Aim: Adapting “escape rooms” for educational purposes is an innovative teaching method. The aim of this study was to ascertain the degree of learning of the residents. A secondary objective was to determine their degree of satisfaction. Methods: A prospective, observational study took place in October 2019. A sepsis-based escape room was designed and carried out. A mix of paediatric medical residents and paediatric nursing residents were enrolled. A prior knowledge test was carried out, which was repeated right at the end of the escape room and then again three months later. Furthermore, all participants completed an anonymous post-study survey. Results: We enrolled 48 residents, 79.2% of whom were women. The mean score for the pre-escape room exam was 7.85/9 (SD 1.65), that for the post-escape room exam was 8.75/9 (SD 0.53), and for the exam three months later, it was 8.30/9 (SD 0.94). Among the participants, 18.8% did not manage to leave before the established 60 min time limit. The results of the satisfaction survey showed high participant satisfaction. Conclusions: The escape room proved to be a valuable educational game that increased students’ knowledge of sepsis management and showed a positive overall perceived value among the participants. Traditionally, educational techniques have been based on lecture-type classes given by teaching staff. In recent years, however, several teaching methods have been proposed that focus on improving learning, in which students become active participants, ceasing to be passive spectators in their training. Trends in global health education are increasingly moving toward a diversification of strategies to improve knowledge and skills .Gamification consists of applying strategies based on games in typically serious environments in order to enhance motivation, convey a message, and teach content through the active participation of the “players” ,3,4. ThiOne of these new gamification techniques is the escape room. An escape room is a physical and mental game experience in which participants are challenged to leave a room in which they are trapped. To do this, they must overcome a series of challenges of different types and follow a story that will lead them to the key that opens the exit door, all before the time allotted ends.Escape rooms were first used in Japan in 2007 .An educational escape room is based on the game-based learning that takes place in a real escape room in order to promote knowledge acquisition, teamwork, and communication among the participants. Several studies have portrayed the successful use of educational escape rooms in a wide variety of disciplines ranging from health care and pharmaceutical practice to telecommunications and mathematics, among others ,35,36,37With the idea of trying out a different form of training for the resident doctors and nurses at our hospital, we used an escape room to develop a clinical case of a paediatric patient affected by sepsis . The aimA prospective and observational study was designed. The paediatric medical and nursing residents at Hospital Sant Joan de Déu  who voluntarily signed the informed consent were included. The escape room was held in our hospital’s simulation room, which is equipped with cameras and microphones, in October 2019. There were no exclusion criteria.Two game masters specializing in critical child care and with extensive experience in teaching and simulation prepared an escape room based on a real case of a paediatric patient with sepsis who was in an emergency room. Nicholson’s RECIPE mnemonic  for meaningful gamification was used to guide the general design . An intrJust before the escape room, a prior knowledge test based on the international guidelines for the management of severe sepsis and septic shock was carried out  Table 1Table 1, At the end of the escape room, all participants underwent a formal debriefing on the diagnosis and initial management of septic shock in paediatrics and completed an anonymous post-study survey with the objective of testing the first two levels of Kirkpatrick’s levels of training evaluation . These mKirkpatrick level 1 aims to measure participant satisfaction with the training and find out to what extent they find the training favorable, engaging, and relevant to their jobs. Level 2 analyzes the degree to which participants acquire the intended knowledge, skills, attitude, confidence, and commitment, based on their participation in the learning event.The sociodemographic variables sex, age, medical or nursing resident, and year of residence were collected. We also recorded the number of recreational escape rooms that the participants had previously completed, the number of clues they asked for, and whether they completed the mission on time.® 21.0 software .Qualitative variables were expressed as absolute and relative values, whereas quantitative variables were defined using mean ± standard deviation or median and interquartile range (IQR), depending on whether they were normally distributed. The comparison of quantitative variables was performed with the Wilcoxon test. The significance level was set at 0.05, and the statistical analysis was carried out with SPSS This study was approved by the institutional Clinical Research Ethics Committee in compliance with the Declaration of Helsinki .A total of 48 participants were recruited: 10 (20.8%) first-year medical residents, 11 (22.9%) second-year medical residents, 9 (18.8%) third-year medical residents, 11 (22.9%) fourth-year medical residents, 4 (8.3%) first-year nursing residents, and 3 (6.3%) second-year nursing residents. All residents are in the paediatric specialty. The mean age was 26.2 ± 2.03 years and 38 (79.2%) were women.As for the participants’ prior experience in recreational escape rooms, the distribution is as follows: 32 (66.7%) participants did less than 5, 14 (29.1%) did from 5 to 15, 1 (2.1%) did from 15 to 30, and 1 (2.1%) participated in more than 30 escape rooms.The mean score for the pre-escape room exam was 7.85/9 (SD 1.65), that for the post-escape room exam was 8.75/9 (SD 0.53), and three months later, this was 8.30/9 (SD 0.94). The analysis according to the type of resident is shown in Of the participants, 18.8% of them, corresponding to two teams (nine people), did not manage to leave before the established 60 min. The mean number of clues the participants asked for or were given by the game master was 10.5 (SD 2.7) overall.Finally, the results of the satisfaction survey are shown in This manuscript presented the results of a new way of learning for paediatric medical residents and paediatric nursing residents: carrying out an escape room based on a septic patient.Learning games are used for various reasons, such as being able to immerse the player; motivating the student; enhancing teamwork, communication, and leadership; and being able to offer a safe environment in which errors have no consequences except within the game itself ,4. The dThe construction of the escape room was performed according to an established order: selection of the problem to be worked on, organization of content that the escape room aimed to deliver, selection of activities and fundamental questions, and selection of means and evaluation of the learning processes. Nicholson’s RECIPE mnemonic  for meaningful gamification was used to guide the general design . Sepsis Due to the novelty of the escape room concept, there is a lack of research that reports on and rigorously examines the potential educational use of these rooms. However, as of late, several articles are being published in a multitude of fields other than medicine, such as pharmacy, physical therapy, chemistry, computer networks, cryptography, mathematics, and programming ,31,32,33An important challenge to take into account is adjusting the level of difficulty, both in the games and in the mastery of the conceptual material for the knowledge area in question ,43. CompAccording to the bibliography consulted, there are no works prior to 2017 that seriously report on the success rate of the use of educational escape rooms, that is, the percentage of students who solve the room before time runs out . This tyTo evaluate this as a learning system, a sepsis test was performed based on international guidelines for the management of severe sepsis and septic shock , both prFinally, as for the perception of the participants regarding educational escape rooms, both the bibliography and our own data mention that the residents enjoyed participating and that, at the same time, they considered the experience to be valuable for learning ,21,45,46The main limitation of the study is the novelty of this type of learning technique, both for students and for the game masters tasked with designing these learning sessions. The different levels of previous experience in recreational escape rooms that participants had may entail a bias. On the other hand, the sample size is still too small to be able to extrapolate these data to the general population. The fact that participants are working with a mannequin in a simulation room can prevent them from performing their usual way of working with patients on a daily basis, despite being previously reminded about the importance of getting into the role. Finally, to improve the test in the future, access to models with racial diversity would be valuable and reduce the inequity that can occur if skin tone is not medically taken into consideration. For example, cyanosis or the sepsis rash can be difficult to detect or look different on a darker skin when compared with a lighter skin. This training gap can lead to inequities in care.Gamification as a new form of learning for paediatric medical residents and nursing residents seems to have a positive educational impact that was sustained over time. In addition, the level of satisfaction of the participants was very good, so this method seems to have great prospects for the future.


Lcapy is an open-source Python package for solving linear circuits symbolically. It uses a superposition of DC analysis, AC (phasor) analysis, transient (Laplace) analysis, and noise analysis. Expressions are evaluated using the computer algebra system SymPy. Lcapy can model circuits comprised of combinations of one-port and two-port networks or circuits specified using a netlist with a Spice-like notation. Lcapy can present the system of equations produced from nodal analysis, modified nodal analysis, loop analysis, and state-space analysis. Expressions can be formatted into many representations, parameterized, and transformed to other domains. Dimensional analysis is performed to reduce user errors and to present results with units. Both continuous and discrete signals are supported. Lcapy produces high-quality output. Textbook quality schematics in a number of different formats can be generated from netlists and customized for different conventions. Expressions can be formatted into LaTeX format for inclusion into a document or numerically evaluated and plotted. An overview of the features and capabilities of Lcapy is presented, along with implementation details and performance considerations. Lcapy is an open-source Python tool to symbolically generate and solve the system of integro-differential equations for a linear electrical, mechanical, or electro-mechanical circuit.Unlike most other circuit analysis programs, Lcapy stores all values and expressions symbolically. It utilizes the powerful Python symbolic mathematics package, Sympy . The priLcapy started in 2013 as a collection of Python classes for handling rational functions for modelling piezo-electric transducers. This was augmented with a collection of component classes that could be organised into networks using parallel and series operators . As comphttps://pypi.org/project/PySpice/), and Qucs  ; Akhab (https://ahkab.github.io/ahkab), a Python program with a SPICE-like command sequence; Sapwin/SapecNG/QSapecNG, Windows-based C++ programs (https://circuitnav.pythonanywhere.com/), a web-based application for generating a symbolic system of equations given a netlist, and SliCAP  , a Python (previously MATLAB) program for teaching circuit analysis.A number of symbolic circuit analysis packages have been developed, primarily through the 1980s and 1990s . These iprograms ; MSCAM , performs modified nodal analysis for each domain, and superimposes the result. A rich variety of transformations is provided to convert between the various domains and to present results in different formats. Lcapy uses an object-oriented approach and provides a comprehensive selection of expression classes to specify the transform domain and data quantity.https://lcapy.readthedocs.io) (This paper is not a comprehensive description of the capabilities of Lcapy (this can be found in the supporting documentation (docs.io) ). InsteaLcapy is an open-source Python package for symbolic linear circuit analysis. Python was chosen since it is platform independent , can be The author has used Lcapy for modelling low-noise operational amplifier circuits, designing analog and digital filters, simulating polyphase power systems, fitting models to electrode impedance data for electromagnetic flowmeters, designing controllers for power-electronics, modelling impedance compensation systems for biomedical instrumentation, and modelling piezoelectric transducers. He and others have also used Lcapy for producing textbook quality schematics, generating exam questions, checking exam and homework answers, and teaching students circuit theory.An object-oriented design was chosen to simplify both the implementation and user interface. The classes provided by Lcapy range from simple expressions, to one-port and two-port networks, to complete netlist models. The object oriented approach makes available methods for a given object clear to the user. For example, time-domain objects have a Laplace transform method but Laplace-domain objects have an inverse Laplace transform method.Lcapy uses an instance-based netlist similar to SPICE  to descrAs well as electrical components, Lcapy netlists can contain masses, springs, and dampers for modelling mechanical or electro-mechanical networks. The mechanical analogue II  is employed where voltage is equivalent to force and current is equivalent to speed. Thus a mass is analogous to an inductor, a spring is analogous to a capacitor, and a damper is analogous to a resistor.Lcapy has three major use cases: network and circuit analysis using combinations of one-port and two-port models; network and circuit analysis using netlists; and symbolic expression manipulation for interpretation of results. These are discussed in the following sections.omega for the angular Fourier domain, and Omega for the normalized angular Fourier domain. These variables can be utilised to create expressions or to specify the result domain for transformations, for exampleLcapy has a number of pre-defined variables associated with different domains: f for the Fourier (frequency) domain, F for the normalized Fourier domain, s for the Laplace (s) domain, t for the time domain, >>> from lcapy import s, t, exp>>> H = s * (s + 2)/ (s + 3) * exp(-2 * s)>>> H(t)δ(t) is the Dirac delta and δ(1)(t) is the first derivative of the Dirac delta. As a consequence of the unilateral Laplace transform, the result is only known for t ≥ 0. However, if the result is known to be zero for t < 0, this can be specified by asserting the causal argument, for example:Here >>> from lcapy import s, t, exp>>> H = s * (s + 2) / (s + 3) * exp(-2 * s)>>> HLcapy can display expressions in a number of forms. In each case the expressions are factored into a rational function and an optional exponential function (the latter is used to support Laplace-domain delays):Rational functions can be represented in many forms. initial_value, final_value, differentiate, integrate, delay, zeros, poles, residues, transient_response, frequency_response, and step_response. For example:Some of the other methods that can be applied to Laplace-domain expressions include: >>> from lcapy import s>>> V = s**3 / ((s + 3) * (s + 4))>>> V.poles This dictionary indicates a pair of real poles, each with a single occurrence.Lcapy also has a number of pre-defined variables associated with discrete-time signals: k for the discrete-frequency sample index, n for the discrete-time sample index, and z for the z-transform domain. These can be used to compose discrete-time signals, for example:>>> from lcapy import delta, n>>> x = delta(n) + 2 * delta(n - 2)>>> x.seq>>> from lcapy import delta, n, z>>> x = delta(n) + 2 * delta(n - 2)>>> x(z)etc. For example,Discrete-time, discrete-Fourier, and Z-domain expressions can be converted to and from sequences. These can be convolved, shifted, transformed, >>> from lcapy import symbols, delta, n>>> a, b = symbols(’a b’)>>> x = a * delta(n - 1) + b * delta(n - 2)>>> x.seqwhere the underscore marks the origin. Similarly, sequences can be converted to expressions: >>> seq).exprNetworks can be constructed by connecting one-port  and two-port components. Basic one-port components include: Vdc (DC voltage source), Idc (DC current source), Vac (AC voltage source), Iac (AC current source), R, G, C, and L. These are augmented by V (generic voltage source), I (generic current source), Y (generic admittance), and Z (generic impedance). More esoteric components include gyrators and constant phase elements.Here is an example that combines an 8 V source in series with a parallel combination of 6 ohm and 3 ohm resistors:>>> from lcapy import V, R>>> net = V(8) + (R(6) | R(3))>>> netnet object is printed, the underlying components forming the network are shown. This is achieved using an abstract syntax tree to represent the component connections.Note, Lcapy overloads the Python | operator to denote a parallel operation. Also note that when the A network can be simplified:>>> net.simplify>>> net.nortonAttributes of one-port networks include: Isc—short-circuit current; Voc—open-circuit voltage; Y—generalized admittance; and Z—generalized impedance.series, parallel, and chain methods. Furthermore, they can be generated from a netlist. For example, the filter shown in Two-port networks can be created from one-port components using ladder descriptions , primiti>>> from lcapy import TSection, L, C, R>>> a = TSection(C(’C’), L(’L’), R(’R’))The same network can be constructed by chaining series and shunt elements:>>> from lcapy import Series, Shunt, L, C, R>>> a = Series(C(’C’)).chain(Shunt(L(’L’))).chain(Series(R(’R’)))A two-port object has methods for generating A (chain) parameters, B (inverse chain) parameters, G (inverse hybrid) parameters, H (hybrid) parameters, S (scattering) parameters, T (transmission) parameters, Y (admittance) parameters, and Z (impedance) parameters . For the >>> a.ZparamsA unique feature of Lcapy is polyphase two-port networks. These use block-matrix forms for the parameter matrices and are useful for modelling power systems. They have methods for converting to symmetrical sequence components.add method of the Circuit class. For example, More complex circuit topologies are best described using netlists. The Lcapy netlist syntax is similar to that of SPICE. Usually nodes are numbered, but they can have symbolic names. The netlist can be loaded from a file or generated incrementally using the Circuit object is collection of components and nodes. The nodes are accessed using Python’s array-indexing notation, for example,A >>> cct[2] - cct[’a7’]Nodes have a number of attributes including: V—the voltage between the node and ground; dpY—the driving-point generalized admittance between the node and ground; dpZ—the driving-point generalized impedance between the node and ground. Thus the potential of node 2 with respect to ground can be found using:>>> cct[2].VCircuit class. Thus components can be accessed using the Python’s attribute notation. For example:Components are dynamically added as attributes of the >>> cct.C1Each component has several attributes as shown in >>> from lcapy import Circuit>>> cct = Circuit(’VRL1.sch’)>>> cct.L1.IThe output is a dictionary showing the superposition of a DC component and a transient-component described in the Laplace domain.The time-domain response can be found using Python’s call notation:>>> cct.L1.I(t)This transforms each component of the superposition into the time domain and sums the result to give:Similarly, the total Laplace-domain response can be found using:>>> cct.L1.I(s)transfer method which implicitly kills any independent sources. For example:A common circuit analysis problem is to find the transfer function between two pairs of nodes. There are several ways this can be achieved with Lcapy. For example, consider the filter network shown in >>> H = cct.transfer>>> H.canonicalA more general approach is to generate a two-port model. For example, the Z-parameters can be found using:>>> tp = cct.twoport>>> tp.ZparamsThe common two-port models are supported, including the A, B, G, H, S, T, Y, and Z-parameter models . They caCircuit class are shown in Other methods of the As well as calculating node voltages and branch currents, Lcapy can also display the equations for nodal analysis, modified nodal analysis, mesh analysis, and state-space analysis. For the following, consider the circuit shown in Lcapy can output the nodal equations by applying Kirchhoff’s current law at each node in a circuit. For example:>>> from lcapy import Circuit>>> cct = Circuit(’example1.sch’)>>> na = cct.nodal_analysis>>> na.nodal_equationslaplace method to convert the circuit. For example:The nodal equations can be formulated in the Laplace-domain using the >>> na = cct.laplace.nodal_analysis>>> na.nodal_equationsThe matrix formulation can also be generated for the Laplace-domain, DC, and phasors. For example:>>> na = cct.laplace.nodal_analysis >>> na.matrix_equationsLcapy can output the mesh equations by applying Kirchhoff’s voltage law around each mesh loop in a circuit. For example:>>> from lcapy import Circuit>>> cct = Circuit(‘example1.sch’)>>> la = cct.loop_analysis>>> la.mesh_equationsState-space analysis is performed by assigning capacitor voltages and inductor currents as state variables. The state equations are found using:>>> from lcapy import Circuit>>> cct = Circuit(’example1.sch’).>>> ss = cct.state_space>>> ss.state_equationsSimilarly, the output equations are found using:>>> ss.output_equationsThe state-space analyser can generate state-transition matrices, system transfer functions, system impulse responses, and the characteristic polynomial. For example, the characteristic polynomial for the circuit in >>> ss.Pt ≥ 0.If any capacitor or inductor in a netlist has an initial value, the transient response is calculated as an initial value problem using Laplace transforms. The result is only valid for Lcapy can numerically simulate a circuit using time-stepping numerical integration. Both the trapezoidal and backward-Euler integrators are provided. Here is an example of use:>>> from lcapy import Circuit>>> from numpy import linspace>>> cct = Circuit(" " "V 1 0 {10 * u(t)}; down… R 1 2 5; right… C 2 3 0.1; right… L 3 0_3 0.5; down… W 0 0_3; right" " ")… >>> tv = linspace>>> results = cct.sim(tv)>>> ax = cct.R.v.plot>>> ax.plot>>> ax.legendsim method takes a NumPy array of time values to numerically evaluate the circuit at. The variable results is a SimulationResults object. This can be indexed by node name. It also has an attribute for each circuit component; for example, results.R1.v is a NumPy array for the time-varying voltage across R1. The plotted result is shown in In this example, the Networks can be created using network synthesis techniques given an impedance or admittance expression. For example:>>> from lcapy import s, Impedance>>> Z = Impedance((4 * s**2 + 3 * s + ’1 / 6’) / (s**2 + 2 * s / 3))>>> Z.networkLcapy provides many network synthesis methods, including recognising the common parallel/serial combinations of resistors, inductors, and capacitors plus more generic methods such as Foster I, II and Cauer I, II .random_network function. This is useful for automated exam question generation. For example:Networks with a random topology can generated with the >>> from lcapy import random_network>>> net = random_networkThis example generates a DC network with four resistors and one voltage source. The kind argument can be ac, dc, or transient. The number of parallel connections can be specified with the num_parallel argument.Lcapy can parameterize a number of first-order, second-order, and third-order Laplace-domain expressions. For example:>>> from lcapy import s>>> H2 = 3 / (s**2 + 2 * s + 4)>>> H2p, defs = H2.parameterize>>> H2pdefs is a dictionary of the parameter definitions. In this case the parameter definitions are:Using the dictionary of definitions, the original expression can be obtained by substituting the parameter definitions into the parameterized expression.Systems higher than first-order can be parameterized in different ways. For example:>>> from lcapy import s>>> H2 = 3 / (s**2 + 2 * s + 4)>>> H2p, defs = H2.parameterize>>> H2pThe polyphase module provides classes for handling polyphase signals. This handles conversions between line voltages and currents to phase voltages and currents. In addition, it can decompose line and phase signals into symmetrical components and vice-versa. In each case, the polyphase signals are stored as vectors. An arbitrary number of phases can be supported, although there are many attributes specifically for three-phase systems, such as Vab that returns the phase-phase voltage between the a and b phases.Here is an example that determines the symmetrical sequence components for three-phase phase-voltages:>>> from lcapy.polyphase import PhaseVoltageVector>>> V = PhaseVoltageVector)>>> V.sequenceα = exp(−j2π/3) for a three-phase system.Expressions have printing methods similar to Sympy. These are summarised in Table 5evaluate method which accepts a NumPy array of values at which to evaluate the expression. Internally, Lcapy uses Sympy’s lambdify function to convert the expression to executable Python code that can be efficiently evaluated.Expressions have an plot method. For Laplace-domain expressions, this is a pole-zero plot. For time-domain expressions, this shows the waveform. In addition, there are specific methods for Bode and Nyquist plots. The plot methods provide automatic labelling and use the Matplotlib library for high-quality output in a number of formats  macros are output producing text-book quality schematics. Lcapy provides a script, schtex, that can convert a netlist into an image, with a number of formats such as pdf or png. This script can also manipulate the netlist, such as renumbering the nodes.Lcapy can draw circuits specified as networks or netlists. Networks are drawn automatically using a number of layout forms, including as a ladder network. Netlists require drawing hints to describe the component orientation, size, color, Lcapy has a config module that can be used for customisation. For example, the printing of Lcapy is implemented using 393 classes within 115 modulesetc.) and from a domain class . Attributes specify the quantity  and the domain .Expressions are represented using one of the 170 dynamic expression classesexpr for general expressions, voltage, current, impedance, admittance, and transfer (for transfer functions). For example, the expr factory function chooses one of the expression classes listed in voltage factory function chooses one of the voltage classes, subclassed from a corresponding expression class.Lcapy expressions are created from factory functions, including: SuperpositionCurrent and SuperpositionVoltage. These represent signals as a superposition of DC, phasor, transient, and noise signals. They collate the results from the different circuit analysis methods before conversion to the time domain, Laplace domain, etc.There are two special superposition classes: Expr class. This facade class provides a common interface to the myriad of Sympy expression classes. Note, Sympy represents an expression using an abstract syntax tree of expression classes  attribute. Noise expressions with different nids are assumed to be independent and thus represent different noise realisations.Lcapy assumes noise signals are zero-mean, wide-sense stationary, Gaussian random processes. Correlations are specified by a one-sided amplitude spectral density. Each noise source is assigned a noise identifier … >>> cct[2].Vi.e., In this example Vn1 defines a white Gaussian noise source with an amplitude spectral density (ASD) of 3 V>>> cct[2].V.nNetlists have a Spice-like syntax and are parsed with a custom parser. A parser based on lex-yacc  was trieNetlists are stored as ordered dictionaries. This simplifies the lookup of components by name and preserves the component order whenever a netlist is converted into another form.Circuit analysis is performed using modified nodal analysis (MNA) . EssentiA is the system matrix. For example, the system of equations for the circuit shown in V1 and V2 are the unknown nodal voltages and IVs is the unknown current through the independent voltage source Vs. Note, since the voltage source is DC, its Laplace-domain voltage is etc.), opamps, gyrators, mutual inductances, transformers, and two-ports. Each component has a stamp method that fills in the system matrices.Lcapy uses a custom implementation of modified nodal analysis and supports controlled sources ), plus the discrete Fourier transform (DFT). The inverse transforms are also supported. Where possible, Lcapy breaks an expression into terms and evaluates each term. All results are cached.Lcapy uses the Lcapy can evaluate most common Laplace transforms. To improve performance, it caches results. Expressions that Lcapy cannot recognise are passed to Sympy. Unfortunately, Sympy uses the Time-domain expressions are determined using an inverse Laplace transform of the corresponding Laplace-domain expression. The Sympy inverse Laplace transform routines were found to be slow and unreliable and so were augmented with a custom method. This was applied when the expression could be factored into the product of a rational function and an exponential, as described by (1). After finding the poles of the rational function, the residues of the partial fraction expansion are determined using a custom algorithm. When pairs of unrepeated conjugate poles are detected, sines and cosines are generated, otherwise exponential functions (and their derivatives for repeated poles) are generated.The Fourier transform computed by Lcapy is>>> from lcapy import expr, f>>> v = expr(’sin(2 * pi * f0 * t)’)>>> v(f)The Fourier transform can also be expressed in terms of angular frequency using a change of variable. For example:>>> V = v(omega)The unilateral Z-transform is computed asThe DTFT is computed as>>> from lcapy import symbol, rect, n, Omega>>> N = symbol>>> x = rect(n/N)>>> x(Omega)F:>>> from lcapy import symbol, sincn, n, F>>> x = sincn(n * ’K’)>>> x(F)OnePort class. Parallel and series combinations are represented by the Par and Ser classes. Both of these combinations are also OnePort objects.The one-port components are sub-classed from the TwoPortModel class. This class contains a 2 × 2 matrix of expressions for the two-port parameters. This is augmented with a vector of two voltages, two currents, or a voltage/current pair to describe the effects of independent sources within the circuit. For example, the equations describing a B-parameter two-port model are:Two-port components are based on the Vb2 and Ib2 model the independent sources within the circuit. Similarly, the equations used to describe a Z-parameter two-port model are:CircuitGraph class to convert a netlist into a graph. The Python package Networkx >>> net.VocThe timing tests were performed on an HP Elitebook 850 G6 using a single core of an Intel i5-8365U CPU running at 1.60 GHz. It is apparent that the maximum computation time increases exponentially with the number of components. The computation time is also highly variable for a given number of components, with over three orders of magnitude variation for 11 components. This is partly due to the MNA matrix size, but it also depends on the number of terms that each matrix element has. The variation of time with the MNA matrix size is not obvious, as shown in O(N3) as expected . On-line documentation can be found at https://lcapy.readthedocs.io. Included in the documentation are tutorials; some using Jupyter notebooks.Documentation is stored as Restructured Text and converted to HTML/PDF using Sphinx  approach . To mitiSympy is an excellent tool but can be confusing for novices since it allows symbols of the same name but with different assumptions. Thus the symbols are not equivalent. Lcapy tries to resolve this problem by keeping a registry of symbols and looking them up by name. The disadvantage of this approach is that symbol assumptions cannot be changed.Lcapy has a number of circuit transformations but further work could add more advanced circuit transformations, similar to programs like SAPWIN . AnotherLcapy is an open-source Python package, designed to generate and solve systems of equations for linear circuit analysis. It supports many aspects of circuit theory, including two-port networks, network synthesis, and polyphase systems. The author is aware of its use for generating exam questions, checking exam and homework answers, teaching students circuit theory and signal processing, simulating polyphase power systems, designing controllers for power-electronics, modelling low-noise operational amplifier circuits, and modelling electromechanical systems. There are many other potential applications.Planned work is to improve compatibility with PySpice and to add a graphical user interface (GUI) to enter and display circuits schematically. This could be achieved by importing and exporting netlists or implementing the GUI in Python and using Lcapy as a module. A GUI is useful from a circuit teaching perspective since components could be selected, the current/voltage interrogated and specific transforms applied.An improvement would be to employ cancellation-free analysis so that all poles and zeros are shown. One approach is to use graph-pair decision diagram techniques  or heuri


We evaluated the association between polymorphisms in the tumor necrosis factor alpha (TNFSchistosoma mansoni.This was a transverse study involving 294 Brazilian patients infected with -α (-G308A) showed a risk association  with UGIB. There was no statistically significant difference in serum TNF-α levels between the clinical groups. The homozygous A/A genotype in TNF-α (-G308A) can be a risk factor for UGIB, in addition to being a potentially predictive factor for the severity of UGIB in schistosomiasis. The polymorphic TNF Schistosoma mansoni (S. mansoni), especially in the Northeast regionSchistosomiasis is a chronic parasitic infection that affects approximately 240 million people worldwide,upper gastrointestinal bleeding (UGIB),,The pathological process of the disease occurs through an immune response to the parasite eggs deposited in the liver, causing a granulomatous inflammatory reactionUGIB is a serious complication of the disease, especially in the advanced hepatosplenic form-α gene is located in the human leukocyte antigen class III on chromosome 6p21.3.-α gene and may influence the function or expression of this protein,,The TNF-α (-G308A) (rs1800629) polymorphism may be a prognostic factor for schistosomiatic PPF-α and its effect on the severity of UGIB in patients with schistosomiasis needs to be better elucidated.The association found between the polymorphisms and the increased level of the TNF-α constituent is a possible explanation of the evolutionary process of PPF-α (-G308A) (rs1800629) and UGIB in S. mansoni endemic populations in the state of Pernambuco, Northeast Brazil.This study analyzed the association between TNFS. mansoni, which determined the TNF-α (-G308A) polymorphism and serum concentrations. Diagnosis of the clinical forms of the disease was determined by the medical reports and physical examination of the patients, in addition to the evaluation of upper abdominal ultrasound to confirm the diagnosis and exclude other liver diseases. This ultrasound evaluation was performed only by the endoscopy operator of the Hospital das Clínicas/ Universidade Federal de Pernambuco (HC-UFPE) Unit, using equipment (Siemens Acuson S2000), with a convex transducer of 3.5-5.0 Mhz. To define the PPF pattern, the NiameyThis cross-sectional study was carried out from August 2020 to May 2021, with 294 individuals infected with These individuals were divided into two groups: Group 1 contained 108 individuals with the hepatosplenic (HE) form, advanced PPF (Pattern E or F), and Group 2 contained 186 individuals with the hepatointestinal (HI) form, with mild PPF (Pattern C) or without fibrosis (Pattern A), without UGIB. All participants were over 18 years of age and were treated at the Gastroenterology Clinic of the Hospital das Clínicas of the Federal University of Pernambuco (HC-UFPE), Recife, Brazil.Patients with a history of other hepatic comorbidities, such as hepatitis B and C (examination performed through serology of antigens and antibodies), cirrhosis, hepatic steatosis, and PPF Pattern D (Niamey's classification) were evaluated using ultrasound, as other clinical forms of schistosomiasis were excluded. Ncol enzyme for single nucleotide polymorphism (SNP) detection in the promoter region TNF-α (rs1800629). In this region, the substitution of the nitrogenous base guanine (G) by adenine (A) occurs at position -308 of geneTo determine GG, GA, and AA genotypes, biological samples were collected (blood) and the DNA was subjected to the polymerase chain technique for restriction fragment polymorphism analysis (PCR-RFLP), being isolated with the An immunoenzymatic assay was performed using a commercial ELISA kit  following the manufacturer's instructions to measure the serum levels of TNF-α. Results are expressed in pg/mL, based on standard curves (sensitivity <1.7 pg/mL). The median cut-off point was the median 32pg/mL and 148 individuals were evaluated at this stage.-α (-G308A) polymorphism and UGIB was evaluated with Fisher’s exact test or Chi-square test at a significance level of 0.05. Epi-Info software version 3.5.5  was used, and the result was considered significant when p< 0.05.Crude prevalence ratio (PR) and 95% confidence intervals (95% CI) were estimated. The association between the TNFThis study was approved by the Ethics and Research Committee of the Center for Health Sciences, UFPE, under protocols 113.199 and 03161512.6.0000.5208.p= 0.192). There was no evidence of an association between serum TNF-α levels and the clinical groups  . There was no statistically significant difference in the sex distribution (=0.2221) .p=0.008) for UGIB, when compared with genotypes GG and GA  polymorphism on UGIB. This is the first study to analyze the influence of genetic factors on UGIB in a group of patients with schistosomiasis in the state of Pernambuco, Northeastern Brazil, a region with a high mortality rate in the countryThis study investigated the impact of the TNFS. mansoni showed that patients with genotype AA had a higher risk of UGIB than those with genotype GG. However, there was no association between serum TNF-α levels in the clinical groups of UGIB.This study involving Brazilian patients infected with ,UGIB is caused by chronic schistosomiasis. UGIB can occur in up to 80% of patients with PPF, with a mortality rate of up to 30% for recurrent episodes. However, not all patients with advanced fibrosis develop esophageal varices, and not all patients with esophageal varices will have UGIB. The frequency of UGIB in Brazil is lower than that in Africa-α (-G308A) polymorphism and PPF has already been studiedS. mansoni after specific treatment and found a protective association between the genotypes GA/AA and the regression of PPF compared to the genotype GG. There was no association between serum TNF-α levels and the exposed groups; however, it is believed that the TNF-α (-G308A) polymorphism can prevent regression of the pattern and grade of PPF, which is a potential prognostic factor in relation to schistosomiasis PPF.The association between the TNFSilva et al. (2017),In humans, although high TNF-α levels are associated with severe hepatic fibrosis,-α (-G308A) polymorphism and PPF. These authors did not exclude the possibility that this polymorphism influenced the development of PPF. Some studies have found no association between this polymorphism and the severity of PPF and hepatic fibrosis-α (-G308A) polymorphism can be associated with hepatic fibrosis. Kusumoto et al. (2006)-α. Some studies have shown that, in comparison with allele G, allele A was associated with increased levels of TNF-α, while others failed to associate the presence of the A allele with different levels of TNF-α productionThe influence of this polymorphism on the expression of TNFIn the current study, although there was no difference in serum TNF-α levels between the clinical groups of UGIB, possibly individuals with the genotype AA maintained a residual production of TNF-α after the specific treatment, contributing to less reabsorption of fibrosis and consequently causing complications by UGIB in this group.The absence of a correlation between TNF-α levels and disease severity, represented by the degree of liver fibrosis, is also shown. There are controversial reports regarding the influence of TNF-α on schistosomiasis pathogenesis. Franco et al. (2021)Another study did not differ between TNF-α levels in obese individuals who underwent bariatric surgery with or without liver fibrosis. The authors reported some limitations to be considered, including a small population of patients, which is mainly due to the high costs of the trials used in this study. In addition, there was no control group, and all individuals evaluated presented some degree of nonalcoholic fatty liver disease, which may limit a better elucidation of the findings and additional extrapolation of these results-α (-G308A) polymorphism and the expression of TNF-α and UGIB intensity in schistosomiasis. Considering the limitation of sample size in this study, as well as possible ethnic variations in the population, further studies with larger samples and in other populations are recommended to better analyze these genotypes and their respective doses of TNF-α serum, to better assess whether there is a connection between the TNF-α (-G308A) polymorphism can be a risk factor for UGIB in the Brazilian population, and therefore, can potentially be a predictive factor for the severity of UGIB resulting from PPF in schistosomiasis.In conclusion, these results suggest that the TNF


The role of inflammatory cytokines, such as TNF-α and IL-8, in gastric carcinogenesis has been investigated, but their impact remains to be further elucidated. H. pylori serostatus in dyspeptic patients, presenting with normal mucosa (NM = 53), chronic gastritis (CG = 94), and gastric cancer (GC = 82), by ELISA. In this study, we measured the serum concentrations of these cytokines and Moderate levels of TNF-α were detected in the NM group (19.9 ± 19.5 pg/ml), which were nearly doubled in patients with CG (35.7 ± 28.0 pg/ml) and drastically declined in GC patients (1.8 ± 5.9 pg/ml). The serum levels of IL-8, however, were not statistically different amongst these three groups. TNF-α serum concentration seemed to undergo up- and downregulation, when moving from NM to CG and from CG to GC, respectively. If confirmed in a prospective study, this cytokine can behave as a serum indicator of gastric inflammation and malignant transformation.  This GC subtype is highly affected by environmental and genetic factors and is preceded by a cascade of precancerous lesions, as previously described by Correa and Piazuelo[2]. According to Correa’s model, primarily, the normal mucosa of the stomach becomes inflamed, otherwise known as gastritis. Gastric inflammation is initiated by several factors, including H. pylori infection, high-salt diet, nitrate intake, etc. Gastritis may then develop into atrophic gastritis, intestinal metaplasia, dysplasia, and ultimately gastric cancer[2].Gastric cancer was recognized as the fourth worldwide cause of cancer death in 2020. Among the different types of GC, the intestinal type is the most prevalent one[3]. IL-8 is another pro-inflammatory chemokine with varying sizes (reviewed in[4]). The activated form of IL-8 is induced in the tumor microenvironment, including cancer cells, macrophages, endothelial cells, and neutrophils and impacts several intracellular signaling cascades via binding to its G protein-coupled receptors (reviewed in[4]). It is involved in carcinogenesis by activating different pathways, including serine/ threonine kinases and protein tyrosine kinases and affects the transcriptome and proteome profile (reviewed in[4]).TNF-α is a critical cytokine, which is mainly produced by macrophages and is involved in many biological processes, such as acute and chronic inflammation. In the process of carcinogenesis, it has a dual role by which it causes tumor cell death and apoptosis, on the one hand, and potentiates tumorigenesis and progression, on the other handH. pylori serostatus in three dyspeptic groups, with varying histopathologic status, including those with NM, CG, and GC. We have also investigated the association of other modifying factors, such as age, gender, ethnicity, family history of GC with the serum levels of IL-8 and TNF-α in general and in each group separately.While alterations in serum concentration of TNF-α and IL-8 have been investigated in several cancers, including GC, the results remain controversial, and the potential of the mentioned cytokines as serum indicators of GC is under debate. In this study, we have evaluated the serum levels of TNF-α and IL-8 and Study populationThe study population included histologically confirmed GC (n = 82) patients who attended Imam Khomeini Hospital, Tehran, Iran, from 2012 to 2017, as well as dyspeptic patients who were referred to the Gastroenterology Department of Amir-Alam Hospital, Tehran, Iran, at the same time period and were diagnosed as either NM (n = 53) or CG (n = 94) following histopathologic analysis. The interview questionnaire comprised of demographic characteristics, including age, gender, ethnicity, smoking habits, FHGC, and prior history of chemotherapy . Of the Serum analysisH. pylori IgG ELISA kit  was used to determine the H. pylori serostatus. To quantify IL-8 and TNF-α serum concentrations, R&D ELISA kit  was used according to the manufacturer’s instructions.For each patient, 2.5 ml of whole blood was collected. The sera were isolated and kept at -70°C until further analysis. A commercial anti-Statistical analysisp values less than 0.05 were considered as statistically significant. The mean serum concentrations of TNF-α and IL-8 were compared amongst different patient groups, using the Kruskal-Wallis test, followed by multiple comparisons using Dunn's multiple comparisons test. Chi-square test was used for the analysis of baseline characteristics in Study populationp < 0.001) and were predominantly male (p < 0.001). Most of the GC patients were of Turk/Turkman (46%), and Kurd/Lor/Lak (26%) ethnicities, whereas in CG patients, Fars (46%) and Turk/Turkman (26%) ethnicities, and in NM patients Turk/Turkman (38%), and Fars (34%) ethnicities predominated the population. The majority of GC tumors were of the noncardia subsite (66%) and intestinal subtype (48%). Analysis of the demographic factors among the patient groups indicated that GC patients were significantly older than the non-GC (CG and NM) subjects , compared to CG (69%), and GC (54%) (p < 0.001). The H. pylori serostatus did not indicate any significant association with age, gender, ethnicity, FHGC, and smoking habits. The NM group showed the lowest percentage of Alterations in serum TNF-α levelsp = 0.0087; p = 0.0012). The maximum concentration of TNF-α was 71.5, 111.4, and 35.8 pg/ml, in the NM, CG, and GC groups, respectively. Combining the three groups showed that the TNF-α concentration was significantly higher in the following subjects: (1) aged <60 vs. those above (p = 0.001), (2) females vs. males (p = 0.027), (3) never vs. ever smokers (p = 0.009), (4) No FHGC vs. those with FHCG (p = 0.010), and (5) H. pylori seropositive vs. negative (p = 0.045) (p = 0.033). Moreover, the TNF-α levels were not significantly different between Hp-seronegative NM and CG patients. Interestingly, the serum levels of TNF-α in CG patients were significantly lower in subjects with FHGC, as compared to those without . Combining the NM and CG groups also revealed a lower TNF-α level in patients with FHGC than those without . According to the herein performed extensive literature review (Supplementary Table 1), our results are not in line with many studies[5–7] which have reported relatively higher serum levels of TNF-α in GC patients, as compared to healthy controls. However, our results are consistent with two other studies indicating a lower concentration of TNF-α in GC patients. Moreover, similar to our experiment, there are several studies in which TNF-α could not be detected in the serum of all or most of the studied patients including GC and healthy individuals[10–12]. The absence of TNF-α production by various tumor cells, including cell lines derived from T-lymphocytes, colon and breast cancers, has also been documented[13]. On the other hand, in a comparative study, serum TNF-α was detectable in patients with ovarian and breast tumors, but not in GC cases[14]. In other studies, TNF-α serum concentrations in GC patients was similar to the controls. Absence or low level of TNF-α in the serum of GC patients can have various reasons. For instance, Ohno et al[11] have suggested that TNF-α is not always secreted, even when its mRNA is detected. These authors showed no expression of TNF-α in GC cell lines, including KATO-III and MKN-45. It has also been hypothesized that a decrease in TNF-α expression might be due to immune suppression in GC patients[9]. Moreover, it has been reported that the half-life of TNF-α is very short, making it difficult to detect, which leads to variable reported levels[17]. Another possible reason for TNF-α suppression in GC patients might be T cell exhaustion. It is well known that the effector T cells are exhausted in chronic infections and cancer and might lose their normal capacity in cytokine production[18]. Accordingly, TNF-α is highly suppressed in the exhausted CD8+ effector T cells[19]. Thus, to gain more information about TNF-α expression in blood cells and tumor-infiltrating immune cells in GC patients, we analyzed the open source high-throughput data from Gene Expression Omnibus (GEO) repository at the transcript level. It should be noted that expression of TNF-α at transcript level does not necessarily reflect its expression at protein level. Supporting the T-cell exhaustion hypothesis, a recent single-cell RNA-seq of both tumor-infiltrating and peripheral blood cells of GC patients showed a marked downregulation of genes involved in the cytokine production of exhausted CD8+ effector T cells[20]. However, TNF-α secretion is not limited to the effector T cells, but is also produced by other cell types such as macrophages and natural killer cells[21]. Similar to ELISA test reports on the sera of patients, the results of high-throughput analysis of immune cells in GC patients have been controversial. In the tumor-infiltrating and blood dendritic cells of GC patients, TNF-α mRNA expression is upregulated compared to the adjacent normal tissue and blood of healthy individuals[20]. Moreover, the suppression of several TNF receptor superfamily genes and receptor-associated factors were reported in peripheral blood leukocytes of GC patients, using RNA-seq analysis (GSE49515 dataset). In a single-cell RNA-seq analysis, the authors reported four clusters of macrophages, two of which expressed higher levels of TNF. At the same time, other subclusters had deficient TNF expression[22], indicating a heterogeneous tumor microenvironment. Therefore, the inconsistent reports on TNF-α expression under cancerous conditions might be due to the heterogeneous tumor microenvironment and the functional state of the immune cells in the blood. Of note, there has been a report on the enhanced effects of chemotherapy, when combined with TNF-α treatment. In this approach, TNF-α induces apoptosis in cancer cells[23]. The serum concentration of TNF-α was significantly higher in the CG patients, as compared to the NM group  . In the Alterations in serum IL-8 levelsP = 0.027). On the other hand, in contrast to TNF-α, the serum IL-8 concentration was lower in GC patients, who received chemotherapy prior to blood sampling . Therefore, we excluded the chemotherapy-treated patients and performed a reanalysis that resulted in the elimination of the statistically significant differences among the studied groups  and Paris for supporting this study.The above-mentioned sampling protocols were approved by the Ethics Committee of the Pasteur Institute of Iran, Tehran . Every patient provided a written informed consent.The data are available upon request.MA: carried out the study and wrote and revised manuscript; ME: provided lab assistance; MT: assisted in gastric sampling; MAM: performed gastric surgery; MEH: performed gastric endoscopy, ET: revised the manuscript; MV: revised the manuscript; MM: wrote and revised the manuscriptNone declaredThis project was supported by an ACIP  grant (ACIP2015-10) from Institut Pasteur Paris; and Grant #833 from Pasteur Institute of Iran, as partial fulfillment of MA Ph.D. dissertation (code: TP-9347). Supplementary Table 1Supplementary Table 2


CD4+ T-cell activation through recognition of Human Leukocyte Antigen II (HLAII)-presented peptides is a key step in the development of unwanted immune response against biotherapeutics, such as the generation of anti-drug antibodies (ADA). Therefore, the identification of HLAII-presented peptides derived from biotherapeutics is a crucial part of immunogenicity risk assessment and mitigation strategies during drug development. To date, numerous CD4+ T-cell epitopes have been identified by HLAII immunopeptidomics in antibody-based biotherapeutics using either their native or aggregated form. Antibody-target immune complexes have been detected in patients with ADA and are thought to play a role in ADA development by enhancing the presentation of CD4+ T-cell epitopes at the surface of antigen presenting cells (APCs). The aim of this study was to investigate the effect of biotherapeutic antibody-target immune complexes on the HLAII peptide presentation of biotherapeutics in human primary monocyte-derived dendritic cells (DCs). The trimeric tumor necrosis factor (TNF) and its biotherapeutic antagonists infliximab (INFL), adalimumab (ADAL), and a single armed Fab’ were used as a model system. The HLAII immunopeptidome of DCs loaded with antagonists or their immune complexes with TNF was analyzed by trapped ion mobility time-of-flight mass spectrometry (timsTOF MS) leading to the identification of ~ 12,000 unique HLAII-associated peptides per preparation. Anti-TNF sequences were detected at a median of 0.3% of the total immunopeptidome, against a majority background of peptides from endogenous and media-derived proteins. TNF antagonist presentation spanned the variable and constant regions in a widespread manner in both light and heavy chains, consistent with previously discovered HLAII peptides. This investigation extends the collection of observed HLAII peptides from anti-TNF biotherapeutics to include sequences that at least partially span the complementary determining regions (CDRs), such as the LCDR1 for both INFL and ADAL. Although antagonist presentation varied significantly across donors, peptides from both bivalent antagonists INFL and ADAL were more highly presented relative to the Fab’. While TNF immune complexes did not alter overall HLAII presentation, a moderate increase in presentation of a subset of peptide clusters was observed in the case of INFL-TNF, which included HCDR2, HCDR3 and LCDR2 sequences. Unprecedented progress has been made with the development of an increasing diversity of protein-based biotherapeutics leading to new treatments for human diseases such as cancers, autoimmune and inflammatory disorders –5. MonocHowever, a major challenge to the development of mAbs is the unwanted anti-drug immune responses, i.e. immunogenicity that, if not identified early, can result in termination of late-stage clinical programs, which is both costly, inefficient and incurs delays for patients who need novel treatment options –8. Indeein vitro analytical strategy that complements T-cell epitope discovery and deimmunization strategies for immunogenicity risk assessment and mitigation   . For exa (MAPPS) , 37, 38.nt HLAII , 39. Thant HLAII –43.In vivo models have suggested that these INFL-TNF immune complexes indeed contribute to mounting an ADA response and production of inflammatory cytokines, an effect also observed with ADAL-TNF immune complexes in macrophages , then DCs were loaded, cultured and harvested prior to HLAII immunopurification, acid elution and timsTOF MS. HLAII peptides derived from the three antagonists INFL, ADAL and Fab’ were detected and analyzed for differences between complex and non-complex conditions. Predominant HLAII presentation of INFL and ADAL-derived peptides was detected. In contrast, Fab’-presentation was found limited to a small percentage of donor-sets. Furthermore, DCs treated with in vitro formed immune complexes increased INFL-presentation, while presentation of peptides from ADAL did not change overall as analyzed by qualitative and quantitative assessments.The work presented herein sought to extend these investigations by using mass spectrometry to detect differences in HLAII presentation of anti-TNF therapeutic-derived peptides in the context of complex formation. The characterization of HLAII immunopeptidomes from DCs loaded with TNF antagonists alone or as immune complexes with TNF enabled the comparison of peptides presented from INFL, ADAL and a single arm Fab’. To this end, In vitro formation of TNF immune complexes with INFL, ADAL and Fab’ antagonists was analyzed and confirmed by diameter-size measurements using dynamic light scattering (DLS). TNF immune complexes were freshly prepared prior to incubation with DCs and analyzed in fourteen independent experiments, i.e., every time prior to administering them to DCs derived from each of the fourteen donors. A total of 126 immunopeptidomes were analyzed. The MS/MS spectra, peptide-spectrum matches (PSMs) upon database searching, and unique peptide identifications were compared between treatments (n=9) and across donors (n=14). T-test, Wilcoxon marched-pairs signed rank test, Mann-Whitney and two-way ANOVA were used in this study. Normalization based on the peak areas of identified endogenous peptides was carried out using dataMAPPs, an immunopeptidomics data processing and visualization platform which employs a ‘global rank-invariant set normalization’ (GRSN) procedure using signals of the most invariant peptides within datasets per donor  was stored in 0.5 mL single use aliquots at -80°C and thawed on ice for 45 min before use. For immune complex formation, the TNF antagonists INFL , ADAL  and a polymer-conjugated Fab’ were prepared at 0.03 mM, independently mixed and incubated for 30 min at room temperature with 0.02 mM TNF in PBS at 1:1 volume to yield a total volume of 500 µL. As controls, TNF or each of the antagonists were incubated in parallel on their own under the same conditions, constituting the following test articles: INFL, ADAL, Fab’, INFL-TNF, ADAL-TNF and Fab’-TNF. Three replicates (9 µL each) were collected at 25°C by dynamic light scattering (DLS) using UNCLE . The hydrodynamic diameters were analyzed with UNCLE v4.10 software. The remaining TNF immune complexes were used immediately on primary monocytic DCs.5 cells/mL in RPMI medium supplemented with 10% (v/v) FBS, GM-CSF (100 ng/mL) and IL-4 (17 ng/mL) for 6 days. Test articles were added at 50 µg/mL to the monocytic DCs in culture on Day 5, followed by LPS stimulation (32 ng/mL) after 4 h, resulting in nine DC cultures for each donor . DCs were harvested 22 h post-LPS addition, washed three times with PBS, counted, and stored at –80°C until further processing.DCs were generated as previously described . BrieflyWhole protein lysates from DCs were prepared as described . BrieflyA dried aliquot per sample was equilibrated on ice and resuspended in 7 µL 3% ACN, 0.1% FA. Peptides were chromatographically separated and analyzed using a nanoElute nano HPLC system coupled to a trapped ion mobility time-of-flight mass spectrometer . A 6.5 µL aliquot of each sample was loaded after equilibration for 3.6 min with mobile phase A (approximately 6 column volumes) onto a 25 cm, 75µm C18 Aurora column  at a flow rate of 400 nL/min using mobile phase A (water plus 0.1% formic acid) and mobile phase B (acetonitrile plus 0.1% formic acid). Sample loading took 27 min. The chromatographic gradient was as follows: 2 to 25% B over 60 min, then 25 to 37% B over 7 min, followed by 67 - 80% B over 10 min, then maintain at 80% B for 10 min before returning 2% B. Total run time was 117.6 min.m/z and ion mobility pane to select features most likely representing peptide precursors rather than singly charged background ions.The timsTOF Pro was operated in PASEF mode with the following settings: mass range 100 to 1,700 m/z, 1/K0 Start 0.6 V·s/cm2, End 1.6 V·s/cm2, ramp and accumulation were set to 166 ms, capillary voltage 1,600 V, dry gas 3 l/min, dry temp 200°C, PASEF settings: 10 MS/MS frames (1.89 seconds duty cycle), charge range 0-5, active exclusion for 0.4 min, target intensity 20,000, intensity threshold 2,500, CID collision energy was 20 to 59 eV depending on the inverse mobility of the precursor. A polygon filter was applied to the Homo sapiens and Bos taurus included 20,352 and 6,009 entries, respectively, and were downloaded from UniProt on 2020-01-31  and analyzed by Byonic version 10.0  and PEAKS Studio X Pro . TimsTOF data files were imported into both database search platforms with a precursor mass error tolerance of 30 ppm and the fragment mass error tolerance to 0.05 Da without enzyme specificity. Cysteinylation, oxidation (M), deamidation (NQ), pyro-glu (QE) were included as variable modifications. For PEAKS database searches, 1% FDR threshold was used (target-decoy approach). HLAII immunopeptidomes from the nine conditions were analyzed by Byonic. PEAKS data was used for additional evaluation and verification of test article derived peptides as well as normalization using the dataMAPPs pipeline . Briefly, the PEAKS database search results were individually exported as DB psm.csv files and analyzed by a modified version of dataMAPPs [(heatMAPPs) following two rounds of peak intensity normalization by the global rank-invariant set normalization (GRSN) procedure in dataMAPPs, which was applied with the default parameters listed in Analyze.R file (MS/MS data were searched against the human proteome with added bovine proteins and light chain (LC) and heavy chain (HC) sequences from TNF antagonists. The Swiss-Prot protein databases from taMAPPs [, R-code in vitro TNF immune complex formation and analysis (The hydrodynamic diameter of TNF and each antagonist was compared to that of freshly formed complexes with TNF in fourteen independent experiments by DLS. Average diameter distribution for TNF was detected at 5.9 ± 0.4 nm (median 5.9 nm) and average sizes of antagonists corresponded to 17.5 ± 2.4 nm (median 16.9 nm), 10.5 ± 1.0 (median 10.4 nm), and 14.9 ± 7.2 nm (median 13.4 nm) for INFL, ADAL and Fab’, respectively  yielded on average a much lower number of DCs compared to D1802 (10.2 x 106). However, average DC yields between treatment conditions were relatively consistent  for HLAII presented peptides independent of DC treatment, followed by bovine proteins from the cell media at ~ ≤20% , the INFL-LCDR2-sequences were only detected in two donors (D1170 and D1214) treated with INFL-TNF. Subtle differences in presentation of LCDR1 and LCDR3 were detected in D1802, D1761 and D2035, where a partial coverage (1-3 residues) was found in TNF immune complex treated DCs. Peptides covering HCDR2 and HCDR3 increased in INFL-TNF in three donors . The three cases where CDR-presentation decreased upon TNF immune complex treatment included sequences spanning HCDR2 and HCDR3 in D1095, HCDR3 and LCDR2 in D1901, HCDR3 in 1802 and HCDR2 in D1289.Presentation of CDR peptides changed in some of the DCs treated with TNF-complexes compared with TNF antagonist controls. The most notable change was for INFL with a ~1.5-fold increase in the peak area using dataMAPPs . Of note, increase in INFL-presentation in DCs treated with INFL-TNF based on peptide counting was concordant with the increase observed in the GRSN-normalized response of peak area from INFL peptides as well as those detected spanning the CDRs  as well as for sequences with low presentation (≤2 peptides), such as LCDR1 and HCDR1 for ADAL-TNF in DCE00 S13, ovenumber of donors and a 1.2- to 5.6-fold increase in number of unique Fab’ and Fab’-CDR peptides was identified with Fab’-TNF treatment . For example, the framework region between HCDR2 and HCDR3, VH71-94, was consistently detected in 50-57% of DC sets analyzed in prior and current investigations . Furthermore, peptides from the light chain region, VL3-19, were presented in 50% of the DCs (7/14 donors). HLAII-presentation of hypervariable regions included HCDR2, HCDR3 and LCDR2 sequences in 64, 42 and 28% donor-sets, respectively, consistent with previous studies , 34. In ADAL-presentation also aligned with prior immunopeptidomics work , includiunique peptides presented from CDR-sequences increased in INFL-TNF, which was confirmed not only by peptide counting but also by analysis of normalized peak area using dataMAPPs -response in vivo , 46. Thiin vivo . Thus, Tin vivo , 66. Of in vivo . This copeptide-clusters based on number of unique peptides, nested sets and peptide abundance) continue to be established. For example, intra- and inter-donor normalization strategies, which are crucial for the comprehensive assessment of HLAII-presentation and relative quantitative interpretation of immunopeptidomics studies, are still being explored. Potentially, peptides presented from proteins internalized from the media such as APOB, HBB, ALBUM could serve as quality controls in HLAII-studies or as a tool to normalize the signal of HLAII-presented peptides. To this end, the generation of a master pool of DCs stimulated with a qualified standard HLAII-presented protein under same conditions as the test article may provide a valuable tool for quality assessment in HLAII immunopeptidomics. Quantitative side-by-side comparisons were not feasible given the known differences in donors analyzed and sample preparation workflows employed  under the review and approval of an Institutional Review Board (IRB). This IRB is in full compliance with good clinical practices as defined under the U.S. food and drug administration (FDA) regulations, U.S. department of health and human services (HHS) regulations, and the international conference on harmonisation (ICH) guidelines. The participants provided their written informed consent that their samples can be used in biomedical research.in silico tools for analysis. MA and GB carried out in silico analysis for peptide normalization to input material. AC, NA-A, and GB assisted with sample preparation. ZL and H-YK enabled DLS analyses. HN supervised the work. All authors critically revised the paper for intellectual content. All authors contributed to the article and approved the submitted version.AC-L and HN designed the study. AC-L acquired data by performing most laboratory experiments and wrote the manuscript; HN, RS, TH, ST, and AC-L conceptualized and developed the workflow for HLAII-immunopeptidomics. MW and GK enabled and performed the LCMS analysis of the DC-datasets by timsTOF MS. MA developed This work has been supported by the Pfizer Worldwide Research, Development and Medical Postdoctoral program.AC, GB, ZL, H-YK, HN, RS, ST, and AC-L are current employees of Pfizer Inc. MA, NA-A, and TH were employed at Pfizer when they contributed to this study. GK and MW are current employees of Bruker Daltonics.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Objective: Obstructive sleep apnea (OSA) is a respiratory disease associated with autonomic nervous system dysfunction. As a novel method for analyzing OSA depending on heart rate variability, fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn) can effectively assess the sympathetic tension limits, thereby realizing a good performance in the disease severity screening. Method: Sixty 6-h electrocardiogram recordings  from the PhysioNet database were used in this study. The performances of minima of Emma-fApEn (fApEn-minima), maxima of Emma-fApEn (fApEn-maxima) and classic time-frequency domain indices for each recording were assessed by significance analysis, correlation analysis, parameter optimization and OSA screening. Results: fApEn-minima and fApEn-maxima had significant differences between the severe OSA group and the other two groups, while the mean value (Mean) and the ratio of low-frequency power and high-frequency power (LH) could significantly differentiate OSA recordings from healthy recordings. The correlation coefficient between fApEn-minima and apnea-hypopnea index was the highest (|R| = 0.705). Machine learning methods were used to evaluate the performances of the above four indices. Random forest (RF) achieved the highest accuracy of 96.67% in OSA screening and 91.67% in severe OSA screening, with a good balance in both. Conclusion: Emma-fApEn may be used as a simple preliminary detection tool to assess the severity of OSA prior to polysomnography analysis. I.Obstructive sleep apnea (OSA) is a chronic and curable upper respiratory tract disease, characterized by repeated blockage of the upper respiratory tract during sleep, which will lead to poor quality of sleep and thus daytime sleepiness 2) Polysomnography is considered as a gold standard for OSA detection et al.et al.HRV reflects slight changes in the heartbeat cycle. It is produced by the interaction between autonomic nervous system (ANS) and internal nervous system of the heart et al.et al.et al.et al.However, many linear indices of HRV are susceptible to the heart rate spontaneous fluctuation et al.et al.et al.The performance of OSA classification is not only affected by features, but also related to the quality of the classifier. Janbakhshi In above studies, most of them were analyzed by comparing the indices of the overall data of OSA groups and control groups, and less attention was paid to the information of some special points in the data. In HRV analysis, the general trend of the RR interval sequences (RRs) reflects the general changes in the ANS tension, and the extrema of the RRs are related to the ANS tension limits.Therefore, in order to analyze the changes of the ANS tension limits in OSA patients through the extrema in the general trend of the RRs, this study proposed the fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn). Due to the small partial fluctuations of the RRs, the multiple moving average method was introduced into the experiment, which reduces the fluctuation effect of the sequence by obtaining segments’ averages of the data several times. Sample entropy has the advantages of anti-noise and anti-interference, but it depends on small tolerances and forward matching of data length II.A.https://www.physionet.org/physiobank/database/apnea-ecg/, which was applied in the Computers in Cardiology Challenge 2000 The database used in this paper was downloaded from The number of apnea and hypopnea events per hour during sleep is defined as the apnea-hypopnea index (AHI), which is an indicator of the severity of sleep apnea. Recordings that contained at least 1 hour with AHI of 10 or more, and at least 100 minutes labeled apnea were classified as the apnea group. Recordings that contained at least 1 hour with AHI of 5 or more, and between 5 and 99 minutes labeled apnea were classified as the borderline group. Recordings that contained at least 1 hour with AHI of less than 5, and fewer than 5 minutes labeled apnea were classified as the control group. In this study, the apnea and control groups were used for analysis. According to AHI, the selected recordings were divided into the normal group , the mild/moderate OSA group , and the severe OSA group .B.The HRV analysis method used in this study is shown in 1)First, the segments with large errors in the beginning and end of the recordings were cut, and then the first 6 hours of the recordings were selected as the ECG signals for the study. Next, the RRs from the ECG signal were extracted according to the Pan-Tompkins algorithm 2)HRV is the small difference in timing between successive normal (sinus) cardiac cycles. We completed the time domain analysis by the time domain measurement of HRV. The common time domain indices for HRV analysis of OSA include the mean value (Mean), the standard deviation of the normal-to-normal intervals (SDNN), and the square root of the mean of the sum of the squares of differences between adjacent the normal-to-normal intervals (RMSSD) of consecutive 5-minute RR intervals. For N points RRs consisting of consecutive 5-minute RR intervals, they are defined as follows:3)The frequency domain analysis is to analyze the law of heart rate change based on the power spectral density of RRs calculated by fast Fourier transform. It is relevant to time domain analysis, and moreover, it can reveal the more complex changes of heart rate. Frequency domain indices commonly used for HRV analysis of OSA patients include low-frequency power , high-frequency power  and LF/HF (LH) for 5-minute epochs. The formulas are as follows:4)The extremum sequences can reflect the tension limits of the ANS, but it cannot be extracted directly from the RRs due to the small partial fluctuation of the RRs. Therefore, the RRs were filtered by the multiple moving average method first, and then the extremum sequences were extracted from them. Next, fApEn of the minima (fApEn-minima) and fApEn of the maxima (fApEn-maxima) were obtained by applying fApEn to calculate the complexity of the minimum and maximum sequences. The calculation method of the indices is shown in The raw RRs were divided into 1-minute non-repeated sequence subsets. First, since gross errors of the RRs can greatly affect the performance of the extremum sequences reflecting ANS tension limits, it is necessary to further remove them by Pauta criterion The multiple moving average method can effectively reduce the small partial fluctuation of the sequence, with which the filtered sequence The comparisons of the raw RRs, the filtered RRs and the extremum sequences between the normal and OSA groups are shown in fApEn can effectively and robustly measure the complexity of the discrete sequence. The calculation details of it Algorithm 3Input:Initialize the following parameters.1)the non-stationary time sequence 2)the parameters Output:the index fordofordofordoend forend forfordofordoend forend forend forThe self-matching was not eliminated in this study, which means Algorithm 1Input:Initialize the following parameters.1)the RRs 2)the point of Output:the new sequence fordocalculate the average of calculate the standard deviation of fordoifthendelete end ifend forend forAlgorithm 2Input:Initialize the following parameters.1)the one-dimensional sequence 2)the repeat counter 3)the interval length Output:the maximum sequence calculate the average length of fordofordoend forfordobuild the one-dimensional sequence calculate the standard deviation of calculate the average of end forbuild the one-dimensional sequence end forextract the maximum sequence According the algorithm 1C.In order to improve the detection performance of the indices and verify the complementarity of them, the random forest (RF) classifier was adopted for OSA and severe OSA screening. RF is composed of tree classifiers. As shown in The design of decision tree needs proper attribute selection measure and pruning method. Most methods choose attribute assignment quality measures to construct decision trees. Gini index, the most commonly used attribute selection measure in decision tree induction, is used to measure the total variance across two OSA classes. It is defined as:In III.A.The different disease states analysis results, including the time-frequency domain indices and the multiple moving averages indices, are listed in The monotonicity analysis results of Mean, LH, fApEn-minima and fApEn-maxima in the different disease states are shown in the B.In this study, we analyzed the correlation of Mean, LH, fApEn-minima and fApEn-maxima with AHI. As shown in C.In the calculation of Emma-fApEn, the multiple moving average method was used to reduce the interference of fine fluctuations in the RRs to the general trend. The moving average method reduced the interference while losing some information contained in the RRs, so the repeat counter The relationships between the absolute value of the correlation coefficient D.The above analysis and the results in Compared with other classifiers, we utilized random forest (RF) to implement the classification of the subjects based on two-fold cross-validation. The classification performance of multiple machine learning classifiers is shown in IV.A.et al.et al.et al. presented that the sample entropy of HRV in OSA patients has the advantage of anti-noise, and the accuracy in OSA screening reached 70.3% et al. reported that variance delay fuzzy approximate entropy (VD_fApEn) provides a method for ANS fluctuation analysis in OSA patients and OSA severity analysis, which showed an improved accuracy (90.0%) The performance of the time-frequency domain indices was also verified by previous studies. The decreases of SDNN and RMSSD in OSA patients reported by Roche Significance analysis showed that Mean and LH were effective indices for OSA screening. The results of the significance analysis also displayed that these time-frequency domain indices cannot discriminate the different severities of the disease. fApEn-minima and fApEn-maxima were significantly different between the severe OSA group and the other groups, which manifested their potential for differentiating severe OSA patients. Through correlation analysis, fApEn-minima (Machine learning methods were used to screen OSA and severe OSA, and the results showed that the accuracy of multi-indices OSA screening reached 96.67% by RF classifier, and the accuracy of severe OSA screening reached 91.67%. Compared with other classifiers, RF can better process high-dimensional data in the face of multi-indices processing and does not need feature screening.https://archive.physionet.org/pn3/ucddb/, which contains ECG recordings from 25 subjects. We extracted and corrected RRs from the database, and calculated the time-frequency domain indices and multiple moving average indices of the RRs. As shown in Validation was implemented on St. Vincent’s University Hospital / University College Dublin Sleep Apnea Database downloaded from B.Moving average method is a simple low-pass filtering method, which can reduce the fluctuation of data and make data evener. Multiple moving average method can effectively obtain the general trend of the sequence by taking the moving average of it for many times. The general trend of the RRs is related to the general changes of ANS tension. The variation of the extrema of the RRs reflects fluctuations of ANS tension limits. Moreover, the multiple moving average method can prevent the selection of inappropriate extrema due to the small partial fluctuation of the RRs. Therefore, multiple moving average method was used to filter the RRs to obtain the general trend of them in this study. Since moving average method can change the mean and standard deviation of the sequence, it is necessary to normalize the RRs after each use of moving average method, which improves the robustness of the model.Compared with approximate entropy and sample entropy, fApEn is less affected by calculated parameters. fApEn has a good robustness to noise, which can evaluate the complexity of biomedical signals more accurately The repeat counter Machine learning methods were used to combine the advantages of multiple effective indices. The performance of different classifiers was compared. Screening results  and ROC C.Sudden fluctuations happen in ANS when sleep apnea occurs. The imbalance between the sympathetic and vagus nerve system can lead to ANS dysfunction However, time-frequency domain analysis is linear and more suitable for stationary signal, while the process of regulating heart rate through ANS is nonlinear and non-stationary. In contrast, non-linear indices of HRV are better to analyze the complex dynamics of the heartbeat The sympathetic nervous system is enhanced and the parasympathetic nervous system is weakened when OSA occurs. Therefore, the low frequency component of HRV increases and the high frequency component decreases, resulting in the periodic fluctuation of HRV. The periodic fluctuations are manifested as the uniform fluctuation of HRV, which lead to the decrease of amplitude variation of the extremum sequences of HRV There are some limitations in this study. First, extremum sequences are greatly affected by the noise in the signal. Denoising method is important when obtaining extremum sequences. The data must conform to independent identically normal distribution V.In this study, Emma-fApEn was proposed as a HRV nonlinear analysis method for OSA research. Mean, LH, fApEn-minima and fApEn-maxima were used to analyze the features of HRV. The results showed that Mean and LH could significantly distinguish OSA patients from normal people. fApEn-minima and fApEn-maxima could significantly distinguish severe OSA patients from other people. Combined with the above four indices, RF was selected as the best classifier and achieved OSA screening with 96.67% accuracy as well as severe OSA screening with 91.67% accuracy. Therefore, the combination of Mean, LH, fApEn-minima and fApEn-maxima screening has an advantage over traditional single-indicator screening in analyzing HRV features and differentiating OSA severities. Moreover, this method also provides a new clinical reference for the diagnosis of OSA.


Hnscr in glucose metabolism.Hypothalamic dysfunction leads to glucose metabolic imbalance; however, the mechanisms still need clarification. Our current study was to explore the role of hypothalamic Hnscr knockout or htNSC‐specific Hnscr overexpression mice, we evaluated the effects of Hnscr on glucose metabolism through GTTs, ITTs, serum indicator measurements, etc. Immunofluorescence staining and Western blotting were performed to test inflammation levels and insulin signalling in hypothalamus. Conditioned medium intervene were used to investigate the effects of htNSCs on neuronal cell line. We also detected the glucose metabolism of mice with htNSCs implantation.Using Hnscr expression decreased in the hypothalamus after high‐fat diet feed. Hnscr‐null mice displayed aggravated systematic insulin resistance, while mice with htNSC‐specific Hnscr overexpression had the opposite phenotype. Notably, Hnscr‐null mice had increased NF‐κB signal in htNSCs, along with enhanced inflammation and damaged insulin signal in neurons located in arcuate nucleus of hypothalamus. The secretions, including sEVs, of Hnscr‐deficient htNSCs mediated the detrimental effects on the CNS cell line. Locally implantation with Hnscr‐depleted htNSCs disrupted glucose homeostasis.This study demonstrated that decreased Hnscr in htNSCs led to systematic glucose imbalance through activating NF‐κB signal and dampening insulin signal in hypothalamic neurons. Hnscr participated in the regulation of high fat diet‐induced insulin resistance. Hnscr knockout aggravated diet‐induced insulin resistance. Mechanistically, loss of Hnscr reduced trim56 level, leading to NF‐κB activation in htNSCs. Hnscr‐deficient htNSCs also contributed to increased inflammation and insulin resistance in neurons finally damaged systematic glucose balance.We discovered the lncRNA  Insulin resistance, the major pathological feature of MS and T2DM, is characterized by reduced sensitivity to insulin in various tissues such as the fat, liver, and muscle. The modulation of insulin resistance and glucose metabolism led by peripheral organs were widely documented, and increasing studies have focused on the regulatory role central nervous system in the process.Recent studies have shown an atypical form of pro‐inflammatory signalling in the hypothalamus, which is known as ‘hypothalamic microinflammation’.Hnscr participated in the regulation of high‐fat diet‐induced insulin resistance. Hnscr was decreased in the hypothalamus after high‐fat diet feeding. Knockout of Hnscr aggravated high‐fat diet‐induced glucose clearance and tolerance, contributing to peripheral insulin resistance. Conversely, htNSC‐specific Hnscr overexpression ameliorated this phenotype. Mechanically, Hnscr stabilized trim56 from degradation, leading to decreased NF‐κB phosphorylation. Through conditioned medium intervention and htNSCs implantation, we found Hnscr‐knockout htNSCs further enhanced inflammation and dampened insulin signal in neurons located in the mediobasal hypothalamic region (MBH) through secretions, finally damaged systematic insulin sensitivity.Here, we discovered the lncRNA 22.1Hnscr, a lncRNA specially enriched in htNSCs, in regulating aging process, and aged Hnscr null mice had aggravated aging‐related physiological phenotype.Hnscr participated in modulating glucose metabolism. To this end, we first detected Hnscr level in mice with a normal chow diet (NCD) and high‐fat diet (HFD) and found decreased hypothalamic Hnscr level after HFD feed  and insulin tolerance tests (ITTs) to evaluate their capacity for glucose tolerance and clearance. Hnscr null mice had worse performance in both GTTs and ITTs with or without high‐fat diet feed, revealed by higher glucose level after glucose or insulin stimulation , and mice injected with AAV‐Sox2‐Scramble were used as controls. Quantitative real‐time reverse transcription PCR (qRT‐PCR) analysis confirmed the highly expressed Hnscr in hypothalamus after AAV‐Sox2‐Hnscr injection  56 (Trim56) aroused our attention. Members of the TRIM protein are reported as important regulators of immunity and inflammation, and some of them were implicated in NF‐κB activation.Hnscr knockout, rather than mRNA level  and treated the CAD, a CNS cell line  led to decreased NF‐κB phosphorylation . And Hnscr‐deficient htNSCs implantation aggravated whole‐body insulin resistance.In our study, we found the regulatory role of Hnscr deficiency in htNSCs increased the vulnerability of NF‐κB activation after TNFα exposure, and vice versa. Besides htNSCs, we also noticed that Hnscr ablation enhanced inflammation in ARH neurons, indicating the possible link between htNSCs and neurons. Further study in vitro demonstrated the secretions from htNSCs mediated the effects. Among the secretions, isolated sEVs can also enhance inflammation and insulin resistance in recipient cells, which suggested the sEVs‐packaged contents might be involved in the process. Still, whether and how htNSC‐derived sEVs functioned on neurons need further investigation.HtNSCs are a group of neural stem cells capable of self‐renew and differentiation, which locate in the mediobasal hypothalamus and the third ventricle wall.Hnscr knockout in htNSCs. Two main populations of neurons, POMC and AgRP, express leptin receptor and insulin receptor,Since hypothalamic insulin signal was reported to be implicated in the regulation of obesity and insulin resistance,Hnscr knockout. Inspired by hepatic insulin resistance in Hnscr‐null mice, we also constructed transgenic mice with liver‐specific Hnscr overexpression to inquire whether Hnscr expression in hepatocytes mediated the effects. Unfortunately, hepatic Hnscr overexpression failed to alter peripheral insulin sensitivity or liver steatosis after HFD feed . Cells were cultured in DMEM (Gibco) with 10% fetal bovine serum (Gibco) and 1% penicillin–streptomycin (Procell). CAD cell line were cultured in 10% DMEM/F‐12 (Gibco) with 10% FBS and 1% PS. All cells were cultured at 37°C, 5% CO4.3Hnscr) were generated from Obio Co . HtNSCs and 293 T cell line were treated with adenovirus at an MOI of 7 for 2–3 days. Cells were stimulated with TNF‐α at a final concentration of 20 ng/ml for 30 min and collected for further qRT‐PCR and Western blot analysis.Adenovirus control (Ad‐Scramble) and Adenovirus‐hnscr  for 48 h, followed by detection of insulin signal and inflammatory level.For conditional medium intervention, the conditioned medium was harvested from htNSCs of wild‐type or 4.5After the intervention, cells were treated with insulin (Sigma) at a final concentration of 100 nmol/L for 20 min. After washing with sterile PBS 3 times, cells were lysed with RIPA Lysis Buffer (Beyotime) mixed with phosphatase inhibitor and protease inhibitor (Selleck) or Trizol (Accurate Biotechnology) for further Western Blot.4.6X, Y, and Z axis values were read using the Bregma point coordinates as the origin. The MBH was located 5.8 mm below the skull surface, 1.7 mm behind the Bregma horizontal line, and 0.25 mm lateral to the midline of the brain.Purified AAVs were generated and bilaterally intra‐MBH injected as previously.The cell implantation method is as previously described.4.7The levels of blood glucose were measured by a glucometer monitor (Sinocare). Mice were intraperitoneal injection with glucose  or insulin . Blood was taken from the tail vein at 0 min, 15 min, 30 min, 60 min, 90 min, and 120 min to measure blood glucose values respectively.4.8The levels of blood glucose were measured by a glucometer monitor (Sinocare). The levels of serum insulin were detected by an ELISA kit . Homeostasis model assessment of insulin resistance (HOMA‐IR) index was calculated using the following formula: fasting glucose levels (mmol/L) × fasting serum insulin (mIU/L)/22.5.4.9Mice fasted for 6 h. Euthanasia was carried out 15 min after the insulin (7 U/kg) injection via a caudal vein. Liver, muscle, and inguinal white adipose tissue (iWAT) were excised and snap frozen for Western Blot detection of insulin signal.4.10Hepatic lipids were extracted with isopropanol in the ratio of liver weight (g):volume (ml) = 1:9. Triglyceride (TG), total cholesterol (TC), free fatty acid (FFA) and alanine transaminase (ALT), aspartate aminotransferase (AST) were measured with a TG kit,TC kit, FFA kit, ALT kit or AST kit respectively, according to the manufacturers' instructions. All these kits were purchased from Elabscience Company .4.11The Western Blot analysis was conducted as previously described.4.12Real‐time PCR was performed as previously described,4.13Frozen liver sections were stained with Oil Red O. Paraformaldehyde‐fixed and paraffin‐embedded liver sections were examined histologically with hematoxylin and eosin (H&E).4.14The immunofluorescence was conducted as previously described.4.15t test or ANOVA followed by Bonferroni post‐test. Data analyses were performed using GraphPad Prism 7.0. p < 0.05 was considered significant.All data are presented as means ± SEM. The statistical significance of the differences between various treatments or groups were evaluated by either Student's Tie‐Jian Jiang designed this study; Ya Liu and Yi‐Fan Guo performed most of the experiments, generated data, and wrote the manuscript; Hui Peng, Hai‐yan Zhou, Tian Su, and Mi Yang analysed the data and designed the figures; Qi Guo, Ye Xiao, Yan Huang, and Tie‐Jian Jiang supervised this study and revised the manuscript.The authors declare that they have no conflict of interest.Figure S1Hnscr knockout induces liver steatosis.  Liver weight and appearance. (c) HE and Oil red staining of representative liver sections. Scale Bar: 50 μm. (d–h) Liver and serum TG, TC, FFA level.  mRNA levels of genes related to lipid metabolism. (k) Serum ALT and AST level. Statistical significance was calculated by two‐tailed Student's t test or two‐way ANOVA .Figure S2 Hypothalamic Hnscr overexpression did not alter liver steatosis. (a) Liver weight. (b) Liver appearance. (c) HE staining of representative liver sections. Scale Bar: 50 μm.Figure S3 Hepatic Hnscr overexpression did not alter insulin sensitivity and liver steatosis. AAV‐Hnscr and the its control AAV‐Scramble were injected to 2‐month old mice through tail vein, following high‐fat diet‐feed for 3 month. (a) qPCR analysis of Hnscr mRNA in liver 1 month after AAVs injection. (b) Fasting and fed blood glucose levels. (c–f) Glucose tolerance tests and insulin tolerance tests.  Liver weight and appearance. (i–m) Liver and serum TG, TC, FFA level. (n) Serum ALT and AST level. Data are presented as mean ± SEM (n = 5–6). Statistical significance was calculated by two‐tailed Student's t test .Click here for additional data file.