Ambient air pollution and epileptic seizures: A panel study in Australia.

OBJECTIVE: Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures. METHODS: This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In the NeuroVista data set, 3273 seizures were recorded using intracranial electroencephalography (iEEG) from 15 participants with refractory focal epilepsy in Australia in 2010-2012. In the seizure diary data set, 3419 self-reported seizures were collected through a mobile application from 34 participants with epilepsy in Australia in 2018-2021. Daily average concentrations of carbon monoxide (CO), nitrogen dioxide (NO2 ), ozone (O3 ), particulate matter ≤10 µm in diameter (PM10 ), and sulfur dioxide (SO2 ) were retrieved from the Environment Protection Authority (EPA) based on participants' postcodes. A patient-time-stratified case-crossover design with the conditional Poisson regression model was used to determine the associations between air pollutants and epileptic seizures. RESULTS: A significant association between CO concentrations and epileptic seizure risks was observed, with an increased seizure risk of 4% (relative risk [RR]: 1.04, 95% confidence interval [CI]: 1.01-1.07) for an interquartile range (IQR) increase of CO concentrations (0.13 parts per million), whereas no significant associations were found for the other four air pollutants in the whole study population. Female participants had a significantly increased risk of seizures when exposed to elevated CO and NO2 , with RRs of 1.05 (95% CI: 1.01-1.08) and 1.09 (95% CI: 1.01-1.16), respectively. In addition, a significant association was observed between CO and the risk of subclinical seizures (RR: 1.20, 95% CI: 1.12-1.28). SIGNIFICANCE: Daily exposure to elevated CO concentrations may be associated with an increased risk of epileptic seizures, especially for subclinical seizures.

A novel dual-wavelength laser stimulator to elicit transient and tonic nociceptive stimulation.

This study aimed to develop a new laser stimulator to elicit both transient and sustained heat stimulation with a dual-wavelength laser system as a tool for the investigation of both transient and tonic experimental models of pain. The laser stimulator used a 980-nm pulsed laser to generate transient heat stimulation and a 1940-nm continuous-wave (CW) laser to provide sustained heat stimulation. The laser with 980-nm wavelength can elicit transient pain with less thermal injury, while the 1940-nm CW laser can effectively stimulate both superficial and deep nociceptors to elicit tonic pain. A proportional integral-derivative (PID) temperature feedback control system was implemented to ensure constancy of temperature during heat stimulation. The performance of this stimulator was evaluated by in vitro and in vivo animal experiments. In vitro experiments on totally 120 specimens fresh pig skin included transient heat stimulation by 980-nm laser (1.5 J, 10 ms), sustained heat stimulation by 1940-nm laser (50-55 °C temperature control mode or 1.5 W, 5 min continuous power supply), and the combination of transient/sustained heat stimulation by dual lasers (1.5 J, 10 ms, 980-nm pulse laser, and 1940-nm laser with 50-55 °C temperature control mode). Hemoglobin brushing and wind-cooling methods were tested to find better stimulation model. A classic tail-flick latency (TFL) experiment with 20 Wistar rats was used to evaluate the in vivo efficacy of transient and tonic pain stimulation with 15 J, 100 ms 980-nm single laser pulse, and 1.5 W constant 1940-nm laser power. Ideal stimulation parameters to generate transient pain were found to be a 26.6 °C peak temperature rise and 0.67 s pain duration. In our model of tonic pain, 5 min of tonic stimulation produced a temperature change of 53.7 ± 1.3 °C with 1.6 ± 0.2% variation. When the transient and tonic stimulation protocols were combined, no significant difference was observed depending on the order of stimuli. Obvious tail-flick movements were observed. The TFL value of transient pain was 3.0 ± 0.8 s, and it was 4.4 ± 1.8 s for tonic pain stimulation. This study shows that our novel design can provide effective stimulation of transient pain and stable tonic pain. Furthermore, it can also provide a reliable combination of transient and consistent stimulations for basic studies of pain perception.

A Portable Real-Time Ringdown Breath Acetone Analyzer: Toward Potential Diabetic Screening and Management.

Breath analysis has been considered a suitable tool to evaluate diseases of the respiratory system and those that involve metabolic changes, such as diabetes. Breath acetone has long been known as a biomarker for diabetes. However, the results from published data by far have been inconclusive regarding whether breath acetone is a reliable index of diabetic screening. Large variations exist among the results of different studies because there has been no "best-practice method" for breath-acetone measurements as a result of technical problems of sampling and analysis. In this mini-review, we update the current status of our development of a laser-based breath acetone analyzer toward real-time, one-line diabetic screening and a point-of-care instrument for diabetic management. An integrated standalone breath acetone analyzer based on the cavity ringdown spectroscopy technique has been developed. The instrument was validated by using the certificated gas chromatography-mass spectrometry. The linear fittings suggest that the obtained acetone concentrations via both methods are consistent. Breath samples from each individual subject under various conditions in total, 1257 breath samples were taken from 22 Type 1 diabetic (T1D) patients, 312 Type 2 diabetic (T2D) patients, which is one of the largest numbers of T2D subjects ever used in a single study, and 52 non-diabetic healthy subjects. Simultaneous blood glucose (BG) levels were also tested using a standard diabetic management BG meter. The mean breath acetone concentrations were determined to be 4.9 ± 16 ppm (22 T1D), and 1.5 ± 1.3 ppm (312 T2D), which are about 4.5 and 1.4 times of the one in the 42 non-diabetic healthy subjects, 1.1 ± 0.5 ppm, respectively. A preliminary quantitative correlation (R = 0.56, p < 0.05) between the mean individual breath acetone concentration and the mean individual BG levels does exist in 20 T1D subjects with no ketoacidosis. No direct correlation is observed in T1D subjects, T2D subjects, and healthy subjects. The results from a relatively large number of subjects tested indicate that an elevated mean breath acetone concentration exists in diabetic patients in general. Although many physiological parameters affect breath acetone, under a specifically controlled condition fast (<1 min) and portable breath acetone measurement can be used for screening abnormal metabolic status including diabetes, for point-of-care monitoring status of ketone bodies which have the signature smell of breath acetone, and for breath acetone related clinical studies requiring a large number of tests.

Determination of breath acetone in 149 type 2 diabetic patients using a ringdown breath-acetone analyzer.

Over 90% of diabetic patients have Type 2 diabetes. Although an elevated mean breath acetone concentration has been found to exist in Type 1 diabetes (T1D), information on breath acetone in Type 2 diabetes (T2D) has yet to be obtained. In this study, we first used gas chromatography-mass spectrometry (GC-MS) to validate a ringdown breath-acetone analyzer based on the cavity-ringdown-spectroscopy technique, through comparing breath acetone concentrations in the range 0.5-2.5 ppm measured using both methods. The linear fitting of R = 0.99 suggests that the acetone concentrations obtained using both methods are consistent with a largest standard deviation of ±0.4 ppm in the lowest concentration of the range. Next, 620 breath samples from 149 T2D patients and 42 healthy subjects were collected and tested using the breath analyzer. Four breath samples were taken from each subject under each of four different conditions: fasting, 2 h post-breakfast, 2 h post-lunch, and 2 h post-dinner. Simultaneous blood glucose levels were also measured using a standard diabetic-management blood-glucose meter. For the 149 T2D subjects, their exhaled breath acetone concentrations ranged from 0.1 to 19.8 ppm; four different ranges of breath acetone concentration, 0.1-19.8, 0.1-7.1, 0.1-6.3, and 0.1-9.5 ppm, were obtained for the subjects under the four different conditions, respectively. For the 42 healthy subjects, their breath acetone concentration ranged from 0.1 to 2.6 ppm; four different ranges of breath acetone concentration, 0.3-2.6, 0.1-2.6, 0.1-1.7, and 0.3-1.6 ppm, were obtained for the four different conditions. The mean breath acetone concentration of the 149 T2D subjects was determined to be 1.5 ± 1.5 ppm, which was 1.5 times that of 1.0 ± 0.6 ppm for the 42 healthy subjects. No correlation was found between the breath acetone concentration and the blood glucose level of the T2D subjects and the healthy volunteers. This study using a relatively large number of subjects provides new data regarding breath acetone in diabetes (T1D and T2D) and suggests that an elevated mean breath acetone concentration also exists in T2D.

Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modelling study.

BACKGROUND: Short-term exposure to ambient PM2·5 is a leading contributor to the global burden of diseases and mortality. However, few studies have provided the global spatiotemporal variations of daily PM2·5 concentrations over recent decades. METHODS: In this modelling study, we implemented deep ensemble machine learning (DEML) to estimate global daily ambient PM2·5 concentrations at 0·1° × 0·1° spatial resolution between Jan 1, 2000, and Dec 31, 2019. In the DEML framework, ground-based PM2·5 measurements from 5446 monitoring stations in 65 countries worldwide were combined with GEOS-Chem chemical transport model simulations of PM2·5 concentration, meteorological data, and geographical features. At the global and regional levels, we investigated annual population-weighted PM2·5 concentrations and annual population-weighted exposed days to PM2·5 concentrations higher than 15 µg/m3 (2021 WHO daily limit) to assess spatiotemporal exposure in 2000, 2010, and 2019. Land area and population exposures to PM2·5 above 5 µg/m3 (2021 WHO annual limit) were also assessed for the year 2019. PM2·5 concentrations for each calendar month were averaged across the 20-year period to investigate global seasonal patterns. FINDINGS: Our DEML model showed good performance in capturing the global variability in ground-measured daily PM2·5, with a cross-validation R2 of 0·91 and root mean square error of 7·86 µg/m3. Globally, across 175 countries, the mean annual population-weighted PM2·5 concentration for the period 2000-19 was estimated at 32·8 µg/m3 (SD 0·6). During the two decades, population-weighted PM2·5 concentration and annual population-weighted exposed days (PM2·5 >15 µg/m3) decreased in Europe and northern America, whereas exposures increased in southern Asia, Australia and New Zealand, and Latin America and the Caribbean. In 2019, only 0·18% of the global land area and 0·001% of the global population had an annual exposure to PM2·5 at concentrations lower than 5 µg/m3, with more than 70% of days having daily PM2·5 concentrations higher than 15 µg/m3. Distinct seasonal patterns were indicated in many regions of the world. INTERPRETATION: The high-resolution estimates of daily PM2·5 provide the first global view of the unequal spatiotemporal distribution of PM2·5 exposure for a recent 20-year period, which is of value for assessing short-term and long-term health effects of PM2·5, especially for areas where monitoring station data are not available. FUNDING: Australian Research Council, Australian Medical Research Future Fund, and the Australian National Health and Medical Research Council.

Seizure Forecasting by High-Frequency Activity (80-170 Hz) in Long-term Continuous Intracranial EEG Recordings.

BACKGROUND AND OBJECTIVES: Reliable seizure forecasting has important implications in epilepsy treatment and improving the quality of lives for people with epilepsy. High-frequency activity (HFA) is one biomarker that has received significant attention over the past two decades, but its predictive value in seizure forecasting remains uncertain. This work aimed to determine the utility of HFA in seizure forecasting. METHODS: We used seizure data and HFA (80-170 Hz) data obtained from long-term, continuous intracranial EEG recordings of drug-resistant epilepsy patients. Instantaneous rates and phases of HFA cycles were used as features for seizure forecasting. Seizure forecasts based on each individual HFA feature, and using a combined approach, were generated pseudo-prospectively (causally). To compute the instantaneous phases for pseudo-prospective forecasting, real-time phase estimation based on an autoregressive model was employed. Features were combined using a weighted average approach. The performance of seizure forecasting was primarily evaluated by the area under the curve (AUC). RESULTS: Of 15 studied patients (median recording duration: 557 days, median seizures: 151), 12 patients with more than 10 seizures after 100 recording days were included in the pseudo-prospective analysis. The presented real-time phase estimation is feasible and can causally estimate the instantaneous phases of HFA cycles with high accuracy. Pseudo-prospective seizure forecasting based on HFA rates and phases performed significantly better than chance in 11 out of 12 patients, although there were patient-specific differences. Combining rate and phase information improved forecasting performance compared to using either feature alone. The combined forecast using the best-performing channel yielded a median AUC of 0.70, a median sensitivity of 0.57, and a median specificity of 0.77. CONCLUSIONS: These findings show that HFA could be useful for seizure forecasting and represent proof of concept for utilizing prior information of patient-specific relationships between HFA and seizures in pseudo-prospective forecasting. Future seizure forecasting algorithms might benefit from the inclusion of HFA, and the real-time phase estimation approach can be extended to other biomarkers. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that high-frequency activity (80-170 Hz) in long-term continuous intracranial EEG can be useful to forecast seizures in patients with refractory epilepsy.

High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed.

For the past 2 decades, high-frequency oscillations (HFOs) have been enthusiastically studied by the epilepsy community. Emerging evidence shows that HFOs harbor great promise to delineate epileptogenic brain areas and possibly predict the likelihood of seizures. Investigations into HFOs in clinical epilepsy have advanced from small retrospective studies relying on visual identification and correlation analysis to larger prospective assessments using automatic detection and prediction strategies. Although most studies have yielded promising results, some have revealed significant obstacles to clinical application of HFOs, thus raising debate about the reliability and practicality of HFOs as clinical biomarkers. In this review, we give an overview of the current state of HFO research and pinpoint the conceptual and methodological issues that have hampered HFO translation. We highlight recent insights gained from long-term data, high-density recordings, and multicenter collaborations and discuss the open questions that need to be addressed in future research.

Temperature-mortality association during and before the COVID-19 pandemic in Italy: A nationwide time-stratified case-crossover study.

OBJECTIVES: To identify the associations of temperature with non-COVID-19 mortality and all-cause mortality in the pandemic 2020 in comparison with the non-COVID-19 period in Italy. METHODS: The data on 3,189,790 all-cause deaths (including 3,134,137 non-COVID-19 deaths) and meteorological conditions in 107 Italian provinces between February 1st and November 30th in each year of 2015-2020 were collected. We employed a time-stratified case-crossover study design combined with the distributed lag non-linear model to investigate the relationships of temperature with all-cause and non-COVID-19 mortality in the pandemic and non-pandemic periods. RESULTS: Cold temperature exposure contributed higher risks for both all-cause and non-COVID-19 mortality in the pandemic period in 2020 than in 2015-2019. However, no different change was found for the impacts of heat. The relative risk (RR) of non-COVID-19 deaths and all-cause mortality at extremely cold (2 °C) in comparison with the estimated minimum mortality temperature (19 °C) in 2020 were 1.63 (95% CI: 1.55-1.72) and 1.45 (95%CI: 1.31-1.61) respectively, which were higher than all-cause mortality risk in 2015-2019 with RR of 1.19 (95%CI: 1.17-1.21). CONCLUSION: Cold exposure indicated stronger impacts than high temperatures on all-cause and non-COVID-19 mortality in the pandemic year 2020 compared to its counterpart period in 2015-2019 in Italy.

Spatiotemporal Patterns of High-Frequency Activity (80-170 Hz) in Long-Term Intracranial EEG.

OBJECTIVE: To determine the utility of high-frequency activity (HFA) and epileptiform spikes as biomarkers for epilepsy, we examined the variability in their rates and locations using long-term ambulatory intracranial EEG (iEEG) recordings. METHODS: This study used continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy. HFA was defined as 80- to 170-Hz events with amplitudes clearly larger than the background, which was automatically detected with a custom algorithm. The automatically detected HFA was compared with visually annotated high-frequency oscillations (HFOs). The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. RESULTS: HFA included manually annotated HFOs and high-amplitude events occurring in the 80- to 170-Hz range without observable oscillatory behavior. HFA and spike rates had high amounts of intrapatient and interpatient variability. Rates of HFA and spikes had large variability after electrode implantation in most of the patients. Locations of HFA and spikes varied up to weeks in more than one-third of the patients. Both HFA and spike rates showed strong circadian rhythms in all patients, and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. CONCLUSION: Analysis of HFA and epileptiform spikes should consider postimplantation variability. HFA and epileptiform spikes, like seizures, show circadian rhythms. However, the circadian profiles can vary spatially within patients, and their correlations to seizures are patient-specific.

Long-term exposure to PM2.5 and fasting plasma glucose in non-diabetic adolescents in Yogyakarta, Indonesia.

BACKGROUND: Indonesia is facing serious air pollution. However, very few studies have been conducted to examine the health risks of air pollution in Indonesia, particularly for adolescents. OBJECTIVE: To assess the association between long-term exposure to ambient particles with a diameter of <2.5 µm (PM2.5) and fasting plasma glucose (FPG) in adolescents. METHODS: A cross-sectional study was conducted in 482 adolescents aged 14-18 years in Yogyakarta, Indonesia in 2016. We finally included 469 (97.30%) participants who had no missing data for data analysis. We collected individual data on socio-demographics, behavioral habits, and health information through standardized questionnaires. Satellite-based PM2.5 concentrations from 2013 to 2016 were assigned based on participants' residential addresses. The association between PM2.5 and FPG was examined using a generalized linear regression model while FPG was modeled as a continuous variable. An ordered logistic regression model was used to assess the relationship between PM2.5 and FPG categories. RESULTS: Every 1 µg/m³ increase in PM2.5 was associated with a 0.34 mg/dL [95 confidence interval (95% CI): 0.08 mg/dL, 0.59 mg/dL] increase in FPG levels. Comparing with the low FPG level (under 86 mg/dL), every 1 µg/m³ increase in PM2.5 was associated with a 10.20% (95% CI: 1.60%, 19.80%) increase in the odds of impaired fasting glucose (IFG) (100-125 mg/dL). Stratified analyses indicated greater effects on participants with hypertension [odds ratio (OR) = 1.30, 95% CI: 1.09, 1.57] and those had higher physical activities (OR = 1.36, 95% CI: 1.09, 1.57). Adolescents' sex, obesity status and different cutoff points of FPG did not modify the association between the exposure to PM2.5 and FPG levels. CONCLUSION: Long-term exposure to PM2.5 was associated with increased FPG levels in Indonesian non-diabetic adolescents.

A fully integrated standalone portable cavity ringdown breath acetone analyzer.

Breath analysis is a promising new technique for nonintrusive disease diagnosis and metabolic status monitoring. One challenging issue in using a breath biomarker for potential particular disease screening is to find a quantitative relationship between the concentration of the breath biomarker and clinical diagnostic parameters of the specific disease. In order to address this issue, we need a new instrument that is capable of conducting real-time, online breath analysis with high data throughput, so that a large scale of clinical test (more subjects) can be achieved in a short period of time. In this work, we report a fully integrated, standalone, portable analyzer based on the cavity ringdown spectroscopy technique for near-real time, online breath acetone measurements. The performance of the portable analyzer in measurements of breath acetone was interrogated and validated by using the certificated gas chromatography-mass spectrometry. The results show that this new analyzer is useful for reliable online (online introduction of a breath sample without pre-treatment) breath acetone analysis with high sensitivity (57 ppb) and high data throughput (one data per second). Subsequently, the validated breath analyzer was employed for acetone measurements in 119 human subjects under various situations. The instrument design, packaging, specifications, and future improvements were also described. From an optical ringdown cavity operated by the lab-set electronics reported previously to this fully integrated standalone new instrument, we have enabled a new scientific tool suited for large scales of breath acetone analysis and created an instrument platform that can even be adopted for study of other breath biomarkers by using different lasers and ringdown mirrors covering corresponding spectral fingerprints.

Temporal and spatial temperature distributions on glabrous skin irradiated by a 1940 nm continuous-wave laser stimulator.

For predicting pain stimulation effects and avoiding damage in 1940nm laser evoked potentials (LEPs) experiments, a 2-layer finite element model (FEM-2) was constructed. A series of experiments were conducted on ex-vivo pig skin pieces to verify temperature distribution predicted by this model. Various laser powers and beam radii were employed. Experimental data of time-dependent temperature responses in different sub-skin depths and space-dependent surface temperature was recorded by thermocouple instrument. By comparing with the experimental data and model results, FEM-2 model was proved to predict temperature distributions accurately. A logarithmic relationship between laser power density and temperature increment was revealed by the results. It is concluded that power density is an effective parameter to estimate pain and damage effect. The obtained results also indicated that the proposed FEM-2 model can be extended to predict pain and damage thresholds of human skin samples and thus contribute to LEPs study.