Stokes’s equation in the fluid domain and Brinkman’s equation in the porous media are combined in the current study which is designated by the Stokes-Brinkman coupling. The current paper gives a theoretical analysis of the Stokes-Brinkman coupling. It has been shown that such a model is a good match for the knee joint. A flow model has been investigated in order to get a better understanding of the convective diffusion of the viscous flow along the articular surfaces between the joints. The Beavers and Joseph slip conditions which are a specific boundary condition for the synovial fluid are used to solve the governing system of partial differential equations for the synovial fluid and the results are provided here. We develop formulas for the interfacial velocity for both flow through special slip condition and analyse the link between the slip parameters  In order to regulate the range of motion between the linked pieces, each joint has a certain form and structural component. Fibrous joints, cartilaginous joints and synovial joints are the three principal kinds of joints in the human body that enable a wide variety of mobility1. These joints are kept in place by connective tissue and are often found in the skulls whereas cartilaginous joints which are somewhat movable, are typically found in the spine or ribs and are hence called, immovable joints1. In conclusion the most mobile joints such as synovial joints in human body account for more than half of all joints and are more often found in the limbs and joints of the spine1. Having fluid in the synovial joints allows them to move more easily because it helps to minimize friction between the articulating surfaces2.Some of them are quite static in their movements because they have a lower degree of flexibility than the mobile synovial joints2. Synovial joints that are not move-able are more stable than synovial joints which are move-able2. As a result, it is believed that the static synovial joints are more prone to damage than the dynamic synovial joints2. This is due to the fact that they have different degrees of freedom from one another.It is common to refer to joints as the points where two or more bones come together, these points are called “edges” in the context of human skeletons3. The pivot joints, saddle joints, hinged joints, ball and socket joints, planar joints and finally condyloid which are most complicated, are among the classes of joints3. The planar joints are made up of bones that have articulating surfaces which are flat or just slightly curved on one side. Consequently, there is gliding movement between the joints3. However, the range of motions is restricted since it does not need rotation. Some examples of synovial joints are metacarpal and tarsal bones such as metatarsal joints in the hands and feet3. There are hinged joints in the ankles, elbows and knee in which one bone moves while the other stays immobile such as door hinged does. It is the only rotational movement that is permitted by pivot joints, which are defined by the fact that their convex articular surfaces are parallel to the longitudinal bone axis4. Among them are the proximal radio-ulnar joint, the middle atlantoaxial joint and the distal radio-ulnar joint. The most common kind of joint known as condyloid joint permits angular movement along two axes4. Consider the joint in the wrist and the finger which can move both side to side and up and down, as two examples of joints that move in this manner. The oval-shaped end of one bone fits into a similarly oval-shaped hollow on the other bone which allows the bones to move together. Some writers refer to it as ellipsoidal joints in their books4.Essentially, each of the synovial joints in the human body has been classified into one of six major groups, which are distinguished by their architecture, composition and forms4. There are greatest degrees of flexibility of any kind of joint in all synovial joints which includes ball and socket joints, allowing movement in all directions. A rounded ball-like structure is present on one end of the bone which fits into a cup-like socket on the other end of the bone in this kind of joint. For example, the shoulder and hip joints, are examples of such joints4. The shoulder, hip and knee have the most degrees of flexibility out of all the synovial joints, making them more prone to the dangers of accidents as well as varying degrees of arthritis which typically affects the synovial joint and produces impairment4. In the medical world, arthritis is defined as inflammation of the joints. Osteoarthritis is a condition that is known to decrease joint function, causing pain and as it progresses from its early stages to its advanced stages, it may eventually result in disability4.The saddle joint is a form of joint that allows angular motion comparable to that of the condyloid joint, however, the saddle joint has a broader range of motion than the condyloid joint. Saddle joints are formed anatomically by two bones that are concave on one side and convex on the other, as in the thumb joint, which has more freedom of movement than the wrist or finger, and has the ability to move back and forth, as well as up and down, in comparison to other joint5. The knee joint is responsible for a variety of functions. It is possible for the knee to be loaded up to seven times the body weight while doing intense activities like walking, running and jumping5. The knee joint is made up of two articulations which are, a medial and lateral tibia femoral articulation, as well as a patella femoral articulation, which are located on each side of the tibia.When it comes to joints, the knee joint is the most complex in addition to being essential in the human body. In the skeletal system, it is made up of two long bones. The femur and the tibia (sometimes known as the shin bone) are the two bones that make up the lower leg, see Fig. 14. An internal shock absorber inside the knee joint, the meniscus, is located in the space between the two bones that make up the joint. When performing hard exercises, the meniscus helps to prevent the knee bones from colliding with one another4. A key contribution of modelling of the knee joint is that, it gives information which is difficult to get via testing and it also allows for the production of realistic models that may be utilized to better surgical and rehabilitation treatments. Equilibrium of forces and moments coming from external loads may be solved by evaluating the influence of individual components and predicting their behaviour rather than calculating the equations. Different types of models may be used to present data, including dynamical system, statistical models, differential equations  and partial differential equations (PDEs)) and theoretical models.Hyaline cartilage covers the articulating surface of each bone and it is the most essential component of the knee joint because it provides the joint with an extraordinarily smooth surface and protects the underlying bones from injuryIn light of previous research, the purpose of this article is to investigate the Stokes equation in the fluid domain and the Brinkman equation in the porous media, both of which are designated by the Stokes Brinkman coupling under the Beavers and Joseph slip conditions, which are a specific boundary condition for the synovial fluid. After the governing equations have been modelled, the appropriate similarity transformations are used in order to change the equations into forms that are self similar. Take note of the fact that such solutions are very uncommon when there is viscous dissipation present. In order to validate the reliability of our findings, we used the Finite Volume technique to find numerical solutions to the resulting self similar equations. In addition, the effects that several relevant factors have on the dimensionless velocity and temperature are depicted and explored in this article.6. The knee joint is the biggest weight bearing joint in the human skeletal system, according to the American Academy of Orthopedic Surgeons, in addition, it is the most complex joint in the human skeletal system4. This difficulty may manifest itself in a variety of ways, including composition, function, geometry, and set-up. To this point, we have concentrated only on the description of the joints, selecting the most appropriate model for synovial fluid that can accurately reproduce the experimental data throughout a given range of deformations6. For the time being, let us develop the balancing laws that describe the flow of synovial fluid with some justification for simplifications. Let us bring these simplifications to a close before we begin putting down the explicit equations6.The most frequently used joint which is the knee joint in the human body and even a very little problem with the knee joint will most certainly have an influence on a person’s ability to do their daily tasks normally. Because of the vital role that the knee joints play in human mobility as well as the fact that they are a major load bearing joint, the important of the knee can not be stressed at any point of the time8. Porous fluids are defined as fluids in porous media in which the movement of momentum within the fluid as a consequence of shear stresses plays an essential role and in which the movement of momentum within the fluid is crucial8. Darcy’s law introduces a term into the momentum balance that takes into consideration viscous transport in this mathematical model which considers the pressure as an independent variable in addition to the pressure and flow velocity vector10. Convection diffusion defines the concentration distribution of a flow when it is applied to it10. The concentration distribution is represented by the scalar field 12. The system of governing equations has the following representation:Viscous Newtonian fluids theory Simply extending the basic theory of viscous fluid flow, it accurately predicts the flow behavior of fluids that have a substructure, such as synthetic lubricants, liquid crystals, synovial fluid, and even animal blood14. The mechanical interactions in a fluid medium are described in such a manner that the idea of couple stresses is introduced, rather than symmetric. Modelling synovial fluid using Stokes theory (1966) is utilized in order to establish connections with the momentum and continuity equations14. Equations that are defined in this theory include the following: D/Dt is a total derivative, vectors 14.Porous media Brinkman viscosity mode Inter-facial flow between a free fluid and a porous medium is well represented by the Brinkman’s equation. A Brinkman viscosity can still be determined but the pore scale flow is still not known16. An a priory estimate of effective viscosity for rough porous media can be determined from the interface slip length and the interior permeability for slip and permeability from unit cell analysis. For irregular porous structures, the Brinkman’s model physical origins show that it is much more accurate17. 16.Power law model The relation related to shear rate and viscosity in a steady shear flow called as Power law model  can be written as18: K and n are curve fitting coefficients18.In case of synovial fluid flow, it is feasible to characterise it in term of the pressure field 18. In the fluid, fluid mass is preserved (continuity equation)in which the time dependent density variation is represented by the first term while the net mass out of the fluid element by its boundaries, is shown by 2nd term which is refereed to as the convective term18. Our assumptions are, the density is constant (in-compressible flow), steady and two dimensional which can be written as,18.The rate of change of momentum equals the sum of the forces on a fluid particle (Newton’s 2nd law of motion)Conservation of momentum equation in components form, in x- direction is defined as,18. Because liquids are in-compressible so the mass conservation equation is continuous and viscous stresses are twice the local rate of linear deformation times the dynamic viscosity21.21. We consider 21. We know that, n is power law index with It is a combination of linear momentum and mass conservation for the fluid in large pores and flow channels as well as Darcy’s equation for regions with unresolved pores that gives rise to the Brinkman’s equation, refer to Eq. .The consideration of unidirectional motion with 21. The boundary of porous medium with a free viscous fluid is the continuity of pressure and the normal mass flux which give rise to wipe up the tangential velocity of the synovial fluid21. Such boundary condition was proposed by Beavers and Joseph (BJ) in 1967 as a empirical formula22. The analysis showed that BJ condition is macroscopic law which describe a phenomena at a macroscopic scale whereas characteristic length is much larger than the pore characteristic size22. Symmetry boundary condition will be defined as,24,24.The conservation of laws of physics are used in fluid flow mathematical equations;x direction, which can be seen from Fig. h separates the two surfaces from one another. Also, u and v are the components of the synovial fluid velocity along x and y directions respectively. The synovial fluid has been represented as viscoelastic fluid and its elasticity is important in joint lubrication. It is surrounded by porous layer which represent synovial membrane. We introduce the conventional assumption of lubrication theory into the Navier-Stokes (NS) equation of motion and ignore the fluctuation of pressure corresponding to a very thin layer of lubrication in the NS equations. The pressure Diffusion process associated with steady convection.The rate of longitudinal diffusion is much lower than the rate of transverse diffusion. According to aforementioned description, the proposed research problem for analysis is physical configured of two rectangular palates of infinite length in kman Eq.  set up wtion Eq.  is buildPartial differential equations may be found in practically every discipline of research and engineering, including mathematics. Only a small number of these instances can be solved analytically to a high degree of precision. It is almost always necessary to use a numerical technique in order to answer the challenges. Non-dimensionalization may also be used to recover the system’s core properties, which might be very useful in certain cases. The method is especially advantageous for system that may be representated by partial differential equations. For numerical solution to aforementioned system, we used finite volume method.Non-dimensionalised boundary conditions:We define the non-dimensional quantities, to Eqs. , 15, 16,By applying non-dimensional quantities  to Eqs. sing Eq.  and withrom Eqs. , 30) to to27\docApply non-dimensionalization technique to Eqs. , 21, suc24,Divide the domain into the sub-domains of finite size(finite control volumes) by a finite number of grid points (like Nodes)24,Over each sub-domain, integrate the governing differential equation (GDE)24.An interpolation function can be considered as a profile assumption for the dependent variable to evaluate the integral expression which give an algebraic quantity at the grid pointsThere are many computational techniques that are most often employed in computational fluid dynamics. These include finite difference, finite volume, finite element and their variations. The current study focuses mostly on the finite volume approach for computing solutions to the aforementioned systems  and 17. Formal integration of the governing equation over a control volume gives the following expression,Nodes at interior:Nodes at le f t boundary: For left boundary node, we use Nodes at right boundary:For right boundary node, we have the following expression;Now we apply FVM to Concentration Eqs. 15) underThe Brinkman’s and Stokes equations for synovial fluid flow at well defined mathematically smooth boundary condition (Beavers and Joseph’s) at 25 in a laminar flow between two parallel plates by taking into account both the homogeneous and heterogeneous reactions of the solvent and solute in the solution. Diabetes patients have increased viscosity of plasma when compared to healthy ones and as a result the diffusion coefficient for diabetes patients is on average higher than that of healthy subjects26. The fluctuation of the mean concentration distribution with axial distance for various values of An analogous conclusion has been reached bythe Fig. .Figure 826. It is also discovered that the cells in the center region get greater nutritional support than the cells in the periphery area. It assists orthopedic surgeons in determining whether or not the joints are functioning properly by using the formula to validate their findings. As a result, the model for steady convective diffusion can be used to develop a mathematical model for articular cartilage regeneration in the future.In this study, it has been shown that the mean concentration distribution decreases with increasing axial distance which can be seen from the Fig. . From Eq30. The Beavers and Joseph slip conditions, which are a specific boundary condition for the synovial fluid, are used to solve the governing system of partial differential equations, and the results are presented here for the first time, illuminating the convective diffusion of the viscous flow along the articular surfaces between the joints. The mechanics of nutrient transport into the synovial joint are of interest, and hence an analytical solution is being developed, along with a numerical simulation (using the finite volume technique). The average concentration was found to be inversely related to both axial distance and time spent in the experiment30. Several graphs have been used to learn about the problem’s many facets, such as its velocity and concentration.The present investigation, the Stokes-Brinkman coupling is used to unite the fluid domain Stokes equation with the porous media Brinkman equation. Evidence suggests that this model accurately represents the knee jointSynovial fluid can be thought of a single component fluid, i.e homogeneous which indicates to use continuous approach. Physically, Brinkman and Stokes solutions suggest comparable velocity behaviour. The outcomes of this study will let us use an alternate equation to comprehend synovial fluid’s homogeneous velocity behaviour, which can be seen from Eqs. , 30 and Synovial fluid has the property of being in-compressible.The content of Hyaluronic acid (HA) in synovial fluid which occurs in the introduction to the model for synovial fluid has an effect on the mechanical reactions of the fluid.29, which can be seen from Fig. Large Darcy number means large permeability and small Darcy number indicates small permeabilityThe current work addresses the use of a specific boundary condition for the flow of synovial fluid with concentration effects in synovial joints. The key findings of this research are as follows;


Early prediction of response to neoadjuvant chemotherapy (NACT) is important to aid personalized treatment in osteosarcoma. Diffusion-weighted Intravoxel Incoherent Motion (IVIM) MRI was used to evaluate the predictive value for response to NACT and survival outcome in osteosarcoma.D, D*, f & D*.f) were estimated using advanced state-of-the-art spatial penalty based IVIM analysis method bi-exponential model with total-variation penalty function (BETV) at 3 time-points and histogram analysis was performed.Total fifty-five patients with biopsy-proven osteosarcoma were recruited prospectively, among them 35 patients were further analysed. Patients underwent 3 cycles of NACT (Cisplatin + Doxorubicin) followed by surgery and response adapted adjuvant chemotherapy. Treatment outcomes were histopathological response to NACT (good-response ≥ 50% necrosis and poor-response < 50% necrosis) and survival outcome  and overall survival (OS)). IVIM MRI was acquired at 1.5T at baseline (t0), after 1-cycle (t1) and after 3-cycles (t2) of NACT. Quantitative IVIM parameters (p = 0.001) higher ADC-25th-percentile  & D-Mean  and lower D*-Mean  were independent predictors of longer EFS  and non-metastatic disease , smaller tumour-volume , lower D*-Mean  and higher D*.f-skewness  were independent predictors of longer OS .Good-responders: Poor-responders ratio was 13 (37%):22 (63%). EFS and OS were 31% and 69% with 16.27 and 25.9 months of median duration respectively. For predicting poor-response to NACT, IVIM parameters showed AUC = 0.87, Sensitivity = 86%, Specificity = 77% at t0, and AUC = 0.96, Sensitivity = 86%, Specificity = 100% at t1. Multivariate Cox regression analysis showed smaller tumour volume (HR = 1.002, IVIM parameters obtained with a 1.5T scanner along with novel BETV method and their histogram analysis indicating tumour heterogeneity were informative in characterizing NACT response and survival outcome in osteosarcoma.The online version contains supplementary material available at 10.1186/s12967-022-03838-1. Neoadjuvant and adjuvant chemotherapy along with surgery have significantly improved the long-term disease-free survival rate of patients with osteosarcoma . Five-yeADC) is a well-established technique for NACT response and outcome evaluation in osteosarcoma, where increase in ADC has been shown to indicate good prognosis [2), have shown evidence for prognostic information for various tumours [Quantitative analysis of advanced functional MRI techniques like Diffusion weighted MRI (DWI) –17, Perfrognosis –17. Usinrognosis , 25, 26.rognosis  DWI, sep tumours –36 along tumours , 38. QuaRecently developed spatial penalty based IVIM analysis method Biexponential model with Total variation penalty function (BE + TV or BETV)  has showFurther, histogram analysis assessing heterogeneity in tumour microenvironment has shown significant improvement in tumour characterisation and predicting the therapeutic response with a more direct correlation with the underlying structural and pathophysiological changes manifested upon tumour progression . NoveltyPatients were enrolled prospectively from March 2016 to March 2018 at Department of Medical Oncology, Dr. B.R.A Institute Rotary Cancer Hospital, All India Institute of Medical Sciences New Delhi, India. Inclusion criteria were treatment naïve patients with biopsy proven osteosarcoma and more than 8 years of age who were planned for NACT. Exclusion criteria were recurrent disease and contradiction to MRI or requiring general anaesthesia for MRI acquisition. All patients underwent NACT, consisting of 3 cycles of Cisplatin and Doxorubicin  every 3 Pathologist, blinded to the clinical status and MRI results, analysed resected lesions, described tumour-size, extent and amount (in percentage) of necrosis relative to the whole tumour volume. Response to NACT was assessed histologically according to the six-grade scale of Salzer-Kuntschik et al. . PatientEndpoints studied were event free survival (EFS) and overall survival (OS). An event was defined as the elapse of secondary tumours or distant metastasis or local recurrence, or death from any cause. EFS was defined as the time interval from the first day of chemotherapy to any of the events or to the last date of follow-up, whichever is first. OS was defined as the time interval from the first day of chemotherapy until death. Patients who were alive without any event at the time of the last follow-up were censored and they were included both in EFS and OS.2) and with matrix size = 192 × 192, TR/TE = 7541/67 ms, slice-thickness/Gap = 5/0.5 mm, voxel-size = 1.3/1.3/5.0 mm, field-of-view = 250 × 250 mm2, and axial slices of 64. IVIM-DWI was acquired at three time-points whereas T1-weighted and T2-weighted images were acquired at time-points t0 and t2.MRI was acquired at three time-points—baseline or pre-NACT (t0), after the 1st NACT cycle  and after the 3rd NACT cycle . MRI acquisition was performed using a 1.5T Philips Achieva® MR scanner with phased-array surface coil or an extremity coil. Conventional T1-weighted, T2-weighted and IVIM-DWI sequences were acquired according to the standard MRI acquisition protocol. T1-weighted and T2-weighted images were acquired using the Turbo-Spin-Echo sequence with TR/TE = 528/10 ms and 3797/60 ms respectively, matrix-size = 512 × 512 and 384 × 384 respectively. Acquisition of IVIM-DWI was performed using free-breathing spin-echo echo-planar imaging (SE-EPI) with a variation of gradient strengths of 11 b-values , Perfusion coefficient (D*) and Perfusion fraction (f) and ADC were evaluated in whole tumour-volume at three time-points t0, t1 and t2. IVIM parameters were evaluated using the state-of-the-art IVIM analysis method BE model with adaptive Total Variation (TV) Penalty function (BETV method) [D*.f) was also calculated voxel-wise in tumour-volume and analysed as it provides the information about vascular changes in terms of relative microvascular perfusion or blood flow, analogous to the blood-flow as measured in perfusion imaging [2 assuming perfusion effect is negligible at higher b-values [Tumour-volume (in cc) at three time-points was determined using region of interest (ROI) drawn manually by an expert radiologist  covering whole tumour on b = 800 s/mm method) . BETV me imaging . ADC wasb-values .2) and Coefficient-of-variation (CV) were calculated as the measure of precision and reproducibility in IVIM parameter estimation respectively. Histogram analysis of imaging parameters was performed in tumour-volume at three time-points. Eleven (n = 11) histogram parameters: mean, standard-deviation(SD), skewness, kurtosis, energy, entropy, 90th, 75th, 60th, 50th and 25th percentiles and their relative percentage changes between time-points t0–t1(ΔI) and time-points t0–t2(ΔII) were calculated for each patient. Quantitative parameters evaluation and histogram analysis was performed using an in-house built toolbox in MATLAB® .Goodness-of-fit , lactate dehydrogenase (LDH) at baseline, and classical prognostic factors like primary tumour site , presence of metastatic disease at diagnosis were evaluated for their potential impact on chemotherapy response and survival outcome as EFS and OS.p < 0.05) of clinical parameters and absolute histogram parameters of ADC, D, D*, f & D*.f and their relative percentage changes (ΔI & ΔII) were evaluated using independent sample t test. Intra-group significant (p < 0.05) changes in parameters across time-points were tested using paired t-test. Predictive performance of statistically significant (p < 0.05) parameters for NACT responsiveness was assessed using receiver-operating-characteristic-curve (ROC) analysis at time-points t0, and t1.Inter-group (between good-response and poor-response) statistical significance (p < 0.05) clinical and imaging parameters on EFS and OS using hazard ratio (HR). Significant histogram parameters derived from ADC, D, D* & f were tested separately for multicollinearity using variance inflation factor (VIF), while VIF ≥ 8 indicated high collinearity. Using Harrells’s c-index [D rank correlation [p < 0.05) after multivariate analysis, and differences were assessed by using a log-rank test. Before inclusion in the Cox analysis, the distributions of the continuous parameters were examined for normality and a multiplicative transformation (× 103–3) as reported earlier [Univariate Cox regression analysis was used to assess the effects of the statistically significant ( c-index  and the relation  (SDRC),  earlier . Analysihttp://www.r-project.org). Workflow of this study is depicted in Fig. Statistical analyses were performed using SPSS v16.0 software (IBM Corporation) and R open-source statistical software  in good-response (n = 13) and poor-response (n = 22) groups at three time-points. Mean R2 value calculated for BETV method fitting were 0.97 ± 0.03 and CV values obtained as ADC:29.6 ± 9.2%, D:30.7 ± 10.1%, D*:96.4 ± 23.2% and f:56.7 ± 12.9%.Table ADC ((1.3 vs. 1.41) × 10−3 mm2/s) and D ((1.21 vs. 1.35) × 10−3 mm2/s) demonstrated similar values among both the response groups with no significant difference (p = 0.36). Mean D* among good-responders was significantly lower than the poor-responders (D* = (23.76 ± 7.56 vs. 30.95 ± 10.80) × 10−3 mm2/s; p = 0.04), whereas, f and D*.f were not significantly different among both the response groups at baseline.At baseline, mean ADC and D showed significant increase among both response groups, however, higher increase among poor-responders were observed than the good-responders (ΔI = 29%↑ vs. 20–22%↑). At t1, mean ADC and mean D values were significantly (p < 0.02) lower among good-responders than poor-responders. D* and D*.f were observed to decrease after 1st cycle of NACT among both response groups. At t1, mean D*.f among good-responders was significantly lower than the poor-responders (D*.f = (2.97 ± 1.02 vs. 4.49 ± 2.32) × 10−3 mm2/s; p = 0.01).At t1, both ADC and D did not increase any further among poor-responders, whereas in good-responders the increase in value was persistent (ΔII-ΔI = 1%↑ vs. 12%↑). D* and D*.f showed significant reduction at t2 in comparison to baseline values. However, in the course of NACT, comparatively a higher reduction in D* and D*.f  were observed among good-responders than the poor-responders. During NACT, f did not change significantly among both the response groups; however, f-mean showed reduction among good-responders as well as poor-responders after NACT.After t1, D*-skewness  was significantly higher and D*-entropy  and D*-90th-percentile ((60.83 ± 18.23 vs. 72.14 ± 8.1) × 10−3 mm2/s; p = 0.03) were significantly lower among good-responders than poor-responders. At baseline, f-entropy was significantly lower among good-responders  and significantly lower D*.f-entropy  were observed among good-responders than poor-responders.Detailed comparison of histogram parameters between good-response and poor-response groups is presented in Additional file ADC and 90th–25th percentile values of D were significantly (p < 0.02) lower among good-responders than poor-responders. Histograms of ADC and D became more negatively skewed among good-responders than poor-responders  after NACT. During NACT, D* and D*.f histograms were more peaked and positively skewed among good-responders than that of poor-responders .At t1, 90th percentile of Illustrative example of IVIM parametric maps and corresponding histograms in tumor volume of representative patients with tumor involving different anatomical regions like femur, tibia and humerus are presented in Figs. p > 0.13) between good-response and poor-response groups. Statistically significant imaging parameters and their ROC curve analysis for chemotherapy response prediction is presented in Table p < 0.05) histogram parameters D*-skewness, D*-entropy, D*-90th- percentile, f-entropy and D*.f-entropy jointly with mean of ADC,D,D*,f &D*.f showed AUC = 0.87 with Sensitivity = 86% and Specificity = 77% in predicting poor-response at t0  histogram parameters ADC-90th-percentile and D-90th–25th-percentiles produced AUC = 0.96 with Sensitivity = 86% and Specificity = 100%; in predicting poor-response to NACT  associated with EFS. These parameters derived from ADC, D and D* respectively had high VIF scores  and after comparing the c-index and SDRC values , both the parameters were tested separately along with the other parameters  as two separate models for multivariate cox analysis. Hazard ratio forest plots for multivariate analysis are depicted in Additional file p = 0.001) and ADC-25th percentile  and EFS-Model-2 including tumour-volume , D-Mean  and D*-Mean  parameters that showed significant and independent association with EFS. Both EFS-Model-1 and EFS-Model-2 met the requirement of proportionality of the covariates , lower level of ADC-25th percentile , D-Mean  and higher D*-Mean  at baseline.Imaging parameters D*-Mean, D*-skewness, D*-90th–25th percentiles, D*.f-skewness, D*.f-kurtosis, D*.f-entropy, D*.f-60th–25th percentiles were significant (p < 0.05). These parameters were derived from D* and thus had high VIF scores and after comparing the c-index and SDRC values  to develop multivariate cox proportional hazard models. Hazard ratio forest plots for multivariate analysis are depicted in Additional file p < 10–3) and D*-Mean  and OS-Model-2 including metastasis , tumour-volume  and D*.f-skewness  parameters that showed significant and independent association with OS. Both OS-Model-1 and OS-Model-2 met the requirement of proportionality of the covariates , larger tumour-volume  and higher levels of D*-mean  and lower D*.f-skewness  at presentation.Univariate analysis for OS showed, imaging parameters This prospective study evaluates the role of non-invasive quantitative IVIM imaging in characterizing tumour microenvironment, predicting chemotherapy response and long-term survival outcome of osteosarcoma at baseline and early in the course of treatment.D*.f predicted poor-response to NACT with high AUC .Histogram analysis of IVIM-DWI parameters showed, microvascular perfusion and its heterogeneity in tumour were significantly higher among poor-responders than good-responders characterizing higher angiogenic changes among poor-responders, thus effectively predicted poor-response to NACT at baseline . While after the first cycle of NACT, diffusion parameters along with ADC-25th percentile, D-Mean and lower D*-Mean as the independent predictor of EFS; while nonmetastatic disease, smaller tumour volume, lower levels of D*-Mean and higher D*.f-skewness were independent predictors of OS. These findings suggest quantitative IVIM parameters and their histogram analyses may be useful for characterizing and quantifying heterogeneity in tumour micro-environment and thereby predicting chemotherapeutic response and outcome in osteosarcoma.For survival outcome, multivariate Cox regression analysis showed smaller tumour volume, higher levels of ADC and D and reduction in D* and f similar to the previous studies in osteosarcoma [IVIM imaging is influenced by the diffusion of free water molecules in the intra & extra-cellular compartments and micro-circulation of water molecules in micro-capillaries. The observations in this study support that cell density and abnormal/immature micro-vessels may decrease in osteosarcoma after chemotherapy resulting an increase in osarcoma , 37, 38 osarcoma , head anosarcoma , cervicaosarcoma , nasophaosarcoma , 34, breosarcoma , hepaticosarcoma  and otheosarcoma , 29.ADC and D values were observed among both the response groups possibly due to high heterogeneity of cellularity in osteosarcoma, similar to the previous studies [ADC and D values in tumour were significantly higher among poor-responders than the good-responders; however, after an initial increase in ADC and D at t1 (ΔI≈29%↑), diffusion did not increase any further (ΔII≈ΔI≈29%↑) among poor-responders. Necrotic tumours with large extracellular space  are often associated with poor response to therapy [ADC and D both were observed to be increased throughout all NACT cycles  indicating possible increase in cell death resulting in an increase in diffusion of water molecules in the tumour. At baseline, higher levels of diffusion parameters were observed to be associated with improved EFS in osteosarcoma patients similar to earlier studies in other tumours [At baseline, similar mean  studies , 15, 37. therapy , 31, 34  therapy . Whereas tumours , 29, 36.D*, f, D*.f) correlate with the process of angiogenesis and reflect changes in relative microvascular perfusion, perfusion volume-fraction and flow in tumour respectively [D* and f were significantly correlated with the micro-vessel density score in murine model colorectal cancer; providing information about tumour perfusion and angiogenesis [D* and D*.f during chemotherapy and lower heterogeneity in tumour microvasculature among good-responders indicated relatively lower angiogenic progression. Lower levels of perfusion parameters were associated with improved EFS and OS in osteosarcoma patients similar to earlier studies in other tumours [D* & f) had higher predictive values (AUC = 0.6–0.8) than diffusion parameters (ADC & D) (AUC = 0.6–0.7) at baseline and after the 1st cycle of chemotherapy in predicting NACT response.IVIM perfusion related parameters . Measurement of true tissue diffusion was observed to be necessary and useful for characterizing chemotherapeutic changes in osteosarcoma, similar to other IVIM studies in literature [osarcoma –17. Howeterature , 30, 31.terature , 32 and terature , 35 evalterature , 54. Thuterature . This reterature  incorporterature  with NNLterature , Ewing sterature , lymphomterature , brain tterature  and prosterature . In thisThere are a few limitations of our study. First, among the available pathological scales to categorise chemotherapeutic response groups, in this study, patients were categorised into good-response (≥ 50% HPE-necrosis) and poor-response(< 50% HPE-necrosis) groups with reference to the earlier study by Picci et al. . SecondlD) and perfusion-related parameters  and their histogram analysis  indicating heterogeneity in micro-vasculature in tumour are useful imaging markers to predict survival outcome at presentation and non-response to NACT before and early in the course of treatment. Therefore, quantitative IVIM analysis with advanced analysis methods can serve as a surrogate marker for characterizing chemotherapeutic response, which can be used for non-invasive monitoring and evaluation of chemotherapy response and treatment outcome in patients with osteosarcoma.In conclusion, clinical parameters such as tumour volume, nonmetastatic disease and ALP can be independent predictors of survival outcome. IVIM diffusion (Additional file 1. Additional Tables and figures.


Europe has faced a massive spread of the varicella-zoster virus through the years. Since the introduction of an effective vaccine, complications and severe forms of chickenpox have been restricted. Nevertheless, among the population, some categories need specific care, such as pregnant women, who present one of the most fragile conditions facing this infection, both for the mother and the fetus. In this review, we highlight how the varicella-zoster virus can be dangerous during pregnancy, underlining the problem of treatment and vaccination, and collect information about the European epidemiology among this particular category of women. Varicella-zoster virus (VZV) is a human-specific α-herpes virus responsible for chickenpox and herpes zoster. VZV is a virus with a lipid-rich envelope acquired from cellular membranes, where viral glycoproteins are inserted. Inside the envelope, a tegument layer formed by regulatory proteins surrounds an icosahedral nucleocapsid core containing the linear double-stranded DNA genome ,2.VZV is responsible for two clinical pictures. Chickenpox is the primary infection, which predominantly affects children, and it is characterized by fever, vesicular, and a pruritic and painful rash, while herpes zoster is typical in adulthood and third age, and it is caused by reactivation of the VZV latency form. Particularly after the primary infection, VZV establishes latency in the sensory nerve root ganglia, and it could reactivate as a consequence of immunosuppression. VZV reactivation leads to viral replication and to the release of newly produced virus particles along the nerve pathway, causing a vesicular rash in the dermatome innerved by the affected sensory ganglion ,3Since chickenpox is extremely contagious, it is one of the most common exanthematous diseases ,4. AlthoSpecifically, varicella is often acquired through the inhalation of saliva droplets containing the infecting VZV and, more rarely, through direct contact with skin lesions of subjects affected by varicella or zoster . Once VZThe epidemiology of chickenpox is different between temperate and tropical climates. In temperate areas, before VZV vaccine introduction, VZV primary infection was hugely common during childhood. In fact, in North America, Europe, and Japan, more than 90% of the population contracted this disease by 15 years of age, whereas in tropical countries, only 25–85% of the child population is infected by VZV . TherefoThe aim of this review was to analyze the current epidemiology of VZV infection among pregnant women in European countries, underlining the effect of VZV infection in pregnant women, its management, and the impact of vaccination on this particular susceptible population and on newborns and children.Varicella pneumonia is the most frequent maternal sequelae of VZV primary infection, affecting 10–20% of pregnant women with chickenpox, while encephalitis and hepatitis are rare complications . Mortaliper se, in which fetal immature-cell-mediated immunity is not able to counteract the virus ..62].Several studies on VZV seroprevalence reported higher seronegativity rates in Italian and Spanish pregnant women, 10.6% and 12%, respectively, compared with other reported European rates (less than 5%) ,50. HoweA Belgian study reported 98% seropositivity among pregnant women , unlike A Norwegian study reported  that 98.Seropositivity to varicella in three different countries was evaluated using TORCH analysis: in Germany, 99.4% of women tested positive for IgG antibodies against VZV; in Poland, 98.1%; and in Turkey, 98.6% .However, VZV vaccination is not recommended in pregnant women, and postpartum administration of VZIG in seronegative women is advisable for prevention in case of further pregnancies  and in nAs regards VZV vaccination, in European countries, the situation is heterogeneous. In fact, this vaccine has been part of the routine vaccination program of four countries: Germany since 2004, Spain since 2006, Greece since 2006, and Latvia since 2008 . AccordiTo date, in 17 European countries, VZV vaccination is recommended for individuals at risk and immunocompromised ones, while there are no policies for 12 nations. Italian policies regarding this vaccination changed from a regional recommendation  to a nationwide program in 2017 ,65.According to the last ECDC report about varicella (2015), in Europe, reported chickenpox cases mostly involved children in the first years of life; in particular, 52–78% of infections occurred in children aged 6 years, and 89–95.9% of cases occurred before adolescence. In order to prevent varicella infection, varicella vaccination has been widely recommended for children and adults in regular or close contact with high-risk individuals. In fact, varicella vaccines are highly effective and safe. The data collected about the single-dose varicella vaccination and the two-dose vaccine showed that both regimens protected efficiently against VZV infection. However, the protection exerted by the two-dose regimen was higher than the single-dose one .In particular, in Germany, which is the most experienced European country in varicella vaccination, after the introduction of universal varicella vaccination for children over 11 months of age in 2004, a reduction of 55% of varicella cases was registered for all ages, and specifically, 63% in the age group 0–4 years and 38% in the age group 5–9-years. Moreover, in 2012, VZV case reduction was 84% for all ages. Accordingly, hospitalization rates due to chickenpox also decreased after routine vaccination introduction .In Italy until 2017, when varicella vaccination became mandatory for each child in order to attend primary education, vaccination against VZV with a two-dose schedule was only recommended for susceptible adolescents of 11–18 years of age and high-risk subjects . However, 8 regions out of 21 introduced the universal chickenpox vaccination in childhood. Subsequently, data from Tuscany and Sicily displayed a significant decrease in varicella incidence and hospitalization rates .In Spain, after the implementation of the varicella vaccination of all susceptible Spanish teenagers between 10 and 13 years of age, a rapid reduction of 77% in the chickenpox incidence rate was registered, and consequently, the hospitalization rate was halved .Two different studies concern VZV seropositivity in newborns: Waaijenborg et al. described that the protection exerted by anti-VZV maternal antibodies lasted for the first 3.4 months in Dutch newborns , and LeuThe latest report by EUVAC described that the highest number of VZV cases is registered among unvaccinated children below the age of 10, with complications and hospitalization rates higher in newborns younger than 1 year and, in the oldest children, older than 15 years .The hospitalization rate was evaluated in different countries: in Germany, Spain, Italy, France, and Greece, it was 14.1–28/100,000 children, while in the UK and Ireland, it was 0.82/100,000 children.Complications in hospitalization primarily occurred in the well-being of children in all the countries where VZV children cases were detected, such as France, Germany, Poland, Switzerland, Spain, Italy, and The Netherlands. The most common ones were bacterial superinfection of skin and tissue, respiratory complications, and hematological, gastrointestinal, and neurological ones .Not only the implementation of routine chickenpox vaccination but also the monitoring of its impact on the population are crucial to assess vaccination effectiveness. An accurate surveillance program should be implemented before vaccine introduction. It should continue after the vaccine introduction, and it should include vaccine coverage, vaccine effectiveness, the occurrence of adverse events, age-specific varicella disease severity and age-specific varicella incidence, herpes zoster cases, and hospitalizations.However, in Europe, the currently retrievable manuscripts about VZV infection and vaccine monitoring are very few and often date back many years. Therefore, there is a need to maintain VZV infection surveillance programs to understand viral circulation in the light of the VZV vaccine era and to protect people ineligible for vaccination.


Appropriate antifungal treatment is crucial for managing IFI. We evaluated the 1,226 moulds causing IFI  were consecutively collected (1/patient) in 43 worldwide hospitals from 2017-2021 and susceptibility tested by CLSI broth microdilution. CLSI interpretation criteria were applied. Pneumonia was the predominant infection type among ICU (81.6%) and non-ICU (71.8%) patients.Aspergillus spp. was the most common mould (87.6%/80.5%). A. fumigatus , A. section Flavi , and A. section Nigri  were the top 3 Aspergillus groups. ISC inhibited 94.2%/91.2%, 100.0%/98.6%, and 96.0%/96.7% of AFM, ASF, and ASN from ICU/non-ICU, respectively, at the epidemiological cut-off values. Similar activities were noted for ISC, PSC, ITC, and VRC against AFM from ICU  and non-ICU  isolates. No difference in the azole activities was noted against ASF from ICU  and non-ICU  or ASN from ICU  and non-ICU  isolates. VRC-non-susceptible (NS) AFM isolates were detected in 9.3% of ICU and 9.0% of non-ICU isolates. Among VRC-NS AFM from ICU/non-ICU, 58.3%/27.5% were wildtype (WT) to ISC, 50.0%/25.0% were ITC-WT, and 75.0%/50.0% remained VRC-WT, respectively. VRC was the most active azole against Scedosporium spp. (SCE) but was less active against Mucorales (MUC) than the other azoles. All azoles showed limited activity against Fusarium spp. (FUS). No difference on azole activities against SCE, MUC, and FUS were noted from ICU and non-ICU isolates.Aspergillus isolates from ICU and non-ICU, including against VRC-NS AFM from ICU. Azoles displayed similar activity against moulds from ICU and non-ICU units.ISC was active against Cecilia G. Carvalhaes, MD, PhD, AbbVie: Grant/Research Support|Cidara: Grant/Research Support|Melinta: Grant/Research Support|Pfizer: Grant/Research Support Paul Rhomberg, BS, MT(ASCP), Cidara: Grant/Research Support|Pfizer: Grant/Research Support Paul Rhomberg, BS, MT(ASCP), Cidara: Grant/Research Support|Pfizer: Grant/Research Support Beth A. Schaefer, BA, MT(ASCP), Pfizer: Grant/Research Support Michael A. Pfaller, MD, Pfizer: Grant/Research Support Mariana Castanheira, PhD, AbbVie: Grant/Research Support|Cidara: Grant/Research Support|GSK: Grant/Research Support|Melinta: Grant/Research Support|Pfizer: Grant/Research Support|Shionogi: Grant/Research Support.


As one of the typical fluorescent cores, dicyanomethylene-4H-pyran (DCM) derivatives exhibit excellent photophysical and photochemical properties, such as large Stokes shift, excellent light stability, and tunable near-infrared (NIR) emission. The luminescence mechanism of DCM probes mainly depends on the intramolecular charge transfer (ICT). Hence, by regulating the ICT process, the probes can specifically act on the target molecule. Accordingly, a series of NIR DCM probes have been constructed to detect the ions, reactive oxygen species (ROS), and biological macromolecules in cells. However, there is no relevant review to summarize it at present. This minireview mainly summarizes the NIR DCM probes based on ICT effect and their applications in biosensors and biological imaging in recent years. This will be beneficial to innovatively construct new DCM probes and actively promote their application in the future. Dicyanomethylene-4H-pyran (DCM) is a typical fluorophore, which shows excellent photophysical and photochemical properties, such as large Stokes shift, excellent light stability, and tunable near-infrared emission . DerivatGenerally, DCM molecules possess a D-π-A configuration, showing a typical ICT effect. ICT effect usually occurs in a molecule with a D-A structure. When the probe with the ICT effect is activated, the charge is apt to flow from the donor (D) segment to the acceptor (A) segment, resulting in the variation of luminescence. Hence, the emission properties of DCM probes can be adjusted as required by tuning the molecular conjugation system or altering the electron donor group. In addition, DCM probes with an “off–on” function have been obtained successfully by introducing fluorescence-quenching groups as well as other active groups. When the probe contacts the target, the ICT mechanism of the DCM probe is reactivated due to the separation of the fluorescence-quenching group by reaction with the target . Thus, tDCM derivatives with “off–on” characteristics have been successfully applied in detection of ions, hydrogen peroxide, and enzymes. In this section, we will systematically and roundly discuss the current applications of “off–on”-type DCM derivatives with NIR emission based on the ICT mechanism.−. The probe was prequenched skillfully with the diphenyl hypophosphite group, which was sensitive to ONOO− , −-targeting probe DCM-2 based on the DCM core , which plays a vital role in a variety of physiological processes. Studies have shown that the imbalance of Hdiseases . Therefo2O2, DCM-6∼7 was obtained with fluorescence quenching because the electron-donating effect of -OH was blocked  design of new-type DCM derivatives with water-solubility, which exhibit great potential in detection of physiological environment, bioimaging, and disease detection drug carriers, etc., 2) developing more applications for DCM probes, such as new iron detection, detection of important active enzymes in the human body, disease screening, and visualization of latent fingerprints. In short, this mini minireview has summarized most of the excellent reports of off–on DCM probes with red emission in this field. It is anticipated that DCM probes with fluorescence activation features will attract more and more interest and greatly flourish.


Traditional serological tumour biomarkers like α‐fetoprotein, prostate‐specific antigens, carcinoembryonic antigens, CA19–9 and CA125 have been widely investigated in clinic, but the specificity has not reached a satisfactory stage for population level.+ T cells both in TILs and in peripheral blood mononuclear cells (PBMCs),Until recently, tumour‐infiltrated T lymphocytes (TILs) were considered to be beneficial tumour‐specific T cells.Next generation sequencing (NGS)‐based T cell receptor (TCR) repertoire quantification has provided methods for TSA recognition and now is extensively used in the identification of tumour‐reactive T lymphocytes.Under physiological conditions, naïve T cells maturing in the thymus will flow through peripheral blood or lymphatic vessels and migrate through high endothelial venules into secondary lymphoid organs where they encounter potential tumour antigens.In this study, we defined a group of circulating T lymphocytes in PBMCs that shared TCRs with TILs as tumour‐associated T cells (TATs). Using the CDR3 sequences of TATs with those in healthy TCRs as input data, we trained a binary model to distinguish TATs from healthy clones. Applying this model on several independent clinical datasets, we acquired the number of TAT sequences in PBMCs for each individual. We then designed a TCR repertoire risk score (TRRS) as the number of TATs our model predicted in the PBMCs divided by the number of detected healthy TCRs from healthy individuals. We demonstrated that the TRRS separated tumour patients from healthy donors effectively. Next, we characterized the transcriptional signatures of TATs in the PBMC populations using multiple single cell RNA sequencing (scRNA‐seq) coupled with TCR sequencing datasets and found that T cell activation pathway was significantly up‐regulated in TATs. Combining the TCR repertoire and transcriptional signatures of TATs,PBMCs_Shared compartment, while clonotypes of TILs that are identical to those of PBMCs are called TILs_Shared compartment. These two compartments have the same TCR clonotypes, but their tissue sources distinguish them. Due to different tissue environment, the frequency of each clonotype and the degree of clonality between the two compartments may differ. Therefore, we specifically named the T cells among PBMCs that share TCRs with TILs as circulating Tumor Associated T cells (cTATs). In contrast, the TCR clonotypes that are unique to TILs are in the TILs_only compartment, and the TCR clonotypes observed only in PBMCs are in the PBMCs_Only compartment. We believe that the PBMCs_Only compartment most likely represents naïve, effector or memory T cells in periphery that are not related to the tumour immune response. T cells in the TILs_Only compartment may largely represent tissue‐resident T cells, which are not in the set of T lymphocytes prevalent in circulation. It should be noted that due to the technical limitation of TCR repertoire sequencing, both TILs_Only and PBMCs_Only compartments actually contain a proportion of overlapped clonotypes that cannot be detected sensitively at present.T cells with identical TCR sequences are thought to be derived from a single naïve T cell, which migrates and circulates among different tissue types and may undergo a functional state transition upon antigen stimulation.This framework allowed us to use TCR sequences as molecular barcodes to track and analyse the function of TATs among TILs and PBMCs. We collected a series of TCR CDR3 β chain sequencing data of paired PBMCs and tumour tissues from the same patient Table  and assiNext, because TCR beta chain variable (TCRBV) genes contributed the most diversity to CDR3 sequences, we analysed the TCRBV gene usage in the different compartments. We found that TRBV genes, such as TRBV06/14/15/20/25, are expressed more frequently in the shared compartments than in the non‐shared compartmentsTILs_Shared compartment : 12.82%–37.51%) was significantly higher (p < 1e−4) than the proportion in the cTATs of the PBMC population  in the proportion of shared TCRs at either the clone or sequence level, possibly due to the limited number of cells captured in single cell TCR‐seq datasets, and many shared clones might be labelled as not shared  than those in PBMCs_Only compartment  than those of the shared compartments and higher than those of the tissue‐only compartments  curve and precision‐recall curve (PRC) Figure  showed tThen, to test whether the TAT prediction probability of our model can be used as a biomarker for differentiating tumour patients from healthy donors, we obtained seven independent datasetsp < 1e−3) than those in the low‐risk group  in the CD4+/ CD8+ ratio between PBMCs from cancer patients and healthy donors  in tumour patients than in healthy donors  in patient PBMCs  technology makes it possible to not only trace the TCR clone sharing relationship between tumour and paired PBMCs samples, but also quantify the transcriptomics patterns at the single cell level.s Figure , implyins Figure , indicats Figure , suggestp < 1e−4) in nearly all the 14 datasets s Figure , Methodss Figure . These r+ TATs and non‐clonal T cells in each dataset. To prevent potential batch effects caused by using different data sources and guarantee a robust analysis, genes that were differentially expressed in more than 10 of the 14 datasets were selected to generate the TAT signature genes  in TATs, while the purine metabolism pathway was significantly depleted (p < 1e−3) in TATs . The ROC demonstrated that the gene module can be satisfactorily generalized across different datasets Figure  satisfac+CD8+ T cells were believed to be tumour‐specific biomarkers in both TILs and PBMC population,p < 1e−4). However, using the normalized expression of this gene alone was not sufficient to distinguish TATs from non‐clonal T cells  PBMC samples and 12 healthy PBMC samples. We named these datasets validation cohort 1 Table . PrincipThe tumour reactive immune response is usually triggered by immunogenic neoantigens expressed on tumour cells. Therefore, we sought to determine whether the tumour immunogenicity score (TIGS)Both the TCR repertoire and transcriptional signatures of TATs have the capacity to distinguish tumour patients from healthy samples. To determine whether the combination of these two types of features can be leveraged to achieve better performance for non‐invasive tumour screening, we performed bulk TCR and RNA sequencing of PBMCs samples from 11 tumour patients and six healthy donors Table . HereaftNext, by integrating these two types of features, we designed a novel tumour screening framework. We first used the TAT prediction model to generate the TRRS for each PBMC sample. Then, we calculated the TAT signature score using bulk RNA‐seq data obtained from the same sample. The two scores were then used to build a binary logistic regression model and define the final cancer risk score as the probability of the model prediction Figure . We founUnderstanding the tumour‐specific T lymphocyte response helps us explore immune signatures of tumourigenesis. In this study, we first separated the T cells into four different compartments based on the TCR‐sharing relationship and analysed the clone expansion differences between these compartments. We used a group of TATs in the PBMC population that shared TCRs with TILs and a group of healthy TCRs to build a binary model. We designed a TRRS, which is the number of predicted TATs among PBMCs divided by the number of healthy TCRs in the healthy TCR pool. We found that TRRS can serve as an indicator to distinguish tumour patients from healthy donors in a series of clinical cohorts with different cancer types. In addition, leveraging the scRNA‐TCR‐seq data of paired tumour tissues and PBMCs, we characterized the transcriptional signatures of TATs and found that T cell activity and cytotoxicity were increased in TATs. TAT signature genes mined from single cell data were also validated with bulk RNA‐seq PBMC data, showing high applicational prospect. Finally, combining the information from the TCR sequence and signature gene level of TATs, we designed an integrated framework for tumour screening and validated it with an independent clinical dataset. This framework considered tumour‐associated TCR repertoire and transcriptional patterns of TATs, providing insights into an alternative strategy of liquid biopsy on the basis of the immune cells.Our method offers a new perspective for tumour screening harnessing the tumour‐associated immune response. Imaging‐based diagnostic assessments, including breast mammogram and low dose computed tomography scan screening methods, are used in limited cancer types.Our method provides a feasibility of tumour screening from the view of tumour‐associated T cells that are antigen‐independent, shifting the diagnostic paradigm from tumour‐modality‐driven to immune‐response‐driven.There are limitations of our study, which should be considered. Our binary predictive model based on TCR sequences from different patients and cancer types does not currently address HLA restriction information. HLA restriction information is very important to the TCR repertoire distribution in patients since it is related to the pattern of the antigen peptides presented. We believe that, when it is accessible, this information can be incorporated into the model, and adding it will improve model performance. Leveraging deep learning architecture allows us to incorporate many types of features to predict TATs. However, we realize that transcriptional signatures are essential but not determinant factors for distinguishing tumours from healthy samples. The immune reaction induced by TSAs may be similar to that included by common pathogen infections. In our study, we have mined publicly available databases of virus/bacteria TCR sequences and removed all the T cell clones overlapped with the sequences in these databases before developing the model or performing differential gene expression analysis, alleviating this problem to a great extent. Notably, the starting material and library construction methods for TCR sequencing can profoundly influence the TCR repertoire detected. Using gDNA as the starting material for TCR sequencing leads to more stable results and allows for better quantification of each single TCR clone, while employing RNA potentially provides information on expression levels. Therefore, consolidating TCR sequencing data from different starting materials and library construction warrants further efforts.The authors declare no potential conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Objectives: Pleural effusion is a common reason for hospital admission with thoracentesis often required to diagnose an underlying cause. This study aimed to determine if the imaging characteristics of TUS effectively differentiates between transudative and exudative pleural fluid. Methods: Patients undergoing TUS with pleural fluid analysis were retrospectively identified at a single center between July 2016 and March 2018. TUS images were interpreted and characterized by established criteria. We determined diagnostic performance characteristics of image criteria to distinguish transudative from exudative pleural effusions. Results: 166 patients underwent thoracentesis for fluid analysis of which 48% had a known malignancy. 74% of the pleural effusions were characterized as exudative by Light’s Criteria. TUS demonstrated anechoic effusions in 118 (71%) of samples. The presences of septations on TUS was highly specific in for exudative effusions (95.2%) with high positive predictive values (89.5%) and likelihood ratio (2.85). No TUS characteristics, even when adjusting for patient characteristics such as heart failure or malignancy, were sensitive for exudative effusions. Conclusions: Among our cohort, anechoic images did not allow reliable differentiation between transudative and exudative fluid. Presence of complex septated or complex homogenous appearance was high specific and predictive of exudative fluid.  Over 1.5 million people develop pleural effusion each year and there is an estimated prevalence of 60% in the intensive care unit (ICU) In this study, we examined the diagnostic performance of TUS in predicting transudative and exudative effusions. This study was approved by the University of Pennsylvania Institutional Review Board (IRB number 828853). The requirement for written informed consent was waived by the board. We retrospectively identified consecutive patients who were evaluated by the procedure service for consideration for thoracentesis with archived pleural effusion images at a single large academic center between July 2016 and March 2018. At our center thoracenteses performed on the acute care medicine floors are performed by a dedicated procedure service staffed by Nurse Practitioners and Physician Assistants supervised by Pulmonary and Critical Care faculty. The service performs approximately 600 thoracenteses per year.Patient clinical, demographic and pleural fluid analysis data were abstracted via chart review by physicians. TUS was performed with a Sonosite Xporte machine using a phased-array sector transducer in the abdominal preset. The other acquisition specifics, including gain, depth, and patient positioning were left to the discretion of the operator. TUS was performed by multiple operators with experience in lung ultrasound. All pleural effusions included a short video clip and/or still images identifying the diaphragm, pleural effusion and lung parenchyma. The TUS images were analyzed retrospectively by board certified pulmonary and critical care physicians (CB and DD) who were blinded to the clinical data (including Light’s criteria) and prior image interpretation. Disagreement between two primary reviewers was arbitrated by an ultrasound fellowship trained physician (NP). The TUS images of the pleural effusions were classified by previously published criteria The decision to complete a thoracentesis was made by the primary team (rather than procedure service performing the imaging and thoracentesis), unless TUS demonstrated lack of adequate volume for safe drainage or other safety concerns. Pleural fluid was sent for analysis and specific tests were ordered by the primary team and typically included: gram stain and culture, pH, lactate dehydrogenase (LDH), cell count and differential, total protein, glucose, and cytology. It was standard practice for the primary team to send serum LDH and total protein at the time of the procedure. Pleural effusions were classified as exudative or transudative based on the Light’s criteria Continuous variables are presented as mean ± standard deviation (SD) or median with interquartile range (IQR) for skewed data. Categorical data are expressed as frequencies and proportions. Logistic regression was used to determine the relationship between sonographic findings and final categorization of the effusion based on the Light’s criteria. Cohen’s kappa coefficient was calculated to determine the inter-observer agreement for the sonographic score. Two by two tables were created to determine the sensitivity, specificity, positive predictive value and negative predictive value. Analyses were performed using Stata version 15 . Our analysis included 166 thoracenteses performed . Patients’ mean BMI was 29.4 and 41.5% of patients were female. Patient demographics characteristics are described in Table 1.Ultrasound images included 118 images that were anechoic, 29 complex and non-septated, 18 complex and septated, and 1 homogenously echogenic. Figure 3 displays the relationship between the TUS findings with a pleural effusion classification of transudative vs exudative. After laboratory analysis, 124 (74%) were exudative by Light’s criteria, while 42 (25%) were transudative. The etiologies of effusion included malignancy (48%), infection (5%), hepatohydrothorax (14%), heart failure (7%) and other (26%). The breakdown of ultrasound image characteristic and fluid sampling is outline in Table 2.Sensitivity of a nonzero sonographic score for an exudate was 29% (95% CI 21-38%), while specificity was 71.4% (95% CI 55-84%). The positive likelihood ratio (LR) of an exudate being present on a thoracentesis following a nonzero sonographic score was 1.0 (95% CI 0.6-1.7). The presence of septations had a specificity of 95.2% (95% CI 84-99%) for an exudate. The positive LR of an exudate being present on thoracentesis following TUS findings of septations was 2.85 (0.7-12). After excluding patients for whom only still images were available, the specificity, PPV and positive likelihood ratio of the presence of septations increased to 96% (95% CI 80-99), 94% (95% CI 73-99), and 4.7 (95% CI 0.66-33.8) respectively. The diagnostic performance of TUS to predict transudative and exudative effusions are described in Table 3 and Table 4. Patient conditions including as decompensated heart failure, hepatohydrothorax, and malignancy had wide distributions of pleural diagnosis (Tables 5-7).There was disagreement between the two primary raters in 18 (11%) cases, most commonly due to assessment of the presence of gain artifacts. Overall agreement between the two raters was substantial with a Cohen’s kappa of 0.75. Overall agreement between the two raters between TUS videos only was almost perfect with a Cohen’s kappa of 0.81. Interrater reliability was decreased between the two raters when only ultrasound still images were used with a Cohen’s kappa of 0.67.In a cohort of 166 pleural effusions, the presence of septations on TUS evaluation was highly specific for exudative fluid, though not predictive for a specific etiology. Nonseptated echoes in the pleural space was only moderately specific, and not sensitive for exudative fluid. Finding anechoic fluid did not reliably differentiate transudative or exudative fluid. 7. Outside of a much lower sensitivity (21% vs 70%) for complex effusions our findings support the conclusions of that study, in a different patient population at a different center. Previous studies have examined the relationship between ultrasound images and pleural fluid characteristics. Yang et al. evaluated a cohort of 320 patients with pleural effusions, finding high sensitivity, but poor specificity of anechoic effusions for transudative effusions These findings support and extends existing primary literature in several ways. The performance characteristics generally mirrored that of the most recent evaluation of TUS by Shkolnik et al, supporting the high specific and PPV of septations for exudative fluid, though the confidence interval for the positive LR cross 1.0 and are wide The study also highlights the importance of video images for interpretation with increases of both testing characteristics and inter-rater agreement when compared to evaluation with still images. When disagreement occurred between the two primary raters, it was most frequently in still images and often due to assessment of the presence of gain artifacts.Future studies should focus further standardization of machine settings and scanning protocols. Additionally, our study highlights the need to identify other ultrasound imaging characteristics to improve diagnostic performance. Studies have identified the presence of pleural masses, pleural thickening >1 cm, pleural nodularity and diaphragmatic thickening >7 mm as TUS findings that are highly suggestive of malignant pleural effusion disease Limitations of our study include describing only a single center, which may limit generalizability, though had similar findings to other studies at other centers. Certain pleural fluid diagnoses may have been more common at our institution and less common at other locations. Some patients in our series did not have a complete set of diagnostic tests reviewed for the study because the tests were ordered by the physician responsible for the patient’s care at the time of thoracentesis. This may have led to some selection bias . Lastly, while there was high degrees of inter-rater reliability, it was not perfect. This highlights the potential need for strict criteria for classification of effusion, and potentially the limitation of retrospective video review in the absence of a standardized scanning methodology. Though used by experienced operators that frequently work together, machine settings including gain were not always consistent. Insufficient gain could inappropriately characterize a complex, nonseptated effusion as anechoic while with too much gain faint artifacts will make anechoic fluid appear to have internal echoes TUS is already part of the standard of care in thoracentesis and using that information beyond simply marking the puncture site can add value. Our analysis suggests that anechoic pleural fluid images do not reliably distinguish between transudative and exudative fluid. While features such as complex septation or homogenous echogenicity are high supportive of exudative fluid, further research is needed to identify other features that would increase diagnostic accuracy. Dr. Cameron Baston receives royalty payments from McGraw Hill for a textbook on POCUS.


Increasing evidence indicates the potential benefits of restricted fluid management in critically ill patients. Evidence lacks on the optimal fluid management strategy for invasively ventilated COVID-19 patients. We hypothesized that the cumulative fluid balance would affect the successful liberation of invasive ventilation in COVID-19 patients with acute respiratory distress syndrome (ARDS).We analyzed data from the multicenter observational ‘PRactice of VENTilation in COVID-19 patients’ study. Patients with confirmed COVID-19 and ARDS who required invasive ventilation during the first 3 months of the international outbreak  across 22 hospitals in the Netherlands were included. The primary outcome was successful liberation of invasive ventilation, modeled as a function of day 3 cumulative fluid balance using Cox proportional hazards models, using the crude and the adjusted association. Sensitivity analyses without missing data and modeling ARDS severity were performed.P = 0.0047). Sensitivity analyses showed similar results.Among 650 patients, three groups were identified. Patients in the higher, intermediate, and lower groups had a median cumulative fluid balance of 1.98 L (1.27–7.72 L), 0.78 L (0.26–1.27 L), and − 0.35 L (− 6.52–0.26 L), respectively. Higher day 3 cumulative fluid balance was significantly associated with a lower probability of successful ventilation liberation ; Date of registration: April 15, 2020.The online version contains supplementary material available at 10.1186/s13054-022-04023-y. Acute respiratory failure necessitating invasive ventilation is considered one of the leading causes of death in patients with COVID-19 . Based oConsiderable evidence from observational studies, clinical trials, and systematic reviews indicates the potential benefits of restricting fluid administration in critically ill patients –7. ExcesPRoVENT-COVID is an investigator-initiated national, multicenter, observational cohort study that included COVID-19 patients with acute respiratory failure requiring invasive ventilation in 22 hospitals in the Netherlands in the first 3 months of the international outbreak. The study protocol was approved by the local institutional review board of Amsterdam University Medical Center (location ‘AMC’) and registered at Clinicaltrials.gov (NCT04346342). The institutional review board waived the requirement for written informed consent at the participating sites. The original study protocol was pre-published elsewhere . The proInvasively ventilated adult patients who met the criteria for ARDS using the Berlin definition  and who The primary exposure of interest was the cumulative fluid balance. Cumulative fluid balance was obtained as a sum of daily fluid balance during the last 24-h, calculated by total fluid input minus total fluid output on a certain day of ICU admission for the first 4 calendar days of invasive ventilation. Insensible fluid loss such as perspiration or evaporative water loss due to respiration was not routinely measured and not included in the cumulative fluid balance calculation. Cumulative fluid balance from day 0 through day 1 was grouped as day 1, and the subsequent days were labelled as day 2 and day 3. Cumulative fluid balance during the first 4 calendar days is referred to hereafter as cumulative fluid balance at day 3.The primary outcome was successful liberation from invasive ventilation at 28 days. We chose this time frame following previous ARDS trials typically because either the subject died or extubated successfully by day 28 . SecondaDescriptive statistics were used to describe the study population and fluid management parameters. Data are presented as numbers and percentages for categorical variables and as means and standard deviation or median and interquartile range according to distribution. The normality of the distributions of quantitative variables was assessed by the Shapiro–Wilk test. Where appropriate, statistical variability is expressed by 95% confidence intervals.Using a mixed effects model, we first examined the crude association between cumulative fluid balance and successful liberation from ventilation at day 28 with successful liberation of ventilation as a dependent variable, fluid balance as (fixed effect) as an independent variable, and hospital as a random intercept effect. To examine potential nonlinearity in the association, the cumulative fluid balance was entered as a restricted cubic spline function with 3 knots distributed equally along the density. The complexity of the spline function was reduced in a stepwise fashion until minimization of the Akaike information criterion (AIC)  covariates in the mixed effects model. Baseline physiological and laboratory variables (Day 0) were collected within one hour of ICU arrival or one hour of initiation of invasive ventilation, in accordance with the pre-published protocol . The setP ≤ 0.05.Conditional on the assumption that the data were missing at random and the severity illness scores were collected differently by hospitals, before imputation, the percentage of missing data in the severity of illness scores in the first 3 days were assessed and addressed by a multi-level multiple imputation method. We imputed 20 datasets using multiple imputation by chained equations . No expoNo formal statistical power calculation was conducted before the study. The sample size was solely based on the available data from the PRoVENT-COVID database.To assess the robustness of the findings toward the missing data and imputation method, we refit the main regression model  on cases with complete data only. To retain the largest possible sample size, only covariates that were significantly associated with the outcome in the main model were included in the sensitivity model.We also estimated the main effect of different classes of ARDS severity (on the day of ICU admission) by including this variable in the adjusted mixed effects model.We identified 687 invasively ventilated COVID-19 and ARDS patients admitted to ICUs between March 1, 2020, and June 1, 2020. The study flowchart is summarized in Fig. The distribution of cumulative fluid balance at day 3 was evaluated for the overall cohort  survival model. Models with 3-, 2-, or 1-knot-restricted cubic splines for cumulative fluid balance had higher AICs  . After adjusting for a predefined set of possible confounding variables . In the post hoc analysis, we assessed the impact of imbalances of chronic baseline hypertension and diabetes mellitus between the tertiles groups and found no difference in the main effect estimate HR 0.83  compared to 0.86  for the model without hypertension and diabetes .Two sensitivity analyses were performed. First, the robustness of our findings was assessed toward the missing data and imputation method , indicating that there was no significant interaction between day 3 cumulative fluid balance and ARDS severity on the association with successful liberation from invasive ventilation  A higher day 3 cumulative fluid balance was associated with a lower probability of successful liberation from invasive ventilation by day 28; (2) these results remained consistent even after adjustment for potential predefined confounding factors and sensitivity analyses; and (3) reduction in duration of invasive ventilation and hospital and ICU length of stay was noted in patients who had lower cumulative fluid balance.Our results add to the growing evidence suggesting the unfavorable effect of higher positive fluid balance on outcomes in critically ill patients –21. HoweEvidence emanating from large trials of ARDS patients has led to an overall practice change that resulted in relatively less aggressive initial fluid management. In 2006, the Fluids and Catheters Treatment Trial reported a causal effect between positive fluid balance and duration of ventilation in ARDS patients . The autSeveral mechanisms may explain the association of higher early cumulative fluid balance and decreased odds of ventilation liberation. Higher positive fluid balance increases the extravascular lung water, and inattention to fluid overload may inadvertently promote counterproductive outcomes, such as pulmonary vascular dysregulation and alveolar edema, contributing to weaning failure. This risk is particularly high among patients with COVID-19 and ARDS because of relatively higher extravascular lung water and pulmonary vascular permeability indices, in distinct contrast to non-COVID ARDS . FurtherHigher cumulative fluid balance has also been suggested in previous studies of non-COVID-19 ARDS patients to be potentially associated with worsened outcomes, such as acute kidney injury and decreased survival . In our The strengths of our study include the size of the multicenter cohort of 22 hospitals that comprised both academic and non-academic institutions, increasing the generalizability of our results. We took careful steps to prevent selection bias that could have been caused by patients who were transferred from other hospitals. Also, trained study coordinators performed careful data collection, and a pre-specified statistical analysis plan was prepared before data acquisition.Our study is subject to several limitations. Although we adjusted for a limited set of pre-specified confounders, our results may be biased by a (large) number of unmeasured confounders. We considered variables observed after day 0 (such as cumulative vasopressor dose or ARDS severity on day 4) as potential mediators as they would presumably be influenced by the cumulative fluid balance and may be associated with the outcome. Adjusting for these variables would introduce bias through over adjustment , 28. OurBased on a priori-defined selection criteria, a trivial fraction (approximately 5%) of patients extubated at day 3 were excluded from the analysis Fig. . It, theThis multicenter study of invasively ventilated COVID-19 and ARDS patients suggests a strong association between higher day 3 cumulative fluid balance and the duration of ventilation, even after adjusting for a predefined set of possible confounding variables. Nevertheless, randomized clinical trials are required to confirm our findings. To the extent that higher positive fluid balance suggests harm and influences weaning outcomes, maintenance of restrictive cumulative fluid balance may improve weaning outcomes in invasively ventilated COVID-19 and ARDS patients.Additional file 1. Figure S1 a. Histogram of model residuals showing zero-inflated count distributions. b: Predicted values vs. model residuals. Figure S2. Models with different spline complexities. Figure S3. Box and violin plots of cumulative fluid balance over days 0–3. Table S1a. Potential predefined confounding variables. b: Post-hoc analysis adjusting for diabetes and hypertension as possible confounding variables. Table S2. Sensitivity analysis to missing data and imputation method.