Tag: nevin manimala

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

Vet Rec. 2026 Mar 13. doi: 10.1002/vetr.70499. Online ahead of print.

ABSTRACT

BACKGROUND: Data evaluating paracetamol combined with NSAIDs in dogs are scarce. Results of clinical studies in dogs investigating intravenous paracetamol vary.

METHODS: Dogs were randomised to either receive 10 mg/kg paracetamol intravenously after induction of anaesthesia and every 8 hours during hospitalisation (test) or not (control). In both groups, meloxicam or robenacoxib was administered at the licensed dose, interval and route. Intraoperative nociception or postoperative pain, defined by a Glasgow composite measure pain scale-short form score greater than 4 of 20, was treated with 0.1 mg/kg methadone intravenously as needed.

RESULTS: Data from 14 dogs that received paracetamol and 13 dogs that did not were analysed. There were no statistically significant differences in clinical or demographic data between the two groups. Median (range) rescue methadone requirements were 0.0 (0.0‒0.2) mg/kg and 0.1 (0.0‒0.3) mg/kg for the test and control groups, respectively (p = 0.17), with no difference intraoperatively (p = 0.72) or postoperatively (p = 0.24). Four test and seven control dogs required rescue analgesia perioperatively (p = 0.17).

LIMITATIONS: Low analgesia requirements in both groups may have resulted in a type two statistical error.

CONCLUSION: When used as part of a multimodal analgesic protocol, paracetamol did not provide significant opioid-sparing effects in the perioperative period.

PMID:41826244 | DOI:10.1002/vetr.70499

Vet Rec. 2026 Mar 13. doi: 10.1002/vetr.70495. Online ahead of print.

ABSTRACT

BACKGROUND: Non-ambulatory tetraparesis or tetraplegia in cats may constitute a diagnostic challenge for general practitioners. Therefore, this study aimed to evaluate if clinical variables from signalment, history, clinical examination and basic ancillary tests are associated with underlying diagnoses in cats with non-ambulatory tetraparesis or tetraplegia.

METHODS: This was a retrospective single-centre study of cases presented between 2010 and 2023. Information on disease onset, progression, neurological and physical examination findings and ancillary tests was analysed across all diagnoses. Diagnostic categories comprising five or more cases were carried forward to univariate and/or multivariable analyses.

RESULTS: Eighty-one cats were included, with 82.7% of cases represented by six conditions: polyneuropathy (PN; n = 26), ischaemic myelopathy (IM; n = 15), spinal cord neoplasia (n = 8), feline infectious peritonitis (n = 7), intracranial neoplasia (n = 6) and spinal cord contusion (n = 5). On multivariable analysis, an age of below 3 years, progressive presentation, normal mentation, reduced spinal reflexes in the thoracic limbs and normal blood tests were statistically associated with PN. Age between 6 and 9 years or older than 9 years and peracute onset of clinical signs were associated with IM.

LIMITATIONS: This was a retrospective study with limited multivariable analysis in certain diagnostic categories. Furthermore, the study included only referral cases, which may not represent the animal population seen by general practitioners.

CONCLUSIONS: PN and IM were the most common causes for non-ambulatory tetraparesis or tetraplegia in this population of cats. Attention to the neurological examination and easy to identify clinical features can be used to determine the most likely differential diagnoses and assist general practitioners in the formulation of a diagnostic plan.

PMID:41826233 | DOI:10.1002/vetr.70495

Semin Nucl Med. 2026 Mar 12:S0001-2998(26)00044-9. doi: 10.1053/j.semnuclmed.2026.02.002. Online ahead of print.

ABSTRACT

Nuclear medicine infection imaging has traditionally relied on semantic visual interpretation supported by simple semi-quantitative indices. While effective, this paradigm is limited by observer dependence, restricted sensitivity to subtle or diffuse disease, and difficulty in standardising interpretation across centres. Advances in quantitative imaging, radiomics and artificial intelligence (AI) are reshaping this landscape. These complementary domains, collectively conceptualised as computomics, extend infection imaging from qualitative pattern recognition toward objective, reproducible and data-driven characterisation of disease. Quantitative imaging converts tracer distribution into measurable biological metrics, ranging from simple region-of-interest count ratios to standardised uptake values and kinetic parameters. Radiomics builds on this foundation by extracting high-dimensional features describing intensity, shape, texture and spatial heterogeneity, revealing image information not appreciable to the human eye. AI, through machine learning and deep learning approaches, integrates quantitative and radiomic data with clinical variables to automate segmentation, enhance reconstruction, support classification, and enable predictive modelling. Together, these tools offer potential to improve differentiation of infection from sterile inflammation, quantify disease burden, monitor therapy response, and standardise interpretation in complex scenarios. Quantitative accuracy and radiomic stability remain highly dependent on acquisition, reconstruction and processing parameters. AI-driven image enhancement and denoising may improve visual appearance while altering voxel statistics, with downstream effects on quantitative metrics and texture features. Variability in feature definitions, segmentation methods and analysis pipelines further limits reproducibility. Consequently, harmonisation, standardisation, transparent validation and physics-informed AI models are essential.

PMID:41826111 | DOI:10.1053/j.semnuclmed.2026.02.002