ChatGPT, a large language model, has shown good performance on physician certification examinations and medical consultations. However, its performance has not been examined in languages other than English or on nursing examinations.We aimed to evaluate the performance of ChatGPT on the Japanese National Nurse Examinations.We evaluated the percentages of correct answers provided by ChatGPT (GPT-3.5) for all questions on the Japanese National Nurse Examinations from 2019 to 2023, excluding inappropriate questions and those containing images. Inappropriate questions were pointed out by a third-party organization and announced by the government to be excluded from scoring. Specifically, these include “questions with inappropriate question difficulty” and “questions with errors in the questions or choices.” These examinations consist of 240 questions each year, divided into basic knowledge questions that test the basic issues of particular importance to nurses and general questions that test a wide range of specialized knowledge. Furthermore, the questions had 2 types of formats: simple-choice and situation-setup questions. Simple-choice questions are primarily knowledge-based and multiple-choice, whereas situation-setup questions entail the candidate reading a patient’s and family situation’s description, and selecting the nurse's action or patient's response. Hence, the questions were standardized using 2 types of prompts before requesting answers from ChatGPT. Chi-square tests were conducted to compare the percentage of correct answers for each year's examination format and specialty area related to the question. In addition, a Cochran-Armitage trend test was performed with the percentage of correct answers from 2019 to 2023.The 5-year average percentage of correct answers for ChatGPT was 75.1% (SD 3%) for basic knowledge questions and 64.5% (SD 5%) for general questions. The highest percentage of correct answers on the 2019 examination was 80% for basic knowledge questions and 71.2% for general questions. ChatGPT met the passing criteria for the 2019 Japanese National Nurse Examination and was close to passing the 2020-2023 examinations, with only a few more correct answers required to pass. ChatGPT had a lower percentage of correct answers in some areas, such as pharmacology, social welfare, related law and regulations, endocrinology/metabolism, and dermatology, and a higher percentage of correct answers in the areas of nutrition, pathology, hematology, ophthalmology, otolaryngology, dentistry and dental surgery, and nursing integration and practice.ChatGPT only passed the 2019 Japanese National Nursing Examination during the most recent 5 years. Although it did not pass the examinations from other years, it performed very close to the passing level, even in those containing questions related to psychology, communication, and nursing. ChatGPT is a large language model developed by OpenAI . Based oArtificial intelligence (AI) applications have been used in the medical field, including medical chatbots, and applications that analyze and summarize electronic medical record systems, perform image diagnosis, analyze and organize the medical literature, and perform patient monitoring ,4. ReleaAlthough physicians and nurses both play critical roles in the health care system, their specialties and responsibilities differ. Physicians focus on diagnosing and treating illnesses, whereas nurses focus on providing direct patient care and support. Nurses monitor patient health, administer medications, assist with activities of daily living, and provide emotional support to patients and their families. Nurses also communicate with other health care professionals to ensure that patients receive the appropriate care. Therefore, their training and responsibilities generally focus more on patient care and communication than on diagnosis and treatment.While passing the national examination does not guarantee the ability to practice in a clinical setting, a different scenario arises when considering the performance on registered nurse licensing examinations. These examinations feature questions that emphasize on patient emotions and communication, contrasting with those found in physician licensing examinations. Notably, if excellent performance can be demonstrated in these nursing examinations, it is likely to pave the way for a significant expansion of AI applications in the medical field. However, the performance of ChatGPT on nursing licensing examinations has not yet been evaluated.We aimed to evaluate the performance of ChatGPT on national examinations for registered nurses in Japan.The data sets included questions and answers from the National Nurse Examinations in Japan from 2019 to 2023 . These eEach year, the Ministry of Health, Labor and Welfare (MHLW) of Japan, which certifies the qualification of registered nurses nationwide, reviews questions among conducted examinations, which cannot be answered with just 1 answer, or questions for which no correct answer exists, based on the MHLW’s own checks and comments from a third-party organization—the Japan Nursing School Association. Then, the MHLW deems these as “inappropriate questions” and removes them from the examinations. The inappropriate questions were excluded from this study. In addition, all questions were screened, and questions containing visual assets, such as clinical images, medical photography, and graphs, were removed because ChatGPT (GPT-3.5) is an interactive language AI that does not support image recognition.Because prompt engineering significantly affects generative output, we standardized the input formats of the questions . QuestioPrompt 1: Simple-choice questionsPlease answer the following questions briefly and by number.Question: <Questionnaire contents>1. <Option 1>2. <Option 2>3. <Option 3>4. <Option 4>Prompt 2: Situation-setup questionsBased on the following situation setup, please answer the questions briefly and by number.Situation-setup: <Situation-setup contents>Question: <Questionnaire contents>1. <Option 1>2. <Option 2>3. <Option 3>4. <Option 4>Based on the scoring criteria of the official nursing examination, the percentage of correct answers provided by ChatGPT (GPT-3.5) was calculated separately for basic knowledge and general questions. We calculated the percentage of correct answers for each of the simple-choice questions  and the situation-setup questions  and conducted a chi-square test to compare the percentage of correct answers between the 2 prompts. Finally, the percentage of correct answers was calculated for each of the 32 subject areas, and areas with higher and lower percentages of correct answers compared with the overall mean and 1 SD were extracted. All statistical analyses were performed using R .This study did not require ethics approval because we only analyzed data from a published database.Five years of the National Nurse Examination data showed that the largest number of inappropriate questions occurred in 2019, with 10 questions having been excluded from the scoring and 2 or 3 inappropriate questions in the other years. The number of questions with figures and tables ranged from 6 to 16. Thus, the number of questions analyzed in this study was 214 of 240 in the lowest year and 232 of 240 in the highest year .The 5-year average percentage of correct answers provided by ChatGPT was 75.1% (SD 3%) for basic knowledge questions and 64.5% (SD 5%) for general questions . ThroughComparing simple-choice questions (prompt 1) and situation-setup questions (prompt 2), the average percentage of correct answers for prompt 1 was 66.3% (SD 3%) and 65.9% (SD 7%) for prompt 2. Differences in the proportions of correct answers between prompts 1 and 2 were not observed throughout the study period . HoweverP=.36; On comparing the percentages of correct answers for each subject area among all questions included in the analysis, the average percentage of correct answers for all areas was 65.9%  lack of up-to-date data (ChatGPT only studied records until 2021) and (2) increased question complexity. Although GPT-3.5 learned data only up to 2021, it is crucial to highlight that ChatGPT is able to answer first-time questions; in other words, it is not simply filling in holes using existing internet sources, as there was no sharp decrease in scores in the 2022 and 2023 examinations. The fact that the situation-setup questions were also answered correctly without significant difference indicates that ChatGPT seemed to do well on the questions dealing with the human mind, such as those involving conversations with patients. Meanwhile, the possibility of losing track of relevant issues in complex situational settings and of having limitations such as difficulty recognizing certain expressions, including the frequent use of hyphens and other expressions, were also shown. If the current version of ChatGPT were used in nursing practice, it could be difficult to assess patients whose situations are complex, such as those requiring treatment for multiple diseases or those with socioeconomic problems. However, this is likely to depend on the amount of information that ChatGPT can store in its short-term memory, which would be resolved in the future models.This study used all questions from the 2019-2023 National Nursing Licensing Examinations in Japan, and the results were highly reliable for the performance assessment of the ChatGPT’s answers with low variability. However, this study has some limitations. First, questions with figures and tables were excluded. Although GPT-3.5, which was used to measure the performance in this study, was unable to judge figures and tables, Wang et al  reportedIn principle, judgments were made based on the content of choices, and computational questions were judged as being correct if the formula and the results of the calculation were correct.In general, AI using a large language model is known to perform better in English than in other languages , althougIn the future, if additional data in the areas of poor performance are acquired and tuned so that questions and options can be understood appropriately without prompt engineering or supplementary human explanation, it is highly likely that the passing criteria will be exceeded in a stable manner. More advanced tools, such as GPT-4 or Bard (developed by Google), superseding the capabilities of ChatGPT, continue to be released and are expected to be used in many clinical situations such as diagnosis, explanation of treatments and drugs, and communication with patients. However, further research will be needed on ethical issues such as the division of roles between human nurses and AI, decision-making responsibilities, and the risks for patients when applied in clinical practice.ChatGPT passed or performed very close to the passing level on the Japanese National Nurse Examinations. With additional learning, prompt engineering, and tuning of ChatGPT, it will likely exceed the passing criteria. ChatGPT has the potential to assist nurses with decisions based on data regarding the patient’s physical condition, and to provide support for psychological issues.


A novel function for the L1 cell adhesion molecule, which binds the actin adaptor protein Ankyrin was identified in constraining dendritic spine density on pyramidal neurons in the mouse neocortex. In an L1-null mouse mutant increased spine density was observed on apical but not basal dendrites of pyramidal neurons in diverse cortical areas  in the L1 cytoplasmic domain was critical for spine regulation, as demonstrated by increased spine density and altered spine morphology in the prefrontal cortex of a mouse knock-in mutant (L1YH) harboring a tyrosine (Y) to histidine (H) mutation in the FIGQY motif, which disrupted L1-Ankyrin association. This mutation is a known variant in the human L1 syndrome of intellectual disability. L1 was localized by immunofluorescence staining to spine heads and dendrites of cortical pyramidal neurons. L1 coimmunoprecipitated with Ankyrin B (220 kDa isoform) from lysates of wild type but not L1YH forebrain. This study provides insight into the molecular mechanism of spine regulation and underscores the potential for this adhesion molecule to regulate cognitive and other L1-related functions that are abnormal in the L1 syndrome. Dendritic spines on cortical pyramidal neurons receive 80%–90% of excitatory glutamatergic synapses in the neocortex , which is highly conserved among L1 family members . An L1 knock-in mouse harboring the FIGQH mutation displays axon targeting errors , Neuron-glial related CAM (NrCAM), and Neurofascin are transmembrane recognition molecules that perform diverse functions in neural development including axon growth and guidance, neuronal migration, cell survival, and synaptic plasticity  are null mutants. L1-/y and wild type (WT) male littermates were analyzed in this study. L1Y1229H (L1YH) mice, mutated in the Ankyrin binding motif FIGQY  at P10–P13 as described , as normal distributions were not assumed. Spine morphologies are classified as mushroom, stubby, and thin spines based on the relative size of the spine head and neck . The proportion of spine morphological types was similarly analyzed in EGFP-labeled cortical neuron cultures.Adult mice (50–150) were anesthetized with isoflurane, brains were isolated and processed for Golgi impregnation using the FD Rapid Golgi Stain Kit (FD NeuroTechnologies) as described . The Sholl center was defined as the midpoint of the cell body at a soma detector sensitivity of 1.5 μm, and the automatic tracing mode was used to seed and trace dendritic arbors. Images in DAT format were subjected to Sholl analysis using Neurolucida Explorer with a starting radius of 10 μm and radius increments of 10 μm. Two factor ANOVA was used to assess significant differences in the mean number of crossings at each distance from the soma with significance set at RRID:AB_300798, host chicken), L1  and MAP2  for 2 h at 4°C. AlexaFluor-conjugated secondary antibodies  were incubated with sections for 2 h at room temperature. Sections were mounted on Superfrost Plus slides with Prolong Glass mountant, cured for 48–72 h, imaged confocally, and deconvolved using AutoQuant 3 software (Media Cybernetics). Microscopy was performed in the UNC Microscopy Services Laboratory .Nex1Cre-ERT2: RCE mice were induced with tamoxifen at P10-P13 and brains were harvested at P20. After transcardial perfusion with 4% PFA, fixed brains were isolated, sectioned, permeabilized, and blocked in 10% normal donkey serum containing 0.3% Triton X-100 in PBS. Sections were stained with antibodies directed against GFP  were precleared for 30 min at 4°C using Protein A/G Sepharose beads. Precleared lysates  were incubated with 2.5 μg nonimmune IgG (NIgG) or L1 monoclonal antibody 2C2 (Abcam #ab24345) together with 1.25 μg L1 monoclonal antibody 5G3 (BD/Pharmingen #554273) for 2 h on ice. Protein A/G Sepharose beads were added for 30 min before washing with RIPA buffer. Samples were subjected to SDS-PAGE (6%) and transferred to nitrocellulose. Membranes were blocked in TBST containing 5% nonfat dried milk and incubated overnight with monoclonal antibodies  against Ankyrin B , washed, and incubated with HRP-secondary antibodies  for 1 h. Blots were developed using Western Bright ECL Substrate (Advansta) and exposed to film for times yielding a linear response of the signal.For preparation of mouse cortical lysates, forebrains (P30) were homogenized in RIPA buffer and centrifuged at 16,000× post-hoc testing (p < 0.05).Cortical neuron cultures were prepared from the forebrains of WT and L1YH embryos at E15.5 as described  in means between independent controls and experimental subjects as described  to discriminate significant differences  due to its importance in cognitive functions Yizhar, . In addiPyramidal neurons in layer 2/3 of the PFC were further analyzed to determine if loss of L1 affected spine morphology or dendritic arborization. Dendritic protrusions acquire different morphologies classified as mushroom, stubby, and thin spines based on the relative size of the spine, head, and neck  of L1-/y mice compared to WT.To determine if the Ankyrin binding site on L1 was required for dendritic spine regulation we focused on layer 2/3 pyramidal neurons in the PFC of L1YH knock-in mice, in which a histidine substitution for tyrosine at position 1229 causes deficiency in binding the actin-spectrin scaffold protein Ankyrin  or L1-/y PFC . Tamoxifen exposure of these mice at P10–P13 induces the expression of EGFP in postmitotic, postmigratory pyramidal neurons, enabling visualization of spines, as well as dendrites, axons, and soma as described  as described . In L1YH neuronal cultures Sema3B-Fc and Sema3F-Fc also induced a significant degree of spine retraction compared to Fc-treated L1YH neurons . Spine densities of L1YH neurons treated with Sema3B-Fc (*p > 0.99) or Sema3F-Fc (*p = 0.91) were not significantly different from similarly treated WT neurons. Spine morphology of WT and L1YH cortical neurons was quantified in Fc-treated cultures to determine if mutation of the L1 Ankyrin binding motif altered the proportion of spine types in vitro. There were no significant differences in the fraction of mushroom, stubby, or thin spines relative to total spines in cultured WT neurons . The relative proportion of spine types in vitro differed somewhat from that in vivo, where mature mushroom spines were more predominant. Cultures contain a diversity of neuronal types that may be altered in their state of differentiation and lack factors present in vivo, which likely influence spine morphology. In summary, these results supported the interpretation that Sema3B- and Sema3F-induced spine retraction is not substantially mediated by L1 interactions at FIGQY in vitro. It should be noted that L1-null (L1-/y) neuronal cultures were not assayed, because breeding requires mating WT males with heterozygous L1 females, yielding litters with a low percentage of L1-/y male embryos.To investigate whether L1-FIGQY interactions were required for Sema3B- or Sema3F-mediated spine retraction, cortical neuron cultures were prepared from forebrains of WT and L1YH embryos (E15.5) and cultured for 14 days Here we show in L1-null mice that the neural cell adhesion molecule L1 constrains dendritic spine density in pyramidal neurons in diverse areas  of the cerebral cortex. This novel function for L1 was restricted to apical dendrites of cortical pyramidal neurons. The Ankyrin binding motif in the L1 cytoplasmic domain (FIGQY) was required for constraining spine density as demonstrated by increased spine density in layer 2/3 pyramidal neurons of the PFC in the L1 mouse mutant harboring a tyrosine to histidine substitution (FIGQH) in the motif. This mutation impaired L1-Ankyrin binding and is a known variant associated with the human L1 syndrome of intellectual disability  in its Ig1 domain necessary for Sema3F-induced spine retraction (Mohan et al., L1 is known to be expressed at the highest levels during postnatal stages in the mouse cortex and to decline with maturation (Demyanenko et al., In conclusion, the present study extends the role of L1 family members in dendritic spine regulation to the prototype of the family, L1. The increased spine density on apical dendrites due to L1 deficiency or perturbation of its Ankyrin binding site may alter excitatory/inhibitory balance in cortical circuits and affect overall behavior. The phenotypes observed in the L1 mouse genetic models studied here may also shed light on the molecular basis of cognitive and other L1-related functions that are abnormal in the L1 syndrome.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved and all mice were handled according to the University of North Carolina Institutional Animal Care and Use Committee policies in accordance with NIH guidelines.KM, SW, JS, VM, BD, EZ, and YP propagated L1YH mouse strains, performed experiments, and analyzed data. DL and MS supervised breeding of L1-/y mice, performed Golgi staining on brain sections of L1-/y mice, and provided advice on experimental design. PM supervised research, analyzed data, and wrote the article. All authors contributed to the article and approved the submitted version.


Heat stress poses a significant challenge to egg production in layer hens. High temperatures can disrupt the physiological functions of these birds, leading to reduced egg production and lower egg quality. This study evaluated the microclimate of laying hen houses using different management systems to determine the impact of heat stress on productivity and hen health. The results showed that the ALPS system, which manages the hen feeding environment, effectively improved productivity and decreased the daily death rate. In the traditional layer house, the daily death rate decreased by 0.045%, ranging from 0.086% to 0.041%, while the daily production rate increased by 3.51%, ranging from 69.73% to 73.24%. On the other hand, in a water-pad layer house, the daily death rate decreased by 0.033%, ranging from 0.082% to 0.049%, while the daily production rate increased by 21.3%, ranging from 70.8% to 92.1%. The simplified hen model helped design the indoor microclimate of commercial layer houses. The average difference in the model was about 4.4%. The study also demonstrated that fan models lowered the house’s average temperature and reduced the impact of heat stress on hen health and egg production. Findings indicate the need to control the humidity of inlet air to regulate temperature and humidity, and suggest that Model 3 is an energy-saving and intelligent solution for small-scale agriculture. The humidity of the inlet air affects the temperature experienced by the hens. The THI drops to the alert zone (70–75) when humidity is below 70%. In subtropical regions, we consider it necessary to control the humidity of the inlet air. Heat stress is a significant issue that affects egg production in layer hens. When layer hens are exposed to high temperatures, their physiological functions are disrupted, which can decrease egg production and quality. This, in turn, results in significant economic losses to poultry farmers. Several factors can affect the quality of eggs, and heat stress is just one of them. Previous studies have shown that the health conditions of layer hens, the thermal environment they are in, the microbial biomass of the breeding environment, and the type of cages they are housed in can all impact egg quality. The thermal environment is a crucial factor that affects the overall health and productivity of poultry. Poultry animals are highly sensitive to changes in temperature and humidity levels, and the thermal environment can impact various aspects of their physiology and behavior. Previous studies have shown that the quality of eggs is affected by multiple factors, such as the health conditions of hens, the thermal environment, the microbial biomass of the breeding environment, and the type of cage. Among them, the thermal environment is the key main factor ,2,3,4. IDue to economic considerations, the traditional battery cage is the primary type of cage used in Taiwan. However, the high temperature can cause heat stress, which can negatively affect the hens and result in poor egg production . Animal Previous studies have highlighted the importance of an appropriate air temperature, humidity, air velocity, and air quality for optimal hen performance. When ambient temperatures are too high or low, a mechanical ventilation system with cooling and heating capabilities is necessary. Moreover, the ventilation system should be adjusted according to the season. The water-pad ventilation system, also known as the tunnel ventilation system, utilizes mechanical ventilation to regulate the indoor temperature of the layer house in response to seasonal changes ,13,14. TTo develop a water-pad system suitable for small-scale agriculture in Taiwan, this study utilized computational fluid dynamics (CFD) to investigate the impact of fan mode on indoor climate control in the water-pad system. A newly constructed layer house at a university experimental farm was chosen as the site for examining the influence of fan mode on indoor temperature distribution. Additionally, we selected two commercial laying hen houses and integrated our self-designed intelligent system into their management system to examine the effect of the intelligent system on the management mode of small-scale agriculture.We developed an Artificial Intelligence Layer Production System (ALPS) to manage farms and installed it in commercial laying houses in Taiwan for intervention. ALPS helps us collect information on the indoor climate in layer houses to validate our numerical computations.Our investigation focused on the effect of humidity and three different fan-operation modes on temperature distribution in a layer house at a university in Taichung, Taiwan. We conducted CFD validation, analyzed the flow of three fan arrangement models, and calculated the Temperature–Humidity Index (THI).First, we built a CFD model based on information collected on-site. We then used the CFD model to calculate the results for the three fan modes. Finally, we compared the temperature distributions of the three models and calculated the THI, taking into account the humidity, to estimate the actual temperature experienced by the hens. In this study, we developed a preliminary version of an Artificial Intelligence Layer Production System (ALPS), as depicted in The diagram in 2, temperature, and humidity sensors  was installed in the house to monitor the environment.TM, DFW Airport, TX, USA) was implanted into the subcutaneous tissue of the nape of 36 hens, and a scanner read the temperature measured by the chip. The temperature recording period was from July 2021 to December 2021. In total, 46 pieces of temperature data were obtained for each hen. The measurement results are shown in The microchip Bio-ThermTM  was used to analyze the flow in space. Equation (1) is the equation of mass conservation.Generally speaking, the airflow in the spaces is incompressible flow. Therefore, the density of the air in the spaces is constant. Therefore, we modify Equation (1) to (2).Equation (3) is the steady-state momentum equation called the Navier–Stokes equation.Equation (4) is the steady-state equation of energy conservation.Twp = 23 °C. The temperature Tlayer was set to 40 °C (the body-surface temperature around the layer) according to the measurement of 36 layers. The velocity of the fans, v, was set to 2, 4, and 6 m/s. Other boundaries, such as the walls, roofs, etc., in the house were set as wall boundaries, and the wall temperature Twall = 25 °C.Boundary conditions were set to solve the Equations (2)–(4) in the study. The boundary conditions were set as follows. The water-pad system at the front of the layer house was set to Twp) to 23 °C.Water pad inlet: Set the front-end water curtain of the hen house as a pressure outlet boundary condition during the simulation, and set the outlet temperature (Tlayer is set to 40 °C (layer surface temperature).Layers: Set the hen flock as a heat source, so that the heat dissipation of the layers mainly comes from the surface of the layer and the heat generated by respiration. The specific heat dissipation value is affected by the environmental temperature and the physical condition of the layer. The temperature Tf set to 30 °C.Fan: The fan outlet is a velocity outlet with temperature Twall to 25 °C.Other boundaries: Set the walls and roof inside the house as wall boundaries, and ignore the evaporation of water vapor on the floor and water tank inside the house. Set the wall temperature This study used commercial software Ansys Fluent R19  and SolidWorks Flow Simulation  as the CFD tools. For this study, the flow model was set to a three-dimensional, fully turbulent, and incompressible flow model under the assumptions of RANS (Reynolds-averaged Navier–Stokes equations). The CFD tools use a semi-implicit method to handle the pressure-linked equations in the velocity-pressure coupling algorithm, and employ a second-order upwind discretization. In the turbulence model, the study used the standard k-ε turbulence model to model the turbulence characteristics of the fluid. The standard k-ε model is the most commonly used model for simulating turbulence in computational fluid dynamics , and is x = 5 m, 50 m, and 95 m, respectively. A total of 15 monitoring points were used to obtain wind speed data as independent references for each grid size. The grid independence results showed that for grid sizes above 13,835,801, the values of the 15 monitoring points reached a convergence criterion of 10−3. The grid independence was shown in The mesh of the model was shown in The temperature–humidity index (THI) was used to assess the impact of environmental conditions on the thermoregulatory mechanisms of animals ,20,21. model of the tunnel structure and conducted field measurements. We took this a step further in order to improve heat build-up and adjusted the fan modes. The results showed that using only the lower exhaust fan (Model 3) was an effective fan mode in this study. However, our findings indicated that water-pad systems might not effectively reduce the thermal stress on animals in areas with high humidity. When the outdoor humidity exceeds 70%, the indoor temperature–humidity index (THI) falls into a dangerous zone. The higher the humidity, the less heat that can be dissipated through evaporation, resulting in a higher perceived temperature for the animals. This is particularly relevant in Taiwan, a country that has a subtropical climate with high temperatures and high humidity; this significantly impacts the heat stress on animals subjected to indoor breeding methods using water-pad structures.The novelty of this study is the use of computational fluid dynamics (CFD) to investigate the impact of the fan mode on indoor climate control in the water-pad system. This represents a new approach to studying the indoor climate of the water-pad system and provides insights into how to optimize the system for improved temperature distribution.We came to the following conclusions for indoor microclimate control in layer houses:The CFD of the simplified model can quickly obtain the assessment of indoor climate control. We verified the calculation results of the CFD model through on-site measurements, and the average error was controlled at 4.4%. We verified that the results show that the hen shape can be simplified to obtain results close to the actual measurement.The temperature in the rear was higher than in the front, an inherent problem in designing the tunnel-type water-pad system layer house. Because the temperature of the hens in the front was transferred to the rear due to the airflow, the hens in the back were exposed to a higher temperature than those in the front. In addition, the air in the rear could not be discharged quickly for the first time due to the fan unit’s resistance, so the rear temperature was in a high-temperature state. According to the results of this study, the length of the layer house was controlled within 30 m, and the Model 3 design was used to control the temperature difference between the front and rear to 0.4 °C.The apparent temperature of the hens was increased due to increased inlet humidity. The THI was located in a dangerous zone when the inlet air humidity was over 80%. When the humidity is below 70%, THI drops to the alert zone. The humidity of the inlet air affected the temperature distribution throughout the house. In subtropical regions, we considered it necessary to control the humidity of the inlet air. This way, the house’s temperature and humidity could be controlled entirely.This study analyzed a layer house with a water-pad system with three fan modes. Based on the conditions of Taiwan’s average relative humidity, which is about 60–80%, and the needs of small-scale agriculture, the research results suggest that Model 3 should be used as the operation mode of layer houses with a water-pad system in small-scale agriculture. It provides small-scale agriculture in Taiwan, or areas with a similar climate to Taiwan’s, with a convenient and usable layer house with a water-pad system that considers the need for energy-saving and intelligent agriculture. It provides solutions for small-scale farming in terms of industrial upgrade and reduced energy consumption in the era of global warming and climate change.A limitation of this study is that the humidity control design has yet to be implemented in commercial layer houses. The main reason for this is that farm operators are concerned about affecting current egg production. Furthermore, we know that increasing wind speed can help reduce perceived temperature. However, the effect of wind speed on the animal’s perceived temperature cannot be directly obtained from the animal’s response, which is research that needs to be continued in the future.


In the present work, the thermal conductivity and thermal expansion coefficients of a new morphology of Ni/graphene composites are studied by molecular dynamics. The matrix of the considered composite is crumpled graphene, which is composed of crumpled graphene flakes of 2–4 nm size connected by van der Waals force. Pores of the crumpled graphene matrix were filled with small Ni nanoparticles. Three composite structures with different sizes of Ni nanoparticles  were considered. The thermal conductivity of Ni/graphene composite was associated with the formation of a crumpled graphene structure (with a high density of wrinkles) during the composite fabrication and with the formation of a contact boundary between the Ni and graphene network. It was found that, the greater the Ni content in the composite, the higher the thermal conductivity. For example, at 300 K,  Nowadays, the demand for new materials with unique mechanical and physical properties is constantly growing. The search for graphene/metal composites is a new and actively developing branch of composite technologies ,2,3,4. TTo date, the mechanical properties of metal matrix composites reinforced by graphene have been widely studied ,13,14,15 mA cm−2 . These nIn addition to high strength, graphene shows unique thermal properties—these include high thermal conductivity and a negative thermal expansion coefficient . It has been experimentally shown that the thermal conductivity of single-layer graphene at room temperature is in the range from 4840 W/(K m) to 5300 W/(K m) ,25,26. TA negative TEC for graphene has been observed in the temperature range from 200 to 400 K ,35,36. TThus, together with the improvement of mechanical properties, graphene reinforcement results in the improvement of thermal properties. The study of such properties of graphene/metal composites is of high importance for the fabrication of new electronic devices such as chemical sensors, energy storage, catalysis, hydrogen storage, batteries, and supercapacitors ,4,40. ThOne of the interesting graphene/metal composite morphologies is a graphene network filled with metal nanoparticles. Such graphene networks, also called graphene aerogel/fiber/crumpled graphene or 3D graphene, exhibit very different morphologies with random porous structures and a pore size from tens to hundreds of nm . For somSuch metals as Cu, Ni, Ag, and Au are of great interest from this point of view, and, in the present work, Ni was chosen as the filler for crumpled graphene. It was shown that Ni nanoparticles grow densely and uniformly inside graphene aerogel . For exaA promising method for estimating the phonon thermal conductivity of such composites is nonequilibrium molecular dynamics (MD). There are several algorithms for calculating the thermal conductivity using the LAMMPS  ,55 packaIn the presented work, the thermal conductivity and TEC of graphene/Ni composites were studied in a wide temperature range by MD simulation. The methodology for the calculation of thermal properties of graphene/metal composites was developed and tested on the graphene/Ni composite. The proposed methodology will further allow for calculations of the thermal properties of such complex structures as graphene networks filled with other atomic clusters. Both metallic fillers and other elements  can be added to the graphene network. This methodology can be applied to calculate the thermal conductivity of crumpled graphene itself, since this structure is proposed for new supercapacitors ,4,40, whIn x-, y-, and z-axis to obtain the three-dimensional (3D) composite precursor, which was then exposed to 300 K temperatures for 20 ps to obtain a more realistic structure with crumpled graphene flakes. After that, hydrostatic compression at temperatures of 300 K was applied to remove empty space from the structure. It should be noted that, during this step, new covalent bonds are not formed between neighboring GFs, since higher temperatures are required , [010], and [001] crystallographic directions, respectively. The lattice parameter was 3.518 Å. The number of Ni atoms was 13,500 and 54,000 atoms, respectively. The equilibration and thermalization of Ni samples were carried out under the same conditions as for the composite.Note that, in this work, the results obtained for a composite were compared with that for pure, single-crystal Ni. Thus, pure Ni fcc crystals with the sizes of 51 × 51 × 51 Å and 205 × 51 × 51 Å were considered. The x-, y-, and z-axis. For each state, three to five measurements of the TCC and TEC values were carried out. The standard deviation of these values ranged from about 5–7% at each temperature. To visualize the structure, the molecular graphics program in VMD  was used, which allows for the display and analysis of structural changes at certain simulation stages 26], the The thermal properties of Ni/graphene composites, where crumpled graphene is a matrix and metal nanoparticles are fillers, were studied by molecular dynamics. It has been shown that the coefficients of thermal expansion and the thermal conductivity of composites strongly depend on the Ni content.The thermal conductivities of the Ni/graphene composites were much lower than for pure Ni, but much higher than for crumpled graphene. The formation of a strongly deformed structure of crumpled graphene during its fabrication resulted in the formation of wrinkles and ripples, which significantly decreased the thermal conductivity in comparison with graphene. An increase in the amount of Ni atoms in the composite resulted in an increase in thermal conductivity due to the addition of an element with higher thermal conductivity. The formation of new contact boundaries between the nickel and carbon phases also had a strong effect on the thermal conductivity of the composite. The thermal conductivity of the Ni/graphene composite can be controlled by the changing Ni content.T > 300 K, it was considerably increased. The higher the Ni content in the composite, the higher the TEC value. At low temperatures, the thermal expansion was defined by the contribution of nickel nanoparticles, while, at high temperatures, the graphene network affected thermal expansion more.The addition of nickel to the crumpled graphene led to an increase in thermal expansion. The TEC value of the CG-8 at.% Ni composite  at a low temperature was closer to the TEC of crumpled graphene, while, at The obtained results, in combination with previously published data on the improvement of the mechanical properties of Ni/graphene composites ,108,109,This work is just the start of the complex study of the physical and mechanical properties of graphene-based composites, which can be used for new electronics and protective coatings. In the future, we plan on considering the thermal properties of Me/graphene composites with metals with a higher thermal conductivity and TEC value than Ni nanoparticles, such as Cu and Al. This will expand the understanding of the thermal properties evolution of Me/graphene composites with metal fillers. This morphology is very new, and additional research is required for a better understanding of their future potential.


Therefore, the primary aim of this study was to assess the changes of lumbar angles, evaluated in a supine position using magnetic resonance imaging (MRI), on a mattress Twenty healthy subjects , aged 32.3 ± 6.5 (mean ± standard deviation), with body mass index 22.4 ± 2.9, completed three evaluations: (i) spine MRI in supine position on a mattress (MAT); (ii) spine MRI in supine position on rigid surface (CON); and (iii) biplanar radiographic imaging in standing position. The following indexes were calculated for both MAT and CON: lumbar lordosis angles L1–L5, L1–S1, L5–S1, and the sacral slope (SS). Further, pelvic incidence (PI) was calculated from the biplanar radiographic images.Main findings were (i) L1–L5 and SS were greater in MAT than CON ; (ii) L5–S1 was lower in MAT than CON (−1.6°); (iii) L1–S1 was greater in MAT than CON only for male subjects (+2.0°); (iv) significant and positive correlations between PI and L1–L5, L1–S1 and SS were observed in both CON and MAT.The use of a mattress determined small but statistically significant changes in lumbar angles.The use of a mattress determines small but statistically significant changes in radiological angles describing the sagittal alignment of the lumbar spine when lying in the supine position.• Lordosis angle L1–L5 was greater in MAT than in CON condition (+2.9°).• Sacral slope was greater in MAT than in CON condition (+2.0°).• Lordosis angle L5–S1 was lower in MAT than in CON condition (−1.6°). Humans  quality , 3.The National Sleep Foundation, USA, highlighted that 93% of people recognize a comfortable mattress as an important instrument being able to get quality sleep . Thus, tThe mechanical characteristics of the mattress are crucial for sleep quality and body comfort. If the mattress is too soft, the mechanical support to musculotendinous structures may be lower leading to higher tension to posterior soft tissue elements, while the intervertebral discs will be under tension at the anterior side. Conversely, if the mattress is too firm, the lumbar section of the spine will not smoothen immediately when lying down . Since sDifferent methods have been described in literature to evaluate bed comfort, such as spine shape reconstruction , 11, eleTo the best of our knowledge, no previous studies examined the changes in spine angles in healthy adults while lying down on surfaces with different rigidities in a supine position. Therefore, the primary aim of this study was to assess the changes of the L1–L5, L1–S1, L5–S1, and sacral slope (SS) angles, evaluated by MRI, between two conditions: (1) in a supine position on a mattress; (2) in a supine position on hard surface. The second aim was to analyze the correlation between the spine angles evaluated in a standing position, using the EOS system, and L1–L5, L1–S1, L5–S1, and SS angles in both supine conditions (mattress and control). We hypothesized to detect significant changes in spine angles between mattress and control conditions.This observational, cross-sectional, pilot study was approved by the Ethics Committee of San Raffaele Hospital (Ref. 158/INT/2020). All procedures were performed in compliance with current national and international laws and regulations governing the use of human subjects (Declaration of Helsinki). The study protocol was registered at clinicaltrials.gov (Ref. No. NCT04638374). All subjects received clear explanation of purpose, methods, potential risks, and benefits of the study, and before the beginning of the experimental procedures, written informed consent was obtained from all participants. The study was conducted at the IRCCS Istituto Ortopedico Galeazzi , in accordance with the STROBE guidelines  for crose.g., scoliosis); contraindications to MRI . Therefore, healthy subjects aged between 18 and 45 years old who met the inclusion criteria were included in the study. All subjects completed the following clinical evaluations to assess the spine angles: (i) spine MRI in supine position on a mattress (MAT); (ii) spine MRI in supine position on hard surface, as control condition (CON); (iii) EOS imaging .Subjects were invited to participate in the study at the Radiology Service of IRCCS Istituto Ortopedico Galeazzi, Milan, Italy. Exclusion criteria were recent fracture; surgery within the past 12 months; history of low back pain in the previous 12 months; spinal disorder including degenerative disease and deformities . Height was rounded to the nearest 1 cm and body mass to the nearest 0.5 kg. Body mass index (BMI) was calculated using the standard formula (weight in kilograms divided by height in meters squared).The mattress used in this study was a medium firm mattress composed of a single layer of polyurethane . The mattress size was adapted for the 1.5-T MRI scanner ; in detail: 50 cm width and 190 cm length. Total mattress thickness was 22 cm. The rigid surface utilized in this study was the standard MRI scanning bed. Figure Standard T2-weighted sagittal images of the lumbar spine were performed with turbo spin-echo sequences for the assessment of the radiological angles . The following indexes were calculated for each subject on the midsagittal slice in both MAT and CON: (i) lumbar lordosis angle (L1–L5) ; (ii) L1–S1 angle; (iii) SS, i.e., the angle between the horizontal line and the superior endplate of the sacrum; and (iv) pelvic incidence (PI), i.e., the angle between the line perpendicular to the sacral plate and the line connecting the midpoint of the sacral plate to the midpoint of the femoral heads.Digitized images of the thoracolumbar spine and pelvis were performed with the EOS Imaging System , which simultaneously acquires images in coronal and sagittal planes, with subjects in standing position. No further calibration procedures were required. The following indexes were calculated for each subject, again using the IDS7 system measurement tools: (i) L1-L5 angle; (ii) L1Statistical analysis was performed using GraphPad Prism version 6.00 .t test.The normality of the distribution of the anthropometric and demographic variables , both for the entire study sample and for male and female subjects separately, was checked using graphical methods and the Shapiro–Wilk test. Since all variables were normally distributed, the baseline differences between genders were checked with unpaired Student’s Four different investigators  manually performed all measurements on MRI and EOS images for all subjects and the analysis was repeated two times, a month away, by only one investigator (S.B.). Intra- and inter-rater reliability was tested for each studied outcome in MAT and CON. For the inter-rater reliability, intraclass correlation coefficient (ICC) estimates and their 95% confidence intervals (CIs) were calculated based on a single rating, consistency, 2-way mixed-effects model. Further, for the intra-rater reliability, ICC estimates, and their 95% CIs were calculated based on a single rating, absolute agreement, 2-way mixed-effects model. As previously described , values –L5 and SS, evaluated by the EOS system, for females. Differences between MAT and CON were tested through paired Student’s t test; further, delta values (MAT minus CON) were calculated for males and females and were compared using unpaired Student’s t tests or with the Mann–Whitney rank test for non-normally distributed variables. Significance was set at p < 0.05. Effect sizes (ES) were used to determine the magnitude of the effect for all significant outcomes of pairwise comparison using Cohen’s d and considered to be either trivial (< 0.20), small (0.21–0.60), moderate (0.61–1.20), large (1.21–2.00), or very large (2.00) [The normality of the distribution of each MRI and EOS measurements, both for the entire study sample and for male and female subjects separately, was checked using graphical methods and the Shapiro–Wilk test. All variables were normally distributed with the exception for L1 small 0.–0.60, mo–L5, L1–S1, L5–S1, and SS in MAT or CON  was tested by the means of the Pearson correlation coefficient. Correlations were considered significant when r > 0.25 and p < 0.05.The existence of a correlation between PI, as evaluated by the EOS system, and L1–L4 level as diagnosed by an expert radiologist (L.M.S.). Data on age, height, body mass, BMI, and spine angles evaluated in a standing position by the EOS are presented in Table Twenty-one subjects met the inclusion criteria and were included in the study . One male subject was dropped a posteriori because of the presence of spondylolisthesis at L3Table –L5 was greater in MAT than CON (+2.9°) with the only exception for female subjects whereas L1-S1 angle was +2.0° greater in MAT than CON only for males . SS was always significantly greater in MAT than CON (+2.0°) and, conversely, L5–S1 was lower in MAT than CON only for the entire sample . Further, no significant differences were observed in the comparisons of delta values between male and female subjects .Table –L5, L1–S1, L5–S1, and SS in CON, MAT, and associated delta values. Significant and positive correlations between PI and L1–L5, L1–S1, and SS were observed both in CON  and MAT  whereas no significant correlations between PI and L5–S1, both in MAT and CON, and between PI and delta values were detected.Multipanel Fig. versus a rigid surface. Further, we also evaluated possible correlations of lumbar angles between a standing and a supine condition (both in MAT and CON).The lack of sleep negatively impact an individual’s cognitive and physical performances, mood, quality of life, social interaction and can lead to a decreased work productivity and increased injury risk too , 22, 23.–L5 and SS were greater in MAT than CON; (ii) L5–S1 was lower in MAT than CON; (iii) L1–S1 was greater in MAT than CON only for male subjects; iv) significant and positive correlations between PI and L1–L5, L1–S1, and SS were observed. Our initial hypotheses were only partially confirmed. The mattress used in the study was composed of a single layer of polyurethane and is typically considered a medium firm mattress. In the comparison between CON and MAT, significant differences were observed for three variables  but not for the L1–S1 angle. Namely, L1–L5 and SS increased when participants were on the mattress, indicating that the mattress significantly influenced some of the lumbar angles, ultimately contributing to an increased sense of comfort. The underlying mechanisms explaining these differences are likely related to effect of mattress firmness on muscular stiffness and pressure distribution when lying supine [i.e., L1–L5) increased whereas lumbosacral lordosis  decreased when subjects were laying down on the mattress compared to the control condition. Further, L1–L5 angle, but not L5–S1, positively correlated with PI in standing position for both MAT and CON, with no significant differences between the two conditions. This result is in line with previous studies showing that the proximal lumbar lordosis has a stronger correlation with PI than distal lumbar lordosis, both in standing and supine positions [The main findings of this study were (i) L1g supine . Noteworositions , 30.–L5, L1–S1, and SS angles in MAT whereas female subjects registered a significant increase only in SS. These results are only partially in line with previous studies showing that range of motion of spine segments during different motion tasks is significantly greater for females than for males [–L3, L3–L4, and L4–L5 compared to males. Legaye et al. [–L5 values than women (61.4° ± 10.2° versus 58.1° ± 10.8°) while Bailey et al. [versus 53.0° ± 1.4°). Korovessis et al. [versus 43° ± 12°). Conversely, the lordosis angle was not significantly different in a supine position . On the contrary, our data suggest no significant differences between sex in L1–L5 angle , L1–S1 angle , SS , and PI .For what concerns gender differences, male participants showed a significant increase in L1or males . Furtheror males  assessede et al.  reportedy et al.  evaluates et al.  found th–S1 in CON, SS in CON, L1–S1 in EOS). Second, a validated assessment with MRI was used in the study so the high quality of the data makes the results reliable and repeatable. In addition, x-ray-based images were acquired with the EOS imaging system, which allows for low-dose biplanar images obtained in standing position. The full trunk, femoral heads, and the pelvis were included in the images with the advantage of having a non-conical projection that is typical of standard x-ray studies. PI, L1–L5, L1–S1 and SS indexes were therefore measured in a highly reproducible way [–L5, L1–S1, SS, but not with L5–S1, measured in both CON and MAT.One strength of this study is that both intra- and inter-rater reliability were tested for each outcome in MAT and CON. In detail, the intra-rater ICC was classified as excellent (> 0.9) for each variable, while the inter-rater ICC was classified as good (0.75–0.9) in seven variables and excellent in three variables ; however, the participants were recruited on the basis of specific inclusion and exclusion criteria, therefore the study sample was very homogenous, and the gender distribution was equal . Additionally, we evaluated how the spinal alignment changed in the very short term, but we don't know how the column behaves may have changed after a full night of sleep. It is likely that the muscles relax with subsequent greater change in spinal angles. Further studies are warranted to evaluate this aspect.Some limitations need to be acknowledged. First, only one type of mattress was used in the study and the patients were evaluated only in the supine position; the results could change by using a mattress with different characteristics  or with the patient laying in a prone position. Second, the sample size is small in the study , the changes of lumbar angles comparing rigid surface and mattress. We observed that the use of a mattress determines small but statistically significant changes in radiological angles describing the sagittal alignment of the lumbar spine when lying in the supine position. Our results do not have a direct clinical impact but they could represent the basis of future studies for the improvement of comfort and sleep quality. Authors also highlight the importance to design short- and long-term randomized controlled trials aiming to study spine alignment, sleep quality, and pain using different sleeping surfaces with various levels of firmness.In conclusion, this is the first study describing, with an accurate protocol and high-quality evaluations (


Autosomal dominant mutations in α-synuclein, TDP-43 and tau are thought to predispose to neurodegeneration by enhancing protein aggregation. While a subset of α-synuclein, TDP-43 and tau mutations has been shown to increase the structural propensity of these proteins toward self-association, rates of aggregation are also highly dependent on protein steady state concentrations, which are in large part regulated by their rates of lysosomal degradation. Previous studies have shown that lysosomal proteases operate precisely and not indiscriminately, cleaving their substrates at very specific linear amino acid sequences. With this knowledge, we hypothesized that certain coding mutations in α-synuclein, TDP-43 and tau may lead to increased protein steady state concentrations and eventual aggregation by an alternative mechanism, that is, through disrupting lysosomal protease cleavage recognition motifs and subsequently conferring protease resistance to these proteins.To test this possibility, we first generated comprehensive proteolysis maps containing all of the potential lysosomal protease cleavage sites for α-synuclein, TDP-43 and tau. In silico analyses of these maps indicated that certain mutations would diminish cathepsin cleavage, a prediction we confirmed utilizing in vitro protease assays. We then validated these findings in cell models and induced neurons, demonstrating that mutant forms of α-synuclein, TDP-43 and tau are degraded less efficiently than wild type despite being imported into lysosomes at similar rates.Together, this study provides evidence that pathogenic mutations in the N-terminal domain of α-synuclein , low complexity domain of TDP-43  and R1 and R2 domains of tau  directly impair their own lysosomal degradation, altering protein homeostasis and increasing cellular protein concentrations by extending the degradation half-lives of these proteins. These results also point to novel, shared, alternative mechanism by which different forms of neurodegeneration, including synucleinopathies, TDP-43 proteinopathies and tauopathies, may arise. Importantly, they also provide a roadmap for how the upregulation of particular lysosomal proteases could be targeted as potential therapeutics for human neurodegenerative disease.The online version contains supplementary material available at 10.1186/s13024-023-00621-8. SNCA triplication and quadruplication leading to familial Parkinson's disease has shown [SNCA, TARDBP and MAPT genes that alter specific amino acids also increase the risk for neurodegenerative disease and are thought to do so by increasing the rate of protein self-association [Despite symptomatic and anatomical differences, neurodegenerative diseases such as Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are all characterized by the neuropathological finding of proteinaceous intraneuronal aggregates on which disease nosology is based and genetic cohorts have been identified . The mecas shown , 8. Mutaociation –11. Howeociation –14 and yociation , 16. ThuGBA, PGRN and CTSD implicated in PD, FTD and AD, respectively [Lysosomes play major roles in cellular protein homeostasis –20. As tectively , 22. Defectively . Althougectively –26, pastectively –30. To dIn this study, we investigated the normal lysosomal clearance of α-syn, TDP-43 and tau and found that a subset of disease mutations extended protein half-life and increased steady-state protein levels. These insights arose following the generation and analysis of comprehensive maps of protease cleavage of these proteins. The maps revealed that areas dense with cleavage sites often overlapped with areas where disease mutations are concentrated. They also predicted mutations that were likely to interfere with protease cleavage. Systematic testing of a subset of these “disruptive” mutations validated these predictions in vitro as well as in cell and iPSC-derived neuronal (iNeuron) models. Disease mutations prolonged α-syn, TDP-43 and tau half-life and increase steady-state protein levels, even when similar rates of lysosomal import of mutant versus wild type protein were observed. Together, this study offers a novel, shared pathobiological mechanism by which genetic and potentially sporadic forms of age-related neurodegenerative disease may develop and implicates lysosomal proteases as therapeutically targetable contributors in neurodegeneration.Cathepsin E (CTSE) (R&D #1294-AS), cathepsin D (CTSD) (R&D #1014-AS), cathepsin G (CTSG) , cathepsin A (CTSA) (R&D #1049-SE), cathepsin L (CSTL) , cathepsin B (CTSB) , cathepsin K (CTSK) , (CTSS) cathepsin S (R&D #1183-CY), cathepsin V (CTSV) (R&D #1080-CY), asparagine endopeptidase (AEP) (R&D #2199-CY), cathepsin H (CTSH) (R&D #7516-CY-010), cathepsin C (CTSC) (R&D #1071-CY), cathepsin O (CTSO) (Abcam #ab267932), cathepsin F (CTSF) (Abcam #ab240858), and cathepsin X (CTSX) (R&D #934-CY).The following primary antibodies  were used: 1) monoclonal mouse anti-GAPDH , 2) mouse anti-FLAG antibody , 3) mouse anti-α-synuclein , 4) mouse anti-TDP-43 , 5) mouse anti-Tau , 6) anti-rabbit anti-Lamp1 antibody , 7) rabbit anti-Cathepsin D , 8) mouse anti-Actin-C4 antibody , 9) rabbit anti-LC3b antibody , and 10) rabbit anti-TFEB antibody .Novel doxycycline-inducible, FLAG-tagged, full-length WT Tau, TDP-43, and α-syn lentiviral plasmid constructs were first designed and synthesized (Epoch Life Sciences) using a puromycin resistant plasmid backbone, pTet-O-Ngn2-Puro . Using these 3 WT plasmid constructs, individual constructs containing single pathogenic point mutations were also generated: Α-syn A30P, E46K, H50Q, G51D and A53T; TDP-43 G298S, A315T, A321G, Q331K and M337V; and tau K257T, N279K, P301S and S305N. With these constructs, lentivirus was made using the psPAX2 packing  and pCMV-VSV-G envelope (Addgene #8454) plasmids. To generate stable lines, SH-SY5Y cells already containing the pLenti CMV rtTA3 Blast construct  were infected and stable lines  were generated using Puromycin 1 mg/mL selection.For in vitro cleavage assays, 1 µg of recombinant full-length human α-syn (Abcam #ab51189), TDP-43 (R&D #AP-190) or 4N2R tau (rPeptide #T-1001–1) was incubated with or without 1 µM of each protease. Proteases requiring pre-activation were performed, as mentioned in Table MSP-MS was performed as previously published by O’Donoghue et al. 2012 . A substMSP-MS reactions were prepared with proteases at 1–70 nM concentration with a library concentration of 500 nM for each peptide (except CTSF at 1 µM) and incubated at 37 °C. Buffers were sodium acetate buffer (50 mM) containing 5 mM DTT and 1 mM EDTA for pH 4.5 and pH 5.5 or sodium citrate (50 mM) containing 5 mM DTT and 1 mM EDTA at pH 3.5. Reactions were monitored at two time points in an end point screening format. Each aliquot taken at a given time point was immediately desalted with C18 zip tips (Millipore-Sigma) and then freeze-dried. Samples were re-suspended in 0.1% formic acid in HPLC-grade water for LC–MS/MS analysis.Reaction conditions for each protease followed manufacturer recommendations for pre-activation and then were assayed at the concentrations and pH buffer conditions mentioned in Table http://prospector.ucsf.edu/prospector/mshome.htm, UCSF) using published methods [Peptide sequencing by LC–MS/MS was performed on a QExactive Plus mass spectrometer (Thermo) equipped with a nanoACQUITY (Waters) ultraperformance LC (UPLC) system and an EASY-Spray ion source (Thermo). Reversed-phase chromatography was carried out with an EASY-Spray PepMap C18 column . Chromatography was performed at a 600-nl/min flow rate during sample loading for 14 min and then at a 400-nl/min flow rate for peptide separation over 90 min with a linear gradient of 2 to 35% (vol/vol) acetonitrile in 0.1% formic acid. Peptide fragmentation was performed by higher-energy collisional dissociation (HCD) on the six most intense precursor ions with a minimum of 2,000 counts, with an isolation width of 2.0 m/z and a minimum normalized collision energy of 25. Data were analyzed using Protein Prospector software, v.6.2.1 ( methods . The pephttps://seaborn.pydata.org/index.html).The percentage contribution of each enzyme to the total number of cleavages in each protein was calculated by dividing the number of cleavage sites for each enzyme with the sum of all enzyme cleavage sites. Since our substrates α-syn, TDP-43 and tau have different amino acid lengths, in order to compare the cleavage patterns, we next normalized the percentage contribution of enzyme cleavages for each substrate with the mean of total percentage contribution for each enzyme across all substrates. Using these mean-normalized values for each enzyme within each substrate, we then performed an unsupervised hierarchical clustering in Python using the clustermap function within the data visualization library called “seaborn”  [https://www.ebi.ac.uk/merops/), assessing the amino acid frequency at the P4’-P4 positions, where all known cathepsins  were previously found to cleave [The resulting peptide identifications via MSP-MS were assessed for specific cleavage in the enzyme-treated sample by subtracting the results from a no-enzyme control incubation. Both endopeptidase and exopeptidase cleavage sites were readily identified. Using these sites, we generated full sequence cleavage maps of α-syn, TDP-43 and tau. We also created diagrams of each cathepsin to identify which amino acids were favored at the P4’ through P4 positions of each enzyme’s specific cleavage motif, using the IceLogos algorithm (iomics.ugent.be/icelogoserver) . We furtedu.au/) . To deteo cleave . Using tFluorescence-based protease activity assays were performed in triplicates in black, flat-bottom 384-well plates  using custom designed fluorescent α-syn, TDP-43 and tau peptide substrates , A53T α-syn (ab256149), WT 2N4R tau (ab199583) and N279K 2N4R tau (ab269006) were purchased from Abcam. Full-length WT TDP-43 and Q331K TDP-43 were synthesized, purified, and obtained from GenScript. Each of these proteins were incubated with 50 μg of human liver lysosomes (Xenotech #H0610.L) in a 50 mM sodium acetate (pH 4.5) buffer buffer containing 2 mM DTT and 1 mM EDTA. Each reaction was allowed to run for 30 min with samples taken at 0-, 10-, 20-, and 30-min time points. Results were then analyzed via Western blot.SH-SY5Y human neuroblastoma cells were obtained from ATCC (CRL-2266) and maintained 1:1 EMEM/F12 media  supplemented with 10% FBS and 1% Penicillin–Streptomycin . Stable lines of all 17 aforementioned constructs were generated by lentiviral infection followed by both blasticidin (5 µg/mL) and puromycin (1 mg/mL) selection. Cells were seeded at 30,000 cells per cm^2 and allowed to grow until ~ 70% confluent. To terminally differentiate into neuron-like cells, SH-SY5Y cells were then treated with retinoic acid as previously published . Once diAll iPSC lines  were obtained from the NIH’s iPSC Neurodegenerative Disease Initiative (iNDI) in association with the Jackson Laboratory . IPSCs wTo confirm trafficking of WT and mutant α-syn, TDP-43 and tau into the lysosomes of our SH-SY5Y cells, we performed a series of lysosome isolation experiments. SH-SY5Y cells were differentiated and treated with MG132 as previously described , 38. Cel34. Briefly, 40 µg of protein were separated on 4–12% SDS-PAGE (Thermo) and then transferred onto nitrocellulose membranes (Bio-Rad). Membranes were blocked at room temperature with Odyssey Blocking Buffer  and incubated at 4 °C overnight with primary antibodies and 1 h at room temperature with appropriate fluorescent secondary antibodies (1:5000) (LI-COR). Immunoreactive bands were visualized using a LI-COR Odyssey CLx image scanner and quantified using ImageJ software.Western blotting was performed as previously describedp value. All data is represented as mean ± SD. Statistical analysis was performed using GraphPad Prism 9 . Western blot quantifications for Figs. p-value < 0.05 was considered significant.Details of the statistical test used for each experiment is in figure legends along with n and Although prior studies have demonstrated that α-syn, TDP-43 and tau can be shuttled to the lysosome for degradation -26, the We first assayed the largest class of lysosomal proteases, the cysteine protease family . Under oCTSD mutations [Next, we tested the serine proteases, cathepsin A (CTSA) and cathepsin G (CTSG). While CTSG was able to digest all three substrates at more neutral pHs, CTSA did not demonstrate any clear protease activity against any of our substrates in this assay Fig. . Lastly,utations . ConsistAlthough most of the enzymes tested are endopeptidases , cathepsins A, C, H, and X are primarily exopeptidases, hydrolyzing mainly N- or C-terminal peptide bonds and exhibiting limited endopeptidase activity. Within the context of our assay, we noted that cathepsins A, H, and X (exopeptidases) as well as the endopeptidase cathepsin O (CTSO) did not demonstrate any detectable activity against α-syn, TDP-43 or tau Fig. b. To conTo map the specific locations at which lysosomal proteases cleave α-syn, TDP-43 and tau, we took advantage of MSP-MS, a rapid, quantitative, reproducible and unbiased method of direct substrate cleavage site identification . To aid At 140 amino acids, α-syn is the smallest of the three proteins assessed. In total, there were 82 cleavages in α-syn by 9 of the 12 proteases tested  contains 441 amino acids. Other than previously reported cleavage by CTSD and CTSS, little is known regarding lysosomal proteases that can digest tau , 50. AllR containe.g., PD versus ALS). Therefore, we performed comparative analysis of the proteases in regards to proteolysis of α-syn, TDP-43 and tau. To do so in proteins of different sizes, we normalized the number of cathepsin cleavages for each substrate relative to their amino acid number. Among these proteases, we found that two cysteine cathepsins, CTSL and CTSS, demonstrated the highest proportion of cleavage sites in α-syn, TDP-43 and tau  P4 to P1 found on the amino (N)-terminal side of the cleavage site and P1’ to P4’ found on the C-terminal side. When expressed in this fashion, many of the cysteine proteases  prefer hydrophobic residues at P2  while AEHaving generated cathepsin cleavage maps for α-syn, TDP-43 and tau, we overlaid the known disease-associated variants onto the maps  [Since it was not feasible to test every mutation against every protease, we utilized two approaches to identify a subset of mutations mostly likely to abrogate cathepsin cleavage. First, we performed in silico comparisons of WT versus mutant protein sequences in the PROSPER database (edu.au/) . This anedu.au/) . The iceedu.au/) , 31, 60 edu.au/)   to test for altered proteolytic cleavage activity as well as non-damaging “control” mutations with lower probability of altering protease cleavage activity Table . We thenUsing this activity assay, the maximal velocities (Vmax) at which each enzyme cleaved the WT and mutant peptides were calculated Fig. . Using tTo better quantify the summed impact of these changes over many different proteases, we assigned “damage points” for each instance in which a mutation decreased a protease’s Vmax for peptide cleavage by 0–25% (1 point), 25–75% (2 points) or greater than 75% 3 points). In contrast, mutations that increased the rate of cleavage of mutant peptides were given a score of -1 point. By summing the point values, we were able to calculate a “Damage Score” for each mutation . When tested within our in vitro protease assays, this mutation decreased CTSE, CTSL and CTSS cleavage of the mutant peptide . Through the NIH’s iPSC Neurodegenerative Disease Initiative (iNDI), we obtained 3 CRISPR-edited, homozygous mutant lines, α-syn A53T, TDP-43 Q331K, and tau N279K, as well as their parent isogenic WT iPSC line . These aAs α-syn, TDP-43 and tau are all intrinsically disordered proteins that are found in disease inclusions and whose mutations are causally linked to neurodegeneration , understAlthough we did observe a significant degree of redundancy in the activity of the lysosomal proteases on the degradation of α-syn, TDP-43 and tau, we were surprised to find that 1) certain proteases, such as CTSB and AEP, had relatively specific non-redundant activity and 2) certain mutations disrupted multiple proteases and likely undercut a significant degree of the redundancy present among the lysosomal proteases. Our correlation analyses also raise the question of whether certain cathepsins are regulated in a coordinated fashion, such as through the TFEB-mediated CLEAR pathway or via an alternative mechanism, in response to these specific lysosomal substrates .While the increased steady-state levels of mutant α-syn, TDP-43 and tau were modest in the cell-based assays, these assays spanned only a few days. In some respects, it is remarkable that point mutations could detectably alter steady state levels of α-syn, TDP-43 and tau at all. Over weeks, years and decades, the impact of half-life extending mutations in α-syn, TDP-43 or tau would likely be compounded. They could be further exaggerated by age-associated declines in lysosome function –68 or the.g., chaperone upregulation, proteasome and autophagy induction, lysosomal secretion, ER and mitochondrial unfolded protein responses, aggregate formation, etc.). As such, it may only be when these compensatory mechanisms are exhausted that neurodegeneration occurs.This model complements and builds upon the idea that certain disease mutations can increase steady state levels of protein directly by enhancing fibril formation while others, like the ones we identify here, do so indirectly via impaired lysosomal degradation. As such, aberrant protein homeostasis and a steady state increase in these proteins may be the unifying feature of these diseases. As terminally differentiated cells that cannot dilute protein concentrations through cell division, neurons have developed many cooperative strategies to deal with aberrant protein homeostasis  impairing the proteolytic cleavage of these proteins in the lysosome could play a role in sporadic cases as well. The practical and disease implications of these findings are multifold. First, they provide a molecular basis by which disease mutations in  regions  and coul regions –72 and p regions , 74. Sim regions . FinallyAlthough our efforts were designed to be comprehensive, there are limitations to our study. Moreover, while the MSP-MS and in vitro fluorescent protease assays are complimentary , on occasion we observed slight discrepancies. For example, in the in vitro protease assay CTSB cleaved the TDP-43 fluorescent peptides spanning the 294–302 region, which we did not observe in MSP-MS. This is likely because the MSP-MS results were limited to two collection time-points, which may miss both the earliest cleavages as well as very late events, although the latter may be less relevant given the multi-protease environment inside the lysosome. Additionally, cell-based experiments were limited to relatively short time intervals and involved differentiated neuronal cells which lack certain aging-hallmarks and whose age-related declines in proteasome function had to be simulated using a proteasomal inhibitor. Future work should address longer-term, in vivo contexts , where the association of end-stage disease hallmarks, such as protein aggregates, in response to cathepsin inhibition can be studied. The contributions of post-translational modifications, which can impact both lysosomal targeting and proteolytic processing , could aThis study comprehensively identifies the arsenal of lysosomal proteases that degrade α-syn, TDP-43 and tau, demonstrating that these proteases have highly specific cleavage patterns and optimal pH settings. This work also provides compelling experimental evidence that certain pathogenic mutations in each of these three proteins can disrupt this lysosomal protease activity, prolong protein half-life, and increase steady state levels in neuronal cell models. Fascinatingly, this novel, alternative mechanism by which disease-causing mutations in α-syn, TDP-43 and tau may lead to neurodegeneration also appears to be shared. These findings therefore enhance our understanding of α-syn, TDP-43 and tau metabolism as well as their impact on normal protein homeostasis and neurodegenerative disease pathobiology. In the future, this work could be leveraged to promote neuronal health by upregulating specific lysosomal proteases.Additional file 1: Figure S1. Proteases that do not cleave α-syn, TDP-43, or tau are active against a fluorogenic casein substrate. Fluorogenic protease activity assays using casein at pH 4.5 was performed on all enzymes that do not cleave α-syn, TDP-43, or tau. The values obtained were normalized to timepoint t = 0 and plotted relative to activity. All enzymes, including both cysteine and serine proteases, show activity against the optimized fluorogenic peptides compared to the substrate only control. Figure S2. PROSPER analysis predicts multiple cathepsin cleavage sites with α-syn, TDP-43, and tau. Cathepsins Gand K, the lysosomal proteases available in the PROSPER database, demonstrate many sites of cleavage throughout the α-syn, TDP-43, and tau substrates. Yellow highlighted amino acids represent the P1 site of predicted CTSK cleavage. Light blue highlighted amino acids represent the P1 of predicted CTSG cleavage. Figure S3. Cathepsin activity assay Vmax tables of α-syn, TDP-43, and tau wild type versus mutant substrates. a, α-Syn, b, TDP-43, and c, tau. Vmax values are listed in nM/min. An arbitrary value of 2000 value was given to protease-enzyme substrate interactions that were too fast to be modeled. Figure S4. Doxycycline-induced expression of mutant α-syn, TDP-43, and tau in differentiated SHSY-5Y cells. a, α-Syn; b, TDP-43; and c, tau. Western blot images demonstrating induced expression of each FLAG-tagged protein by our plasmid constructs with 24 h of doxycycline treatment results in measurable protein whose half-life can be readily quantified over days. These results also demonstrate minimal leakiness of these constructs in the absence of doxycycline. Figure S5. Proteasomal inhibitor, MG132, upregulates markers of autophagy. a., Western blot images demonstrating that Lamp1, cathepsin D, LCBIIb, and TFEB are all upregulated after MG132 treatment at 24, 72, and 120 h, compared to the no treatment condition. b., Quantification of the results of A. Experiments were performed in triplicate. Figure S6. Full Western blots of differentiated SH-SY5Y and iNeuron protein half-life experiments. a. Western blot images demonstrating induced expression of each FLAG-tagged protein whose half-lives were quantified over 5 days. The green channel was used to measure each FLAG tagged protein. The red channel was used to measure GAPDH, which was used as the loading control and method by which protein quantification was normalized. b. Western blot images demonstrating iNeurons whose protein levels were quantified at day 0 and day 5 of MG132 treatment. The green channel was used to measure either α-syn, TDP-43, or tau expression. The red channel was used to measure GAPDH, which was used as the loading control and method by which protein quantification was normalized. Figure S7.Table S1. α-Syn, TDP-43, and tau peptide library. Amino acid sequences for all peptides within the library tested in the described MSP-MS experiments in Figs. 2, 3, 4 and 5. Table S2. Pre-activation steps, protease concentrations, tested pHs, and timepoints for in vitro and mass spectrometry experiments. Cathepsin A, C, H, and X required pre-activation for all experiments in Figs. 1, 2, 3, 4, 5 and Fig. S1. Protease concentration and mass spectrometry timepoints were chosen and optimized based on the known rate of cleavage for each protease. Table S3. WT and mutant α-syn, TDP-43, and tau sequences with high rank order prediction scores of cleavage for select cathepsins. Each mutation and companion wild-type sequences for a, α-Syn; c, TDP-43, and c, tau are represented alongside the rank order prediction scores of PROSPER predicted sites of cleavage for the available cathepsins B, K, L, S, D and E. Table S4. Fluorogenic peptide sequences for WT and mutant α-syn, TDP-43, and tau. A, α-Syn; B, TDP-43, and C, tau amino acid sequences tested in the described fluorogenic protease activity assays in Figs. 2, 3, and 4.Additional file 2.Additional file 3.


Clostridioides difficile infection (rCDI) substantially diminishes patients’ health-related quality of life (HRQL). The gut microbiome is known to play an important role in rCDI, but little is known about its relationship with HRQL. In this study, we assessed the relationship between the gut microbiome and HRQL in patients with rCDI from REBYOTA’s  phase 3 PUNCH CD3 trial (NCT03244644).Recurrence of C. difficile Quality of Life Survey (Cdiff32) via its total score and physical, mental, and social domain scores .HRQL and relative abundance  of gut microbiota at the class level were collected at baseline and weeks 1, 4, and 8. The Microbiome Health Index , defined as (Bacteroidia + Clostridia) / (Gammaproteobacteria + Bacilli), was analyzed using log transformation and as a binary indicator with MHI > 7.2 indicating a healthy microbiome. HRQL was measured using the disease-specific Correlations between microbiota data  and Cdiff32 scores were estimated. Associations of Cdiff32 scores with microbiota data and MHI > 7.2 were estimated using mixed effects analyses adjusted for baseline patient characteristics. Analyses used all available data . P-values were adjusted for multiple testing via the Benjamini-Hochberg procedure; correlations were interpreted as moderate if ≥ 0.3 and large if ≥ 0.5 .Table 1). Significant associations of microbial relative abundances and log MHI with all Cdiff32 scores were found in mixed effects analyses  (Table 2). MHI > 7.2  was associated with an improvement of 14.2 to 18.4 points in Cdiff32 scores (vs. MHI ≤ 7.2).A total of 176 out of 262 (67.2%) patients had Cdiff32 and microbiota data  in PUNCH CD3. All four Cdiff32 scores correlated positively with log MHI (∼0.6), Clostridia (∼0.5) and Bacteroidia (∼0.3), and negatively with Gammaproteobacteria (∼−0.5) and Bacilli (∼−0.3) (Our study found moderate to large and statistically significant associations between a healthy gut microbiome and improved HRQL in patients with rCDI.Paul Feuerstadt, MD, FACG, AGAF, Ferring/Rebiotix Pharmaceuticals: Advisor/Consultant|Ferring/Rebiotix Pharmaceuticals: Grant/Research Support|Merck and Co.: Advisor/Consultant|Seres Therapeutics: Advisor/Consultant|Seres Therapeutics: Grant/Research Support|Takeda Pharmaceuticals: Advisor/Consultant Ken Blount, PhD, Ferring Pharmaceuticals: Employee Amy Guo, PhD, Ferring Pharmaceuticals: Employee Min Yang, MD, PhD, Analysis Group, Inc.: I am an employee of Analysis Group, Inc., which has received consulting fees from Ferring for the conduct of this study. Viviana García-Horton, PhD, Analysis Group, Inc.: I am an employee of Analysis Group, Inc., which has received consulting fees from Ferring for the conduct of this study. Mirko Fillbrunn, PhD, Analysis Group, Inc.: I am an employee of Analysis Group, Inc., which has received consulting fees from Ferring for the conduct of this study. Glenn S. Tillotson, PhD, Dynavax: Advisor/Consultant|Ferring Pharmaceuticals: Advisor/Consultant|Peggy Lillis Foundation: Honoraria|Spero Therapeutics: Advisor/Consultant Yipeng Gao, PhD, Merck and Co.: Grant/Research Support Erik R. Dubberke, MD, MSPH, Abbott: Advisor/Consultant|AstraZeneca: Advisor/Consultant|Ferring Pharmaceuticals: Advisor/Consultant|Ferring Pharmaceuticals: Grant/Research Support|Merck and Co.: Advisor/Consultant|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Seres Therapeutics: Advisor/Consultant|Summit: Advisor/Consultant|Theriva Biologics: Grant/Research Support Kevin W. Garey, PharmD, MS, Acurx: Grant/Research Support|Ferring: Advisor/Consultant|Paratek: Grant/Research Support