Daily Counts Research Articles

In Case You Haven't Heard…

Mondays and New Year's Day have the highest suicide risk according to new research, “Association of holidays and the day of the week with suicide risk: multi‐country, two‐stage, time series study,” published in The BMJ Oct. 23, according to a news release. An international research team used an advanced statistical technique called standardized two‐stage time series analysis to investigate suicide risk patterns. They analyzed data from 740 locations across 26 countries, gathered from the Multi‐country Multi‐city Collaborative Research Network database. This dataset comprised 1.7 million suicide cases, including daily suicide counts along with daily mean temperature data spanning from January 1971 to December 2019. The results revealed that suicide risk was highest on Mondays across all countries, though the effect of weekends varied. New Year's Day was associated with an increase in suicide risk in all countries, while Christmas showed a mixed pattern. Although there was an overall decreasing trend in suicide risk on other national holidays, the risk tended to increase after these holidays in most countries. Notably, the findings also showed that men are more vulnerable to variations related to the day of the week and New Year's Day compared to women. Researchers hope that this large‐scale study can lead to timely interventions, potentially saving countless lives.

Efficacy and safety of diclazuril nanoemulsion in control of Eimeria tenella in broilers

BackgroundNanotechnology has the potential to reduce drug dosage while increasing efficacy; thus, the current work intends to synthesize diclazuril nanoemulsion and assess its performance against experimental coccidiosis in broilers.MethodsDiclazuril nanoemulsion (DZN) was formulated and characterized by zeta seizer and zeta potential. The formulated DZN was evaluated in vivo against Eimeria tenella infected chicks. DZN and DZ were used in 2 programs; therapeutic and prophylactic. A total of 210 one-day-old broiler chicks were distributed equally into six groups. The controls were negative uninfected untreated and positive infected untreated (G1 & G2). Therapeutic groups (G3 & G4) treated by DZ and DZN after appearance of the clinical signs of coccidiosis and continued for 5 days. Prophylaxis groups (G5 & G6) received DZ and DZN at 3 days before challenge and continued for 5 days after infection. The treatments dosages were 10 mg/mL for DZ of commercial origin and 2.5 mg/mL for the prepared DZN. All groups (except negative control) orally infected then followed up for clinical signs of coccidiosis, mortality rate, oocysts count, performance, hematological and biochemical parameters in addition to histopathological lesions.ResultsThe therapeutic groups showed that both treated groups (DZ and DZN) revealed similar results including good body weight gain, a low lesion caecal score, a low daily and total oocyst shedding count, and a low mortality rate. Regarding the biochemical parameters, all parameters were affected during infection then restored after the 12th day post infection. However, in the prophylactic groups, showed mild clinical signs and the blood pictures and biochemical parameters were nearly like the control negative without infection.ConclusionDZN at a quarter dose of standard DZ produced the same outcomes as DZ at 10 mg/mL. Furthermore, DZN does not impair the typical safety of diclazuril in treated chicks.

Staggered botulinum toxin-a injections into parotid and submandibular glands prior to four-duct ligation for pediatric sialorrhea.

This study aimed to assess the effectiveness of four-duct ligation following Botulinum toxin-A injections into the parotid and submandibular glands in pediatric patients with sialorrhea resistant to nonsurgical treatments. Prior research has individually explored either surgical or Botulinum toxin interventions; however, the safety and efficacy of the combined approach to these treatments have yet to be documented. Patients were assessed before surgery and 6, 12, and 24months post-operatively. Evaluations involved interviews with parents and caregivers, conducted face-to-face or by telephone, using the Drooling Severity and Frequency Scales. Additionally, metrics such as the daily count of bib changes and the hourly frequency of saliva wiping were recorded. Quality-of-life assessments were performed before and after the surgical procedures. All complications associated with the interventions were carefully recorded. The study group consisted of 25 participants, with a median age of 6years (range 2-17years). Each participant was diagnosed with a neurological, metabolic, or genetic disorder. Evaluations were conducted periodically, culminating in a final follow-up at 24months. The treatment demonstrated a 100% success rate at six months post-operation (25/25 patients), which slightly decreased to 96% at the 12-month mark (24/25 patients) and further to 84% by the 24-month follow-up (21/25 patients). Major complications were not observed. However, minor complications were reported in six patients (24%): three exhibited temporary facial swelling (12%), two encountered minor bleeding (8%), and one experienced excessive dryness (4%). The combination of Botulinum toxin-A injection 3 weeks prior to the four-duct ligation procedure demonstrated high success rates and substantially reduced the incidence of infectious and cosmetic complications. Consequently, this staggered approach to combination treatment is recommended for managing pediatric sialorrhea cases that are resistant to non-surgical interventions.

Enhancing Daily Crash Count Prediction using Deep Learning with Window Size Selection and Seasonality Predictor Integration

The escalating need for proactive safety measures, coupled with advancements in data collection and analytical techniques, has significantly refined the accuracy of crash count predictions, shifting from annual scales to finer daily or hourly estimates. This research places emphasis on recurrent neural networks (RNNs), specifically the long short-term memory (LSTM) model, acknowledged for effectively managing sequential data in time series predictions. Paramount considerations encompass the treatment of input data, including the decision to incorporate temporal features alongside endogenous historical target values, and the establishment of an optimal window size for data input. Despite the critical nature of these facets, exhaustive studies concurrently investigating both under controlled conditions are scarce. This research addresses this gap, assessing diverse scenarios featuring distinct temporal treatments and window sizes, and employing an LSTM model with uniform fine-tuned parameters to ensure a fair comparison. Findings indicate a significant variation in performance among models employing different window sizes and month predictor integration, under identical RNN structures and LSTM configurations. The best model outperformed the least effective by roughly 30% concerning root mean square error (RMSE) and ranking correlation between predicted and actual target crash counts in the test datasets. Unlike seasonality predictor treatment, diverse window size selections did not lead to statistically significant differences in model performance. Generally, a window size of 3 performed worst under most conditions, followed by a size of 14. Sizes of 7 and 28 performed best in nearly an equal numbers of cases.

Expanding Short-Term Pedestrian Volume Counts to Average Annual Daily Counts at Intersections: Temporal Pattern Indices and a Comparison of Statistical Land Use and Simple Threshold Methods

Quantifying pedestrian exposure for intersections is important for developing pedestrian-centric strategies. Exposure is typically expressed as the annual-average daily pedestrian traffic (AADPT). AADPT can be directly calculated when continuous counts (CCs) are available. However, CCs are rarely available for all intersections, requiring estimation methods. This work explores the steps involved in expanding pedestrian short-term counts (STCs) to AADPT using factor groups. Day-of-week-of-month expansion factors were used to expand 8-h STCs. The first step consists of grouping sites with similar pedestrian activity patterns (factor groups). Five factor groups were identified, and three pedestrian temporal pattern indicators were proposed to characterize them. The thresholds of these indicators for grouping sites with CCs are available in this work. The second step involves the challenge of associating sites where only STCs are available (unknown traffic patterns) with a particular factor group. Multinomial logistic regression (MLR) was developed to link factor groups to land use, socioeconomic, and operational attributes. Explanatory variables that represent commercial and industrial areas and the number of schools and transit stops were found to be significant. Simpler methods were also introduced to identify factor groups strongly affected by schools. The last step assesses the expansion of STCs to AADPT using the proposed approaches. The results showed improved performance when compared with averaging expansion factors across all sites. It was also demonstrated that, in certain cases, simple approaches that rely exclusively on the identification of sites that are highly affected by schools present similar performance to more complex approaches, such as MLR.

Impact of motivational feedback on levels of physical activity and quality of life by activity monitoring following knee arthroplasty surgery—protocol for a randomized controlled trial nested in a prospective cohort (Knee-Activity)

BackgroundEvidence on how to improve daily physical activity (PA) levels following total knee arthroplasty (TKA) or medial uni-compartmental knee arthroplasty (mUKA) by motivational feedback is lacking. Moreover, it is unknown whether a focus on increased PA after discharge from the hospital improves rehabilitation, physical function, and quality of life. The aim of this randomized controlled trial (RCT) nested in a prospective cohort is (a) to investigate whether PA, physical function, and quality of life following knee replacement can be increased using an activity monitoring device including motivational feedback via a patient app in comparison with activity monitoring without feedback (care-as-usual), and (b) to investigate the potential predictive value of PA level prior to knee replacement for the length of stay, return to work, and quality of life.MethodsThe study is designed as a multicenter, parallel-group, superiority RCT with balanced randomization (1:1) and blinded outcome assessments. One hundred and fifty patients scheduled for knee replacement (TKA or mUKA) will be recruited through Odense University Hospital, Denmark, Vejle Hospital, Denmark and Herlev/Gentofte Sygehus, Denmark. Patients will be randomized to either 12 weeks of activity monitoring and motivational feedback via a patient app by gamification or 'care-as-usual,' including activity monitoring without motivational feedback. The primary outcome is the between-group change score from baseline to 12-week follow-up of cumulative daily accelerometer counts, which is a valid proxy for average objectively assessed daily PA.DiscussionImproving PA through motivational feedback following knee replacement surgery might improve post-surgical function, health-related quality of life, and participation in everyday life.Trial registrationClinicalTrials.gov, ID: NCT06005623. Registered on 2023–08-22.Trial statusRecruiting.

An assessment of excess mortality during the COVID-19 pandemic, a retrospective post-mortem surveillance in 12 districts – Zambia, 2020–2022

BackgroundThe number of COVID-19 deaths reported in Zambia (N = 4069) is most likely an underestimate due to limited testing, incomplete death registration and inability to account for indirect deaths due to socioeconomic disruption during the pandemic. We sought to assess excess mortality during the COVID-19 pandemic in Zambia.MethodsWe conducted a retrospective analysis of monthly-death-counts (2017–2022) and individual-daily-deaths (2020–2022) of all reported health facility and community deaths at district referral health facility mortuaries in 12 districts in Zambia. We defined COVID-19 wave periods based on a sustained nationally reported SARS-CoV-2 test positivity of greater than 5%. Excess mortality was calculated as the difference between observed monthly death counts during the pandemic (2020–2022) and the median monthly death counts from the pre-pandemic period (2017–2019), which served as the expected number of deaths. This calculation was conducted using a Microsoft Excel-based tool. We compared median daily death counts, median age at death, and the proportion of deaths by place of death (health facility vs. community) by wave period using the Mann-Whitney-U test and chi-square test respectively in R.ResultsA total of 112,768 deaths were reported in the 12 districts between 2020 and 2022, of which 17,111 (15.2%) were excess. Wave periods had higher median daily death counts than non-wave periods (median [IQR], 107 [95–126] versus 96 [85–107], p < 0.001). The median age at death during wave periods was older than non-wave periods (44.0 [25.0–67.0] versus 41.0 [22.0–63.0] years, p < 0.001). Approximately half of all reported deaths occurred in the community, with an even greater proportion during wave periods (50.6% versus 53.1%, p < 0.001), respectively.ConclusionThere was excess mortality during the COVID-19 pandemic in Zambia, with more deaths occurring within the community during wave periods. This analysis suggests more COVID-19 deaths likely occurred in Zambia than suggested by officially reported numbers. Mortality surveillance can provide important information to monitor population health and inform public health programming during pandemics.

Monitoring of the Fall Armyworm (Spodoptera frugiperda Walker) Moth for the Determination of Efficient Chemical Control in Zea mays L.

Objective: To determine the population dynamics of the fall armyworm (FAW), identify the critical period of highest infestation and to determine the number of agrochemicals applications for its control. Design/methodology/approach: A monitoring and capture of male FAW moths was conducted using plastic traps with sexual attraction pheromones. A daily count of captured moths was performed during the crop season, the data were plotted to determine the period of highest infestation and the optimal timing for chemical control. Additionally, the number of insecticide applications for FAW control was evaluated, with treatments including none (T0), one (T1), two (T2), three (T3), and four (T4) applications. A randomized complete block design with nine repetitions was used. Before each application, the number of plants with visible damage and its intensity were counted using the Davis visual scale. Statistical analysis of the measured variables was conducted. Results: The results showed that moths were evenly distributed across the planted surface, and two periods of higher infestation were identified: between 32 to 35 and 70 to 76 days after planting, respectively. The biological cycle of the FAW was between 38 to 41 days. The analysis of variance showed statistical differences (p ≤ 0.001) among the treatments. Findings/conclusions: Using plastic traps with sexual attraction pheromones is an efficient method for capturing, monitoring, reducing the population, estimate the length of the biological cycle, and identifying the highest infestation period of the FAW. Moreover, two insecticide applications during the periods of highest infestation resulted in optimal control of FAW.

A - 98 Physical Activity and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) in Older Adults with Dementia with Lewy Bodies (DLB) and Alzheimer’s Disease (ad)

Abstract Objective Past studies have suggested an association between physical activity and neuropsychological scores in cognitively healthy older adults, but similar studies in adults with dementia have been inconsistent. This pilot study is one of the first to consider dementia subtypes and the relationship between daily physical activity and cognitive performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Methods We administered the RBANS to participants with DLB (n = 11) and ad (n = 8) and then measured physical activity using triaxial wGT3X-BT actigraphy devices for two weeks. Two-sample t-tests were performed comparing participants with DLB and ad on physical activity, RBANS domains, and UPDRS scores (see Table 1). Linear mixed models with a random intercept were used to assess the association between activity counts and RBANS scores, adjusting for dementia subtype. Results Compared to participants with DLB, ad participants were significantly more active (p = 0.035) and had lower UPDRS Part III scores (p &amp;lt; 0.001). We found no significant association between daily activity counts and total RBANS scores (Beta = −2.77, 95% CI -6.0, −0.47, p = 0.091). Conclusions To our knowledge, this is the first study to compare differences between physical activity and RBANS scores, accounting for dementia subtypes. Participants with DLB were less physically active than participants with ad; this is unsurprising given that parkinsonian signs and symptoms are a core DLB feature. We found no significant associations between daily activity and cognitive performance, but we lacked the sample size to account for potential contributory variables. Larger explorations of physical activity, RBANS domain score, and dementia subtype are necessary.

Aerobiology of Olive Pollen (Olea europaea L.) in the Atmosphere of the Iberian Peninsula

Olea europaea L. pollen is one of the main causes of pollinosis and respiratory diseases in the Iberian Peninsula (IP). The aim of this study was to provide a pollen calendar in different regions of the IP, which could help allergists and allergic patients in the management of Olea europaea allergic diseases, and to update/complement what has already been reported on olive trees’ aeropalynology in this region. Airborne Olea pollen dynamics were analyzed over a period of 8 years in a total of 21 localities, 7 in Portugal and 14 in Spain. Airborne pollen monitoring was carried out using the Hirst-type spore trap method and following the recommendations of the Quality Control Working Group of the European Aerobiology Society. The daily pollen count, the annual pollen profile, the Annual Pollen Integral (APIn), the Seasonal Pollen Integral (SPIn) and the Pollen Peak, all expressed in number of pollen grains per cubic metre of air, together with the main pollen season and its characteristics, the Start Day, the End Day and the length of the pollen season, were calculated for each sampling station. Differences in mean Olea pollen concentration between odd and even years were also analyzed. On average, the main pollen season (MPS) started in April/May and ended in June, with Pollen Peaks recorded in May, except in Burgos, where it was recorded in June. The longest MPS occurred in Lisbon, Oviedo and Valencia (53 days) and the shortest in Vitoria (25 days). A high daily pollen concentration (i.e., &gt;200 grains/m3) was recorded between 1 and 38 days along the year in all sampling stations of the southwest quadrant of the IP and in Jaén. A biannual pattern, characterized by alternating years of high and low pollen production, was found in the southwest of the IP. In conclusion, the study provided a deeper understanding of the pollination behaviour of olive trees in the IP and allowed the establishment of a representative Olea pollen calendar for this region. In addition, our results suggest the usefulness of investigating more detailed relationships between annual Olea pollen, allergen sensitization and symptoms, both for allergists involved in the study and management of allergic respiratory diseases caused by this species and for the self-management of disease in allergic subjects.

Toward a dynamic national transportation noise map: Modeling temporal variability of spectral traffic noise emission levels.

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM). The method incorporates three models that predict temporal variability of traffic volume, predict temporal variability of different traffic classes, and use Traffic Noise Model (TNM) 3.0 equations to give traffic noise emission levels based on vehicle numbers and class mix. Location-specific features are used to predict average class mix across CONUS. VROOM then incorporates dynamic traffic class mix data to obtain dynamic traffic class mix. TNM 3.0 equations then give estimated equivalent sound level emission spectra near roads with up to hourly resolution. Important temporal traffic noise characteristics are modeled, including diurnal traffic patterns, rush hours in urban locations, and weekly and yearly variation. Examples of the temporal variability are depicted and possible types of uncertainties are identified. Altogether, VROOM can be used to map national transportation noise with temporal and spectral variability.

A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID‐19 in Quebec

ABSTRACTInfectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instance, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) that caused the global COVID‐19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID‐19 test‐positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID‐19 cases, the number of hospitalizations and the number of ICU admissions due to COVID‐19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization.

Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN

This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.

Spatial Patterns of Tourism: Towards Legally-Based Tourism Village Regulations Based on Physical Carrying Capacity Analysis

Lowita Beach was a tourist area in Suppa District, Pinrang Regency, with 11 tourist attractions. The annual increase in visitors to Lowita Beach can compromise tourist comfort and damage the area's natural habitat. This is the basis for researchers to analyse the Physical Carrying Capacity (PCC) of Lowita Beach. This study aimed to determine the characteristics of tourist activities, assess the physical carrying capacity of the area, and formulate the role of tourist villages in managing the Lowita Beach area. The research approach used was research and development, as initiated by Borg and Gall: the calculation of Physical Carrying Capacity (PCC), area parameters, and the number of tourists. The study population comprised 93 tourists who visited the Lowita Beach area. Data was collected through the completion of questionnaires, interviews, measurements, and field observations. A quantitative-descriptive approach was used for data analysis. The Physical Carrying Capacity (PCC) analysis reveals that Lowita Beach's ideal capacity is 1 visitor per day, yet it currently hosts an average of 35 visitors daily, significantly surpassing this threshold. This overcapacity indicates that the beach is experiencing environmental strain and underscores the need for management strategies to align visitor numbers with sustainable limits. In conclusion, despite the physical carrying capacity (PCC) of Lowita Beach being 17 visitors per day, the present daily visitor count suggests that the beach is currently facing overcrowding, albeit not to a significant extent. Hence, it is necessary to implement policy interventions from the tourist villages of Tasiwalie and Wiringtasi, which should include the establishment of standardized operating procedures and fostering collaboration among stakeholders, in order to ensure sustainable tourism management at Lowita Beach.

Associations between short-term exposure to airborne carbonaceous particles and mortality: A time-series study in London during 2010–2019

Exposure to ambient particulate matter (PM) has been identified as a major global health concern; however, the importance of specific chemical PM components remains uncertain. Recent studies have suggested that carbonaceous aerosols are important detrimental components of the particle mixture. Using time-series methods, we investigated associations between short-term exposure to carbonaceous particles and mortality in London, UK. Daily counts of non-accidental, respiratory, and cardiovascular deaths were obtained between 2010 and 2019. For the same period, daily concentrations of carbonaceous particles: organic (OC), elemental (EC), wood-burning (WC), total carbon (TC) and equivalent black carbon (eBC) were sourced from two centrally located monitoring sites (one urban-traffic and one urban-background). Generalized additive models were used to estimate the percentage change in mortality risk associated with interquartile range increases in particulate concentrations. Lagged effects up to 3 days were examined. Stratified analyses were conducted by age, sex, and season, separate analyses were also performed by site-type. For non-accidental mortality, positive associations were observed for all particle species at lag1, including statistically significant percentage risk changes in WC (0.51% (95%CI: 0.19%, 0.82%) per IQR (0.68 μg/m3)) and OC (0.45% (95%CI: 0.04%, 0.87% per IQR (2.36 μg/m3)). For respiratory deaths, associations were greatest for particulate concentrations averaged over the current and previous 3 days, with increases in risk of 1.70% (95%CI: 0.64%, 2.77%) for WC and 1.31% (95%CI: −0.08%, 2.71%) for OC. No associations were found with cardiovascular mortality. Results were robust to adjustment for particle mass concentrations. Stratified analyses suggested particulate effects were greatest in the summer and respiratory associations more pronounced in females. Our findings are supportive of an association between carbonaceous particles and non-accidental and respiratory mortality. The strongest evidence of an effect was for WC; this is of significance given the rising popularity of wood-burning for residential space heating and energy production across Europe.

Supporting Examinations for Hair Disorders

Hair evaluation methods are grouped into three main categories: non-invasive methods (for example: questionnaire, daily hair counts, standardized wash test, 60-s hair count, global photographs, dermoscopy, hair weight, contrasting felt examination, phototrichogram, TrichoScan, and polarizing and surface electron microscopy), semi-invasive methods (for example: trichogram and unit area trichogram), and invasive methods (for example: scalp biopsy). This article explores supporting examinations for the diagnosis of hair disorders.

Association between precipitation and mortality due to diarrheal diseases by climate zone: A multi-country modeling study.

Precipitation could affect the transmission of diarrheal diseases. The diverse precipitation patterns across different climates might influence the degree of diarrheal risk from precipitation. This study determined the associations between precipitation and diarrheal mortality in tropical, temperate, and arid climate regions. Daily counts of diarrheal mortality and 28-day cumulative precipitation from 1997 to 2019 were analyzed across 29 locations in eight middle-income countries (Argentina, Brazil, Costa Rica, India, Peru, the Philippines, South Africa, and Thailand). A two-stage approach was employed: the first stage is conditional Poisson regression models for each location, and the second stage is meta-analysis for pooling location-specific coefficients by climate zone. In tropical climates, higher precipitation increases the risk of diarrheal mortality. Under extremely wet conditions (95th percentile of 28-day cumulative precipitation), diarrheal mortality increased by 17.8% (95% confidence interval [CI] = 10.4%, 25.7%) compared with minimum-risk precipitation. For temperate and arid climates, diarrheal mortality increases in both dry and wet conditions. In extremely dry conditions (fifth percentile of 28-day cumulative precipitation), diarrheal mortality risk increases by 3.8% (95% CI = 1.2%, 6.5%) for temperate and 5.5% (95% CI = 1.0%, 10.2%) for arid climates. Similarly, under extremely wet conditions, diarrheal mortality risk increases by 2.5% (95% CI = -0.1%, 5.1%) for temperate and 4.1% (95% CI = 1.1%, 7.3%) for arid climates. Associations between precipitation and diarrheal mortality exhibit variations across different climate zones. It is crucial to consider climate-specific variations when generating global projections of future precipitation-related diarrheal mortality.

Longitudinal Cough Frequency Monitoring in Persistent Coughers: Daily Variability and Predictability

PurposeWe determined the cough counts and their variability in subjects with persistent cough for 30 days.MethodsThe Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analyzed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.ResultsThe mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS) have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as the percentage of days when cough frequencies fall within that day’s 95% confidence interval. Overall Predictability was the mean of the 30-One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).ConclusionThere is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. If confirmed in future studies, the clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.

Add-On Bifidobacterium Bifidum Supplement in Children with Attention-Deficit/Hyperactivity Disorder: A 12-Week Randomized Double-Blind Placebo-Controlled Clinical Trial.

We conducted a 12-week randomized double-blind placebo-controlled clinical trial to investigate the potential impact of Bifidobacterium bifidum (Bf-688) supplementation on attention-deficit/hyperactivity disorder (ADHD). Children with ADHD who were already receiving a stable dose of methylphenidate (MPH) treatment were enrolled and were randomly assigned to two groups: one receiving add-on Bf-688 (daily bacterial count of 5 × 109 CFUs) (n = 51) and the other receiving a placebo (n = 51). All participants underwent assessments using Conners' Continuous Performance Test (CPT) and Conners' Continuous Auditory Test of Attention (CATA). Additionally, fecal samples were collected at the beginning of the trial (week 0) and at the endpoint (week 12). Remarkably, the group receiving Bf-688 supplementation, but not the placebo group, exhibited significant improvements in omission errors in CPT as well as Hit reaction time in both CPT and CATA. Gut microbiome analysis revealed a significant increase in the Firmicutes to Bacteroidetes ratio (F/B ratio) only in the Bf-688 group. Furthermore, we identified significant negative correlations between N-Glycan biosynthesis and Hit reaction time in both CPT and CATA. Our results demonstrate that the probiotic Bf-688 supplement can enhance neuropsychological performance in children with ADHD, possibly by altering the composition of the gut microbiota, ultimately leading to reduced N-Glycan biosynthesis.

Extreme Temperatures and Stroke Mortality: Evidence From a Multi-Country Analysis.

Extreme temperatures contribute significantly to global mortality. While previous studies on temperature and stroke-specific outcomes presented conflicting results, these studies were predominantly limited to single-city or single-country analyses. Their findings are difficult to synthesize due to variations in methodologies and exposure definitions. Within the Multi-Country Multi-City Network, we built a new mortality database for ischemic and hemorrhagic stroke. Applying a unified analysis protocol, we conducted a multinational case-crossover study on the relationship between extreme temperatures and stroke. In the first stage, we fitted a conditional quasi-Poisson regression for daily mortality counts with distributed lag nonlinear models for temperature exposure separately for each city. In the second stage, the cumulative risk from each city was pooled using mixed-effect meta-analyses, accounting for clustering of cities with similar features. We compared temperature-stroke associations across country-level gross domestic product per capita. We computed excess deaths in each city that are attributable to the 2.5% hottest and coldest of days based on each city's temperature distribution. We collected data for a total of 3 443 969 ischemic strokes and 2 454 267 hemorrhagic stroke deaths from 522 cities in 25 countries. For every 1000 ischemic stroke deaths, we found that extreme cold and hot days contributed 9.1 (95% empirical CI, 8.6-9.4) and 2.2 (95% empirical CI, 1.9-2.4) excess deaths, respectively. For every 1000 hemorrhagic stroke deaths, extreme cold and hot days contributed 11.2 (95% empirical CI, 10.9-11.4) and 0.7 (95% empirical CI, 0.5-0.8) excess deaths, respectively. We found that countries with low gross domestic product per capita were at higher risk of heat-related hemorrhagic stroke mortality than countries with high gross domestic product per capita (P=0.02). Both extreme cold and hot temperatures are associated with an increased risk of dying from ischemic and hemorrhagic strokes. As climate change continues to exacerbate these extreme temperatures, interventional strategies are needed to mitigate impacts on stroke mortality, particularly in low-income countries.