The entry of leukocytes into tissues requires well-coordinated interactions between the immune cells and endothelial cells which form the inner lining of blood vessels. The molecular basis for recognition, capture, and adhesion of leukocytes to the endothelial apical surface is well studied. This review will focus on recent advances in our understanding of events following the firm interaction of leukocytes with the inner surface of the blood vessel wall. We will discuss how leukocytes initiate the transmigration (diapedesis) process, trigger the opening of gaps in the endothelial barrier, and eventually move through this boundary. To bring them into a position where they can perform their immune functions, they need to first exit the vascular system. This requires sophisticated mechanisms that allow the leukocytes to recognize injured or infected tissue areas from within the vasculature. Recognition goes hand in hand with adhesion to the luminal surface of endothelial cells. These initial events are mediated by cytokine-induced endothelial adhesion molecules, such as the selectins, that mediate the capturing and rolling of leukocytes at the vessel wall. Selectins and chemokines presented on the endothelial cell surface trigger the activation of leukocyte integrins, which initiates leukocyte arrest and supports the crawling to appropriate exit sites. This well-studied multi-step process of leukocyte docking has been described in several excellent reviews9. We will first give a short overview about endothelial adhesion receptors and signaling processes that support the diapedesis process. Considering this, we will then discuss what is currently known about the following questions: what determines the exit sites where the transmigration (diapedesis) process occurs and how do leukocytes recognize such sites? Which routes do leukocytes take to transmigrate through the endothelial barrier—the paracellular pathway through junctions or the transcellular route through the body of an endothelial cell—and what determines which route is taken? How are paracellular or transcellular gaps or pores through the endothelial barrier formed? How do leukocytes and endothelial cells maintain the barrier integrity and prevent plasma leakage during the diapedesis process?Here, we will focus on recent advances in our understanding of the subsequent steps of leukocyte extravasation, which are less well understood. For more extended discussions, the reader is also referred to some other recently published excellent reviewsLβ2) and Mac-1 (αMβ2) and the β1-integrin VLA-4 (α4β1) on leukocytes, of which the first two bind to endothelial intercellular adhesion molecule 1 (ICAM-1) and the latter binds to vascular cell adhesion protein 1 (VCAM-1). These endothelial adhesion molecules also act as signaling receptors and are instrumental for the initiation of signaling events, which affect the actomyosin cytoskeleton as well as adhesive structures at endothelial junctions, thereby facilitating and enabling the transmigration process 17.After capturing, rolling, and arrest, leukocytes crawl on the endothelial surface until they start to diapedese through the endothelial barrier. Arrest and crawling are mediated mainly by the β2-integrins LFA-1  and associated catenins, which is important for the opening of endothelial junctions30.Other signaling steps that are triggered by ICAM-1 and VCAM-1 are summarized in excellent reviews31 and was followed by many others such as the junctional adhesion molecules JAM-A, -B, and -C35; endothelial cell-selective adhesion molecule (ESAM)36; CD9938; CD99L241; and the nectin-related poliovirus receptor (PVR)42. All of these proteins are enriched at endothelial cell contacts. Although not much is known about how they facilitate the diapedesis process, some of them were shown to act in a sequential manner. PECAM-1 was found to act before CD9937, whereas PVR functions after PECAM-1 and before the CD99 step42. ICAM-2 was reported to support one of the earliest steps in the diapedesis process followed by JAM-A and then PECAM-143. Interference with some of these receptors, such as PECAM-1, CD99, and CD99L2, led to the accumulation of leukocytes between the endothelium and the basement membrane, suggesting that these proteins were also involved in mechanisms that enable leukocytes to overcome the basement membrane45.Several endothelial membrane proteins support the transmigration of leukocytes through the endothelial cell barrier. Platelet and endothelial cell adhesion molecule 1 (PECAM-1) was the first adhesion molecule that was shown to be involved in this process46. PECAM-1 was suggested to trigger the mobilization of this compartment to the plasma membrane, whereas crosslinking of CD99 would trigger a second wave of vesicle traffic to cell contacts47. The latter process was linked to the interaction of CD99 with ezrin, soluble adenylyl cyclase, and protein kinase A (PKA)47. Engagement of PECAM-1 by leukocytes was reported to cluster the Ca2+ channel TRPC6 at sites of diapedesis, and TRPC6 was found to act downstream of PECAM-1, affecting the recycling of LBRC vesicles16. This report also suggested that Ca2+ signals triggered by PECAM-1 via TRPC6 were rather local and different from global cellular Ca2+ signals that are found after leukocyte docking to endothelial cells, which are mediated by Ca2+ store release from the endoplasmic reticulum , would support the diapedesis process, possibly by serving as a membrane reservoir that helps accommodate the body of the transmigrating leukocyte at gaps between endothelial cellsticulum .48. Several mechanisms were recently discussed which could determine such exit sites and allow leukocytes to identify them. Since ICAM-2 seems to act at the most apical position of all diapedesis-supporting cell surface receptors43, it might be a candidate for a receptor that could help leukocytes to identify endothelial cell contacts. ICAM-2 is diffusely expressed over the whole apical surface of endothelial cells in postcapillary venules but is enriched at endothelial cell contacts49. Apical ICAM-2 supports as a ligand for Mac-1 neutrophil crawling and interfering with its function altered the stop-and-go intervals of leukocyte crawling49. It is an attractive speculation that the increased expression levels of ICAM-2 at cell contacts might influence leukocytes in their decision to stop and start diapedesing.Crawling on the luminal surface of postcapillary venules was reported to help leukocytes find appropriate exit sites50. Intravascularly adherent platelets and neutrophils, in turn, recruited inflammatory monocytes to these sites of extravasation. These interactions required the interaction of P-selectin with leukocyte PSGL-1, which contributed to the activation of leukocyte integrins. Blockade of these multi-cellular interactions reduced leukocyte extravasation. These findings provide mechanistic understanding for the previously well-documented important contribution of platelets to leukocyte extravasation54.Recently, platelets were described as pathfinders for leukocyte extravasation. It was shown that platelets adhered under inflammatory conditions at endothelial junctions in the smallest venular microvessels and captured neutrophils via CD40-CD40L/CD154-dependent interactions55. In contrast, another study showed that lymphocytes transmigrated preferentially through local sites of reduced endothelial cell stiffness, which were characterized by low levels of F-actin and were also found at sites of transcellular migration56. Interestingly, long time exposure of endothelial cells to flow stabilizes junctions and this increased the fraction of lymphocytes that transmigrated through a transcellular route56.Another aspect that may determine a transmigration site is the stiffness of endothelial cells. It was found that endothelial stiffness supported the spreading and transmigration of neutrophils. A gradient of increasing stiffness (measured by atomic force microscopy) from the center to the periphery of endothelial cells drove crawling neutrophils toward cell junctions, promoting transmigration through a paracellular routein vitro andin vivo. Direct analysis by intravital three-dimensional video microscopy showed that 90% of extravasating neutrophils use the paracellular route and that only 10% use the transcellular route, and this was found under different inflammatory stimuli35. In line with this, stabilizing endothelial junctions in genetically modified mice by replacing endogenous VE-cadherin with a VE-cadherin-α-catenin fusion construct strongly inhibits neutrophil extravasation in lung and cremaster and lymphocyte recruitment into inflamed skin by 70 to 80%57.In vitro, more than 90% of neutrophils, monocytes, and lymphocytes transmigrate through human umbilical vein endothelial cells (HUVECs) via the paracellular route58. The low efficiency of the transcellular route is enhanced up to 30% for leukocyte transmigration through cultured microvascular endothelial cells59. In addition, higher expression levels of ICAM-1 on endothelial cells correlated with a higher percentage of transcellular events61. The latter study also showed that higher ICAM-1 expression levels correlated with reduced crawling distances of cells under flow. Interestingly, this study also reported that the few lymphocytes that still transmigrated through endothelial cells lacking ICAM-1 and ICAM-2 were unable to crawl prior to diapedesis and used exclusively the transcellular route61. Thus, it is tempting to speculate that leukocytes, which are hindered to reach their optimal site of exit at junctions or which reside longer than normal at a site on the apical endothelial surface, may tend to use a transcellular route. In agreement with this concept, interfering with the integrin Mac-1 on neutrophils, which inhibited crawling, enhanced the percentage of transcellular migrationin vivo48. Likewise, T cells deficient for the Rac1 guanine nucleotide exchange factor (GEF) Tiam1 showed reduced crawling and used the transcellular diapedesis route with enhanced efficiency62.Leukocytes transmigrate through a paracellular route, which requires the opening of endothelial junctions, and through a transcellular route, which does not require junction opening but is often close to junctions. Both processes have been well documentedin vivo63 and enhancing the adhesive activity of VE-cadherin by replacing it with a VE-cadherin-α-catenin fusion protein in knock-in mice strongly inhibits leukocyte extravasation in several tissues57.In vitro studies revealed that leukocyte-triggered clustering of ICAM-1 modulated tyrosine phosphorylation of VE-cadherin, which was linked to the opening of junctions and transmigration efficiency29. The transmigration of B16 melanoma cells through cultured endothelial cells was reported to require endothelial FAK activity, which triggered the phosphorylation of Y658 of VE-cadherin64.In vivo studies with mice carrying tyrosine/phenylalanine (Y/F) point mutations in Y731 or Y685 of VE-cadherin revealed that leukocyte-induced dephosphorylation of Y731 was required for neutrophil extravasationin vivo30  or histamine triggers rapid dissociation of VE-PTP from VE-cadherin66. Each of these different stimuli triggers the same signaling cascade that leads to VE-PTP/VE-cadherin dissociation67. It could be demonstrated that this dissociation is necessary for the induction of vascular permeability and for inflammation-induced neutrophil extravasationin vivo68. Interestingly, VE-PTP is able to dephosphorylate Y685 but not Y731 of VE-cadherin30. Thus, it is likely that VE-PTP affects the regulation of vascular permeability via Y685 of VE-cadherin but that the substrate relevant for the role of VE-PTP in leukocyte diapedesis is probably plakoglobin66. A role of VE-PTP for the control of endothelial cell integrityin vivo was also shown in a recent study that reported the induction of VE-PTP by hypoxia via HIF2α69. Besides VE-cadherin, VE-PTP also associates with the tyrosine kinase receptor Tie-270, which is important for vascular remodeling in embryonic development71 , which associates with VE-cadherin and supports its adhesive activityment71 . Interfe2+ signals and phosphorylation of myosin light chain kinase (MLCK)11, which has been implicated in the modulation of endothelial junctions74. Inhibition of MLCK prevents neutrophil diapedesis75, and also Rho kinase was shown to be involved76. In agreement with this, the endothelial microfilament system is required for the transmigration of monocytes77. These reports are in line with our previous findings that showed that stimulation of the endothelial tyrosine kinase receptor Tie-2 can inhibit neutrophil recruitment to endotoxin-stimulated mouse lungs73. These effects were even observed in mice conditionally gene-inactivated for VE-cadherin. Since Tie-2 activation was found to reduce radial stress fiber formation and blocked MLC phosphorylation by a Rap-1- and Rac-1-dependent mechanism, these results suggest that leukocyte extravasation opens endothelial junctions by a two-step mechanism: downregulation of VE-cadherin function and active, actomyosin-mediated pulling on endothelial cell contacts73.In addition to counteracting the function of VE-cadherin, leukocytes also need to trigger mechanisms that actively open gaps for extravasation. As mentioned above, clustering of ICAM-1 triggers endothelial Ca78. Furthermore, it was suggested that regulated membrane fusion events requiring NSF  and SNARE (soluble NSF attachment protein receptor) complex proteins would be required in endothelial cells for transcellular leukocyte diapedesis59. Endothelial cells are often rather flat in large areas of their bodies, with a thickness of no more than 1 μm at their edges. It may be possible that at certain sites the apical and the basal membranes fuse directly. Alternatively, intracellular vesiculo-vacuolar organelles inside endothelial cells might fuse with each other and with the apical and basal membranes to form a short “channel” that could accommodate transmigrating leukocytes on a transcellular route79. It is interesting that several of the adhesion receptors that are involved in the paracellular transmigration of leukocytes, such as PECAM-1, CD99, and JAM-A, were also found encircling transcellularly migrating leukocytes80. Antibodies against these antigens also interfered with the transcellular transmigration, although the role of these antigens in transcellular migration is unknown at present.For the transcellular migration pathway, it is less clear how the required transcellular gaps are formed. It was shown that membrane trafficking-related proteins such as vasodilator-stimulated phosphoprotein (VASP) and caveolin are involved in the formation of the transcellular pathway84 and at different times during the inflammatory process86. Thus, mechanisms must be in place to ensure a tight endothelial barrier, although leukocytes open endothelial junctions and transmigrate through them. Recently, it was reported that such a mechanism is based on ICAM-1-triggered activation of RhoA, mediated by the RHO GEFs Ect2 and LARG . It was shown that, under such conditions, local bleedings (petechiae) occur at sites of local inflammation93. It is an interesting concept that the composition of the basement membrane and low expression regions of certain components of the basement membrane represent preferred sites of transmigration95, which may even have an impact on the ability of associated endothelial cells to serve as preferred entry sites. Likewise, pericytes could have guiding effects in this respect96. Finally, it was recently discovered that the transmigration process is not always unidirectional and that under certain conditions neutrophils can also revert the direction and move back into the circulation98. It will be interesting to elucidate the physiological and pathophysiological relevance of this process.Recent years have provided the first mechanistic insights into the process of leukocyte diapedesis through the endothelial barrier. Although several cell surface adhesion receptors have been identified at endothelial cell contacts that are involved in the transmigration process (and not in leukocyte capturing and docking), the first signaling mechanisms triggered by PECAM-1 and by CD99 were identified only recently. It will be a major goal in the future to elucidate the potential roles of the various receptors for the following functions: signaling to leukocytes to convey the information that an exit site has been reached; opening and possibly sealing of gaps in the endothelial barrier; participation in leukocyte migration; and facilitating mechanisms that enable leukocytes to overcome the basement membrane. The last step of migration through the basement membrane is still enigmatic


Refugee Health Screener-15 (RHS-15) as a screening tool for perinatal depression among migrant women living on the Thai-Myanmar border. The Structured Clinical Interview for the Diagnosis of DSM-IV Disorders (SCID) was used as the gold-standard comparator. Complete results were obtained for 235 Burmese-speaking and 275 Sgaw Karen-speaking women. Despite displaying reasonable psychometric properties, a number of shortcomings associated with the RHS-15 limited its utility in this setting. The Likert-type response categories of the RHS-15 proved problematic in this low-literacy population. Combined with the relative superiority and greater ease of administration of the SCID, the RHS-15 is not recommended as the tool of choice for detecting perinatal depression in this setting.Perinatal depression is common, and left untreated can have significant and long-lasting consequences for women, their children and their families. Migrant women are at particular risk of perinatal depression as a result of a multitude of stressors experienced before, during and after migration. Identification of perinatal depression among migrant women—particularly those living in low- and middle-income regions—remains challenging, partly due to the lack of locally-validated and culturally appropriate screens tools. This study formally validates Burmese and Sgaw Karen versions of the  Maternal mental health is an important global health challenge and a cornerstone to reducing global inequalities. Perinatal mental disorders are common, and the global burden falls disproportionately upon low- and middle-income countries (LMIC), where an estimated 16% of pregnant and post-partum women experience depression . UntreatMigrant women face a multitude of stressors which place them at increased risk of mental disorders –8. Risk Better identification of affected individuals is key to promoting mental health. Culturally valid tools are essential for detecting illness, quantifying the disease burden, targeting care and monitoring treatment response . For manA previous study of over 600 migrant women conducted on the Thailand-Myanmar border between February 2014 and April 2015 found that the Edinburgh Postnatal Depression Scale (EPDS), one of the most widely-used screening tools for perinatal depression, had poor acceptability among local staff and patients . Women iThe Thailand-Myanmar border is home to an estimated 200,000 labour migrants and 145,000 refugees fleeing decades of conflict, poverty and lack of opportunity in Myanmar , 16. LabMigrants in this region constitute a heterogeneous group of Karen, Burman and Burman Muslim ethnicities, each with their own languages, traditions and religious backgrounds. Sgaw Karen, the language of one of Myanmar’s largest ethnic group, is the most commonly spoken language. A smaller proportion speaks Burmese, the official language of Myanmar. The Shoklo Malaria Research Unit (SMRU) has provided maternity care to this border population since 1986. In collaboration with Thailand Public Health, care was initially motivated by the very high rates of maternal mortality due to malaria which were reduced by the antenatal clinic (ANC) services . ANC forParticipants were first trimester pregnant migrant women attending SMRU antenatal clinics (ANC) at MLA, MKT and WPA. Women were eligible if they were aged 18 years or over, their estimated gestational age (EGA) as determined by ultrasound dating scan was less than 14 weeks, they had a viable pregnancy, planned to deliver at SMRU and were willing and able to participate.Ethics approval was granted by the University of Oxford Tropical Research Ethics Committee (OxTREC 28–15), Mahidol University Faculty of Tropical Medicine Ethics Committee (TMEC 15–045) and the Tak Border Community Advisory Board (T-CAB 6/2/2015), a committee of local community representatives who assess the acceptability of proposed research .Refugee Health Screener-15 (RHS-15) is a fifteen-item screen for symptoms of depression, anxiety and post-traumatic stress disorder (PTSD) developed in conjunction with refugees from Myanmar, Bhutan and Iraq recently resettled in the United States  is maximised, to identify the point of optimal balance between sensitivity and specificity. We conducted a Receiver Operating Characteristic (ROC) analysis to calculate the area under the curve (AUC) for the RHS-15 and thereby assess criterion accuracy (the proportion of results correctly identified) and validity . We condOf 630 eligible women who attended ANC during the study period, 569 (90.2%) participated . Women wThe reliability of the Burmese RHS-15 as determined using Cronbach’s alpha was 0.63. Omitting unhelpful individual items of the RHS-15 did not improve the reliability significantly producing a range of alpha values between 0.56 and 0.66. The sensitivity, specificity and likelihood ratios for each cut-off of items 1–14 of the Burmese RHS-15 are shown in The reliability of the Sgaw Karen RHS-15 as determined using Cronbach’s alpha was 0.56, with values ranging between 0.48 and 0.52 when individual items were omitted. The sensitivity, specificity and likelihood ratios for each cut-off of items 1–14 of the Sgaw Karen RHS-15 are shown in This study examines the validity of the Burmese and Sgaw Karen RHS-15 as a screening tool for perinatal depression in women on the Thai-Myanmar border. The combined prevalence of major and minor depression during the first trimester of pregnancy as assessed using the SCID was 7.7%. This is line with estimates reported in a systematic review of refugees resettled in Western countries, which found a pooled prevalence of major depression of 5% . OverallOn the Burmese RHS-15, the optimal cut-off for items 1–14 was ≥14, yielding good sensitivity and specificity at 81.8% and 76.1%, respectively. The distress thermometer in Burmese performed slightly less well with a sensitivity of 77.3% and specificity of 67.6% at the optimal cut-off of ≥4. On the Sgaw Karen RHS-15, the optimal cut-off for items 1–14 was ≥15, yielding good sensitivity and specificity at 88.2% and 81.0%, respectively. The distress thermometer in Sgaw Karen performed significantly less well: at the optimal cut-off of ≥4 sensitivity and specificity were low at 52.9% and 69.2%, respectively. Reliability as determined by Cronbach’s alpha was low in both languages . However, as the RHS-15 assesses for anxiety and post-traumatic stress disorder as well as depression, the low alpha values may reflect the multiple dimensions of the test rather than poor internal consistency of the scale .et al. found significantly higher sensitivity (100%), specificity (91%) and reliability  among Burmese participants [et al.’s small sample of 50 Burmese participants, of whom only six had a diagnosis of depression, suggests that results must be interpreted with caution. Furthermore Hollifield et al.’s group of ‘Burmese’ participants in fact include four distinct ethnicities  and two interview languages (Burmese and Sgaw Karen). Not disaggregating results by language or ethnicity may have masked differences between groups.In the original validation of the RHS-15 among refugees in Washington State, USA, Hollifield icipants . A numbeA number of important contextualising factors need to be considered when interpreting findings. Firstly, factors relating to language and literacy are likely to have influenced results. Participants found it difficult to select the appropriate Likert-type response category on the RHS-15, and it was often necessary to discuss at length the distinctions between ‘a little bit’, ‘moderately’ and ‘quite a bit’–none of which have a direct translation into Burmese or Sgaw Karen. Previous studies have described the challenges of using Likert scales in low-literacy populations –36. The A further constraint may have been the linguistic characteristics of Burmese and Sgaw Karen. As an ethnic language with a strong oral tradition, Sgaw Karen in particular has a limited scope for nuanced questioning and linguistic precision, especially around Western constructs of mental distress and its symptoms. The absence of a Burmese or Sgaw Karen word for depression means that alternative words with similar meanings are needed to convey its meaning. A further important consideration arose from the relative superiority of the comparator instrument. It became apparent during the study that in this particular setting, the SCID had a number of strengths over the RHS-15. The SCID proved not only more straight-forward to administer, but also elicited a greater level of detail and depth than the RHS-15. The open questions format of the SCID allowed women to describe their feelings in their own words, and often, valuable additional information around their personal and social circumstances emerged which helped to contextualise their psychological state. The SCID therefore added a breadth and depth to the clinical history that far exceeded what was elicited by the RHS-15. Importantly, on average, the SCID saved time. For women with no symptoms of depression, the SCID took less time to administer than the RHS-15. For women with symptoms, the SCID took longer to administer than the RHS-15, butremained faster than the alternative of administering the RHS-15 first followed by the SCID, as would be required by a screen-positive RHS-15.Added to these two contextual factors came the fact that despite good sensitivity and specificity of certain aspects of the RHS-15, a number of its properties were sub-optimal. The fact that overall, 44% (225/510) of women screened positive on the RHS-15 using the optimal thresholds for our population is problematic given that each of these screen-positive women requires further assessment in the form of a diagnostic interview. Follow-up of almost one in every two women attending ANC is unfeasible in a resource-constrained setting such as ours with numerous competing health priorities. This limitation of the RHS-15, along with women’s difficulty with the Likert-type scale and the relative superiority of the SCID, led to the decision not to continue using the RHS-15 in our setting.The greatest strength of our study is that, to our knowledge, it is the first to use the RHS-15 in a LMIC setting. The majority of today’s migration flows occur within LMIC but despite this, research on migrants in LMIC is severely lacking . UnderstThere were a number of limitations to our study. Firstly, gold standard assessments of depression were made by non-specialists. The SCID was designed to be administered by a clinician or trained mental health professional, rather than by minimally-trained healthcare workers. The lack of mental health expertise in our setting meant that specialist involvement was not an option available to us, and this is likely to be the case in other LMIC settings , 24. HowA second limitation was that due to staffing constraints, SCID assessors were not blinded to RHS-15 scores at two out of the three study sites. Because blinding was only possible at one site, and because this site also had a lower prevalence of depression, it was not possible to explore whether there was an association with blinding status and depression. We are therefore unable to say whether or not blinding had a significant effect on the diagnosis of depression using the SCID. Thirdly, the fact that the RHS-15 was verbally-administered by interviewers rather than self-completed may have affected results. Face-to-face, interviewer-administered questions may be prone to social desirability bias and a lower willingness to disclose sensitive information . HoweverFinally, the gold standard SCID has not itself been validated in this setting. The SCID remains one of the most widely used diagnostic instruments globally and was selected based on the lack of any alternative, locally-developed tools. The possibility that culturally-specific symptoms of depression were missed cannot be ruled out. Somatic symptoms, for example, are common in non-Western cultures but may not have been picked up by the SCID which focuses on psychological symptoms , 40. In As is typical of other resource-constrained settings, mental health services are severely lacking on the Thai-Myanmar border. Mental disorders during pregnancy and the post-partum period have immediate and long-term consequences for mothers, children and wider society. The lack of appropriate, locally validated screening tools limit the ability to identify and monitor disorders such as perinatal depression, perpetuating its low priority on the global agenda for marginalised and vulnerable populations. Although the RHS-15 demonstrated good sensitivity and specificity on the Thai-Myanmar border, the SCID was able to elicit more detailed and culturally-relevant information in the same amount of time, leading us to choose the SCID as the tool of choice in our setting. Further research is required on the role of health literacy in mental health screening. With the majority of global migration flows occurring within LMIC regions, research from these regions is an urgent research priority. As long as the needs of the most vulnerable communities remain under-researched, these needs will also be remain insufficiently addressed.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(PDF)Click here for additional data file.


Mortality outcomes in trials of low-dose computed tomography (CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.A decision tree model with three scenarios  was developed to compare screening results in a simulated Chinese urban cohort . Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality (primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Among the 100,000 subjects, there were 448, 541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively (17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7 and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, low-dose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths (relative numbers) with low-dose CT screening in the worst and optimal cases were 16 (5.4%) and 288 (40.2%) over no screening, respectively.In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China. However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed. Lung cancer is the most common malignant tumor and the dominant cause of cancer-related deaths in China . The 5-yThe United States National Lung Screening Trial (NLST) has reported an encouraging 20% reduction in lung cancer mortality with low-dose CT screening over chest X-ray screening . HoweverIn this study, we simulated a cohort of 100,000 urban residents and offered them one-off lung cancer screening . The age and sex structures of this cohort were based on data from the China Population & Employment Statistics Yearbook 2014 . AccordiDecision analysis is widely used to make choices between different paths in Chaotics and uncertain conditions , especiaIndividuals with findings suggestive of lung cancer on low-dose CT images were asked to undergo further investigation, such as bronchoscopy and percutaneous biopsy, to determine the diagnosis and then either accept or refuse surgical treatment. Additionally, we set up branches in the path corresponding with “missed diagnosis” caused by false negative imaging results (merging into the no screening path described below), “false diagnosis” caused by false positive imaging results, and the “over diagnosis” issue  recently raised by researchers .The overall path was similar to that in the low-dose CT screening scenario, with the only difference being that the variables were adjusted to chest X-ray screening.Individuals with lung cancer detected and further diagnosed according to symptoms or medical examinations for other diseases took the usual health care treatment path.All the three scenarios were stratified by early  and non-early stage cancers, considering the impact of disease stage on the acceptance rate of surgery and survival possibility after surgery.Additionally, the following assumptions were made for the decision tree. First, surgery is a determinant for lung cancer mortality (except for indolent cancers), and although non-surgical treatments could prolong patient survival, their small impact on the final outcomes was not taken into account . Second,Table Lung cancer prevalence of the screened cohort was calculated as follows.R is the age- and sex-specific rate of smoking reported in the Global Adult Tobacco Survey (GATS) China 2010 Country Report [First, age- and sex-specific lung cancer incidences in the general Chinese urban population  is the average course of lung cancer [Further, because lung cancer prevalence is relatively stable in China, the following formula was used to calculate age- and sex-specific lung cancer prevalence in the screened cohort   was analAdditionally, to explore the influences of criteria of age, smoking, and sex on the prevalence of lung cancer in the screened cohort (and therefore on screening outcomes), with the same procedures described above, we further calculated different Lung cancer death was the primary outcome measure in this study. Using base-case analysis, we calculated the absolute and relative reductions in lung cancer deaths with low-dose CT screening over chest X-ray screening and no screening. False diagnosis, death due to false diagnosis, and QALY as secondary outcome measures were simultaneously calculated and compared. Univariate sensitivity analysis  was performed to investigate the influences of screening conditions on outcomes in different scenarios, and a tornado diagram was plotted to vividly display the magnitude of the influence of each variable . Optimal and worst cases of reductions in lung cancer deaths with low-dose CT screening over other scenarios were determined by considering combinations of all possible ranges of the variables. All the analyses were performed with Treeage Pro 2011 software .Age and sex distribution of the simulated cohort in the base-case analysis is displayed in Table Lung cancer prevalence in the screened cohort was the factor that most strongly influenced the death reduction with low-dose CT screening over no screening Fig. . The varFor the secondary outcome measures (not shown in figures), the factor that most influenced the number of false diagnosis was specificity of low-dose CT ; lung cancer prevalence had a tiny negative influence (0.01%). Three factors were related to number of deaths due to false diagnosis with low-dose CT screening, namely, death possibility due to false diagnosis , low-dose CT specificity , and lung cancer prevalence . The factor that most influenced DALY was lung cancer prevalence (58.7%), followed by low-dose CT sensitivity (13.5%), low-dose CT specificity (11.0%), and proportion of early-stage cancers among lung cancers detected with low-dose CT . Notably, a threshold of QALY was detected in lung cancer prevalence ; the gain of QALY in low-dose CT screening over no screening would diminish if screening was performed among populations with a low prevalence of lung cancer.Considering the possible ranges of all influential factors, reductions in lung cancer deaths with low-dose CT screening over no screening were 281 versus 297  in the worst case and 428 versus 716  in the optimal case, and those over chest X-ray screening were 281 versus 270  in the worst case and 428 versus 669  in the optimal case.The influences of upper and lower age limits on prevalence of lung cancer in the screened cohort and on differences in numbers of lung cancer deaths between the three scenarios are shown in Table Figure This study evaluated the outcomes of lung cancer screening in China, including the influences of several factors on screening effects. According to the base-case analysis of 100,000 urban smokers aged 45–80 years, low-dose CT screening decreased lung cancer deaths by 24.2% over no screening and by 17.2% over chest X-ray screening. There were fewer deaths even in the worst case analysis , and the reduction was as high as 43.6%  in an optimal situation. Thus, in terms of mortality outcome, our findings indicate that screening old smokers in urban China with low-dose CT would reduce lung cancer deaths. However, the effect of low-dose CT screening on lung cancer death reduction was inferior to that of chest X-ray screening in the worst situation . On average, low-dose CT screening resulted in more than three-fold false diagnoses and even deaths due to false diagnosis and over-treatment. Additionally, the gain in QALYs with low-dose CT screening (250 years over no screening and 147 years over chest X-ray screening) was not as pronounced as that for mortality. Thus, better screening strategies are required to optimize the outcomes of low-dose CT screening.Being a country with a large lung cancer burden , China In the present study, sensitivity analysis showed that the prevalence of lung cancer in the screened cohort was the factor with the strongest influence on reduction in lung cancer deaths, indicating that the selection criteria for eligibility for lung cancer screening programs should be carefully and rigorously defined . AlthougRegarding other factors that influenced screening effects, the sensitivity of low-dose CT had a relatively important positive influence on the reduction in lung cancer deaths; so was the proportion of early-stage cancer among lung cancers detected with low-dose CT screening, reinforcing the need to develop highly sensitive screening techniques . The posFor individuals who do not have lung cancers, false positive results remain the most critical issue of low-dose CT screening –45. ThisIn our study, gains in QALYs of low-dose CT screening over chest X-ray screening and no screening were less impressive than reduction in lung cancer deaths. This was mainly because unscreened individuals with lung cancer can enjoy a higher quality of life during the symptom-free interval before diagnosis of their cancers, whereas individuals with false-positive diagnoses experience a decreased quality of life as a result of anxiety and unnecessary diagnosis or treatment. For example, in the base-case analysis, these two sources resulted in loss of 562 QALYs in the low-dose CT screening scenario than in the no screening scenario . Thus, close attention should also be paid to physiological reactions if we wish to provide participants in the low-dose CT screening scenario with better quality of life .Limitations remain in this study. First, the decision tree we used is a simplified representation of complex clinical paths and individuals’ behaviors. For example, we dichotomized the distribution of cancer stages into early and non-early; full I–IV staging  would have made the model more accurate. This was mainly because of lack of large domestic studies from which we could draw robust estimates of screening and clinical parameters. Additionally, the absence of transition possibilities in lung cancer stage among the extremely heterogeneous histological types of lung cancer prevented us from employing stage transition Markov models. Second, for the same reasons, we only considered baseline screening in this study, whereas some programs in China already adopted a baseline and annual repeat screening fashion , in whicMortality outcome derived from this decision analysis were in favor of conducting low-dose CT screening in urban China for early detection and treatment of lung cancer. However, for nationwide promotion in the future, further work should be done to optimize the effects of screening, minimize false positive diagnoses, and improve participants’ actual quality of life, such as by developing selection criteria for screening that are more appropriate to Chinese participants, standardizing diagnostic and treatment methods, and adapting screening protocols according to availability of funding and other local conditions.


This model was verified in single factor survival analysis (p=0.004447558) and significant analysis with recurrence time (p=0.001474831) by using independent datasets from TCGA. The constructed 9-genes model was stable and effective, which might serve as prognostic signature to predict the survival of GC patients and monitor the long-term treatment of GC.Gastric cancer (GC) is a common malignant tumor with high incidence and mortality. Reasonable assessment of prognosis is essential to improve the outcomes of patients. In this study, we constructed and validated a prognostic gene model to evaluate the risks of GC patients. To identify the differentially expressed genes between GC patients and controls, we extracted Gene expression profiles of GC patients (N=432) from Gene Expression Omnibus database and then stable signature genes by using Robust likelihood-based modeling with 1000 iterations. Unsupervised hierarchical clustering of all samples was performed basing on the characteristics of gene expressions. Meanwhile, the differences between the clusters were analyzed by Kaplan Meier survival analysis. A 9-genes model was obtained (frequency = 999; p=1.333628e Gastric cancer (GC) is the third leading cause of cancer-related death worldwide . AccordiEpigenetic alterations, especially aberrant DNA methylation, and microRNA (miRNA) expression play a central role in many cancers –11. In mAs to GC, apart from the disease staging, Lauren’s classification is another accurate well-recognized class-ification system, subdividing GC as two histomorphologic subtypes – “intestinal-differentiated” and “diffuse-undifferentiated” . HoweverIn this study, gene expression profiles of gastric cancer were analyzed to figure out the key genes affecting the prognosis of patients. With repeated Kaplan Meier survival analysis and Robust likelihood-based modeling, a 9-genes expression signature was finally identified serving as prognostic model for gastric cancer. The stability and effectiveness were verified by an extra dataset in the Cancer Genome Atlas (TCGA). The 9-gene prognostic model can function as effective prognostic tool to identify groups of patients at risk of relapse or metastasis. Besides, this might also be useful to monitor cancer survivors following treatment.In total, 17418 expression profiles of 432 formalin fixed paraffin embedded tumor tissues of GC patients were obtained in dataset GSE26253.Schematic diagram for a multi-step strategy to identify gene signature for prognosis in GCwas listed in Figure A total of 11420 differentially expressed genes were identified, including 17418 probes expressing in 432 patient samples. Single-factor survival analysis identified 798 probes with significantly differential expressions (p < 0.05). Hereinto, the top 20 significantly differentially expressed genes were listed in Table One thousand Robust likelihood-based results showed that the frequency of a 9-gene feature was 999, suggesting the 9-gene as a prognostic feature. One random result of the Robust likelihood-based survival analysis was in Table Multivariate survival analysis of 9 prognostic genes was performed to check the effect of the overall genes on the prognosis of GC. Then according to the area under the ROC curve (AUC) values and Pearson’s correlations, Kaplan Meier survival analysis was performed to estimate the differences of survival outcomes between groups Figure . MultivaThe expression profiles of 9-gene feature were analyzed in unsupervised hierarchical clustering Figure . This feKaplan Meier survival analysis on the prognostic differences of Cluster1 and Cluster2 were performed and shown in Figure According to the classification, the impact factors of each sample was calculated, the activated was marked as 1, or, was 0.Samples were grouped again basing on the activated impact factors of every sample . And Kaplan Meier univariate survival analysis was performed to analyze the differences of each group with significant p value Figure . In FiguFinally, the ≥5Genes cluster model was identified as the final model of the 9-gene prognostic feature. That is, patient with five genes of the 9-genes prognostic feature identified activated was suggested as high risk.The expressing profiles of these 9 feature genes were obtained from TCGA, basing on which 415 samples of GC patients were clustered into high- and low- risk group following the ≥5Genes mode. The Kaplan Meier univariate survival analysis of each cluster were shown in Figure Gastric cancer (GC) is a highly lethal malignancy with poor five-year survival rate comparing with any solid tumors, improving only modestly over the last 50 years, from 12 % to 22 % . The remIn many previous studies, gene signatures were ever identified when analyzing expressions in residual gastric cancer cells after neoadjuvant chemotherapy, composing of GSK3B, the β-catenin gene CTNNB1, NOTCH2 and many other genes , 20. BesIn this study, the expressing profiles of GC were analyzed and the most significant feature genes were identified, with which a stable and effective 9-genes prognostic feature  was constructed. To solve the over-fitting issues in this study, we go for simpler models over more complicated models using fewer parameters and remove one parameter without much increasing the (out-of-sample prediction) error.To perform the validation process, we tried Cross-Validation, a standard way to find out-of-sample prediction error is to use 5-fold cross validation. In this case, we used 9 differentially expressed genes to build the model, and then evaluate the error on the 4 genes left out of the model. To repeat this 4 times, we used a different set of 5Gene each time. This is a better representative of the error. Combing with Kaplan Meier univariate and Robust likelihood-based survival analysis, as well as clustering analysis, the optimistic method to classify the GC patients into high- and risk- group was the no less than 5 genes model (≥5Genes).Because the data in TCGA (The Cancer Genome Atlas) are generated by larger teams of research and more standardized than those in GEO, the predictive value basing on the TCGA cohort is less than that basing on the GEO cohort. To achieve an advanced molecular classifier and predictors, we are able the take good advantage of the large-scale profiling data in GC at TCGA. It was verified that the 9-5Gene clustering model can effectively classify patients from both the selected dataset and the extra one into high- and low- risk group with significant differences on survival time and reoccurrence risk.In the previous studies, some of these 9 genes were identified differentially expressing in other cancer patients and that some were analyzed to explain the bio-functions. Hereinto, NR1I2 (nuclear receptor subfamily 1 group I member 2) was identified involved in the pharmacogenetics of irinotecan, which might help to predict the toxicity of low-dose irinotecan . These NIn previous study, C1orf198 was identified as novel gene in colon, gastric and pancreatic cancer, however, the GO functions of this gene were unknown yet . Gene CSTraditionally, tumor node metastasis (TNM) staging is the common classification system. Besides, another two well-recognized classification systems: the Lauren classification, subdividing GC into intestine and diffuse types; and the alternative World Health Organization (WHO) system that divides gastric cancer into papillary, tubular, mucinous (colloid), and poorly cohesive carcinomas. However, comparing with the novel microarray technologies for GC subtypes by gene expression profiling , the preIn conclusion, the constructed 9-genes feature was effective and stable in classifying the samples from GC patients. And the ≥5Genes cluster model worked well in clustering patients into high- and low-risk groups with significant differences on both survival time and reoccurrence. Besides, most of the 9 genes were the first time finding associated with GC and can influencing the prognosis of patients. This feature gene model might contribute a lot to the development to predict the GC patients and help improve the outcomes by offering effective prognostic evaluation.http://www.ncbi.nlm.nih.gov/geo/) for prognostic modeling on platform Illumina HumanRef-8 WG-DASL v3.0. Dataset in the Cancer Genome Atlas (TCGA) [Microarray gene expression profiles of gastric cancer, GSE26253 , were dos (TCGA)  were usehttp://www.cbioportal.org/), as well as the clinical follow-ups for validation of the constructed prognostic model.Standardized data of GSE26253 were downloaded and the probe-level data were converted into the corresponding genetic symbols to remove the non-matched probes. The corresponding RNA-sequencing data were obtained from cBioPortal in Cancer Genomics  the median expression level of this gene in every sample was more than 20% of the total median expressions of all genes in every sample; 2) the variance of expression level of one gene in every sample was higher than 20% of the total expression variances of all genes in every sample.Then single-factor survival analysis of the differentially expressed genes was performed by using Survival in R Language . Genes wFor the utility of the seed genes in clinical diagnosis, Robust likelihood-based survival models , 38 were1) The samples were randomly divided into the training set with N*(1 − p) samples and the validation set with N*p samples, with p = 1/3. In subsequence, a gene was fitted to the training set of samples, obtaining the parameter estimate for this gene. Then we evaluated log likelihood with the parameter estimate and the validation set of samples. This evaluation was repeated for each gene.2) The above procedure was repeated for 10 times, thus we obtained 10 log-likelihood of each gene. The best gene, g (1), with the largest mean log likelihood was selected.We searched the next best gene by evaluating every two-gene model and selected an ideal one with the largest mean log likelihood.3) This forward gene selection procedure was continued resulting in a series of models. Akaike information criterions (AICs) for all the candidate models were computed and an optimal model with the smallest AIC was selected finally.Hereinto, 300 samples were repeated in procedure of Robust likelihood-based survival model for 1000 times. The most frequent gene combinations were finally selected as final prognostic feature genes.Multivariate survival analysis of feature genes was performed to check the effect of the overall genes on the prognosis of gastric cancer. Estimated relative risks of poor prognosis were expressed as adjusted p value and corresponding 95 % confidence intervals (95 % CI). Multivariate survival analysis was used to evaluate the effect of each specific parameter. For multivariate analyses, Cox proportional hazards regression was applied. Then survival ROC  in R Lanhttp://kmplot. com/analysis/index.php?p=service&cancer=gastric) was performed to identify the prognostic differences between samples after classification. Meanwhile, the expressing differences between the profiles of feature genes and that of the normal tissues as well.According to the expression profiles of the feature genes, each sample was classified by the unsupervised hierarchical clustering, then Kaplan Meier Survival analysis  The positive-impacting factors will be marked as active ones when their expressions were higher than the median.2) The negative-impacting factors will be marked as active ones when their expressions were lower than the median.3) Calculating the numbers of the active positive- or negative- impacting genes of every patient, patients were then classified according to the active numbers .Diversiform clustering algorithms were obtained, and then Kaplan Meier univariate survival analysis of these different algorithms was performed to calculate their influences on the prognosis of GC. Repetitive training of random samples in each model (samples contain at least a death event) to verify the model's stability. Finally, the optimistic clustering model was selected.Selecting the ideal classification model, level 3 released gene level expression data of GC were downloaded from TCGA. The data processing and quality control were done by Broad Institute's TCGA workgroup. These patient samples of GC patients downloaded from another dataset were clustered into high- and low- risk group following the ≥5Genes mode. The Kaplan Meier univariate survival analysis was applied as well to compare the differences of the GC patients between high- and low- risk group in survival time and recurrence.


A single viral variant is transmitted in the majority of HIV infections. However, about 20% of heterosexually transmitted HIV infections are caused by multiple viral variants. Detection of transmitted HIV variants is not trivial, as it involves analysis of multiple viral sequences representing intra-host HIV-1 quasispecies.env gp120. The Maximum Likelihood phylogeny and distribution of pairwise raw distances were assessed at each sampling time point  time points per patient, range 2–12 time points per patient).We distinguish two types of multiple virus transmission in HIV infection: (1) HIV transmission from the same source, and (2) transmission from different sources. Viral sequences representing intra-host quasispecies in a longitudinally sampled cohort of 42 individuals with primary HIV-1C infection in Botswana were generated by single-genome amplification and sequencing and spanned the V1C5 region of HIV-1C Transmission of multiple viral variants from the same source (likely from the partner with established HIV infection) was found in 9 out of 42 individuals . HIV super-infection was identified in 2 patients  with an estimated rate of 3.9 per 100 person-years. Transmission of multiple viruses combined with HIV super-infection at a later time point was observed in one individual.Multiple HIV lineages transmitted from the same source produce a monophyletic clade in the inferred phylogenetic tree. Such a clade has transiently distinct sub-clusters in the early stage of HIV infection, and follows a predictable evolutionary pathway. Over time, the gap between initially distinct viral lineages fills in and initially distinct sub-clusters converge. Identification of cases with transmission of multiple viral lineages from the same source needs to be taken into account in cross-sectional estimation of HIV recency in epidemiological and population studies. The majority of HIV infections are associated with transmission of a single founder virus, with transmission of multiple HIV-1 lineages occurring in about 20% of heterosexual cases . MultivaIdentification of the multiplicity of virus transmission in HIV infection is challenging because it requires multiple viral sequences representing intra-host HIV quasispecies. Molecular techniques, such as single-genome amplification and sequencing, or next-generation sequencing, can be applied to address multiplicity of HIV transmission, which remains a subject of special studies.multiplicity of virus transmission in the context of HIV infection has not been well defined with the exception of two extreme scenarios, transmission of a single founder virus and HIV super-infection. High homogeneity of viral quasispecies soon after infection is associated with the effective transmission of a single HIV variant . Similarly, distinct viral lineages separated by other patients’ sequences in the phylogenetic tree provide compelling evidence for transmission of multiple HIV variants, often as a super-infection. However, the interpretation of intermediate scenarios remains uncertain, as well as thresholds and criteria for multiplicity of HIV transmission. Technically, even a single nucleotide difference between identified intra-host HIV quasispecies could be interpreted as transmission of multiple viral variants. However, the clinical or epidemiological relevance of transmitted HIV quasispecies with minor differences is still unclear.The term Multivariant HIV infection has been associated with elevated HIV-1 RNA set point , 30–33 aIn this study we focus on transmission of multiple HIV lineages from the same source. The goal of the study was to identify transmission of multiple virus lineages from the same source based on the inferred phylogeny and distribution of viral pairwise distances of viral sequences representing intra-host HIV quasispecies. Better understanding of HIV transmission and the ability to distinguish between transmissions of multiple virus variants from a single source and those from multiple sources should assist in the analysis of HIV transmission networks and their dynamics.Tshedimoso study [The study on primary HIV-1C infection in Botswana, the so study , 38–45, Tshedimoso study of primary HIV-1C infection in Botswana [env gp120, about 1,200 bp in length . The initial set of viral sequences included 225 time points; eight time points with fewer than four sequences each were excluded . The total number of 217 time points analyzed in this study represented 42 patients, median 5 (IQR: 4–6) time points per patient, range from 2 to 12 time points per patient. The analyzed time points were represented by a total of 2,524 sequences, approximately 12 sequences per patient per time point. Participant characteristics are described elsewhere [Viral sequences were generated within the Botswana , 38–45. lsewhere , 43, 44.Codon-based multiple sequence alignment of viral sequences was performed by Muscle , 47 withThe Maximum Likelihood (ML) phylogeny was reconstructed for each time point of sampling  time points per patient, range from 2 to 12 time points per patient) using PhyML , 50. TheThe distribution of pairwise raw distances of viral sequences per subject per time point was analyzed by dist.dna  the presence of potential sub-clusters within each set of viral sequences representing intra-host HIV-1C quasispecies at a single time point. In this paper the term “sub-clusters” indicates clusters within the pool of HIV sequences representing intra-patient viral quasispecies. Sub-clusters were defined by a specific topology in the inferred ML phylogenetic tree: presence of a monophyletic patient-specific lineage with sub-clusters, which was evident by a combination of relatively long branches separating sub-clusters of viral sequences and short branches within each cluster. Such a topology was considered to be associated with transmission of multiple viral variants from the same (or a closely related) source of presumably established (chronic) HIV infection.To standardize identification of sub-clusters based on phylogeny of viral sequences representing intra-host HIV-1C quasispecies, we developed a simple test using R packages ape  and statBased on the results of simulation studies, the value of 0.20 has been chosen as the threshold for ratio withinss to betweenss, although the value of 0.15 could also be considered. In this study, within each set of viral sequences representing intra-host HIV-1C quasispecies at a given time point, sub-clusters were considered present if the ratio values for both sub-clusters were greater than zero and less than 0.20.The statistical analysis was performed in R version 3.3.1 . The proHIV-1C evolutionary dynamics are exemplified by the following four scenarios: (1) transmission of a single founder virus, (2) transmission of multiple viruses from the same source partner, (3) super-infection, and (4) transmission of two founder viruses followed by an HIV super-infection. The inferred ML trees and distribution of pairwise distances are presented at the specified time points of sampling expressed as days post-seroconversion.Extreme homogeneity of viral quasispecies in most cases is a hallmark of transmission of a single viral variant , the structure of the monophyletic clade includes multiple  sub-clusters with relatively low diversity within each sub-cluster  and conTransmission of distinct HIVs can occur over the course of HIV infection. In contrast to a monophyletic clade, viral sequences representing super-infection fall into different parts of the inferred phylogenetic tree and are separated by HIV sequences from other patients. Upon HIV super-infection, long branches separate distinct viral variants. Multiple  peaks can be found on the histogram of pairwise distances reflecting distances within and between two viral variants . Transmission of multiple viral variants from the same source was evident in 9  cases. HIV-1 super-infection was identified in 2 cases . The estimated rate of HIV-1C super-infection is 3.9 per 100 person-years. Transmission of multiple viruses combined with HIV super-infection at a later time point was observed once. The population frequency of HIV transmission as multiple variants from the same source remains unclear and warrants further studies.The frequency of multiple-variant transmission from the same source  appeared to be larger than the frequency of HIV superinfection , the difference reached statistical significance at the 5% level .Multiplicity of HIV transmission has important implications for design and development of treatment and prevention strategies, and particularly for advancing HIV vaccine research. The extreme cases in multiplicity of HIV transmission, such as transmission of a single founder virus, or of substantially distinct multiple viral variants, are well defined. These cases can be detected relatively easily, e.g., by phylogenetic inference of viral sequences representing intra-host HIV quasispecies. However, HIV transmission of closely related multiple viral variants remains uncertain, and the criteria for identification of such multiplicity have not been defined, nor have its clinical or epidemiological relevance.Tshedimoso study [env gp120. Transmission of at least two distinct HIV variants from the same source partner was demonstrated in 17% (7 of 42) of cases. The frequency of HIV super-infection was 5% (2 of 42) of cases, similar to the rate in MSM [In this study we utilized HIV sequences representing intra-host viral quasispecies from a prospectively sampled cohort of 42 individuals in Botswana who were enrolled in a primary HIV-1C infection project, the so study –45. Virae in MSM .The identification of closely related multiple viral variants might be challenging. The transient nature of distinct sub-clusters requires sampling during the early stage of HIV infection. If the early time points of sampling are missed, the topology and branch length in the phylogenetic tree and distribution of pairwise distance might not be informative. Moreover, within a short time after transmission of multiple viral variants, the elevated branch lengths and the extended pairwise distances could be interpreted as evidence for an established (chronic) HIV infection, leading to a misclassified recent HIV infection. This phenomenon and sub-optimal sampling could complicate the use of viral diversity as a marker of HIV recency in population studies. However, knowledge of the pattern of multivariant HIV transmission from the same source and its frequency in different populations could help to refine the estimation of HIV recency. If analysis of HIV recency relies on viral diversity, an adjustment for transmission of multiple viral variants could improve accuracy and result in more precise estimation of HIV recency.Sub-clustering of viral sequences could be defined by a topology of the inferred ML phylogenetic tree—presence of a monophyletic patient-specific lineage with sub-clusters, accompanied by a specific distribution of virus pairwise distances. However, such identification could be subjective, as the criteria for identification of sub-clusters are not well defined. To alleviate this problem and reduce subjectivity in identification of sub-clusters within the pool of HIV sequences representing intra-host viral quasispecies, we suggested a simple method based on the ratio withinss to betweenss. Our intention was to assess the extent to which the ratio withiness to betweeness can be used as a more objective surrogate for subjective interpretation of phylogeny plus pairwise distance distribution. We performed simulation studies see , and fouA simplistic diagram in In summary, the results of this study suggest that upon HIV infection, transmission of closely related multiple viral variants from the same source can be distinguished from transmission of viral variants from different sources. The proposed simplistic model highlights the dynamics of multivariant HIV transmission from the same source. The frequency of this transmission in different populations needs to be addressed in future studies.Multiple HIV lineages transmitted from the same source produce a monophyletic clade in the inferred phylogenetic tree. Such a clade has transiently distinct sub-clusters in the early stage of HIV infection, and follows a predictable evolutionary pathway. Over time, the gap between initially distinct viral lineages fills in and initially distinct sub-clusters converge. Identification of cases with transmission of multiple viral lineages from the same source needs to be taken into account in cross-sectional estimation of HIV recency in epidemiological and population studies.


Antibody–drug conjugates (ADCs) take advantage of the specificity of a monoclonal antibody to deliver a linked cytotoxic agent directly into a tumour cell. The development of these compounds provides exciting opportunities for improvements in patient care. Here, we review the key issues impacting on the clinical success of ADCs in cancer therapy. Like many other developing therapeutic classes, there remain challenges in the design and optimisation of these compounds. As the clinical applications for ADCs continue to expand, key strategies to improve patient outcomes include better patient selection for treatment and the identification of mechanisms of therapy resistance. The search for the “magic bullet” to selectively deliver a cytotoxic agent to the site of a cancerous cell has been the goal of clinical oncology for more than 100 years . AntibodThis review will provide a summary of the key factors leading to the emerging clinical success of ADCs and identify the challenges and advancements that have defined this exciting area of cancer therapy.The appropriate selection of the antigen-binding site is a critical developmental step for the eventual success of an antibody–drug conjugate. As such, the most effective antigens share certain characteristics. The monoclonal antibody selected, as the basic structural element of the ADC, binds to a target antigen present on the surface of the cell that is accessible via the bloodstream. Following binding, the complex must be rapidly internalised, allowing the release the cytotoxic agent within the tumour cell  7,8]. I,8. I7,8]The chemical conjugation of the antibody and the cytotoxic payload has a major influence on the pharmacokinetics, selectivity, and the therapeutic index of ADC-based therapies . A linkeConventional ADC conjugation involves methods of alkylation of reduced interchain disulphides and alkylation or acylation of lysine residues . These cImproving the antibody site for linker conjugation has been greatly enhanced through advancements in protein engineering. Antibody site-specific alterations enhance linker conjugation and result in homogeneous ADCs. These alterations can be mediated through enzymatic conjugation or insertion of reactive cysteine or chemoselective functional groups of unnatural amino acid residues into the antibody protein sequence . The covN-succinimidyl-4-(2-pyridyldithio) butanoate (SPDB), N-succinimidyl-4-(2-pyridyldithio)-2-sulfo butanoate (sulfo-SPDB), maleimidomethyl cyclohexane-1-carboxylate (MCC), and hydrazone . The. The2]. The mechanisms of resistance to ADCs are complex, and may be influenced by multiple factors. Primary resistance to ADC based therapy may occur. However, even in initially responding patients as treatment continues, as is the case for T-DM1, acquired resistance inevitably develops ,71. A crChronic in vivo exposure to ADCs has generated multiple resistance models of interest. Using continuous exposure to trastuzumab-maytansinoid ADCs (TM-ADC), a TM-ADC resistant breast cancer cell lines 361-TM and JIMT1-TM was developed which allowed for proteomic and transcriptional profiling to be performed . This stIn genome-wide association studies of breast cancer cell lines following resistance to antimitotic agents such as paclitaxel and monomethyl-auristatin-E (MMAE), a striking breast cancer subtype sensitivity spectrum to these anti-mitotic agents was observed . Basal-lAntibody–drug conjugates are an exciting treatment strategy for cancer patients. Lessons learned from the development of approved ADCs are informing the selection of novel targets, linkers, payloads, and patient populations most likely to benefit from future ADC-based therapies. With more than 100 clinical trials currently underway, there is great promise for new therapeutic approvals in the future. Importantly, the field must address ways to widen the narrow therapeutic index of ADC-based therapies, which will drive the future development of this class of cancer therapeutics.