There is a difficulty reconciling the anti-humanistic approach of systems theory (as developed by Niklas Luhmann) with methodologies centering the individual. Society is viewed as a constellation of systems - politics, law, economy, media - and not the agglomeration of billions of individuals. This fore fronting of systems, and the unknowability of the cognitive processes of individuals, makes empirical operationalization of systems theory difficult. Can an interview method ever be accepted as a robust empirical operationalization of a theory which views society as a collection of intertwined and self-reinforcing systems, recursively communicating according to their own internalized contingencies? This paper attempts a theoretical justification for a methodology where individuals (researcher and interviewees) observe a system from which, theoretically, the human agent is excised and the focus is only on the construction of systemic possibilities. Reconciling theory and method is an important step in recognizing the possibilities of systems theory as a way of examining society.
Virtual screening (VS) is a powerful approach to exploring a vast chemical space, encompassing libraries of millions to billions of compounds. However, the low hit rates of VS require testing numerous candidates to validate true binders, followed by iterative optimization cycles, which makes experimental validation costly and time-consuming. Here, we report COMBINAUT, an automated parallel synthesis platform that generates diverse chemical scaffolds to accelerate hit validation and refinement. Using a faculty-wide collection of in-house building blocks, the system enables enumeration of over 22.9 million compounds, each designed for parallelized synthesis within 32 h using repurposed solid-phase peptide synthesis equipment. Using this platform, we performed large-scale VS targeting the allosteric pocket of the immuno-oncology target, C-C chemokine receptor 2 (CCR2). Our approach facilitated the rapid synthesis and testing of 100 VS hits spanning diverse molecular architectures. In radioligand binding assays, we successfully validated nine hits with distinct scaffolds, including completely novel CCR2 ligand chemotypes. Iterative hit-to-lead optimization using the automated workflow produced cell-active CCR2 antagonists. This work demonstrates the synergy of automated synthesis and VS, enabling the efficient exploration of chemical space and the rapid discovery of novel ligands.
Large language models (LLMs) are deep learning-based artificial intelligence models that have achieved remarkable success in natural language processing. Typically composed of neural networks with billions of parameters, they are trained on massive unlabeled datasets using self-supervised or semi-supervised learning. Beyond language, LLMs hold immense potential for addressing complex bioinformatics challenges. This review provides a comprehensive overview of transformer-based model applications in genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. We discuss critical components, including tokenization strategies for diverse biological data, transformer architectures, attention mechanisms, and pretraining approaches. We also survey currently available foundation models and their downstream applications across bioinformatics domains. Finally, we highlight major challenges that remain insufficiently addressed in prior reviews and outline future perspectives and design principles for next-generation biological language models, offering practical guidance for both users and developers.
Extreme temperature variability (ETV) is a key dimension of climate risk for billions of residents. However, how urbanization shapes global ETV divergence remains unclear. Here, we assemble a 1950-2020 panel of 10,522 cities and demonstrate that ETV trajectories diverge by development status as measured by the human development index (HDI). ETV intensifies in high-development cities, whereas it slowly weakens in low-development cities, resulting in a current gap of roughly 1.66 °C. Decomposing ETV into event frequency and intensity reveals that cumulative ETV is driven mainly by intensity. Using an interpretable machine-learning framework, we find that aerosols are the urban factor most strongly associated with ETV after controlling for climate and geography. Blue-green space is consistently associated with lower ETV, whereas urban morphology has a smaller and context-dependent effect. These findings link global ETV inequality to urban governance and support targeted management that focusses on limiting volatility under climate risk.
A significant proportion of global photosynthetic carbon fixation relies on the pyrenoid, a biomolecular condensate found in the chloroplast of most unicellular algae, where the CO2-fixing enzyme Rubisco is exposed to saturating concentrations of the gas. In this review, we highlight recent advances in our understanding of the molecular basis of diverse pyrenoids. Phase separation of phylogenetically distant Rubiscos is mediated by convergently evolved linker proteins, with an emerging theme of pyrenoid condensation being organized via Rubisco-binding motifs. To minimize CO2 leakage out of the pyrenoid, starch sheaths and protein shells have evolved to surround the pyrenoid in various algal lineages. Crucially, the pyrenoid is a biomolecular condensate with an increasingly well-defined function that has evolved multiple times over the past billions of years. The emerging similarities and differences of these various pyrenoids will inform our appreciation of phase separation in biology and empower engineering efforts aimed at enhancing photosynthetic CO2 assimilation.
Peptide quantitative structure-activity relationship (PepQSAR) has attracted much attention in the bio- and cheminformatics communities as a well-established computational peptidology strategy to statistically correlate the sequence/structure and activity/function of bioactive peptides (BAPs). In this study, a new concept termed DeepPepQSAR that integrates deep learning into traditional PepQSAR is proposed to quantitatively model, predict, and interpret the BAP universe in an all-in-one manner, that is, massive BAP samples with diverse activity types (i.e. antimicrobial, antiviral, hemolytic, anticancer, antigen, ACE-inhibitory, antioxidant, domain-binding, etc.) are merged into a single all-in-one DeepPepQSAR framework for artificial intelligence (AI)-driven big-data BAP discovery. A novel PepImage map is described to graphically represent both the sequence features of length-varying peptides and the activity types tested for these peptides, which is then fed into a dual-path, single-/multiple-channel convolutional neural network (CNN) for training, developing, and validating DeepPepQSAR regression models. We also practice the CNN-based DeepPepQSAR methodology on extrapolative navigation across a large-scale molecular diversity space covering billions of peptidic fragment candidates generated systematically from various food-derived proteins (FDPs) for AI-driven antimicrobial food peptide (AMFP) and antihypertensive food peptide (AHFP) discovery. Consequently, 14 AMFP peptides and 10 AHFP peptides are determined to have good antibacterial and ACE-inhibitory profiles, in which 4 and 2 peptides exhibit high potencies, respectively.
More than one billion people are exposed to flood risk globally, with this number projected to double by 2050. Global flood models underpin risk assessment and adaptation planning, yet typically assume that river bankfull capacity corresponds to a fixed two-year return period, neglecting spatial and temporal variability in channel characteristics. Here, we evaluate how inundated areas and population exposures respond when forced with empirically-derived bankfull capacities in the Mississippi basin using the Fathom Global Flood Model. We find that present-day bankfull flows generally correspond to return periods of less than one year, leading to systematic underestimation of flood extent (9-152%) and exposure (15-472%) across 5-, 20- and 100-year flood events, with the largest discrepancies for more frequent floods. We further show that historical changes in channel morphology can influence flood impacts at magnitudes comparable to projected climate change over multi-decadal timescales, depending on emission scenarios. Our work highlights a key structural limitation in current global flood modelling frameworks with implications for risk assessments.
Mycotoxigenic fungi, principally Aspergillus, Fusarium, and Penicillium spp. contaminate 60-80% of global feed commodities and cause multi-billion-dollar losses through animal health impairment, reduced productivity, and food chain carry-over. Despite decades of reliance on synthetic antifungals (azoles, polyenes, echinocandins), the emergence of resistant strains and growing regulatory pressure demand fundamentally different solutions. Existing reviews have catalogued plant-derived phytochemicals as promising alternatives yet have failed to provide an integrated, mechanistically grounded, and practically deployable framework that (i) explains why phytochemicals overcome synthetic resistance, (ii) ranks candidate compounds according to translational readiness, and (iii) guides rational multi-component formulation. This review addresses those gaps by introducing three integrative conceptual frameworks proposed for feed-system applications. The Multi-Barrier Phytogenic Defence (MBPD) Framework, which conceptualises antifungal action as five overlapping but mechanistically distinct barriers- specifically, membrane disruption, mycotoxin-gene silencing, oxidative equilibrium perturbation, enzymatic cascade interference, and signal-transduction blockade explains how simultaneous engagement of multiple barriers makes resistance evolution geometrically less probable than with single-target drugs. A Resistance-Indexed Phytochemical Selection (RIPS) model that scores candidate compounds against six criteria (multi-target coverage, ergosterol independence, efflux-pump evasion, biofilm penetration, generally recognized as safe (GRAS) status, and stability in feed matrices) to produce a prioritised shortlist for formulators. A Translational Readiness Score (TRS) applicable across the pipeline from in-vitro evidence to commercial feed inclusion, addressing the persistent gap between laboratory efficacy and practical livestock outcomes. These frameworks are validated against published minimum inhibitory concentration (MIC) data, gene-expression studies, and in-vivo trials, and are used to re-evaluate the evidence base for key compounds including thymol, eugenol, cinnamaldehyde, citral, curcumin, and resveratrol. These six compounds were selected because they represent the highest-scoring candidates under the RIPS model and collectively illustrate the full range of MBPD barrier engagement; the rationale for this selection is detailed in Sects. 5 and 6. The review concludes with a research agenda and a recommendation for an evidence-based regulatory pathway that, taken together, offer a roadmap for sustainable mycotoxin control in global feed systems.
The World Health Organization (WHO) reports that more than one billion people globally suffer from substantial disability. Involving and integrating individuals with disabilities into society is a sustainable global development goal. Autism spectrum disorder (ASD) is a complex condition that affects individuals from an early age and interferes with their daily functioning. It notably hinders social and communicative skills, blocking individuals' ability to interact effectively with other people. This study introduces an artificial intelligence (AI) learning-based model that identifies the most influence features on the ASD individuals' learning outcomes. Based on this AI learning model, this research develops a platform that promotes the ASD individuals' educational skills, helps their representatives and the community to customize the learning requirements according to their needs and abilities. This platform resolves the challenges associated with ASD people during their daily learning activities and interactions to deliver tailored solutions aligned with the unique environments of the ASD people. The experimental results show significant enhancements in ASD individuals' learning development, personal progress, and community enrollment. A comparative assessment against leading approaches further validated the model's effectiveness in improving ASD individuals' educational outcomes and fostering cultural inclusion.
Robust object detection in haze-degraded unmanned aerial vehicle imagery remains challenging because atmospheric corruption progressively weakens neural representations throughout the detection pipeline, while the aerial viewpoint further intensifies scale variation and small-object ambiguity. Existing solutions usually depend on image dehazing, external priors, or heavy multimodal systems, which often suffer from object-restoration mismatch, limited robustness, or excessive complexity. In this paper, we present the Multi-stage Purified Representation Network (MPRNet), a lightweight end-to-end detection framework that improves degraded-scene representation learning through architecture-level feature purification. The proposed network performs semantic purification in deep representations and multi-scale purification during cross-scale feature interaction, thereby reducing haze-related noise propagation and improving the recovery of weak small-object cues. Experimental results on three hazy-scene benchmarks show that MPRNet achieves competitive detection accuracy and consistent performance across both synthetic and real hazy scenes. On the main benchmark, MPRNet achieves 55.3% mean average precision, improving the strongest competing method by 3.3 percentage points while requiring only 10.3 million parameters and 30.9 billion floating-point operations. Edge-side evaluation on Jetson Xavier NX further reports 33.5-36.1 milliseconds of inference latency together with low memory occupancy, providing supplementary evidence that the proposed model offers a favorable accuracy-efficiency trade-off and is suitable for edge deployment.
The study aimed to examine the economic and epidemiological burden of human papillomavirus (HPV) on both men and women in the Czech Republic. It extended beyond the typically studied cervical cancer to encompass a rising incidence of non-cervical HPV-related cancers. The utilization of administrative healthcare claims data enabled the identification of HPV-related diseases using ICD-10 codes. For each identified disease, the proportion corresponding to disease cases directly attributable to HPV was analyzed in terms of the associated healthcare costs. Furthermore, the years of life lost (YLL) and indirect costs associated with premature mortality were calculated using gender-specific life expectancies and average salaries, employing the human capital approach. The findings indicate that there were over 100,000 incident cases of HPV-related diseases between 2018 and 2020, with the majority of these occurring in females (84.2%), and the average age of the patient was 40.6 y. The total medical costs incurred by HPV-related diseases exceeded 1 billion CZK (€41.1 million) over the study period (2018-2020), with an estimated 27,436 y lost due to premature mortality. The indirect costs, attributable exclusively to productivity losses from premature mortality, amounted to over 3.29 billion CZK (€127.7 million). These results highlight the substantial financial and health burdens HPV imposes on the Czech healthcare system, underscoring the necessity for informed policy-making and cost-effective HPV interventions, including enhanced vaccination and preventive programs.
Whether Archean arc-like volcanism reflects subduction remains debated. We present high-resolution geochemical data from a well-preserved 3.13-3.10 Ga arc-like volcanic succession in Australia's Pilbara Craton, a rare Archean analog of modern arc volcanism retaining fluid-mobile element concentrations consistent with primary magmatic values. The sequence records three primitive lava series typical of modern arcs: tholeiitic, calc-alkaline, and the oldest stratigraphically extensive genuine boninites. Geochemical modelling shows this melt diversity requires at least two mantle sources with distinct depletion histories. The mantle H2O required for fluid-assisted melting to produce these lavas substantially exceeds primitive mantle, approaching the H2O-saturated solidus of modern mantle wedges. We infer hydrous melting was triggered by dripduction, the short-lived inclined foundering of hydrated lithosphere without laterally continuous plate boundaries, in an off-plateau setting. Dripduction locally recycled surface water and generated arc-like magmas without self-sustained plate tectonics, possibly promoting mantle-ocean-atmosphere volatile exchange during the Archean.
Access to MRI is limited by lengthy exam times and inefficient utilization. Focused protocols can reduce exam times, but workflow variability and inefficient room turnaround contribute to conservative scheduling with long exam slots. To develop and evaluate a high-throughput clinical MRI suite architecture and workflow, using an AI-prescribed free-breathing chemical shift-encoded (CSE) MRI exam to quantify liver proton density fat fraction (PDFF) in under 5 min of total MRI room time. Prospective. 24 healthy volunteers in two cohorts: 12 research staff (7 women/5 men; age 26.8 ± 5.8 years) and 12 community volunteers (6 women/6 men; age 41.3 ± 13.5 years). 1.5 T; free-breathing 2D multi-echo gradient echo CSE-MRI. Each participant underwent three nonconsecutive CSE-MRI exams in a continuously queued workflow to characterize timing and PDFF repeatability. Workflow intervals were recorded from timestamped video review and image metadata. Staff cohort exams included two CSE-MRI acquisitions to assess within-exam repeatability, while community cohort exams included one to simulate clinical practice. Three radiologists (8/13/14 years of experience) independently evaluated AI-automated prescriptions for complete liver coverage and rated CSE-MRI image quality (five-point Likert scale). Student's t-tests; Gwet's AC2; repeatability coefficients (RCs) with bootstrap 95% confidence intervals; Bland-Altman analysis. p < 0.05 was significant. Diagnostic image quality was achieved in all 72 exams (median PDFF Likert score 5/5, inter-rater AC2 ≥ 0.86). Total MRI room times averaged 4:09 ± 0:14 min (staff) and 3:35 ± 0:34 min (community). Turnaround times averaged under 2 min, enabling throughput of 16.1 exams per hour in the community cohort. Automated prescription achieved complete liver coverage in all exams. PDFF RCs were 0.78% (staff within-exam), 0.99% (staff between-exam), and 1.21% (community between-exam) absolute PDFF. The proposed high-throughput MRI workflow achieved over 16 exams per hour with highly repeatable liver fat quantification, demonstrating a framework for improving MRI utilization and access. 1. 2. Liver disease affects over two billion people globally, and magnetic resonance imaging (MRI) can detect liver fat buildup that signals early disease. Liver MRI exams frequently take 30–60 min per patient, limiting access. This study tested a faster workflow combining three innovations: a redesigned MRI suite with movable tables that enable simultaneous patient preparation and imaging; software that automatically positions imaging volumes over the liver; and an imaging method that does not require breath‐holding, which slows conventional liver MRI. With this approach, over 16 patients could be imaged per hour while keeping fat measurements accurate and reliable.
Over their more than 1 billion years of evolutionary history, streptophytes have repeatedly transitioned between unicellular, simple multicellular and complex multicellular forms. Rather than a linear trajectory toward increasing complexity culminating in land plants, streptophyte evolution is now understood as highly dynamic. Phylogenomic analyses reveal an early origin of multicellularity followed by lineage-specific losses, reductions and secondary gains of complexity. Filamentous and parenchymatous body plans in Charophyceae and Coleochaetophyceae are derived rather than direct precursors of land plant multicellularity, while zygnematophyte algae - the sister group to embryophytes - exhibit dramatic reductions to unicellular or filamentous forms despite retaining genetic toolkits linked to multicellular traits. We argue that streptophyte multicellular evolution is best explained by regulatory changes at the cellular level rather than gene gain or loss alone. Variation in cell division patterns, polarity establishment, cell wall sensing and intercellular connectivity plays a central role in shaping body plan diversity. We further propose that rewiring protein-protein interaction networks, mediated by intrinsically disordered regions and short linear motifs, represents a key mechanism driving repeated evolutionary innovation. Integrating comparative genomics, ancestral state reconstruction and functional cell biology will clarify how conserved molecular toolkits generated streptophyte diversity and ultimately facilitated the emergence of land plants. Im Verlauf ihrer mehr als eine Milliarde Jahre umfassenden Evolutionsgeschichte haben Streptophyten wiederholt Übergänge zwischen einzelligen, einfach vielzelligen und komplex vielzelligen Organisationsformen vollzogen. Anstatt einer linearen Entwicklung hin zu zunehmender Komplexität, die schließlich in den Landpflanzen gipfelt, wird die Evolution der Streptophyten heute als hochdynamischer Prozess verstanden. Phylogenomische Analysen weisen auf einen frühen Ursprung der Vielzelligkeit hin, auf den stammlinienabhängige Verluste, Reduktionen und sekundäre Zugewinne an Komplexität folgten. Die filamentösen und parenchymatischen Baupläne der Charophyceae und Coleochaetophyceae sind demnach abgeleitete Formen und keine direkten Vorläufer der Vielzelligkeit der Landpflanzen. Demgegenüber zeigen die Zygnematophyceae – die Schwestergruppe der Embryophyten – eine ausgeprägte Reduktion zu einzelligen oder filamentösen Formen, obwohl sie weiterhin genetische Ausstattung besitzen, die mit vielzelligen Merkmalen in Zusammenhang steht. Wir vertreten die Auffassung, dass die Evolution der Vielzelligkeit bei den Streptophyten am besten durch Veränderungen der zellulären Regulation erklärt werden kann und nicht allein durch den Gewinn oder Verlust von Genen. Unterschiede in den Mustern der Zellteilung, der Etablierung von Zellpolarität, der Wahrnehmung der Zellwand sowie der interzellulären Konnektivität spielen eine zentrale Rolle bei der Ausbildung der Vielfalt von Körperbauplänen. Darüber hinaus schlagen wir vor, dass die Umgestaltung von Protein–Protein‐Interaktionsnetzwerken, vermittelt durch intrinsisch ungeordnete Regionen und kurze lineare Motive, einen entscheidenden Mechanismus für wiederholte evolutionäre Innovationen darstellt. Die Verknüpfung von vergleichender Genomik, Rekonstruktionen von anzestralen Zuständen und funktioneller Zellbiologie wird dazu beitragen zu klären, wie konservierte molekulare Werkzeugsätze die Diversität der Streptophyten hervorbrachten und letztlich die Entstehung der Landpflanzen ermöglichten.
Temperatures could routinely exceed the optimal levels for photosynthesis as global warming intensifies, imposing thermal stress on the productivity of vegetation. We utilized satellite-derived canopy temperature and gross primary productivity data from 2003 to 2024 to identify the ecosystem-level optimal canopy temperature ([Formula: see text]) for global photosynthesis and the extent of any thermal acclimation, which may offset warming impacts. Our findings indicate that across the globe, heat-induced restrictions on global photosynthesis are worsening, and areas subjected to thermal limitations have expanded by 1.7 billion hectares (57% increase) over the last 22 years. The number of days per year with high thermal suppression of photosynthesis during that period has increased sharply, averaging 28 days globally, and is especially high in tropical forests (117 days) and key agricultural regions (39 days). We demonstrate that vegetation acclimation to higher canopy temperature is partially mitigating emerging heat stress, but it is insufficient to keep up with the rate of global warming, with more than 90% of vegetated areas across the globe exhibiting only partial acclimation. A key feature of our analysis is the use of canopy-level temperatures, which more accurately represent the actual temperatures that vegetation physiologically responds to, rather than air temperature used in previous research. This difference accounts for our identified more rapidly intensifying vegetation response to warming than that estimated by other analyses. Overall, our canopy-level analysis reveals an escalating threat to global vegetation productivity and highlights the need for climate models to have refined land components, which often rely on air-temperature forcing and simplified acclimation schemes. Required are targeted ecosystem management strategies for adaptation to further global warming.
Obesity represents a critical public health challenge. As of 2021, over two billion adults were overweight or living with obesity,1 conditions that increase the risk of multiple chronic diseases and premature mortality. Although effective weight-loss strategies exist, post-intervention weight regain remains the central obstacle to long-term management.
Escalating global temperatures threaten economic stability by worsening occupational heat stress and reducing workforce productivity. Despite advancements in macroeconomic modeling, current risk assessments rely on coarse annual aggregations and ignore internal labor mobility, thereby masking highly unequal sub-national vulnerabilities and underestimating how labor mobility buffers cascading supply-chain losses. Here we present a high-resolution, agent-based dynamic supply chain network model that integrates empirical daily mobility data across 313 Chinese cities to quantify the spatiotemporal cascading economic impacts of occupational heat exposure. We show that annual heat stress costs China 2933.5 billion CNY (2.6% of GDP), with systemic propagation through supply chains driving 59% of these losses. Crucially, labor mobility redistributes risk: net labor inflows into industrialized, high-heat southeastern regions provide a factor-compensation effect that buffers cascading losses by offsetting direct local productivity shocks, saving a net 7.2 billion CNY directly and 24.6 billion CNY indirectly nationwide. Under a 2030 warming scenario (SSP3-7.0), total losses expand 1.6-fold to 4672.9 billion CNY, though integrated multi-level adaptations-combining industrial restructuring with work-hour shifting-can mitigate these future losses by 30%. These findings reveal that demographic mobility dictates the economic geometry of climate vulnerability, highlighting that resilient climate adaptation requires synchronized network-level interventions rather than isolated local policies.
Hypertension, a major contributor to global cardiovascular disease related deaths, has seen a rise in low- and middle-income countries, especially in South Asia (SA) where ongoing demographic and lifestyle transitions are constantly elevating the cardiometabolic risk. We aimed to generate policy-relevant region-specific estimates of hypertension prevalence and temporal trends in SA in the past 25 years. A comprehensive systematic search of online databases (PubMed, Web of Science, Scopus), WHO STEPS surveys, and demographic and health surveys were conducted. Of 11 773 journal articles and 50 reports identified, 33 articles and 25 reports met inclusion criteria after screening by two independent reviewers. The pooled prevalence of hypertension in SA has increased from 24.6% (95%CI 18.5-30.6) in 2000-04 to 26.0% (95%CI 20.3-31.7) in 2020-25. According to the latest data, Pakistan reports the highest (46.2%, 95%CI 45.2-47.2), while Bangladesh reports the lowest (20.4%, 95%CI 19.8-21.1) prevalence of the region. Hypertension prevalence has risen in most countries in SA in the past two decades, with Sri Lanka witnessing the steepest rise. Although hypertension has historically been more prevalent in urban populations, rural prevalence has seen a sharp rise over the years, likely linked to rapid urbanization. Nearly 1.3 billion adults in SA (37.5%; 95%CI 30.4-44.5) are pre-hypertensive. The region demonstrated a high Hypertension Epidemicity Index of 60.1%, indicating a substantial epidemic potential in the future, highlighting the need for urgent translation of existing policies into effective implementation to mitigate growing cardiovascular burden.
Anxiety disorders, depressive disorders, migraine, and rheumatoid arthritis are common chronic conditions that contribute substantially to disability, recurrent care needs, and productivity losses, yet remain comparatively under-prioritised in health policy. In Mexico, these conditions disproportionately affect women, but their economic burden has not been comprehensively quantified from a gender perspective. To estimate the direct and indirect economic burden of anxiety disorders, depressive disorders, migraine, and rheumatoid arthritis in Mexico among adults aged 20 years and older between 2005 and 2021, from a societal perspective and by gender. We conducted a cost-of-illness analysis from a societal perspective. Direct costs were estimated by combining condition-specific prevalence from the Global Burden of Disease Study 2021 (GBD 2021) with normative per-case treatment costs derived from national clinical guidelines and official cost sources. Indirect costs were valued using three complementary approaches: the Human Capital Approach (HCA), based on predicted annual labour income from nationally representative employment surveys; a GDP-per-capita benchmark (1 DALY = 1 GDP per capita); and a willingness-to-pay approach using the value of a statistical life year (VSLY) transferred to Mexico following OECD methods. All costs were expressed in 2021 international dollars (Int$). Between 2005 and 2021, the four disorders accounted for 28.8 million DALYs lost. Migraine was the most prevalent condition, but depressive disorders generated the highest direct costs (Int$310.5 billion) and the largest share of indirect costs (41.1%). Indirect costs totalled Int$106.8 billion under the HCA, Int$582.2 billion under the GDP-per-capita valuation, and Int$2.9 trillion under the willingness-to-pay approach. Under the GDP-per-capita benchmark, the combined economic burden of the four disorders reached approximately Int$1.2 trillion over the study period. Women consistently bore a greater burden than men across all four conditions and under all valuation methods; total indirect costs borne by women were 2.0 times higher for anxiety disorders, 2.1 times higher for depressive disorders, 2.2 times higher for migraine, and 3.8 times higher for rheumatoid arthritis. Anxiety disorders, depressive disorders, migraine, and rheumatoid arthritis impose a substantial and unequally distributed economic burden in Mexico. The persistent excess burden among women indicates that these high-disability chronic disorders should be understood not only as a public health problem, but also as a health equity concern. More gender-responsive priority setting, stronger continuity of care, and better financial protection may help reduce both disability and its downstream economic consequences in Mexico and other LMICs with segmented health systems.
Dysphagia affects over 15 million US adults. The initial workup often involves performing an esophagogastroduodenoscopy (EGD), and in 32% of cases, the EGD is normal, prompting further evaluation. This study aimed to identify the most cost-effective approach for diagnosing and managing nonobstructive dysphagia (NOD). We compared the cost of multiple scenarios in the initial workup of NOD: (1) esophageal high-resolution manometry (HRM); (2) esophageal impedance planimetry (EndoFLIP); or (3) empiric dilation followed by HRM or EndoFLIP if dilation fails. Distributions of HRM and EndoFLIP diagnoses in NOD were determined from prior published large cohorts, which were then cross-referenced to determine the costs of different clinical scenarios. Approaches using EndoFLIP compared with HRM with or without empiric dilation had notably higher average per-patient costs of ∼$300 to $430. HRM-first strategies remained more cost-effective in all scenarios unless the HRM failure rates exceeded 48.8%. Our study found potential savings of $1 to $2 billion when performing HRM instead of EndoFLIP in the initial workup of NOD. The difference is attributed to the relatively higher cost of EndoFLIP and the more frequent need for follow-up testing, which often includes HRM. However, the cost of EndoFLIP, particularly when normal during the initial EGD, may be justified as patient comfort and tolerability are critical unmeasured components of patient care.