Playing an instrument may promote a parafunctional behavior within the cranio-cervical-mandibular-complex with unknown repercussions. The aim of this study was to find any association between the dental inter-arch relationship and the practice of a wind or string instrument.n = 50) and string instrumentalists (n = 27), had a lateral cephalogram taken to compare six cephalometric parameters following the Rickett’s analysis . The Fisher test was performed to compare, with a 95% statistical confidence, if both groups have similar frequency distributions for each cephalometric parameter.A sample of 77 musicians, divided in two groups of wind (p = 0.011).No statistical differences were found for the maxilla position, mandible position, facial type, skeletal class and upper incisor inclination. Statistical differences were found for the lower incisor inclination (Playing a wind instrument showed to have little orthopaedic influence at the craniofacial morphology, on contrary it may influence the lower incisor inclination with its osseous base. Craniofacial morphology has been associated to distinct aspects such as genetic factors and the influence of the local environmental factors, where the activity and function of the masticatory system is important . Some auIf one takes in consideration that the masticatory system is part of a complex neuromuscular system that coordinates different tasks, which primary functions are mastication, swallowing and speech, and the secondary functions are breathing and the expression of emotions. For this purpose one should bear in mind that the activity of playing a wind or a string instrument can promote a parafunction within the cranio-cervical-mandibular-complex (CCMC), since playing a saxophone means that there will require an isometric contraction of the masseter muscle while stabilizing the mouthpiece within the orofacial structures. In the same way, a string instrument like for example a violin requires an asymmetrical pattern and activity of postural muscles such as the sternocleidomastoid muscle during musical performance. Beyond this, during the stabilization of the violin, there will probably be an hyperfunction of the elevator muscles of the masticatory system, since it is common to clench his teeth while holding the instrument between the angle of the mandible, the shoulder and the chest .The central nervous system coordinates these different functions of the stomatognathic system, which in some cases can be altered by several factors, such as dental occlusion, neuromuscular dysfunction, periodontal mechanoreceptors and pain .Some attempts have been made in the past in order to associate musical performance and an eventual effect on modifying facial morphology when playing violin and viola . On anotTherefore it would be interesting to study if there is any association between the dental inter-arch relationship and the practice of a specific wind or string instrument. The purpose of this study is to compare six cephalometric parameters between wind and string instrumentalists.n = 50; 64.9%) and 27 string instrumentalists . The majority of musicians were associated to the orchestra national of Porto, Casa da Música, and the students from a Master of Science degree in Music and Performing arts from Superior School of Music and Performing Arts of Oporto (ESMAE). The wind instrumentalists were clarinetists (n = 10), saxophonists (n = 8), bassoon players (n = 4), tuba players (n = 4), trumpeters (n = 10), oboists (n = 1), french horn players (n = 4), trombone players (n = 4) and flautists (n = 5). The string instrumentalists were violinists (n = 14), violoncellists (n = 6), viola players (n = 4), contrabass players (n = 2) and one pianist. The inclusion criteria were adults (> 18 years old), with more than 10 years of experience and no prior orthodontic treatment nor any history of maxillofacial surgery neither mandibular injuries.The present study was composed by a sample of 77 participants, 50 wind instrumentalists  plane was parallel to the floor. To all participants was given a lead vest to minimize the radiation exposure.To ensure intraexaminer reliability, all measurements were repeated in a number of cases by doing again the analysis. For each lateral cephalogram it was determined six cephalometric parameters following the Rickett’s analysis Fig. :Fig. 2R1) maxilla position with the base of the skull  mandible position with the base of the skull  and the Frankfort horizontal plane);3) facial type ;4) sagittal skeletal relation  ;5) upper incisor inclination in relation with the maxilla upper mandibular incisor inclination – angle formed between the incisor’s axis and the plane A-Pog);6) lower incisor inclination in relation with the mandible (lower mandibular incisor inclination – angle formed between the incisor’s axis and the plane A-Pog);The IBM SPSS Statistics version 24.0 was used to obtain the variables distribution and posteriorly perform a Fischer exact test to compare the wind instrumentalists group with the string instrumentalists group searching for differences in the variable distribution. The null hypothesis (H0) for the present study was "wind and string instrumentalists have equal cephalometric parameters". Thus, the alternative hypothesis (H1) stated wind and string instrumentalists have differences regarding cephalometric parameters.The participants were students of the Escola Superior de Música e Artes do Espectáculo (ESMAE), aged between 18 and 31 years old, playing their instrument for a minimum of 10 years and reported playing approximately 4 to 5 h per day, 6 days per week. From 77 participants, 50 participants played wind instruments and 27 played string instruments. The group of wind instrumentalists was composed by 36 male and 14 female participants. On contrary, the group of string instrumentalists were 21 females and 6 male patients.n = 54; 70.1%); for the mandible the majority had promandible , regarding the facial type most of the sample showed to be brachifacial  and having a skeletal sagittal Class II . For the upper incisor the majority of the sample showed to have a pro-inclination regarding the osseous base (maxilla) ; and the lower incisor a orthoposition in relation to the osseous base (mandible) .Table p = 0.642), , ,  and  respectively, Table p = 0,011), Table Subsequently, it was evaluated the influence of the type of instrument, wind or string, on the orofacial parameters that were revealed from the cephalometric analysis. A Fisher test was used to compare the six variables between wind instrumentalists and the string instrumentalists. No statistically significant differences were found when compared the maxillary sagittal position, the mandibular sagittal position, the facial type, the sagittal skeletal class and the upper central incisor position , and when subdividing the sample in wind and string instrumentalists it was possible to observe a similar size of 68% and 74% respectively. Regarding the sagittal position of the mandible, there was a higher difference in the percentage of this occurrence between each group. The wind instrumentalists presented 36% of individuals with orthomandible and 56% with promandible, whereas the string instrument players presented uniforme distributions with 48 and 48%, for the orthomandible and promandible. None of these results were statistically significant, nevertheless there is a higher percentage of individuals with promandible in the wind instrumentalist group compared with the string instrument players.The results of our findings demonstrated that the majority of the sample had promaxillary (Regarding the facial pattern of these instrument players, it was possible to observe that both groups had higher percentages of brachyfacial individuals. A previous study highlighted that viola and violin players had a higher prevalence of brachyfacial individuals, manifested by smaller facial heights, greater lengths of mandibular corpus than in controls , which wAccordingly to the relationship of the upper incisor or the lower incisor with the maxilla and mandible, respectively, it was possible to verify that there was statistical significance on the last parameter. The string instrumentalist group showed to have a higher prevalence of the lower central incisor orthopositioned in relation to the mandible.In relation to the eventual issue of the teeth position, more specifically the central incisor and the embouchure of wind instrumentalists it has been emphasized that tooth irregularities have a negative influence on embouchure comfort and performance. Van der Weijden concluded for example that a Class I relationship without malocclusion seems appropriate for every type of wind instrument and the association of these distinct factors to musical performance is still limited .The current investigation is in agreement with these two issues mentioned above, in the sense that eventually a Class I relationship without malocclusion would be more favorable for the practice for any kind of wind instrument, single reed instrument, double reed instrument or brass instrument. However, there is still lack of sustained information regarding the embouchure mechanism of wind instrument players and the orofacial issues, in particular their craniofacial morphology. Therefore the results are compared with the systematic review of van der Weijden, it is possible to report that there isn’t an accordance regarding the fact reported that the more extreme the malocclusion, the greater the interference with wind instrumentalist’s performance and embouchure comfort. Theoretically this is true, but within the sample of our study we could confirm that 66% of the wind instrument players presented a Class II malocclusion. Dividing the sample into groups of each wind instrument is was possible to notice that in the group of 10 trumpet players, 6 musicians presented a Class II malocclusion, comparing to the group of 10 clarinet players where 8 musicians presented also a Class II malocclusion.Nevertheless, even with this prevalence of Class II malocclusion within the clarinet players, there could be a dolichofacial pattern associated to counter clockwise mandibular rotation that occurs during the embouchure mechanism. This could provide a greater vertical growth in the molar region, since the mouth is maintained in an open position. But what really happens in terms of craniofacial morphology is the opposite, since within our sample of 50 wind instrument players, there are only 20% of musicians with a dolichofacial pattern, where the majority present a brachyfacial pattern 66%.If one follows the functional and biomechanical reasons to explain the craniofacial morphology of wind instrument players, all brass instrumentalists could eventually have a Class III malocclusion since the embouchure mechanism involves the protrusion of the lower jaw to obtain the most convenient alignment between the upper and lower jaw during the performance. On our sample two trombone players were classified with a Class I malocclusion, two trombone players with a Class II malocclusion, four trompa players with a Class I malocclusion, one tuba player with a Class I malocclusion, three tuba players with a Class II malocclusion, and from the ten individuals that played trumpet only one presented a Class III malocclusion.The cephalometric measurements of our research do not confirm that playing a wind or string instrument can influence the craniofacial morphology, since the only statically significant result obtained was the position and relation of the lower central incisor with the osseous base. There are many factors that can contribute for the dental inter-arch relationship, in the three planes of space, namely the sagittal inter-arch relationship, the transversal dimension or the development of anterior open bite or overbite. The wind instrument players can play and practice for more than 5 h per day, nevertheless this time can’t be considered as a continuous force in order to produce or stimulate facial growth or its development. Moreover, it is important to highlight, that in both, wind and string groups, a heterogeneity in the number of the different instruments was present. Ideally, the number of each type of instrument should be similar.In order to be able to affirm that playing an wind instrument may influence the craniofacial morphology of an individual, there would be the need to provide longitudinal studies where the investigation should be targeted to carry out lateral cephalograms and dental casts to young musicians at the age they start playing their musical instrument, which normally occurs around the age of 7–9 years old, and compare with a control group that does not play any kind of musical instrument. These individuals should be followed until the age of 14–15 years old, where all the permanent teeth would be erupted, not taking in consideration the wisdom tooth.It is known that there are damaging oral habits like thumb sucking, tongue thrusting or finger sucking with significant repercussions on the development of a malocclusion. The clinician should try initially to eliminate these noxious habits by non-invasive means and then implement an orthodontic treatment, if necessary . There cThe findings of this work are in accordance to the research carried out by Rindischeber et al., where the cephalometric analysis of 61 wind instrumentalists showed similar, normal facial morphology within all groups . HoweverWhen studying the craniofacial morphology of wind instrumentalists, some crucial aspects should be considered regarding the time of duration of the applied force, the intensity and the direction. For example, a saxophone player will promote more intrusive forces on the lower incisal teeth due to the embouchure since there is a labioretrusion of the lower lip over the central incisors, comparing for example with the lateral forces applied by the mouthpiece of a trompa player on the lower central incisors . Other sFor both groups a higher prevalence of Class II malocclusion, brachyfacial type, promaxillary and a pro-positioned upper incisor was found. Similar percentages of orthomandible and promandible was found in both groups. The only statically significant difference found between both groups was the position and relation of the lower central incisor with the mandible, which was more orthopositioned in the wind instrumentalist group.Playing a wind instrument may have little dentofacial influence in the characteristics of an individual, on the other hand insignificant and/or none craniofacial influence.


The aim of this study is to explore mania as a network of its symptoms, inspired by the network approach to mental disorders.Network structures of both cross‐sectional and temporal effects were measured at three time points  in a sample of 100 involuntarily committed patients diagnosed with bipolar I disorder with severe manic features and hospitalized in a specialized psychiatric ward.Elevated mood is the most interconnected symptom in the network on admission, while aggressive behavior and irritability are highly predictive of each other, as well as language‐thought disorder and “content” . Elevated mood is influenced by many symptoms in the temporal network.The investigation of manic symptoms with network analysis allows for identifying important symptoms that are better connected to other symptoms at a given moment and over time. The connectivity of the manic symptoms evolves over time. Central symptoms could be considered as targets for clinical intervention when treating severe mania. In this work, manic symptoms are studied as a network of mutually influencing components in a sample of severe inpatients. Elevated mood is the most interconnected symptom. Elevated mood is the most interconnected symptom in the manic network on admission.Aggressive behavior is connected to irritability, and language‐thought disorder to abnormal content.The network approach can be useful to monitor the evolution of the connectedness of manic symptoms over time.The assumption of stationarity may be violated in our sample.Spearman's rho was used as correlation input instead of Pearson's correlation coefficient.Only three time points were measured in this study.1Investigating the way symptoms of mental disorders arise in a given patient or group of patients has always been a domain of interest in psychiatric research  is studied as a measure of absolute connectivity and therefore importance in the network structures  with a manic episode of a bipolar I disorder; the hospitals that perform such an expert psychiatric assessment in the Brussels region of Belgium use the criteria in the DSM‐IV as a reference for reporting the diagnosis; the patient undergoes the psychiatric expertise in one hospital and is then transferred to another hospital for the involuntary commitment, which lasts for 40 days on average. As a routine, blood and urine analysis were realized as part of the early clinical evaluation of patients to exclude a manic symptomatology secondary to another problem . All included patients were not taking any treatment on admission, either because they stopped treatment themselves before admission, or because they never had treatment before: only 6% of patients were committed for the first time, while the rest (94%) of patients were committed before.All manic patients included in the study were examined and followed by the same psychiatrist (the first author of this study) and were treated with a standard set of drugs following the local protocol for the treatment of manic patients: In the first stage of treatment , an association of typical and atypical antipsychotic drugs is administered, with a mood stabilizer as well as soporific and anxiolytic drugs when necessary and depending on the symptoms presented. In the second stage of treatment and when the clinical presentation is stable, patients are usually left with an atypical antipsychotic, a mood stabilizer, as well as a soporific and/or anxiolytic drug when necessary. The data sets supporting the findings of this study are available from the corresponding author upon reasonable request.2.1.2t0), halfway through the commitment period , and on discharge . The symptoms assessed . The packages needed for the analyses were bootnet , is a partial correlation network and is estimated as the inverse of the variance–covariance matrix. Three separate GGMs were estimated for t0, t1, and t2. GVAR can be seen as a multivariate multiple regression on the previous measurement occasion; a nonzero element in the temporal matrix means that a given variable B is predicted over time by another variable A: this prediction is known as Granger causality  recently introduced in the network literature Epskamp,  was usedGranger, , because2.2.3Node predictability was estimated for the 11 symptoms in the three data sets and is reported in Table t0 and t1, t1 and t2, and t0 and t2.The Network Comparison Test (NCT) was performed to compare global strength in the three networks , with the reference number CE 2020/39 and the project title “Modeling severe mental disorders using complex systems and machine learning.”33.1M = 44.5, SD=:14.5); 47% of them were female, and 53 of them were male. The descriptive statistics for each symptom at each time point are reported in Table Patients were 20–72 years old  and language‐thought disorder is the most interconnected node at t1 and t2.Network predictability estimates for the three time points are reported in 3 and represented in Figure t0 and t2 (p = .0014); however, t0 and t1, as well as t1 and t2 do not present significant differences in global strength .The NCT reported a statistically significant difference between the global strength of 3.4The temporal network estimated through the GVAR adapted for panel data is represented in Figure 4This work tackled the important issue of the study of mania as a network of symptoms while using the YMRS to assess manic symptoms.t0 and t1 means that, when mania is untreated, the two symptoms are highly predictive of one another; if patients are irritable, then there will be a high chance for them to be aggressive as well, and vice versa. However, at t1, aggressive behavior becomes predictive of insight instead; this can be interpreted as a hint that stable manic patients that tend to have no insight, also tend to show greater signs of aggressive behavior , controlling for all other symptoms in the network.The strong connection between aggressive behavior and irritability at the two time points Language‐thought disorder and content are highly connected at all time points; from a network point of view, this means that the presence of an abnormal communication with the patient  may suggest that there is an ongoing delusional or hallucinatory process (and vice versa), controlling for all other symptoms. The association of language‐thought disorder and delusions or hallucinations is recurrent in the literature; recent works on psychosis also suggest that the less patients are in control of their thought process, the more likely they suffer from hallucinations , it informs the clinician that when the patient is stable (t2) a loss of insight (if not present before) or an increased energy may be the sign of a rising mood; this finding is supported by recent literature  all other symptoms in the network at t0. However, when patients become more stable, language‐thought disorder becomes the most predictable node in the network; this is likely due to the strong connection that it shares with content, which also presents high estimates at t1 and t2. This phenomenon is described as “centrality corruption”  is also highly connected at t0 and t2. This means that the network presents a different connectivity at the two time points; it is worthy of note that the networks compared by NCT are slightly different than the ones estimated, because they have regularized partial correlation estimated with a Pearson's rho input. Although other comparisons between network structures are described in this work, the results from NCT offer supplementary arguments in favor of a difference in network structures on admittance and discharge.The NCT reported a statistically significant difference between the global strength of networks at Granger causal effects were explored in the temporal network. Three examples of variables that receive many effects are sleep, increased motor activity and mood. From a network point of view, it is interesting to see how other variables have temporal effects on mood, which from a categorical perspective, is supposed to be the direct representation of the cause, the mood disorder itself; mood receiving many incoming edges in the temporal network supports a complex system view of the concept of mania.t0 and low scores at t2: this leads to a less good model fit. Second, and for the same reason regarding patient scores, Spearman's ρ correlation was used instead of Pearson's as an input for the GGM because the distribution of the data is skewed. Third, although GVAR can be estimated from three measurement occasions, it would be optimal to obtain a sample with many more time points, and with more subjects to obtain more accurate estimates. Fourth, all patients included in this study were examined by one physician, which is a potential limitation for the reliability of the measures obtained from the routine YMRS interviews. Five, the nature of our patient sample is extremely rare, which is likely to limit the generalizability of our findings.The results of this work should be interpreted in light of five limitations. First, the assumption of stationarity (the variables have the same mean and the same standard deviation over time) is likely violated in our sample, because patients present high scores at 5This study expanded the network theory of mental disorders to mania. Manic symptoms were interconnected in a network structure, and specific associations between symptoms, both static  and dynamic  nature were investigated, as well as the importance of symptoms in the self‐determination of the network. Future work may endeavor to replicate our results in other population, as well as in patient with a less severe condition.None.Giovanni Briganti and Charles Kornreich collected the data. Giovanni Briganti performed the statistical analyses and drafted the manuscript. Paul Linkowski and Charles Kornreich reviewed the manuscript.https://publons.com/publon/10.1002/brb3.2010.The peer review history for this article is available at


ObjectiveZenker’s diverticulum is a pulsion outpouching from the posterior pharyngeal wall. The anatomy of the wall has been proposed to be dehiscent in the region of the Killian’s triangle, between the thyropharyngeus muscle  and the cricopharyngeus muscle. A dehiscence is a bursting open, splitting or gaping along natural or sutured lines. To the best of our knowledge, there have not been any studies to histologically analyze the posterior pharyngeal wall and the exact location of the dehiscence. We thus aim to determine the presence and characteristics of this area of possible dehiscence. MethodsFifty-eight cadavers were analysed. A portion of tissue was excised within the borders of the Killian’s triangle, being the inferior border of the oblique inferior constrictor muscle and the superior border of the cricopharyngeus muscle from the posterior wall of the pharynx. Four longitudinal sections were sampled from each cadaver including: left lateral, left medial, right medial and finally the right lateral aspect of the posterior pharyngeal wall. These samples were then embedded in wax and cut with a microtome at 5 microns. They were then placed on microscope slides and stained with Haematoxylin and Eosin and analysed in terms of thickness and histology.ResultsThere was significant overlapping of the thyropharyngeus and cricopharyngeus muscles seen macroscopically in all cadavers that were dissected. No obvious area of dehiscence was found in any of the specimens, however, there were variations in the thickness of the posterior pharyngeal wall within the thyropharyngeus muscle. When comparing the left- and the right-hand sides of the thyropharyngeus, the mean measurement of the left medial muscle sample was found to be significantly thinner than the mean measurement of the right medial muscle sample . The average of both the thickest and thinnest muscle measurements for each of the four samples was then compared. The average left medial muscle layer was found to be significantly thinner than the average right medial muscle layer .ConclusionThis study demonstrated that the left thyropharyngeal muscle was thinner than the right. However, no dehiscent areas were found in any of the specimens. Significant overlapping of the cricopharyngeus and thyropharyngeus muscles was noted. Thus, we propose that the hypopharyngeal pouch, given enough intraluminal pharyngeal pressure, may occur between the fibres of the inferior pharyngeal constrictor muscle rather than between the cricopharyngeus and the inferior pharyngeal constrictor muscles. As a dehiscence occurs between a natural or sutured line, of which there is neither in the thyropharyngeus muscle, we propose that the term Killian’s dehiscence is a misnomer and that the defect instead meets the definition of a hernia.  Zenker’s diverticulum is an outpouching of the mucosa and submucosa located in the posterior hypopharyngeal wall ,2. The eThe area that has been postulated to be the site is known as Killian’s dehiscence as described by Killian in 1908 .Killian stated that the triangle is always present yet some authors argue that there is considerable overlap of the inferior constrictor muscular fibres and thus have cast doubt regarding the presence and prevalence of the dehiscence ,2.Cadaver studies have demonstrated the replacement of normal muscle and connective tissue with fibro adipose tissue ,5. This To the best of our knowledge, there have not been any studies to histologically analyse the posterior pharyngeal wall to explore the exact location of the Killian’s dehiscence. We thus aim to determine the possible location for the development of a Killian’s Dehiscence. Fifty-eight cadavers  were dissected in the study after approval from the University of the Witwatersrand Human Research Ethics Committee number W-CJ-140604-1. A portion of tissue was excised from the posterior pharyngeal wall and from the inferior border of the oblique inferior constrictor muscle till the superior border of the cricopharyngeus muscle.Four longitudinal sections were sampled from each cadaver, including left lateral, left medial, right medial and finally the right lateral aspect of the posterior pharyngeal wall. The adventitial layer of the pharynx was inked with red or green in order to identify the left and right sides of the excised portion of the tissue during later histological analysis Figures  and 2. L The 28 male cadavers had a mean age of 81 years and a mean height of 173 cm. The 29 female cadavers had a mean age of 76 years and a mean height of 162 cm. All cadavers were Caucasian in ethnicity. No obvious area of dehiscence was found in any of the specimens; however, there were variations in the thickness of the muscle of the posterior pharyngeal wall. The exact location of Killian’s dehiscence was not identified in any of the specimens as we found the muscle layer to be continuous. When comparing the left- and the right-hand sides, the mean of the thickest measurement of the left medial muscle sample was found to be significantly thinner than the mean of the thickest measurement of the right medial muscle sample .The average of both the thickest and thinnest muscle measurements for each of the four samples was then compared. The average left medial muscle layer was found to be significantly thinner than the average right medial muscle layer . Therefore, the muscular layer in the left medial aspect of the posterior pharyngeal was significantly thinner than the right medial muscular layer.The left lateral thickest muscle measurement was found to be significantly thicker than its corresponding left medial thickest muscle measurement . The left lateral thinnest muscle measurement was found to be significantly thicker than its corresponding left medial thinnest muscle measurement  and the average right lateral muscle sample was significantly thicker than the average right medial muscle sample . These measurements may be appreciated in Figure The pharyngeal pouch has been described over 150 years ago and was postulated to occur in a dehiscent area known as the Killian’s dehiscence as described by Killian [Kelly and Kuncl dissected 21 non-fixed specimens and only reported the triangle in six of those cadavers. They, however, did not strictly define the boundaries of the triangle. In our study of 58 specimens, no obvious area of dehiscence was identified during the time of cadaveric dissection . FurtherOne of the challenges that have previously been encountered was the variation described in the pattern of the hypopharyngeal muscles. We found the muscle fibres to be flat and thick in the overlapping regions, which was in keeping with studies done by Anagiotos. They described the existence of a triangle in 9 out of 15 males and 11 out of 32 females. However, none of their specimens had a pharyngeal pouch . There have been many theories as to why the pharyngeal pouch has a left-sided predominance. Killian attributed this due to the muscle wall weakness on the left side; this is in keeping with our findings, as we found a thinner left lateral wall in our study.The pharyngeal phase of swallowing depends on a pressure gradient from the oropharynx to the hypopharynx. During swallowing, a negative pressure is generated, at the junction of the hypopharynx and oesophagus, by elevation of the larynx. Coupled with this is the cricopharyngeal relaxation that allows the bolus to pass to the oesophagus ,9. CrossFrieling et al. described an abnormal sequence of pharyngeal and oesophageal contractions . BarthelSome authors have suggested a primary myopathy of the cricopharyngeus muscle Table . There mA hernia is a term used to describe a bulge or protrusion of an organ through the structure or muscle that usually contains it. To date, the exact pathogenesis of the pharyngeal pouch has still not been fully understood and rather been considered as a field of ongoing work. This study demonstrates that the left posterior pharyngeal wall muscle is thinner than the right. However, no dehiscent areas were found in any of the cadaveric specimens that were looked at, but rather the presence of considerable overlap between the thyropharyngeus and cricopharyngeus muscles. Thus, we propose that the term “Killian’s dehiscence” is a misnomer as there is no true anatomical dehiscence as there is no splitting along a natural line within the muscle. However, the area of weakness as a result of a thinner muscle layer may predispose to the formation of the pharyngeal diverticulum as a herniation through the muscle of the inferior pharyngeal constrictor given enough pharyngeal intra-luminal pressure. We propose the term: Maharaj-Fitchat hernia. Zenker’s diverticulum is a pulsion outpouching from the posterior pharyngeal wall. The anatomy of the wall has been proposed to be dehiscent in the region of the Killian’s triangle. In our study, no obvious area of dehiscence was found in any of the specimens, however, there were variations in the thickness of the posterior pharyngeal wall. The exact location of Killian’s dehiscence was not identified on any of the specimens. When comparing the left- and the right-hand sides, the mean of the thickest measurement of the left medial muscle sample was found to be significantly thinner than the mean of the thickest measurement of the right medial muscle sample. This study demonstrated that the left posterior pharyngeal wall was thinner than the right. However, no dehiscent areas were found on any of the specimens.


Titin, the third filament of striated muscle, has been largely neglected in mechanical models of the sarcomere. The titin filament spans the entire length of the half‐sarcomere forming extensible links in the I‐band of the sarcomere between the tips of the thick filaments and the Z‐band. These titin links act as molecular springs that account for the passive force response to stretch of a resting muscle ‐like domains in the I‐band titin might contribute to the mechanical work done by muscle during contraction . L.F. was funded by a Sir Henry Dale Fellowship awarded by the Wellcome Trust and the Royal Society (210464/Z/18/Z).


Comprehensive characterization of geometric distortions for MRI simulators and MRI‐guided treatment delivery systems is typically performed with large phantoms that are costly and unwieldy to handle. Here we propose an easily implementable methodology for MR distortion determination of the entire imaging space of the scanner through the use of a compact commercially available distortion phantom. The MagphanRT phantom was scanned at several locations within a MR scanner. From each scan, an approximate location of the phantom was determined from a subset of the fiducial spheres. The fiducial displacements were determined, and a displacement field was fitted to the displacement data using the entire multi‐scan data set. An orthogonal polynomial expansion fitting function was used that had been augmented to include independent rigid‐body transformations for each scan. The rigid‐body portions of the displacement field were thereafter discarded, and the resultant fit then represented the distortion field. Multi‐positional scans of the phantom were used successfully to determine the distortion field with extended coverage. A single scan of the phantom covered 20 cm in its smallest dimension. By stitching together overlapping scans we extended the distortion measurements to 30 cm. No information about the absolute location or orientation of each scan was required. The method, termed the Multi‐Scan Expansion (MSE) method, can be easily applied for larger field‐of‐views (FOVs) by using a combination of larger phantom displacements and more scans. The implementation of the MSE method allows for distortion determination beyond the physical limitations of the phantom. The method is scalable to the user’s needs and does not require any specialized equipment. This approach could open up for easier determination of the distortion magnitude at distances further from the scanner’s isocenter. This is especially important in the newly proposed methodologies of MR‐only simulation in RT and in adaptive replanning in MR linac systems. Patient dependent distortion arises from magnetic susceptibility and chemical shift effects and is more difficult to correct for than system dependent distortion. For this reason, the distortion correction efforts have mostly been concentrated on the latter. Patient dependent distortion is generally also smaller in magnitude.System distortion arises due to inhomogeneities in the main magnetic field and due to nonlinearities in the induced gradient fields.Another issue arises with MR‐only simulation in the context of stereotactic body radiation therapy (SBRT) treatments. During these treatments the use of non‐coplanar beams are often utilized with beams entering and exiting the patients at much greater distances from the isocenter than what is seen in conventional treatments. With increased distance from the isocenter the distortion increases and therefore a need for accurate knowledge about the distortion at these off‐target locations is essential to properly account for and protect organs‐at‐risk.Phantoms are commercially available to accurately quantify MRI distortions. One of the challenges that remains, however, is measuring distortion over the entire MR bore in a practical way. Liquid‐filled phantoms large enough to cover torso‐sized regions of the bore are heavy to handle and unwieldy in a clinical environment. Nonliquid‐filled phantoms can cover a larger region, but are often still unwieldy due to their weight, and perform only distortion measurements. It is desirable to have a phantom that is small enough to be handled easily in a clinical environment and can perform a multitude of measurements so that an overall quality control protocol is simplified.Here we propose an easily executed methodology for enlarging the dimensions of the MR distortion map through the use of a compact commercially available distortion phantom. By importing several off‐center locations into the distortion calculation algorithm, we can effectively emulate a whole‐bore phantom for accurate, reproducible, and practical determination of distortion beyond the physical volume of the phantom and facilitate the increased use of MRI in radiation therapy.22.AThe MagPhanRT phantom  was used in this study Fig. .12 TheThe phantom [Fig. 2 with a slice thickness of 1 mm. The scanning FOV was chosen to encompass the entire phantom (380 x 300 x 240 mm3 with 240 slices in the Superior/Inferior (Sup/Inf) direction). The 3D vendor supplied distortion correction was applied to all scans. A Siemens Biograph mCT 128 slice CT scanner  was used for internal sphere position verification . The geometric distortion of the CT scanner was evaluated with the CatPhan phantom  and was determined to be 0.1 mm using the vendor recommended protocol.The phantom was scanned in a Siemens 3T Skyra Magnetom MRI scanner  using the body coil and a gradient echo 3D sequence (TurboFLASH) with TE, TR, and flip angle of 2.44 ms, 2200 ms, and 13 degrees, respectively. The pixel size was 1.5625 × 1.5625 mmThe phantom was placed on the table such that the dimensions of larger coverage lay in the axial plane. The FOV was extended in the third direction with two additional scans, one with the patient table translated five centimeters in the Sup direction, and one with the patient table translated five centimeters in the Inf direction.2.BA workflow chart of the Multi‐Scan Expansion (MSE) methodology of going from a single scan limited FOV (LFOV) to a multi‐scan extended FOV (EFOV) for distortion determination is presented in Fig. 2.B.1All calculations were performed with custom code written in Matlab, Rev 2016b . The fiducial spheres are segmented using Normalized Cross Correlation (NCC). The template for the NCC calculation is generated mathematically from the known dimension of the sphere on a grid. The grid density is five times higher than the voxel density in each of the three dimensions, for a total oversampling factor of 125. The final template pixels are generated by volume averaging at the original voxel size followed by down sampling to generate a template with voxel spacing matching that of the original image. This oversampling‐averaging gives the template sensitivity to partial‐volume effects from the finite voxel resolution. After locating the spheres approximately with NCC, the NCC function is interpolated by a factor of eight in each direction using sinc interpolation to provide sub‐voxel resolution on the sphere location.The segmented fiducial location determined within the image will be referred to as the ‘segmented’ location. The manufacturing tolerance of the sphere locations is approximately 0.5 mm. A CT scan of the phantom is performed to further increase the accuracy of the known locations, segmented with the same NCC technique, bringing the uncertainty down to approximately 0.2–0.3 mm for typical voxel dimensions. These locations then represent the best‐known position of the spheres, but in an arbitrary coordinate system different from that of the MR scanner. This coordinate system will be referred to as the ‘phantom’ coordinate system. A rigid‐body transformation  will be needed to know the actual location of the fiducials in the scanner. The means to obtain this transformation are described subsequently.2.B.2An approximate location and orientation of each phantom module within the scanner is determined by first locating three spheres of slightly larger diameter (1.5 cm) than the rest of the fiducial spheres. The spheres are located near the central portion of the phantom, arranged at the vertices of a scalene triangle so that they can be uniquely identified. The Orthogonal Procrustes algorithm is then used to generate a rigid‐body transformation that best transforms the locations of the three spheres in the phantom coordinate system to their locations in the coordinate system of the MR scanner. Because the phantom is itself a rigid body, this transformation can then be applied to any sphere in the phantom to transform its coordinates from the phantom coordinate system to the coordinate system of the MR scanner. The location of each fiducial sphere arrived at in this manner will be referred to as the ‘designed’ location. Because only three spheres are used, and because the sphere locations are subject to geometric distortion in the scanner, the rigid‐body transformation computed thus far (and hence the ‘designed’ location) is still approximate. The residual error is in the form of a small rigid‐body transformation that, as explained below, will be determined from the full distortion fit and does not propagate to the distortion measurements.2.B.3Φmnpi areFrom the segmented locations of each sphere determined from the MR image, a displacement vector at each location is calculated as the difference between the segmented location and the designed location. The displacements are fit to an expansion of Chebyshev polynomials:Um is a Chebyshev polynomial of the second kind of order m, and Amnpi is the fitting coefficient to be determined for the Φmnpi basis function. The choice of polynomial basis is not critical for performing such a fit, as polynomial bases can be constructed from one another with simple linear combinations.In these basis functions, The appropriate order of the fit in Eq.  is goverAmong the basic functions described above are terms with no spatial dependence , which represent simple translations by a distance δ along the x, y, or z axes:Also among the basis function set described above are functions where each component is proportional to only x, y, or z, where either m, n, or p is 1 and the other two are zero. The subspace spanned by these components can be described in linear combination of these vectors that include terms to represent distortions corresponding to small, linearized rotations and translations. Small rotations about the x, y, and z axes, respectively, are represented by the following distortion basis functions:Thus, by simple rearrangement of the basis functions described in Eq. , the trad→x,y,z=∑d→x,y,z represents the difference between the apparent location of the point that appears in the image at location x,y,z and the designed location. The form of Eq. . The second term with the Rj basis functions represents a small rigid‐body rotation of all fiducials about the origin of the scanner coordinate system. The remaining terms represent geometric distortions of the image, rather than translations or rotations.Equation  is an exm of Eq.  has a siBecause of the approximate rigid‐body transformation performed using the Procrustes Algorithm on the three larger spheres, the translational and rotational terms in Eq.  will be Φmnp basis functions) are common to all image series, as they are a property of the scanner, not the placement of the phantom. We can thus extend Eq.  will yield.When combining multiple image series to extend the FOV, distinct translational and rotational terms need to be applied for each separate image series to account for the different phantom placements for each series. The distortion terms  in Eq.  in Eq. . DiscardAcquire multiple MR image series of the phantom, with the phantom placed in different locations, with substantial degree of overlap in coverage region. To get the translational and rotational information about the alignment system, one of the phantom acquisitions should be performed at the center of the device as determined by the alignment system.Segment the fiducials from each series to get an absolute (apparent) location of each fiducial markerFor each series, determine an approximate location of each phantom by any convenient method, such as using a small number of landmark fiducials that can be segmented easily.dk→ for each fiducial k, which is found within the image at location xk,yk,zk.Based on the approximate location of the phantom, use the known design of the phantom fiducials and the measured location within the images to determine a displacement measurement Perform the fit described in Eq.  to the cThe translational and rotational terms in Eq.  for the The translational and rotational components of all other series can be discarded, and the true distortion field of the MR scanner determined by the terms in the last summation in Eq. .The procedure is as follows:In applying the fit with Eq. , TikhonoIt is useful to understand the impact of center frequency on such distortion measurements. The center frequency is generally calibrated immediately prior to a set of acquisitions, with the actual patient or phantom in the scanner. Errors in the center frequency calibration manifest themselves as translations of the entire image in the readout direction, with a magnitude inversely proportional to the readout bandwidth. Generally, such translational errors will amount to a small fraction of a pixel dimension. In this technique, it is not important to recalibrate center frequency between phantom placements, as the translational and rotational information for all but the center placement is discarded. Any errors in the center frequency calibration will be reflected in the translational measurement of the displacements that comes out of the fit in Eq. .2.CTo evaluate the distortion correction accuracy, a high‐quality CT of the phantom was acquired. This CT image was used as our “gold standard” for determining the sphere fiducial locations within the phantom. The corrected MR images were compared against the CT image and the overall absolute difference in sphere fiducial position and the dependence of absolute difference in sphere position on distance from the MR isocenter was determined.A further evaluation of the distortion correction was performed based on the internal distances between sphere positions within the individual scans. In this evaluation, the EFOV vendor‐corrected MR images and the CT images were used directly. The EFOV vendor‐corrected images refer here to the use of the multiple translated sets of MR images, each of which has had the vendor’s standard built‐in distortion correction applied. The EFOV vendor‐corrected MR images were also further corrected based on the distortion determined through the MSE method. These images are hereafter termed MSE corrected MR images. The relative distances between the sphere positions within the two MR image sets were compared with that determined from the CT images. This eliminated the need to define the location of the phantom in any of the scans. The absolute difference in distance between each sphere between the MR image sets and the CT was calculated for all different sphere combinations (number of distances compared = n(n + 1)/2 − n (n = number of spheres)).33.A±5 cm translation in the Sup/Inf direction [Fig. The number of fiducial locations was increased from 505 to 1515 positions through the inclusion of a ± 0.2 mm. Two out of the 505 spheres had a positional difference of >1 mm (1.1 mm difference). The largest difference in the determined distortion was in the Left/Right (L/R) direction where the 95% confidence interval (CI) was −0.56 to 0.55 mm. The differences in the determined distortion in the Sup/Inf or in the anterior/posterior (Ant/Post) direction were more than a factor of 2 smaller.The differences in the distortion maps determined through LFOV and EFOV were evaluated by comparing the distortion at the sphere positions from the central phantom position in the EFOV data set to the LFOV data set Fig. . The phy3.BFigures To further validate the MSE method and to remove any assumption about the absolute position of the phantom, the internal distances between all fiducial spheres were determined for each image set for each phantom position Fig. . Again, 4±5 cm translation in the Sup/Inf direction to demonstrate our methodology. However, conceptually the methodology is not limited to translational offsets, nor is it limited to inclusion of only three scans, but can instead be scaled to the user’s requirement. The implementation is also independent of the direction of FOV extension. Because no information about the absolute position or orientation of each module is assumed, modules from different scans can be easily combined to enlarge the FOV of the distortion measurement.In this study we present an easily executed methodology for extending the FOV of the determined MR distortion map beyond the physical boundaries of a single phantom. The methodology increases the number of distortion sampling points, the FOV, and the resolution of the distortion sampling. This is achieved through image acquisition of a standard distortion phantom in different positions within the scanner. In this study, we used a ± 0.2 mm for the overlapping sphere positions. This is within the uncertainty of the segmentation . This demonstrates that the distortion fits in the overlap region for the two approaches are equivalent. We further demonstrate that the application of the MSE methodology for distortion correction allows for a twofold reduction of the residual mean geometric errors (as measured by inter‐sphere distances).The analysis of the distortion magnitude determined with LFOV and EFOV showed a mean difference in sphere position of 0.3 3 and a total weight of 5 kg, overcoming some of the limitations with these systems.The standard approach for determining the distortion map for the entire FOV of an MR scanner is through large phantoms that fill the entire bore. While these phantoms do allow for direct determination of the distortion map, the size and reported weights of up to 60 kg makes them practically hard to handle.Although this multi‐scan approach requires several acquisitions of the phantom, each taking several minutes, there are substantial practical benefits of enhancing the ability of a smaller phantom to cover larger FOV. Routine quality assurance protocols are often performed daily or weekly, and don’t necessarily require large FOV for the distortion measurement. A smaller phantom that can be used to cover the larger FOV on a less frequent basis has clinical value in keeping the primary quality control protocol efficient to execute, and prevents the need for an entirely different phantom to cover large fields of view.In our study we used distance measurements in 3D phantom images acquired at different offset from the isocenter as surrogate metrics for evaluating the accuracy of our propose method. An alternative, more direct analysis would involve a comparison between the distortion map determined through the proposed methodology and a distortion map determined through the use of a whole‐bore phantom. Along these lines, the validity of the FOV extension was verified in the region of overlap between the LFOV and EFOV data sets, but no such verification could be performed in the parts that extended beyond this region. Furthermore, only one specific imaging protocol was used for MR image acquisition in the current study. Different imaging protocols can have different distortion patterns. The largest difference between protocols arises from using 2D vs 3D acquisition techniques, particularly if the vendor distortion correction is not activated in the slice direction on a 2D acquisition. The other main source of protocol‐to‐protocol variation depends upon the relative importance of gradient nonlinearity vs static magnetic field inhomogeneity. Distortion arising from gradient nonlinearity will be largely independent of protocol (other than the 2D vs 3D issue), whereas distortion arising from static magnetic field inhomogeneity will be sensitive to readout bandwidth as well as the orientation of the scan plane and readout axis direction. The methodology presented here can be repeated to characterize distortion on a protocol‐specific basis.5In summary, we have presented a methodology that could enable the user to use a standard distortion phantom to extend the mapping of MR distortion beyond the physical dimensions of the phantom. The methodology is easily implementable using compact distortion phantoms readily available at most institutions, does not require any specialized equipment, and is scalable to the user’s needs. This approach could open up for easier determination of the distortion magnitude at distances further from the scanner’s isocenter, which is especially important in newly proposed methodologies of MR‐only simulation in RT and in adaptive re‐planning in MR linac systems.Richard Mallozzi and Joshua Levy are employed at The Phantom Laboratory Inc. Emil Schüler and Dimitre Hristov have nothing to disclose.


Organogenesis is a complex coordinated process of cell proliferation, growth, migration, and apoptosis. Differential growth rates, particularly during cardiogenesis, play a role in establishing morphology. Studies using stereological and cell sorting methods derive averages of morphogenetic parameters for an organ. To understand tissue composition and differential growth, the researcher must determine a number of morphogenetic parameters in the developing organ. Such measurements require sectioning to enable identification of organ borders, tissue components and cell types, three‐dimensional (3D)‐reconstruction of sections to visualize morphology and a 3D‐measurement scheme to build local morphogenetic information. Although thick the section confocal microscopy partially solves these issues, information loss at the section surface hampers the reconstruction of 3D morphology. Episcopic imaging provides the correct morphology but lacks histological procedures to identify multiple cell types. The 3D‐measurement scheme is based on systematic sampling, with overlapping sample volumes, of the entire organ in the aligned image stack. For each sample volume, morphogenetic variables are calculated and results projected back to the cube (boxel) at the sample volume center. Boxel size determines spatial resolution of the final quantitative 3D‐reconstruction whereas size of the sample volume determines the precision of the morphogenetic information. The methods described here can be used to measure tissue volume, proliferation and cell size, to determine contribution and distribution of cell types in a tissue and to display this information in a quantitative 3D‐reconstruction. Anat Rec, 302:49–57, 2019. © 2018 Wiley Periodicals, Inc. Growth, the increase in tissue volume, can be defined quantitatively in terms of increase in the number of cells or increase in the volume of cells or a combination of both. Therefore, measurements must be performed at both the tissue and cellular levels to determine the mode and extend of organ or tissue growth. Moreover, different cell types that make up the tissue may proliferate and increase in volume in a cell type specific way and thus contribute differentially and specifically to the growth of the organ as a whole. These different contributions may qualitatively and quantitatively change with ongoing development. Development of the heart is an excellent example in which researchers constantly struggle with the changing three‐dimensional (3D) morphology during development and the contribution of extracardiac cell populations. In short  to high (white). When section thickness is taken into account, the individual 2D‐pixels display the information contained in the 3D‐voxels of which the pixels are projections. The pixel value range occurring in unstained parts of the tissue is defined as background whereas foreground, or signal, is found in the specifically stained tissue components. Foreground and background areas both represent cross sections through 3D‐structures. Discrete 3D‐particles, for example, cell nuclei are represented by profiles in 2D‐images  11.5. Deformation as a result of the heating of the section is negligible. However, one should carefully check whether no parts of the tissue were lost during heating.The most frequently used detergents of this family are Triton‐X100, Nonidet‐P40, or Tween‐20. These detergents dissolve membranes and thus enable antibodies to penetrate the tissue more easily. However, the need for detergent treatment depends on the antibody and the developmental stage of the embryo. Most antibodies that require detergent treatment to successfully stain embryos at late developmental stages do not require such treatment when sections are taken from embryos of E12.5 or younger. When detergent treatment is essential for young embryos the detergent concentration should be carefully titrated to avoid dissolving the tissue.To reduce the amount of antibody required, incubations are performed in droplets covering the sections with slides lying flat. A hydrophobic barrier surrounding the section is applied with a PaP pen  to keep the droplets of incubation medium in place. It is essential to prevent evaporation of the medium by placing the slides in a humidified closed box. Washing steps in between incubation steps should be done in large volumes and are most easily and efficiently performed with the slides placed in a rack, allowing easy repetitions.The dilution factor of an antibody depends on the specific antibody and should be determined in a pilot experiment. The optimal antibody concentration is the dilution which shows the strongest specific staining intensity in combination with the lowest background staining. To increase this difference one can consider increasing the salt concentration (up to 1.5 M NaCl) or decreasing the incubation temperature down to 4°C. To allow comparison between positive and negative tissue compartments, it is essential that the sections used for testing dilutions comprise both tissue compartments.All incubation steps using fluorescent dyes have to be performed in the dark to protect the dyes from photobleaching. To avoid photobleaching the stained sections should be stored in the dark at 4°C and the number of excitation and emission cycles should be minimized. Further reduction of the fluorescence as a result of chemical bleaching of the fluorescent dyes can be achieved by using an anti‐fading mounting medium. Many (expensive) commercial media are available but a cheap alternative is supplementing 1% 1,4‐diazabicyclo[2.2.2]octane (DAPCO) and 50% glycerol to phosphate‐buffered saline pH 7.6. Finally, image acquisition should be performed in a standardized way to avoid bleaching differences between sections.If the specificity and/or intensity of the staining are not good enough to enable efficient identification of cell types, tissues or organ compartments, the entire series of the sections should be discarded and the staining should be repeated on newly fixed or sectioned tissue.Images of each of the sections containing part of the organ are acquired using a microscope and camera setup  of the image .The choice of the filter set is important for the recorded fluorescence to be specific for the applied staining. In a fluorescence microscope a filter set comprises three components: an excitation filter, a dichroic and an emission filter. The fluorescence filter set should be carefully selected to allow the recording of the specific excitation of the interrogated fluorophore only. Bleed‐through of other fluorophores present in the same section should be carefully accessed, to avoid over‐projection or identifying false positives. In Table X–Y movement of the scanning stage needs to be calibrated with the X–Y matrix of the CCD camera. The overlapping regions are then cropped off by the software. For easy acquisition of large image sets (many sections and slides) custom made macros can be used. Especially when tiling and stitching is applied, chromatic aberration should be evaluated when a large difference between the wave lengths of the emitted light of the used fluorophores is present and when also the periphery of the lens is used.When the tissue area is larger than the field of view, you have to use tiling and stitching software, like the IPP Stage Pro module (MediaCybernetics), to acquire sets of overlapping images. When tiling and stitching is performed, the In our experience, storage of the image files that are generated in the course of a quantitative 3D‐reconstruction requires a folder structure that starts with a main folder identifying the original specimen and then forms a tree structure that parallels the generation of different types of images in the quantitative 3D reconstruction process. Such a folder structure provides easy systematic access to the original images as well as the aligned, segmented, and 3D‐Tiff images for each of the fluorescence channels that are recorded. This folder structure helps to keep track of the images, and thus enables the researcher to back‐track in case the quality check indicates that some steps in the procedure should be redone.To reconstruct the 3D morphology of the original organ and the distribution of cell types in this organ, the 2D images of the sections have to be processed into a 3D‐reconstruction and the different cell types have to be identified in these sections. The 3D image analysis and reconstruction procedures thus consist of 3D‐alignment, segmentation and identification of specifically stained nuclei Fig. .http://www.amira.com), has to be used. Alignment of the images, acquired from the stained sections, should be done on as many tissues or organs as possible, not just the organ‐of‐interest. Most often alignment can be done on the image showing the background staining or the auto‐fluorescence of the tissue. The 3D‐reconstruction software performs an iterative procedure to align pairs of images. A narrow range of gray value within the tissue background often suffices to obtain a satisfactory result. When the 3D alignment is optimized and of sufficient quality, the alignment parameters can be saved and applied to the images of the other channels, because these channels were recorded without moving the microscope stage.To create a 3D‐reconstruction from sections, a dedicated software package, like Amira  or 16‐bit  images. When the camera does not generate images in these formats, image conversion has to be performed. In case the camera produces 12‐bit  images, these have to be converted to 16‐bit images without stretching, to avoid change or loss of information. In the resulting 16‐bit image only the first 4,096 gray values are used; the unused gray values can be ignored in the display of the images in the 3D‐reconstruction program.http://www.bulkrenameutility.co.uk).Frequently the software can only handle consecutively numbered images. This may require that the researcher renumbers the stack of images for each channel. For easy renumbering of images freely available programs can be found online, for example, Bulk Rename Utility ; the overlapping structures in the two consecutive images are then shown in white. This inspection and correction is most efficiently done by starting with the middle section pair of the image stack and working both directions. When too many artifacts due to tissue compression are identified, the image set should be discarded and one should start with sectioning a new specimen.For the 3D‐measurement procedure, the tissue‐of‐interest should be identified in the images of the channel in which this tissue is specifically stained. This process of dividing an image into different regions  is referred to as image segmentation. Segmentation effectively converts the grayscale images into binary images in which the tissue (or stained structure) of interest (foreground) is distinguished from the remaining tissue and glass (background).In a stack of aligned images the specific signal of stained tissue  can be isolated from the background using a threshold‐mode in the 3D‐reconstruction software. Since the staining intensity may vary between sections, it may be necessary to set this threshold manually per image: the selected area is often shown in a contrasting color and should match the area of the stained tissue‐of‐interest. When a threshold is automatically set a quality control per image is needed. If no specific staining is available, one can manually trace the tissue‐of‐interest. This is not preferred as it is laborious and prone to bias introduced by the researcher.Differences in staining intensity between sections may severely hamper the consistency of the segmentation. When many images require adjustments of the segmentation threshold or when manual tracing of the tissue‐of‐interest is required it is better to discard the images and restart the staining procedure.The 3D‐measurement protocol it based on the identification and counting of nuclear profiles to allow the calculation of labeling index or cell density. The identification of all nuclear profiles in histologically stained sections is often hampered by gradients in staining intensity that inevitably occur. These staining gradients can be removed by subtracting a low pass filtered image (in our case a 10 × 10 pixel filter) from the original image resulting in a local‐maxima image. In this local‐maxima image a threshold can then be set to select the nuclear profiles. The threshold depends on the size of the kernel used in the low pass filter which needs to be set such that in the image the majority of the objects will be single nuclear profiles. In some cases, especially when nuclei are densely packed and the sections are relatively thick, nuclear profiles will overlap. In that case, image analysis functions to split such clusters of profiles, based on the curvature of their boundaries or the pixel value profile of their interior, are available in most image analysis packages. The 3D‐Measurement Toolbox applies such a function and divides profiles in two when the profile area is larger than twice the median profile area  and is then determined as a multiple of the section thickness.The size of the sample volume depends firstly on the required number of profiles that has to be counted to reach sufficiently precise results and secondly on the desired spatial resolution of the morphogenetic information displayed in the quantitative 3D‐reconstruction. The required size of the sample volume is especially important for a valid estimation of the labeling index. Given the ever‐present biological variation of about 30%, a labeling index of 0.5 ± 0.075  was considered to be sufficiently precise. Such a precision can be reached when 171 profiles are counted per sample volume  the area of the tissue‐of‐interest, (2) the total number of profiles of nuclei, and (3) the number of profiles of the specifically stained cell type. Nuclear profiles are only counted within the tissue of interest within the sample volume. The values of morphogenetic variables that are measured for each sample volume, are converted into gray values and projected to the center boxel of the sample volume in the image stack. For each variable, this results in a 3D‐Tiff image in which the stack of images is represented as a 3D‐matrix of pixel values  can be derived from the number of profiles per tissue area ), the section thickness (t) and mean nuclear diameter (D) according to Abercrombie's equation . This equation corrects for the fact that the observed number of nuclear profiles per unit area in a section depends on the number of nuclei per unit volume, the size of the nuclei and the thickness of the section. It therefore also corrects for the fact that some nuclei may be counted twice because their profiles were present in the serial sections in the sample volume. The calculation of number per volume should be performed per cell type. When the diameter of the specifically stained nuclei differs from the diameter of the other nuclei, the respective diameters should be included in the calculation of the number of nuclei per unit volume (NV) per cell type.The number of nuclei per unit volume (Cell size = 1/NV. The labeling index (LI) is defined as the fraction of cells that is specifically stained and can be calculated from the number of nuclei per unit volume: LI = NV,positive/.The cell size, defined as the average tissue volume per nucleus, is the inverse of the number of nuclei per unit volume: t) times the sum of the observed tissue area (A): Vtissue= t.∑(A). The number of nuclei in the sample volume can then be calculated by multiplication of the number of nuclei per unit volume and the tissue volume: N = NV. Vtissue and pooled for the total tissue of interest to determine the total cell number.The tissue volume per sample volume is calculated according to the Cavalieri principle as the section thickness  and the smallest at the measurement level  to 100% cardiomyocytes (yellow). To open Figure Click here for additional data file.


The cardiac hydatid cyst (HC) is a rare pathology and mostly is endemic in livestock raising countries. Patients do not have a specific presentation so it is mainly a diagnosis based on imaging. Finding HC anywhere in the body warrants looking for another hydatid in other organs. This is a case report of a young male who presented with nonspecific symptoms and during diagnostic workup, it happened that he has combined hepatic and cardiac HCs. The cardiac cyst was located intramurally in the interventricular septum and expanding down mostly to the left side of the diaphragmatic surface of the heart and partly crossing intramurally to the diaphragmatic surface of the right ventricle. Emergency open-heart surgery was performed; the endocyst was removed while intramural ectocyst was drained to prevent potential future residual space. Hydatid cyst (HC) is a human parasitic disease caused by the cestode tapeworm Echinococcus granulosus, which infests the guts of dogs as its definitive host while human beings are accidental hosts and are infested by the ingestion of ova in vegetables or water contaminated with dog feces . HC was The distribution of echinococcosis in the heart depends on the blood supply to that part of the heart. The coronary circulation is the main pathway by which the parasitic larvae reach the heart and because of rich coronary blood supply, the left ventricular wall is the most common cardiac location (60%), followed by the right ventricle 10%), pericardium (7%), left atrium (6%-8%), and right atrium 3%-4%) [%-4% 3, . Cardiac%, pericaTransthoracic echocardiography is the elementary imaging technique but it carries several well-described limitations, including operator dependence, restricted field of view in patients with large body habitus, and limited views of the left ventricular apex and the right heart chambers. CCT provides useful information in detecting specific findings and performs better than transthoracic echocardiography by distinguishing cysts from solid tumors such as myxomas or fibromas through using density measurements and analysis of enhancement after IV contrast injection .We are presenting a case of a 15-year-old male from a rural area living on a farm and raising livestock. He presented with six months history of easy fatigability, shortness of breath, and dull right upper abdominal pain. Physical examination revealed that the patient had tachycardia at rest and hepatomegaly and no other significant findings. Chest X-ray (CXR) showed clear lung fields and mildly enlarged heart. Ultrasound examination of the abdomen revealed few cysts in the liver without any associated lymphadenopathy in the coeliac group of lymph nodes. As part of the workup, the patient had a CT scan of the abdomen, chest, and brain. The CT showed one cyst in the heart, four cysts in the liver, but no cysts in the lungs or the brain Figures .The cystic nature of the lesions and their locations in multiple organs made us think of the HC as the pathology. The serology test for HC using the ELISA test came positive, which helped to confirm the diagnosis. The patient was scheduled for urgent open-heart surgery, fearing the risk of potential rupture of the cyst and the grave consequences which come with that. The patient underwent conventional median sternotomy and aortocaval cannulation with aortic cross-clamp and cardioplegic heart arrest. CT images helped to locate the cardia cyst and its relation to the cardiac chambers. The location of the cyst was confirmed by direct examination of the heart when it was arrested. The cyst was totally located intramurally in the interventricular septum and expanding down mostly to the left side of the diaphragmatic surface of the heart and partly crossing intramurally to the diaphragmatic surface of the right ventricle making a visible and palpable soft cystic bulge. The myocardium overlying the cyst on the left side of the posterior descending coronary artery on the diaphragmatic surface of the heart was fibrosed, making part of the ectocyst wall. This was the most epicardially prominent part of the cyst and was chosen as the site of aspiration and later incision to evacuate and remove the cyst. Initially, a 50 mL syringe was used to aspirate the cyst and make it less tense to avoid rupturing it during incision Figure .Around 200 mL of clear fluid was aspirated, and the bulge disappeared. After the evacuation of most of the fluid, a small surgical incision of the fibrotic ectocyst was performed from the same site of aspiration Figure .A surgical discard sucker tip was introduced via the incision, and some residual fluid was aspirated. The incision was enlarged to admit forceps to take the cyst out. There was a residual cavity left behind intramurally caused by the cyst. The residual cavity was inspected to make sure that no part of the cyst was left behind, and the wall was intact, and no communication with cardiac chambers was noticed as no blood was ever seen to collect. The cyst and the fluid were sent to the histopathological and cytological exam Figure .The cavity left draining to the base of the heart without closure of the surgical incision, hoping that the normal filling of the ventricular chambers will eventually lead to the collapse of the residual cavity and prevent any future collection. The patient weaned successfully from the heart and lung machine, and a mediastinal drain was left on the diaphragmatic surface of the heart and another on the anterior surface of the heart. The patient was then discharged to the cardiac intensive care unit (CICU). After a few hours the patient was extubated from the ventilator without any pharmacological support. After 24 hours, CXR and echocardiography were repeated, and both were normal. The patient was discharged home after six days very stable and was referred to general surgery to deal with the liver cysts in the future.Human acquires hydatid disease through ingesting food or milk or water contaminated by dog feces containing the ova of the parasite , 11. CarCardiac HC is a rare and very serious and life-threatening cardiac pathology. It does not have a typical clinical presentation, where patients may present with unexplained arrhythmia and shortness of breath. Usually, it is located intramurally and mostly in the left ventricular wall and can be single or multiple cysts. Physicians should keep a high index of suspicion among patients in areas endemic with the disease. Because of the potentially systemic nature of the disease, finding a HC anywhere in the body should trigger a search in other parts. Surgical management of cardiac HC requires urgent surgical removal of the endocyst using epicardial incision to avoid intracardiac contamination with the infective HC fluid and to ensure drainage of the residual ectocyst to the pericardial space and providing no communication between the cardiac chambers and the residual cavity.


EPAS1 from Denisovans into the ancestors of Tibetans.Admixture is increasingly being recognized as an important factor in evolutionary genetics. The distribution of genomic admixture tracts, and the resulting effects on admixture linkage disequilibrium, can be used to date the timing of admixture between species or populations. However, the theory used for such prediction assumes selective neutrality despite the fact that many famous examples of admixture involve natural selection acting for or against admixture. In this paper, we investigate the effects of positive selection on the distribution of tract lengths. We develop a theoretical framework that relies on approximating the trajectory of the selected allele using a logistic function. By numerically calculating the expected allele trajectory, we also show that the approach can be extended to cases where the logistic approximation is poor due to the effects of genetic drift. Using simulations, we show that the model is highly accurate under most scenarios. We use the model to show that positive selection on average will tend to increase the admixture tract length. However, perhaps counter-intuitively, conditional on the allele frequency at the time of sampling, positive selection will actually produce shorter expected tract lengths. We discuss the consequences of our results in interpreting the timing of the introgression of  For example, when the ancestors of modern ethnic Tibetans first colonized the extremely high elevation Tibetan Plateau their success in adapting to these harsh conditions stemmed in part from genes acquired from a Denisovan-like ancestral population . There There are many other works which have used coalescent theory to study the dynamics of selective sweeps. In particular,  expandedThe distribution of ancestry tract lengths around a locus under selection can reveal the strength of selection and the timing of admixture without requiring the assumption of neutrality. In this work, we present an efficient way to calculate the expected ancestry tract length distribution, and we analyze the dependence of this distribution on a range of different biological parameters. In particular, it is evident that for a given admixture proportion, selection results in longer tract lengths of introgressed segments. On the other hand, when conditioning on the allele frequency at the time of sampling, positive selection actually results in shorter ancestry tracts during adaptive introgression.We model the tract length distribution around a selected locus. In other words, we want to find the probability that an ancestry tract ends at a certain distance from the selected locus because of a recombination event within the admixed population. We imagine a stochastic process along the length of the chromosome where transitions between ancestries at a given locus are caused by recombination (at this locus) such that chromosomes on the left and on the right of the recombination site are derived from different ancestral populations. As shown in , the strIndeed, if a neutral locus is far away from the selected locus, the surrounding genomic region is nearly independent of the impact of selection, and the transitions between the ancestry types should occur almost as expected under neutrality. On the other hand, if a given site is tightly linked to a selected locus, the probability of transitions to a different ancestry would be affected by the allele frequency changes in the selected locus. Because a 2-locus framework cannot model this dependency, it does not provide enough information regarding the local transition rates in genomic regions proximal to a selected site. Instead, in this work, we use a 3-locus model to calculate transition rates between different ancestries in two loci as a function of the distance to a third selected locus.α denote a locus under selection with two possible alleles A standing for the selected allele and a standing for neutral allele. We are interested in the scenario when allele A is introduced into the population through an adaptive introgression event that includes instantaneous replacement of a given proportion of individuals, the admixture fraction, in the recipient population. Such an introgression event is commonly termed an “ancestry pulse” in related works, e.g.,  so that there are 3 loci on the chromosome: γ, following each other sequentially in this order on the chromosome. Let α and β, β and γ. We are following the ancestry of β and γ. In other words, we track the lineage leading to each locus to the time of introgression, while simultaneously tracking how recombination and coalescence act on the segment. If at the time of introgression the ancestral locus is on a haplotype carrying allele A, then, by definition, its ancestry is from the introgressed population (denoted ancestry type 1). If, in contrast, the ancestral locus is linked to allele a, then it comes from the recipient population (denoted ancestry type 0). Ancestry type 0 corresponds to the recipient population and ancestry type 1 corresponds to the donor population.Let α.The importance of this 3-locus model is that we can calculate transition rates between ancestry types, and hence, we can numerically calculate the distribution of tract lengths of ancestry type 1 (or type 0) near the locus Under the coalescent with recombination, the model can be considered Markovian backward in time, when conditioned on the allele frequency path. To describe the dynamics of the adaptive introgression 3-locus model, we need to enumerate the possible states, which describes the ancestry configurations across the three loci, and we need to find the transition rates between them.α, β and γ. At α we track the allelic state, A or a, which also indicates ancestry. In β and γ we only need to know if the given chromosome is ancestral to the observed chromosome or not. We use the notation β or γ that is ancestral to the observed chromosome. The model has 6 possible states. Each state represents an ancestral configuration for an observed chromosome with three loci: β and γ on a chromosome carrying allele A at the selected locus α,β and γ on different chromosomes, both carrying allele A,β and γ on different chromosomes, and the chromosome ancestral to the observed chromosome at β is carrying allele A, while the chromosome ancestral to the observed chromosome at γ is carrying allele a,β and γ on different chromosomes, and the ancestral chromosome at β is carrying allele a, while the ancestral chromosome at γ is carrying allele A,β and γ are on different chromosomes, both carrying allele a,β and γ are on the same chromosome carrying allele a.t by T is the time since introgression, does not necessarily equal We denote the frequency of the selected allele at time α and β occurs at rate β and γ at rate Recombination acts at a rate proportional to the recombination distances between loci. We assume that recombination between loci t. If recombination occurs between loci α and β, then ancestral material at loci β and γ remain on the same ancestral chromosome. The allele at the site α on this ancestral chromosome will be A with probability a with the probability t. If a recombination event occurs on a chromosome with configuration β and γ, then it is split into two chromosomes, and there are two different possible ancestry configurations. In both configurations the first ancestral chromosome is A or a) at the selected locus α at the rate A and a, where a, and the equation describing the probability of being at a certain state at time t isA), anda).Consider for example the ancestry configuration state et al. 2001) which is based on lsoda  at that time we conclude that it comes from the recipient population (donor population respectively), hence it has ancestry type 0 (ancestry type 1 respectively). Our goal is to calculate the expected length of the introgression tract containing the locus under selection, i.e., haplotypes with allele A. The fact that the tract ends at a certain position on a genome means that all loci (β) to the left of this position has ancestry type 1 and a loci (γ) to the right of this position has ancestry type 0. Moreover, all the loci between α and β have ancestry type 1, and β and γ are infinitesimally close to each other. The mathematical derivation of this idea is in the next subsection. We also note that this process is a type of of Sequentially Markov Coalescent (SMC) model  model  which coA, i.e., the set of ancestry tracts with ancestry type 1 at the selected site. In the given model the probability that a locus is of ancestry type 1 or type 0 is equal to the probability that the ancestral chromosome carries the allele A or a respectively at time of introgression As previously discussed, we are interested in transitions of ancestry in loci near the introgressed selected allele. More precisely, we seek the distribution of lengths of ancestry tracts that contain r of a Markov process Now we consider a new Markov process, which describes the ancestry at a locus while moving along the chromosome away from the selected locus. The process is only approximate because the real process along the length of the chromosome is not Markovian. The states of this Markov process are again ancestries of type 0 or 1. By definition, transition rate between states In order to estimate how accurate our deterministic approximation is, we compared our results with the average tract length estimated from simulations using the forward-in-time simulation framework SELAM . Brieflyhttps://github.com/vlshchur/DAIM.The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article. The code is available at the GitHub repository associated with this project We simulate both relatively weak and strong selection, and we allow selection to act in different periods of time. We mostly explore the accuracy through the comparison of expected tract length and its variance. We also present the distribution of break-points of tracts containing selected allele. The tract length is the sum of two independent random variables, one representing the distance to the left of the selected mutation and the other representing the distance to the right of the selected mutation.As the waiting time to a recombination event along the length of the genome is exponentially distributed, one might be inclined to think that tracts lengths also are exponentially distributed. However, this is not the case as illustrated in In all of the scenarios that we explored, the relative error does not exceed ithin 2%  and 2. TAs previously discussed, the logistic function is a good approximation for the allele frequency trajectory only when selection is strong. Still, we wondered if the deterministic approach would give consistent results for a case of neutral admixture without adaptive introgression. Following Liang, Nielsen  the expeC’ model . Notice C’ model  , and to the Denisovan introgression into Tibetans (0.06%). We found that the logistic approximation performs poorly for the Denisovan-like scenario, and is not very accurate . The code is available at the GitHub repository associated with this project https://github.com/vlshchur/DAIM.Calculation of the expected tract length for 100 introgression scenarios takes just 106 sec for a Python implementation executed on MacBook Pro (2.9 GHz Intel Core i5) when using the logistic function to approximate the allele frequency trajectory. Transition rates are evaluated at 1000 points (different values of The deterministic approach facilitates the calculation of expected tract lengths for a wide array of parameters. In The strength of selection has a stronger effect on the expected tract length than the admixture proportion. For example, consider an adaptive introgression model with time The new approximation facilitates investigation of another important dependency, which would otherwise require prohibitively large numbers of simulations, namely the dependency of the expected tract length on the observed allele frequency. In A well-known example of adaptive introgression is Denisovan introgression into Tibetans which facilitated altitude adaptation by introducing an adaptive allele of EPAS1 affecting red blood cell production into the ancestors of modern Tibetans (The admixture proportion is very small, and the logistic function will therefore be quite inaccurate. For a range of values of selection coefficient (We summarize the dependency of the introgression time on the strength of selection in Adaptive introgression is an important and common phenomenon in evolutionary genetics . In thise.g., (Our results illustrate that selection should be carefully considered and incorporated when studying adaptive introgression events. More generally, this framework opens new possibilities for understanding the timing of admixture and the strength of adaptive introgression across a wide range of populations. Our work therefore lays the groundwork for the development of new inference frameworks that can detect adaptive introgression using tract lengths and allele frequencies. Particularly, in light of the fact that adaptive introgression is common historically, and may be accelerated in the future by anthropogenic impacts such as climate change,