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Correction: Diagnosis challenges and accessibility barriers to migraine management in Southeast Asia: results from the South-East Asia Local breAch on MigraiNe Treatment (SEALANT) study (The Journal of Headache and Pain, (2026), 27, 1, (47), 10.1186/s10194-026-02295-1)
(2026-12-01)
Rattanawong, Wanakorn
;
Hiransuthikul, Akarin
;
Anukoolwittaya, Prakit
;
Pongpitakmetha, Thanakit
;
Thanprasertsuk, Sekh
In the section Survey and outcomes of this article, the components of the questionnaire were incorrectly labeled using the manuscript-style headings “Introduction,” “Methods,” “Results,” “Discussion,” and “Conclusion.” These terms were intended solely to denote internal sections of the questionnaire and do not correspond to the standard structural sections of the manuscript. To avoid confusion, these labels have been replaced with “Section 1,” “Section 2,” “Section 3,” “Section 4,” and “Section 5,” respectively. The correct and incorrect version of the text is presented below and the original article has been corrected. The survey comprised five main sections, each designed to explore different aspects of migraine care and physician perspectives. Section 1 collected demographic data. Section 2 focused on barriers to migraine diagnosis, covering issues such as diagnostic accuracy, time to diagnosis, hospital workload, and use of headache diaries and patient education. Section 3 explored acute migraine treatment, including medication availability, the proportion of patients using acute medications, issues of medication underuse (timing and efficacy), and awareness of MOH. Section 4 explored preventive treatment, including access to preventive medications, availability of calcitonin gene-related peptide (CGRP)–targeted therapies, and physicians’ views on their use. Section 5 explored migraine-related stigma and its impact on patients’ quality of life; these findings will be reported separately. The survey comprised five main sections, each designed to explore different aspects of migraine care and physician perspectives. Section “Introduction” collected demographic data. Section “Methods” focused on barriers to migraine diagnosis, covering issues such as diagnostic accuracy, time to diagnosis, hospital workload, and use of headache diaries and patient education. Section “Results” explored acute migraine treatment, including medication availability, the proportion of patients using acute medications, issues of medication underuse (timing and efficacy), and awareness of MOH. Section “Discussion” explored preventive treatment, including access to preventive medications, availability of calcitonin gene-related peptide (CGRP)–targeted therapies, and physicians’ views on their use. Section “Conclusion” explored migraine-related stigma and its impact on patients’ quality of life; these findings will be reported separately.
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Author Correction: Predicting biomass global warming potential with FT-NIR spectroscopy (Scientific Reports, (2025), 15, 1, (33725), 10.1038/s41598-025-10584-z)
(2026-12-01)
Gyawali, Prakash
;
Shrestha, Bijendra
;
Phanomsophon, Thitima
;
Posom, Jetsada
;
Pornchaloempong, Pimpen
Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-025-10584-z, published online 30 September 2025 The original version of this Article contained errors. In the original version of this article, the climate-carbon effect value was not included as introduced in AR6. As a result, in the Materials and methods section, under the subheading ‘Estimation of global warming potential and emission of greenhouse gas (GHG)’, where: “Step 1: (CO<inf>2</inf>, CH<inf>4</inf>, N<inf>2</inf>O) emissions (kg) = Mass of Fuel (kg) × Carbon emission factor (kg TJ-1) × HHV (kg TJ-1) Step 2: Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) +(GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) +(GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions)” now reads: “Step 1: GHG (CO<inf>2</inf>, CH<inf>4</inf>, or N<inf>2</inf>O) emissions (kg) = Mass of Fuel (kg) × Specific GHG emission factor (kg TJ-1) × HHV (TJ kg-1) Step 2: Total GWP (kg CO<inf>2</inf>e) = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) + (GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) + (GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions)” In addition, under the subheading ‘Model development and validation’, where: “The model was optimized by selecting wavenumbers through various variable selection methods, including the Correlation Method (CM), Variance Method (VM), Co-Variance Method (COVM), and Variable Importance Projection (VIP). The spectral data were pretreated using raw spectra, standard normal variate (SNV), as well as first derivative and second derivative transformations. The following spectra pretreatment methods: Standard Normal Variate (SNV) is for corrects scatter effects and baseline variations35,” now reads: “The model was optimized by selecting wavenumbers through various variable selection methods, including the Correlation Method (CM), Variance Method (VM), Co-Variance Method (COVM), and Variable Importance Projection (VIP). The following spectra pretreatment methods: Standard Normal Variate (SNV) is for correcting scatter effects and baseline variations35,” In addition, Equations 3, 4 and 6 contained typesetting errors. As a result, Equation 3: (Formula presented.) now reads, (Formula presented.) Equation 4: (Formula presented.) now reads: (Formula presented.) And Equation 6 (Formula presented.) now reads: (Formula presented.) Furthermore, under the Results section, subheading ‘Predicting performance of biomass GWP using PLSR’, where: “The model developed with CM (reduction of 1150 wavenumber of full range to 325 wavenumber) of 1st derivative spectra, gave best performance with R2<inf>P</inf> was 0.87 (Table 4).” now reads: “The model developed with COVM (reduction of 1150 wavenumber of full range to 325 wavenumber) of 1st derivative spectra, gave the best performance with R2<inf>P</inf> was 0.85 (Table 4).” Under the subheading ‘Prediction result of HHV using PLSR’, where: “This approach reduced the number of variables from 1150 to 365 wavenumbers, significantly enhancing the model’s performance (R2<inf>C</inf> of 0.98 and R2<inf>P</inf> of 0.87)” now reads: “This approach reduced the number of variables from 1150 to 365 wavenumbers, significantly enhancing the model’s performance (R2<inf>C</inf> of 0.98 and R2<inf>P</inf> of 0.86)” Under the subheading ‘Regression coefficient and x-loading of GWP model’, where: “Prominent peaks were identified and the bond vibration interpretation is shown in Table 7 and the vibration indicated by Workman and Weyer38 at the wavenumbers in bold were not found or not related to biomass.” now reads: “Prominent peaks were identified, and the bond vibration interpretation is shown in Table 7, which was indicated by Workman and Weyer38.” Equation 8: GWP for CO<inf>2</inf>, or CH<inf>4</inf>, or N<inf>2</inf>O Emissions = (1×CO<inf>2</inf> Emission) or (29.8×CH<inf>4</inf> Emission) or (273×N<inf>2</inf>O Emission) = (1×112 HHV) for CO<inf>2</inf> Emission or (29.8×30 HHV) for CH<inf>4</inf> Emission or (273×4 HHV) for N<inf>2</inf>O Emission = 112.0×HHV for CO<inf>2</inf> Emission or 894.0×HHV for CH<inf>4</inf> Emission or 1092.0×HHV for N<inf>2</inf>O Emission and GWP total = 2098.0 (HHV, TJ kg-1) = 0.000002098 kJ kg-1 = 0.000002098 Jg-1 now reads: GWP for CO<inf>2</inf>, or CH<inf>4</inf>, or N<inf>2</inf>O Emissions = (1×CO<inf>2</inf> Emission) or (27.2×CH<inf>4</inf> Emission) or (273×N<inf>2</inf>O Emission) = (1×112 HHV) for CO<inf>2</inf> Emission or (27.2×30 HHV) for CH<inf>4</inf> Emission or (273×4 HHV) for N<inf>2</inf>O Emission = 112.0×HHV for CO<inf>2</inf> Emission or 816.0×HHV for CH<inf>4</inf> Emission or 1092.0×HHV for N<inf>2</inf>O Emission and GWP total (kg CO<inf>2</inf>e) = 2020.0 (HHV, TJ kg-1) = 0.000002020 kJ kg-1 = 0.000002020 J g-1 Moreover, Tables 1 and 3 have been corrected. Incorrect Table 1: Remarks IPCC guideline Calculation of CO<inf>2</inf> emission Higher Heating Value = 17932000 J kg-1 CO<inf>2</inf> emission factor = 112 kg TJ-1, we can follow these steps: Convert HHV to TJ kg-1: Since 1 TJ = 1012 J, we need to convert the HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932×10-3 TJ kg-1 CO<inf>2</inf> emission (kg) = Mass of fuel (kg) × CO<inf>2</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CO<inf>2</inf> emission (kg) = 1 kg × 112 kg TJ-1 × 0.017932 TJ kg-1 Therefore, the CO<inf>2</inf> emission from stationary fuel combustion with an HHV of 17932000 J kg-1 and using the default CO<inf>2</inf> emission factor of 112 kg TJ-1 would be approximately 2.0083×10-3 kg of CO<inf>2</inf> per kg of fuel. CO<inf>2</inf> emission factor: 112 kg dry matter TJ-1 (typical for wood combustion) Calculation of CH<inf>4</inf> emission Hight Heating Value = 17932000 J kg-1 CH<inf>4</inf> emission factor = 30 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ= 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = Mass of Fuel (kg) × CH<inf>4</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CH<inf>4</inf> emission (kg) = 1 kg × 30 kg TJ-1 × 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = 5.3796×10-4 kg of CH<inf>4</inf> per kg of fuel CH<inf>4</inf> emission factor: 30 kg dry matter TJ-1 Calculation of N<inf>2</inf>O emission Hight Heating Value = 17932000 J kg-1 N<inf>2</inf>O emission factor = 4 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ = 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932 ×10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = Mass of Fuel (kg) × N<inf>2</inf>O emission factor (kg TJ-1) × HHV (TJ kg-1) N<inf>2</inf>O emission (kg) = 1 kg × 4 kg TJ-1 × 1.7932×10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = 7.1728×10-5 kg of N<inf>2</inf>O per kg of fuel N<inf>2</inf>O emission factor 4 kg dry matter /TJ The concept of global warming potential (GWP) was introduced in IPCC –AR1 (Shine et al. 1990) to compare the greenhouse effects of different greenhouse gases relative to a reference gas, normally taken as carbon dioxide, under this definition, CO<inf>2</inf> would have a GWP value of 1. Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions)+(GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions)+(GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions) Total GWP =1×2.0083×10-3+29.8× 5.379 ×10-4+273×7.1728×10-5 Total GWP=0.0376 kg CO<inf>2</inf>e This calculation is by 100 years based GWP of emission gases followed AR6 7 Correct Table 1: Remarks IPCC guideline Calculation of CO<inf>2</inf> emission Higher Heating Value = 17932000 J kg-1 CO<inf>2</inf> emission factor = 112 kg TJ-1, we can follow these steps: Convert HHV to TJ kg-1: Since 1 TJ = 1012 J, we need to convert the HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932×10-5 TJ kg-1 CO<inf>2</inf> emission (kg) = Mass of fuel (kg) × CO<inf>2</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CO<inf>2</inf> emission (kg) = 1 kg × 112 kg TJ-1 × 1.7932 × 10-5 TJ kg-1 Therefore, the CO<inf>2</inf> emission from stationary fuel combustion with an HHV of 17932000 J kg-1 and using the default CO<inf>2</inf> emission factor of 112 kg TJ-1 would be approximately 2.0083 × 10-3 kg of CO<inf>2</inf> per kg of fuel. CO<inf>2</inf> emission factor: 112 kg dry matter TJ-1 (typical for wood combustion) Calculation of CH<inf>4</inf> emission Higher Heating Value = 17932000 J kg-1 CH<inf>4</inf> emission factor = 30 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ= 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = Mass of Fuel (kg) × CH<inf>4</inf> emission factor (kg TJ-1) × HHV (TJ kg-1) CH<inf>4</inf> emission (kg) = 1 kg × 30 kg TJ-1 × 1,7932×10-5 TJ kg-1 CH<inf>4</inf> emission (kg) = 5.3796×10-4 kg of CH<inf>4</inf> per kg of fuel CH<inf>4</inf> emission factor: 30 kg dry matter TJ-1 Calculation of N<inf>2</inf>O emission Higher Heating Value = 17932000 J kg-1 N<inf>2</inf>O emission factor = 4 kg TJ-1 Convert HHV unit to TJ kg-1 1 TJ = 1012 J HHV from J kg-1 to TJ kg-1: HHV in TJ kg-1 = 17932000 J kg-1 / 1012 = 1.7932 × 10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = Mass of Fuel (kg) × N<inf>2</inf>O emission factor (kg TJ-1) × HHV (TJ kg-1) N<inf>2</inf>O emission (kg) = 1 kg × 4 kg TJ-1 × 1.7932×10-5 TJ kg-1 N<inf>2</inf>O emission (kg) = 7.1728×10-5 kg of N<inf>2</inf>O per kg of fuel N<inf>2</inf>O emission factor 4 kg dry matter /TJ-1 The concept of global warming potential (GWP) was introduced in IPCC –AR1 (Shine et al. 1990) to compare the greenhouse effects of different greenhouse gases relative to a reference gas, normally taken as carbon dioxide, under this definition, CO<inf>2</inf> would have a GWP value of 1. Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) + (GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) + (GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions) Total GWP = 1×2.0083×10-3+ 27.2× 5.3796 × 10-4+ 273×7.1728×10-5 Total GWP= 0.03622 kg CO<inf>2</inf>e This calculation is by 100 years based GWP of emission gases followed AR6 7 Incorrect Table 3: Calibration set Prediction set Parameter Method NT NC Max Min Mean SD NP Max Min Mean SD GWP IPCC Guidelines 197 147 0.03905 0.03080 0.03564 0.00180 50 0.038943 0.033002 0.03577 0.00165 HHV (J g-1) Bomb Calorimeter 197 147 18616 16405 16976 910 50 17950 15268 17051 787 Correct Table 3: Parameter Method NT Calibration Set Prediction Set NC Max Min Mean SD NP Max Min Mean SD GWP (kg CO<inf>2</inf>e) IPCC Guidelines 197 147 0.03905 0.03080 0.03564 0.00180 50 0.038943 0.033002 0.03577 0.00165 HHV (J g-1) Bomb Calorimeter 197 147 18616 16405 15268 910 50 17950 16976 17051 787 Finally, the legends of Tables 4 and 7 have been updated: “Table 4: Prediction of GWP of biomass of fast-growing tree and agriculture residue by PLSR. N: Number of samples in calibration set, R2<inf>c</inf>: coefficient of determination of calibration set, n: number of samples in prediction set, R2<inf>p</inf>: coefficient of determination of prediction set, RPD: ratio of prediction to deviation, CM: correlation method, VM: variance method, COVM: co-variance method, VIP: variable. Significant values are in [bold].” now reads: “Table 4. Prediction of GWP of biomass of fast-growing tree and agriculture residue by PLSR. N: Number of samples in calibration set, R2<inf>c</inf>: coefficient of determination of calibration set, n: number of samples in prediction set, R2<inf>p</inf>: coefficient of determination of prediction set, RPD: ratio of prediction to deviation, CM: correlation method, VM: variance method, COVM: co-variance method, VIP: variable important projection, FstDev: 1st derivative, SecDev: 2nd derivative. Significant values are in [bold].” “Table 7. The function groups corresponding to the wavenumber shown in regression coefficient plot and x-loading plot of models for GWP and HHV. *1ν, fundamental stretching vibration; 2ν, 1st overtone of fundamental stretching vibration; 3ν, 2nd overtone of fundamental stretching vibration; 5ν, 4th overtone of fundamental stretching vibration; 1δ, fundamental bending (deformation) vibration; 3δ, 2nd overtone of fundamental bending (deformation) vibration; 1, symmetric stretching vibration; 2, bending vibration; 3, asymmetric stretching vibration; and + is combination. now reads: “Table 7. The function groups corresponding to the wavenumber shown in regression coefficient plot and x-loading plot of models for GWP and HHV. 1ν, fundamental stretching vibration; 2ν, 1st overtone of fundamental stretching vibration; 3ν, 2nd overtone of fundamental stretching vibration; 5ν, 4th overtone of fundamental stretching vibration; 1δ, fundamental bending (deformation) vibration; 3δ, 2nd overtone of fundamental bending (deformation) vibration; and + is combination.” The original version of this Article has been corrected.
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Erratum to “Modeling and numerical simulation of control policies for co-infection Leishmaniasis–Chagas disease in Brazil via classical and fractional RK4-scheme” [Comput. Biol. Chem. 123 (2026) 108990] (Computational Biology and Chemistry (2026) 123, (S1476927126001155), (10.1016/j.compbiolchem.2026.108990))
(2026-08-01)
Aalam, Balal
;
Daniyal-ur-Rehman
;
Ghaffar, Maryam
;
Pongsumpun, Puntani
The publisher regrets that the incorrect graphical abstract was displayed in the published online version of the article. The incorrect graphical abstract has been replaced with the correct graphical abstract provided below.[Figure presented] The publisher would like to apologise for any inconvenience caused.
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Corrigendum to “The apolipoprotein gene: a modulating role on brain volume and cognitive function in carriers of the fragile X premutation” [Neurobiology of Disease 2026 Feb 2; 220:107292, Page 1–13]
(2026-07-01)
Jiraanont, Poonnada
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Wang, Jun Yi
;
Durbin-Johnson, Blythe
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Hwang, Ye Hyun
;
Hessl, David
The authors regret that they omitted to submit updated captions for Figs. 1, 2 and 3 during revision. The correct figure captions are as follows. Fig. 1 Representative segmentations of white matter hyperintensities (A-F), whole brain (G-L), cerebellum (G-L), brainstem (G-L), and lateral ventricles (LV, G-L) in a healthy control (A, D, G, & J), a premutation carrier without FXTAS (B, E, H, & K), and a premutation carrier with FXTAS (C, F, I, & L) all at the age of 62 years. (A-C) Axial views of the FLAIR scans showing segmented white matter hyperintensities including the splenium sign of the corpus callosum (dash arrow) in C. (DF) Coronal views of the FLAIR scans showing the segmented white matter hyperintensities including the splenium sign of the corpus callosum (dash arrow) and the MCP sign (straight arrows) in F. (G-I) Sagittal views of the T1 images showing the segmentation of the brain, cerebellum, brainstem, and LV. (J-L) Coronal views of the T1 images showing the segmentation of the brain, cerebellum, brainstem, and LV.
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Correction: Navigating the winds of change: strategic foresight and the power of weak signals (Sustainability Science, (2026), 10.1007/s11625-025-01786-5)
(2026-07-01)
Jabbour, Jason
;
Davidson, Debra J.
;
Carlsen, Henrik
;
King, Nicholas
;
Lucatello, Simone
In this article, several affiliations were incorrect: the affiliation for Simone Lucatello should have been ‘Instituto Mora-SECIHTI, Mexican Ministry for Science, Humanities and Technological Innovation, Mexico’; Nadejda Komendantova ‘International Institute for Applied Systems Analysis, Laxenburg, Austria’; Diana Mangalagiu ‘Neoma Business School, France and University of Oxford, United Kingdom’; Felix Moronta-Barrios ‘International Centre for Genetic Engineering and Biotechnology, Trieste, Italy’; Michelle Mycoo ‘The University of the West Indies, St. Augustine, Trinidad and Tobago’; Anne-Sophie Stevance ‘International Science Council, Paris, France’ The original article has been corrected.
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Correction to “A Career in Catalysis: Daniel Resasco”
(2026-02-20)
Resasco, Joaquin
;
Albanese, Jimmy Faria
;
Alvarez, Walter
;
Crossley, Steven
;
Haller, Gary L.
In the original manuscript, José E. Herrera was inadvertently omitted from the author list. He contributed to the section on carbon nanotubes. The author list has been corrected to reflect his contribution.
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Correction: Sustainable urban waste collection using a hybrid heuristic–genetic approach: a Bangkok case study (Frontiers in Sustainability, (2026), 6, (1716538), 10.3389/frsus.2025.1716538)
(2026-01-01)
Hamontree, Chaowalit
;
Koiwanit, Jarotwan
;
Sinchai, Ananta
Waste Management An incorrect number was provided for School of Engineering, King Mongkut's Institute of Technology Ladkrabang. The correct number is 2565-02-01-074. The original version of this article has been updated.
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Correction: Analysis of fluid flow across a 2D bluff body in a tandem arrangement with varying aspect ratios near a moving wall ((2026), (Front. Mech. Eng.), 11, 1727981, (10.3389/fmech.2025.1727981))
(2026-01-01)
Pant, Pawan Kumar
;
Chamoli, Sunil
;
Pant, Naval
;
Joshi, Hitesh
;
Rana, Saurav
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Correction: Fractional ABC Dynamics and Nonlinear Transmission Analysis of Dengue–Malaria Co-infection with Reinfection (Earth Systems and Environment, (2026), 10.1007/s41748-026-01258-5)
(2026-01-01)
Lamwong, Jiraporn
;
Pongsumpun, Puntani
The authors wish to correct an error in Figs. 2, 3, 4, 5 and 6 of the above-referenced article (https://doi.org/10.1007/s41748-026-01258-5), with regard to the omission of several subfigures in the online published version. Specifically, subfigures 2g–2q in Fig. 2, subfigures 3g–3q in Fig. 3, subfigures 4g–4h in Fig. 4, subfigures 5g–5h in Fig. 5, and subfigures 6g–6h in Fig. 6 were omitted. The corrected figures are provided below. Time–series dynamics of the dengue–malaria co-infection model under varying fractional orders (ABC derivative) Three-dimensional surface representations of the dengue–malaria co-infection dynamics under varying fractional orders (ABC derivative) Three-dimensional surface plots comparing the effects of different mosquito biting rates on the dengue–malaria co-infection dynamics at the fractional order Three-dimensional surface plots illustrating the impact of varying transmission probabilities from aedes mosquitoes to humans during primary dengue infection at the fractional order Three-dimensional surface plots comparing the effects of different transmission probabilities from dengue-infected humans to aedes mosquitoes during primary infection at the fractional order
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Correction to: Filling the data gap on CGRP mAb therapy in low- to middle-income countries in Southeast Asia: insights from a real-world study in Thailand (The Journal of Headache and Pain, (2024), 25, 1, (150), 10.1186/s10194-024-01859-3)
(2025-12-01)
Anukoolwittaya, Prakit
;
Hiransuthikul, Akarin
;
Pongpitakmetha, Thanakit
;
Thanprasertsuk, Sekh
;
Rattanawong, Wanakorn
In this article reference 13 was Asawavichienjinda T, Imruetaijaroenchoke W, Phanthumchinda K (2020) Thai-version Migraine Disability Assessment (MIDAS) questionnaire: concurrent validity, test-retest reliability, internal consistency, and factors predictive for migraine-related disability. Asian Biomed (Res Rev News) 14(4):139–150 but it should have been Vongvaivanich K, Yongprawat T, Jindawong N, Chansakul C (2018) Test-Retest Reliability of the Thai Migraine Disability Assessment (Thai-MIDAS) Questionnaire in Thai Migraine Patients. Bangk Med J 14(1):10–10. The original article has been updated. The authors would like to apologize for any inconvenience caused.

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