World’s Largest Eye Bank has Served a Nation for Three Decades

This is an Editorial and does not have an abstract

Effect of the COVID-19 Pandemic on Seizure Control Status in Patients With Epilepsy

Background: Previous studies have shown that patients with epilepsy (PWE) perceived significant disruption in the quality and provision of care due to the coronavirus disease 2019 (COVID-19) pandemic. The present study aimed to investigate the effect of this pandemic on seizure control status and changes in seizure frequency in PWE. Methods:A consecutive sample of adult PWE registered in the database of Shiraz Epilepsy Center (Shiraz, Iran) was included in the study. In July 2021, phone interviews were conducted with all selected patients. Information such as age, sex, last seizure, seizure type, and frequency during the 12 months before the study, and history of COVID-19 contraction was extracted. The seizure control status of the patients in 2019 (pre-pandemic) was compared with that during the COVID-19 pandemic. Data were analyzed using SPSS software with the Fisher’s exact test and Pearson’s Chi squared test. P Results: A total of 158 patients were included in the study, out of which 62 (39.2%) patients had a stable seizure control status, 47 (29.7%) had fewer seizures, and 50 (31.6%) had more seizures. Breakthrough seizures were reported by 32 (34.4%) patients. Seizure frequency increased in 18 (27.7%) and decreased in 46 (70.7%) patients. Conclusion: Overall, the COVID-19 pandemic has not been a major precipitating factor nor has it affected the seizure control status of PWE. In treated epilepsy, a fluctuating course with periods of seizure freedom followed by relapses is part of its natural history

Contributions of Iranian Journal of Medical Sciences (IJMS) during the COVID-19 Pandemic

Since late 2019, the world and since early 2020, Iran has been experiencing a catastrophic pandemic of the coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).1 This fatal virus has a high potential for person-to-person transmission; therefore, this deadly outbreak has caused massive job losses, various psychiatric problems, and increasing difficulties for all businesses worldwide.2-5 Production of scientific data and its timely dissemination are the essential elements of an effective response to any crisis including the current pandemic. The current study aimed to determine the early contributions of the Iranian Journal of Medical Sciences (IJMS) during the COVID-19 pandemic in 2020. We retrieved all the IJMS publications in 2020 and divided the published articles into three main categories: clinical sciences, basic sciences, and editorials. We also identified and highlighted the studies related to COVID-19

Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis

Summary Background A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed – last updated on March 11, 2021 – including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings  368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68–0·73). Interpretation We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding MING fonds

Modélisation par approches biométriques du vieillissement et du rajeunissement numérique du visage, intégration de facteurs comportementaux liés au mode de vie

Cette thèse a pour objectif de modéliser, par approches biométriques, l’évolution dans le temps du visage humain, en partant de l’âge enfant, jusqu’à un âge adulte. Ces travaux sur le vieillissement rentrent dans le cadre des activités de recherche du groupe biométrie du laboratoire LiSSi (UPEC).Comme il est connu, l’évolution des traits dues au vieillissement dépend deplusieurs facteurs intrinsèques ou extrinsèques, dont : la génétique, l’origine ethnique, le mode de vie, etc. En considérons les modèles paramétriques proposés dans cette thèse, nous exploitons entre autres, les similitudes des caractéristiques extraites chez des individus d’une même catégorie d’âge. Ces similitudes sont intégrées dans nos modèles afin de pouvoir estimer l’apparence faciale à un âge spécifique. Contrairement aux nombreuses études traitant les modèles prédictifs de vieillissement facial, cette thèse propose pour la première fois un modèle réversible permettant également le rajeunissement numérique de l’apparence du visage que nous appellerons, modèle de prédiction arrière d’apparence. Quant à la prédiction avant, notre contribution s’est orientée vers la proposition d’un modèle non-linaire paramétrique de vieillissement permettant de prendre en considération les facteurs accélérateurs de vieillissements liés au mode de vie des individus. De manière générale, nous nous sommes intéressés aux conséquences de certaines addictions de type (drogues, alcool,exposition au soleil, etc.), sur le vieillissement prématuré du visage. Par conséquent,nous avons proposé des modèles sensibles à certains de ces facteurs en se basant sur des analyses statistiques. Comme retombés socio-économiques, cette étude a pour objectif de sensibiliser les jeunes personnes par rapport aux dangers liés à la consommation excessives de certaines substances, voire à l’addiction à certaines pratiques.Les études que nous avons menées durant cette thèse, ont nécessité la constitution d’une base de données contenant plus de 1600 images faciales. Cette base de données a permis le développement 30 modèles de visages «Face Templates». Suite à cela, nous avons créé une base de données d’évaluation, appelée «Face Time-Machine (FaceTiM)». Constituée à partir de 120 sujets, cette base de données est mise à disposition des chercheurs afin qu’ils puissent reproduire les résultats que nous avons obtenus, évaluer les performances, et enfin contribuer à l’amélioration des modèles proposésThe main focus of this thesis is to model the evolution trajectory of human face from infancy to senility using the biometrics facial features.The manifestation of facial changes caused by ageing depends on different factors such as genetic, ethnicity and lifestyle. Nevertheless, individuals in the same age group share some facial similarities. These resemblances can be employed to approximate the facial appearance of an individual in the bygone or the forthcoming years.Unlike numerous studies dealing with predictive face ageing models, for the first time, this thesis proposes the first Backward Facial Ageing Model aiming at digitally rejuvenate an adult face appearance down to its early childhood. We also present the Forward Facial Ageing Model to predict the adult face appearance in its future by taking into account the naturalageing trajectory. The main purpose of Forward Facial Ageing Model is to have a base model for the supplementary ageing models such as behavioural models.In this thesis for the first time in face ageing studies, the effects of different lifestyle behaviours are integrated into the facial ageing models. The Behavioural Facial Ageing Models predict the feature of a young face in case of having the high-risk lifestyle habits. The main attempt of these models is to illustrate the adverse effects of unsafe lifestyle behaviourson the senility of the face, aiming to prevent the youth from becoming involved in these habits. The Facial Ageing Modeling Database, contains over 1600 facial images, is collected to construct the models and 30 Face Templates for the purpose of the face ageing studies.Besides, the Face Time-Machine Database from 120 subjects is created and published to testand evaluate the results. For the proposed approach face contour and different components are modified non-linearly based on an estimated geometrical model related to the trajectory of growth or ageing. Moreover, the face texture is adapted by mapping a Face Template to the estimated geometrical model. Then, the effects of each lifestyle habit are set up to the primal predictive model.The evaluations of the results indicate that the proposed models are remarkably accurate to estimate the correct face appearance of an individual in the target age. While the simulated facial images are realistic and have the appearance, geometrical and textural characteristics of the target age, the personal identity and details of the input face images are preserve

Modélisation par approches biométriques du vieillissement et du rajeunissement numérique du visage, intégration de facteurs comportementaux liés au mode de vie

The main focus of this thesis is to model the evolution trajectory of human face from infancy to senility using the biometrics facial features.The manifestation of facial changes caused by ageing depends on different factors such as genetic, ethnicity and lifestyle. Nevertheless, individuals in the same age group share some facial similarities. These resemblances can be employed to approximate the facial appearance of an individual in the bygone or the forthcoming years.Unlike numerous studies dealing with predictive face ageing models, for the first time, this thesis proposes the first Backward Facial Ageing Model aiming at digitally rejuvenate an adult face appearance down to its early childhood. We also present the Forward Facial Ageing Model to predict the adult face appearance in its future by taking into account the naturalageing trajectory. The main purpose of Forward Facial Ageing Model is to have a base model for the supplementary ageing models such as behavioural models.In this thesis for the first time in face ageing studies, the effects of different lifestyle behaviours are integrated into the facial ageing models. The Behavioural Facial Ageing Models predict the feature of a young face in case of having the high-risk lifestyle habits. The main attempt of these models is to illustrate the adverse effects of unsafe lifestyle behaviourson the senility of the face, aiming to prevent the youth from becoming involved in these habits. The Facial Ageing Modeling Database, contains over 1600 facial images, is collected to construct the models and 30 Face Templates for the purpose of the face ageing studies.Besides, the Face Time-Machine Database from 120 subjects is created and published to testand evaluate the results. For the proposed approach face contour and different components are modified non-linearly based on an estimated geometrical model related to the trajectory of growth or ageing. Moreover, the face texture is adapted by mapping a Face Template to the estimated geometrical model. Then, the effects of each lifestyle habit are set up to the primal predictive model.The evaluations of the results indicate that the proposed models are remarkably accurate to estimate the correct face appearance of an individual in the target age. While the simulated facial images are realistic and have the appearance, geometrical and textural characteristics of the target age, the personal identity and details of the input face images are preservedCette thèse a pour objectif de modéliser, par approches biométriques, l’évolution dans le temps du visage humain, en partant de l’âge enfant, jusqu’à un âge adulte. Ces travaux sur le vieillissement rentrent dans le cadre des activités de recherche du groupe biométrie du laboratoire LiSSi (UPEC).Comme il est connu, l’évolution des traits dues au vieillissement dépend deplusieurs facteurs intrinsèques ou extrinsèques, dont : la génétique, l’origine ethnique, le mode de vie, etc. En considérons les modèles paramétriques proposés dans cette thèse, nous exploitons entre autres, les similitudes des caractéristiques extraites chez des individus d’une même catégorie d’âge. Ces similitudes sont intégrées dans nos modèles afin de pouvoir estimer l’apparence faciale à un âge spécifique. Contrairement aux nombreuses études traitant les modèles prédictifs de vieillissement facial, cette thèse propose pour la première fois un modèle réversible permettant également le rajeunissement numérique de l’apparence du visage que nous appellerons, modèle de prédiction arrière d’apparence. Quant à la prédiction avant, notre contribution s’est orientée vers la proposition d’un modèle non-linaire paramétrique de vieillissement permettant de prendre en considération les facteurs accélérateurs de vieillissements liés au mode de vie des individus. De manière générale, nous nous sommes intéressés aux conséquences de certaines addictions de type (drogues, alcool,exposition au soleil, etc.), sur le vieillissement prématuré du visage. Par conséquent,nous avons proposé des modèles sensibles à certains de ces facteurs en se basant sur des analyses statistiques. Comme retombés socio-économiques, cette étude a pour objectif de sensibiliser les jeunes personnes par rapport aux dangers liés à la consommation excessives de certaines substances, voire à l’addiction à certaines pratiques.Les études que nous avons menées durant cette thèse, ont nécessité la constitution d’une base de données contenant plus de 1600 images faciales. Cette base de données a permis le développement 30 modèles de visages «Face Templates». Suite à cela, nous avons créé une base de données d’évaluation, appelée «Face Time-Machine (FaceTiM)». Constituée à partir de 120 sujets, cette base de données est mise à disposition des chercheurs afin qu’ils puissent reproduire les résultats que nous avons obtenus, évaluer les performances, et enfin contribuer à l’amélioration des modèles proposé

Backward Face Ageing Model (B-FAM) for Digital Face Image Rejuvenation

International audienceFacial ageing modelling has been an active research topic in the field of anthropology. Considering the fact that ageing is a non-uniform and a non-linear process for different face types (e.g. origins, gender etc.), dealing with a reliable face-ageing model may considerably help investigators working in some specific fields such as forensics. Unlike numerous studies dealing with forward or predictive face models, in this study, the authors propose a backward model aiming at estimating childhood face images using their corresponding adult face appearance as an input. For the proposed approach, face contour and different components are modified non-linearly, based on an estimated geometrical model. On the other hand, the face texture is estimated by mapping a reference face texture to the estimated geometrical model. This approach will show that it will be possible to `digitally' rejuvenate an adult person's face down to it being 3-4 years old. For evaluation purposes, a database has been created from 112 subjects. Results have been evaluated using both objective (face recognition system) and subjective (human perception) criteria. The most promising and interesting results will be highlighted further ahead