The effects of simultaneous respiratory infections on the nasal shedding of Mycoplasmopsis bovirhinis in dairy calves.

Although there are several studies that described the possible participation of Mycoplasmopsis bovirhinis (formerly, Mycoplasma bovirhinis) in respiratory disease in calves worldwide, none of these evaluated the effects of concomitant infections on the shedding of this organism. Accordingly, this study evaluated the effects of simultaneous respiratory infections in dairy calves on the nasal shedding of M. bovirhinis. A statistical two-step model, using univariable and multivariable with logistic regression was developed to investigate and predict the possible effects of simultaneous infections by Histophilus somni, Mannheimia haemolytica, Pasteurella multocida, bovine coronavirus (BCoV), and ovine gammaherpesvirus 2 (OvGHV2) in dairy calves on the nasal shedding of M. bovirhinis. The multivariable analysis demonstrated that dairy calves infected with OvGHV2 have 2.59 times likelihood of nasal shedding of M. bovirhinis relative to calves not infected by OvGHV2, while the odds of nasal shedding of M. bovirhinis was 3.46 times higher in dairy calves infected by M. haemolytica. In contrast, simultaneous respiratory infections in dairy calves by H. somni, P. multocida, and BCoV had no direct effect on the nasal shedding of M. bovirhinis. Consequently, infections by OvGHV2 and M. haemolytica may be possible risk factors for the nasal shedding of M. bovirhinis in dairy calves. These results demonstrated the importance of disease modeling in veterinary medicine to predict and understand the complex outcomes of associations in animals concomitantly infected by several disease pathogens.

Induced-pluripotent stem cells and neuroproteomics as tools for studying neurodegeneration.

The investigation of neurodegenerative diseases advanced significantly with the advent of cell-reprogramming technology, leading to the creation of new models of human illness. These models, derived from induced pluripotent stem cells (iPSCs), facilitate the study of sporadic as well as hereditary diseases and provide a comprehensive understanding of the molecular mechanisms involved with neurodegeneration. Through proteomics, a quantitative tool capable of identifying thousands of proteins from small sample volumes, researchers have attempted to identify disease mechanisms by detecting differentially expressed proteins and proteoforms in disease models, biofluids, and postmortem brain tissue. The integration of these two technologies allows for the identification of novel pathological targets within the realm of neurodegenerative diseases. Here, we highlight studies from the past 5 years on the contributions of iPSCs within neuroproteomic investigations, which uncover the molecular mechanisms behind these illnesses.

Modeling the impact of child vaccination (5-11 y) on overall COVID-19 related hospitalizations and mortality in a context of omicron variant predominance and different vaccination coverage paces in Brazil.

Background: Developing countries have experienced significant COVID-19 disease burden. With the emergence of new variants, particularly omicron, the disease burden in children has increased. When the first COVID-19 vaccine was approved for use in children aged 5-11 years of age, very few countries recommended vaccination due to limited risk-benefit evidence for vaccination of this population. In Brazil, ranking second in the global COVID-19 death toll, the childhood COVID-19 disease burden increased significantly in early 2022. This prompted a risk-benefit assessment of the introduction and scaling-up of COVID-19 vaccination of children. Methods: To estimate the potential impact of vaccinating children aged 5-11 years with mRNA-based COVID-19 vaccine in the context of omicron dominance, we developed a discrete-time SEIR-like model stratified in age groups, considering a three-month time horizon. We considered three scenarios: No vaccination, slow, and maximum vaccination paces. In each scenario, we estimated the potential reduction in total COVID-19 cases, hospitalizations, deaths, hospitalization costs, and potential years of life lost, considering the absence of vaccination as the base-case scenario. Findings: We estimated that vaccinating at a maximum pace could prevent, between mid-January and April 2022, about 26,000 COVID-19 hospitalizations, and 4200 deaths in all age groups; of which 5400 hospitalizations and 410 deaths in children aged 5-11 years. Continuing vaccination at a slow/current pace would prevent 1450 deaths and 9700 COVID-19 hospitalizations in all age groups in this same time period; of which 180 deaths and 2390 hospitalizations in children only. Interpretation: Maximum vaccination of children results in a significant reduction of COVID-19 hospitalizations and deaths and should be enforced in developing countries with significant disease incidence in children. Funding: This manuscript was funded by the Brazilian Council for Scientific and Technology Development (CNPq - Process # 402834/2020-8).

Mathematical modeling and investigation on the role of demography and contact patterns in social distancing measures effectiveness in COVID-19 dissemination.

In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.

Modeling Early Neural Crest Development via Induction from hiPSC-Derived Neural Plate Border-like Cells.

Neural crest cells (NCCs) are a multipotent and transient cell population that gives rise to many important tissues during human embryogenesis. Disturbances that occur during NCCs development may lead to numerous types of diseases and syndromes, which are called neurocristopathies. NCCs in vitro modeling enables the access to cellular, genetic, and biochemical information about the neural crest development and its derivatives. By using cells derived from patients with neurocristopathies it is possible to study the cellular and genetic mechanisms behind each disease in a specific and trustworthy manner, as well as to contribute to the development of prospective treatments. Here, we describe a protocol of 19 days, capable of efficiently generating NCCs from human induced pluripotent stem cells (hiPSCs). This differentiation process recapitulates the intermediate stage of neural plate border-like cells (NBCs), the epithelial to mesenchymal transition (EMT), and enables further generation of NCCs derivatives, such as Schwann cells, smooth muscle cells, melanocytes, peripheral neurons, adipocytes, osteoblasts, and chondrocytes.

A process-based model to simulate sugarcane orange rust severity from weather data in Southern Brazil.

Forecasting the severity of plant diseases is an emerging need for farmers and companies to optimize management actions and to predict crop yields. Process-based models are viable tools for this purpose, thanks to their capability to reproduce pathogen epidemiological processes as a function of the variability of agro-environmental conditions. We formalized the key phases of the life cycle of Puccinia kuenhii (W. Krüger) EJ Butler, the causal agent of orange rust on sugarcane, into a new simulation model, called ARISE (Orange Rust Intensity Index). ARISE is composed of generic models of epidemiological processes modulated by partial components of host resistance and was parameterized according to P. kuenhii hydro-thermal requirements. After calibration and evaluation with field data, ARISE was executed on sugarcane areas in Brazil, India and Australia to assess the pathogen suitability in different environments. ARISE performed well in calibration and evaluation, where it accurately matched observations of orange rust severity. It also reproduced a large spatial and temporal variability in simulated areas, confirming that the pathogen suitability is strictly dependent on warm temperatures and high relative air humidity. Further improvements will entail coupling ARISE with a sugarcane growth model to assess yield losses, while further testing the model with field data, using input weather data at a finer resolution to develop a decision support system for sugarcane growers.

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil.

Livestock movements create complex dynamic interactions among premises that can be represented, interpreted, and used for epidemiological purposes. These movements are a very important part of the production chain but may also contribute to the spread of infectious diseases through the transfer of infected animals over large distances. Social network analysis (SNA) can be used to characterize cattle trade patterns and to identify highly connected premises that may act as hubs in the movement network, which could be subjected to targeted control measures in order to reduce the transmission of communicable diseases such as bovine tuberculosis (TB). Here, we analyzed data on cattle movement and slaughterhouse surveillance for detection of TB-like lesions (TLL) over the 2016-2018 period in the state of Rio Grande do Sul (RS) in Brazil with the following aims: (i) to characterize cattle trade describing the static full, yearly, and monthly snapshots of the network contact trade, (ii) to identify clusters in the space and contact networks of premises from which animals with TLL originated, and (iii) to evaluate the potential of targeted control actions to decrease TB spread in the cattle population of RS using a stochastic metapopulation disease transmission model that simulated within-farm and between-farm disease spread. We found heterogeneous densities of premises and animals in the study area. The analysis of the contact network revealed a highly connected (~94%) trade network, with strong temporal trends, especially for May and November. The TLL cases were significantly clustered in space and in the contact network, suggesting the potential for both local (e.g., fence-to-fence) and movement-mediated TB transmission. According to the disease spread model, removing the top 7% connected farms based on degree and betweenness could reduce the total number of infected farms over three years by >50%. In conclusion, the characterization of the cattle network suggests that highly connected farms may play a role in TB dissemination, although being close to infected farms was also identified as a risk factor for having animals with TLL. Surveillance and control actions based on degree and betweenness could be useful to break the transmission cycle between premises in RS.

Neuroprogenitor Cells From Patients With TBCK Encephalopathy Suggest Deregulation of Early Secretory Vesicle Transport.

Biallelic pathogenic variants in TBCK cause encephaloneuropathy, infantile hypotonia with psychomotor retardation, and characteristic facies 3 (IHPRF3). The molecular mechanisms underlying its neuronal phenotype are largely unexplored. In this study, we reported two sisters, who harbored biallelic variants in TBCK and met diagnostic criteria for IHPRF3. We provided evidence that TBCK may play an important role in the early secretory pathway in neuroprogenitor cells (iNPC) differentiated from induced pluripotent stem cells (iPSC). Lack of functional TBCK protein in iNPC is associated with impaired endoplasmic reticulum-to-Golgi vesicle transport and autophagosome biogenesis, as well as altered cell cycle progression and severe impairment in the capacity of migration. Alteration in these processes, which are crucial for neurogenesis, neuronal migration, and cytoarchitecture organization, may represent an important causative mechanism of both neurodevelopmental and neurodegenerative phenotypes observed in IHPRF3. Whether reduced mechanistic target of rapamycin (mTOR) signaling is secondary to impaired TBCK function over other secretory transport regulators still needs further investigation.

Movement Restriction and Increased Surveillance as Efficient Measures to Control the Spread of Highly Pathogenic Avian Influenza in Backyard Productive Systems in Central Chile.

During the last 5 years there has been an alarming number of reports of highly pathogenic avian influenza worldwide. However, little is known about the status of this disease in South America. Chile has been the only country in South America where an HPAI outbreak was reported. This outbreak occurred in 2002 and was due to an H7N3 HPAI, where the most plausible hypothesis that explained the entrance of the disease to the country, had relation to migratory wild birds. Commercial poultry farms in Chile are highly integrated and have high biosecurity standards. Nevertheless, poultry backyard production systems lack biosecurity measures and are widely distributed. Since 2002 outbreak, avian influenza viruses have been identified in wild birds and different animal species kept in backyard productive systems (BPS) in Chile. The aim of this study was to simulate the possible natural history of HPAI after its introduction to BPS in central Chile and to simulate different intervention strategies. To do so, the North American Animal Disease Spread Model version 3.3 was used. The results showed that a median of 15,930 BPS would be affected if HPAI spread among BPS in central Chile, representing 97.8% of the current amount of BPS existing in study zone. Movement restrictions, pre-emptive destruction, passive surveillance, tracing of infected premises and combinations of the three, where the intervention strategies tested in the simulation model. From all the interventions simulated, movement restrictions together with increasing surveillance (through increasing passive surveillance and good tracing of infected premises) had the biggest effect, reducing the median number of infected BPS in 90.8%. However, more studies are needed to more accurately estimate local contact rates. These results can guide the official veterinary services to consider potential mechanisms to control or prevent an HPAI emergency situation.

Modeling ALS using iPSCs: is it possible to reproduce the phenotypic variations observed in patients in vitro?

Amyotrophic lateral sclerosis (ALS) is a fatal disease that leads to progressive degeneration of motoneurons. Mutations in the C9ORF72, SOD1, TARDBP and FUS genes, among others, have been associated with ALS. Although motoneuron degeneration is the common outcome of ALS, different pathological mechanisms seem to be involved in this process, depending on the genotypic background of the patient. The advent of induced pluripotent stem cell (iPSC) technology enabled the development of patient-specific cell lines, from which it is possible to generate different cell types and search for phenotypic alterations. In this review, we summarize the pathophysiological markers detected in cells differentiated from iPSCs of ALS patients. In a translational perspective, iPSCs from ALS patients could be useful for drug screening, through stratifying patients according to their genetic background.

A Three-Dimensional Alzheimer's Disease Cell Culture Model Using iPSC-Derived Neurons Carrying A246E Mutation in PSEN1.

Alzheimer's disease (AD) is a chronic brain disorder characterized by progressive intellectual decline and memory and neuronal loss, caused mainly by extracellular deposition of amyloid-ß (Aß) and intracellular accumulation of hyperphosphorylated tau protein, primarily in areas implicated in memory and learning as prefrontal cortex and hippocampus. There are two forms of AD, a late-onset form that affects people over 65 years old, and the early-onset form, which is hereditable and affect people at early ages ~45 years. To date, there is no cure for the disease; consequently, it is essential to develop new tools for the study of processes implicated in the disease. Currently, in vitro AD three-dimensional (3D) models using induced pluripotent stem cells (iPSC)-derived neurons have broadened the horizon for in vitro disease modeling and gained interest for mechanistic studies and preclinical drug discovery due to their potential advantages in providing a better physiologically relevant information and more predictive data for in vivo tests. Therefore, this study aimed to establish a 3D cell culture model of AD in vitro using iPSCs carrying the A246E mutation. We generated human iPSCs from fibroblasts from a patient with AD harboring the A246E mutation in the PSEN1 gene. Cell reprogramming was performed using lentiviral vectors with Yamanaka's factors (OSKM: Oct4, Sox2, Klf4, and c-Myc). The resulting iPSCs expressed pluripotency genes (such as Nanog and Oct4), alkaline phosphatase activity, and pluripotency stem cell marker expression, such as OCT4, SOX2, TRA-1-60, and SSEA4. iPSCs exhibited the ability to differentiate into neuronal lineage in a 3D environment through dual SMAD inhibition as confirmed by Nestin, MAP2, and Tuj1 neural marker expression. These iPSC-derived neurons harbored Aß oligomers confirmed by Western Blot (WB) and immunostaining. With human iPSC-derived neurons able to produce Aß oligomers, we established a novel human hydrogel-based 3D cell culture model that recapitulates Aß aggregation without the need for mutation induction or synthetic Aß exposure. This model will allow the study of processes implicated in disease spread throughout the brain, the screening of molecules or compounds with therapeutic potential, and the development of personalized therapeutic strategies.

Understanding transmission and control of the pork tapeworm with CystiAgent: a spatially explicit agent-based model.

BACKGROUND: The pork tapeworm, Taenia solium, is a serious public health problem in rural low-resource areas of Latin America, Africa and Asia, where the associated conditions of nuerocysticercosis (NCC) and porcine cysticercosis cause substantial health and economic harms. An accurate and validated transmission model for T. solium would serve as an important new tool for control and elimination, as it would allow for comparison of available intervention strategies, and prioritization of the most effective strategies for control and elimination efforts. METHODS: We developed a spatially-explicit agent-based model (ABM) for T. solium ("CystiAgent") that differs from prior T. solium models by including a spatial framework and behavioral parameters such as pig roaming, open human defecation, and human travel. In this article, we introduce the structure and function of the model, describe the data sources used to parameterize the model, and apply sensitivity analyses (Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC)) to evaluate model parameters. RESULTS: LHS-PRCC analysis of CystiAgent found that the parameters with the greatest impact on model uncertainty were the roaming range of pigs, the infectious duration of human taeniasis, use of latrines, and the set of "tuning" parameters defining the probabilities of infection in humans and pigs given exposure to T. solium. CONCLUSIONS: CystiAgent is a novel ABM that has the ability to model spatial and behavioral features of T. solium transmission not available in other models. There is a small set of impactful model parameters that contribute uncertainty to the model and may impact the accuracy of model projections. Field and laboratory studies to better understand these key components of transmission may help reduce uncertainty, while current applications of CystiAgent may consider calibration of these parameters to improve model performance. These results will ultimately allow for improved interpretation of model validation results, and usage of the model to compare available control and elimination strategies for T. solium.

Modeling Inflammation on Neurodevelopmental Disorders Using Pluripotent Stem Cells.

Neurodevelopmental disorders (ND) are characterized by an impairment of the nervous system during its development, with a wide variety of phenotypes based on genetic or environmental cues. There are currently several disorders grouped under ND including intellectual disabilities (ID), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorders (ASD). Although NDs can have multiple culprits with varied diagnostics, several NDs present an inflammatory component. Taking advantage of induced pluripotent stem cells (iPSC), several disorders were modeled in a dish complementing in vivo data from rodent models or clinical data. Monogenic syndromes displaying ND are more feasible to be modeled using iPSCs also due to the ability to recruit patients and clinical data available. Some of these genetic disorders are Fragile X Syndrome (FXS), Rett Syndrome (RTT), and Down Syndrome (DS). Environmental NDs can be caused by maternal immune activation (MIA), such as the infection with Zika virus during pregnancy known to cause neural damage to the fetus. Our goal in this chapter is to review the advances of using stem cell research in NDs, focusing on the role of neuroinflammation on ASD and environmental NDs studies.

The use of iPSC technology for modeling Autism Spectrum Disorders.

Autism Spectrum Disorders (ASDs) are a group of neurodevelopmental disorders that influence social skills, involving communication, interaction, and behavior, usually with repetitive and restrictive manners. Due to the variety of genes involved in ASDs and several possible environmental factors influence, there is still no answer to what really causes syndromic and non-syndromic types of ASDs, usually affecting each individual in a unique way. However, we know that the mechanism underlying ASDs involves brain functioning. The human brain is a complex structure composed of close to 100 billion cells, which is a big challenge to study counting just with post mortem tissue investigation or genetic approaches. Therefore, human induced pluripotent stem cells (iPSC) technology has been used as a tool to produce viable cells for understanding a working brain. Taking advantage of patient-derived stem cells, researchers are now able to generate neurons, glial cells and brain organoids in vitro to model ASDs. In this review we report data from different studies showing how iPSCs have been a critical tool to study the different phenotypes of ASDs.

Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes as a Model to Study Trypanosoma cruzi Infection.

Chagas disease (ChD) is one of the most neglected tropical diseases, with cardiomyopathy being the main cause of death in Trypanosoma cruzi-infected patients. As the parasite actively replicates in cardiomyocytes (CMs), the heart remains a key target organ in the pathogenesis of ChD. Here we modeled ChD using human induced pluripotent stem cell-derived CMs (iPSC-CMs) to understand the complex interplay between the parasite and host cells. We showed that iPSC-CMs can get infected with the T. cruzi Y strain and that all parasite cycle stages can be identified in our model system. Importantly, characterization of T. cruzi-infected iPSC-CMs showed significant changes in their gene expression profile, cell contractility, and distribution of key cardiac markers. Moreover, these infected iPSC-CMs exhibited a pro-inflammatory profile as indicated by significantly elevated cytokine levels and cell-trafficking regulators. We believe our iPSC-CM model is a valuable platform to explore new treatment strategies for ChD.

Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys.

Mapping yellow fever (YF) risk is often based on place of infection of human cases, whereas the circulation between nonhuman primates (NHP) and vectors is neglected. In 2008/2009, YF devastated NHP at the southern limit of the disease in the Americas. In view of the recent expansion of YF in Brazil, we modeled the environmental suitability for YF with data from 2008/2009 epizootic, the distribution of NHP (Alouatta spp.), and the mosquito (Haemagogus leucocelaenus) using the maximum entropy algorithm (Maxent) to define risk areas for YF and their main environmental predictors. We evaluated points of occurrence of YF based on dates of confirmed deaths of NHP in three periods, from October 2008 to: December 2008, March 2009, and June 2009. Variables with greatest influence on suitability for YF were seasonality in water vapor pressure (36%), distribution of NHP (32%), maximum wind speed (11%), annual mean rainfall (7%), and maximum temperature in the warmest month (5%). Models of early periods of the epizootic identified suitability for YF in localities that recorded NHP deaths only months later, demonstrating usefulness of the approach for predicting the disease spread. Our data supported influence of rainfall, air humidity, and ambient temperature on the distribution of epizootics. Wind was highlighted as a predicting variable, probably due to its influence on the dispersal of vectors infected with YF in fragmented landscapes. Further studies on the role of wind are necessary to improve our understanding of the occurrence of YF and other arboviruses and their dispersal in the landscape.

Genetic Susceptibility to Leprosy-From Classic Immune-Related Candidate Genes to Hypothesis-Free, Whole Genome Approaches.

Genetics plays a crucial role in controlling susceptibility to infectious diseases by modulating the interplay between humans and pathogens. This is particularly evident in leprosy, since the etiological agent, Mycobacterium leprae, displays semiclonal characteristics not compatible with the wide spectrum of disease phenotypes. Over the past decades, genetic studies have unraveled several gene variants as risk factors for leprosy per se, disease clinical forms and the occurrence of leprosy reactions. As expected, several of these genes are immune-related; yet, hypothesis-free approaches have led to genes not classically linked to immune response. The PARK2, originally described as a Parkinson's disease gene, illustrates the case: Parkin-the protein coded by PARK2-was defined as an important player regulating innate and adaptive immune responses only years after its description as a leprosy susceptibility gene. Interestingly, even with the use of powerful hypothesis-free study designs such as genome-wide association studies, most of the major gene effect controlling leprosy susceptibility remains elusive. One hypothesis to explain this "hidden heritability" is that rare variants not captured by classic association studies are of critical importance. To address this question, massively parallel sequencing of large segments of the human genome-even whole exomes/genomes-is an alternative to properly identify rare, disease-causing mutations. These mutations may then be investigated through sophisticated approaches such as cell reprogramming and genome editing applied to create in vitro models for functional leprosy studies.

Modeling Inflammation in Autism Spectrum Disorders Using Stem Cells.

Recent reports show an increase in the incidence of Autism Spectrum Disorders (ASD) to 1 in every 59 children up to 8 years old in 11 states in North America. Induced pluripotent stem cell (iPSC) technology offers a groundbreaking platform for the study of polygenic neurodevelopmental disorders in live cells. Robust inflammation states and immune system dysfunctions are associated with ASD and several cell types participate on triggering and sustaining these processes. In this review, we will examine the contribution of neuroinflammation to the development of autistic features and discuss potential therapeutic approaches. We will review the available tools, emphasizing stem cell modeling as a technology to investigate the various molecular pathways and different cell types involved in the process of neuroinflammation in ASD.

Autism spectrum disorders and disease modeling using stem cells.

Autism spectrum disorders (ASD) represent a variety of disorders characterized as complex lifelong neurodevelopment disabilities, which may affect the ability of communication and socialization, including typical comportments like repetitive and stereotyped behavior. Other comorbidities are usually present, such as echolalia, hypotonia, intellectual disability and difficulties in processing figured speech. Furthermore, some ASD individuals may present certain abilities, such as eidetic memory, outstanding musical or painting talents and special mathematical skills, among others. Considering the variability of the clinical symptoms, one autistic individual can be severely affected in communication while others can speak perfectly, sometimes having a vocabulary above average in early childhood. The same variability can be seen in other clinical symptoms, thus the "spectrum" can vary from severe to mild. Induced pluripotent stem cell technology has been used to model several neurological diseases, including syndromic and non-syndromic autism. We discuss how modeling the central nervous system cells in a dish may help to reach a better understanding of ASD pathology and variability, as well as personalize their treatment.

Induced pluripotent stem cells for modeling neurological disorders.

Several diseases have been successfully modeled since the development of induced pluripotent stem cell (iPSC) technology in 2006. Since then, methods for increased reprogramming efficiency and cell culture maintenance have been optimized and many protocols for differentiating stem cell lines have been successfully developed, allowing the generation of several cellular subtypes in vitro. Gene editing technologies have also greatly advanced lately, enhancing disease-specific phenotypes by creating isogenic cell lines, allowing mutations to be corrected in affected samples or inserted in control lines. Neurological disorders have benefited the most from iPSC-disease modeling for its capability for generating disease-relevant cell types in vitro from the central nervous system, such as neurons and glial cells, otherwise only available from post-mortem samples. Patient-specific iPSC-derived neural cells can recapitulate the phenotypes of these diseases and therefore, considerably enrich our understanding of pathogenesis, disease mechanism and facilitate the development of drug screening platforms for novel therapeutic targets. Here, we review the accomplishments and the current progress in human neurological disorders by using iPSC modeling for Alzheimer's disease, Parkinson's disease, Huntington's disease, spinal muscular atrophy, amyotrophic lateral sclerosis, duchenne muscular dystrophy, schizophrenia and autism spectrum disorders, which include Timothy syndrome, Fragile X syndrome, Angelman syndrome, Prader-Willi syndrome, Phelan-McDermid, Rett syndrome as well as Nonsyndromic Autism.