Nevin Manimala

Proteomics Clin Appl. 2026 Mar;20(2):e70042. doi: 10.1002/prca.70042.

ABSTRACT

PURPOSE: Coronavirus disease 2019 (COVID-19) has highlighted significant neurological complications in severe cases. Cerebrospinal fluid (CSF) proteomics could reveal biomarkers related to clinical outcome among critically ill patients.

EXPERIMENTAL DESIGN: We performed high-resolution proteomic analyses of CSF samples from 29 intensive care unit (ICU) patients with severe COVID-19 and 19 controls. Differentially expressed proteins and associated pathways were identified through bioinformatic and statistical analyses.

RESULTS: Proteomic analysis identified 488 significantly altered proteins between COVID-19 patients and controls. Proteins linked to coagulation, inflammation, and blood-brain barrier dysfunction (e.g., SERPINC1, KNG1, PLG) were elevated in patients who survived ICU admission. Conversely, proteins associated with metabolic disruption, cellular stress, and neuroinflammation (e.g., FABP3, PDIA4) were upregulated in non-survivors. Pathway enrichment analyses confirmed involvement of immune activation, inflammatory responses, and coagulation cascades.

CONCLUSIONS AND CLINICAL RELEVANCE: CSF proteomics in severe COVID-19 patients reveals potential biomarkers predictive of patient outcomes. These findings support the involvement of systemic inflammation and blood-brain barrier disruption in COVID-19 pathophysiology, suggesting novel targets for personalized intervention strategies.

PMID:41830303 | DOI:10.1002/prca.70042

Huan Jing Ke Xue. 2026 Mar 8;47(3):1995-2006. doi: 10.13227/j.hjkx.202501083.

ABSTRACT

Heavy metals surrounding waste incineration plants undergo cross-media migration and enrich in the soil-water system, resulting in a systematic deterioration of the physical and chemical properties of the soil and posing potential hazards to the ecological environment and human health. Taking the heavy metals in the soil, sediment, surface water, and groundwater around a waste incineration plant in Jiangxi Province as the research objects, descriptive statistical analysis was conducted on the contents (or concentrations) of heavy metals in multi-media. The spatial distribution characteristics of heavy metals within the soil-water system were analyzed, and the sources of heavy metals in the soil were deciphered by comprehensively using correlation analysis and the Absolute Principal Component Analysis-Multiple Linear Regression (APCS-MLR) model. The results showed that heavy metals around the waste incineration plant were mainly enriched in the soil and sediments. The content of Cd in the soil exceeded the risk screening value, and the contents of Cd and Zn exceeded the soil background values in Jiangxi Province. The average values of As, Cr, Ni, Pb, and Zn in the sediments exceeded the sediment background values in Jiangxi Province. The concentrations of heavy metals As and Pb in the groundwater exceeded the groundwater standard (Class Ⅲ). The variation coefficients of As and Cd in the soil were 53.97% and 39.84%, respectively, which belonged to a strong variation degree and exhibited obvious characteristics of point source pollution. The average content of Zn in the soil samples was higher than that in the sediment samples, while the contents of As, Cd, Cr, Cu, Ni, and Pb in the sediments were higher than those in the soil. The results of the correlation analysis between the distance from the riverbank and the cumulative concentration of heavy metals indicated that the concentration of heavy metals in the groundwater was affected by the recharge of surface water. The contents of heavy metals As, Cd, Ni, and Pb in the soil were higher in the southeast wind direction, which was obviously affected by the perennial dominant wind direction of the waste incineration plant, while Cr, Cu, and Zn were not significantly affected by the wind direction. The contents (concentrations) of heavy metals in the sediments and surface water generally showed a trend of first increasing and then decreasing along the flow direction of the surface water. The contents (or concentrations) of the other six heavy metals except Cr were higher near the sewage outlet and in the lower reaches of the river, among which the influence of As was the most obvious, which may have been related to the industrial activities of the waste incineration plant. The heavy metals in the soil mainly came from industrial sources of waste incineration, agricultural sources of pesticides and fertilizers, and natural sources of parent materials, with corresponding contribution rates of 31.14%, 28.14%, and 40.72%, respectively. The industrial sources of waste incineration had different degrees of influence on the seven heavy metals. The influence on As and Cd was the most significant; the influence on Ni and Pb was general; and the influence on Cu, Cr, and Zn was relatively weak. Among them, As and Cd mainly came from industrial sources, Ni and Pb mainly came from industrial and natural sources, while Cu, Cr, and Zn mainly came from agricultural and natural sources. It can provide data support and scientific basis for comprehensively investigating the heavy metal pollution status of multi-media and developing strategies for the prevention and control of heavy metal pollution around waste incineration plants.

PMID:41830275 | DOI:10.13227/j.hjkx.202501083

Huan Jing Ke Xue. 2026 Mar 8;47(3):1833-1844. doi: 10.13227/j.hjkx.202502182.

ABSTRACT

Henan Province plays a crucial strategic role in maintaining national food security. Exploring the collaborative evolution of water resources and cultivated land resources, as well as their driving factors, is of significant importance for achieving the goal of building up strength in agriculture. Using Henan Province as the study area, this study constructed an evaluation index system for the carrying capacity of water resources and cultivated land resources based on statistical data from 2005 to 2023. The entropy method was employed to determine the weights of the evaluation indices and quantitatively assess the carrying capacity levels. The Haken model was used to analyze the synergistic effect between the two resources, while a modified gravity model and social network analysis were applied to reveal the characteristics of the synergistic network. Additionally, the GeoDetector was employed to explore the driving factors of the collaborative relationship. The results indicate that: ① From 2005 to 2023, the water resource carrying capacity index of Henan Province increased by 0.123, rising from a low capacity level to a higher level, while the cultivated land resource carrying capacity index increased by 0.132, rising from low to high capacity, with Pingdingshan City still maintaining a general capacity level. ② From 2005 to 2023, the synergistic degree between water resource carrying capacity and cultivated land resource carrying capacity increased from 0.424 to 0.557, rising from low-level synergy to high-level synergy. Except for Pingdingshan City, which was at a medium-level synergy, all regions in the province achieved high-level or above synergy. ③ The synergistic effect between water resource carrying capacity and cultivated land resource carrying capacity in Henan Province had formed a complex, multi-threaded spatial network structure. During the study period, the stability and connectivity of the spatial network improved, with the intermediary roles of cities weakening, resource control becoming more decentralized, and the network becoming more balanced. The connections and interactions between regions became more significant. ④ Cultivated land resource carrying capacity, as a sequence parameter, determined the current level of water resource carrying capacity and dominated the path and direction of their synergy. Per capita water resources, residents’ consumption level, agricultural electricity intensity, and per capita net income of rural residents were the core driving forces of the synergy between water and cultivated land resource carrying capacity, which were simultaneously influenced by multiple factors and interactions. The findings provide decision-making references for the collaborative evolution and dynamic adaptation of water and cultivated land resources in Henan Province.

PMID:41830261 | DOI:10.13227/j.hjkx.202502182

Huan Jing Ke Xue. 2026 Mar 8;47(3):1657-1664. doi: 10.13227/j.hjkx.202503326.

ABSTRACT

Alzheimer’s disease (AD) is a common neurodegenerative disease. The usage of its therapeutic drugs is increasing with the intensification of population aging. These drugs cannot be completely metabolized in the human body and enter wastewater systems in the form of the originals or metabolites. In-depth investigation of the occurrence characteristics of these drugs in wastewater is of great significance for effective control and management. The concentrations and associated factors of four main AD drugs (donepezil, rivastigmine, galantamine, and memantine) and their metabolites in the influent of 210 wastewater treatment plants across 31 Chinese provinces were analyzed. The results indicated that detection rates of the above drugs in wastewater were high, with concentrations ranging from 7.47-21.60 ng·L^-1. Among them, the concentration of donepezil was significantly higher in East China and the Northwest and Northeast regions than that in Central China; the concentrations of rivastigmine and galantamine in Southwest China were significantly higher than those in East China; and the concentrations of memantine in Northwest and North China were significantly higher than those in East China. The above results indicated that the occurrence of these drugs showed a significant regional difference. Further, the AD drugs and metabolites with detection rates above 90% and excretion rates exceeding 20% (donepezil, rivastigmine metabolite, galantamine metabolite, and memantine) were chosen as biomarkers to evaluate AD prevalence. The prevalence of AD in different regions was estimated by wastewater-based epidemiology method, and the results were highly consistent with official statistical data. These results showed that the concentrations of AD drugs in wastewater influent were closely related to the AD prevalence. Additionally, correlation analysis also found that socioeconomic factors (such as stress, aging population, level of economic development, and health care services) had a significant positive correlation with the AD prevalence, indicating that socioeconomic factors may influence the occurrence of AD drugs in wastewater by affecting the AD prevalence. These results provide a scientific basis for further understanding of the characteristics of AD drugs in wastewater treatment plants and the development of corresponding control measures.

PMID:41830246 | DOI:10.13227/j.hjkx.202503326

Huan Jing Ke Xue. 2026 Mar 8;47(3):1576-1585. doi: 10.13227/j.hjkx.202502011.

ABSTRACT

As pivotal pillars of China’s national energy security strategy, resource-based cities can leverage the data-sharing capabilities, real-time transmission features, and low marginal cost advantages inherent in digital-real integration to forge new pathways for overcoming the “resource curse” and “transition inertia” dilemmas. Based on panel data of China’s resource-based cities from 2011 to 2022, this study constructs a multidimensional econometric framework incorporating two-way fixed effects models, mediation and moderation effect models, and threshold regression analysis to systematically deconstruct the operational impacts and mechanistic drivers of digital-real integration in propelling green and low-carbon urban transitions. The results showed that: ① Digital-real integration demonstrated statistically significant positive effects on green low-carbon transition in resource-based cities, with robustness confirmed through multiple empirical tests. ② Mechanism tests revealed that digital-real integration significantly facilitated green and low-carbon transition in resource-based cities through innovation-driven effects and environmental regulation effects, whereas industrial optimization effects demonstrated no significant driving force. Concurrently, government intervention exhibited a negative moderating effect on this transition process driven by digital-real integration. ③ Heterogeneity tests revealed significant differential effects across three dimensions: typology of resource-based cities, economic development levels, and digital technology innovation capacities. ④ Threshold effect tests confirmed that higher digital economy policy supply levels intensified the green and low-carbon transition effects of digital-real integration.

PMID:41830239 | DOI:10.13227/j.hjkx.202502011

Huan Jing Ke Xue. 2026 Mar 8;47(3):1433-1448. doi: 10.13227/j.hjkx.202501278.

ABSTRACT

As China’s 2030 carbon peak target approaches, carbon emission reduction efforts have become increasingly urgent and crucial. Resource-based cities, characterized by their reliance on high-carbon industries, play a pivotal role in the nation’s carbon peak progress. This study focuses on 108 resource-based cities from 2000 to 2022, employing the RF-RFECV algorithm to identify key factors influencing carbon emissions in these cities and utilizing the SHAP algorithm to evaluate feature importance. Furthermore, a BO-CNN-BiLSTM-attention prediction model is constructed, combined with scenario analysis to simulate the dynamic pathways of carbon peaking in resource-based cities under low-carbon, baseline, and high-speed scenarios. The results indicate the following: ① From the perspective of influencing factors, energy consumption was the most critical driver of carbon emissions in resource-based cities, reflecting their dependence on energy-intensive industries. The GDP of the primary industry and population density had a negative impact on carbon emissions, while the other six variables exerted a positive influence. ② In terms of city types, the impact of energy consumption on regenerative cities gradually declined, the development of secondary industries varied in its influence across different city types, and urbanization levels had the most significant impact on growing resource-based cities. ③ According to the peak scenario simulations, under the baseline and high-speed scenarios, carbon emissions in resource-based cities will continue to rise before 2040, whereas under the low-carbon scenario, emissions are projected to peak by 2034. Based on these findings, resource-based cities should achieve low-carbon transformation and sustainable development by improving energy efficiency, developing renewable energy, advancing green finance, adjusting industrial structures, and establishing carbon emission trading markets.

PMID:41830227 | DOI:10.13227/j.hjkx.202501278

Int Endod J. 2026 Mar 13. doi: 10.1111/iej.70142. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is transforming medical imaging, yet its economic impact in dentistry remains largely unexplored.

AIM: This study evaluated the cost-effectiveness of AI-assisted detection of apical periodontitis on panoramic radiographs, including downstream clinical decision-making.

MATERIAL AND METHODS: Using data from a randomised study on AI-assisted detection of apical lesions, a decision-analytic model was established to analyse costs and effectiveness from a German mixed-payer perspective.

RESULTS: AI support reduced average costs per case and increased treatment effectiveness, outperforming unaided examiner performance. These gains were primarily driven by improved specificity, reducing false-positive detection. However, effects varied by examiner experience; junior clinicians achieved the greatest cost savings and effectiveness gains, whereas senior examiners showed reduced sensitivity and slightly lower effectiveness at similar costs.

CONCLUSION: AI-assisted diagnostics offer significant potential to improve cost-effectiveness by reducing overtreatment, with benefits being most pronounced among less experienced practitioners. Adapting AI systems to individual examiners or experience levels might further enhance clinical and economic impact.

PMID:41826269 | DOI:10.1111/iej.70142

Am J Hum Biol. 2026 Mar;38(3):e70242. doi: 10.1002/ajhb.70242.

ABSTRACT

OBJECTIVES: Changes in the timing of puberty may reflect shifts in population health, including the rising prevalence of overweight and obesity. Therefore, this study aimed to analyze the longitudinal association between body mass index (BMI) in childhood and pubertal development 5 years later among Brazilian students.

METHODS: This longitudinal study included 494 students aged 7-10 years. Data were collected in 2007 and 2012. BMI z-scores were calculated. Pubertal development was self-assessed using Tanner stages, and girls reported age at menarche. Structural equation modeling was used to assess the effects of the 2007 BMI on sexual maturation (SM) in 2012, adjusting for socioeconomic status (SES), birth weight, breastfeeding, physical activity, and dietary patterns (DP).

RESULTS: No statistically significant association between BMI and SM was observed in either sex. Among boys, higher adherence to DP IV (milk, coffee with milk, cheese, breads/biscuits) (β = -0.21) and higher SES (β = -0.21) were associated with normal/late SM. Among girls, a higher 2007 BMI z-score (β = -0.27) had a direct negative effect on age at menarche, while DP II (ultra-processed foods) showed an indirect negative effect on age at menarche, mediated by the 2007 BMI z-score (β = -0.05).

CONCLUSIONS: This study found that in girls, higher childhood BMI was associated with an earlier age at menarche. In boys, DP IV and SES were associated with normal/late SM. These findings highlight the significance of monitoring puberty timing at the population level and the need for sex-sensitive, prospective research to elucidate the determinants of earlier puberty, especially in low- and middle-income countries.

PMID:41826258 | DOI:10.1002/ajhb.70242

J Med Imaging Radiat Oncol. 2026 Mar 13. doi: 10.1111/1754-9485.70084. Online ahead of print.

ABSTRACT

BACKGROUND: Beta Thalassaemia Major is a hereditary disorder characterised by defective protein synthesis in the beta-globin chain, leading to chronic anaemia. As a consequence of repeated blood transfusions used to manage anaemia, excessive iron accumulation occurs, primarily in the liver and other vital organs, leading to organ damage.

PURPOSE: To determine the correlation between liver iron concentration and iron accumulation in the liver due to blood transfusions in paediatric patients diagnosed with Beta Thalassaemia Major, using shear wave elastography and liver magnetic resonance imaging apparent diffusion coefficient measurements.

MATERIALS AND METHODS: A prospective study was conducted from January 2025 to April 2025 on 77 paediatric patients diagnosed with Beta Thalassaemia Major, utilising liver shear wave elastography, liver apparent diffusion coefficient, T2* values and serum ferritin levels.

RESULTS: Descriptive statistics for the continuous variables of the patients: elasticity median (10.39 ± 4.39 kPa), velocity median (1.81 ± 0.36 m/s), liver ADC (0.52 ± 0.42), liver T2* (4.93 ± 5.15), LIC (9.45 ± 5.7). The median elasticity values differed significantly across the iron overload severity groups (p = 0.006). A post hoc analysis was performed to identify the groups responsible for this difference. The analysis revealed that the differences originated from comparisons between the mild (9.58 ± 3.62) and severe (14.44 ± 5.91) groups, the moderate (9.47 ± 3.24) and severe (14.44 ± 5.91) groups and the severe (14.44 ± 5.91) and normal (8.16 ± 2.44) groups (p^ac < 0.001; p^bc < 0.001; p^cd = 0.003).

CONCLUSION: We demonstrated a correlation between liver shear wave elastography and liver apparent diffusion coefficient values with MRI T2* and LIC in the group of paediatric patients diagnosed with Beta Thalassaemia Major who had a high liver iron burden.

PMID:41826250 | DOI:10.1111/1754-9485.70084

Med Decis Making. 2026 Mar 13:272989X261423177. doi: 10.1177/0272989X261423177. Online ahead of print.

ABSTRACT

BackgroundClinical trial designs are typically narrowly focused on error control in hypothesis testing, but this approach is inadequate in many contexts, particularly when a decision maker intends to, or must, consider multiple relevant clinical and health economic outcomes under uncertainty. Value-of-information (VoI) metrics can be used to estimate the monetary value of data collection to the decision maker. Adaptive trial designs use prespecified decision rules as data are collected and analyzed to modify the ongoing trial design. To date, VoI considerations have rarely been integrated into this approach, partly due to the computational burden.MethodsWe propose a value-driven adaptive design that refocuses trial design on VoI as a metric to direct trial adaptations. Specifically, a VoI analysis is performed at each interim analysis to determine whether or not the trial should proceed to the next analysis (i.e., determine whether further data collection is sufficiently valuable). We provide methods to compute the expected net benefit of perfect information, expected net benefit of sampling (ENBS) for the next analysis, and the ENBS for subsequent sequential analyses. Our approach is flexible to any statistical model, decision model, and research cost function and does not require distributional assumptions about the net benefit.ResultsWe describe our method in detail and demonstrate its implementation via a case study comparing infant immunoprophylaxis and maternal vaccination to prevent respiratory syncytial virus-related medical attendances.ConclusionsOur value-driven adaptive design aligns pragmatic clinical trial design with the requirements of decision makers. Designs with VoI-based adaptations have the potential to improve the cost-effectiveness of clinical trials.HighlightsOur value-driven adaptive design is a new method that uses the expected net benefit of sampling to define stopping rules at interim analyses (i.e., to determine if further data collection is sufficiently valuable).Our method orients trial designs to efficiently produce evidence to inform the decision maker.

PMID:41826246 | DOI:10.1177/0272989X261423177