition of these four distinct morphotypes. Morphological, anatomical, and fruit architectural traits, together with group-specific phytochemical profiles and SCoT molecular markers, provided robust evidence for the differentiation of four distinct morphotypes within the Z. simplex complex. However, given the moderate resolution of SCoT markers (37.77% polymorphism), formal varietal status is not proposed at this stage. We recommend that future studies employ higher-resolution molecular approaches, such as single nucleotide polymorphism (SNP) analysis and DNA barcoding, to validate whether these morphotypes represent genetically distinct and reproductively coherent varieties. Pending such validation, these taxa should be treated as morphotypes, thereby establishing a preliminary integrative framework for their identification.
Across thousands of known millipede species, genetic data is extremely limited. Many interesting biological processes in millipedes are ill-defined, such as the production of a defensive hydrogen cyanide secretion and UV fluorescence. We describe a transcriptomic dataset of the millipede Cherokia georgiana, including predicted coding regions and functional annotations. This de novo transcriptome will facilitate future research in understanding gene expression under a variety of conditions. Next-generation sequencing was conducted on polyA-enriched mature messenger RNAs using Illumina 2 x 150 paired-end sequencing. Total number of assembled contigs was 146,956. Transcripts were compared against the NCBI non-redundant (nr) protein database using DIAMOND BLASTx to identify sequence similarity to known proteins. Transcripts with sequence similarity to genes associated with cyanogenesis, including mandelonitrile oxidase and hydroxynitrile lyase, are present in this transcriptome. The data presented here enhances existing knowledge of millipede biology and provides a valuable reference for future research in myriapod evolution and ecology.
Sexual reproduction relies on meiotic recombination and the accurate segregation of homologous chromosomes to generate viable, genetically diverse gametes. While the molecular mechanisms of recombination and chromosome segregation are well studied, the upstream regulatory cues that drive expression of key meiotic genes remain poorly understood, especially in metazoans. Emerging evidence suggests that post-transcriptional regulation plays a central role in initiating and coordinating the meiotic program in both fruit flies and mammals. Here, we identify the RNA-binding protein Ataxin-2 (Atx2) as a crucial regulator of meiosis in Drosophila melanogaster. We show that Atx2 positively regulates meiotic factors, especially components of the synaptonemal complex (SC), a structure essential for pairing, recombination and segregation of homologous chromosomes. In Atx2-depleted germ cells, SC component mRNA and protein levels are markedly reduced, leading to defective SC assembly and maintenance. Consequently, homologous chromosomes fail to pair properly, which is essential for proper homolog segregation and the prevention of aneuploidy. These findings uncover Atx2 as a key regulator of the SC and highlight an underappreciated layer of gene regulation essential for accurate meiotic chromosome segregation and fertility.
Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, yet conventionally measured plant characteristics have limited value as NUE proxies. Here we show that artificial intelligence (AI) can uncover previously unrecognized phenotypic variation associated with NUE, revealing genetic variation that is largely missed by conventional phenotypes. We trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy. The learned features were defined as deep phenotypes. Compared with conventional phenotypes, deep phenotypes showed greater phenotypic variation and higher heritability, enabling the identification of 523 significant loci compared with 21 for conventional phenotypes. We next investigated candidate genes underlying these loci and used these findings to interpret the learned features. Lower CNN layers primarily reflected visual patterns overlapping with conventional phenotypes, whereas deeper layers encoded additional features associated with N-responsive genetic variation. To validate candidate genes identified by the AI framework, we functionally characterized Liguleless2 (LG2), a basic-leucine zipper (bZIP) transcription factor, and demonstrated that lg2 mutants exhibit enhanced root architecture and increased N uptake efficiency. Field trials of 200 hybrids across diverse N environments further supported the AI findings, with each beneficial allele increasing ear weight by an average of 18 g per plot under low-N conditions. These results show how integrating AI and biology can uncover biologically relevant variation underlying complex traits such as NUE and enhance the interpretability of AI models.
The 2021 WHO classification reclassified "IDH-mutant glioblastoma (GBM)" as "Astrocytoma, IDH-mutant, grade 4." This study aims to provide real-world validation of this reclassification using the specific ICD-O-3 code (9445/3) from the Surveillance, Epidemiology, and End Results (SEER) "Transition Era" (2018-2022) and develop a machine learning (ML)-based prognostic model. Patients diagnosed with IDH-mutant GBM (9445/3) and GBM NOS (9440/3) were identified. Propensity Score Matching (PSM) and Inverse Probability of Treatment Weighting (IPTW) were employed to minimize bias. A doubly robust Cox regression model was constructed to quantify survival benefits. Nine ML algorithms were integrated to develop a prognostic signature, which was interpreted using SHAP (Shapley Additive exPlanations) analysis. Of 13,443 patients, 312 were IDH-mutant. After matching, the IDH-mutant group exhibited significantly superior overall survival (OS) and cancer-specific survival (CSS) (p < 0.001). IDH mutation emerged as a potent independent favorable prognostic factor, associated with a 65.0% lower mortality risk (HR = 0.350, p < 0.001). Subgroup analysis confirmed robust benefits from chemotherapy. The Random Forest (RF) model achieved the best performance (Test AUC = 0.698). SHAP analysis identified IDH status, chemotherapy, and age as the top predictors. This study provides compelling evidence in support of the clinical rationale for the WHO 2021 reclassification. Despite a favorable prognosis, aggressive multimodal therapy was strongly associated with improved survival, though potential indication bias necessitates cautious interpretation and prospective validation. The developed ML model serves as a robust tool for personalized risk stratification.
Myeloid cells, including microglia and perivascular macrophages, are central to Alzheimer's disease (AD) neurobiology, yet their role remains incompletely understood. We profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 donors spanning the lifespan and showing varying degrees of AD neuropathology. We delineated six subclasses comprising 13 transcriptionally distinct subtypes and identified adaptive changes associated with aging and AD progression. Here we show that a disease-associated microglial subtype, characterized by elevated GPNMB expression and enriched for polygenic AD risk, expands with AD pathology and shows increased phagocytic activity. We identify MITF as an upstream regulator required to maintain this microglial state. Cell-cell interaction analyses prioritize APOE-SORL1 and APOE-TREM2 signaling pairs associated with disease progression. Using human and mouse models, we demonstrate that the neuroprotective effects of this microglial subtype depend on TREM2. These findings provide mechanistic insights into myeloid cell function in aging and AD, aiding therapeutic discovery.
Deubiquitylases modulate cellular processes by removing monoubiquitin or cleaving polyubiquitin chains. The ARISC-RAP80 complex partners with BRCA1-BARD1 to form the BRCA1-A supercomplex, which recognises K63-linked ubiquitin chains at DNA damage sites. ARISC-RAP80 contains multiple ubiquitin-binding sites, yet how these influence recognition and cleavage of K63-polyubiquitylated substrates remains unknown. We discover that a composite three-subunit interface allows ARISC-RAP80 to position K63-linked polyubiquitin chains in its catalytic site. Substrate recognition is further supported by RAP80 and non-catalytic ubiquitin-binding sites that impose a compact conformation on K63-polyubiquitylated substrates. This mechanism exploits the inherent flexibility of long ubiquitin chains and differs considerably from other deubiquitylases. Structure-guided mutageneses validate ubiquitin chain interactions, and cell-based assays demonstrate a functional role of the observed interfaces in chromatin recruitment. Our findings define mechanisms of polyubiquitin chain decoding and cleavage by ARISC-RAP80, linking ubiquitin reading and erasing functions to BRCA1-A mediated DNA damage responses.
Somatic cell nuclear transfer (SCNT, or cloning) in wild animal species is considered a potential tool in biodiversity conservation to restore genetic diversity in animal populations and overcome the detrimental effects of inbreeding, climate change, or diseases in rare and endangered animal populations. In January 2026, the Committee for Companion Animals, Non-Domestic and Endangered Species (CANDES) of the International Embryo Technology Society (IETS) organized a round table discussion on future directions in wildlife cloning. This paper reflects the content of discussions among experts in assisted reproductive technologies (ARTs) and specialists in wild animal conservation. After reviewing the advantages and limitations of SCNT, several research priorities were identified. Importantly, strengthening open communication and data exchange among experts was highly recommended to ensure that progress in the field is effective. It was also emphasized that regulatory and ethical frameworks are still required to better integrate this technology into animal conservation efforts. Overall, SCNT remains a highly specialized technology with a distinct, yet currently limited, role in biodiversity conservation. Its true value does not lie in immediate population recovery, but in its capacity to preserve genetic material, generate critical scientific insight, and inspire new collaborations across different disciplines.
Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy with a poor prognosis, necessitating the exploration of novel molecular mechanisms driving its progression. CircRNA, which have emerged as critical regulators in cancer biology, have been implicated in various tumorigenic processes. However, their specific roles in SKCM remain inadequately understood. Bioinformatics analyses of TCGA and GEO datasets identified circ17399 as a candidate oncogenic circRNA. Functional validation was performed using in vitro (A375, A2058 cells) and in vivo models. Techniques included qRT-PCR, dual-luciferase reporter assays, RNA FISH, Western blot, Transwell assays, and MeRIP-qPCR. Circ17399 knockdown/overexpression, miR-150-3p modulation, and ALKBH5/FOXM1 interaction studies were conducted to dissect its regulatory network. Circ17399 was significantly upregulated in SKCM tissues and correlated with poor prognosis. Mechanistically, circ17399 sponged miR-150-3p to derepress ITM2C, enhancing SKCM cell proliferation, migration, and invasion. Concurrently, circ17399 bound ALKBH5, reducing m6A methylation on FOXM1 mRNA, thereby stabilizing FOXM1 and promoting tumor progression. In vivo, circ17399 knockdown suppressed tumor growth and metastasis, while overexpression exacerbated malignancy. Circ17399 promotes melanoma progression by competitively binding miR-150-3p to upregulate ITM2C and recruiting ALKBH5 to reduce m6A methylation of FOXM1, enhancing its stability and oncogenic function. These findings unveil a dual-axis regulatory mechanism in SKCM pathogenesis and position circ17399 as a promising diagnostic biomarker and actionable therapeutic target for melanoma intervention.
Learning requires adaptive changes in neuronal circuits, but how neurons encode learning content in their activity patterns to construct memories remains poorly understood. Using longitudinal multi-site recordings in freely moving male mice performing a sensory discrimination task, we discover the emergence of burst-coding neurons (BCNs) across cortical, thalamic, and extrathalamic regions. BCNs encoded task rules through the presence or absence of bursts, with their proportion increasing as learning progressed. Decoding analyses reveal that BCNs act as the principal carriers of rule information within the thalamocortical system. BCN burst rates scaled with stimulus valence, collapsed when contingencies were degraded, and inverted after repeated rule reversals, demonstrating that bursts dynamically track associative context during learning. Indeed, pharmacological and focal genetic suppression of thalamocortical bursting disrupted learning and task performance, establishing neuronal bursts as context-sensitive drivers of associative learning. These findings identify a burst-based neural code for stimulus-outcome associations in the thalamocortical system and provide causal evidence linking cellular firing dynamics to reward contingency learning.
Rare and ultra-rare genetic diseases (GDs) involve complex, multidimensional burdens not fully captured by clinical endpoints, highlighting uncertainties in the availability, scope, and quality of Health-Related Quality-of-life patient-reported outcome measures (HRQoL-PROMs). To identify PROMs developed or validated to assess HRQoL in rare and ultra-rare GDs, map their content using the International Classification of Functioning, Disability and Health (ICF), and evaluate their measurement properties according to COSMIN methodology. Original studies reporting PROM development or measurement properties in rare or ultra-rare GDs were included. PubMed, Embase, PsycINFO, Web of Science, registries, outcome-measure repositories, reference lists, and citation tracking were searched from inception to March 26, 2026, without language restrictions. Study selection, data extraction, and risk-of-bias (RoB) assessment were performed independently. Methodological quality was evaluated using the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) RoB checklist. Measurement properties were rated as sufficient, insufficient, indeterminate, or inconsistent, and certainty of evidence was assessed using a modified GRADE approach. PROM content was mapped to ICF components and synthesized narratively; no meta-analysis was conducted due to heterogeneity. Fifty-nine studies were included, covering 45 PROMs across 29 rare or ultra-rare GDs. Instruments were predominantly disease-specific, although generic and adapted measures were also identified. PROMs mainly addressed body functions and activities/participation, while environmental factors, social participation, stigma, and access-to-care domains were consistently underrepresented. Internal consistency and construct validity were most frequently assessed, whereas responsiveness, measurement error, and cross-cultural validity were rarely evaluated. Only three PROMs (HAE-QoL, NF1-AdQoL, EPP-QoL) were classified as COSMIN category A and recommended; most were category B, and five were category C. The evidence is limited and methodologically weak, and many QoL-PROMs fail to capture key multidimensional aspects of QoL; more rigorous, patient-centered, disease-specific development and validation are needed. This review examined questionnaires used to assess health related quality of life in people with rare and ultra-rare genetic diseases. Although 45 PROMs were identified, only three had enough evidence to be recommended. Many questionnaires focused mainly on symptoms and physical functioning, while important aspects such as social participation, stigma, care access, and environmental support were often missing. Future PROMs should be developed with strong patient involvement and validated across age groups, languages, and disease contexts.
Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world. El cambio climático y la pérdida de hábitat están impulsando respuestas evolutivas rápidas en poblaciones de todo el mundo, lo que crea una necesidad urgente de realizar predicciones evolutivas en conservación y agricultura. Los enfoques de predicción pueden agruparse en tres escalas temporales: modelos basados en rasgos, que utilizan ecuaciones multivariadas de genética cuantitativa para proyectar respuestas fenotípicas correlacionadas hasta aproximadamente 20 generaciones; análisis basados en alelos, que modelan la dinámica de las frecuencias alélicas hasta 100 generaciones; y puntuaciones compuestas de adaptación, que integran numerosos efectos de pequeña magnitud para generar predicciones a plazos más largos. Sin embargo, estos enfoques han permanecido en gran medida desconectados. Aquí presentamos un marco bayesiano que integra estos tres enfoques complementarios de predicción evolutiva. Nuestro marco combina datos genómicos, fenotípicos y ambientales para generar predicciones probabilísticas que representan explícitamente la incertidumbre. Mostramos cómo estas predicciones pueden validarse mediante evolución experimental, experimentos de campo, ejemplares históricos y trasplantes recíprocos. Una vez validadas, pueden contribuir a los programas de conservación y agricultura al ayudar a predecir qué poblaciones corren riesgo de extinguirse en el futuro, optimizar los programas de mejoramiento genético para las condiciones climáticas futuras y planificar la gestión de los ecosistemas frente al cambio ambiental. Al favorecer una transición hacia enfoques más predictivos en biología evolutiva, este marco podría mejorar nuestra capacidad para gestionar la biodiversidad y salvaguardar la seguridad alimentaria en un mundo cambiante. 气候变化和生境丧失正在推动世界各地的种群产生快速的进化响应，因此，保护生物学和农业领域迫切需要开展进化预测。这类预测可按时间尺度分为三类:基于性状的模型利用多变量数量遗传学方程，预测最多约20代内相互关联的表型响应；基于等位基因的分析模拟最多100代内等位基因频率的动态变化；复合适应评分则汇总许多小效应，以对更长时间尺度作出预测。然而，这些方法迄今仍大多彼此割裂。本文提出一个贝叶斯框架，将这三类互补的进化预测方法整合起来。该框架结合基因组、表型和环境数据，产生明确呈现不确定性的概率预测。我们说明了如何通过实验进化、野外实验、历史标本和互惠移植实验来验证这些进化预测。经验证的预测可推动保护和农业项目，帮助预测哪些种群未来面临灭绝风险，优化面向未来气候条件的育种计划，并规划环境变化背景下的生态系统管理。通过推动进化生物学转向更具预测性的方法，该框架或可提高我们在不断变化的世界中管理生物多样性并维护粮食安全的能力。. As mudanças climáticas e a perda de habitat estão impulsionando respostas evolutivas rápidas em populações de todo o mundo, o que cria uma necessidade urgente de previsões evolutivas em conservação e agricultura. Essas previsões podem ser agrupadas em três escalas temporais: modelos baseados em caracteres, que utilizam equações multivariadas de genética quantitativa para projetar respostas fenotípicas correlacionadas por até cerca de 20 gerações; análises baseadas em alelos, que modelam a dinâmica das frequências alélicas por até 100 gerações; e escores compostos de adaptação, que integram numerosos efeitos de pequena magnitude para gerar previsões em horizontes temporais mais longos. No entanto, essas abordagens permaneceram, em grande parte, desconectadas. Apresentamos aqui um arcabouço bayesiano que integra essas três abordagens complementares de previsão evolutiva. Nosso arcabouço combina dados genômicos, fenotípicos e ambientais para gerar previsões probabilísticas que representam explicitamente a incerteza. Mostramos como essas previsões podem ser validadas por meio de evolução experimental, experimentos de campo, espécimes históricos e transplantes recíprocos. Uma vez validadas, elas podem contribuir para programas de conservação e programas agrícolas ao ajudar a prever quais populações correm risco de extinção no futuro, otimizar programas de melhoramento genético para condições climáticas futuras e planejar o manejo de ecossistemas diante das mudanças ambientais. Ao favorecer uma transição para abordagens mais preditivas na biologia evolutiva, esse arcabouço pode melhorar nossa capacidade de gerir a biodiversidade e salvaguardar a segurança alimentar em um mundo em transformação.
A clinical case of successful oral cavity sanitation using general anesthesia in a patient with a rare genetic disease, progressive fibrodysplasia ossificans progressiva (PFO), is presented. Special attention is paid to the technical features of performing oral cavity sanitation and general anesthesia in the context of progressive phenomena of false ankylosis of maxillofacial structures with a total lack of mouth opening function. An individualized algorithm of actions is selected at the stage of endotracheal anesthesia with nasotracheal intubation, oral cavity sanitation, and subsequent rehabilitation. The presented case demonstrates the existence of problematic patients, the lack of information about them in the medical community, and the importance of interdisciplinary collaboration between dentists, pediatricians, orthopedists, traumatologists, anesthesiologists, and other specialists to optimize treatment outcomes and prevent potential risks in the management of patients with FOP. The positive treatment outcome indicates a significant improvement in the patient's quality of life and the absence of complications. Представлен клинический случай успешной санации полости рта с использованием общей анестезии у пациентки с редким генетическим заболеванием — фибродисплазией оссифицирующей прогрессирующей (ФОП). Особое внимание уделено техническим особенностям выполнения санации и общего обезболивания на фоне прогрессирующих явлений ложного анкилоза челюстно-лицевых структур с тотальным отсутствием функции открывания рта. Индивидуально подобран алгоритм действий на этапе эндотрахеального наркоза с назотрахеальной интубацией, санацией полости рта и последующей реабилитацией. Представленный случай свидетельствует о существовании проблемных пациентов, дефиците информации о них во врачебной среде и демонстрирует целесообразность междисциплинарного сотрудничества стоматологов, педиатров, ортопедов, травматологов, анестезиологов и других узких специалистов, что необходимо для оптимизации результатов лечения, профилактики потенциальных рисков при лечении пациентов с ФОП. Полученный положительный результат лечения свидетельствует о значительном улучшении качества жизни пациентки и отсутствии осложнений.
To investigate whether immunosuppressant (IS) use contributes to the inverse association between rheumatoid arthritis (RA) and Alzheimer's disease (AD). We analyzed NHANES 2011-2014 data, including 217 RA patients aged ≥ 60 years on prescription medications. Cognitive function was assessed using the Digit Symbol Substitution Test (DSST), CERAD, Animal Fluency Test (AFT), and a global cognition z-score (Z-score). Associations between IS use and cognition were evaluated using multivariable linear regression. Additionally, two-sample Mendelian randomization (TSMR) and multivariable MR (MVMR) analyses were performed with GWAS datasets for RA, AD, and IS to examine potential causal effects. We observed that patients with RA using IS performed better in z.DDST (β: 0.335, p = 0.036) and Z-score (β: 0.214, p = 0.032) after adjusting for covariates, with sex-specific differences in cognitive domains. TSMR indicated that genetically predicted RA was associated with lower AD risk (OR: 0.936, p = 4.531E-04), and this effect was largely mediated by IS use. MVMR further validated the independent neuroprotective effect of IS on AD (OR = 0.884, p = 0.003) after adjusting for glucocorticoid and NSAID use, while these other medications showed no significant association with AD risk. These results suggest that the reduced risk of AD observed in RA patients may be partly related to IS use, highlighting a potential role of IS in improving cognitive function and modulating AD risk.