Salmonella invades both phagocytic and non-phagocytic host cells and adopts an intracellular lifestyle in a membrane-bound compartment during infection. Therefore, a systemic overview of Salmonella adaptations to distinct host cells together with host remodeling will assist us in charting the landscape of host-pathogen interactions. Central to the Salmonella-host interplay are bacterial virulence factors (effectors) that are injected into host cells by type III secretion systems (T3SSs). Despite great progress, functional studies of bacterial effectors have experienced daunting challenges as well. In the last decade, mass spectrometry-based proteomics has evolved into a powerful technological platform that can quantitatively measure thousands of proteins in terms of their expression as well as post-translational modifications. Here, we will review the applications of high-throughput proteomic technologies in understanding the dynamic reprogramming of both Salmonella and host proteomes during the course of infection. Furthermore, we will summarize the progress in utilizing affinity purification-mass spectrometry to screen for host substrates of Salmonella T3SS effectors. Finally, we will critically discuss some limitations/challenges with current proteomic platforms in the context of host-pathogen interactions and highlight some emerging technologies that may offer the promise of tackling these problems.As a model pathogen,  Salmonella can cause local intestinal disease (enteritis and diarrhea) in humans or, in some cases, systemic disease (enteric/typhoid fever). The group of typhoidal Salmonella contains serovars Typhi, Paratyphi A, Paratyphi B, and Paratyphi C, whereas other serovars are referred as non-typhoidal Salmonella, including Salmonella Typhimurium [Salmonella Typhimurium which we will refer to as Salmonella unless otherwise indicated. As an intracellular pathogen, Salmonella can invade different types of human cells including epithelial cells and macrophages, which is essential for bacterial virulence. Salmonella pathogenesis is largely mediated by two type III secretion systems (T3SSs) encoded within Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2). These nanomachines are able to transport virulence factors  directly into host cells to manipulate diverse cellular processes. The SPI-1 effectors mostly mediate the initial bacterial invasion into host cells, while those encoded within SPI-2 contribute to pathogen intracellular survival and multiplication. Upon internalization, Salmonella resides in a membrane-bound compartment known as Salmonella-containing vacuoles (SCVs).As a group of Gram-negative bacilli that parasitize in human and animal intestines, himurium . Here inSalmonella pathogenesis has been extensively studied by using various genetic, biochemical and cell biology methods. These approaches have built up most of our knowledge of Salmonella infection biology. Nevertheless, there are notable obstacles that hamper us from further understanding host-pathogen interactions on the molecular level. For instance, we often lack a systemic snapshot of the host-pathogen interplay in that the bulk of research has focused on characterizing individual pathogen genes/proteins. In addition, among the 40–60 Salmonella type III effectors identified so far, only a handful of those are well characterized in terms of their biochemical activities, host targets, and functional roles in pathogenesis. In reality, the function of many more effectors (particularly those encoded by SPI-2) are yet to be defined. Another unfortunate truth is that they are often more challenging to work with, or in other words, difficult to tackle by using traditional methods .As one of the model bacterial pathogens, Recently, mass spectrometry (MS)-based proteomics has witnessed major technical breakthroughs. Owing to highly efficient nanoflow liquid chromatography (nano LC) and modern mass spectrometers with high sensitivity, throughput and resolution, current proteomic platforms have achieved enormous progress in analytical depth as well as the proteome coverage of complex samples. In the last decade, the number of detected proteins by LC-MS techniques has increased from 1000 to almost 10,000 in a single cell type .Salmonella-host interactions. Herein, we intend to summarize the utilization of proteomic technologies on elucidating the functional interplay between Salmonella and host cells. With respect to proteomic applications in the broader area of host-pathogen interactions , there are some excellent reviews published lately [Salmonella-host interactions.We believe that it has come to the moment when MS-based proteomics can make substantial impact on the research of d lately ,4. FurthSalmonella proteome exhibits high plasticity upon interactions with its mammalian host. Conversely, host cells will have to reprogram their gene expression to fight against bacterial invasion and their intracellular proliferation [Salmonella and host cells upon infection would allow us to chart a panoramic view of the host-pathogen interplay and generate hypotheses that warrant further investigations.Bacteria in general can sense environmental cues and quickly adapt to their surroundings. Such adaptations are ultimately reflected in alterations on the molecular level and, in particular, the protein level. Therefore, the feration . TherefoSalmonella as a facultative intracellular pathogen, it would be most informative to examine the proteome of bacteria isolated from infected host cells. For a long time, however, it had been technically challenging to physically isolate intracellular pathogens [Salmonella recovered from cultured cell lines or in vivo infection models.Given athogens . Therefoathogens . These sathogens . Herein Salmonella proteome during infection was reported by the Bumann and Mann groups in 2006 [Salmonella strain expressing green fluorescent protein (GFP). In total, they identified 370 and 835 Salmonella proteins from mouse spleen and caecum, respectively, most of which were metabolic enzymes nonessential for Salmonella virulence. In addition to in vivo studies, in the same year Shi et al. quantitatively analyzed the intracellular Salmonella proteome by using cell culture models, in which 315 bacterial proteins were identified from infected RAW 264.7 macrophages with 39 up-regulated proteins after infection [Salmonella proliferation inside macrophages. The seminal work on the  in 2006 . Prior tnfection . Follow-Salmonella encodes ~4500 proteins, the proteome coverage of the above two studies still awaits further improvement to increase the analytical depth. In 2013, the Bumann group extended the previous work and detected 1182 bacterial proteins from an infected mouse spleen by using improved sorting and proteomic technologies [Salmonella metabolism in infected host tissues and demonstrated that the pathogen exploits diverse host nutrients available only in scarce amounts. Though not reported in this study, it would be interesting as well to analyze bacterial proteins in the in vivo dataset other than metabolic enzymes . In particular, quantitative assessment of the in vivo proteome together with control groups can be quite illuminating from the perspective of bacterial pathogenesis. Given that Campylobacter by the Galán group [Salmonella proteome within infected epithelial cells [Salmonella infection ) and their proteomes were quantitatively profiled together with extracellular populations. We reasoned that time-course analyses of the pathogen proteome are likely to reveal Salmonella’s adaptations to its intracellular niche within the host. At 6 hpi , the most striking alteration was the massive induction of bacterial metal uptake  and phosphate utilization systems, unveiling an overall shortage of metal and phosphate within the SCV  as well as severe degeneration of bacterial flagellar and chemotaxis systems . To illuminate its potential roles in bacterial pathogenesis, in collaboration with the Song group, we again exploited intracellular bacterial proteomics [ydcR (ΔydcR) to that of its parental strain would reveal those candidate proteins under the regulatory controls of YdcR. Such a strategy turned out to be quite successful in nailing down the target proteins. The expression of SrfN, a known virulence factor, was found to be strictly dependent on YdcR during infection, and further biochemical assays validated the direct regulation of srfN by YdcR. Importantly, competitive infection reveals that the loss of YdcR largely phenocopied that of SrfN and led to reduced bacterial fitness in a murine infection model, thereby suggesting a physiological role in pathogenesis.Among those differentially regulated proteins of intracellular oteomics . Given iSalmonella can be further internalized by phagocytic cells, such as macrophages [Salmonella encounters different host environments in macrophages; therefore, it would be intriguing to uncover those differences to which pathogens further adapt. Our group profiled the intracellular Salmonella proteome inside infected murine macrophages (RAW 264.7 cells) [Salmonella (in addition to shared patterns). Notably, differential regulation of bacterial T3SSs, chemotaxis and flagellar systems showed substantially faster kinetics in macrophages relative to epithelial cells. Most strikingly, we noted massive induction of a complete set of Salmonella proteins encoded on his operon in RAW 264.7 cells while these bacterial enzymes were barely detectable during infection of HeLa cells. Follow-up experiments further established that markedly lower histidine levels in RAW 264.7 cells together with a hisG mutation of the strain (SL1344) drove up the abnormally high expression of the His enzymes. In other words, the defective biosynthetic pathway renders the bacterial strain hypersensitive to histidine shortage in host macrophage cells.After breaching the intestinal epithelium, rophages  where it7 cells) . Upon coSalmonella-induced filaments (SIFs) [Salmonella-containing vacuoles (SCVs), Salmonella establishes an intertwined tubular network of SIFs connected to SCVs. The authors examined the proteome of two bacterial mutants, ΔssaV and ΔsseF, which either lack a tubular network or have a defective one. In comparison to the WT strain, both mutants had elevated expression of bacterial proteins in response to nutritional starvation. In addition, the SPI-2-deficient mutant (ΔssaV) exhibited features indicative of higher stress levels due to host antimicrobial defenses such as reactive oxygen species (ROS). These proteomic findings further support the roles of SIFs in the Salmonella intracellular lifestyle. Importantly, remodeling of the host endosomal system by SPI-2 T3SS effectors allows the pathogen to acquire nutrients and defend against host antimicrobial molecules.A recent study by the Hensel group employed intracellular bacterial proteomics in murine macrophages to study the roles of s (SIFs) . While rS. Typhimurium infection [Salmonella infection. Notably, iNOS and prostaglandin (PG)-endoperoxide synthase 2  were up-regulated, and their induction was previously shown to contribute to host defenses. Recently, our group examined protein expression of host epithelial cells  during Salmonella infection [Salmonella-host interactions remain to be defined.Host-pathogen interactions are a two-way street. Surveying the host proteome can help understand host defense mechanisms during bacterial infection. Due to the greater complexity of mammalian proteomes, proteomic profiling of host cells, in general, is lagging behind that of pathogens. In 2009, Shi et al. reported the first proteomic analyses of host cells upon nfection . They confection . Other pSalmonella replication [Salmonella infection of host cells often accompanies the formation of extensive membrane networks extending from the mature SCV for nutrient supply. To understand the source and function of these networks, Vorweck et al. immunoprecipitated SseF, a SPI-2 effector embedded in such membranes [Salmonella redirects ER membrane trafficking to its intracellular niche. Rather than profiling the whole proteome, subsets of the host proteome  can be analyzed as well in response to bacterial infection. In principle, such analyses often afford greater details at the price of comprehensiveness. It has been shown that intact Golgi membranes are required for efficient lication . To invelication . To furtembranes . Using aSalmonella, SCVs have also been an intense subject of study, though their physical isolation is still technically challenging. Santos et al. examined the proteome of SCVs purified from HeLa cells at 30 min and 3 h, representing the early and maturing SCVs, respectively [Salmonella infection of macrophages, bacterial pathogens induce secretion of specific cytokines from these phagocytes and interfere with the host secretory pathways. For example, IL-4 and IL-10 suppress the host immune responses against Salmonella, while cytokines such as IL-1β and IL-18 are able to activate inflammasome. Hui et al. analyzed the extracellular proteome of macrophages (human THP-1 cells) upon Salmonella infection [Due to their vital function for intracellular ectively . With nonfection . Their pSalmonella SPI-2 T3SS. Importantly, cathepsin inhibition was able to suppress Salmonella-induced cell death and reduce gasdermin D expression, thus illustrating a functional role of cathepsin in non-canonical inflammasome regulation. In a recent study, the Typas and Krijgsveld groups performed host spatiotemporal proteomics during macrophage infection by combining click chemistry and pulsed stable isotope labeling of amino acids in cell culture (SILAC) . They moSalmonella during infection [Salmonella and harvested at 15 min, 1 h and 6 h post infection prior to affinity enrichment of crosslinked host proteins. In total, 442 proteins were identified over three time points. Consistent with previous findings, host proteins associated with early endocytosis were enriched at 15 min and 1 h, while late endosomal and lysosomal proteins  were overrepresented at 6 h. In addition to known SCV markers and/or interacting host components, this work also identified novel substrates that bind to bacterial pathogens during early stages of infection, including CD98, CD180, CD147 and CD11b. It is conceivable that the developed strategy can be broadly applied to understand the interplay between hosts and other bacteria, as well as viruses.Lately, the Tao group developed a chemical probe that permits the capture of host proteins that directly interact with the surface of intracellular nfection . Briefly2 [Post-translational modifications (PTMs) play a crucial role in the interplay between pathogens and host cells. In the last decade, significant advances have been made in proteomic technologies towards large-scale profiling of several common PTMs such as phosphorylation, glycosylation, acetylation, and ubiquitination. Among the proteomic studies of all PTMs, phosphoproteomics is arguably the most established area in which diverse and robust enrichment strategies have been developed for phosphoproteins or phosphopeptides, such as immobilized metal affinity chromatography (IMAC) and TiO2 .Salmonella infection [sopB strains together with mock infection. Overall, they found higher phosphorylation levels of proteins related to regulation of apoptosis, transmembrane transport, nuclear organization, and cell proliferation and lower phosphorylation levels of those related to cytoskeleton organization, protein complex assembly, and cell polarity. Further bioinformatic analyses revealed that phosphosites of differing levels during infection were targeted by Akt, protein kinase C, and Pim. Moreover, they found that the SPI-1 T3SS effector SopB could impact half of the host phosphorylation events upon bacterial infection. In a subsequent study, the same group investigated the global impact of Salmonella SPI-2 effectors on the host phosphorylation events [ssaV). Intriguingly, they found differential modulation of host phosphorylation events and cellular processes by SPI-2 effectors in a cell type-dependent manner. Furthermore, they identified HSP27 as a kinase substrate of the type III effector SteC. Biochemical and cellular assays demonstrated that SteC induces actin rearrangement by phosphorylating multiple sites of HSP27.In 2011, the Foster group conducted quantitative phosphoproteomics of host epithelial cells during the initial stages of nfection . They men events . They quSalmonella encodes several T3SS effector proteins , which harbor E3 ubiquitin ligase activity and presumably directly target host ubiquitination processes. The Behrends and Dikic groups utilized ubiquitinome profiling to measure the dynamic alterations in the global ubiquitination events of host epithelial cells upon Salmonella infection [In addition to phosphorylation, protein ubiquitination is central to many fundamental cellular processes and is also an important player at the functional interface of host-pathogen interactions. Indeed, the host ubiquitin system is a frequent target of many bacterial virulence factors ,32. For nfection . OverallSalmonella infection by using immunoblotting assays [Salmonella was profoundly impacted by host SUMO status, suggesting a crucial role of this PTM in bacterial pathogenesis. In a following study, the same group enriched and analyzed the SUMOylated proteins from Salmonella-infected host cells as well as uninfected controls [Salmonella promotes long-lived yet functionally deficient Rab7 for its own benefit of intracellular survival and proliferation. Small ubiquitin-related modifier (SUMO) is a ubiquitin-like protein and SUMOylation can regulate a variety of protein functions in many cellular pathways . The Srig assays . Moreovecontrols . Among tSalmonella type III effectors SseK1 and SseK3. These two effectors are arginine glycosyltransferases that can covalently attach N-acetyl glucosamine (GlcNAc) to protein substrates. By taking advantage of an antibody that can recognize and enrich GlcNAcylated peptides, they aimed to globally identify glycosylated host proteins during Salmonella infection. They found that SseK1 modified the signaling adaptor TRADD, while SseK3 modified the signaling receptors TNFR1 and TRAILR [Recently, the Hartland group employed glycoproteomics to screen for host glycosylation substrates of d TRAILR . In collSalmonella infection, secreted toxins and effectors are the bacterial factors that directly engage in host-pathogen interactions. Therefore, identifying these virulence factors and understanding how they work is a fundamentally important theme in bacterial pathogenesis. In fact, proteomic tools can make substantial contributions to such endeavors.In addition to the global profiling of protein expression and PTMs, MS-based proteomics has also evolved into a powerful technique for studying protein-protein interactions. In the context of Salmonella type III effectors have already been reported by various biochemical, genetic, and bioinformatic methods. Nevertheless, we often lack a comprehensive survey of these effectors and whether we are still missing some effectors remains to be answered. Proteomic profiling of bacterial culture supernatant  can be utilized as a high-throughput screen for novel effector candidates. In 2011, the Heffron group analyzed bacterial secretome from Salmonella culture under SPI-2-inducing conditions [ssaL mutant as a control, they were able to identify 20 known SPI-2 effectors. Among 12 candidate proteins for novel effectors, they successfully validated six proteins as bona fide substrates translocated by SPI-2 T3SS. Almost at the same time, the groups of Finlay and Foster collaboratively applied secretome analysis and identified several novel substrate candidates of SPI-2 T3SS as well [Over the years, many nditions . By usin as well . In addiSalmonella effectors delivered by SPI-1 T3SS [invA) as a control, we identified STM1239 (which we renamed SopF) as a novel substrate of T3SS. Further immunoblotting and β-lactamase reporter assays confirmed T3SS-dependent secretion and translocation of SopF. In collaboration with the Zhou group, we performed some initial characterization of this newly identified effector, ascertaining its contribution to bacterial virulence. Recently, an independent study from the Shao group confirmed the discovery of SopF as a novel type III effector [Salmonella-induced autophagy (xenophagy) by covalently modifying a component of host vacuolar ATPase via its ADP-ribose transferase activity. We also contributed in this work to mapping the ADP-ribosylation site (s) of this host factor by tandem mass spectrometry.In 2017, our group undertook quantitative secretome profiling to catalogue I-1 T3SS . By usineffector . RemarkaSalmonella effectors, which are scattered throughout the literature and include SopB, SopE, GogB, SteE, SifA, and SopA [As for bacterial virulence factors, a significant bottleneck of their functional studies is the discovery of host-interacting partners. When one embarks on such tasks, affinity purification-mass spectrometry (AP-MS) is arguably one of the most practiced methods. Indeed, AP-MS has helped uncover many host substrates of and SopA ,46,47,48E. coli, immunoprecipitation of protein complexes, and MS analyses. In addition to previously described interactions, they were able to identify 11 novel protein interactions, three of which were further validated by reciprocal co-immunoprecipitation [Salmonella effectors  and collectively identified 54 high-confidence host interactors [Next, we would like to review some reports in which large-scale AP-MS experiments were conducted to reveal host interactome for a panel of bacterial effectors. In 2011, the Finlay and Foster groups collaboratively screened a total of 24 SPI-2 effectors using strategies combining overproduction in mammalian cells or pitation . In 2016eractors . Among tDespite the large body of the work discussed above, the overall success rate of AP-MS is not particularly encouraging, at least in some cases, in identifying host targets of individual effectors. There are two major complications, in our opinion, that could account for an unsuccessful practice. First of all, stable protein interactions/complexes (that can withstand and survive the whole purification scheme) have been assumed for positive outcomes. We know in reality that many effector-substrate interactions can be relatively weak or transient, which would be most likely lost during AP-MS experiments. Secondly, due to the sensitivity of MS measurements, one would pick up many non-specific binding partners (in particular when overexpression occurs) that mask the real targets from being identified. Salmonella effectors that modulate host intracellular trafficking  [Some emerging methods have shown promise in addressing the issues discussed above. Proximity labeling is one of these techniques. A prototype of this tool is BioID for proximity-dependent biotin identification . Conceptd SopD2) . NotablySalmonella-host interactions. Regarding proteome-wide expression profiling, the analyses of bacterial pathogens seem to provide us more insights into bacterial pathogenesis largely owing to relatively compact pathogen proteomes (and higher coverage). Though isolation of intracellular bacteria used to be technically challenging, we have perfected a differential centrifugation-based approach, permitting routine analyses of bacteria recovered from host cells. With such proteome datasets, we are able to chart a dynamic overview of pathogen biology during infection based on current knowledge of known protein functions. Nonetheless, we believe the better utility of such data is to formulate hypothesis, from which new studies can be initiated. We would like to raise attention in this regard because many altered proteins are not well characterized or cannot be classified into known pathways/networks (omitted from the main text). Furthermore, we would like to encourage the practice of analyzing intracellular proteome of bacterial mutants, particularly when a mutant fails to exhibit observable phenotypes in traditional assays. With WT bacteria as a reference, large-scale proteomic analysis of mutant strains can provide valuable clues by generating a molecular phenotype. Another future direction would be bacterial proteomes from in vivo infections, though we have seen a few successful examples as discussed early in As discussed above, proteomic analyses offer us a global and systemic view of the delicate balance of Salmonella-host interactions. In addition to generating networks of signaling pathways  and new hypotheses, another direction we would like to encourage is the utilization of PTM profiling as a powerful screen for effector substrates. For example, Salmonella  encodes type III effectors harboring enzymatic activities that can directly engage in host phosphorylation and ubiquitination pathways. Quantitative PTM profiling of host cells infected by Salmonella WT and mutant strains can help us tease out candidate host targets. In the future, we would anticipate expansion of PTM profiling to other less common modifications when more enrichment tools become available. Though less prevalent than their mammalian counterparts, PTM profiling of bacterial pathogens can be explored as well. Indeed, large-scale analyses of protein acetylation in Salmonella have been reported [On the host side, the proteome coverage in expression profiling has been expanded largely due to significantly improved proteomic platforms in the last decade. That being said, the covered proteome is still not as comprehensive as we would like it to be. In many cases, greater coverage comes with the price of extensive sample fractionations before LC-MS measurements, which substantially drives up the analysis time and lowers the throughput. Prior to the arrival of mass spectrometers with superb sequencing rates, we may have more success with subproteome analyses with reduced sample complexity and greater analytical depth. Subproteomes can be either organelles or other subcellular structures. Furthermore, PTMs can be considered as subproteomes as well. We have summarized some great applications of PTM profiling in the context of reported ,53.Salmonella-host interactions is the functional study of bacterial effectors. A common rate-limiting step in such endeavors is the relentless search for their host targets. Traditional AP-MS has fulfilled its duty to some extent in interrogating effector-target interactions, and smarter tools are definitely needed with the hope of better precision and accuracy. As discussed above, the efficiency of regular AP-MS would drop substantially when weak and/or dynamic protein-protein interactions are involved. Several emerging techniques can potentially overcome this difficulty including proximity-labeling methods such as BioID, as discussed above (Legionella type IV effector SetA harboring glycosyl-transferase activity [Salmonella and host cells as well as the broad field of host-pathogen interactions.A center stage of ed above . A recened above . Lately,activity . Regardl


Since its cloning more than 30 years ago, the thyrotropin receptor (TSHR) has emerged as a pivotal player in thyroid physiology and pathophysiology. In particular, hyperthyroidism due to autoimmune disease or thyroid autonomy is linked with TSHR activation via autoantibodies or mutations respectively. This review summarises clinical aspects of constitutive TSH receptor activation by naturally occurring somatic or germline TSHR mutations resulting in TSH-independent thyroid function and cell proliferation. The group of Dumont and Vassart were among the first to suggest that any molecular lesion leading to constitutive activation of the cAMP cascade could be responsible for the growth and functional properties of autonomous thyroid nodules . In suppFunctional characterisation of the identified TSHR mutations has mostly been performed in COS-7 cells and has demonstrated constitutive adenylylcylase activation, in addition to activation of phospholipase C-protein kinase C signalling by some mutations , 4, 18. Distinct biological properties of various TSHR mutations and gsp were subsequently demonstrated in rat thyroid follicular cells and human thyrocytes , 25. TheHowever, the precise mechanism that drive the evolution from thyrocytes harbouring a gain-of-function TSHR mutation to a clinically apparent toxic thyroid nodule are still far from being understood, adding to the ongoing discussion that additional alterations may be required. In this context, a second hit mutation was identified in enhancer of zeste homologue 1 (EZH1) in toxic thyroid nodules by whole-exome sequencing . This muConstitutively activating TSHR mutations may also occur as germline mutations and in the situation cause hereditary non-autoimmune hyperthyroidism that is transmitted in an autosomal dominant manner , 32. TheIn sum, constitutive activation of the cAMP cascade, predominantly via TSHR mutations, is the molecular hallmark of thyroid autonomy in its various presentation as solitary toxic adenoma, toxic multinodular goitre or even hereditary non-autoimmune hyperthyroidism . The ide


The trait of extraversion is one of the longest-standing domains that captures the social dimension of personality and can potentially explain the covariation of a wide variety of behaviors. To date, there is a growing recognition that human behavior should be specified not only through the psychological mechanisms underlying each trait but also through their underlying neurobehavioral systems. While imaging studies have revealed important initial insights into the structural and functional neural correlates of extraversion, current knowledge about the relationships between extraversion and brain structures is still rather limited, especially with regard to the relationship between extraversion and white matter (WM). In this study, we aimed to investigate WM microstructure in extraversion in greater depth. Thirty-five healthy volunteers  underwent magnetic resonance imaging, as a part of a larger project aimed at investigating the longitudinal effect of motor training. WM integrity was assessed using the diffusion tensor imaging technique combining multiple diffusion tensor measures. Extraversion was assessed by the Eysenck Personality Questionnaire-Revised. Voxelwise correlation analyses between fractional anisotropy, axial diffusivities, and radial diffusivities maps and extraversion score showed decreased connectivity in the right inferior fronto-occipital fasciculus and forceps major among individuals who had high extraversion ratings. In conclusion, individual differences in extraversion may reflect differential organization of the WM tracts connecting frontal cortex, temporal, and occipital areas, which are related to socioemotional and control functions. Personality traits involve individual tendencies to express specific patterns of cognition, emotion, and behavior  as well as task-based studies show anatomic variations in different brain areas, with a variety of both positive and negative correlations identified between extraversion and neuroanatomic measures in several regions. Some studies reported a positive association between extraversion and gray matter (GM) in the OFC and sub-cortical regions related to emotional processing  technique. DTI assesses WM integrity by measuring directional water diffusion  and behaviors related to selected personality traits, such as novelty-seeking, reward dependence, and creativity  subscale = 23 items (item example: “Do you have many friends?”); Psychoticism (P) subscale = 23 items (item example: “Do you enjoy to insult people who you love?”); and Neuroticism (N) subscale = 24 items (item example: “Have you often felt guilty?”); on which participants answered choosing between YES and NO. Reliability coefficients for the Extraversion and Neuroticism scales ranged from 0.85 to 0.90 . In the current study, the alpha coefficients were .68, .67, and .79, respectively.1.3b factor 0 and 1000 sec/mm2; number of acquired signals, four). A 2D fluid-attenuated inversion recovery (FLAIR) T2-weighted scan was also used to exclude the presence of small vessel ischemic disease and other supra- or infra-tentorial brain lesions .Imaging data were acquired using a Siemens 1.5-T MAGNETOM Avanto  whole body scanner equipped with a 12-element designed Head Matrix coil, as part of the standard system configuration. Diffusion-weighted images (DWIs) were acquired using an axial pulsed-gradient spin-echo echo-planar sequence , with diffusion-encoding gradients applied in 12 noncollinear directions  examined all MRIs. Participants were excluded when more than three lesions with a maximum diameter of 5 mm were detected in the subcortical or periventricular WM on axial FLAIR images ;   scores. The statistical threshold was established with a family-wise error-corrected p value (pFWE) < .05 with multiple comparison corrections using threshold-free cluster enhancement (TFCE) .Randomize tool (5000 permutations) was used to examine the statistical correlation between FA maps at T2 and EPQ’s subscale  and forceps major (posterior).pFWE < .05 TFCE-corrected) was found between FA values and Extraversion  was found between FA values and E subscale, we then examined possible correlation between Extraversion and RD  and demyelination (RD) in WM within the right IFOF and forceps major (posterior). These WM fibers interconnect brain regions that are implicated in emotional processing and cognitive control functions related to extraversion.The IFOF is a large WM tract connecting the frontal , temporal, and occipital lobes and constitutes one of the major efferent and afferent neuronal projections to the frontal lobes. It runs from the lateral aspect of the frontal lobe, passing through the extreme and external capsules, as well as the temporal lobe stem, before radiating into the posterior temporal and occipital cortex  or the splenium form the forceps major  and agency  and (2) impulsivity, which emerges from the interaction of extraversion and a second independent trait (constraint).In the current study, we found decreased connectivity (low FA/AD together with high RD) values in the right IFOF and in the forceps major among individuals who had high extraversion ratings. Notably, the negative correlation between extraversion and WM integrity in right IFOF, which interconnects frontal-temporal-occipital regions, suggests that lateralization of these regions may play a role in expressions of extraversion. Right lateralization has been associated with an increased tendency toward negative emotions and withdrawal tendencies  and that the quality of these factors may depend on the strength of communication between them and the involved brain regions. However, we did not find increased FA in WM tracts interconnecting brain areas associated with regulatory control. Also, we did not find significant correlations between extraversion and WM tract integrity in other brain areas, for example, the uncinate fasciculus, longitudinal (arcuate) fasciculus, and insula, which were found in anatomical studies to be associated with extraversion  and extraversion, such that the findings should be interpreted cautiously and future research should investigate the nature and implications of the current observations, by employing the effective connectivity method, which can provide information about causation Sporns, .Furthermore, we used DTI images of 12 diffusion directions with four scan repetitions. Although more scan repetition seems to be related to a higher signal-to-noise ratio as well as to more reliable FA and tractography data Jones, , it has 5.Our study revealed a link between extraversion and WM microstructure in a healthy sample, thereby providing insight into neurobiological mechanisms of extraversion. Evidence from functional and structural connectivity studies has shown that interindividual differences in extraversion and personality-related traits have biological bases in the brain. Whereas neuroscientists have begun to use DTI to examine the structural correlates of various psychiatric and neurological disorders  as compared to adaptive behaviors in different populations and to further research the neural underpinnings of behavioral differences as a function of extraversion.


Are patients with depression associated with impairment on behavioral tests of reward processing compared with healthy control individuals?In this systematic review and meta-analysis of data from 48 case-control studies of reward-processing tasks, patients with depression showed a small to medium impairment in reward processing across all tasks. They showed medium to large impairments in reward bias, small to medium impairments in option valuation and reinforcement learning, and small (nonsignificant) impairments in reward response vigor.In this systematic review and meta-analysis, depression is associated with behavioral reward-processing impairments, although this could vary depending on the precise subcomponent measured. Dysfunctional reward processing is a leading candidate mechanism for the development of certain depressive symptoms, such as anhedonia. However, to our knowledge, there has not yet been a systematic assessment of whether and to what extent depression is associated with impairments on behavioral reward-processing tasks.To determine whether depression is associated with impairments in reward-processing behavior.The MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies that investigated reward processing using performance on behavioral tasks by individuals with depression and nondepressed control groups, published between January 1, 1946, and August 16, 2019.Studies that contained data regarding performance by depressed and healthy control groups on reward-processing tasks were included in the systematic review and meta-analysis.Summary statistics comparing performance between depressed and healthy groups on reward-processing tasks were converted to standardized mean difference (SMD) scores, from which summary effect sizes for overall impairment in reward processing and 4 subcomponent categories were calculated. Study quality, heterogeneity, replicability-index, and publication bias were also assessed.Performance on reward-processing tasks.The final data set comprised 48 case-control studies . The mean age was 37.85 years and 58% of the participants were women. These studies used tasks assessing option valuation (n = 9), reward bias (n = 6), reward response vigor (n = 12), reinforcement learning (n = 20), and grip force (n = 1). Across all tasks, depression was associated with small to medium impairments in reward-processing behavior . When examining reward-processing subcomponent categories, impairment was associated with tasks assessing option valuation , reward bias , and reinforcement learning  but not reward response vigor . The medication status of the major depressive disorder sample did not explain any of the variance in the overall effect size. There was significant between-study heterogeneity overall and in all subcomponent categories other than option valuation. Significant publication bias was identified overall and in the reinforcement learning category.Relative to healthy control individuals, individuals with depression exhibit reward-processing impairments, particularly for tests of reward bias, option valuation, and reinforcement learning. Understanding the neural mechanisms driving these associations may assist in designing novel interventions. This systematic review and meta-analysis evaluates whether depression is associated with impairments in reward-processing behavior. This includes reward processing,5 which describes how organisms use reinforcement-related perceptions to guide goal-directed behaviors. A reward-processing framework is especially useful for understanding symptoms associated with motivation, such as reduced interest and activity,6 which warrant better understanding because they are associated with poorer outcomes8 and treatment response.9It is now well established that depression is associated with disrupted cognitive processing,6 reward processing proceeds according to the following sequence of cognitive operations: (1) option generation, the generation of potentially rewarding behavioral options; (2) decision-making, where options are subjected to a cost-benefit evaluation, which balances the utility of potential rewards against associated costs , resulting in the selection of one of the options; (3) anticipation, an anticipatory or preparatory phase associated with physiological arousal before the reward is obtained; (4) action and effort, engagement in action to obtain the reward goal; (5) consummation, the hedonic effect arising from interacting with the reward goal ; and (6) reinforcement learning, learning how to modify behavior in future interactions with similar stimuli using an update signal.Reward processing can be divided into a number of subcomponents. According to 1 conceptualization,10Not all of these subcomponents are straightforward to assess using objective behavioral tasks; anticipation and hedonic impact are typically assessed using physiological responses and self-report, respectively. Over the past 2 decades, reward-processing dysfunction in depression has been the focus of numerous studies, typically using tasks falling into the following 4 categories.6 Studies investigating this domain of reward processing, eg, using the Cambridge Gambling Task, have reported that individuals with depression were less willing than control individuals to place high bets when reward probabilities were high.11Part of subcomponent 2 in the previous section, option valuation describes the process by which individuals evaluate reward-related options when given explicit information about possible options . An individual’s choice is assumed to reflect the weights that they place on potential rewards and costs .12 Individuals with depression have been reported to exhibit weaker reward biases than control individuals.12Also thought to reflect subcomponent 2, reward bias is measured while individuals make difficult decisions  that are rewarded asymmetrically, distinguishing this process from option valuation. Information relating to potential rewards/losses/probabilities is typically not provided explicitly. The reward bias measure, derived from signal detection theory, reflects an individual’s tendency to choose more frequently rewarded stimuli, regardless of perceptual accuracy.13 and the Cued Reinforcement Reaction Time Task.14Part of subcomponent (4), reward response vigor reflects the speed with which an individual executes an action to obtain a reward. The difference between this and the former 2 types of measure is that here, the measure relates to the actual action taken, not simply the choice to take it. This category includes tasks such as the Monetary Incentive Delay Task6 Studies using probabilistic learning tasks report that individuals with depression use feedback less effectively than control individuals to accumulate reward.15Part of subcomponent 6, reinforcement learning describes the process by which an individual uses feedback to change their behavior in the future. Changes in behavior over time are assumed to reflect the updating of value expectations assigned to available behaviors.18 all of which identified lower striatal responses. The striatum, part of the basal ganglia, connects reciprocally with prefrontal areas,  as well as the midbrain, which signals the discrepancy between expected and received reward.20 Together these areas form part of the brain’s reward circuit, which modulates reward-related behavior and learning.20 However, to our knowledge, there has been no meta-analysis of the behavioral reward-processing literature, although several narrative reviews exist.27 While narrative reviews can provide a useful overview of the field and an opportunity to develop theoretical accounts, they cannot directly address whether disruptions in reward processing are consistent across samples or quantify the magnitude of identified effects.Three meta-analyses have examined abnormal reward-related neural processing in depression,Therefore, we aimed to produce a quantitative summary of this literature by conducting a systematic review and meta-analysis of reward-processing behavior in depression. This is an important step in determining whether reward-processing dysfunction is useful for understanding depression. The aims of our meta-analysis were to clarify (1) the nature and extent of differences between depressed and healthy groups on behavioral measures of reward processing and (2) the relative strength and consistency of differences within different reward-processing subdomains.deci* or reward* or motivat* or incentiv* or effort*) and (depress*) and (task* or paradigm* or battery*). The inclusion criteria were as follows: (1) case-control design; (2) included a healthy control group; (3) included a group with major depressive disorder (MDD), assessed according to DSM-IV/DSM-5 or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision criteria; (4) participants were 18 years or older (because there are important differences between developing and adult reward systems)28; (5) participants performed a reward-processing task; and (6) task rewards were explicit, ie, money, points, water, or food . Although it could be argued that these are social rewards, the distinction between purely informational feedback and socially rewarding feedback is unclear in such contexts. A focused search for social reward tasks in depression yielded no eligible studies. The final 2 criteria were (7) samples did not overlap with other included data sets and (8) studies reported data on a behavioral measure of reward processing that could be converted to a case-control standardized mean difference (SMD) score. If articles were otherwise suitable but did not contain such data, the data were requested from the authors.The Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for articles published between January 1, 1946, and August 16, 2019, inclusive, with titles or abstracts containing the terms . For some tasks, this was not possible . Articles were independently assessed by D.C.H. and A.N. Conflicts were highlighted using the Covidence software package (Covidence) and resolved through in-person discussion.29 Studies were rated on whether cases and controls were sampled from the same population, how precisely they were defined  using a restricted maximum likelihood estimator model.33 Heterogeneity was assessed using a point estimate of the among-study variance of true effect sizes (2τ) and the approximate proportion of total variability (2I).34 Sensitivity analysis involved stepwise removal of studies to assess the effect of their removal on effect size and heterogeneity.The meta-analysis was performed in the statistical packages metafor35 and the Egger test36 (using a 2-sided significance threshold of P = .10 because the Egger test has low power when the number of studies is low). Studies missing owing to publication bias were estimated and imputed using the trim-and-fill method. Moderator analyses used random-effects categorical or metaregression models, overall, and in subcomponent categories. Replicability indexes were calculated by reducing the observed median power of studies by the discrepancy between it and the percentage of significant results (within a given category).37Funnel plot asymmetry was assessed using visual inspection of a contour enhanced funnel plot80 containing 3154 participants  were analyzed. Although we searched primarily for case-control studies, we also attempted to identify relevant longitudinal, population-based studies; none were identified. Data from 48 studiesg .Across all studies, there was a small to medium reward processing impairment in depressed compared to healthy groups . Analysis of the 4 subcomponent categories  revealed2τ = 0.15; 2I = 68%). There was no interstudy heterogeneity in the option valuation category . There was low to moderate heterogeneity in the reward response vigor category . There was substantial to considerable heterogeneity in the reward bias  and reinforcement learning  categories. Excluding 1 study59 reduced heterogeneity in the reward bias category to moderate to substantial  and increased the effect size to large . Excluding 1 study14 eliminated heterogeneity in the reward response vigor category ; the effect size remained small to negligible and nonsignificant . Removing individual studies did not reduce interstudy heterogeneity either in the other categories or in the overall sample.Overall interstudy heterogeneity was substantial  explained 0% of the variance in the global effect size, with no effect of medication status in any sub-component category.In those studies that reported summary statistics of anhedonia scores (n = 18) or cold-cognitive task performance (n = 8), SMDs of respective measures used as continuous moderators revealed no significant effects of either on reward processing .P < .001) and nonsignificant effect  than those including nonelderly samples . Studies that clearly matched groups for sex yielded a larger effect than those that did not (eResults in the Studies including an exclusively elderly sample (n = 4) yielded a smaller . It was not possible to assess publication bias, median power, or R index scores in the option valuation or reward bias categories owing to the low number of studies in both. Publication bias was significant in the reinforcement learning category  and nonsignificant in the reward response vigor category. Median power and R index scores were low in both reward response vigor  and reinforcement learning  categories.Overall publication bias was significant  reward-processing impairments relative to healthy groups. However, there were potentially important differences between reward-processing subcomponents.82The largest impairment was observed in the reward bias category , which is unlikely to be owing to impairment in non–reward-based processing  because the signal detection theory discrimination measure did not differ significantly between the groups in most included studies. From this meta-analysis, the reward bias impairment in depression appears most robust. Prior computational analysis suggests that this may be driven by lower reward valuation.83 Future studies that investigate reinforcement learning impairments in depression may benefit from using tasks that allow the contributions of hot and cold cognitive impairment to be dissociated.83The reinforcement learning category yielded a small to medium effect . Many tasks in this category could not allow reward response to be disentangled from confounds such as working memory impairment.24 The reward response vigor category exclusively contained tasks measuring reaction times, which may be vulnerable to attentional confounds. While these cannot be fully controlled for, we go some way toward doing so by including only tasks that compared responding in more vs less rewarded conditions. While reward response vigor yielded a nonsignificant summary effect , the single study30 that assessed grip force production yielded a large effect . Speculatively, this might indicate that reward-processing impairments in depression are associated more with fatigability than the speed of action, consistent with the higher prevalence of anergia than psychomotor retardation in depression.84 Further research on the effect of reward on grip force in depression is warranted.Individuals with depression exhibited a small to medium impairment in option valuation , which is associated with “decisional anhedonia” resulting from impaired cost-benefit decision-making.R index values suggest that significant results may be difficult to replicate. However, the R index is conservative and less precise when true power is low.37 Assuming that future studies can expect to yield effect sizes comparable with the overall summary effect in this meta-analysis , they will require sample sizes of 133 per group to achieve a power of 0.8 at a significance of .05 (2-tailed). This is considerably larger than the mean sample size in the included studies (33 per group).85 None of the included studies controlled for personal/household income, a potentially important oversight in studies that use monetary rewards because depression is associated with lower household income86 87 than the general population. Future studies that use monetary rewards should assess income levels.Moderator analysis revealed no significant association for between-study variation and either anhedonia or cold cognitive performance on reward-processing impairment. These results should be interpreted cautiously owing to the low number of studies that could be included and heterogeneity in measuring these constructs. The assessment of within-study variation in such factors would have been more informative, but the necessary correlation coefficients were rarely reported; future studies should report correlations between reward processing and anhedonia and/or cold cognitive impairment. Those studies that recruited exclusively elderly participants yielded a nonsignificant summary effect . Speculatively, this may be owing to the effect of healthy aging on reward processing.88 this is an important omission. Fourth, there was significant heterogeneity, overall and in all categories except option valuation, making the interpretation of the summary effects less clear. Fifth, there was significant publication bias overall and in reinforcement learning particularly, resulting in a potential overestimation of the summary effects. Sixth, we were unable to examine 2 important reward-processing components, the anticipation and hedonic effect of rewards. A literature search for studies in these categories yielded 5 studies,93 which investigated the hedonic effect of oral sucrose solutions in healthy vs depressed groups. However, of these studies, too few contained suitable data for the meta-analysis to include a hedonic effect category. Seventh, this systematic review and meta-analysis summarizes the findings of case-control studies, which do not inform us about the causal relationship between reward-processing impairment and depression or its treatment. Longitudinal studies examining reward processing in depression are needed to answer these important questions. Eighth, the effect size estimates were unadjusted for covariates and so may be affected by confounds.Several limitations of our analyses merit comment. First, reward processing comprises a heterogeneous set of processes, which we categorized according to 4 subcomponents. However, there are several potential ways to measure function in each category. Therefore, this meta-analysis sometimes combines dissimilar measures in its summary statistics. For example, option valuation contains studies that probe the effect of reward on the willingness to exert effort (in 3 cases) and to take risks (in 6 cases). Second, the medicated samples were often not entirely medicated, used a variety of medications (even within-study), and at different doses. Therefore, the nonsignificant moderation result for medication status is difficult to interpret. The effect of medication on reward processing in depression is best investigated in the context of randomized clinical trials. Third, we did not investigate response to punishment (because very few of the studies investigated responses to punishment separately from reward). Given the importance of sensitivity to punishment in some cognitive models of depression,24 Research on reward processing may therefore be a credible route to better characterizing mechanistic heterogeneity within depression, as well as potentially highlighting novel targets for treatment.Conducting a meta-analysis of 48 studies, we found that depression was reliably associated with small to medium reward-processing impairments overall and of varying magnitudes across several reward-processing subdomains. This is important because the cognitive and neural mechanisms underlying reward processing and its subdomains are relatively well understood.


Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models. For kNN models, we selected the best model among the models with euclidean and manhattan metrics and with k from the range . For random forest, the best model has been chosen by performing grid search on the number of trees in the range .Images corresponding to intra-host viral populations have been labeled based on the stage of infection as recent or chronic and used to train the following machine learning classification models: Stochastic Gradient Descent (SGD), Decision Tree, Gaussian Naive Bayes (Gaussian NB), Linear Support Vector Machine (Linear SVM), Random Forest and  library . DiffereTrained classifiers have been validated based on their accuracy, area under the curve (AUC), precision, and recall. Accuracy (Acc) is defined as the proportion of test cases correctly classified, as either recent or chronic. Precision (Prec) measures the fraction of the correctly classified populations within each predicted infection class, while recall (Rec) measures the fraction of the true recent or chronic populations that are correctly predicted. Validation has been performed via stratified 10-fold cross-validation. Specifically, in addition to the standard 10-fold cross-validation, we employ "leave-one-outbreak-out" cross-validation and random undersampling methods to balance the datasets. In our current data, some of the samples come from the same HCV outbreak. Such samples are close to each other by their nucleotide composition, thus their presence may lead to over-fitting of any particular method. In "leave-one-outbreak-out" cross-validation, data from each of these outbreaks was used in the validation set, while other samples are used in the training sets. Random undersampling has been performed to balance the difference in sizes of datasets of recent and chronic hosts. In this method, chronic dataset size is reduced by random subsampling to match the recent dataset size.k-means clustering and mini-batch k-means clustering. As before, we used models’ implementations from python scikit-learn library [We cluster images representing intra-host viral populations into transmission clusters using standard clustering algorithms {agglomerative hierarchical clustering,  library . SeveralNormalized Mutual Information (NMI) , homogenStratified 10-fold cross-validation has been initially performed to analyze the performance of several classification methods trained using the normalized image data. Fig. Linear SVM model has been analyzed further with leave-one-outbreak-out and random undersampling validation combined with 10-fold cross-validation. Table k-means, mini-batch k-means and hierarchical clustering models are shown in Table The results of All experimental results discussed above have been obtained using the default image resolution 480 × 480. We analyzed impact of image resolution on the classification and clustering performance. Resolution values varied from 50 × 50 to 550 × 550 with step size of 50. Fig. A previously published model  classifiWe also compared clustering sensitivity and AUROC (of the inference of genetic relatedness between a pair of HCV samples) for our method and consensus-based approach  forThe sequence-image normalization method described here provides a way to transform genomic data into image data which can be directly employed by machine learning methods. The proposed preprocessing method was specifically designed to addresses multiple challenges that currently impede applications of machine learning and deep learning methods to viral studies. These challenges could be thematically classified as follows:High-throughput sequencing technologies are prone to errors and biases, which may significantly affect viral data. Indeed, frequencies of minor viral variants are often comparable with the level of sequencing noise; however, such variants should not be simply discarded based on some frequency threshold, since often they are the ones responsible for transmission, immune escape or therapy failure , 34–36. Another important problem is sampling and sequencing bias resulting in significant irregularities in the number and length of viral sequences from different infected individuals. If classifiers capture these artificial differences as significant associations, it may result in overfitting and decline of accuracy. Thus, application of machine learning to heterogeneous viral population data should be preceded by a preprocessing step to eliminate these irregularities via normalization procedure. However, selection of an appropriate normalization approach is challenging. For instance, if we use text classification techniques for preprocessing, difference in the number of sequences among different files needs to be controlled either by truncation or padding. This preprocessing, however, causes data loss (in case of truncation) or introduces irrelevant data (in case of padding). An optimal preprocessing method should not introduce such issues.n. It is usually achieved by identifying numerical features that are relevant to the problem under consideration. They can include various diversity measures [Before applying machine learning methods to classification of heterogeneous viral populations, genomic data should be mapped into the euclidian space Rmeasures , popula-measures , physicomeasures  and otheAn optimal feature selection method should be able to capture the entire population structure using a relatively simple and easily contractible data representation. Furthermore, it should use a standard universal data format, which has a fixed number of features and is applicable to different problems. Since genomic data is essentially a textual information, it is tempting to utilize well-developed machinery from the text classification domain , 39 for Clustering of intra-host viral populations requires an inter-population distance measure, which takes into account complex population structures. It has been shown that among simple alignment-based population distance measures, the minimal distance between population variants allows to achieve the highest clustering accuracy . HoweverOur proposed preprocessing method converts the viral population genomic data sampled by NGS into a scaled image. Irregularities in the data are thus handled by generating a fixed size image. The number of features in this case remains same. Therefore, it can be directly used for machine learning applications without any explicit feature selection methods. High accuracy of machine learning classification and clustering techniques based on image representation applied to several molecular epidemiology tasks signifies validity of our approach. The case of infection staging is particularly illustrative. Previous studies demonstrated that diversity of intra-host viral populations often increases with progression of HCV infection , 32, 37.Here, we propose a novel method for generation of a fixed set of features representing heterogeneous viral populations, which is widely applicable for various classification and clustering tasks addressed by machine learning. The method converts sequence data into fixed-size images, thus reducing several issues associated with comparison of viral populations by machine learning methods. Simplicity of the sequence image normalization method allows for a robust conversion of genomic data into numerical data. Image data also help in visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.


In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters , were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior. Driving requires the integration of sensory, motor, and cognitive functions . BecauseDespite the importance of identifying characteristics of older drivers who are cognitively normal but at high risk of causing fatal traffic accidents, a standardized method for assessing the driving ability of healthy older drivers has not yet been established. Although on-road driving tests are recognized as the gold standard assessment for measuring driving ability, it is not practical to perform driving tests for all older drivers because of the cost involved . One preThe current study had two main aims. First, we categorized participants into unsafe or safe drivers using a new sensing method for the objective evaluation of on-road driving ability of healthy older people on the basis of vehicle behavior using a data recorder and video cameras. Second, to describe the neurobiological features associated with unsafe driving, we built a classification model to distinguish unsafe from safe drivers based on gray matter volume data using a linear support vector machine (SVM) approach.The present study recruited 32 healthy older individuals aged over 65 years from the local community through online advertisements at the University of Tokyo and the Musashisakai Driving School . All participants were diagnosed as “cognitively normal” using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) diagnostic classification: (1) Mini-Mental State Examination (MMSE) score between 24 and 30; (2), Clinical Dementia Rating (CDR) score of 0; (3) normal memory function measured by education-adjusted scores on the Logical Memory II subscale of delayed paragraph recall from the Wechsler Memory Scale—Revised (WMS-R) . ParticiThe general cognitive function of each participant was assessed using the Raven’s Colored Progressive Matrices (RCPM). The Rey Auditory Verbal Learning Test (RAVLT), the Rey–Osterrieth Complex Figure Test (ROCFT), the Clock Drawing Test (CDT), and the Everyday Memory Checklist (EMC) were used to evaluate memory and visuospatial function. We estimated attentional/executive function using the Stroop Test (ST), the Trail Making Test (TMT) A and B, and the Dysexecutive Questionnaire (DEX). We investigated subjective driving ability using the DBQ. Depression severity was evaluated using the Geriatric Depression Scale (GDS). Handedness was assessed using the Edinburgh Handedness Inventory . The CDTIn addition to these neuropsychological tests mentioned above, we performed the Cambridge Neuropsychological Test Automated Battery (CANTAB), which is a computer-based test battery widely used in neurocognitive studies  . SpecifiWe adopted binocular functional visual acuity to assess the visual function associated with driving ability. First, we measured corrected distance visual acuity (CDVA) using Landolt vision charts. Second, we calculated corrected distance functional visual acuity (CDFVA) with the AS-28 FVA Measurement System  . CDFVA cMR images were acquired using a 3.0-T MRI scanner  with an 8-channel head coil. High-resolution T1-weighted images were acquired using a magnetization-prepared rapid acquisition with gradient echo sequence . All images were first visually checked for scanner artifacts and anatomical anomalies.recon-all processing pipeline for cortical reconstruction and volumetric segmentation (http://surfer.nmr.mgh.harvard.edu/). The cortical processing stream in FreeSurfer included Talairach transformation, removal of non-brain tissue, segmentation of subcortical white matter and gray matter tissue, intensity normalization and atlas registration. After these automatic steps, a triangular mesh model of the cortical surface consisting of over 150,000 vertices per hemisphere was generated, and the cortical surface was parcellated into 74 distinct cortical regions of interest (ROIs) based on curvature values of the surface for each hemisphere according to the Destrieux atlas  cameras, and dual brake controls . The datWe evaluated participants’ driving ability at intersections with a stop sign using on-road driving test data, because older drivers most frequently cause traffic accidents at intersections without traffic lights . At inteThe purpose of the current study was to build a classification model to dissociate unsafe drivers from safe drivers using a machine learning. Based on a previous study suggesting that combining neuroimaging and clinical data could improve the accuracy of predicting cognitive decline , we madeIn the present study, a linear SVM was used to create the classification model. The linear SVM method is widely used as a powerful supervised learning methodology in binary classification . In geneAfter fixing C for the linear SVM classifier, we evaluated the performance of the linear SVM classifier using leave-one-out cross-validation (LOOCV), which is a widely used validation method for accurately assessing the performance of predictive models. Specifically, LOOCV maximizes the training sample and avoids possible case partition bias, even with small sample sizes . In our We used scikit-learn in Python 3.7.0 for machine-learning analyses .t-test, chi-squared test, or multivariate analysis of variance in the group comparison between safe drivers and unsafe drivers. IBM SPSS software Statistics 25 for Mac OS  was used for the statistical analysis. We used a liberal statistical threshold of P < 0.05.For clinical data, we adopted a two-tailed We classified 21 participants as safe drivers and 11 participants as unsafe drivers . There wC = 0.050) successfully differentiated unsafe drivers from safe drivers with accuracy of 87.5% (sensitivity of 81.8% and specificity of 90.5%).The linear SVM classifier  using clinical features and gray matter volume data distinguished unsafe drivers from safe drivers with an accuracy of 87.5% (sensitivity of 63.6% and specificity of 100%). While 36 parameters were selected as features for the final classification model at least once throughout cross-validation procedures , five paIn the present study, the linear SVM classifier using both clinical features and gray matter volume data differentiated unsafe drivers from safe drivers with an accuracy of 87.5% . Furthermore, in the final classification model, age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus (of Jensen), the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus, were selected as consistent features, suggesting that regional gray matter volume changes in these four cortical regions are strongly associated with a high risk of unsafe driving among healthy older people.One advantage of the present study is that we objectively evaluated on-road driving behaviors. In previous structural MRI studies investigating the neural basis of driving ability among older people, interviews or questionnaires were often utilized for driving evaluation . FurtherOur final classification model successfully dissociated unsafe drivers from safe drivers with 87.5% accuracy. We consider that the accuracy of our model was relatively high, because in two previous studies using only neuropsychological tests to predict driving ability, prediction accuracies were 66 and 90%, respectively . FurtherThe current results revealed that the risk of unsafe driving increases with age. A recent meta-analysis of global longitudinal cohort data revealed that all cognitive domains, particularly attentional function, decline with age . DrivingThe current data revealed that regional gray matter volume changes are highly predictive of driving ability in healthy older people. This finding suggests that gray matter volume accurately reflects changes in cortical structure related to decreased driving ability among healthy older people. One previous study examining the association of cortical changes with driving ability among older people reported that gray matter volume was correlated with driving ability, supporting the current results . In the The superior part of the precentral sulcus is located in the dorsal premotor cortex (PMC) including the frontal eye field (FEF). The FEF plays a decisive role in saccade programming and shows enhanced responses to a visual stimulus when it is the saccade target . The sulThe right orbital part of the inferior frontal gyrus plays a crucial role in decision making . A previThe superior frontal sulcus has been repeatedly shown to contribute to working memory in functional MRI studies using the N-back task . In our The results of the current study should be interpreted with caution because of several limitations. First, the number of participants per group was relatively small. In general, small numbers of participants can induce over-fitting. To mitigate this problem, we created a sparse model using the LASSO algorithm. However, it would be optimal to train our classification model with an independent cohort to generalize the model. Future studies with larger samples at multiple sites may be useful for addressing this issue. Second, although we measured on-road driving ability using a data recorder and CCD cameras, we only evaluated one aspect of driving ability. Because we decided not to use other data, including data regarding the pedal position, gaze and footwork, to simplify the classification criteria, it may be valuable to establish a new objective method with which to measure various types of driving ability. For instance, examining driving behaviors when turning right at an intersection or making a lane change could provide useful results. Third, we used a cut-off value of 5 km/h to divide participants into unsafe and safe drivers based on the past findings . HoweverOverall, we built a reliable classification model for identifying the on-road driving ability of healthy older individuals with an accuracy of 87.5%. Five parameters , were consistently selected as features for the final classification model. Importantly, the current findings revealed the neural bases of unsafe driving in healthy older people, suggesting that age and gray matter volume data can provide useful information for identifying unsafe drivers, potentially leading to the development of new interventions to prevent fatal traffic accidents.The data that support the findings of this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by the ethics committees of the University of Tokyo and Keio University. The participants provided their written informed consent to participate in this study.YY, BY, JH, MS, and MM designed the study. YY, BY, JH, HY, KN, MY, MK, and KK acquired the data. YY, BY, JH, and RU analyzed and interpreted the results of the data. YY, BY, JH, and MM drafted the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Continued smoking after a cancer diagnosis increases mortality, risk of recurrence, and negatively impacts treatment effectiveness. However, utilization of tobacco use cessation treatment among cancer patients remains low. We conducted a clinical trial assessing patient preferences, treatment acceptability, and preliminary effectiveness  of three tobacco treatment options among cancer patients at an academic health center. Implementation strategies included electronic referral and offering the choice of three treatment options: referral to external services, including the quitline (PhoneQuit) and in-person group counseling (GroupQuit), or an internal service consisting of 6-week cognitive behavioral therapy delivered via smartphone video conferencing by a tobacco treatment specialist (SmartQuit). Of 545 eligible patients, 90 (16.5%) agreed to enroll. Of the enrolled patients, 39 (43.3%) chose PhoneQuit, 37 (41.1%) SmartQuit, and 14 (15.6%) GroupQuit. Of patients reached for 12-week follow-up (n = 35), 19 (54.3%) reported receiving tobacco treatment. Of all patients referred, 3 (7.7%) PhoneQuit, 2 (5.4%) SmartQuit, and 2 (14.3%) GroupQuit patients reported 7-day point prevalence abstinence from smoking at 12 weeks. Participants rated the SmartQuit intervention highly in terms of treatment acceptability. Results indicate that more intensive interventions may be needed for this population, and opportunities remain for improving reach and utilization. Upward of 17% of cancer survivors in the United States are current tobacco users ,5,6,7,8.Promoting tobacco cessation among cancer patients has been referred to as the “fourth pillar of cancer care”, along with surgery, radiotherapy, and chemotherapy . DeliverDespite these efforts, adherence to tobacco treatment guidelines is suboptimal. Survey data indicate that although most oncologists screen their patients for tobacco use advise them to quit, only 44% discuss medication options and 39% provide cessation treatment or referral their patients to cessation services . WhereasDue to the competing demands of complex cancer care, cancer centers are challenged with implementing strategies that both assess tobacco use and link tobacco users with tobacco treatment services ,27. TobaThis study attempts to address this knowledge gap by investigating implementation strategies designed to improve uptake of tobacco treatment in cancer care. Given the barriers to incorporating tobacco treatment in oncology care, we used strategies designed to complement existing clinical practices  and extend tobacco treatment beyond the clinical visit. We took a patient-centered approach by multiple tobacco treatment options. Treatment options included an in-house dedicated 6-week cognitive behavioral therapy (CBT) program delivered via mobile phone video teleconference (SmartQuit) in addition to referral to existing state-run evidence-based tobacco treatment programs—a quitline (PhoneQuit) and in-person group sessions (GroupQuit). The SmartQuit treatment options was provided in order to offer intensive tobacco use treatment that would be convenient and accessible for cancer patients. We conducted this pilot trial as part of a larger study investigating the feasibility of implementing multi-level strategies to improve the access and utilization of tobacco treatment services for cancer patients. The objective of this trial was to assess provider counseling practices, program reach, factors influencing cancer patients’ treatment choices, and treatment acceptability. As a secondary objective, we investigated preliminary effectiveness  of tobacco treatment among cancer patients. This implementation study consisted of two components: (1) provider and staff training on best practices, and (2) providing patients with multiple tobacco treatment options. All other components of standard care  remained in place during study activities.Prior to patient enrollment, the study team conducted a 30 min training session for clinic staff based on the American Society of Clinical Oncology’s Tobacco Cessation Guide for Oncology providers and staff . The traThe trial was conducted among cancer patients visiting outpatient clinics in the University of Florida Health System, a large academic health center in the Southeastern U.S. Patients were eligible if they were ≥18 years old, had a cancer diagnosis, and were current smokers (defined as at least one cigarette in the previous week). Patients who were not ready to quit but interested in reducing their smoking or obtaining more information about quitting were eligible. Informed consent was obtained for all participants. Participants who completed the follow-up telephone call at 12 weeks received a $20 gift card. This study was approved by the University of Florida Institutional Review Board and is registered with ClinicalTrials.gov under NCT03482583, 2018.We recruited participants from three oncology clinics from May 2018 to July 2019. Potential participants were identified by generating a daily list of scheduled patients with a “current smoker” status listed in the EHR. Clinicians and staff additionally identified patients. After eligible patients were identified, the RA alerted clinical staff who then asked patients if they were willing to talk to an RA about a smoking cessation study. If the patients agreed, the RA explained the study in detail and assessed eligibility. Eligible patients could choose either the standard referral to free state-run cessation services (PhoneQuit or GroupQuit) or the additional SmartQuit option. Only participants with smartphones were eligible for SmartQuit. If patients were interested in cessation services but did not want to participate in the study, they were referred to the state-run cessation program.The state of Florida provides free tobacco cessation services through a Quitline (PhoneQuit) and in-person group sessions (GroupQuit). Individuals who participate in these programs receive counseling and are eligible to receive a free 2-week supply of nicotine replacement therapy (NRT). The programs offer information about other cessation medications, including prescription options. If participants are interested in prescription cessation medications, they are instructed to contact their physician. GroupQuit sessions are held in every Florida county and are offered as a one-time 90-min program or as a series of four to six weekly sessions lasting 60 min each. PhoneQuit consists of three 15 to 20 min sessions. Content and materials for both programs are evidence-based and developed by the program. For patients choosing GroupQuit or PhoneQuit who had not already been referred to the state-run program, the RA provided a referral request form to the patient’s clinician or nurse clinician or nurse, who then entered a referral to the state-run tobacco cessation program the EHR system. Once the EHR referral was placed, the patient received a proactive telephone call from the service staff to complete enrollment.We developed the evidence-based SmartQuit intervention to leverage the convenience and growing ubiquity of smartphone technology to deliver CBT and verify abstinence . SmartQu®, a HIPAA-compliant video conferencing application, on the patient’s smartphone. The RA also provided the patients with a smartphone-compatible CO monitor , assisted them with downloading the accompanying Smokerlyzer© mobile application, and trained the patient on their use. The patients were told that the CO monitor would be used to verify smoking abstinence but they were free to use it for personal CO tracking if desired.For those choosing SmartQuit, the RA assisted with download of VidyoTo assess provider adherence to tobacco treatment guidelines, the RAs administered PEIs after recruitment with all participants (regardless of tobacco treatment choice) in REDCap via a tablet. The PEI is a brief patient-reported assessment of provider delivery of counseling . A PEI iA telephone survey was administered by a trained research staff member at week 12, regardless of group. The survey assessed treatment acceptability and smoking outcomes. Up to three contact attempts were made for each participant.Treatment Acceptability Questionnaire (TAQ): The TAQ consisted of 16 items scored on a 0 to 10 scale . The questions assessed aspects of treatment acceptability, such as helpfulness in reducing smoking and thoughts and feelings related to the treatment. Questions also related to barriers and facilitators of tobacco treatment, including open-ended questions assessing suggestions for improvement. Smoking Status: We collected self-reported prolonged abstinence  and past-7-day point prevalence of cigarette smoking [ smoking . The TTS smoking .SPSS 25  was used to conduct statistical analysis . DescripA total of 90 participants enrolled in the study and 35 (38.9%) completed the follow up survey. Most participants were non-Hispanic white and approximately one-third resided in rural areas. About half of the participants received a cancer diagnosis in the previous six months and the majority (65.6%) had diagnoses of head and neck or lung cancer. Baseline participant characteristics are presented in A total of 545 cancer patients receiving care in the clinics during the study period were identified in the EHR as smokers. Of these, we enrolled 90 (16.5%) patients in the study. All GroupQuit and PhoneQuit enrollees received an electronic referral to external cessation services and study staff made at least three attempts to schedule initial sessions with the SmartQuit participants. Rates of enrollment differed among clinics, with the largest proportion of patients reached in the Medical Oncology clinic see . Reasons for tobacco treatment choice are presented in Smoking outcomes are presented in About half of the patients completing follow-up received some form of tobacco treatment program see . Among pThe current study reports on an implementation trial designed to promote uptake of evidence-based tobacco treatment in oncology clinics. We offered patients a choice of three treatment options and explored motivations for patients’ treatment choice as well as the acceptability of each treatment option. We additionally explored smoking cessation outcomes in our study sample. To better understand patient preferences for tobacco treatment, we used a pragmatic design and offered patients multiple treatment options rather than using random assignment. Each treatment option had unique features which appealed to different patients; GroupQuit offered face-to-face group support, PhoneQuit offered brief support that could be obtained from home, and SmartQuit offered more intensive support and could also be completed at home. The variation in treatment selection and reasons for choice suggest that multiple treatment options may enhance patient engagement. Treatment choice was most often motivated by convenience and perceived effectiveness of the treatment. Notably, for patients choosing SmartQuit and PhoneQuit, convenience was more frequently endorsed as a reason for treatment choice than the expectation that the treatment would be effective. Convenience may have been especially important in this study as nearly one-third of participants resided in rural areas. These findings should be interpreted in light of the fact that patients without smartphones were not able to participate in the SmartQuit intervention.Participants reported relatively high provider adherence to screening and counseling best practices, consistent with previous literature ,27. HoweWhile the SmartQuit treatment option was only provided in the context of this research study, the electronic referral to state-run cessation services  remains part of standard care for all patients in this study’s health system. As oncology providers typically deliver tobacco screening and counseling, but do not routinely provide cessation treatment ,27, the Provider counseling and referral is an important first step in addressing tobacco use in cancer patients, but treatment utilization is necessary in order to achieve cessation for most patients. About half of patients reached for follow-up completed some form of tobacco cessation treatment, which is higher than treatment utilization rates resulting from primary care referral to cessation programs ,36,37. HUsing a modified intent-to-treat approach  yielded lower abstinence rates than found in a meta-analysis of tobacco treatment interventions for cancer patients . One expOur smartphone-based intervention received high treatment acceptability ratings from patients. As expected, the technology-based SmartQuit program was rated highly for convenience and flexibility. The majority of cancer patients are older adults, representing the age group least likely to use the Internet or own smartphones; however, technology use in this population is rapidly increasing. From 2011 to 2016, smartphone use among adults over 65 rose from 11% to 42% . A recenA limitation of this study is the low proportion of patients who completed follow-up data collection, which may not have provided a representative sample and limited our ability to measure the effect of cessation treatment on patient outcomes. Further, our study only included patients who expressed interest in smoking cessation. We also did not evaluate the reasons patients refused treatment; this information could provide insight on how to optimize the timing and design of tobacco treatment interventions. While the prescription of cessation medication is a component of usual care in the participating clinics, another limitation of the research is that we did not collect data on cessation medication use or take additional steps to ensure that prescription cessation medication was prescribed to patients. Additionally, we did not conduct treatment fidelity checks for the SmartQuit sessions. Finally, participating clinics were not fully staffed with research assistants at all times, so the potential reach of the implementation strategies used may be underestimated. Provision of evidence-based tobacco cessation treatment is a critical step to improving cancer treatment outcomes. This study demonstrates the feasibility of using implementation strategies to incorporate tobacco treatment into routine oncology care; however, we demonstrate that there is much room for improvement in recruitment, follow-up, and treatment effectiveness. It is clear that more intensive efforts are required to successfully address tobacco use in oncology settings, which will require dedicated funding and resources. Implementation of tobacco treatment in oncology settings may benefit from a combination of more intensive approaches  and strategies that reach a larger number of patients . Future work should focus on developing multi-level strategies to improve reach, patient engagement, and effectiveness.


Posterior circulation strokes are potentially devastating events that carry a significant risk of morbidity and mortality. Acute basilar artery occlusion stroke is a rare posterior circulation stroke that needs emergent management. We report the case of a 67-year-old woman who developed an acute basilar artery occlusion. We achieved complete recanalization of the occluded basilar artery and its branches with endovascular mechanical thrombectomy. It is possible to achieve excellent results with mechanical thrombectomy in acute basilar artery occlusion if timely diagnosis and reperfusion can be done. We are not aware of any previous publication from Nepal describing this technique in acute basilar artery occlusion. Here, we report a patient with an acute BAO who was successfully treated with MT. We are not aware of any previous publication from Nepal describing this technique in acute BAO.Acute basilar artery occlusion (BAO) is a devastating subtype of posterior circulation stroke. It carries a risk of significant morbidity and mortality.A 67-year-old woman presented with weakness of the right side of the body and left-sided facial deviation for five hours. She had hypertension for five years and atrial fibrillation for 20 days, for which she was taking regular medications. She had no remarkable past medical history. On magnetic resonance imaging and magnetic resonance angiography, we found an acute infarct in the anterior part of the pons with complete occlusion of the entire basilar artery (BA) from its origin . After w1 In contrast to posterior circulation strokes, MT has been established through several international randomized trials in anterior circulation strokes. As there is a dearth of high-quality studies in posterior circulation stroke, standard therapeutic protocols or devices have not been uniformly established or adopted. The various treatment modalities that have been tried or are available for posterior circulation stroke include intravenous thrombolysis, intra-arterial thrombolysis, MT, and stenting or angioplasty if vascular stenosis is present. In recent times, endovascular techniques are gradually being established as safe and effective, resulting in good clinical outcomes in posterior circulation strokes.2Posterior circulation strokes constitute only one-fifth of all acute ischemic stroke (AIS) events; however, they cause substantial morbidity and mortality.3 A vexing problem with BAO is that clinical presentations are highly variable, often delaying rapid diagnosis and subsequent revascularization therapy.4 Among patients treated conventionally with antiplatelets or anticoagulants, there is a high rate of poor outcomes in patients with BAO despite recent advances in stroke management.1 The Basilar Artery International Cooperation Study (BASICS) did not find the superiority of any one of the three treatment strategies for BAO -antithrombotics (antiplatelets or systemic anticoagulants), primary intravenous thrombolysis, and intra-arterial therapy .1 However, the findings of the BASICS study should be interpreted with caution as it was an observational study, and high-quality endovascular devices were not available to perform MT. The ENDOSTROKE study5 found that MT using a stent-retriever is associated with a high recanalization rate (79%), but recanalization alone does not accurately predict clinical outcome. Age, hypertension, stroke severity, collateral status, and the type of imaging modality used were independent predictors of clinical outcomes.5 In a recently published study, Gory et al6 compared the two common MT approaches for revascularization of BAO using either direct-aspiration first-pass technique (ADAPT) or stent-retriever devices. They concluded that the ADAPT technique achieves a significantly better complete reperfusion rate (87%) with a shorter duration of the endovascular procedure, and a lower rate of periprocedural complications.6 In a smaller study, an even higher complete reperfusion rate (89%) was achieved in patients with BAO with MT techniques.7 Even though the ‘gold-standard’ method of revascularization has not been definitively established, evidences strongly favor MT as the most effective treatment of acute BAO.8BA strokes are rare posterior circulation events that account for only around 1% of all reported strokes.Acute BAO is a potentially fatal diagnosis, and timely reperfusion is paramount for good clinical outcomes. As the territory of the posterior circulation is very small compared with anterior circulation, even a small infarct can lead to life-threatening complications. Furthermore, diagnostic tools optimized to detect the penumbra zone of the posterior circulation territories are not readily available. Perfusion modalities do not accurately reflect the posterior circulation perfusion and diffusion states. Despite these challenges, we could achieve a complete recanalization of the BA and its distal branches using thrombo-aspiration.9 These numbers have greatly improved in recent times. We do not have proper data regarding the clinical profile of patients with acute BAO from Nepal; however, with the recent development of neurointervention facilities in the country, it is possible to achieve excellent outcomes, if early diagnosis and timely revascularization can be done.Before the advent of modern endovascular techniques, over 80% of patients with acute BAO receiving conventional treatment reportedly suffered debilitating morbidity.JNMA Case Report Consent Form was signed by the patient and the original article is attached with the patient's chart.None.