Indirect Fistula: A New Terminology for Cerebrospinal Fluid Fistula With Different 'Apparent Origin' and 'Real Origin'.

Fistulas are abnormal communications between body cavities. They can occur between the CNS and the extracranial space, presenting clinically as CSF leaks. Due to the variety of features, multiple classifications have been implemented to better study this pathology. A systematic review was conducted using the Scopus, Medline, and Web of Science databases. Observational studies such as cohort studies, case reports, case series, cross-sectional studies, systematic reviews, and publications that assess the classification of CSF leaks were included. The systematic review identified 29 publications that met the required criteria for inclusion. Although the primary focus of most of these publications was not on classification, they briefly mentioned it. The included publications describe classifications according to etiology, exiting flow pressure, anatomic site, and some new classification proposals. Of the 29 included studies, 11 referred to the appearance of CSF rhinorrhea or otorrhea with no relationship between the cause or site of origin and the site of the CSF leak. However, none of these publications names this situation. These results clearly indicate that a term for this circumstance needs to be established; none of the previously listed publications provide a name for this condition. This systematic review aims to demonstrate the necessity of implementing a new term to describe CSF leaks where the 'apparent origin' does not correspond to the 'real origin.' The results show no existing term that considers such cases; therefore, we propose the term 'Indirect Fistula' to designate these cases.

Epiploic Appendagitis: Systematic Review of a Distinctive Pathology.

Introduction: Epiploic appendagitis (EA) is an essential cause of abdominal pain that can be confused with more typical causes such as acute diverticulitis and appendicitis. Epiploic appendagitis accounts for 1% of all cases of abdominal pain in adults. The scarcity of information has limited its recognition as an essential nonsurgical cause of acute abdominal pain.Methods: We performed a systematic review of all EA cases published. We searched Scopus, Medline, Web of Science, and Google Scholar to retrieve all available studies from January 2000 to November 2023.Results: 196 case reports and case series were analyzed, with 371 patients with EA included. The mean age at the time of diagnosis was 39 years. Most patients were male (59%). The primary presenting symptoms were pain (100%), tenderness (59.5%), and rebound tenderness (27.4%). The left abdomen was the most common localization of pain (53%). The most frequently identified differential diagnoses were acute appendicitis (26.4%) and acute diverticulitis (16.1%). Most patients (53%) were treated conservatively, and 98 (26.4%) underwent surgical treatment. A significant difference in the choice of treatment was found for signs and symptoms such as rebound tenderness, nausea, anorexia, and diarrhea.Conclusions: Acute EA is an essential clinical condition of rare occurrence that might present a diagnostic challenge, as it can masquerade as another acute abdominal pain etiology. The optimal management of EA is conservative, so a higher recognition by surgeons and emergency physicians is essential to avoid unnecessary surgical interventions and their associated consequences.

Simultaneous spine extradural and intradural teratomas in a pediatric patient: A rare presentation with insights in the flawed migration of germ cells theory.

Spinal teratomas are infrequent lesions in the pediatric population. These lesions can be extradural, intradural or intramedullary. We present a case of an 8-month-old boy that was assessed for underdevelopment of motor milestones. The neurologic examination revealed hyporeflexia, decreased sensation and flaccid paraplegia. MRI of the spine revealed two simultaneous and independent lesions in the extradural and intradural compartment. A laminectomy was performed for the T4-T7 vertebrae with total resection of both lesions. The histopathological analysis confirmed both lesions to be mature cystic teratomas. At the 1-year follow-up, the patient remained with no recovery of neurological function. A debate takes place regarding the etiology of formation of these lesions in the spine. The simultaneous presentation of two independent lesions in this patient could contribute to define the flawed migration of germ cells theory as the etiology for formation of teratomatous lesions in the spine.

SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild.

We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and illuminance. SfSNet is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real-world images. This allows the network to capture low-frequency variations from synthetic and high-frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation. We also introduce a companion network, SfSMesh, that utilizes normals estimated by SfSNet to reconstruct a 3D face mesh. We demonstrate that SfSMesh produces face meshes with greater accuracy than state-of-the-art methods on real-world images.

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). This forces us to rethink the universe of possible solutions to the problem of inverse optics in vision. Second, deep learning models indicate that high-level visual representations of faces cannot be understood in terms of interpretable features. This has implications for understanding neural tuning and population coding in the high-level visual cortex. Third, learning in deep networks is a multistep process that forces theoretical consideration of diverse categories of learning that can overlap, accumulate over time, and interact. Diverse learning types are needed to model the development of human face processing skills, cross-race effects, and familiarity with individual faces.

Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition.

Single-unit responses and population codes differ in the "read-out" information they provide about high-level visual representations. Diverging local and global read-outs can be difficult to reconcile with in vivo methods. To bridge this gap, we studied the relationship between single-unit and ensemble codes for identity, gender, and viewpoint, using a deep convolutional neural network (DCNN) trained for face recognition. Analogous to the primate visual system, DCNNs develop representations that generalize over image variation, while retaining subject (e.g., gender) and image (e.g., viewpoint) information. At the unit level, we measured the number of single units needed to predict attributes (identity, gender, viewpoint) and the predictive value of individual units for each attribute. Identification was remarkably accurate using random samples of only 3% of the network's output units, and all units had substantial identity-predicting power. Cross-unit responses were minimally correlated, indicating that single units code non-redundant identity cues. Gender and viewpoint classification required large-scale pooling of units-individual units had weak predictive power. At the ensemble level, principal component analysis of face representations showed that identity, gender, and viewpoint separated into high-dimensional subspaces, ordered by explained variance. Unit-based directions in the representational space were compared with the directions associated with the attributes. Identity, gender, and viewpoint contributed to all individual unit responses, undercutting a neural tuning analogy. Instead, single-unit responses carry superimposed, distributed codes for face identity, gender, and viewpoint. This undermines confidence in the interpretation of neural representations from unit response profiles for both DCNNs and, by analogy, high-level vision.

Facial expression is retained in deep networks trained for face identification.

Facial expressions distort visual cues for identification in two-dimensional images. Face processing systems in the brain must decouple image-based information from multiple sources to operate in the social world. Deep convolutional neural networks (DCNN) trained for face identification retain identity-irrelevant, image-based information (e.g., viewpoint). We asked whether a DCNN trained for identity also retains expression information that generalizes over viewpoint change. DCNN representations were generated for a controlled dataset containing images of 70 actors posing 7 facial expressions (happy, sad, angry, surprised, fearful, disgusted, neutral), from 5 viewpoints (frontal, 90° and 45° left and right profiles). Two-dimensional visualizations of the DCNN representations revealed hierarchical groupings by identity, followed by viewpoint, and then by facial expression. Linear discriminant analysis of full-dimensional representations predicted expressions accurately, mean 76.8% correct for happiness, followed by surprise, disgust, anger, neutral, sad, and fearful at 42.0%; chance \(\approx\)14.3%. Expression classification was stable across viewpoints. Representational similarity heatmaps indicated that image similarities within identities varied more by viewpoint than by expression. We conclude that an identity-trained, deep network retains shape-deformable information about expression and viewpoint, along with identity, in a unified form-consistent with a recent hypothesis for ventral visual stream processing.

Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?

Previous generations of face recognition algorithms differ in accuracy for images of different races (race bias). Here, we present the possible underlying factors (data-driven and scenario modeling) and methodological considerations for assessing race bias in algorithms. We discuss data-driven factors (e.g., image quality, image population statistics, and algorithm architecture), and scenario modeling factors that consider the role of the "user" of the algorithm (e.g., threshold decisions and demographic constraints). To illustrate how these issues apply, we present data from four face recognition algorithms (a previous-generation algorithm and three deep convolutional neural networks, DCNNs) for East Asian and Caucasian faces. First, dataset difficulty affected both overall recognition accuracy and race bias, such that race bias increased with item difficulty. Second, for all four algorithms, the degree of bias varied depending on the identification decision threshold. To achieve equal false accept rates (FARs), East Asian faces required higher identification thresholds than Caucasian faces, for all algorithms. Third, demographic constraints on the formulation of the distributions used in the test, impacted estimates of algorithm accuracy. We conclude that race bias needs to be measured for individual applications and we provide a checklist for measuring this bias in face recognition algorithms.

Seeing through disguise: Getting to know you with a deep convolutional neural network.

People use disguise to look unlike themselves (evasion) or to look like someone else (impersonation). Evasion disguise challenges human ability to see an identity across variable images; Impersonation challenges human ability to tell people apart. Personal familiarity with an individual face helps humans to see through disguise. Here we propose a model of familiarity based on high-level visual learning mechanisms that we tested using a deep convolutional neural network (DCNN) trained for face identification. DCNNs generate a face space in which identities and images co-exist in a unified computational framework, that is categorically structured around identity, rather than retinotopy. This allows for simultaneous manipulation of mechanisms that contrast identities and cluster images. In Experiment 1, we measured the DCNN's baseline accuracy (unfamiliar condition) for identification of faces in no disguise and disguise conditions. Disguise affected DCNN performance in much the same way it affects human performance for unfamiliar faces in disguise (cf. Noyes & Jenkins, 2019). In Experiment 2, we simulated familiarity for individual identities by averaging the DCNN-generated representations from multiple images of each identity. Averaging improved DCNN recognition of faces in evasion disguise, but reduced the ability of the DCNN to differentiate identities of similar appearance. In Experiment 3, we implemented a contrast learning technique to simultaneously teach the DCNN appearance variation and identity contrasts between different individuals. This facilitated identification with both evasion and impersonation disguise. Familiar face recognition requires an ability to group images of the same identity together and separate different identities. The deep network provides a high-level visual representation for face recognition that supports both of these mechanisms of face learning simultaneously.

Face Space Representations in Deep Convolutional Neural Networks.

Inspired by the primate visual system, deep convolutional neural networks (DCNNs) have made impressive progress on the complex problem of recognizing faces across variations of viewpoint, illumination, expression, and appearance. This generalized face recognition is a hallmark of human recognition for familiar faces. Despite the computational advances, the visual nature of the face code that emerges in DCNNs is poorly understood. We review what is known about these codes, using the long-standing metaphor of a 'face space' to ground them in the broader context of previous-generation face recognition algorithms. We show that DCNN face representations are a fundamentally new class of visual representation that allows for, but does not assure, generalized face recognition.

Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms.

Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

Disturbed sensory perception of changes in thermoalgesic stimuli in patients with small fiber neuropathies.

The assessment of functional deficits in small fibre neuropathies (SFN) requires using ancillary tests other than conventional neurophysiological techniques. One of the tests with most widespread use is thermal threshold determination, as part of quantitative sensory testing. Thermal thresholds typically reflect one point in the whole subjective experience elicited by a thermal stimulus. We reasoned that more information could be obtained by analyzing the subjective description of the ongoing sensation elicited by slow temperature changes (dynamic thermal testing, DTT). Twenty SFN patients and 20 healthy subjects were requested to describe, by using an electronic visual analog scale system, the sensation perceived when the temperature of a thermode was made to slowly change according to a predetermined pattern. The thermode was attached to the left ventral forearm or the distal third of the left leg and the stimulus was either a monophasic heat or cold stimuli that reached 120% of pain threshold and reversed to get back to baseline at a rate of 0.5 °C/s. Abnormalities seen in patients in comparison to healthy subjects were: (1) delayed perception of temperature changes, both at onset and at reversal, (2) longer duration of pain perception at peak temperature, and (3) absence of an overshoot sensation after reversal, ie, a transient perception of the opposite sensation before the temperature reached again baseline. The use of DTT increases the yield of thermal testing for clinical and physiological studies. It adds information that can be discriminant between healthy subjects and SFN patients and shows physiological details about the process of activation and inactivation of temperature receptors that may be abnormal in SFN.

PARKIN-coding polymorphisms are not associated with Parkinson's disease in a population from northeastern Mexico.

Early- and late-onset Parkinson's disease (EOPD and LOPD) have been associated with mutations in the PARKIN gene. Several studies have reported association of Parkinson's disease (PD) with different polymorphisms in different ethnic populations. To study the role of PARKIN polymorphisms as risk factors for PD in a genetically homogeneous northeastern Mexican population, four previously described coding polymorphisms (Ser167Asn, Val380Leu, Arg366Trp, and Asp394Asn) were analyzed by using the PCR-RFLP technique. This case-control study comprised 117 unrelated patients (mean age 59+/-12 years, range 25-83 years) and 122 healthy unrelated control subjects (mean age 50+/-15 years, range 25-85 years). The homozygous Trp366 and Asn394 genotypes were not present in our study. The Ser167Asn and Val380Leu polymorphisms were not associated with this disease. For the control group, Ser167Asn and Val380Leu were in Hardy-Weinberg disequilibrium. Given that the main causes of Hardy-Weinberg disequilibrium in controls are selection bias or genotyping error, a competing risk of death associated with the mutant gene could be an explanation of this disequilibrium and lack of association.

Using stereo matching with general epipolar geometry for 2D face recognition across pose.

Face recognition across pose is a problem of fundamental importance in computer vision. We propose to address this problem by using stereo matching to judge the similarity of two, 2D images of faces seen from different poses. Stereo matching allows for arbitrary, physically valid, continuous correspondences. We show that the stereo matching cost provides a very robust measure of similarity of faces that is insensitive to pose variations. To enable this, we show that, for conditions common in face recognition, the epipolar geometry of face images can be computed using either four or three feature points. We also provide a straightforward adaptation of a stereo matching algorithm to compute the similarity between faces. The proposed approach has been tested on the CMU PIE data set and demonstrates superior performance compared to existing methods in the presence of pose variation. It also shows robustness to lighting variation.

Topiramate as an adjunctive treatment in migraine prophylaxis.

BACKGROUND: Anticonvulsants now are commonly used for headache prevention. Topiramate, one of the newer anticonvulsants, recently has been demonstrated to be effective as monotherapy for migraine prophylaxis. OBJECTIVE: To assess the efficacy, safety, and tolerability of topiramate as adjunctive prophylactic therapy for migraine. MATERIAL AND METHODS: A prospective trial involving patients with more than 3 migraine attacks per month was performed. Patients continued their usual prophylactic treatment. Baseline analgesic use and frequency and duration of migraine attacks were recorded. A 4-point visual analog scale evaluated severity. Laboratory tests, electrocardiogram, and computed tomography or magnetic resonance imaging were performed before study entry. After informed consent was obtained, patients were instructed to take 25 mg of topiramate per day, with 25- to 50-mg weekly increments to a maximum of 100 mg per day. Safety was assessed at the first month; tolerability and efficacy were assessed every week for the first month and then every month for 3 months. Effectiveness was assessed by comparing baseline and on-treatment migraine status, and data were analyzed by the Fisher exact test. RESULTS: Twenty-five women and 11 men (mean age, 44 years) were evaluated. Existing prophylactic treatment was either propranolol or flunarizine (or both) in 80% of the patients. At 3 months of therapy with topiramate, headache frequency decreased from 17 to 3 episodes per month, headache duration from 559 to 32 minutes, and intensity from 9 to 1 by visual analog scale (P <.001). Improvement in frequency and severity of migraine was observed in 83% of patients. Slight or no changes in headache were observed in 6 patients. Tolerability was good in 30 patients. The most common side effects were acroparesthesias, weight loss, sleepiness, and headache worsening. No adverse interaction with propranolol or flunarizine was observed. CONCLUSIONS: These results suggest that topiramate is efficacious and safe as an adjunctive treatment in patients with migraine whose prior response to prophylactic management has been less than satisfactory.