Efficacy of administering a sugar-free flavor before dental injections on pain perception in children: A split-mouth randomized crossover clinical trial.

BACKGROUND: Sweet taste administration before dental injections helps to control associated pain in children. AIM: To evaluate the efficacy of using a sugar-free flavor on pain perception during dental injections. DESIGN: Children (n = 84) aged 4-9 (mean 6.71 ± 1.55) years who required buccal infiltration bilaterally participated in this split-mouth randomized crossover study. On the test side (flavor visit), infiltration injections were applied after receiving a sugar-free flavor. On the control side (no flavor visit), sterile water was administered. Demographic characteristics, body mass index (BMI), and sweet taste preference (STP) were recorded. Pain perception during injection was measured using heart rate (HR), sound, eyes, and motor (SEM) scale, and Wong-Baker Faces pain scale (WBFPS). RESULTS: Most children had healthy weight (72.6%) and equal STP (32.1%). In the test side, mean HR during injection, HR differences before and during injection, and SEM scores were significantly lower (p < .001, for all). There was no significant difference in the WBFPS between both visits. Flavor had a significant effect on pain reduction (p = .001 for HR, p = .000 for SEM), whereas age, gender, BMI, STP, and treatment side did not. Treatment sequence had a significant effect on total SEM scores (p = .021); children who received the flavor during their first visit had lower SEM scores. CONCLUSION: Using a sugar-free flavor before dental injections helps in reducing associated pain in children.

ChatGPT for phenotypes extraction: one model to rule them all?

Information Extraction (IE) is a core task in Natural Language Processing (NLP) where the objective is to identify factual knowledge in textual documents (often unstructured), and feed downstream use cases with the resulting output. In genomic medicine for instance, being able to extract the most precise list of phenotypes associated to a patient allows to improve genetic disease diagnostic, which represents a vital step in the modern deep phenotyping approach. As most of the phenotypic information lies in clinical reports, the challenge is to build an IE pipeline to automatically recognize phenotype concepts from free-text notes. A new machine learning paradigm around large language models (LLM) has given rise of an increasing number of academic works on this topic lately, where sophisticated combinations of different technics have been employed to improve the phenotypes extraction accuracy. Even more recently released, the ChatGPT1 application nevertheless raises the question of the relevance of these approches compared to this new generic one based on an instruction-oriented LLM. In this paper, we propose a rigorous evaluation of ChatGPT and the current state-of-the-art solutions on this specific task, and discuss the possible impacts and the technical evolutions to consider in the medical domain.Clinical relevance- Deep phenotyping on electronic health records has proven its ability to improve genetic diagnosis by clinical exomes [10]. Thus, comparing state-of-the-art solutions in order to derive insights and improving research paths is essential.

Communicable Disease Surveillance in Lebanon during the Syrian Humanitarian Crisis, 2013-2019.

Lebanon has been one of the most affected countries by the Syrian humanitarian crisis. The national communicable disease surveillance was enhanced to detect outbreaks among Syrians. In this study, we aim to describe the findings of the communicable disease surveillance among Syrians in Lebanon, compare it to residents' data, and describe the implemented surveillance activities between 2013 and 2019. During the study period, data on communicable diseases was mainly collected through the routine national surveillance system and an enhanced syndromic surveillance system. Predefined case definitions and standard operating procedures were in place. Data collection included both case-based and disease-specific reporting forms. Descriptive data and incidence rates were generated. Information was disseminated through weekly reports. Activities were conducted in close collaboration with different partners. The most commonly reported diseases were: viral hepatitis A, cutaneous leishmaniasis, mumps, and measles. Hepatitis A incidence increased in 2013 and 2014 among Syrians as well as residents. For leishmaniasis, the incidence increased only among Syrians in 2013 and decreased after that. An outbreak of mumps was reported among Syrians between 2014 and 2016, with a peak in 2015 concomitant with a national outbreak. Outbreaks of measles were reported among Syrians and residents in 2013, 2018, and 2019. The infrastructure of the well-implemented surveillance system in Lebanon has been utilized to monitor the health status of Syrians in Lebanon, early detect communicable diseases among this population, and guide needed preventive and control measures. This highlights the importance of having a flexible surveillance system that can be adapted to emergencies and the importance of sharing results with involved partners.

Socio-political and organizational influences on national infectious disease surveillance for refugees: A qualitative case study in Lebanon.

Infectious disease surveillance provides actionable information on displaced populations and helps identify outbreaks. Though not a signatory to the 1951 Refugee Convention, Lebanon has experienced large refugee influxes (e.g. Palestinians in 1948, Syrians in 2011), yet information on socio-political and organizational influences shaping surveillance targeting refugees is limited. We thus aimed to examine how Lebanese socio-politics affected infectious disease surveillance for refugees in Lebanon. We conducted a qualitative multimethod single case study of government engagement with refugee infectious disease surveillance (2011-2018) drawing from document analysis, semi-structured observations, and semi-structured key informant interviews at four surveillance sites in Lebanon. We analysed data thematically, using deductive and inductive coding. National politics delayed government and thus its epidemiological surveillance program's (ESU) engagement with refugee disease surveillance, largely due to Lebanon not being a 1951 Refugee Convention signatory and internal policy disagreements. Thus, it was initially difficult for the ESU to lead surveillance activities, though it later became more active. The ESU was limited by unclear reporting mechanisms and resources and its reliance on aggregated surveillance data prevented provision of data-informed responses. Though the ESU led surveillance nationally, and we identified positive provincial level collaborations due to individual efforts, some partners still conducted parallel surveillance. We found no systematic approach to infectious disease surveillance for refugees. The ESU could improve surveillance for refugees by collaborative strategic planning with partners for preparedness, surveillance, reporting, and sustainable resource allocation during refugee crises. Further suggestions include collecting disaggregated data, and piloting potentially more efficient syndromic surveillance, based on symptom clusters, for refugee populations.

Effectiveness of the Pfizer-BioNTech Vaccine against COVID-19-Associated Hospitalizations among Lebanese Adults ≥75 Years Old-Lebanon, April-May 2021.

In Lebanon, the nationwide vaccination against COVID-19 was launched in February 2021 using the Pfizer-BioNTech vaccine and prioritizing elderly people, persons with comorbidities, and healthcare workers. Our study aims to estimate the post-introduction vaccine effectiveness (VE) of the Pfizer-BioNTech vaccine in preventing COVID-19 hospitalizations among elderly people ≥75 years old in Lebanon. A case-control study design was used. Case patients were Lebanese, ≥75 years old, and hospitalized with positive PCR results during April-May 2021, and randomly selected from the database of the Epidemiological Surveillance Unit at the Ministry of Public Health (MOPH). Each case patient was matched by age and locality to two controls. The controls were hospitalized, non-COVID-19 patients, randomly selected from the MOPH hospital admission database. VE was calculated for fully (2 doses ≥14 days) and partially vaccinated (≥14 days of the first or within 14 days of the second dose) participants using multivariate logistic regression. A total of 345 case patients and 814 controls were recruited. Half were females, with a mean age of 83 years. A total of 14 case patients (5%) and 143 controls (22%) were fully vaccinated. A bivariate analysis showed a significant association with gender, month of confirmation/hospital admission, general health, chronic medical conditions, main income source, and living arrangement. After adjusting for a month of hospital admission and gender, the multivariate analysis yielded a VE of 82% (95% CI = 69-90%) against COVID-19-associated hospitalizations for those fully vaccinated and 53% (95% CI = 23-71%) for those partially vaccinated. Our study shows that the Pfizer-BioNTech vaccine is effective in reducing the risk for COVID-19-associated hospitalizations of Lebanese elderly people (≥75 years old). Additional studies are warranted to explore VE in reducing hospitalizations for younger age groups, as well as reducing COVID-19 infections.

Data-driven systems to detect physical weakening from daily routine: A pilot study on elderly over 80 years old.

The use of telemonitoring solutions via wearable sensors is believed to play a major role in the prevention and therapy of physical weakening in older adults. Despite the various studies found in the literature, some elements are still not well addressed, such as the study cohort, the experimental protocol, the type of research design, as well as the relevant features in this context. To this end, the objective of this pilot study was to investigate the efficacy of data-driven systems to characterize older individuals over 80 years of age with impaired physical function, during their daily routine and under unsupervised conditions. We propose a fully automated process which extracts a set of heterogeneous time-domain features from 24-hour files of acceleration and barometric data. After being statistically tested, the most discriminant features fed a group of machine learning classifiers to distinguish frail from non-frail subjects, achieving an accuracy up to 93.51%. Our analysis, conducted over 570 days of recordings, shows that a longitudinal study is important while using the proposed features, in order to ensure a highly specific diagnosis. This work may serve as a basis for the paradigm of future monitoring systems.

Infectious disease surveillance for refugees at borders and in destination countries: a scoping review.

BACKGROUND: Data on infectious disease surveillance for migrants on arrival and in destination countries are limited, despite global migration increases, and more are needed to inform national surveillance policies. Our study aimed to examine the scope of existing literature including existing infectious disease surveillance activities, surveillance methods used, surveillance policies or protocols, and potential lessons reported. METHODS: Using Arksey and O'Malley's six-stage approach, we screened four scientific databases systematically and 11 websites, Google, and Google Scholar purposively using search terms related to 'refugee' and 'infectious disease surveillance' with no restrictions on time-period or country. Title/abstracts and full texts were screened against eligibility criteria and extracted data were synthesised thematically. RESULTS: We included 20 eligible sources of 728 identified. Reporting countries were primarily European and all were published between 1999 and 2019. Surveillance methods included 9 sources on syndromic surveillance, 2 on Early Warning and Response (EWAR), 1 on cross-border surveillance, and 1 on GeoSentinel clinic surveillance. Only 7 sources mentioned existing surveillance protocols and communication with reporting sites, while policies around surveillance were almost non-existent. Eleven included achievements such as improved partner collaboration, while 6 reported the lack of systematic approaches to surveillance. CONCLUSION: This study identified minimal literature on infectious disease surveillance for migrants in transit and destination countries. We found significant gaps geographically and on surveillance policies and protocols. Countries receiving refugees could document and share disease surveillance methods and findings to fill these gaps and support other countries in improving disease surveillance.

A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities.

Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i.e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of the activities of daily living (ADLs) in these datasets in comparison with the actual distribution in real life. In this work, we first show the effects of this problem on the sensitivity of the ML algorithms and on the interpretability of the reported specificity. Then, we propose a reliable design of an ML-based fall detection system that aims at discriminating falls from the ambiguous ADLs. The latter are extracted from 400 days of recorded activities of older adults experiencing their daily life. The proposed system can be used in neck- and wrist-worn fall detectors. In addition, it is invariant to the rotation of the wearable device. The proposed system shows 100% of sensitivity while it generates an average of one false positive every 25 days for the neck-worn device and an average of one false positive every 3 days for the wrist-worn device.

Calculating the serial interval of SARS-CoV-2 in Lebanon using 2020 contact-tracing data.

INTRODUCTION: The first detected case in Lebanon on 21 February 2020 engendered implementation of a nationwide lockdown alongside timely contact-tracing and testing. OBJECTIVES: Our study aims to calculate the serial interval of SARS-CoV-2 using contact tracing data collected 21 February to 30 June 2020 in Lebanon to guide testing strategies. METHODS: rRT-PCR positive COVID-19 cases reported to the Ministry of Public Health Epidemiological Surveillance Program (ESU-MOH) are rapidly investigated and identified contacts tested. Positive cases and contacts assigned into chains of transmission during the study time-period were verified to identify those symptomatic, with non-missing date-of-onset and reported source of exposure. Selected cases were classified in infector-infectee pairs. We calculated mean and standard deviation for the serial interval and best distribution fit using AIC criterion. RESULTS: Of a total 1788 positive cases reported, we included 103 pairs belonging to 24 chains of transmissions. Most cases were Lebanese (98%) and male (63%). All infectees acquired infection locally. Mean serial interval was 5.24 days, with a standard deviation of 3.96 and a range of - 4 to 16 days. Normal distribution was an acceptable fit for our non-truncated data. CONCLUSION: Timely investigation and social restriction measures limited recall and reporting biases. Pre-symptomatic transmission up to 4 days prior to symptoms onset was documented among close contacts. Our SI estimates, in line with international literature, provided crucial information that fed into national contact tracing measures. Our study, demonstrating the value of contact-tracing data for evidence-based response planning, can help inform national responses in other countries.

Global burden of influenza-associated lower respiratory tract infections and hospitalizations among adults: A systematic review and meta-analysis.

BACKGROUND: Influenza illness burden is substantial, particularly among young children, older adults, and those with underlying conditions. Initiatives are underway to develop better global estimates for influenza-associated hospitalizations and deaths. Knowledge gaps remain regarding the role of influenza viruses in severe respiratory disease and hospitalizations among adults, particularly in lower-income settings. METHODS AND FINDINGS: We aggregated published data from a systematic review and unpublished data from surveillance platforms to generate global meta-analytic estimates for the proportion of acute respiratory hospitalizations associated with influenza viruses among adults. We searched 9 online databases (Medline, Embase, CINAHL, Cochrane Library, Scopus, Global Health, LILACS, WHOLIS, and CNKI; 1 January 1996-31 December 2016) to identify observational studies of influenza-associated hospitalizations in adults, and assessed eligible papers for bias using a simplified Newcastle-Ottawa scale for observational data. We applied meta-analytic proportions to global estimates of lower respiratory infections (LRIs) and hospitalizations from the Global Burden of Disease study in adults ≥20 years and by age groups (20-64 years and ≥65 years) to obtain the number of influenza-associated LRI episodes and hospitalizations for 2016. Data from 63 sources showed that influenza was associated with 14.1% (95% CI 12.1%-16.5%) of acute respiratory hospitalizations among all adults, with no significant differences by age group. The 63 data sources represent published observational studies (n = 28) and unpublished surveillance data (n = 35), from all World Health Organization regions (Africa, n = 8; Americas, n = 11; Eastern Mediterranean, n = 7; Europe, n = 8; Southeast Asia, n = 11; Western Pacific, n = 18). Data quality for published data sources was predominantly moderate or high (75%, n = 56/75). We estimate 32,126,000 (95% CI 20,484,000-46,129,000) influenza-associated LRI episodes and 5,678,000 (95% CI 3,205,000-9,432,000) LRI hospitalizations occur each year among adults. While adults <65 years contribute most influenza-associated LRI hospitalizations and episodes (3,464,000 [95% CI 1,885,000-5,978,000] LRI hospitalizations and 31,087,000 [95% CI 19,987,000-44,444,000] LRI episodes), hospitalization rates were highest in those ≥65 years (437/100,000 person-years [95% CI 265-612/100,000 person-years]). For this analysis, published articles were limited in their inclusion of stratified testing data by year and age group. Lack of information regarding influenza vaccination of the study population was also a limitation across both types of data sources. CONCLUSIONS: In this meta-analysis, we estimated that influenza viruses are associated with over 5 million hospitalizations worldwide per year. Inclusion of both published and unpublished findings allowed for increased power to generate stratified estimates, and improved representation from lower-income countries. Together, the available data demonstrate the importance of influenza viruses as a cause of severe disease and hospitalizations in younger and older adults worldwide.

PulseNet Lebanon: An Overview of Its Activities, Outbreak Investigations, and Challenges.

Background: Foodborne diseases are still a major health issue in Lebanon, although some steps have been taken forward in food safety. To this purpose, PulseNet Lebanon, a foodborne diseases tracking network, was established in 2009, through the collaboration between the Ministry of Public Health (MoPH) and the American University of Beirut (AUB). Materials and Methods: Three papers published regarding the PulseNet project were summarized. Initially, clinical and food samples, collected within the surveillance network scope, were identified by using the respective API for Salmonella and Listeria spp. Salmonella spp. were further serotyped by using the Kauffman and White method. Campylobacter spp. were determined by the 16 S rRNA sequencing method. Antimicrobial susceptibility to a number of antibiotics was determined by using the disk diffusion method for Samonella and Campylobacter spp. Genomic diversity was determined by using pulsed field gel electrophoresis (PFGE) and random amplified polymorphic DNA (RAPD). Results: Results indicated that 290 clinical and 49 food isolates were identified as Salmonella. Serotyping revealed the prevalence of ten and seven serotypes in the clinical and food samples, respectively. Fifty-one isolates from chicken ceca and carcass were identified to be Campylobacter spp. Fifty-nine samples were identified to be Listeria monocytogenes. Antimicrobial susceptibility testing revealed a wide range of resistance among the different samples. PFGE showed a variation in pulsotypes among the Salmonella serotypes. PFGE also linked certain outbreaks to their food sources. This method also demonstrated 13 subtypes with 100% similarity among the L. monocytogenes isolates. Finally, the Camplyobcater spp. were grouped into nine clusters with a minimum similarity of 43.5% using RAPD. Conclusion: This summary of results shows the importance of implementing a "farm-to-fork" approach in the surveillance of foodborne disease outbreaks in Lebanon, allowing the detection of pathogens causing foodborne disease outbreaks in a timely fashion.

Influenza-associated severe acute respiratory infections in 2 sentinel sites in Lebanon-September 2015 to August 2016.

BACKGROUND: Given the sparse information on the burden of influenza in Lebanon, the Ministry of Public Health established a sentinel surveillance for severe acute respiratory infections (SARI) to identify the attribution of influenza to reported cases. We aim to highlight the proportion of influenza-associated SARI from September 1st, 2015 to August 31st, 2016 in 2 Lebanese hospitals. METHODS: The study was conducted in 2 sentinel sites located in Beirut suburbs and southern province of Lebanon. WHO's 2011 standardized SARI case definition was used. Data from September 1, 2015 to August 31, 2016 were reviewed, and all-cause hospital admission numbers were obtained. Nasopharyngeal swabs were collected and tested by RT-PCR. Descriptive and bivariate analyses were conducted using STATA 13. RESULTS: The 2 sentinel sites reported 746 SARI cases during the studied time frame: 467 from the southern province site and 279 from the Beirut suburbs site. SARI reports peaked between January and March 2016. All, except 4, cases were sampled, and a co-dominance of influenza B (43%) and influenza A (H1N1) (41%) was evident. A high proportion of cases was reported in children <2 years 274 (37%). The proportional contribution of influenza-associated SARI to all-cause hospital admissions was high in children <2 years in the south (4.5% [95% CI: 3.1-6.5]) and in children <5 years in Beirut (0.7% [95% CI: 0.6-0.8]). CONCLUSION: This is the first study to highlight the proportion of influenza-associated SARI in 2 hospitals in Lebanon. The findings will be beneficial for supporting respiratory prevention and immunization program policies.

Molecular epidemiology and antimicrobial resistance of Salmonella species from clinical specimens and food Items in Lebanon.

INTRODUCTION: Foodborne illnesses can be due to a wide range of bacteria, one of the most common being Salmonella. In this study, PulseNet International was implemented in Lebanon to identify circulating pathogens at the species and strain levels, determine antimicrobial resistance, and link food sources and clinical cases during outbreaks. METHODOLOGY: Clinical and food Salmonella isolates received from the Epidemiological Surveillance Unit, Ministry of Public Health (ESUMOH) and the Lebanese Agriculture Research Institute (LARI) between 2011 and 2014 were identified to the species level using API 20E. Serotyping was carried out using the Kauffman and White scheme. Antimicrobial susceptibility to a panel of antimicrobials was tested by the disc diffusion method. The DNA fingerprinting patterns were determined using Pulsed-Field Gel Electrophoresis (PFGE) followed by BIONUMERICS analysis. RESULTS: 290 clinical and 49 food isolates were identified to be Salmonella. The serotyping of the isolates revealed the prevalence of ten serotypes in the clinical isolates and seven serotypes within the food isolates; S. Enteritidis and S. Typhimurium being the two most common. Antimicrobial susceptibility test showed resistance to tested antimicrobials among both clinical and food isolates. PFGE results showed a wide range of pulsotypes by the different serovars. These pulsotypes were then used to confirm the linkage of two outbreaks to their food sources. CONCLUSION: This study calls out to set and implement food safety regulations and emphasizes the importance of surveillance through a "farm-to-fork" approach in identifying widely circulating food borne pathogens.

Genotypic and virulence characteristics of Listeria monocytogenes recovered from food items in Lebanon.

INTRODUCTION: Listeria monocytogenes is the agent of listeriosis, a life threatening foodborne disease for immunocompromised patients and pregnant women. This bacterium is not routinely screened for in Lebanon and there is lack of data about the prevalent strains and their potential pathogenicity. To that purpose, this study was undertaken to characterize L. monocytogenes from various food products, by assessing the in vitro biofilm forming ability, detecting their virulence potential, and characterizing them at the strain level. METHODOLOGY: Fifty-nine isolates were obtained from the Lebanese Agriculture Research Institute (LARI). They were collected in 2012-2013 from local and imported food products in the Lebanese market. Biofilm formation was measured using the Microtiter Plate Assay. PCR amplification was performed for three main virulence genes; hly, actA, and inlB. Pulsed field gel electrophoresis (PFGE) and BIONUMERICS analysis were carried out. RESULTS: Lebanese isolates from cheese and raw meat showed higher biofilm formation than imported and Lebanese seafood isolates. A total of 100% of the isolates were PCR positive for hly and actA genes and 98.3% for inlB gene. PFGE analysis demonstrated the prevalence of 13 different subtypes with 100% similarity. Detected subtypes were grouped into 6 clusters of 90% genomic similarity. Clustered subtypes were particular to the country of origin. CONCLUSION: This study highlights the presence of L. monocytogenes in the Lebanese food market with high pathogenic potential and stresses the importance of enhanced surveillance and the implementation of strict regulations on local and imported food. Future investigations may be conducted on a larger food selection.