These results indicate that although squid undergo major ecological transitions and morphology changes from paralarvae to adults, all life stages demonstrate flexibility in escape jet responses and produce escape jets of surprisingly high propulsive efficiency.Throughout their lives, squids are both predators and prey for a multitude of animals, many of which are at the top of ocean food webs, making them an integral component of the trophic structure of marine ecosystems. The escape jet, which is produced by the rapid expulsion of water from the mantle cavity through a funnel, is central to a cephalopod's ability to avoid predation throughout its life. Although squid undergo morphological and behavioral changes and experience remarkably different Reynolds number regimes throughout their development, little is known about the dynamics and propulsive efficiency of escape jets throughout ontogeny. We examine the hydrodynamics and kinematics of escape jets in squid throughout ontogeny using 2D/3D velocimetry and high-speed videography. All life stages of squid produced two escape jet patterns: (1) ‘escape jet I’ characterized by short rapid pulses resulting in vortex ring formation and (2) ‘escape jet II’ characterized by long high-volume jets, often with a leading-edge vortex ring. Paralarvae exhibited higher propulsive efficiency than adult squid during escape jet ejection, and propulsive efficiency was higher for escape jet I than escape jet II in juveniles and adultsThis article has an associated First Person interview with the first author of the paper. Summary: In this study, we examine the hydrodynamics and kinematics of escape jets in squids throughout ontogeny using 2D/3D velocimetry and high-speed videography. Nautilus, octopuses, cuttlefishes and squids, are well known for their rapid escape jet responses. Unlike many octopuses and demersal cuttlefishes that can burrow and hide from predators, many squids reside exclusively in the water column throughout ontogeny with predators approaching them from all directions. Thus, they require highly effective escape responses for survival.Escape responses are used by many animals as their primary survival tactic against predation . Typical2), where inertia and viscosity have similar relative effects on flow (3–106), where inertial forces dominate and continuous swimming is advantageous  ‘jet mode I’, where ejected fluid rolls into an isolated vortex ring and (2) ‘jet mode II’, where ejected fluid forms into a leading vortex ring that pinches off from a long trailing jet . Paralarvae squid that are early in their development likely have less control of jet dynamics, and consequently, we predict that older squid (juveniles and adults) will produce more hydrodynamic escape jet patterns than paralarvae. These hypotheses were tested by documenting the kinematics, velocity and vorticity of escape jets, and using direct measurements of jet wake properties  to determine propulsive efficiency.ω/Dω<3, and (2) escape jet II, which consisted of elongated trails of concentrated vorticity with and without discernible leading-edge vortex ring separation and an Lω/Dω>3 . Kinematic traces of escape jet I and II are characterized by a decrease in mantle diameter and funnel diameter widening at the beginning of the jet. This sequence, in addition to a single fin flap, results in peak swimming velocity  than escape jet II (0.13 s)  in juveniles and adults. Funnel aperture and mantle diameter in paralarvae could not be measured reliably due to low spatial resolution.A total of 59 escape jets were considered for this study . Only sequences in which the animal was away from the water surface or working section walls were analyzed. All of the data presented are for tail-first escape jets. Escape jets consisted of vortical regions of variable length. Two different hydrodynamic patterns were observed: (1) escape jet I, where the jet structures consisted of spherical vortex rings with an L Lω/Dω>3  and 4. Aructures . No diffvelocity . AlthougF6,86=11.42, P<0.001, Wilk's Δ=0.31, η2=0.112). The average velocity was significantly different among the size classes , with the paralarvae having a higher relative average velocity  than both the juveniles  and adults  , where paralarvae were higher  than juveniles  and adults  . Paralarvae had significantly higher peak acceleration  than juveniles  and adults  (Significant differences were found among kinematic swimming variables throughout ontogeny (MANOVA:  DMLs−1) . The meaDML s−1) . AdditioDML s−2) .Fig. 6.F2,59=3.94, P=0.025). Tukey post hoc tests revealed that paralarvae had higher propulsive efficiency (94.7±5.3%) than the adults (88.2±11.8%) during the mantle contraction phase; however, neither paralarvae nor adults were found to have different propulsive efficiency than the juveniles (93.8±2.8) . Propulsive efficiency increased as mean swimming speed increased . However, propulsive efficiency did not increase as the peak velocity increased .Significant differences in propulsive efficiency were found among the three size classes (ANOVA: 3.8±2.8) . No diffThe primary survival tactic against predation is a fast and efficient escape response . Squid fjet 700–6000) while paralarvae ranged from 70–150 (Rejet 20–50) in this study. The measurements of propulsive efficiency derived from properties of the jet wake  indicate that paralarvae exhibit higher propulsive efficiency during jet ejection than adult squid for escape jets, which is surprising given swimming at intermediate Re is generally considered more difficult than at higher Re. The efficiency advantage of paralarvae is likely a product of several factors. Paralarvae produced a jet that had a significantly higher funnel angle relative to the horizon (76.34±13.64°) than juveniles (14.86±2.72°) and adults (20.84±8.09°) , which was more aligned with their direction of motion. These results are consistent with Although paralarvae, juveniles, and adults exhibited similar escape jet flow patterns, differences in propulsive efficiency and kinematics throughout ontogeny were observed, likely originating from morphological and ecological differences, as well as physical constraints associated with their Re environment. Older squid Re ranged from 8000–17,000 (ReSarsia tubulosa has been shown to modify its swimming kinematics to maintain high propulsive efficiency (approximately 60–75%), which were calculated using similar techniques to this paper , as propulsive efficiency tends to increase with speed in squid , while consistently providing lift at low/intermediate speeds (<2.0 DML s−1) to counteract negative buoyancy. The lack of complex fin activity and appreciable thrust production during escape jets may be attributed to the constraints of the fin musculature and its inability to produce high forces at the high shortening velocities required for an escape jet  than juveniles (8.8±2.9 DML s−1) and adults (4.2±1.8 DML s−1) when normalized by dorsal mantle length. The same pattern was seen in peak velocity among the three size classes, where paralarvae reached five times the peak velocity of juveniles and adults. Paralarvae also exhibited significantly greater peak acceleration than juveniles and adults. These results are consistent with the findings of Loligo vulgaris paralarvae exhibit maximum linear accelerations of 817 DML s−2, while juveniles reached accelerations of 316 DML s−2, and adults only reached 162 DML s−2. The ability of paralarvae to reach such high velocity and acceleration is a great advantage given the high rate of predation at this early life history stage  is similar to those reported in larval zebrafish when performing a C-start escape [50–65 body lengths (BL) s−1]   s−1] . Juvenilt start) . Howevert start) . Both lat start) . Ecologit start) , and havt start) . Paralart start) , requirit start) . Squid at start)  because t start) .In this study, we determined that squid have flexibility in escape responses, which was evident by the observation of two different escape jet modes throughout ontogeny. Escape jet I is more efficient in juveniles and adults and may be the mode used when a threat is not eminent. Escape jet II is less efficient than escape jet I and may be used when a predatory attack is unavoidable, making a rapid escape integral for survival. Having high propulsive efficiency and the ability to swim quickly are key advantages for squid as they escape oncoming predators. Throughout all life history stages, squids are prey targets for many marine predators, including fish, marine mammals, sea birds and even other cephalopods, making them an integral component of marine food webs . TherefoD. pealeii Lesueur [dorsal mantle length (DML)=0.18 cm] and juvenile (DML=3.0–5.0 cm) and adult L. brevis Blainville (DML=5.1–7.0 cm) were used for this research. Paralarval D. pealeii are comparable to paralarval L. brevis, which are extremely difficult to obtain, because both species have similar body size, fin size and shape, and ecological niches during early ontogenetic stages . A UNIQ UP-1830CL video camera  outfitted with a VZM 450i zoom lens  and an optical filter allowing only 532 nm wavelengths was synchronized with laser pulses and used for data collection . For analysis of the DPIV data, each image was subdivided into a matrix of 32×32 pixel interrogation windows. Using a 16 pixel offset, cross-correlation was used to determine the particle displacement within interrogation windows comprising the paired images. These cross correlations were performed using Pixelflow™  (I) and the excess kinetic energy of the jet (E) were computed from:θ is the azimuthal component of vorticity, r is the radial coordinate, z is the longitudinal coordinate along the jet axis, ψ is the Stokes stream function, and ρ is the fluid density. The area integrals were computed using a 2D version of the trapezoidal rule. Only motion produced during jet ejection was considered because paralarvae tend to sink rapidly during refilling and work done by the propulsive system, not work done by gravity, was of interest. Therefore, propulsive efficiency (ηp) was calculated using the equation:x is the animal displacement during mantle contraction. Lω) was computed based on the extent over which the jet vorticity field was ≥20% of maximum vorticity, and jet diameter (Dω) was the distance (perpendicular to the jet centerline) between vorticity cores, regions where vorticity was ≥90% of peak jet vorticity. The Lω/Dω ratio is an important metric for jet performance as it linked to the physical limit of vortex ring formation, propulsive performance, and thrust augmentation  was used to collect 2D hydrodynamic data from paralarval squid. We provide a general description of the approaches below, and refer the reader to CA, USA)  and INSI−1), after which they were exposed to a range of flow velocities until they swam steadily against oncoming flow. The seeding particles were illuminated with the pulsed laser described above with the beam expanded to illuminate the volume of the water tunnel test section, and a V3V-8000 probe and INSIGHT 4G V3V software  were used to collect paired DDPTV images of flows around the squid at 7 Hz with ΔT=2 ms. Optical filters allowing only 532 nm wavelengths were used with the probe. In many sequences, firing the dual lasers following extended periods of steady swimming served as the trigger for escape jetting. However, in some sequences, the squid did not respond to the initial laser pulses, but did exhibit an escape response within an extended laser firing sequence. All of the juvenile/adult escape jets presented in this study occurred while the squid swam against free-stream flow, i.e. no escape jet was initiated from a resting start.Defocusing digital particle tracking velocimetry (DDPTV) data were collected for juvenile/adult size classes. Again, we provide a short description of the approach below and refer the reader to I) associated with the vortical 3D flow was computed from:x is the position vector, ρ is the fluid density, and the integral is computed over the volume of the vortex (E) was computed from:Lω,Dω, and ηp was computed using Eqn. (3) above.Approximately 75,000–125,000 particles were identified in each DDPTV image with triplet yields (matches of particles among the three cameras in the probe) of ∼50–60%. A relaxation method for particle tracking  was usede vortex . Excess Video of the squid was collected simultaneously with the DPIV and DDPTV data using two high-speed DALSA Falcon video cameras . To prevent overexposure of frames from the laser, each high-speed camera was fitted with a filter to block 532±5 nm wavelengths. For DPIV experiments, the cameras collected images from dorsal and lateral perspectives; for DDPTV experiments the cameras collected images from ventral and lateral perspectives. A series of two to four, 500-watt halogen lights, equipped with optical filters having low transmission at 532 nm, provided illumination.Frame-by-frame position tracking of the squid body features was accomplished using DLTdv digitizing software . Video wpost hoc tests used for multiple comparisons (SPSS). Two-tailed t-tests were used to compare kinematic variables between escape jets I and II. ANOVAS were used to compare propulsive efficiency among the ontogenetic stages, with subsequent Tukey post hoc tests for comparisons. Logarithmic regressions were performed to analyze propulsive efficiency and swimming speed. All means are presented with standard deviation, unless otherwise noted.Statistical analysis was performed in SPSS . All data were tested for normality using Shapiro-Wilk tests. Multivariate analysis of variance (MANOVA) was performed to compare kinematic swimming variables in paralarvae, juveniles and adult squid. Follow-up ANOVAs for significant variables were performed, with Tukey


It act as central biological regulator in critical immune functions, but its dysregulation has been linked with a number of diseases. Inhibition of TNF-α has considerable therapeutic potential for diseases such as cancer, diabetes, and especially autoimmune diseases. Despite the fact that many small molecule inhibitors have been identified against TNF-α, no orally active drug has been reported yet which demand an urgent need of a small molecule drug against TNF-α. This study focuses on the development of ligand-based selective pharmacophore model to perform virtual screening of plant origin natural product database for the identification of potential inhibitors against TNF-α. The resultant hits, identified as actives were evaluated by molecular docking studies to get insight into their potential binding interaction with the target protein. Based on pharmacophore matching, interacting residues, docking score, more affinity towards TNF-α with diverse scaffolds five compounds were selected for in vitro activity study. Experimental validation led to the identification of three chemically diverse potential compounds with the IC There are two types of TNF reported: tumor necrosis factor alpha (TNF-α) and tumor necrosis factor beta (TNF-β). Both are structurally and sequentially related to each other and compete for binding to their receptor2. TNF-α initiate many biological signaling pathways upon binding with two specific receptors, tumor necrosis factor receptor type I (TNFR1 or p55) and tumor necrosis factor receptor type II (TNFR2 or p75). Under normal physiological environment expression of TNFR2 is limited to immune cells, endothelial cells and nerve cells while TNFR1 expressed on almost all nucleated cells and play a key role in triggering the TNF-α signaling pathways3. Upon activation with binding to receptors, TNF-α initiate many major signaling pathways such as, activation of c-Jun N-terminal kinase, mitogen activated protein kinase (MAPK), extracellular signal regulated kinase (ERK) and transcription factor NFκB4. Physiologically it is an important regulator of immune functions, but its dysregulation has been linked with cancer, neurological diseases and, especially autoimmune diseases5.Tumor necrosis factor is a pleiotropic pro-inflammatory cytokine, having effects on immune cells and plays a key role in cell proliferation, cell metabolism, inflammation, cell differentiation and apoptosis10. These drugs bind to the interface of TNF-α dimer and for a complex, which hinders the binding of TNF-α receptors thus stops the activation of downstream signaling complexes that induce inflammation and other signaling pathways. Drugs reported till to date are proteins or antibodies, with higher molecular weight and are associated with various side effects for instance, tuberculosis, congestive heart failure, lupus, demyelinating disease, injection site reaction, production of auto-antibodies and systematic side effects14. In 2005 He et al.21 reported the crystal structure of TNF-α dimer in complex with small molecule SPD304. The active site of TNF alpha at the interface of dimer consists of sixteen contact residues, including six tyrosine residues: Leu57, Tyr59, Ser60, Gln61, Tyr119, Leu120, Gly121, Gly122 and Tyr151 from chain A while Leu57, Tyr59, Ser60, Tyr119, Leu120, Gly121 and Tyr151 from chain B. Tyr119 is the most crucial residue, its chi-1 angles rotates to form a dimer by accommodating compound binding. Interaction of SPD304 with crucial residues slightly changed the symmetry of trimer, promoting the dissociation of monomer and stabilize the TNF-α dimer. This structure provide the direction for the development of small molecule inhibitors that can act as direct inhibitors of TNF-α by the stabilizing the dimer structure. To best of our knowledge no orally active small molecule drug is reported yet against TNF-α. Therefore, the identification of small molecules that can inhibit TNF-α regulated pathway presents a promising and current focus area.Many TNF-α antagonists, icluding Infliximab, Certolizumab, Etanercept, Golimumab, and Adalimumab have revolutionized for therapeutic management of different autoimmune diseases, notably rheumatologic inflammatory diseases50 32.5 ± 4.5 µM, 6.5 ± 0.8 µM and 27.4 ± 1.7 µM, respectively.Thus, in the present study, an integrated virtual screening approach was employed to explore new TNF-α inhibitors. The hits selected through virtual screening were then corroborated by using an in vitro assay. On the basis of TNF-α inhibition activity, compound 1, 2 and 3 showed the most promising inhibitory activity with the IC50 in the range of 0.001–100 µm) against TNF-α were retrieved from the literature (Supplementary Table 25. All the ligands were sketched in MOE26 using builder module. Ligands were subjected to protonation and energy minimize using MMFF94 force field27. The decoys were generated by submitting the set of active compounds to DUD-E website (http://dude.docking.org). For virtual screening, our In-house database of natural and synthetic molecules was utilized containing ~ 10,000 compounds. The database converted into the 3D format followed by protonation, structure correction and minimization using MOE26.Twenty six reported biologically active inhibitors . Table In this approach, conformations of selective active compounds are aligned, and common pharmacophore features are generated where physicochemical functionalities are overlapped. These conformations were calculated from OMEGA, implemented in LigandScout 4.3The generated pharmacophore model was validated in order to examine its potential to differentiate between active and decoys compounds in the dataset. For this purpose, an external test set was prepared using 26 confirmed actives and 1750 decoys or inactive for the validation of pharmacophore hypothesis.Various statistical parameters such as percent ratio of actives, percentage yield of actives, false negatives, false positives, enrichment factor (EF), and area under the ROC curve (AUC) were further calculated.The validated pharmacophore model was used as a 3D query to retrieve the potential compounds from the inhouse natural product and synthetic compounds library . Default settings were used for virtual screening (25 conformers generated per entry under omega-fast settings). The resultant compounds were subjected to drug-like assessment employing the ADMET Descriptors and Filter by Lipinski rule. The compounds that have obeyed these criteria were upgraded to the molecular docking studies.21 was retrieved from Protein Data Bank. Missing residues in the crystal structure were added and refined by using MODELLER 9.1830. Polar hydrogen atoms were added while nonpolar were merged and Gasteiger charges were applied. Lamarckian genetic algorithm was used for all the calculations. The coordinates of the crystal structure was saved into pdbqt format for docking calculation. A grid box of centers 28 Å × 26 Å × 30 Å along with dimensions − 19.515 Å × 74.84 Å × 33.894 Å, centered on cognate ligand served as spacing grid which encompassed the entire binding site. The pose with the highest binding affinity and reasonable able conformation were chosen for further analysis. The finger prints of protein–ligand interactions were analyzed by using PLIF module in MOE. Furthermore, the compounds were visually analyzed by using Chimera31.A campaign of high throughput docking was performed by using MGLTools of AutoDock 4.2. The crystal structure of TNF-α with PDB ID 2AZ5l-glutamine , 10 mmol/L HEPES  and 1 mmol/L sodium pyruvate . Cells were harvested and 2 × 105 cells/mL were then added to 24-well tissue culture plates. Differentiation of cells was performed by adding 20 ng/mL of phorbol myristate acetate (PMA) , and by incubation at 37 °C for 24 h in 5% CO2. Cells were then activated with 50 ng/mL of E. coli lipo-polysacchride B , and treated with different concentration of test compounds  and, were then incubated at 37 °C for 4 h in 5% CO. The supernatants were analyzed for the level of TNF-α using Human TNF-α ELISA Kit .THP-1 cells (Human monocytic leukemia cells) were obtained from Biobank facility, ICCBS, University of Karachi, which was purchased from the ECACC . The cells were cultured in RPMI-1640, supplemented with 10% FBS , 50 µmol/L mercaptoethanol , 5.5 mmol/L glucose , 2 mmol/L; 2SO4) and plate was then read at wavelength of 450 nm in ELISA reader .Briefly the working concentration of 4 μg/mL in PBS from 720 μg/mL of mouse anti-human TNF-α capture antibodies were used for coating of 96 well ELISA plate. The 100 μl of TNF capture antibody per well was added and plate was incubated overnight at RT. The plate was then blocked by adding 300 μl of reagent diluent in each well and incubated for 1 h at RT. 100 μL/well of collected supernatants was then added each in triplicate and plate was then incubated for 2 h at RT. The 100 μL of detection antibody diluted to 250 ng/mL (working concentration) in reagent diluent was then added to each well and plate was incubated for 2 h at RT. Next 100 μL of 1:200 dilution of streptavidin-HRP in reagent diluent was added in each well in dark and incubated for 20 min at RT. Substrate solution was prepared by mixing color reagent A and color reagent B (provided in the kit) in 1:1 ratio and 100 μL from this mixture was added to each well in dark, plate was incubated for 20 min at RT. The reaction was stopped by adding 50 μL of stop solution (2 N H4 cells/mL in DMEM supplemented with 10% FBS were plated into 96-wells flat bottom plate and incubated overnight at 37 ºC in 5% CO2. Different concentrations of test compounds (350–1 µM) were added to the plate in triplicates and incubated for 48 h. 50 µL of 0.5 mg/mL MTT was added to each well and plate was then further incubated for 4 h. MTT was aspirated and 100 µL of DMSO was then added to each well. The extent of MTT reduction to formazan within cells was calculated by measuring the absorbance at 540 nm, using spectrophotometer . The cytotoxic activity was recorded as concentration causing 50% growth inhibition (IC50) for 3T3 cells.Cytotoxicity of compounds on NIH-3T3 fibroblast cells was evaluated by MTT colorimetric assay. The cell line was provided by ICCBS Biobank facility which was purchased from . Briefly 100 μL of 6 × 10the presence of chemical features that possibly interact with tyrosine residues , which is crucial for TNF-α inhibition.ability to select active compounds with good fitness score according to their biological activity with minimum deviation and.ability to picked active compounds from the pool of active and decoys dataset. This criteria declare Hypo_7 as best hypothesis as it yields the pharmacophore fit score range that imitate the activity trend and difference in their magnitude as illustrated in Table Prior to the pharmacophore model generation, key features of the reported 28 active compounds from 10 different classes  were identified by superposing them to determine potential overlapped chemical features with the LigandScout. This procedure has generated 12 hypothesis . This suggested that the virtual hits might act in similar way as reference compound SPD304 inhibit the protein activity.Compound 1 showed good inhibitory potential against TNF-α with the binding affinity of − 8.4 kcal/mol. Detailed molecular interaction pattern of compound 1 demonstrated that chlorobenzene ring, establish π–π stacking interaction with the aromatic ring of TyrB59 and TyrB119 while π-alkyl interaction with LeuA57 and TyrA59. Similarly, fluorobenzene ring involve in mediating π-alkyl interaction with TyrA59, GlnA61, TyrA119, and TyrA151. Moreover protein–ligand contacts were stabilized by two hydrogen bond interactions between hydroxyl group of the ligand and the side chain of SerA60 Fig. a.Figure Compound 2, 3 and 4 were belong to chemical class of thiourea with binding affinities − 7.1, − 7.0 and − 6.6 kcal/mol respectively. Compound 2 and 3 were more active as compared to compound 4 based on TNF-α inhibition activity. Upon evaluating the molecular interactions, it was suggested the compound 2 and 3 showed similar type of interactions , except compound 4 as compare to standard pentoxifyllin (IC50 = 340.6 ± 7.54 µM), against TNF-α. Similarly compound 5 was the derivative of Flurbiprofen; the most important NSAIDs , which is widely used to treat arthritis36, previously synthesized by Momin et al.37, from our institute. Compound 5 showed inhibition against TNF-α with the IC50 value of 117.7 ± 1.1 µM. Moreover, all compounds were found to be non-toxic on NIH-3T3 cell line when compared to the standard drug cyclohexamide (Table Compounds obtained from in silico studies were evaluated for their TNF-α inhibition activity through in vitro studies using Pentoxifyllin as standard drug. Table 50 32.5 ± 4.5 µM, 6.5 ± 0.8 µM and 27.4 ± 1.7 µM, respectively. The identified inhibitors have potential to act as anti-inflammatory agent and may serve as a starting point in developing novel drugs.Inhibition of TNF-α has emerged as a potential therapeutic to treat tumor and especially autoimmune diseases. Currently, no orally active FDA approved drug against TNF-α is reported. Small-molecule drugs that can regulate TNF-α levels or activity may provide an economic alternative to antibody therapeutics. In the current investigation we aim to identify novel small molecule from inhouse database that obey the pharmacophoric features of TNF-α inhibitors. The statistical evaluation of the developed pharmacophore model highlights its ability to discriminate between active and decoys. The screened compounds were subjected to molecular docking to investigate their binding mode. Subsequently, 5 compounds were identified with high docking score and demonstrated key interaction as was noticed for reference compound. Experimental validation indicated that three of these compounds exhibit strong TNF-α inhibitory potential with the ICSupplementary Information.


Protein kinases are crucial components of the cell-signalling machinery that orchestrate and convey messages to their downstream targets. Most often, kinases are activated upon a phosphorylation to their activation loop, which will shift the kinase into the active conformation. The Dual specificity mitogen-activated protein kinase kinase 4 (MKK4) exists in a unique conformation in its inactive unphosphorylated state, where its activation segment appears in a stable α-helical conformation. However, the precise role of this unique conformational state of MKK4 is unknown. Here, by all-atom molecular dynamics simulations (MD simulations), we show that this inactive state is unstable as monomer even when unphosphorylated and that the phosphorylation of the activation segment further destabilizes the autoinhibited α-helix. The specific phosphorylation pattern of the activation segment has also a unique influence on MKK4 dynamics. Furthermore, we observed that this specific inactive state is stable as a dimer, which becomes destabilized upon phosphorylation. Finally, we noticed that the most frequent MKK4 mutation observed in cancer, R134W, which role has not been disclosed to date, contributes to the dimer stability. Based on these data we postulate that MKK4 occurs as a dimer in its inactive autoinhibited state, providing an additional layer for its activity regulation. MKK4 has a serine (SER257) and a threonine (THR261) residues in its activation segment that can be phosphorylated One of the kinases, which has two phosphorylation sites in the close proximity of each other in its activation segment, is the dual specificity mitogen-activated protein kinase kinase 4 (MKK4). MKK4 is encoded by the MKK4 has been suggested to play a crucial role in certain physiological functions and disease development; notably, it has a decisive function in liver regeneration free 0.271) is crystallized with a short p38 peptide that is bound on top of the N-terminal lobe. This structure is particularly interesting, as a part of its activation segment (ILE250–ARG264) appears in an ordered autoinhibited conformation, forming a long α-helix that protrudes from the kinase (SI free 0.378) and 3vut , are lower quality with two disordered regions: ASP263–GLY283 and GLN316–VAL320. Furthermore, 3vut is missing its C-terminal part after ALA374.Currently three MKK4 structures are available in the RCSB Protein Data Bank  with Ensemble Optimization Method (EOM) Here, we conducted microsecond timescale  all-atom MD simulations to study the dynamics of autoinhibited MKK4. We studied the role and influence of all phosphorylation patterns in the activation segment. Finally, we investigated putative dimerization of MKK4 and the effect of phosphorylation to the stability of the homodimer, including the most common MKK4 mutant R134W to these simulations. Our results highlight the instability of the autoinhibited state as a monomer and suggest that it may exist as a stable dimer only when unphosphorylated.22.1cross link proteins tool. First, the PRO277 residue was deleted, the rotamer of TYR284 was changed (to prevent the clash) and the sequence PRO277–GLY283 (PSASRQG) was added to link the chain. The terminal PRO389 was mutated to ALA as a terminal PRO residue distorts the dynamics . The systems were prepared with Protein Preparation Wizard with default settings (Cap-termini) For the simulations we used the autoinhibited MKK4 structure 3alo +-ions. The water was described with TIP3P model Desmond MD engine was used for the simulations 2.2The dimer assembly (lowest energy assembly) was obtained with PDBePISA server (v.1.52) 2.3Root-mean-square fluctuations (RMSFs) of protein backbone and Root-mean-square deviations (RMSDs) of Cα-atoms were calculated using Maestro Simulation interaction analysis tool . RMSDs of residue intervals used for angle calculations were conducted with MDAnalysis.analysis.rms module 2.4The PCA was conducted with GROMACS (version 2019) covariance analysis tools  2.5Secondary structure analysis was conducted with Maestro Simulation interaction analysis tool . The percentage of secondary structure elements (%SSE) throughout the simulations was calculated with Python 3.7.2.6Angle calculations between subunits of MKK4 were conducted using open-source MDAnalysis library for Python 3.7 In order to confirm that the selection of particular ASH residues is not critical for angle value, we performed validation by testing different intervals. Residues ILE250–ALA264 (ASH), that are forming one side of the angle, were switched to ILE250–ALA259 and VAL255–ALA264 SI . This wa2.7The distances between subunits of MKK4 were calculated using GROMACS (version 2019) gmx distance tool Distance calculations within the N-lobe MKK4 were done as above, using the following residue intervals of each subunit: VAL116–VAL120; VAL120–ILE127; PHE164–LEU168; ALA111–GLY114.2.8The interaction networks analysis related to 2.9Results were plotted with Seaborn library for Python 3.7 33.13.1.1First, we investigated dynamics of autoinhibited MKK4 with all possible phosphorylation patterns in its activation segment . Four different phosphorylation states of MKK4 in this region are possible: unphosphorylated (Up), monophosphorylated at SER257 (pS257) or at THR261 (pT261) and double phosphorylated (pS257 + pT261) A. Overal3.1.2i.e. original crystal structure conformation) is clearly disfavoured with the double phosphorylated system. In PC2, movements of activation segment and C-lobe loop are dominating . According to the PCA phosphorylation affects to the MKK4 dynamics, as each system displays individual profile in their PC  score plots A. In PC1minating A. With P3.1.3In addition to the differences among the systems observed by RMSF and PCA, a system-specific movement is evident even by visual examination of the simulation trajectories. The activation segment appears to display a system specific shift that clearly deviates from the crystal conformation. This is also demonstrated by the resulting end conformations of the simulations SI .To validate these visual observations, we further evaluated this movement by angle calculations . This al3.1.4As phosphorylation status clearly affects MKK4 dynamics, we next conducted interaction network analysis to determine the potential changes in the protein’s interaction patterns. First, we analysed the salt-bridges and their frequencies in the close proximity of the ASH of each individual system A. EspeciSystems also display differences in their hydrophobic interactions B. A spec3.23.2.1int = −53.5 kcal/mol; ΔGdiss = 10.3 kcal/mol) and not as a monomer . In this homodimer assembly the apical activation segment region is stabilized and buried within the dimer interface , ILE250–ALA264 (ASH), VAL286–THR302 (αF-helix) A. To notNext, we evaluated the relative movement of function-related structural elements in the dimer using angle calculations as described for monomer systems C. StrikiThe distance analysis did not reveal distortion in the dimer with the phosphorylated wild-type systems, whereas the angle calculations suggested that it clearly exists. Average end conformations of the systems reveal that in the systems where the ASH is unstable (based on angles), the dimer subunits begin to twist related to each other  Biggest movements in the MKK4 monomer occur in the activation segment helix and in the C-lobe loop between the αF- and αH-helices. This high flexibility observed in simulations perhaps reflect to the fact why high-quality MKK4 crystal structures are unavailable. Moreover, the results here are in agreement with the recent solution structure analysis of MKK4 by SAXS Although the autoinhibited MKK4 conformation is unstable as monomer even when unphosphorylated, our simulation results support the possibility that this unique inactive autoinhibited state is stable as a dimer. This observation is in agreement with the results obtained from the PDBePISA assembly evaluation The origin of the autoinhibited MKK4 configuration is from the co-crystallized complex with ANP and p38α peptide The most frequent MKK4 mutation in cancer, R134W, which role has not been disclosed to date, affects MKK4 dynamics on the putative inactive dimer interface. This mutation leads to dramatic alterations in the N-lobe interactions, demonstrated by the changes in frequency and nature of the interactions within the residue 134 in the N-lobe. Based on this, R134W may be an activating mutation via distorting the autoinhibited dimer state of MKK4. On the other hand, the timescale of the simulations is not sufficient to disclose a full disruption of the dimer complex. Therefore, this alteration with its shifted N-lobe interactions may even lead to enhanced stability of the inactive dimer regardless of the putative scaffold protein. This would mean that R134W is an inactivating MKK4 mutation. Overall, additional experimental evidence is required to disclose the role of this cancer associated mutation.Our results demonstrate that the autoinhibited state of MKK4 is unstable as monomer and stable as dimer. Moreover, different phosphorylation patterns and the R134W mutation have all individual consequences for MKK4 dynamics. Better understanding of conformational changes and dimerization of protein kinases, occurring either due to phosphorylation (activation) processes or oncogenic mutations, is needed to provide comprehensive framework for disease causality. This will ensure and support a rational inhibitor design in a disease specific context related to aberrantly behaving protein kinases, which are currently the main target class in ongoing projects of the pharmaceutical industry T.P. performed simulations and designed the research. E.S, A.P. and T.P. analysed the data. Original draft was written by E.S. and T.P. All authors participated in writing the final manuscript. Figures were prepared by E.S and T.P.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area. The present paper draws on a systematic selection of published literature from 2000 to 2019, in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues. The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013. To date, articles on system dynamics have focused on a variety of healthcare topics. The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS. Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models’ quality were discussed.  Complex and intractable healthcare problems, as well as limited progress in discovering effective solutions, continue to challenge the healthcare industry ,2. In coOperations research (OR) approaches, especially simulation methods, have been used to specify complex problems in healthcare since the 1960s . Many heSystem dynamics (SD) simulation emerged in late 1950 as a result of focusing on the behavior of complex systems over a specific period . The maiIn general, the SD model can be developed through specific steps, including a clear explanation of the problem, generating a qualitative diagram of the system structure, converting the qualitative hypothesis to a quantitated simulation model, testing the model, and informing policy decisions about the model’s implications . The maiThe main objective of the present study is to review recent papers that utilize the SD method to analyze a variety of healthcare problems. We attempt to provide an overview of SD’s applications in healthcare by discussing different modeling approaches, procedures, and quality. This review is structured as follows: The first part presents the main outcomes of published review papers in healthcare. The Methodology section discusses the inclusion and exclusion criteria and the risk of bias. The Results section provides the outputs of the literature search, while the Discussion section explains healthcare aspects and research areas of studies. Finally, the quality of the reviewed papers is evaluated in the Quality Assessment section.To date, there have been several literature reviews regarding the applications of simulation methods in healthcare. Some of the highly cited literature reviews published between 2000 and 2019 are shown in Two methodologies were used to summarize the published papers of interest: (a) the literature review, and (b) the systematic literature review. Of the 19 papers considered here, six papers used the literature review method, while 13 papers used the systematic literature review method. Of the systematic review papers, only He and associates  used theWeb of Science was the most popular database among the selected review papers, followed by PubMed, Science Direct, and Google Scholar. The outcomes of the review papers can be classified into five distinct groups, including (a) the categorization of the included papers based on country, publication source, and year; (b) grouping by areas of research; (c) comparing simulation techniques; (d) discussing trends of topics or trends of simulation techniques; and (e) describing specific areas of research in detail.Two major sections in the selected review papers are suggestions for research and study limitations. In the suggestions portions of the papers, many of the review papers offer interesting topics for future research, while others attempt to describe their upcoming studies. Additionally, a few review papers suggest novel methodologies for conducting a literature review. The limitations sections in the review papers can be categorized into different groups. The first limitation is identifying and selecting papers published in a specific time frame. The second limitation is the inability to identify individual relevant records resulting from the use of a restricted number of search keywords. The third limitation is not covering all search databases, while the fourth limitation is restricting the search to specific regions or countries.RQ1.What are the main problems in the healthcare sector that have been studied in the past using the SD approach?RQ2.How was the SD approach utilized to model and address complex healthcare problems?RQ3.What can be learned from the past SD simulation research in the healthcare area that will support high-quality research in the future?The present systematic literature review followed PRISMA guidelines  and contTo answer the above research questions, a search strategy was developed to identify and review the relevant scientific papers. The search strategy included (a) defining keywords and identifying the relevant materials, (b) filtering the articles written by two authors, and (c) addressing the risk of bias . As mentHealth care, healthcare, or health-care is the improvement or maintenance of health via the diagnosis, prevention, and treatment of illness, disease, injury, and other mental and physical impairments in individuals . PhysiciWeb of Science and Google Scholar were used as database search tools in the present review. The search conducted in this review was divided into two steps: (a) the first set of keywords was used to discover relevant articles , and (b) a new set of keywords was developed based on the identified papers, which was used to discover additional articles. The second set of keywords is shown in After the development of the main database and the identification of all relevant papers, a formal screening process was applied to the database based on the exclusion and inclusion criteria. The inclusion criteria were as follows:Articles related to healthcare;Articles published from 2000 to 2019;Articles related to research questions;Articles written in English.Articles with SD models or an SD procedure/framework;Papers written in other languages;Chapters of books;Review papers;Articles from secondary sources that were not free or open access;Letters, newspaper articles, viewpoints, presentations, anecdotes, duplicated studies, short papers, and posters.The exclusion criteria were as follows:In this systematic review, bias could occur by (a) applying inclusion/exclusion criteria and (b) specifying the dimensions of the included papers. To address the first type of bias, two researchers (authors) independently reviewed the titles, abstracts, and conclusions of the identified records to choose articles for full-text review. Subsequently, the two authors compared their selected articles to create a unified list. After reading the full texts of the selected articles, the authors decided whether or not to include the articles. If there was an agreement between the two researchers, the article was considered and included. Disagreements between the two authors were resolved in sessions with the third author. To address the second type of bias, two authors independently specified aspects of healthcare and modeling in the included papers then compared the results, resolving disagreements by consulting with the third author. A chart of the selection strategy according to PRISMA guidelines is displayed in Interestingly, only 15% of the included papers were published between 2000 and 2009, and there was a significant increase in the number of included papers after 2013. To confirm these results, we first compared the trend of included papers with that of unscreened records, as shown in Secondly, we compared our results with those of other literature reviews. Kunc and associates  indicateOf the 253 included papers, 207 were published in journals and 46 were presented as conference papers. The most popular sources of publication among the included papers, in order, were the Journal of the Operational Research Society, System Dynamics Review, Winter Simulation Conference, PLoS One, Systems Research and Behavioral Science, and Health Care Management Science.The papers included in the present review are categorized into different aspects of healthcare, as shown in The most popular research area among the reviewed papers is patient flow. Since healthcare facilities are under pressure to decrease delays and reduce costs, an investigation into streamlining patient flow is beneficial . The objIt has been reported that more than 75% of instances of delayed discharge were a result of delays in making long-term care arrangements for elderly patients ,37. SeveThe key variables of the simulation models include the patients awaiting discharge, inpatients waiting for rehabilitation, patients in care, length of stay, admission rate, bed capacity and demand, and waiting lists. The main output variables are the average daily occupancy, patient waiting time, number of patients, number of delayed discharges, and post-acute accessibility. The core part of the stock-flow diagram among the included papers for the patient flow model is illustrated in In order to develop an expanded SD model, auxiliary variables such as the average daily arrivals, referrals, and walk-in admissions can be linked to the patient admission/arrival rate flow. Different auxiliary variables can be linked to patients in hospital or patients on hospital wards such as the numbers of occupied beds, patients, and empty beds. A variety of auxiliary variables can be connected to, and have an impact on, the patient treatment rate such as the treatment time, treatment conditions, and size of hospital staff. The patient in assessment for discharge can be linked to different auxiliary variables, including the inpatient discharge, the patient needs for other treatment, the patient needs convalescence, and the patient needs rehabilitation. The core part of the causal loop diagram for the reviewed papers is shown in Another popular research area in healthcare is obesity. Increasing rates of obesity and the incidence of being overweight are putting pressure on the economy and health services . Many diPapers report different results for reducing and preventing obesity. For example, Abdel-Hamid  indicateThe most popular scenarios among the reviewed papers are changing the sensitivity of weight to diet, healthy food availability, weight loss rates, the availability of educational programs, and preventing fast food advertising. The key variables among the reviewed papers include the average daily energy intake, the expected weight of a user, change in expected weight, energy expenditure, energy metabolism, body composition, the effect of diet on weight, the sensitivity of weight to exercise, overweight adults, obese adults, normal-weight children, overweight children, obese children, physical activity, changes in average weight per year, participation in sports, junk food consumption, levels of physical activity, healthy weight, healthy food choices, and mental health. The main output variables of the reviewed papers in this area include weight, change in weight, carbohydrate intake and oxidation, fat intake and oxidation, change in physical activity, the number of overweight and obese individuals, and the prevalence of being overweight and obesity. The results indicate that qualitative studies, building causal loop diagrams, and integrating SD with other simulation methods are not common in obesity papers. The core part of the stock-flow diagram among the included papers for the obesity model is shown in To expand the core model, different auxiliary variables can be linked to the weight loss rate and weight gain rate, including changes in average weight per day/month, engaging in weight loss/gain behaviors, daily energy balance, and healthy weight.Studying the application of SD in HIV/AIDS and tuberculosis (TB) is popular among researchers. In many countries, AIDS is the main cause of death in the adult population and a significant source of excessive demands on existing treatment and medical resources . With thSome of the main programs are mentioned in the following paragraph. Several studies have investigated the consequences of highly active antiretroviral therapy (HAART) ,59,60. HInterestingly, almost all the papers mention model calibration, and different types of data are used for this purpose  data , and datWhile Dangerfield and associates  indicateThe key variables in the developed models include the program participants, undiagnosed HIV-positive individuals with AIDS, family awareness, the support of family and friends, undiagnosed HIV, and diagnosed HIV. The main output variables include the annual HIV incidence, cumulative TB deaths, cumulative deaths from AIDS, cumulative MDRTB deaths, cumulative deaths from AIDS in non-TB individuals, and HIV infections and incidences.One of the main research areas of healthcare is workforce demand. The objectives of the selected papers on workforce demand are planning and predicting the number of workforces, including medical specialists ,66, the A study by Barber and López-Valcárcel  indicateThe critical variables among the models include immigration, emigration, the targeted workforce, the general population, births, deaths, graduated students and medical specialization schools, demand based on the population, and retirement. The main output variables among these papers include the number of specialists, population prediction, workforce demand and supply prediction, care supply, and demand. The core part of the stock-flow diagram for the included papers for workforce demand is shown in Auxiliary variables can be linked to flows, including the application rate, immigrant workforce, national exam pass rate, graduation rate, national exam application rate, graduate school enrolment rate, rate of choosing a hospital as a workplace, and employment rate.conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living [The main area of non-communicable diseases is chronic diseases, defined as y living . It has y living . For exay living . It is ry living . The autThe average simulation time among the selected papers is 32.4 years, although a study by de Andrade and associates  used 144A study by Tejada and associates  indicateKenealy and associates  concludeSeveral studies indicate that the rates of diagnosed diabetes and prediabetes can be decreased by different interventions such as improving the clinical management of diabetes, reducing obesity prevalence, and innovative healthcare delivery, while other studies project that the population with diabetes will increase in the U.S. and Japan ,92.The key variables among the reviewed papers are access to treatment/therapies, age, adherence to treatment/therapies, the rate of chronic disease diagnoses, chronic disease mortality, moderate and severe stages of chronic disease, the population in different age groups, population growth, and the population with disease. The main output variables among the reviewed papers include the treatment/therapy cost, number of disease incidents and diagnoses, number of deaths, and fraction of the population with the disease or those at-risk. The core part of the stock-flow diagram for chronic diseases is displayed in Some studies implement the entire core part of the stock-flow diagram ,93,94,95One of the main research areas in healthcare is hybrid modeling and the comparison of simulation methods. The main objective of the selected papers is to discuss the integration or comparison of SD with other simulation methods in a healthcare context. Interestingly, all papers are qualitative studies, with the majority being conference papers published at the Winter Simulation Conference. The main results of the reviewed papers include (a) the advantages of using hybrid models ,101,102,Chlamydia—one of the research areas of communicable diseases—was studied by five papers ,116,117.A handful of papers present SD models as the main results and outcome, including for a methadone maintenance treatment , water rTo build guidelines for SD applications in healthcare, we targeted (a) the categorization and analysis of relevant papers and (b) an assessment of the quality of SD models. The assessment criteria were adopted from a previous study since they provide an analytical and quantitative approach to analyzing the reviewed papers . HoweverA good quality paper is one that has clear objectives and results, provides detailed information about developing the SD model, presents model variables, and, in the case of quantitative studies, describes scenarios, model validation, outcome variables, and the source of the input data. The quality of the papers is categorized into four groups: “good”, “medium”, “low”, and “very low”. The results of this quality assessment are shown in It should be noted that more than 50% of the reviewed papers belong to the good- and medium-quality categories. Furthermore, the observed trend in quality has not increased over the 20 years. In fact, in the last five years, there has been a considerable number of papers classified as “bad” and “very bad” quality. Generally, papers belonging to the aspects of communicable diseases, non-communicable diseases, and disorders and stress exhibited higher quality than those belonging to other healthcare aspects, which may indicate evidence-based practice in these areas. It is important to note that we only assessed the information given in the papers and their additional/attached files. Moreover, we found that some highly cited papers did not present their models, and therefore, we adjusted the criteria to assess the papers based on model description or model presentation and not only based on model presentation.The clarity of the study objectives is vital to modeling efforts and is related to the type of problems under consideration . The revThe quality of data was assessed based on two parameters: (a) the engagement of experts and stakeholders in terms of the survey, interview, discussion, community-based model development, and group model building, and (b) the presentation of a source of data for model calibration. A total of 65 out of the 253 papers engaged experts and stakeholders in their studies or used interviews or surveys in their research.The reviewed papers have been categorized into different groups: presenting stock-flow diagrams, presenting causal loop diagrams, presenting SD frameworks, and describing SD model development. A total of 26 out of the 253 papers present no SD model, 11 of these 26 papers describe different frameworks for hybrid modeling, and the remaining 15 papers describe model development. The reviewed papers present 139 causal loop diagrams and 249 stock-flow diagrams. A total of 32 out of the 253 papers only present the causal loop diagram, 74 present both the casual loop diagram and the stock-flow diagram, and 121 only present the stock-flow diagram.The time horizon is one of the main aspects of stock-flow diagram development and should go back far enough in history to show the emergence of problems and their consequences and symptoms. The time horizon must also extend far enough into the future to elucidate the effects of different policies and delays . The choDirect structural tests, including empirical and theoretical tests;Structure-oriented behavioral tests, such as extreme condition tests and sensitivity tests;Behavioral pattern tests, considered to be model calibration .The model validation can be a problematic task and should, therefore, be performed by engaging stakeholders and experts during the model-building process, where the stakeholders should understand and trust the behavior and internal structure of the model . RelyingIt should be noted that out of the 235 papers selected for this review, 187 studies were carried out quantitatively. A total of 115 out of these 187 papers discussed calibration tests by comparing the model results with different sources, including (a) actual/historical/experimental data, and/or (b) data extracted from the literature reviews, and/or (c) data from surveys. A total of 57 reported direct structural tests, including empirical tests and theoretical tests. Finally, 104 studies performed sensitivity tests by evaluating the response of the model to fluctuation in the values of certain parameters.A total of 187 quantitative papers contain a total of 707 and an average of four outcome variables in the forms of charts, tables, and graphs. The cost, number, and percentage of specific parameters over time are the most common forms of the outcome variable. The most popular outcome variables of the reviewed papers are the number of patients, individuals, or doctors; the cost of the treatment, disease, or program; and the mortality or diagnosis rate.The included papers presented a total of 410 scenarios. The main types of scenarios were changing the value of established variables, adding new feedback loops or variables to the model, and removing specific variables. Common scenarios among the reviewed papers include program, legislation, or guideline adaptation; running the model with and without a specific system or feedback loop; changing the percentage or size of the population or population growth; changing the level of health services; changing hospital performance indicators; changing the rate of birth, death, or disease; and changing the treatment procedure or cost. The included papers used a total of 8627 variables in their models and an average of 22 variables per model. Moreover, papers included 2835 equations in their models and an average of 11 equations per stock-flow diagram.Typically, SD simulation modeling has been used to study and explain high-level problems in complex healthcare systems, mainly related to the impact of strategy and policy decisions on health outcomes, and to design future healthcare systems . The maiFinally, to generate a better picture of the included papers, the titles and abstracts were also analyzed. The current paper offers a comprehensive review of the critical healthcare issues under consideration by the SD application community. A carefully chosen set of keywords was used for discovering relevant articles. Following PRISMA guidelines, we explored three main research questions related to fundamental problems in the healthcare sector that have been studied in the past using the SD approach. These questions focused on how the SD methodology has been utilized in the past and what could be learned from the application of the SD simulation approach. Finally, the specific criteria for inclusion were defined and applied to the subsequent analysis focusing on developing useful SD applications in the healthcare sector. In general, reviewing and assessing contributions related to SD applications in the healthcare sector is somewhat tricky because there is no unique or standardized way of developing and presenting such papers. However, the main criterion applied here was the overall organization of the manuscript and the quality of the technical content from which the reader could learn about the current trends in applying the SD approach to healthcare problems.RQ1.What are the main problems in the healthcare sector that have been studied in the past using the SD approach? To answer this question, the included records have been reviewed, and the main problems were extracted and are represented in RQ2.How was the SD approach utilized to model and address complex healthcare problems? To answer this question, essential aspects of the SD approach, including “core part of SD diagrams”, “key variables”, and “output variables” are represented in the Discussion section.RQ3.What can be learned from the past SD simulation research in the healthcare area that will support high-quality research in the future? In order to answer this question, the main aspects of high-quality SD research have been assessed in the quality assessment section, such as the clarity of the objectives and results, the description of the scenarios, the source of the input data, and model validation.The research questions were answered as follows:This paper contributes to the body of knowledge related to developing system dynamics and simulation models that have been used in the past to analyze complex healthcare systems. We draw on a systematic literature selection and present a comprehensive view of system dynamics’ applications to the healthcare sector. The study includes papers from 2000 to 2019 and demonstrates the broad range of interests in the research community and the variety of healthcare topics. We demonstrate that the most popular research topics include patient flow, obesity, workforce demand, and HIV/AIDS. Moreover, the quality of the reviewed papers has been assessed using a proposed ranking system. Finally, the need for improving the quality of the system dynamics models was discussed.


Mycobacterium tuberculosis-infected zebrafish and human macrophages, suggesting its utility in tuberculosis therapy.Tumor necrosis factor (TNF) is a critical host resistance factor against tuberculosis. However, excess TNF produces susceptibility by increasing mitochondrial reactive oxygen species (mROS), which initiate a signaling cascade to cause pathogenic necrosis of mycobacterium-infected macrophages. Here, using the zebrafish, we identify the mechanism of TNF-induced mROS in tuberculosis. Excess TNF in mycobacterium-infected macrophages elevates mROS production by reverse electron transport (RET) through complex I. TNF-activated cellular glutamine uptake increases the Krebs cycle intermediate succinate. Oxidation of this elevated succinate by complex II drives RET, thereby generating the mROS superoxide at complex I. The complex I inhibitor, metformin, a widely used anti-diabetic drug, prevents TNF-induced mROS and necrosis of  Mycobacterium marinum (Mm) have revealed the mechanisms behind this dual effect  is both a host resistance and susceptibility factor in tuberculosis (TB) –3. Findil effect –6. Althol effect , 7–9. Thl effect , 9. LTA4rm of TB . In cohorm of TB . These frm of TB . Moreoverm of TB .To gain mechanistic understanding of TNF-mediated pathogenic macrophage necrosis, we returned to the zebrafish larva. We found that excess TNF, acting through the kinase RIP3 and one of its substrates, PGAM5, increases mitochondrial reactive oxygen species (mROS) such as superoxide and hydrogen peroxide in infected macrophages  5, 6). . 6). Thehi animals, mROS in infected macrophages were further increased to 3.6-6.6-fold over uninfected macrophages  bacterial determinant is needed (hi mROS originated from superoxide production by the electron transport chain (ETC), we asked if it was inhibited by compounds that disrupt mitochondrial electron transport. mROS were inhibited by four compounds that disrupt mitochondrial electron transport through distinct mechanisms . TNF. TNF4). rophages . TNF didrophages . Moreoves needed . To confchanisms . Thus, T2•−) through single electron donation to O2 (2•− production at complex I is generated by two distinct mechanisms (2•− (2•− which dismutates to H2O2) , generating in the process a small amount of the mROS superoxide . Th. Th2•−) to H2O2) . These tto H2O2)  12, 14), 142•−) to H2O2) . By cont animals . Two oth in TNFhi larvae through injection of its mRNA. AOXs, which are present in plants, fungi, and some invertebrates but are absent in vertebrates, catalyze the transfer of electrons from the CoQH2 pool directly to O2, bypassing Complexes III and IV . AO. AOhi mRI and IV  (15). Thmplex IV  15, 16)hi mROS, CoQ pool .Mycobacterium tuberculosis (Mtb), the agent of human TB, using a leucine and pantothenic acid Mtb auxotroph that can be used in containment level 2 facilities , 1713, 1trations —increasetrations . Diethyltrations —performetrations . Thus, ihi macrophages without affecting mROS in wild-type macrophages (hi mROS. Two different inhibitors of glutaminase 1 (GLS1), BPTES and CB-839 (telaglenastat), performed as expected, inhibiting mROS in TNFhi but not wild-type macrophages  removed mROS in both wild-type and TNFhi animals , as previously proposed  increased succinate levels over baseline . Moreoveproposed , 21. Accproposed . Finallyproposed  and for proposed  5, 6). , 6. hi sWe dissected the interactions between TNF, mROS, and mycobacteria and what roles they play at distinct steps of the pathway. We had shown that both TNF and mycobacteria are required to increase mitochondrial succinate, which is required to induce mROS. Because exogenous succinate could induce mROS in wild-type animals in both infected and uninfected macrophages , we concWe have previously shown that the mROS are required for macrophage necrosis . We now To determine if mycobacteria were required for necrosis downstream of mROS induction, we examined if diethyl succinate and DEBM could also kill uninfected macrophages by enumerating macrophages in infected and uninfected animals . DiethylSimilar results were observed in human macrophages derived from the monocytic cell line THP-1. We had previously shown that TNF induces necrosis in Mtb-infected THP-1 cells through the same interorganellar pathway downstream of mROS as in Mm-infected zebrafish  6). We . We 6). We had previously shown that blocking mROS using scavengers such as N-acetyl cysteine inhibited TNF-induced macrophage necrosis and restored resistance . Four ofThough long thought to be an in vitro artifact, moderate levels of RET and resultant increases in mROS have important homeostatic roles in cell differentiation and oxygen sensing . HoweverWe also considered our findings in the light of work using cultured macrophages, which has shown that succinate is responsible for generating proinflammatory responses to lipopolysaccharide (LPS), a key virulence determinant of Gram-negative bacteria . LPS cauLTA4H genotype, and, given the association of TNF with necrotic lung granulomas  (with yellow fluorescent macrophages) (ZFIN ID: ZDB-FISH-150901-6828)  (ZFIN ID: ZDB-GENO-960809-7) and the transgenic line 01-6828) . Sample 26206 ΔleuD ΔpanCD  expressing tdTomato, mWasabi, or EBFP2 were grown as previously described (Mm M strain (ATCC #BAA-535) and Mtb H37Rv strain, mcescribed , 61. Forhi animals were created by injecting recombinant zebrafish soluble TNF (2-Xros injection. TTFA (thenoyltrifluoroacetone) (1 μM) (Cambridge Bioscience), atpenin A5 (2.5 nM) (Insight Biotechnology), diethyl succinate (500 nM) (reagent plus 99% Sigma), and DEBM  (1 μM) (Sigma) were administered 2 hours before MitoTracker Red CM-H2-Xros injection. DM-Glutamate (dimethyl glutamate) (60 μM) (Cambridge Bioscience) was administered 3 hours before MitoTracker Red CM-H2-Xros injection. DNP (2.4-dinitrophenol) (100 nM) (Agilent Technologies) was administered 3.5 hours before MitoTracker Red CM-H2-Xros injection. Rotenone (6.25 nM) (Sigma), piericidin A (50 nM) (Stratech Scientific), strobilurin B (100nM) (Insight Biotechnology), metformin (20 μM) , phenformin (20 μM) (Sigma), nigericin (5 μM) (Sigma), diazoxide (50 nM) (Cambridge Bioscience), UK5099 (10 μM) (Cambridge Bioscience), and M-pyruvate (methyl pyruvate) (50 nM) (Fisher Scientific) were administered 4 hours before MitoTracker Red CM-H2-Xros injection. DM-malonate  (10 μM) (Sigma), perhexiline (10 μM) (Stratech Scientific), 4-BrCA (4-bromocrotonic acid) (10 μM) (Insight Biotechnology), GPNA (10 μM) (Cambridge Bioscience), BPTES (5 μM) (Cambridge Bioscience), telaglenastat (5 μM) (Cambridge Bioscience), and R-162 (1 μM) (Cambridge Bioscience) were administered 5 hours before MitoTracker Red CM-H2-Xros injection. In experiments to assess cording, perhexiline was removed 5 hours after TNF administration, diethyl succinate and DEBM were administered for 10 hours and then removed, and metformin, phenformin, DM-malonate, diazoxide, and telaglenastat were added 1 dpi and removed 2 dpi. After drug removal, the larvae were maintained in fresh E2/2 medium for the rest of the experiment. In experiments to assess macrophage numbers, diethyl succinate and DEBM were administered 1 dpi for 24 hours until macrophage number was assessed 2 dpi. For experiments quantifying mitochondrial ROS production, drugs were added before MitoTracker Red CM-H2-Xros injection as indicated above and maintained during imaging.TNFuble TNF  as previuble TNF . To asseuble TNF . FCCP . The T7 promoter (5′-TAATACGACTCACTATAGG-3′) followed by the zebrafish Kozak sequence 5′-GCCGCCACC-3′ were inserted before the start codon by PCR. mRNA was synthesized using the mMessage mMachine kit (Ambion) and the polyA Tailing kit (Ambion). Approximately 2-4 nl of injection solution  from RIP3 e2/i2-splice-blocking (5′-TTTTAGAAATCACCTTGGCATCCAG-3′) and PGAM5-translation-blocking morpholino (5′-AGCGCCCTCCGAAAAGACATGCTTC-3′) (Gene Tools) were diluted to 0.15 mM in injection solution . ApproxiAOX-expressing 2 dpf larvae were treated with different concentrations of KCN for an hour. Heart rate (beats per minute) was assessed as a readout of cyanide poisoning of complex IV of the electron transport chain  in absenFluorescence microscopy was performed as described . Mycobac2-Xros, a cell-permeable fluorogenic probe for ROS which is targeted to the mitochondrion and produces red fluorescence upon oxidation by diverse ROS (Fisher Scientific) (w200Tg(mpeg1:YFP) larvae were infected 2 dpf. For all experiments where TNFhi animals are used, larvae were microinjected 1 dpi via CV with phosphate buffered saline (PBS) containing TNF and 50 mM MitoTracker Red CM-H2-Xros or PBS containing vehicle for TNF and MitoTracker Red CM-H2-Xros , larvae were prepared for confocal imaging and maintained at 28.5°C within a heated incubation chamber attached to the confocal microscope. Images of the CHT of each larva were taken starting 30-60 min after MitoTracker Red CM-H2-Xros administration. Mitochondrial ROS production was quantified using maximum projection images as MitoTracker Red CM-H2-Xros maximum fluorescence intensity per macrophage using NIS-Elements. When not otherwise stated in the figure legend, the mean of maximum MitoTracker Red CM-H2-Xros fluorescence was quantified only in Mm- or Mtb- infected macrophages.Mitochondrial ROS production was assayed by fluorescence intensity of MitoTracker Red CM-Hentific) , 6. Tg was added to culture medium 30 min before image acquisition. Macrophages were imaged using a Nikon Ti-E inverted microscope fitted with a 20X objective  and 2-5 arbitrary images per well acquired with NIS Elements (Nikon). Cell necrosis was quantified using a previously described method (THP-1 cells (ATCC TIB-202) were differentiated into macrophages and infected with single-cell suspensions of mCherry- or tdTomato-expressing Mtb mcP-values were defined as follows: Not significant, P>0.05; *P<0.05; ** P<0.01; ***P<0.001; and ****P<0.0001. The statistical tests used for each figure can be found in the corresponding figure legend. Where the n value is given and not represented graphically in the figure, n represents the number of zebrafish used for each experimental group.The following statistical analyses were performed using Prism 7 (GraphPad): two-way ANOVA or one-way ANOVA with Dunn’s or Tukey’s post-test and Fisher’s exact test. Error bars represent the standard error of mean. Post-test https://fiji.sc/) for image analysis of macrophage death, GraphPad Prism 7.0  for data graphing and statistical analyses, and CorelDRAW (CorelDRAW Graphics Suite x5) for figure preparation.The following software was used: NIS-Elements for image acquisition in wide-field and confocal microscopy, ImageJ (Supplementary Materials


This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference. Extremely popular for statistical inference, Bayesian methods are gaining importance in machine learning and artificial intelligence problems. Indeed, in many applications, it is important for any device not only to predict well, but also to provide a quantification of the uncertainty of the prediction.x the observations, and The main problem when one is to apply Bayesian statistics is that the computation of the estimators is expensive and sometimes not feasible. Bayesian estimators are based on the posterior distribution on parameters given by:π(θ|x)=L samples from the posterior in hidden state models. They allow thus the computation of a Bayesian version of filters  in  by:2)π˜ recover  as a speure; see ,32,33,34π˜ recoveure; see .It appears that this idea was already popular in the machine learning theory community, where distributions like ibutions ,37,38. Wibutions ,40. Refeibutions ,42,43 emibutions  was studibutions . Many puibutions ,49,50,51ibutions  for a reSuch distributions were also well known in game theory and in prediction with expert advice since the 1990s ,54. We rFinally, many aggregation procedures studied in high-dimensional statistics can also be written under the form of ; see 59,62,63,64Many works have been done in the past few years to compute estimators based on The nature and accuracy of these approximations are extremely different from one algorithm to the other, and some of them are not well understood theoretically. Below, we group these algorithms into three groups. In Monte Carlo methods based on Langevin diffusions were introduced in physics in the 1970s . Let  algorithm was proposed in population genetics for models where the likelihood is far too complex to be computed, but where it is relatively easy to sample from it ,88. It bLaplace’s method provides a Gaussian approximation of the posterior centered on the Maximum Likelihood Estimator (MLE) and whose covariance matrix is the inverse of the Fisher information. This approximation can be theoretically justified in parametric models under appropriate regularity conditions thanks to the Bernstein–von Mises theorem. We refer the reader to Chapter 13 in  for a coThe extension of the Bernstein–von Mises theorem to nonparametric or semiparametric models is a quite technical and important research direction; see for example ,100,101 A huge number of methods are based on the idea of using optimization algorithms to find the best approximation of q is absolutely continuous with respect to p, and Variational approximations are based on the Kullback–Leibler divergence l papers ,105, to l papers , and to l papers . Note thl papers  in the el papers , economil papers ,112,113,l papers , naturall papers , and vidl papers , among ol papers , the gral papers ,115,118,l papers . The conl papers . The scol papers . Referenl papers  pointed |π(·|x))=argminq∈l papers ,130,131.l papers  proved teference  containsThe choice of the Kullback–Leibler divergence in  and 4)  4) was iergence,  and (4) ergence,  by replaeference  used theeference  discusseeference  discusseeference ,140,141 eference  and the eference ,144.The very popular Expectation Propagation algorithm (EP) was introduced by . EP can stead of . This waussed in  and by [ussed in , who proussed in . Let us ussed in , which iussed in  focuses x. Note that this is typically the kind of approximation provided by the MCMC and sequential Monte Carlo methods, but in these methods, the q and q and Finally, let us mention approximations by discrete distributions, of the form posed in . Referenposed in  proposedccessful ,155,156.ccessful .The objective of this Special Issue is to provide the latest advances in approximate Monte Carlo methods and in approximations of the posterior: the design of efficient algorithms, the study of the statistical properties of these algorithms, and challenging applications.


The review aimed to identify factors influencing opioid prescribing as regular pain-management medication for older people.Chronic pain occurs in 45%–85% of older people, but appears to be under-recognised and under-treated. However, strong opiate prescribing is more prevalent in older people, increasing at the fastest rate in this age group.This review included all study types, published 1990–2017, which focused on opioid prescribing for pain management among older adults. Arksey and O’Malley’s framework was used to scope the literature. PubMed, EBSCO Host, the UK Drug Database, and Google Scholar were searched. Data extraction, carried out by two researchers, included factors explaining opioid prescribing patterns and prescribing trends.A total of 613 papers were identified and 53 were included in the final review consisting of 35 research papers, 10 opinion pieces and 8 grey literature sources. Factors associated with prescribing patterns were categorised according to whether they were patient-related, prescriber-driven, or system-driven. Patient factors included age, gender, race, and cognition; prescriber factors included attitudes towards opioids and judgements about ‘normal’ pain; and policy/system factors related to the changing policy landscape over the last three decades, particularly in the USA.A large number of context-dependent factors appeared to influence opioid prescribing for chronic pain management in older adults, but the findings were inconsistent. There is a gap in the literature relating to the UK healthcare system; the prescriber and the patient perspective; and within the context of multi-morbidity and treatment burden. This scoping review aimed to ascertain what factors influence opioid prescribing as non-palliative pain-management medication for older people; the results will be used to inform practice development and training within the CCG.Most health and care services in the UK are commissioned by groups of GP Practices known as Clinical Commissioning Groups (CCGs) and our local CCG  provided the catalyst for this study. There is little UK literature concerning not only et al., A scoping literature review is a comprehensive and systematic approach that allows for a broad research question and incorporates all sources, including grey literature, compared to a standard systematic review that focuses on a ‘narrow range of quality assessed studies’. We used Arksey and O’Malley’s  frameworThe review was guided by the following inclusion and exclusion criteria:Literature, including all study designs and publication types, from Peer-Reviewed Journals from January 1990 to September 2017Grey literature  from January 1990 to September 2017Literature in English language only (resource restrictions meant that translation services could not be used)Papers that involve older adult participants  regardless of setting , or looked at external perceptions of older adult pain managementPapers on opioid prescribing for pain management for older peopleBachelor and Masters dissertations; unpublished doctoral thesesprescribing of opioids)Papers specifically focusing on opioid use, rather than prescribing Papers on palliative or end-of-life carePapers on opioids as substitution  rather than pain managementshould prescribe, rather than what affects current prescribingGuidelines addressing how clinicians or ‘opiate’ or ‘oxycodone’ or ‘oxycontin’ or ‘fentanyl’ or ‘hydrocodone’ or ‘Co-dydramol’ or ‘hydromorphone’ or ‘meperidine’ or ‘pethidine’ or ‘morphine’ or ‘codeine’ or ‘alfentanil’ or ‘dihydrocodeine’ or ‘diamorphine’ or ‘meptazinol’ or ‘pentazocine’ or ‘papaveretum’ or ‘remifentanil’ or ‘buprenorphine’ or ‘tramadol’ or ‘tapentadol’ or ‘dipipanone’ or ‘buprenorphine’ AND‘opioid’ or ‘older person’ or ‘older people’ or ‘elders’ or ‘elderly’ or ‘geriatric’ AND‘older adult’ or ‘prescribing’ or ‘prescribed’ AND‘prescription’ or ‘pain’‘pain management’ A conceptual diagram was developed to focus the literature search on the intersection of 4 topics  carried out study selection. At each stage of selection, the authors first worked together to establish a consistent approach, and then independently. Sources were categorised into those that should be included in the final synthesis, those that did not meet the inclusion criteria, and those where both researchers were uncertain. A third researcher (P.W.) independently reviewed papers where the primary reviewers remained uncertain.Papers meeting selection criteria were then summarised in Table The initial search identified 626 papers with 360 remaining once duplicates were removed. These papers were screened by title and abstract; 116 were excluded, leaving 244 to assess by reading full text. Nine of the 116 excluded sources were non-English language. Figure Of the 244 sources selected for full-text screening, 181 were excluded including 10 where full-text could not be obtained by institutional subscriptions and/or contacting the author. The full-text articles were categorised according to type: research (170), grey literature (47), and opinion pieces (27). Each reviewer assessed articles from all categories to ensure a consistent approach.The authors identified 14 systematic reviews but none addressed the same question as to the current scoping review. We screened these for relevant primary sources of which all but one were duplicates, and the remaining one was later removed as it did not meet the inclusion criteria.Sixty-one remaining articles were included for data extraction using a data extraction tool to ensure a systematic approach. A further 10 sources were excluded. A final set of 53 sources was included in the scoping review, consisting of 35 research papers, 10 opinion papers, and 8 grey literature sources.Key items from each source were charted using a uniform approach and including author, setting/country, methodology, sample size, aims/objectives, and key findings Table . Sourcesn = 23, 43%) suggested that opioids were being under-prescribed for older adult pain management. However, the patterns diverged depending on the source. While the same proportion of research papers (40%) and opinion papers (40%) suggest under-prescribing is an issue, this figure was much higher in the grey literature . Overall, less than a quarter of papers identified over-prescribing as an issue, with no opinion papers addressing the over-prescription of opioids  influencing factors on (2) opioid prescribing for (3) pain management in (4) older people]. Some reviews have looked at potentially inappropriate prescribing for older people  with extensive knowledge of the topic, who reviewed ongoing findings.broad overview and identify gaps in the evidence. This approach avoided favouring academic perspectives over that of practitioners and allowed us to capture differing discourse trends within types of literature, for example, that under-prescribing was discussed more commonly in grey literature compared to academic sources.Scoping reviews do not rate the quality or level of evidence provided therefore recommendations for practice cannot be graded; the aim is to provide a n = −10) full-text articles could not be obtained (despite contacting the authors). An equally small (n = 9) number of non-English papers could not be assessed. As is true for most reviews, available sources did not include literature from the global south and disproportionately captured North American and European perspectives. In a similar way, the identification of relevant sources was predominately digital, with limited opportunities to hand-search sources, which may not be entered into online databases and cannot be found via online search engines.A drawback of the review was that for a very small number of sources , dosages increase/decrease, and prescriptions are discontinued or changed for another type of opioid. There was also a gap in how prescribing decisions were perceived by older people and their carers  should be explored within the context of multi-morbidity and treatment burden. While there are few attitudinal studies to date and none are based in the UK, these studies suggest that ageism, in particular, may play a significant role (Kaasalainen


In contrast, feedforward cortical axons, another major source of extrinsic input to extrastriate areas, are less spatially divergent and more typically terminate in layer 4. Functional implications are briefly discussed, including comparisons with the better investigated rodent brain.Pulvino-cortical (PC) projections are a major source of extrinsic input to early visual areas in the macaque. From bulk injections of anterograde tracers, these are known to terminate in layer 1 of V1 and densely in the middle cortical layers of extrastriate areas. Finer, single axon analysis, as reviewed here for projections from the lateral pulvinar (PL) in two macaque monkeys ( The visually-related pulvinar in nonhuman primates (NHP) is a complex nucleus consisting of multiple retinotopically organized subnuclei, each with a mix of morphologically and neurochemically distinct cell types ,2,3,4 anVisual processing in occipitotemporal areas is generally thought to involve interactions of pulvino-cortical (PC) projections with extrinsic and intrinsic cortico-cortical (CC) connections. While detailed microcircuitry data will be necessary to further understand these interactions, experiments in galago have identified several functionally relevant comparisons of the V1 and PL projections to V2, in that these appear to target similar layers and both lack parvalbumin. Projections from the PL to V2, however, had larger boutons, suggestive of greater synaptic efficacy .In this brief review, I emphasize results at single axon, Golgi-like resolution of PL axons in comparison with CC axons in early visual areas V1, V2, V4, and area MT  of NHP. This finer level of resolution demonstrates that PL axons in these areas are morphologically distinct from CC in arbor number, arbor size, and overall spatial topology, as well as in laminar organization. For more extensive reviews of pulvinar organization and connectivity, see ,12,13,14n= two monkeys). CC comparisons are drawn from a series of similar studies of anterogradely labeled single axons after tracer injections in V1, V2, or V4 [Results are largely based, except for V1, on serial reconstructions of 25 axons anterogradely labeled by tracer injections in the PL at the lateral border of the nucleus, near the representation of horizontal meridian  , where they form a plexus generally parallel to the pia ,15. The A comparison of input to mouse V1 from LG and lateral posterior nuclei (LP) concludes that LG input to layer 1 is topographically organized but input from LP is only coarsely topographic, with much larger receptive fields and it originates from widely dispersed locations in the visual field. This contrast was interpreted to suggest that LP inputs provide contextual information about the visual scene, with spatial extension beyond the strong retinotopic bias of local V1 neurons: “LP inputs may therefore contribute to surround modulation of V1 neurons, or to state-dependent or behavioral modulation of visual responses across visual space” . In galaIn our material, PL axons had more spatially separate arbors that diverged over a larger cortical territory than the CC axons terminating in the same area. In area V2, most V1 axons had single arbors, less than 0.4 mm in diameter; and those with multiple arbors formed patches over a hollow domain of about 1.2 mm × 0.3 mm . In arean = 2 axons, 3.0 × 2.6 mm, and 2.0 × 1.25 mm). One of the PL axons in V4 had at least eight smaller collaterals in addition to two arbors in layer 4. This complex, mixed topology—terminal clusters together with linear segments—has been reported for a V4 feedback axon terminating in V1  and forre 12 in . In the Why PL arbors extend over wider territories in V2 and V4 than feedforward CC in the same target area is unclear. A straightforward explanation might be the apparently larger receptive fields of PL neurons . Given tA differential relationship to cortical modularity, notably the cytochrome oxidase (CO) compartments in area V2 should also be considered. Bulk injections of WGA-HRP in the PL produce stripelike terminations coinciding with the CO compartments in V2 , and then = 6) have been reconstructed in area MT [Only a few axons from the PL In the early visual cortical areas V2 and V4, feedforward CC axons preferentially target the middle layers 3 and/or 4, sometimes with collaterals in layer 5 ,18,27. AA single PL axon, terminating in V2 or V4, commonly has one or several arbors in layers 3 and/or 4, defined as “main arbor” on the basis of larger size and greater number of boutons. In area V2, additional arbors variably occur in layers 1, 5, and/or 6. Of seven PL axons terminating in the lunate sulcus, four had collaterals in layer 1. Of nine axons terminating in inferior occipital sulcus, only one had collaterals in layer 1, and the others had small collaterals in layers 5 and/or 6. This strongly contrasts with the organization of CC projections to these areas, where terminations outside the middle layers are not usual. Moreover, the arbors of a single PL axon are spatially distributed and offset, rather than vertically aligned see .Two axons reconstructed from the PL to V4 each had two arbors in layers 3 and 4, with smaller collaterals spatially separate in layer 5 . Axons Interestingly, there is some indication that CC projections in association cortical areas may also have a spatially distributed, multi-laminar termination pattern . In botThere is physiological evidence in mice that that thalamo-cortical inputs from the posterior medial nucleus (POm) have layer-specific functional effects in relation to pyramidal cells in layer 2  vs. those in the deeper layers .Multi-laminar thalamo-cortical (TC) terminations have been observed from the medial geniculate body to auditory cortex of the macaque . In thisPL axons projecting to area V1 terminate only or mainly in layer 1 . Injections of double retrograde tracers in V1 and V2 result in a proportion of double labeled neurons in the inferior and lateral pulvinar (from 5%–12%) . This isQuantitative bouton features, as detected at the light microscopic level, can be a useful clue to synaptic strength and efficacy. In the geniculo-cortical pathway, for example, magnocellular axons have larger boutons than the parvocellular ,24. AnotOn the subject of connectional strength, there are several relevant recent studies in rodents. In mouse somatosensory cortex, for example, there is evidence that higher-order thalamic inputs from POm are stronger, with longer lasting effects, than cortical inputs . Anothern = 87) were found to have a mean area of 0.47µm2 in contrast with those from area V1 (n = 88), with a mean area of 0.36 µm2. By comparison, geniculo-cortical terminations were measured as having a mean area of 0.70 µm2 (magnocellular) or 0.52 µm2 (parvocellular). In the macaque, a subset of larger PL boutons (diameter = 1.0–2.0 µm) was found intermingled within a dense PL focus in V2 [Comparable physiological data for CC and PC connections are not available in NHP; but several relevant morphological differences have been observed. In area V2 of bush baby , PC boutus in V2 . One migus in V2 ; but theThe density of PL boutons has been reported as less than that of CC, as compared focus-to-focus (PL vs. CC) within V2 . This mOn the basis of available data, PL axons Table 1 in ) appear  in  appeIntrinsic pyramidal cell collaterals and feedback CC projections to V1 and V2 typically have a large number of boutons, exceeding 1000 boutons per neuron ,23.In summary, as briefly reviewed here, cortically projecting axons from PL evince a variety of phenotypes in terms of laminar and spatial distribution. Those projecting to area V1 preferentially target layer 1 and are hypothesized to be widely divergent, those to areas V2 and V4 terminate in multiple layers with spatially distributed arbors, and, curiously, at least some of the PL axons terminating in area MT form small, relatively compact arbors. These results, however, are based on two injection sites at the lateral edge of PL. A larger sample size is necessary to establish whether the apparent variability  might derive from neurochemical or other cell-type specific properties of PL neurons and, importantly, to assess to what extent neurons at different retinotopic locations or in different visual pulvinar nuclei (inferior pulvinar) exhibit similar properties.What is the synaptic convergence of PC and CC axons , especially in relation to the multi-laminar arbors reviewed here for PL axons?How do PC axons interact with other systems: the numerically dense intrinsic collaterals of pyramidal cells, feedback projections in layer 1, and the various inhibitory populations?What is the composition of neuron ensembles postsynaptic to the different arbors of a given axon? Is this related to finer modularity within the target area, at least for V2 and V4?How do PL and other visual PC axons compare, structurally and functionally, with homologous thalamic terminations in other less visual species, such as rodents?At the microcircuitry level, there are multiple questions in need of further investigation.