HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications.

PURPOSE: Magnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI. METHODS: This work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient's facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area. RESULTS: Using the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient's face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively. CONCLUSIONS: This work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.

Coronally advanced flap with connective tissue graft for the treatment of multiple recession defects: Case Report

Mucogingival deformities are a group of conditions that affect many patients, having an impact on function and esthetics; they can be congenital, developmental, or acquired. Gingival recession is defined by the American Academy of Periodontology as the location of the gingival margin apical to the amelocemental junction. They can be localized or generalized and include one or more sides of the tooth. Among the treatments, subepithelial connective tissue grafting and acellular dermal matrix can be considered, whose objectives are root coverage and keratinized tissue width. Case report: A 54-year-old female patient diagnosed with mucogingival deformities around the tooth, was treated with subepithelial connective tissue graft to obtain root coverage in recessions RT2 and RT3. Conclusion: The purpose of using a bilaminar technique where a flap is made, and a connective tissue graft is placed to cover gingival recessions is to obtain better esthetic results.

Surgical Management of Temporal Lobe Epilepsy Secondary to Epidermoid Cysts: A Case Report With Review of the Literature.

Epidermoid cysts represent roughly 1% of all intracranial tumors. They are frequently located in the cerebellopontine angle but rarely extend to the supratentorial brain. Epilepsy is an extremely uncommon manifestation of this neoplasm. We suggest the surgical management of a 35-year-old male who presented with a six-month history of intractable temporal lobe epilepsy. His seizures were characterized by a focal onset in the form of déjà vu experiences, followed by a secondarily generalized tonic-clonic seizure. Imaging revealed a heterogeneous cystic mass in the right cerebellopontine angle, extending supratentorially causing a mass effect on the mesial temporal region. Gross total resection was achieved through a combined subtemporal-retrosigmoid approach. Histopathology revealed an epidermoid cyst. The patient was entirely seizure-free at the three-month follow-up. Epidermoid cysts may present with epileptic seizures. Seizure freedom can be achieved with surgical management in most cases. The patient's symptoms, imaging findings, and epileptogenic focus must be considered to select the appropriate surgical strategy.

Recesión mandibular única tratada con colgajo desplazado lateral: Reporte de un caso

Introducción: Hoy en día, uno de los motivos de consulta de los pacientes es la inconformidad con su apariencia estética debido a la presencia de recesiones gingivales. Dentro de las diversas técnicas para tratar este tipo de defectos se encuentra el colgajo posicionado lateral, el cual, se utiliza para la cobertura radicular de recesiones gingivales únicas en dientes mandibulares y cuyos resultados han demostrado ganancia en altura de tejido queratinizado, ganancia en inserción clínica y una cobertura radicular completa. Reporte de Caso: Paciente femenino de 32 años, diagnosticado con defecto mucogingival único en torno a pieza dental 3.1 y que fue tratado con un colgajo posicionado lateral en combinación con proteínas derivadas de la matriz del esmalte, con el objetivo de cubrir la recesión gingival. Conclusión: La técnica de colgajo posicionado lateral es un tratamiento viable y eficaz para la cobertura radicular completa de defectos mucogingivales únicos en torno a diente, donde además se obtiene ganancia en altura de encía queratinizada, así como de nivel de inserción clínica.

Introduction: Nowadays, one of the reasons for consultation of patients is the dissatisfaction with their aesthetic appearance due to the presence of gingival recessions. Among the various techniques to treat this type of defects is the laterally positioned flap, which is used for root coverage of single gingival recessions in mandibular teeth and whose results have shown gain in height of keratinized tissue, clinical attachment gain and complete root coverage. Case Report: 32-year-old female patient, diagnosed with a single mucogingival defect around tooth 3.1 and treated with a laterally positioned flap in combination with enamel matrix derivatives, in order to cover the gingival recession. Conclusion: The laterally positioned flap technique is a viable and effective treatment for complete root coverage of single mucogingival defects around the tooth. In addition, there is a gain in keratinized gingiva and in clinical attachment.

Nutritional and antioxidant profile of the Physalis fruit grown in three Andean regions of Peru.

Background: Physalis peruviana L. fruit contains nutritional and bioactive compounds of immense importance to public health and represents a potential ingredient for the development of functional foods and beverages. Objective: This study aimed to determine the chemical and nutritional composition as well as the antioxidant capacity of the P. peruviana L. fruit grown in Peru in three areas of the Central Andean region. Material and methods: Proximal and physicochemical analyses and estimation of mineral content, vitamin C, total carotenoids, total polyphenols, and antioxidant capacity (2, 2-diphenyl-1-picrylhydrazyl [DPPH] and 2, 2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) [ABTS] assays) were performed using standardized methods. Results: The fruits were collected from three regions of the Peruvian Andes (Ancash, Cajamarca, and Cusco). The results showed that the content of potassium (306.54-327.60 mg/100 g) and iron (12.93-14.47 mg/kg) was prominent. The Physalis fruit had high levels of vitamin C (47.20-52.20 mg/100 g), total polyphenols (68.17-83.40 mg equivalents of gallic acid/100 g), and carotenoids (1.12-1.73 mg ß-carotene/100 g). Higher values for antioxidant capacity were obtained with the ABTS method (896-1003.33 µmol Trolox/100 g) than with the DPPH method (290-309 µmol Trolox/100 g). Conclusions: This study confirms that the P. peruviana fruit has properties that could provide important health benefits and that it could be used for the development of functional foods and food supplement.

Enhancing microsurgical skills in neurosurgery residents of low-income countries: A comprehensive guide.

Background: The main objectives of this paper are to outline the essential tools, instruments, and equipment needed to set up a functional microsurgery laboratory that is affordable for low-income hospitals and to identify cost-effective alternatives for acquiring microsurgical equipment, such as refurbished or donated instruments, collaborating with medical device manufacturers for discounted rates, or exploring local suppliers. Methods: Step-by-step instructions were provided on setting up the microsurgery laboratory, including recommendations for the layout, ergonomic considerations, lighting, and sterilization processes while ensuring cost-effectiveness, as well as comprehensive training protocols and a curriculum specifically tailored to enhance microsurgical skills in neurosurgery residents. Results: We explored cost-effective options for obtaining microsurgery simulators and utilizing open-source or low-cost virtual training platforms. We also included guidelines for regular equipment maintenance, instrument sterilization, and establishing protocols for infection control to ensure a safe and hygienic learning environment. To foster collaboration between low-income hospitals and external organizations or institutions that can provide support, resources, or mentorship, this paper shows strategies for networking, knowledge exchange, and establishing partnerships to enhance microsurgical training opportunities further. We evaluated the impact and effectiveness of the low-cost microsurgery laboratory by assessing the impact and effectiveness of the established microsurgery laboratory in improving the microsurgical skills of neurosurgery residents. About microsutures and microanastomosis, after three weeks of training, residents showed improvement in "surgical time" for ten separate simple stitches (30.06 vs. 8.65 min) and ten continuous single stitches (19.84 vs. 6.51 min). Similarly, there was an increase in the "good quality" of the stitches and the suture pattern from 36.36% to 63.63%. Conclusion: By achieving these objectives, this guide aims to empower low-income hospitals and neurosurgery residents with the necessary resources and knowledge to establish and operate an affordable microsurgery laboratory, ultimately enhancing the quality of microsurgical training and patient care in low-income countries.

Encefalitis por ameba de vida libre: reporte de caso / Free-living amoeba encephalitis: case report

La encefalitis amebiana granulomatosa es una enfermedad considerada atípica debido a la baja incidencia desde que fue descubierta. Sin embargo, conforme han avanzando los métodos diagnósticos, en retrospectiva, se ha descrito la presencia de esta enfermedad desde principios del siglo XX, identificando a Acanthamoeba spp y Balamuthia mandrillaris como agentes más comunes.Es una enfermedad de difícil diagnóstico debido al amplio abanico clínico que puede presentar, siendo el sitio anatómico dentro del sistema nervioso central quien determinará estas características. El conocimiento y sospecha de la misma permitirá un abordaje terapéutico oportuno aunque, en la mayoría de los casos, con un desenlace fatal para el paciente.A continuación se presenta el caso clínico de un paciente masculino de 26 años de edad originario de la ciudad de Durango, México, sin antecedentes de importancia, de ocupación ladrillero, quien sufre golpe con ladrillo en región naso-orbitaria, ingresando al hospital al tercer día del evento con diagnóstico de celulitis periorbitaria izquierda y nasal, cefalea de leve intensidad, rinorrea abundante, hemiparesia fasciocorporal izquierda, parestesia de extremidad superior izquierda, diplopía y vértigo postural. Los estudios imagenológicos revelaron lesiones heterogéneas en el lóbulo occipital, hemisferio cerebeloso izquierdo y tallo cerebral. Se realizó escisión de lesión tumoral cerebelar, con estudio anatomopatológico, reportando encefalitis granulomatosa por presencia de ameba de vida libre. El paciente evolucionó hacia deterioro rostro-caudal declarándose su fallecimiento a los 16 días de su estancia intrahospitalaria.En el Estado de Durango, México, no se cuenta con casos reportados por infección de ameba de vida libre

Granulomatous amoebic encephalitis is a disease considered atypical due to the low incidence since it was discovered. However, as diagnostic methods have progressed, in retrospect, the presence of this disease has been described since the beginning of the 20th century, identifying Acanthamoeba spp. and Balamuthia mandrillaris as the most common agents.A disease that is difficult to diagnose due to the wide clinical range that it can present, being the anatomical site within the central nervous system that will determine these characteristics. The knowledge and suspicion of it will allow a timely therapeutic approach although, in most cases, with a fatal outcome for the patient.The clinical case is presented below of a 26 year old male patient from the city of Durang, México, with no significant history, of brick occupation, who suffers a blow with a brick in the naso-orbital region, follows, entering our hospital on the third day of the event with a diagnosis of left periorbital and nasal cellulitis, mild headache, profuse rhinorrhea, left fasciocorporeal hemiparesis, left upper limb parestesia, diplopia, and postural vertigo.Imaging studies were performed observing heterogeneous lesions in the occipital lobe, left cerebellar hemisphere and brain stem; excision of the cerebellar tumor lesion being carried out, with pathological study, reporting granulomatous encephalitis due to the presence of free-living amoeba. The patient progresses to face-caudal deterioration, declaring his death after 16 days of hospital stay.Within the state of Durango, Mexico, there are no reported cases of free-living amoeba infection

GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications.

HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon's action, with their incorporation being a neurosurgeon's task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of -0.93 dB, -0.6 dB, and -1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation.

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification.

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

Species Delimitation of Scavenger Flies in the Valley of Mexico.

Identification of species involved in cadaveric decomposition, such as scavenger Diptera, is a fundamental step for the use of entomological evidence in court. Identification based on morphology is widely used in forensic cases; however, taxonomic knowledge of scavenger fauna is poor for many groups and for many countries, particularly Neotropical ones. A number of studies have documented the utility of a DNA barcoding strategy to assist in the identification of poorly known and diverse groups, particularly in cases involving immature states or fragmented organisms. To provide baseline knowledge of the diversity of scavenger Diptera in the Valley of Mexico, we generated a DNA barcode collection comprised of sequences of the cytochrome c oxidase subunit 1 (COI) gene for all families sampled at a nature reserve located in this region. We collected and identified specimens on the basis of morphology and a species delimitation analysis. Our analyses of 339 individuals delineated 42 species distributed across nine families of Diptera. The richest families were Calliphoridae (9 species), Sarcophagidae (7 species), and Phoridae (6 species). We found many of the species previously recorded for the Valley of Mexico, plus 18 new records for the region. Our study highlights the utility of DNA barcoding as a first-step strategy to assess species richness of poorly studied scavenger fly taxa.

Zinc Deficiency in Men Over 50 and Its Implications in Prostate Disorders.

Research has been consistently showing the role of zinc (Zn) in prostate function. In this article, we review the current literature on the anatomy and main functions of the prostate, highlighting the role of zinc. In particular, we will review the etiology of benign prostate enlargement (BPH), its prevalence in men over 50, the likelihood of BPH becoming prostate cancer (PCa), and explain the relationship of zinc and apoptosis in the prostate cells and the implications for BPH and PCa. We present a model that explains how endogenous factors provoke excretion of zinc or limit zinc absorption, and how exogenous factors like nutrition and drugs regularly used in men over 50 can significantly decrease zinc status and thereby increase the risk of BPH. Finally, we explain how Zn amino acid (AA) complexes may be capable of avoiding antagonists and inhibitors of zinc absorption, thereby increasing the bioavailability of zinc for the necessary biological processes in the prostate.

Computational study of the ro-vibrational spectrum of CO-CO2.

An accurate ab initio ground-state intermolecular potential energy surface (PES) was determined for the CO-CO2 van der Waals dimer. The Lanczos algorithm was used to compute rovibrational energies on this PES. For both the C-in and O-in T-shaped isomers, the fundamental transition frequencies agree well with previous experimental results. We confirm that the in-plane states previously observed are geared states. In addition, we have computed and assigned many other vibrational states. The rotational constants we determine from J = 1 energy levels agree well with their experimental counterparts. Planar and out-of-plane cuts of some of the wavefunctions we compute are quite different, indicating strong coupling between the bend and torsional modes. Because the stable isomers are T-shaped, vibration along the out-of-plane coordinates is very floppy. In CO-CO2, when the molecule is out-of-plane, interconversion of the isomers is possible, but the barrier height is higher than the in-plane geared barrier height.

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images.

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.