Percutaneous nephrostomy versus ureteral stent in hydronephrosis secondary to obstructive urolithiasis: A systematic review and meta-analysis.

Objective: To assess if there is a preferable intervention between retrograde ureteral stent (RUS) and percutaneous nephrostomy (PCN) tube, in cases of upper urinary tract stone obstruction with complications requiring urgent drainage, by evaluating outcomes regarding urinary symptoms, quality of life (QoL), spontaneous stone passage, and length of hospital stays, since there is no literature stating the superiority of one modality over the other. Methods: We searched MEDLINE and other sources for relevant articles in June 2019 without any date restrictions or filters applied. The selection was done first by the title and abstract screening and then by full-text assessment for eligibility. Only randomized controlled trials or cohort studies in patients with hydronephrosis secondary to obstructive urolithiasis that presented comparative data between PCN and RUS placement concerning at least one of the defined outcome measures were included. Lastly, MEDLINE database and PubMed platform were screened again using the same terms, from June 2019 until November 2022. Results: Of 556 initial articles, seven were included in this review. Most works were considered of moderate-to-high quality. Three studies regarding QoL showed a tendency against stenting, even though only one demonstrated statistically significant negative impact on overall health state. Two works reported significantly more post-intervention urinary symptoms in stenting patients. One article found that PCN is a significant predictor of spontaneous stone passage, when adjusted for stone size and location. Findings on length of hospital stays were not consistent among articles. Conclusion: PCN appears to be the intervention better tolerated, with less impact on the patient's perceived QoL and less post-operative urinary symptoms, in comparison with RUS. Nevertheless, further studies with larger samples and a randomized controlled design are suggested.

Multiple mask and boundary scoring R-CNN with cGAN data augmentation for bladder tumor segmentation in WLC videos.

Automatic diagnosis systems capable of handling multiple pathologies are essential in clinical practice. This study focuses on enhancing precise lesion localization, classification and delineation in transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence. Despite deep learning models success, medical applications face challenges like small and limited datasets and poor image characterization, including the absence lack of color/texture modeling. To address these issues, three solutions are proposed: (1) an improved texture-constrained version of the pix2pixHD cGAN for data augmentation, addressing the tradeoff of generating high-quality images with enough stochasticity using the Fréchet Inception Distance (FID) measure. (2) Introducing the Multiple Mask and Boundary Scoring R-CNN (MM&BS R-CNN), a new mask sub-net scheme where multiple masks are generated from the different levels of the mask sub-net pipeline, improving segmentation accuracy by including a new scoring module to refine object boundaries. (3) A novel accelerated training strategy based on the SGD optimizer with the second momentum. Experimental results show significant mAP improvements: the data generation scheme improves by more than 12 %; MM&BS R-CNN proposed architecture is responsible for an improvement of about 1.25 %, and the training algorithm based on the second-order momentum increases mAP by 2-3 %. The simultaneous use of all three proposals improved the state-of-the-art mAP by 17.44 %.

Navigation systems and 3D imaging in percutaneous nephrolithotripsy: improving outcomes and safety.

PURPOSE OF REVIEW: Percutaneous nephrolithotripsy (PCNL) is the first-line management option for large kidney stones (>2 cm). It remains, however, a demanding procedure with considerable morbidity. The present narrative review summarizes recent knowledge from original research studies investigating navigation systems/three-dimensional (3D) imaging in PCNL, particularly on publications during the past 12-18 months. RECENT FINDINGS: Navigation systems and 3D imaging are primarily used for preoperative planning, with few intra-operative applications. Patient management and residents' training could benefit from their use. SUMMARY: Navigation systems and 3D imaging technology have emerged as a potential game-changer in PCNL. Even though available evidence is currently scarce/inconclusive, the safety/efficacy of navigation systems and 3D dimensional imaging use in PCNL appears promising. This advanced technology offers precise anatomical mapping, improved visualization, and surgical accuracy. Enabling a comprehensive preoperative evaluation and improving guidance, navigation systems, and 3D imaging technology may improve the safety and efficacy of PCNL. With continuous technological evolution, it is expected that improvements/innovations will offer further aid in such demanding procedures. Familiarization and cost reduction are necessary for widespread application, while larger-scale prospective studies and well designed randomized controlled trials are still needed.

SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery.

This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. Participants were assigned the task of developing algorithms to track the movement of soft tissues, represented by bounding boxes, in stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The metric used for ranking the methods was the Expected Average Overlap (EAO) score, which measures the average overlap between a tracker's and the ground truth bounding boxes. Coming first in the challenge was the deep learning submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs ARFlow to estimate unsupervised dense optical flow from cropped images, using photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses deep learning for surgical tool segmentation on top of a non-deep learning baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The results from this challenge show that currently, non-deep learning methods are still competitive. The dataset and benchmarking tool created for this challenge have been made publicly available at https://surgt.grand-challenge.org/. This challenge is expected to contribute to the development of autonomous robotic surgery and other digital surgical technologies.

TREK-1 inhibition promotes synaptic plasticity in the prelimbic cortex.

Synaptic plasticity is one of the putative mechanisms involved in the maturation of the prefrontal cortex (PFC) during postnatal development. Early life stress (ELS) affects the shaping of cortical circuitries through impairment of synaptic plasticity supporting the onset of mood disorders. Growing evidence suggests that dysfunctional postnatal maturation of the prelimbic division (PL) of the PFC might be related to the emergence of depression. The potassium channel TREK-1 has attracted particular interest among many factors that modulate plasticity, concerning synaptic modifications that could underlie mood disorders. Studies have found that ablation of TREK-1 increases the resilience to depression, while rats exposed to ELS exhibit higher TREK-1 levels in the PL. TREK-1 is regulated by multiple intracellular transduction pathways including the ones activated by metabotropic receptors. In the hippocampal neurons, TREK-1 interacts with the serotonergic system, one of the main factors involved in the action of antidepressants. To investigate possible mechanisms related to the antidepressant role of TREK-1, we used brain slice electrophysiology to evaluate the effects of TREK-1 pharmacological blockade on synaptic plasticity at PL circuitry. We extended this investigation to animals subjected to ELS. Our findings suggest that in non-stressed animals, TREK-1 activity is required for the reduction of synaptic responses mediated by the 5HT1A receptor activation. Furthermore, we demonstrate that TREK-1 blockade promotes activity-dependent long-term depression (LTD) when acting in synergy with 5HT1A receptor stimulation. On the other hand, in ELS animals, TREK-1 blockade reduces synaptic transmission and facilitates LTD expression. These results indicate that TREK-1 inhibition stimulates synaptic plasticity in the PL and this effect is more pronounced in animals subjected to ELS during postnatal development.

A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images.

Ultrasound (US) imaging is a widely used medical imaging modality for the diagnosis, monitoring, and surgical planning for kidney conditions. Thus, accurate segmentation of the kidney and internal structures in US images is essential for the assessment of kidney function and the detection of pathological conditions, such as cysts, tumors, and kidney stones. Therefore, there is a need for automated methods that can accurately segment the kidney and internal structures in US images. Over the years, automatic strategies were proposed for such purpose, with deep learning methods achieving the current state-of-the-art results. However, these strategies typically ignore the segmentation of the internal structures of the kidney. Moreover, they were evaluated in different private datasets, hampering the direct comparison of results, and making it difficult to determination the optimal strategy for this task. In this study, we perform a comparative analysis of 7 deep learning networks for the segmentation of the kidney and internal structures (Capsule, Central Echogenic Complex (CEC), Cortex and Medulla) in 2D US images in an open access multi-class kidney US dataset. The dataset includes 514 images, acquired in multiple clinical centers using different US machines and protocols. The dataset contains the annotation of two experts, but 321 images with complete segmentation of all 4 classes were used. Overall, the results demonstrate that the DeepLabV3+ network outperformed the inter-rater variability with a Dice score of 78.0% compared to 75.6% for inter-rater variability. Specifically, DeepLabV3Plus achieved mean Dice scores of 94.2% for the Capsule, 85.8% for the CEC, 62.4% for the Cortex, and 69.6% for the Medulla. These findings suggest the potential of deep learning-based methods in improving the accuracy of kidney segmentation in US images.Clinical Relevance- This study shows the potential of DL for improving accuracy of kidney segmentation in US, leading to increased diagnostic efficiency, and enabling new applications such as computer-aided diagnosis and treatment, ultimately resulting in improved patient outcomes and reduced healthcare costs.1.

A loss-of-function NCSTN mutation associated with familial Dowling Degos disease and hidradenitis suppurativa.

Dowling Degos disease (DDD) is a rare autosomal dominant genodermatosis characterized by acquired, slowly progressive reticulated pigmented lesions primarily involving flexural skin areas. Mutations in KRT5, POGLUT-1 and POFUT-1 genes have been associated with DDD, and loss-of-function mutations in PSENEN, a subunit of the gamma-secretase complex, were found in patients presenting with DDD or DDD comorbid with hidradenitis suppurativa (HS). A nonsense mutation in NCSTN, another subunit of the gamma-secretase, was already described in a patient suffering from HS and DDD but whether NCSTN could be considered a novel gene for DDD is still debated. Here, we enrolled a four-generation family with HS and DDD. Through Whole Exome Sequencing (WES) we identified a novel nonsense mutation in the NCSTN gene in all the affected family members. To study the impact of this variant, we isolated outer root sheath cells from patients' hair follicles. We showed that this variant leads to a premature stop codon, activates a nonsense-mediated mRNA decay, and causes NCSTN haploinsufficiency in affected individuals. In fact, cells treated with gentamicin, a readthrough agent, had the NCSTN levels corrected. Moreover, we observed that this haploinsufficiency also affects other subunits of the gamma-secretase complex, possibly causing DDD. Our findings clearly support NCSTN as a novel DDD gene and suggest carefully investigating this co-occurrence in HS patients carrying a mutation in the NCSTN gene.

Functional recovery of injured cavernous nerves achieved through endogenous nerve growth factor-containing bioactive fibrous membrane.

Radical prostatectomy is a highly successful treatment for prostate cancer, among the most prevalent manifestations of the illness. Damage of the cavernous nerve (CN) during prostatectomy is the main cause of postoperative erectile dysfunction (ED). In this study, the capability of a personalized bioactive fibrous membrane to regenerate injured CN was investigated. The fibrous membrane bioactivity is conferred by the selectively bound nerve growth factor (NGF) present in the rat urine. In a rat model of bilateral CN crush, the implanted bioactive fibrous membrane induces CN regeneration and restoration of erectile function, showing a significantly increased number of smooth muscle cells and content of endothelial and neuronal nitric oxide synthases (eNOS; nNOS). In addition, the bioactive fibrous membrane promotes nerve regeneration by increasing the number of myelinated axons and nNOS-positive cells, therefore reversing the CN fibrosis found in untreated rats or rats treated with a bare fibrous membrane. Therefore, this personalized regenerative strategy could overcome the recognized drawbacks of currently available treatments for CN injuries. It may constitute an effective treatment for prostate cancer patients suffering from ED after being subject to radical prostatectomy. STATEMENT OF SIGNIFICANCE: The present work introduces a unique strategy to address post-surgical ED resulting from CN injury during pelvic surgery (e.g., radical prostatectomy, radical cystoprostatectomy, abdominoperineal resection). It comprises a bioactive and cell-free fibrous implant, customized to enhance CN recovery. Pre-clinical results in a rat model of bilateral CN crush demonstrated that the bioactive fibrous implant can effectively heal injured CN, and restore penile structure and function. This implant selectively binds NGF from patient fluids (i.e. urine) due to its functionalized surface and high surface area. Moreover, its local implantation reduces adverse side effects. This tailored regenerative approach has the potential to revolutionize the treatment of ED in prostate cancer patients following radical prostatectomy, overcoming current treatment limitations.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Zika virus: Critical crosstalk between pathogenesis, cytopathic effects, and macroautophagy.

Zika virus (ZIKV) is a re-emerging positive-sense RNA arbovirus. Its genome encodes a polyprotein that is cleaved by proteases into three structural proteins (Envelope, pre-Membrane, and Capsid) and seven nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5). These proteins have essential functions in viral replication cycle, cytopathic effects, and host cellular response. When infected by ZIKV, host cells promote macroautophagy, which is believed to favor virus entry. Although several authors have attempted to understand this link between macroautophagy and viral infection, little is known. Herein, we performed a narrative review of the molecular connection between macroautophagy and ZIKV infection while focusing on the roles of the structural and nonstructural proteins. We concluded that ZIKV proteins are major virulence factors that modulate host-cell machinery to its advantage by disrupting and/or blocking specific cellular systems and organelles' function, such as endoplasmic reticulum stress and mitochondrial dysfunction.

Serotonergic Modulation of the Excitation/Inhibition Balance in the Visual Cortex.

Serotonergic neurons constitute one of the main systems of neuromodulators, whose diffuse projections regulate the functions of the cerebral cortex. Serotonin (5-HT) is known to play a crucial role in the differential modulation of cortical activity related to behavioral contexts. Some features of the 5-HT signaling organization suggest its possible participation as a modulator of activity-dependent synaptic changes during the critical period of the primary visual cortex (V1). Cells of the serotonergic system are among the first neurons to differentiate and operate. During postnatal development, ramifications from raphe nuclei become massively distributed in the visual cortical area, remarkably increasing the availability of 5-HT for the regulation of excitatory and inhibitory synaptic activity. A substantial amount of evidence has demonstrated that synaptic plasticity at pyramidal neurons of the superficial layers of V1 critically depends on a fine regulation of the balance between excitation and inhibition (E/I). 5-HT could therefore play an important role in controlling this balance, providing the appropriate excitability conditions that favor synaptic modifications. In order to explore this possibility, the present work used in vitro intracellular electrophysiological recording techniques to study the effects of 5-HT on the E/I balance of V1 layer 2/3 neurons, during the critical period. Serotonergic action on the E/I balance has been analyzed on spontaneous activity, evoked synaptic responses, and long-term depression (LTD). Our results pointed out that the predominant action of 5-HT implies a reduction in the E/I balance. 5-HT promoted LTD at excitatory synapses while blocking it at inhibitory synaptic sites, thus shifting the Hebbian alterations of synaptic strength towards lower levels of E/I balance.

5-HT-dependent synaptic plasticity of the prefrontal cortex in postnatal development.

Important functions of the prefrontal cortex (PFC) are established during early life, when neurons exhibit enhanced synaptic plasticity and synaptogenesis. This developmental stage drives the organization of cortical connectivity, responsible for establishing behavioral patterns. Serotonin (5-HT) emerges among the most significant factors that modulate brain activity during postnatal development. In the PFC, activated 5-HT receptors modify neuronal excitability and interact with intracellular signaling involved in synaptic modifications, thus suggesting that 5-HT might participate in early postnatal plasticity. To test this hypothesis, we employed intracellular electrophysiological recordings of PFC layer 5 neurons to study the modulatory effects of 5-HT on plasticity induced by theta-burst stimulation (TBS) in two postnatal periods of rats. Our results indicate that 5-HT is essential for TBS to result in synaptic changes during the third postnatal week, but not later. TBS coupled with 5-HT2A or 5-HT1A and 5-HT7 receptors stimulation leads to long-term depression (LTD). On the other hand, TBS and synergic activation of 5-HT1A, 5-HT2A, and 5-HT7 receptors lead to long-term potentiation (LTP). Finally, we also show that 5-HT dependent synaptic plasticity of the PFC is impaired in animals that are exposed to early-life chronic stress.

An overview of Zika virus genotypes and their infectivity.

Zika virus (ZIKV) is an enveloped, single-stranded RNA arbovirus belonging to the genus Flavivirus. It was first isolated from a sentinel monkey in Uganda in 1947. More recently, ZIKV has undergone rapid geographic expansion and has been responsible for outbreaks in Southeast Asia, the Pacific Islands, and America. In this review, we have highlighted the influence of viral genetic variants on ZIKV pathogenesis. Two major ZIKV genotypes (African and Asian) have been identified. The Asian genotype is subdivided into Southwest Asia, Pacific Island, and American strains, and is responsible for most outbreaks. Non-synonymous mutations in ZIKV proteins C, prM, E, NS1, NS2A, NS2B, NS3, and NS4B were found to have a higher prevalence and association with virulent strains of the Asian genotype. Consequently, the Asian genotype appears to have acquired higher cellular permissiveness, tissue persistence, and viral tropism in human neural cells. Therefore, mutations in specific coding regions of the Asian genotype may enhance ZIKV infectivity. Considering that mutations in the genomes of emerging viruses may lead to new virulent variants in humans, there is a potential for the re-emergence of new ZIKV cases in the future.

Transcriptome Meta-Analysis Confirms the Hidradenitis Suppurativa Pathogenic Triad: Upregulated Inflammation, Altered Epithelial Organization, and Dysregulated Metabolic Signaling.

Hidradenitis suppurativa (HS) is an inflammatory skin condition clinically characterized by recurrent painful deep-seated nodules, abscesses, and sinus tracks in areas bearing apocrine glands, such as axillae, breasts, groins, and buttocks. Despite many recent advances, the pathophysiological landscape of HS still demands further clarification. To elucidate HS pathogenesis, we performed a meta-analysis, set analysis, and a variant calling on selected RNA-Sequencing (RNA-Seq) studies on HS skin. Our findings corroborate the HS triad composed of upregulated inflammation, altered epithelial differentiation, and dysregulated metabolism signaling. Upregulation of specific genes, such as KRT6, KRT16, serpin-family genes, and SPRR3 confirms the early involvement of hair follicles and the impairment of barrier function in HS lesioned skin. In addition, our results suggest that adipokines could be regarded as biomarkers of HS and metabolic-related disorders. Finally, the RNA-Seq variant calling identified several mutations in HS patients, suggesting potential new HS-related genes associated with the sporadic form of this disease. Overall, this study provides insights into the molecular pathways involved in HS and identifies potential HS-related biomarkers.

Analysis of Current Deep Learning Networks for Semantic Segmentation of Anatomical Structures in Laparoscopic Surgery.

Semantic segmentation of anatomical structures in laparoscopic videos is a crucial task to enable the development of new computer-assisted systems that can assist surgeons during surgery. However, this is a difficult task due to artifacts and similar visual characteristics of anatomical structures on the laparoscopic videos. Recently, deep learning algorithms have been showed promising results on the segmentation of laparoscopic instruments. However, due to the lack of large public datasets for semantic segmentation of anatomical structures, there are only a few studies on this task. In this work, we evaluate the performance of five networks, namely U-Net, U-Net++, DynUNet, UNETR and DeepLabV3+, for segmentation of laparoscopic cholecystectomy images from the recently released CholecSeg8k dataset. To the best of our knowledge, this is the first benchmark performed on this dataset. Training was performed with dice loss. The networks were evaluated on segmentation of 8 anatomical structures and instruments, performance was quantified through the dice coefficient, intersection over union, recall, and precision. Apart from the U-Net, all networks obtained scores similar to each other, with the U-Net++ being the network with the best overall score with a mean Dice value of 0.62. Overall, the results show that there is still room for improvement in the segmentation of anatomical structures from laparoscopic videos. Clinical Relevance- The results of this study show the potential of deep neural networks for the segmentation of anatomical structures in laparoscopic images which can later be incorporated into computer-aided systems for surgeons.

A deep learning method for kidney segmentation in 2D ultrasound images.

Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide. Nevertheless, the number of available methods that effectively work in such devices is still limited. In this work, we evaluate the performance of the U-NET architecture to segment the kidney in 2D US images. To accomplish this task, we studied the possibility of using multiple sliced images extracted from 3D US volumes to achieve a large, variable, and multi-view dataset of 2D images. The proposed methodology was tested with a dataset of 66 3D US volumes, divided in 51 for training, 5 for validation, and 10 for testing. From the volumes, 3792 2D sliced images were extracted. Two experiments were conducted, namely: (i) using the entire database (WWKD); and (ii) using images where the kidney area is > 500 mm2 (500KD). As a proof-of-concept, the potential of our strategy was tested in real 2D images (acquired with 2D probes). An average error of 2.88 ± 2.63 mm in the testing dataset was registered. Moreover, satisfactory results were obtained in our initial proof-of-concept using pure 2D images. In short, the proposed method proved, in this preliminary study, its potential interest for clinical practice. Further studies are required to evaluate the real performance of the proposed methodology. Clinical Relevance- In this work a deep learning methodology to segment the kidney in 2D US images is presented. It may be a relevant feature to be included in the recent portable US ecosystems easing the interpretation of image and consequently the clinical analysis.

Detection of bladder cancer with feature fusion, transfer learning and CapsNets.

This paper confronts two approaches to classify bladder lesions shown in white light cystoscopy images when using small datasets: the classical one, where handcrafted-based features feed pattern recognition systems and the modern deep learning-based (DL) approach. In between, there are alternative DL models that had not received wide attention from the scientific community, even though they can be more appropriate for small datasets such as the human brain motivated capsule neural networks (CapsNets). However, CapsNets have not yet matured hence presenting lower performances than the most classic DL models. These models require higher computational resources, more computational skills from the physician and are more prone to overfitting, making them sometimes prohibitive in the routine of clinical practice. This paper shows that carefully handcrafted features used with more robust models can reach similar performances to the conventional DL-based models and deep CapsNets, making them more useful for clinical applications. Concerning feature extraction, it is proposed a new feature fusion approach for Ta and T1 bladder tumor detection by using decision fusion from multiple classifiers in a scheme known as stacking of classifiers. Three Neural Networks perform classification on three different feature sets, namely: Covariance of Color Histogram of Oriented Gradients, proposed in the ambit of this paper; Local Binary Patterns and Wavelet Coefficients taken from lower scales. Data diversity is ensured by a fourth Neural Network, which is used for decision fusion by combining the outputs of the ensemble elements to produce the classifier output. Both Feed Forward Neural Networks and Radial Basis Functions are used in the experiments. Contrarily, DL-based models extract automatically the best features at the cost of requiring huge amounts of training data, which in turn can be alleviated by using the Transfer Learning (TL) strategy. In this paper VGG16 and ResNet-34 pretrained in ImageNet were used for TL, slightly outperforming the proposed ensemble. CapsNets may overcome CNNs given their ability to deal with objects rotational invariance and spatial relationships. Therefore, they can be trained from scratch in applications using small amounts of data, which was beneficial for the current case, improving accuracy from 94.6% to 96.9%.

An overview of Zika virus genotypes and their infectivity

ABSTRACT Zika virus (ZIKV) is an enveloped, single-stranded RNA arbovirus belonging to the genus Flavivirus. It was first isolated from a sentinel monkey in Uganda in 1947. More recently, ZIKV has undergone rapid geographic expansion and has been responsible for outbreaks in Southeast Asia, the Pacific Islands, and America. In this review, we have highlighted the influence of viral genetic variants on ZIKV pathogenesis. Two major ZIKV genotypes (African and Asian) have been identified. The Asian genotype is subdivided into Southwest Asia, Pacific Island, and American strains, and is responsible for most outbreaks. Non-synonymous mutations in ZIKV proteins C, prM, E, NS1, NS2A, NS2B, NS3, and NS4B were found to have a higher prevalence and association with virulent strains of the Asian genotype. Consequently, the Asian genotype appears to have acquired higher cellular permissiveness, tissue persistence, and viral tropism in human neural cells. Therefore, mutations in specific coding regions of the Asian genotype may enhance ZIKV infectivity. Considering that mutations in the genomes of emerging viruses may lead to new virulent variants in humans, there is a potential for the re-emergence of new ZIKV cases in the future.

Can narration and guidance in video-enhanced learning improve performance on E-BLUS exercises?

INTRODUCTION: This study aimed to compare trainees' laparoscopic performance concerning the peg-transfer (PT) and needle-guidance (NG) exercises after watching the original European Basic Laparoscopic Urologic Skills (E-BLUS) video or after watching a video-mentored tutorial (VMT) with 'tips and tricks', narration and didactic illustrations. MATERIAL AND METHODS: An experimental, unblinded, parallel, 2-intervention, 2-period randomized trial with an allocation ratio of 1:1 was conducted. Forty-two participants were randomized into 2 groups. Prior to task initiation, Group 1 watched the VMT in both trials and Group 2 watched, firstly, the original E-BLUS examination video and, in the second trial, the VMT. Each participant performed 2 trials for each exercise. Outcome measures were task time and total number of errors. RESULTS: In the first period, participants who visualized the PT and NG VMT had fewer errors than participants who visualized the E-BLUS video (p = 0.001 and p = 0.014, respectively). In the second period, after watching the VMT, a decrease in the total number of errors in PT and NG exercises was observed in the participants who previously watched the E-BLUS video (p = 0.001 and p = 0.002, respectively). In the second period, a decrease in median task time was observed for Group 1 and 2 after watching the PT VMT (p ≤0.001 and p = 0.003, respectively) and NG VMT (p = 0.005 and p = 0.01, respectively). CONCLUSIONS: The use of VMT can lead to a smaller number of errors and, if coupled with deliberate practice, could lead to a shorter task time in exercise performance among participants with no previous laparoscopic experience.

In silico analysis of molecular interactions between HIV-1 glycoprotein gp120 and TNF receptors.

Proinflammatory microenvironmental is crucial for the Human Immunodeficiency Virus Type 1 (HIV-1) pathogenesis. The viral glycoprotein 120 (gp120) must interact with the CD4+ T cell chemokine receptor (CCR5) and a co-receptor C-X-C chemokine receptor type 4 (CXCR4) to let the virus entry into the host cells. However, the interaction of the viral particle with other cell surface receptors is mandatory for its attachment and subsequently entry. Tumor Necrosis Factor receptor type I (TNFR1), type II (TNFR2) and Fas are a superfamily of transmembrane proteins involved in canonical inflammatory pathway and cell death by apoptosis as responses against viral pathogens. In our study, we performed an in silico evaluation of the molecular interactions between viral protein gp120 and TNF receptors (TNFR1, TNFR2 and Fas). Protein structures were retrieved from Protein Databank (PDB), and Molecular Docking and dynamics were performed using ClusPro 2.0 server and GROMACS software, respectively. We observed that gp120 is able to bind TNFR1, TNFR2 and Fas receptors, although only the TNFR2-gp120 complex demonstrated to produce a stable and durable binding. Our findings suggest that gp120 may act as an agonist to TNF-α and also function as an attachment factor in HIV-1 entry process. These molecular interaction by gp120 may be the key to HIV-1 immunopathogenesis. In conclusion, gp120 may stimulate pro-inflammatory and apoptotic signaling transduction pathways mediated by TNFR2 and may act as an attachment factor retaining HIV-1 viral particles on the host cell surface.