Design method for eliminating spectral line tilt in a multiple sub-pupil ultra-spectral imager (MSPUI).

A multiple sub-pupil ultra-spectral imaging system designed with a single spectrometer and detector can simultaneously detect multiple-channel spectra with ultra-high spectral resolution. However, due to using a prism in the system's front end, the nonlinear dispersion introduces spectral line tilt in the imaging spectra. This phenomenon can lead to bias in the final spectral data. To eliminate this issue, we propose a new design by introducing a second prism to correct this spectral tilt in the system. The angle of spectral line tilt generated by the nonlinear dispersion of the first prism is derived. It provides the theoretical basis for characterizing the second complementary prism. Finally, a UV multiple sub-pupil ultra-spectral imaging system is designed. The system employs two pupil separation prisms and one flat panel array to segment the pupil in three channels, each operating within spectral ranges of 180∼210 nm, 275∼305 nm, and 370∼400 nm, respectively. The spectral resolutions in all three channels are better than 0.1 nm. The corrected spectral line tilt is less than 1/3 of a pixel in the two channels with pupil separation prisms. At a Nyquist frequency of 30 lp/mm, the modulation transfer functions of all three channels are greater than 0.7, ensuring imaging quality. The design results indicate that the method proposed in this paper, utilizing complementary prisms, can effectively correct the spectral line tilt caused by the nonlinear dispersion of the pupil separation prisms. This design approach can be a reference for developing multiple sub-pupil ultra-spectral imaging systems.

Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western Taiwan.

The application of statistical models has excellent potential to provide crucial information for mitigating the challenging issue of ozone (O3) pollution by capturing its associations with explanatory variables, including reactive precursors (VOCs and NOX) and meteorology. Considering the large contribution of O3 in degrading the air quality of western Taiwan, three-year (2019-2021) hourly concentration data of VOC, NOX and O3 from 4 monitoring stations of western Taiwan: Tucheng (TC), Zhongming (ZM), Taixi (TX) and Xiaogang (XG), was evaluated to identify the effect of anthropogenic emissions on O3 formation. Owing to the high-ambient reactivity of VOCs on the underestimation of sources, photochemical oxidation was assessed to calculate the consumed VOC (VOCcons) which was followed by the source identification of their initial concentrations. VOCcons was observed to be highest in the summer season (16.7 and 22.7 ppbC) at north (TC and ZM) and in the autumn season (17.8 and 11.4 ppbC) in southward-located stations (TX and XG, respectively). Results showed that VOCs from solvents (25-27%) were the major source at northward stations whereas VOCs-industrial emissions (30%) dominated in south. Furthermore, machine learning (ML): eXtreme Gradient Boost (XGBoost) model based de-weather analysis identified that meteorological factors favor to reduce ambient O3 levels at TC, ZM and XG stations (-67%, -47% and -21%, respectively) but they have a major role in accumulating the O3 (+38%) at the TX station which is primarily transported from the upwind region of south-central Taiwan. Crucial insights using ML outputs showed that the finding of the study can be utilized for region-specific data-driven control of emission from VOCs-sources and prioritized to limit the O3-pollution at the study location-ns as well as their accumulation in distant regions.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques.

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 µg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine.

Introduction: Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities. Methods: A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine. Results: Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD. Conclusion: This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.

Links between oral microbiome and insulin resistance: Involvement of MAP kinase signaling pathway.

Oral dysbiosis contributes to periodontitis and has implications for systemic diseases. Diabetes mellitus is a common metabolic disorder characterized by impaired glucose regulation. AMP-activated protein kinase (AMPK) plays a vital role in regulating glucose uptake and glycogenesis in the liver. This study aimed to investigate the association between periodontal bacteria and diabetes mellitus. A clinical trial was conducted to explore the association between oral bacteria and hyperglycemia. Additionally, we elucidated the molecular mechanisms by which periodontal bacteria cause insulin resistance. In the clinical trial, we discovered significant alterations in the expression levels of Fusobacterium nucleatum (Fn) and Tannerella forsythia (Tf) in patients with diabetes compared with healthy controls. Furthermore, Fn and Tf levels positively correlated with fasting blood glucose and glycated hemoglobin (HbA1C) levels. Moreover, we explored and elucidated the molecular mechanism by which Fusobacterium nucleatum culture filtrate (FNCF) induces cytokine release via the Toll-like receptor 2 (TLR2) signaling pathway in human gingival epithelial Smulow-Glickman (S-G) cells. This study investigated the effects of cytokines on insulin resistance pathways in liver cells. The use of an extracellular signal-regulated kinase (ERK) inhibitor (U0126) demonstrated that FNCF regulates the insulin receptor substrate 1 and protein kinase B (IRS1/AKT) signaling pathway, which affects key proteins involved in hepatic glycogen synthesis, including glycogen synthase kinase-3 beta (GSK3ß) and glycogen synthase (GS), ultimately leading to insulin resistance. These findings suggest that ERK plays a crucial role in hepatocyte insulin resistance.

Source-apportionment and spatial distribution analysis of VOCs and their role in ozone formation using machine learning in central-west Taiwan.

This study assessed the machine learning based sensitivity analysis coupled with source-apportionment of volatile organic carbons (VOCs) to look into new insights of O3 pollution in Yunlin County located in central-west region of Taiwan. One-year (Jan 1 to Dec 31, 2021) hourly mass concentrations data of 54 VOCs, NOX, and O3 from 10 photochemical assessment monitoring stations (PAMs) in and around the Yunlin County were analyzed. The novelty of the study lies in the utilization of artificial neural network (ANN) to evaluate the contribution of VOCs sources in O3 pollution in the region. Firstly, the station specific source-apportionment of VOCs were carried out using positive matrix factorization (PMF)-resolving six sources viz. AAM: aged air mass, CM: chemical manufacturing, IC: Industrial combustion, PP: petrochemical plants, SU: solvent use and VE: vehicular emissions. AAM, SU, and VE constituted cumulatively more than 65% of the total emission of VOCs across all 10 PAMs. Diurnal and spatial variability of source-segregated VOCs showed large variations across 10 PAMs, suggesting for distinctly different impact of contributing sources, photo-chemical reactivity, and/or dispersion due to land-sea breezes at the monitoring stations. Secondly, to understand the contribution of controllable factors governing the O3 pollution, the output of VOCs source-contributions from PMF model along with mass concentrations of NOX were standardized and first time used as input variables to ANN, a supervised machine learning algorithm. ANN analysis revealed following order of sensitivity in factors governing the O3 pollution: VOCs from IC > AAM > VE ≈ CM ≈ SU > PP ≈ NOX. The results indicated that VOCs associated with IC (VOCs-IC) being the most sensitive factor which need to be regulated more efficiently to quickly mitigate the O3 pollution across the Yunlin County.

Development of a metabolomics-based data analysis approach for identifying drug metabolites based on high-resolution mass spectrometry.

A metabolomics-based approach to data analysis is required for drug metabolites to be identified quickly. This study developed such an approach based on high-resolution mass spectrometry. Our approach is a two-stage one that combines a time-course experiment with stable isotope tracing. Pioglitazone (PIO) was used to improve glycemic management for type 2 diabetes mellitus. Consequently, PIO was taken as a model drug for identifying metabolites. During Stage I of data analysis, 704 out of 26626 ions exhibited a positive relationship between ion abundance ratio and incubation time in a time-course experiment. During Stage II, 25 isotope pairs were identified among the 704 ions. Among these 25 ions, 18 exhibited a dose-response relationship. Finally, 14 of the 18 ions were verified to be PIO structure-related metabolite ions. Otherwise, orthogonal partial least squares-discriminant analysis (OPLS-DA) was adopted to mine PIO metabolite ions, and 10 PIO structure-related metabolite ions were identified. However, only four ions were identified by both our developed approach and OPLS-DA, indicating that differences in the designs of metabolomics-based approaches to data analysis can result in differences in which metabolites are identified. A total of 20 PIO structure-related metabolites were identified by our developed approach and OPLS-DA, and six metabolites were novel. The results demonstrated that our developed two-stage data analysis approach can be used to effectively mine data on PIO metabolite ions from a relatively complex matrix.

Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.

In this study, mean monthly and diurnal variations in fine particulate matters (PM2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NOx, O3, nitrate (NO3 -), and sulfate (SO4 2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NOx concentration due to a decrease in traffic flow under the NOx-saturated regime was observed to enhance the secondary NO3 - and O3 formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NOx, O3, NO3 -, and SO4 2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH3, HNO3, and H2SO4, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Pathophysiology of Chronic Migraine: Insights from Recent Neuroimaging Research.

PURPOSE OF REVIEW: Chronic migraine (CM) is a highly disabling primary headache disorder with a substantial impact on patients' quality of life. Episodic migraine (EM) and CM are dynamic states; CM usually evolves from EM alongside increased headache frequency, comorbidities, and medication overuse, supporting the notion that migraine is a spectrum disorder. This narrative review aims to summarize neuroimaging studies to better understand the pathophysiology of CM. RECENT FINDINGS: Positron emission tomography studies have revealed abnormal energy metabolism and metabolic changes in the dorsal rostral pons in individuals with CM, suggesting that this structure has a key role in the pathophysiology of migraine generation and chronification. Magnetic resonance spectroscopy studies have suggested that thalamocortical pathway dysfunction may contribute to migraine chronification, while functional magnetic resonance imaging studies have highlighted that hypothalamic activity may be involved. Recent evidence highlights functional and structural alterations in cortical and subcortical pain-related brain regions in patients with CM. Whether these functional and structural abnormalities of the brain cause migraine chronification or are a consequence of repeated attacks is still debated. In the future, imaging patterns that predict the transformation from EM to CM should be identified.

Genetic Variants Associated With Subjective Cognitive Decline in Patients With Migraine.

The genetic association between subjective cognitive decline (SCD) and migraine comorbidity remains unclear. Furthermore, single nucleotide polymorphisms (SNP) associated with SCD have not been identified previously. Migraineurs were genotyped using an Affymetrix array. The correlation between different SNP variants in migraineurs with or without SCD and non-migraine controls was investigated. Migraineurs with or without SCD were further divided for the analysis of relevant SNP variants linked to migraine with aura (MA), migraine without aura (MoA), episodic migraine (EM), and chronic migraine (CM). Significant connectivity between SNPs and clinical indices in migraineurs and non-migraine controls with SCD were assessed using multivariate regression analysis. The rs144191744 SNP was found in migraineurs (p = 3.19E-08), EM (p = 1.34E-07), and MoA(p = 7.69E-07) with and without SCD. The T allele frequency for rs144191744 in TGFBR3 was 0.0054 and 0.0445 in migraineurs with and without SCD (odds ratio, 0.12), respectively. rs2352564, rs6089473 in CDH4, rs112400385 in ST18, rs4488224 and rs17111203 in ARHGAP29 SNPs were found, respectively, in non-migraineurs (p = 4.85E-06, p = 8.28E-06), MoA (p = 3.13E-07), and CM subgroups (p = 1.05E-07, 6.24E-07) with and without SCD. Rs144191744 closely relates to SCD with the all-migraine group and the EM and MoA subgroups. In conclusion, rs144191744 in TGFBR3 was significantly associated with SCD in migraineurs, especially in the EM, MoA, and female patient subgroups. Furthermore, three SNPs (rs112400385, rs4488224, and rs17111203) were associated with SCD in migraineurs but not in non-migraine controls.

A Metallomic Approach to Assess Associations of Plasma Metal Levels with Amnestic Mild Cognitive Impairment and Alzheimer's Disease: An Exploratory Study.

Alzheimer's disease (AD) involves the abnormal activity of transition metals and metal ion dyshomeostasis; however, the potential of trace metal biomarkers in predicting cognitive decline has not been evaluated. This study aimed to assess the potential of 36 trace elements in predicting cognitive decline in patients with amnestic mild cognitive impairment (aMCI) or AD. Participants (9 controls, 23 aMCI due to AD, and 8 AD dementia) underwent comprehensive cognitive tests, including the Mini-Mental State Examination (MMSE) and trace metal analysis. The correlations between the plasma trace element levels and annual MMSE changes during follow-up were analyzed. We found that an increase in disease severity was linked to lower plasma levels of boron (B), bismuth (Bi), thorium (Th), and uranium (U) (adjusted p < 0.05). Higher baseline calcium levels (r = 0.50, p = 0.026) were associated with less annual cognitive decline; those of B (r = −0.70, p = 0.001), zirconium (r = −0.58, p = 0.007), and Th (r = −0.52, p = 0.020) with rapid annual cognitive decline in the aMCI group; and those of manganese (r = −0.91, p = 0.035) with rapid annual cognitive decline in the AD group. Overall, our exploratory study suggests that plasma metal levels have great potential as in vivo biomarkers for aMCI and AD. Larger sample studies are necessary to confirm these results.

Identification of Novel Genetic Variants Associated with Insomnia and Migraine Comorbidity.

Purpose: Although insomnia and migraine are often comorbid, the genetic association between insomnia and migraine remains unclear. This study aimed to identify susceptibility loci associated with insomnia and migraine comorbidity. Patients and Methods: We performed a genome-wide association study (GWAS) involving 1063 clinical outpatients at a tertiary hospital in Taiwan. Migraineurs with and without insomnia were genotyped using the Affymetrix Axiom Genome-Wide TWB 2.0. We performed association analyses for the entire cohort and stratified patients into the following subgroups: episodic migraine (EM), chronic migraine (CM), migraine with aura (MA), and migraine without aura (MoA). Potential correlations between SNPs and clinical indices in migraine patients with insomnia were examined using multivariate regression analysis. Results: The SNP rs1178326 in the gene HDAC9 was significantly associated with insomnia. In the EM, CM, MA, and MoA subgroups, we identified 30 additional susceptibility loci. Multivariate regression analysis showed that SNP rs1178326 also correlated with higher migraine frequency and the Migraine Disability Assessment (MIDAS) questionnaire score. Finally, two SNPs that had been previously reported in a major insomnia GWAS were also significant in our migraineurs, showing a concordant effect. Conclusion: In this GWAS, we identified several novel loci associated with insomnia in migraineurs in a Han Chinese population in Taiwan. These results provide insights into the possible genetic basis of insomnia and migraine comorbidity.

Altered glucose metabolism and its association with carbonic anhydrase 8 in Machado-Joseph Disease.

Machado-Joseph disease (MJD), also known as spinocerebellar ataxia type 3 (SCA3), is an autosomal dominant neurodegenerative disease. This disorder is caused by polyglutamine (polyQ)-containing mutant ataxin-3, which tends to misfold and aggregate in neuron cells. We previously demonstrated a protective function of carbonic anhydrase 8 (CA8) in MJD disease models and a decreased glycolytic activity associated with down-regulated CA8 in a human osteosarcoma (OS) cell model. Given that a reduction in body weight accompanied by gait and balance instability was observed in MJD patients and transgenic (Tg) mice, in this study, we aimed to examine whether metabolic defects are associated with MJD and whether CA8 expression is involved in metabolic dysfunction in MJD. Our data first showed that glucose uptake ability decreases in cells harboring mutant ataxin-3, but increases in cells overexpressing CA8. In addition, the expressions of glucose transporter 3 (GLUT3) and phosphofructokinase-1 (PFK1) were significantly decreased in the presence of mutant ataxin-3. Consistently, immunohistochemistry (IHC) showed that GLUT3 was less expressed in cerebella of aged MJD Tg mice, indicating that the dysfunction of GLUT3 may be associated with late-stage disease. On the other hand, transient down-regulation of CA8 revealed decreased expressions of GLUT3 and PFK1 in HEK293 cells harboring wild-type (WT) ataxin-3, but no further reduction of GLUT3 and PFK1 expressions were observed in HEK293 cells harboring mutant ataxin-3. Moreover, immunoprecipitation (IP) and immunofluorescence (IF) demonstrated that interactions exist between ataxin-3, CA8 and GLUT3 in MJD cellular and Tg models. These lines of evidence suggest that CA8 plays an important role in glucose metabolism and has different impacts on cells with or without mutant ataxin-3. Interestingly, the decreased relative abundance of Firmicutes/Bacteroidetes (F/B) ratio in the feces of aged MJD Tg mice coincided with weight loss and metabolic dysfunction in MJD. Taken together, our results are the first to demonstrate the effects of CA8 on glucose metabolism and its involvement in the metabolic defects in MJD disease. Further investigations will be required to clarify the underlying mechanisms for the metabolic defects associated with MJD.

Sex Differences in Chronic Migraine: Focusing on Clinical Features, Pathophysiology, and Treatments.

PURPOSE OF REVIEW: This review provides an update on sex differences in chronic migraine (CM), with a focus on clinical characteristics, pathophysiology, and treatments. RECENT FINDINGS: Approximately 6.8-7.8% of all migraineurs have CM, with an estimated prevalence of 1.4-2.2% in the general population. The economic burden caused by CM, including medical costs and lost working ability, is threefold higher than that caused by episodic migraine (EM). Notably, the prevalence of migraine is affected by age and sex. Female migraineurs with CM experience higher levels of headache-related disability, including longer headache duration, higher frequency of attacks, and more severely impacted efficiency at work. Sex hormones, including estrogen, testosterone, and progesterone, contribute to the sexually dimorphic characteristics and prevalence of migraine in men and women. Recent neuroimaging studies have indicated that migraine may have a greater impact and cause greater dysfunction in the organization of resting-state functional networks in women. Accumulating evidence suggests that topiramate, Onabotulinumtoxin A and calcitonin gene-related peptide (CGRP) monoclonal antibodies are effective as the preventative treatments for CM. Recent evidence highlights a divergence in the characteristics of CM between male and female populations. The data comparing the treatment response for CM regarding sex are lacking.

Helical structure motifs made searchable for functional peptide design.

The systematic design of functional peptides has technological and therapeutic applications. However, there is a need for pattern-based search engines that help locate desired functional motifs in primary sequences regardless of their evolutionary conservation. Existing databases such as The Protein Secondary Structure database (PSS) no longer serves the community, while the Dictionary of Protein Secondary Structure (DSSP) annotates the secondary structures when tertiary structures of proteins are provided. Here, we extract 1.7 million helices from the PDB and compile them into a database (Therapeutic Peptide Design database; TP-DB) that allows queries of compounded patterns to facilitate the identification of sequence motifs of helical structures. We show how TP-DB helps us identify a known purification-tag-specific antibody that can be repurposed into a diagnostic kit for Helicobacter pylori. We also show how the database can be used to design a new antimicrobial peptide that shows better Candida albicans clearance and lower hemolysis than its template homologs. Finally, we demonstrate how TP-DB can suggest point mutations in helical peptide blockers to prevent a targeted tumorigenic protein-protein interaction. TP-DB is made available at http://dyn.life.nthu.edu.tw/design/ .

2022 Taiwan Guidelines for Acute and Preventive Treatment of Cluster Headaches.

The Treatment Guideline Subcommittee of the Taiwan Headache Society evaluated Taiwan's guidelines for the acute and preventive treatment of cluster headaches on the basis of the principles of evidence-based medicine. The subcommittee assessed the quality of clinical trials and levels of evidence and referred to the treatment guidelines of other countries. Over the course of several panel discussions, the subcommittee members reached a consensus regarding the major roles of, recommended levels of, clinical efficacy of, adverse events in, and clinical precautions for the acute and preventive treatment of cluster headaches. Thus, the subcommittee updated the previous version of the guidelines published in 2011. Most cluster headaches occurring in Taiwan are episodic, and very few patients develop chronic cluster headaches. Cluster headaches cause extreme pain over a short period and are accompanied by ipsilateral autonomic symptoms; therefore, immediate treatment can provide considerable relief. Treatment options can be categorized into acute and preventive types. Among the treatment methods currently available in Taiwan for cluster headaches, high-flow pure oxygen inhalation has demonstrated the best evidence and effectiveness for acute attacks, followed by triptan nasal spray; therefore, these are recommended as first-line treatments. Oral steroids and suboccipital steroid injections can be used as transitional preventative treatments. Verapamil is recommended as a first-line treatment for maintenance prophylaxis. Drugs such as lithium, topiramate, and calcitonin gene-related peptide (CGRP) monoclonal antibodies are recommended as secondline treatments. Noninvasive vagus nerve stimulation is the recommended instrumental therapy. The effectiveness of surgical treatment, such as sphenopalatine ganglion stimulation, is supported by a high level of evidence; nevertheless, because few patients have chronic cluster headaches in Taiwan, no clinical records are available for use as a reference. Transitional prophylaxis and maintenance prophylaxis can be used simultaneously according to the individual condition of the patient, and the transitional prophylaxis can be gradually discontinued once the maintenance prophylaxis takes effect. Steroids should not be used for more than 2 weeks as transitional prophylaxis. Maintenance prophylaxis should be administered until the end of the bout period (no attacks for 2 weeks) and then gradually tapered off. Key words: cluster headaches, oxygen therapy, triptans, steroids, CGRP monoclonal antibodies, noninvasive vagus nerve stimulation.

A machine learning model for predicting PM2.5 and nitrate concentrations based on long-term water-soluble inorganic salts datasets at a road site station.

In this study, long-term variations in the concentrations of PM2.5, water-soluble inorganic salts (WIS), and gaseous precursors measured by a roadside air quality monitoring station were investigated from 2017 to February 2021 to examine the formation mechanism of secondary inorganic PM2.5. A new machine learning model using WIS data as input variables was further developed to predict PM2.5 and nitrate concentrations for source tracing and effective control strategy development. The results showed that a reduction in the NOx concentration under VOC-limited O3 formation regime could offset the consumption of OH and O3, causing an increase in secondary NO3- and PM2.5 formation during fall and winter seasons. A good agreement was obtained between the predicted and measured PM2.5 values, with R2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.81, 6.81 µg/m3, and 5.10 µg/m3, respectively. The nitrate ([NO3-]) prediction model could predict ∼59% of the atmospheric nitrate concentration. The sensitivity analysis of the input variables in the present model further revealed that NO3- and VOC were two important pollutants dominating the variation trend of PM2.5. It is recommended that decision makers should focus more on the reduction of VOC and O3 to reduce secondary PM2.5 formation during winter in central Taiwan. Real-time measurements of the chemical composition of PM2.5, taken as the regulatory air quality monitoring items are needed in the future.

Identification of genetic risk loci for depression and migraine comorbidity in Han Chinese residing in Taiwan.

Introduction: The genetic association between depression and migraine has not been well investigated in Asian populations. Furthermore, the genetic basis of depression and comorbid migraine subtypes remains nebulous. Hence, in the current study we investigate the susceptibility loci associated with depression and migraine comorbidity in the Han Chinese population in Taiwan. Methods: We perform a genome-wide association study involving 966 migraine patients, with or without comorbid depression. Genotyping is performed using participant genomic DNA. Association analyses are performed for the entire migraine cohort (subgroups: episodic migraine, chronic migraine, and migraine with or without aura). Results: Results show that the single nucleotide polymorphism variants of the CDH4 intron region (rs78063755), NTRK3-AS1 downstream region (rs57729223), and between LINC01918 and GPR45 (rs2679891) are suggestively associated with depression. Twenty additional susceptibility loci occur within the subgroups. A multivariate association study demonstrated that a variant in the intron region of CDH4 rs78063755 was associated with Beck Depression Inventory and Migraine Disability Assessment scores. Discussion: The findings of this study identify several genetic loci suggestively associated with depression among migraine patients in the Han Chinese population. Moreover, a potential genetic basis has been characterized for depression and migraine comorbidity, thus providing genetic candidates for further investigation.

Shared Patterns of Brain Functional Connectivity for the Comorbidity between Migraine and Insomnia.

Migraine is commonly comorbid with insomnia; both disorders are linked to functional disturbance of the default mode network (DMN). Evidence suggests that DMN could be segregated into multiple subnetworks with specific roles that underline different cognitive processes. However, the relative contributions of DMN subnetworks in the comorbidity of migraine and insomnia remain largely unknown. This study sought to identify altered functional connectivity (FC) profiles of DMN subnetworks in the comorbidity of migraine and insomnia. Direct group comparisons with healthy controls, followed by conjunction analyses, were used to identify shared FC alterations of DMN subnetworks. The shared FC changes of the DMN subnetworks in the migraine and insomnia groups were identified in the dorsomedial prefrontal and posteromedial cortex subnetworks. These shared FC changes were primarily associated with motor and somatosensory systems, and consistently found in patients with comorbid migraine and insomnia. Additionally, the magnitude of FC between the posteromedial cortex and postcentral gyrus correlated with insomnia duration in patients with comorbid migraine and insomnia. Our findings point to specific FC alterations of the DMN subnetwork in migraine and insomnia. The shared patterns of FC disturbance may be associated with the underlying mechanisms of the comorbidity of the two disorders.