Science, Baughn et al. revealed that TDP-43 acts as a steric block in STMN2 pre-mRNA processing preventing inclusion of a deleterious cryptic exon, and present strategies for substituting this function to compensate for the loss of nuclear TDP-43 in a variety of neurodegenerative diseases (NDs) including amyotrophic lateral sclerosis (ALS).1In a recent study published in 3 TDP-43 is, at least in humans, essential for expression of functional full-length STMN2 protein. It binds to the pre-mRNA of STMN2 and prevents inclusion of a cryptic exon (exon 2a) located in the first intron. Nuclear loss of TDP-43 in NDs induces exon 2a inclusion that introduces an in-frame stop-codon as well as a premature polyadenylation signal resulting in a severely truncated mRNA, and loss of functional STMN2 protein  is a microtubule-associated protein specifically expressed in neurons and required for axon outgrowth and maintenance, as well as for axon regeneration after injury in vitro. Loss or reduction of this protein in mice leads to progressive sensory and motor neuropathy resembling some crucial, but not all, features of ALS.ein Fig. . This abSTMN2 mRNA. Previous data suggested that TDP-43 directly binds to a 24 bp GU-rich motif in exon 2a of the STMN2 pre-mRNA. Replacing this motif with a 19 bp stem-loop forming sequence of the bacteriophage MS2 by genome editing of human neuroblastoma cell line SH-SY5Y led to deleterious exon 2a inclusion in STMN2 mRNA. To rescue these defects, Baughn et al. expressed the MS2 coat protein (MCP) that binds to the introduced 19 bp stem-loop in STMN2 pre-mRNA with high affinity. Here, both expression of MCP alone or fused to an inactive TDP-43 variant lacking the RNA-binding domains similarly prevented exon 2a inclusion and restored correct STMN2 pre-mRNA splicing, supporting the hypothesis that TDP-43 functions as a simple steric block. This was further confirmed by introducing the human MS2-edited exon 2a including flanking regions into the first intron of the Stmn2 gene of murine Neuro2a cells. In mice, exon 2a is not conserved and Stmn2 pre-mRNA processing is not dependent on TDP-43. However, this humanization of mouse Stmn2 induced similar processing defects as reported in the human SH-SY5Y cells, and could be rescued by expression of MCP. Additionally, in SH-SY5Y cells homozygously expressing the ALS-related N352S variant of TDP-43 and displaying the described defects of STMN2 pre-mRNA processing, the authors show that not only MCP, but also the nuclease-dead CRISPR effector RfxCas13d (dCasRx) can compensate for the steric block function of TDP-43 when guided to the right position in exon 2a. Further genome editing of SH-SY5Y cells revealed that the 3’ splice acceptor site, but not the premature polyadenylation signal, of exon 2a is responsible for cryptic splicing after TDP-43 depletion. Taken together, these very elegant experiments, that were also controlled for possible effects on endogenous TDP-43 expression, leave little doubt that TDP-43 functions as a steric block for the cryptic 3’ splice acceptor site of deleterious exon 2a in STMN2 pre-mRNA processing.1Baughn et al. moved an important step forward toward a STMN2-based therapeutic approach for TDP-43 proteinopathies. First, they addressed molecular mechanisms of TDP-43-dependent cryptic exon 2a inclusion in STMN2 pre-mRNA processing -based approach to compensate for TDP-43 function in ing Fig. . 250 ASOStmn2 in ALS model mice expressing the TDP-43 variant Q331K does not induce misprocessing of Stmn2 pre-mRNA, or worsen phenotypes such as reduced grip strength. Considering that these mice do not show TDP-43 pathology, these results confirm the authors’ hypothesis that indeed nuclear loss of TDP-43 is required to induce misprocessing of Stmn2 pre-mRNA.1 However, this finding emphasizes existence of additional TDP-43-dependent mechanisms beyond nuclear loss that contribute to NDs. Nonetheless, humanization of Stmn2 in ALS model mice more closely resembling human TDP-43 neuropathology may represent an excellent approach to quantify the contribution of TDP-43 induced reduction of Stmn2 protein to the degenerative phenotype, and may simultaneously provide an ideal model to determine potential benefits of restoring Stmn2 expression using ASOs.Besides these highly promising results, Baughn et al. additionally found that homozygous humanization of In conclusion, while Baughn et al. report substantial progress in the development of a STMN2-based treatment strategy for TDP-43 proteinopathies, it remains to be determined if rescuing expression of a single downstream target of TDP-43 is sufficient to markedly delay disease progression in humans. Nevertheless, this approach is among the most promising for treating sporadic NDs linked to TDP-43, and is definitively worth being further followed-up.


In this regard, two graphene sensors with different ZnO loadings of 5 wt.% and 20 wt.% were prepared, and their responses towards NO2 at room temperature and different conditions were compared. The experimental results demonstrate that the graphene loaded with 5 wt.% ZnO NPs (G95/5) shows better performance at detecting low concentrations of the target gas than the one loaded with 20 wt.% ZnO NPs (G80/20). Moreover, measurements under dry and humid conditions of the G95/5 sensor revealed that the material is very sensitive to ambient moisture, showing an almost eight-fold increase in NO2 sensitivity when the background changes from dry to 70% relative humidity. Regarding sensor selectivity, it presents a significant selectivity towards NO2 compared to other gas compounds.This paper investigates the effect of decorating graphene with zinc oxide (ZnO) nanoparticles (NPs) for the detection of NO According to the World Health Organization (WHO), annually, 7 million premature deaths occur worldwide  caused ben death . Furtheren death . To limi2 [2 at room temperature, other alternatives to MOX sensors have been investigated, such as chemiresistors. Among these alternatives, it is worth mentioning graphene [2, in order to boost the graphene-based sensor performance. In particular, it was proven that the addition of MOX to graphene has resulted in substantial enhancement of its properties and sensing abilities, with ZnO as the leading MOX for this type of addition. Furthermore, ZnO is a semiconductor with a band gap of 3.37 eV and exciton binding energy of 60 meV, which makes it very useful for making stable devices. It is also considered thermally and chemically stable, which makes it also perfect for the production of devices for a long period of use [2 detection by making graphene decorated by ZnO nanoparticle gas sensors [Many types of gas sensors emerged intending to continuously monitor trace level concentrations of the previously mentioned gases, such as gas chromatographic systems , as well2 ,15. Howe2 . In this2 . For detgraphene , MXenes graphene , transitgraphene ,21,22 orgraphene . Graphengraphene . But notd of use . But mosd of use . Recent d of use . Additio sensors .In this work, we have synthesized graphene/ZnO nanocomposites using a novel procedure that uses moderate temperatures and pressures jointly with non-toxic solvents, allowing the mass production of the nanocomposites. These nanocomposites have been employed to manufacture chemoresistive gas sensors, which have been characterized. Their performance is then compared with the one of previously reported sensors making use of similar materials.G80/20 and G95/5 nanomaterials, with a weight ratio of 80:20 and 95:5, were synthesized by the insertion of zinc oxide nanoparticles in the pristine graphene nanoplatelets, using a novel nanotechnological process based on patented procedures (Patent number ES2678419A1). Briefly, pristine graphene nanoplatelets were dispersed in malonic acid (molar rario 1:6), in which the starting ZnO had been previously dissolved. After homogenization, ZnO nanoparticles were precipitated between graphene nanoplatelets with a basic solution (NaOH 2.5 M) under controlled parameters, such as vigorous agitation, temperature (50 °C) and nanomaterial proportion according to the weight ratio. Bare graphene, as well as pure ZnO, were also prepared to have reference samples.2O3) substrates via the spray coating method while heating the substrate to 70 °C. 2, CO, H2, H2S, ethanol and NH3.The different nanomaterials were deposited on commercial alumina . Samples were outgassed at 423 K for 12 h under vacuum (6 mTorr) to eliminate chemisorbed volatiles before the adsorption isotherm was measured. Surface areas were calculated using the Brunauer-Emmett-Teller theory (BET) method.Morphological characterization of the nanocomposite was conducted through a Transmission Electron Microscopy (TEM)  operated at 100 kV, and the images were taken on the powder samples without any previous treatment.The two obtained sensors were characterized using different techniques, such as Raman via a Raman spectrometer , with a laser wavelength of 514 nm to check the crystallinity of the materials, and a Field Emission Scanning Electron Microscope (FESEM) using a Carl Zeiss AG-Ultra 55  to study the surface morphology and to check the distribution of the NPs on the graphene layer and Photoluminiscece (PL) measurements to analyze the defects in the sensing layer. These later measurements were performed at room temperature using a chopped Kimmon IK Series He-Cd laser (325 nm and 40 mW). Fluorescence was dispersed with an Oriel Corner Stone 1/8 74,000 monochromator, detected using a Hamamatsu H8259-02 with a socket assembly E717-500 photomultiplier, and amplified through a Stanford Research Systems SR830 DSP. A filter in 360 nm was used to stray light. All spectra were corrected for the response function of the setups. FESEM was also used to determine the deposited nanocomposite layer thickness using a cross-sectional view.3. A continuous stream of dry air  and diluted gases were passed through the testing chamber with a total flow of 100 mL/min via a set of Bronkhorst mass-flow controllers. The target gases from calibrated bottles  were further diluted by means of the mass flow controllers set. The resistance changes were continuously acquired using an Agilent HP 34972A multimeter. The humidity effect on the sensing performance was assessed by humidifying the gas stream through a controller evaporator mixer (CEM). The sensing responses were calculated using this formula R (%) = ((Rg − Ra)/Ra) × 100, where Rg and Ra correspond to the resistance level after and before gas exposure, respectively.Gas sensing measurements were conducted by placing the different sensors in an airtight Teflon chamber with a volume of 35 cm−1 for the D-band, 1576 cm−1 for the G-Band, and the 2D band at 2696 cm−1. Despite the presence of the ZnO in the graphene, the peak corresponding to the NPs is absent in the spectrum due to its low concentration of 5%. The same graphene peaks were observed also in −1, 1579 cm−1, and 2704 cm−1, although in this spectrum a new peak is present at 415 cm−1, which can be attributed to the ZnO nanoparticles since its specific peak usually appears at around 430 cm−1. These typical peak positions were widely reported for graphene samples, but peak intensities vary depending on the crystallinity of the samples [2 bonded carbon, and by performing a polarization study of the band under uniaxial strain, it is possible to determine the orientation of the graphene on the substrate [−1, revealing that a thin graphene layer was achieved [ samples . Specifiubstrate . Additioubstrate ; specifiachieved . In addiachieved .The obtained FESEM images of the layers present on the surface of the G95/5 and G80/20 sensors using a back-scattered electron detector (BSE) shows a black layer which is the graphene layer, big grey chunks corresponding to the alumina substrate surface, and the zinc oxide nanoparticles can be detected by the bright spots. Some ZnO nanoparticles can be seen in EDX analysis with a quantitative elemental analysis can be found in the 2/g was obtained by means of BET analysis. In For the G95/5 sample, a surface area of 392.734 mThe quantity and type of defects in ZnO can be estimated based on PL measurements . Figure 2 gas according to the European Environment Agency is 106 ppb (200 ug/m3), gas measurement tests of different dilutions of this target gas were conducted in the range of 50 to 500 ppb at room temperature. The ZnO sensor operated at room temperature exhibited a high resistance (in the range of 10 MΩ), leading to very noisy behavior. In this case, no noticeable response was obtained in the concentration range studied. −1 in this concentration range. 2 in dry conditions. As expected, the figure shows a p-type behavior of the sensor, with the resistance decreasing when put in contact with NO2, which is an oxidizing gas. As can be seen in 2 from the gas sensitive surface, thus fully regaining its baseline.Since the hourly limit of exposure to NO2, it was the subject of further measurements closer to real conditions (in other words under humidity), to check the effect of ambient moisture on sensor performance. Two different relative humidity percentages of 20 and 70% were applied, while doing the gas tests towards different concentrations of NO2 of 50, 150, 250, 350 and 500 ppb at room temperature. As can be seen in Since the G95/5 sensor was able to efficiently detect very low concentrations of NOOne can see that increasing the moisture level, the sensitivity of the senor is increased.The limit of detection (LOD) was calculated for the three cases using the equation:2 under dry conditions. However, the LOD increased to 47 ppb when the relative humidity was increased to 20% and to 36.9 for 70% humidity.Here, Sy represents the standard deviation of y-residuals obtained from the calibration curves, while b corresponds to the slope. The sensor exhibited a LOD of 21.9 ppb for NO2 concentration.These results imply that in a practical situation this sensor should be operated jointly with a humidity sensor, in order to enable using the correct calibration curve for determining NO2 of 5.1% seems to be better than the works presented in the references [2 tested are way higher: 10 times higher for [To have an idea of the position of this work compared to the literature, the sensing performance of the G95/5 sensor was compared with previously reported sensors and gathered in ferences ,37 with ferences . Meanwhiferences ,39 have gher for  and 200 2 for detecting other target gases. In this regard, 500 ppb of NO2, 200 ppm of H2, 20 ppm of H2S and ethanol 25 ppm of CO and 50 ppm of NH3 were detected at room temperature. As can be seen in 2, although the concentration of those gases is more than 40 times higher. The highest response is obtained for hydrogen, but is less than half the one obtained for NO2, due to the concentration being 200 times higher. This proves the high selectivity of this sensor to nitrogen dioxide gas (NO2).The selectivity of the sensors was evaluated by repeating the same experimental conditions when detecting NOFinally, to check the long-term stability of the sensors, measurements shown in 2, which is a strong oxidant gas (electron-withdrawing gas), the interaction between the graphene layer and the gas results in a decrease in the sensor resistance. Conversely, when detecting reducing gases, such as NH3 (electron donor gas), the sensor resistance increases [Bare graphene is a mild p-type nanomaterial, and when gas molecules get adsorbed on its large surface area, the local carrier concentration quickly changes, inducing resistance changes. In the case of NOncreases .Considering the use of p-type graphene loaded with n-type ZnO nanoparticles, it is expected that a p-n junction will form on the contact surface. Specifically, electrons from the ZnO conduction band probably flows to the graphene, resulting in a depletion layer at the interface of the two materials. As a result, ZnO@Graphene films with a ratio of 20/80 exhibit a higher resistance baseline than their 5/95 counterparts. This is because the ZnO nanoparticles transfer electrons to the graphene, reducing the concentration of majority carriers in p-type graphene. Consequently, the conductivity of the sensing film is reduced.When ZnO@Graphene is exposed to air, oxygen molecules capture electrons from the valence band and become adsorbed onto the sensor surface (1).2 molecules interact with the sensor surface, two distinct reactions may occur. Firstly, NO2 can adsorb onto the hybrid nanocomposite (2). Secondly, NO2 may interact with adsorbed oxygen species, which can lead to changes in the resistivity of the sensitive film (3). In both cases, NO2 adsorption captures free electrons from the sensitive film, leading to a significant increase in the conductivity [When NOuctivity .2 molecules [However, the p-n junction also enhances the sensor’s sensitivity by expanding the depletion region when the film interacts with NOolecules . Figure It is interesting to note that G95/5 demonstrates a higher sensing performance than G80/20. In other words, a higher content of ZnO does not necessarily translate into better sensitivity, despite the higher flow of electrons from ZnO to graphene. This is probably because a lower amount of nanoparticles brings graphene sheets closer and better interconnected. As a result, the charge transfer is more efficient compared to higher amounts of nanoparticles, which may obstruct the conduction paths along the graphene sheets .2, causing an increase in sensitivity towards the target gas [Finally, it is worth noting that ambient moisture usually has an enhancing effect on the sensitivity of graphene-based sensors . Considerget gas .2 sensing at room temperature. The synthesis method has been already scaled up for the mass production of gas sensitive nanomaterials (from few g up to kg). ZnO(5%)-loaded graphene sensors clearly outperform pure graphene or pure ZnO sensors when operated at room temperature. The loading of the graphene with two different percentages of ZnO has shown that the lower the loading, the better the sensing capabilities of the sensor, since the G80/20 was not capable of detecting NO2 at concentrations lower than 250 ppb. Meanwhile, G95/5 could detect as low as 50 ppb of the target gas. Furthermore, the G95/5 sensor showed very good responses, stability, and reproducibility in the range of 50-250-500 ppb of NO2 The sensing performance becomes significantly enhanced under a humid atmosphere to reach almost five-fold, the response under a dry atmosphere, which makes it a very promising material for NO2 detection for industrial use in farms or factories.This work reports the fabrication of a graphene-based sensor, decorated with ZnO nanoparticles using a novel and patented nanotechnological method for enhanced NO


The implementation of an energy storage system (ESS) as a container-type package is common due to its ease of installation, management, and safety. The control of the operating environment of an ESS mainly considers the temperature rise due to the heat generated through the battery operation. However, the relative humidity of the container often increases by over 75% in many cases because of the operation of the air conditioner which pursues temperature-first control. Humidity is a major factor which can cause safety issues such as fires owing to insulation breakdown caused by condensation. However, the importance of humidity control in ESS is underestimated compared to temperature control. In this study, temperature and humidity monitoring and management issues were addressed for a container-type ESS by building sensor-based monitoring and control systems. Furthermore, a rule-based air conditioner control algorithm was proposed for temperature and humidity management. A case study was conducted to compare the conventional and proposed control algorithms and verify the feasibility of the proposed algorithm. The results showed that the proposed algorithm reduced the average humidity by 11.4% compared to the value achieved with the existing temperature control method while also maintaining the temperature. An energy storage system (ESS) is a system that has the flexibility to store power and use it when required. An ESS can be one of the solutions to mitigate the intermittency effect of variable renewable energy (VRE), such as photovoltaic and wind power ,2,3. An Apart from indoor temperature, indoor humidity is highly correlated with battery safety problems. The Pacific Northwest National Laboratory (PNNL) and International Business Machines Corporation (IBM) consider humidity as an ESS operating environment factor and recommend an appropriate range ,13. In a2 concentration and humidity ratio data, while Tanyer et al. , and P is the ESS output [W]. Equation (2) represents the heat Equation (1) represents the amount of heat When the lumped model is applied to the ESS, the temperature change in the ESS can be expressed using Equation (3).By substituting Equations (1) and (2) into Equation (3), Equation (4) can be obtained as:In Equation (4), s Rth=τC , is usedP ≠ 0, which implies that the ESS is operating. When P = 0, the ESS does not operate, and Equation (5) can be expressed as Equation (7), where Equation (5) can be expressed as Equation (6) when The predicted battery temperature T.The absolute humidity The target absolute humidity As shown in The battery temperature increases as per the operation of the ESS, which affects the battery life. The battery temperature can be predicted using the ESS charging–discharging schedule and environmental temperature.[P1] Measure [P2] Calculate the [C1] Check whether the safety range is maintained by comparing the battery temperature prediction value with the battery temperature upper limit value, [C2] Compare [C3] If [P3] Turn off the air conditioner when additional air conditioning is not required.[C4] When the air conditioner is not being operated, check whether the predicted temperature exceeds the temperature range compared to the upper limit of the temperature maintenance range, [P4] Set the air conditioner set temperature [P5] Set The steps of the rule-based air conditioner control algorithm are as follows:η was 94%, which was set as the average operating efficiency of the ESS calculated for one year. The input parameter setting values for each case are listed in To verify the feasibility of the proposed air conditioner-based indoor temperature and humidity control algorithm, the testing was performed in an ESS container environment. For the ESS used in this study, an operating environment within a temperature range of 18~28 °C and relative humidity of 35~75% is recommended. The case study consisted of two cases depending on the application of the proposed air conditioner control algorithm. Case 1 consisted of an ESS operating environment that considered only conventional temperature control. The experiment was conducted by setting the air conditioner set temperature Each case was operated for one similar consecutive day to verify the effectiveness of the proposed algorithm. While the indoor temperature remained constant during the period of operation, the indoor humidity was divided into approximate ranges of 60–70% and 52–63% during the operation period. This could be caused by the air conditioner operation algorithm related to the indoor and outdoor temperatures and humidity.The cost of maintaining the environment inside the container was mostly used to run the air conditioner. In this study, a sensor-based control system was developed to manage the indoor temperature and humidity of a container-type ESS. The data from the sensors were stored in a database system built to collect and monitor the environmental data of the ESS container. A rule-based air conditioner control algorithm was proposed based on the ESS operation data to solve the problem of the humidity exceeding an appropriate range. The proposed algorithm enhances the operating environment of an ESS container by controlling the operation of commercial air conditioners using thermal and humidity models. Based on the proposed algorithm, the indoor temperature of the ESS container was maintained at 21 °C on average, whereas the relative humidity was suppressed to a value of less than 75%. The average indoor humidity was maintained at 59.4%, which was 11.4% lower than the value achieved with the existing temperature control method. In addition, the standard deviation of the humidity was reduced from 10 to 5.6%. These results indicate that the battery management environment improved, which would consequently slow battery life reduction. In addition, the proposed method can reduce energy consumption by 10.7% compared to the current daily energy consumption without any additional equipment in the case study, resulting in economical operation.The proposed algorithm controls the set temperature of the air conditioner to manage the indoor temperature and humidity of the ESS; therefore, the ESS container operation environment is improved. However, this requires frequent air-conditioner operation to prevent the reverse flow of moisture from the indoor evaporator. If the air conditioner fan control can be combined with the indoor temperature control of the ESS, better results can be obtained. It is assumed that the air conditioner has sufficient capacity to maintain room temperature throughout the year. If the energy management system considers energy consumption for managing the room environment, the battery operation schedule can be changed. In addition, although this study estimated the battery temperature based on the lump-sum capacitance model, advanced battery models can be used to accurately estimate battery temperature.This study sought appropriate technology to economically control temperature and humidity by making the most of the existing facilities. Future research should take into account the combination of sealing, control, and energy consumption in indoor environment management and explore how the optimal operation schedule of batteries can be achieved.


In June 2023, text-based multiple-choice questions were submitted to GPT-4.0 and GPT-3.5. The AAO provides the percentage of humans who selected the correct answer, which was analyzed for comparison. All questions were classified by 10 subspecialties and 3 practice areas . Out of 1023 questions, GPT-4.0 achieved the best score (82.4%), followed by humans (75.7%) and GPT-3.5 (65.9%), with significant difference in accuracy rates . Both GPT-4.0 and GPT-3.5 showed the worst results in surgery-related questions (74.6% and 57.0% respectively). For difficult questions (answered incorrectly by > 50% of humans), both GPT models favorably compared to humans, without reaching significancy. The word count for answers provided by GPT-4.0 was significantly lower than those produced by GPT-3.5 ; however, incorrect responses were longer (P < 0.02). GPT-4.0 represented a substantial improvement over GPT-3.5, achieving better performance than humans in an AAO BCSC self-assessment test. However, ChatGPT is still limited by inconsistency across different practice areas, especially when it comes to surgery.To compare the performance of humans, GPT-4.0 and GPT-3.5 in answering multiple-choice questions from the American Academy of Ophthalmology (AAO) Basic and Clinical Science Course (BCSC) self-assessment program, available at  In order to understand and “generate” human-like language, LLMs are “pretrained” on a substantial volume of text data from the internet, employing a neural network architecture called “Transformer”. The latest versions of the model are GPT-3.5 and GPT-4.0, launched in March 2022 and March 2023 respectively. Both models are available and optimized for natural conversation in the ChatGPT web app; however, GPT-4.0 is currently accessed under a paid monthly subscription2. In the ophthalmology setting, ChatGPT has been tested for triaging patient symptoms3 and for answering patient and care giver questions about specific ocular diseases5. To our knowledge, four papers analyzed the performance of LLMs in answering multiple-choice questions from simulations of board certification exams11, but only one included direct comparison between humans, GPT-3.5 and GPT-4.0 in a limited sample of American Academy of Ophthalmology (AAO) Basic and Clinical Science Course (BCSC) questions9.The growing interest around ChatGPT has led to a considerable number of recent publications investigating the impact of AI language models in the medical environment, from both an educational and a workflow perspectivehttps://www.aao.org/education/self-assessments to all the subscribed members; furthermore, the comparative performance of these three respondents according to different categorization of the questions, including subspecialties and practice areas, was calculated.The purpose of this study was to compare the overall performance of humans, GPT-4.0 and GPT-3.5 on a larger sample of multiple-choice questions of the AAO BCSC self-assessment program available at https://www.aao.org/education/self-assessments) were submitted in June 2023 to ChatGPT, using both GPT-4.0 and GPT-3.5 language models.In this comparative cross-sectional study, the multiple-choice questions included in the self-assessment test from the clinical education section of the AAO official website  Cataract/Anterior Segment, (2) Cornea/External Disease, (3) Glaucoma, (4) Neuro-Ophthalmology/Orbit, (5) Ocular Pathology/Oncology, (6) Oculoplastics/Orbit, (7) Pediatric Ophth/Strabismus, (8) Refractive Mgmt/Intervention, (9) Retina/Vitreous, (10) Uveitis. Additionally, a masked investigator further classified questions according to practice area: (1) Diagnostics/Clinics, (2) Medical treatment, (3) Surgery. Since ChatGPT cannot analyze multimedia content, questions containing images were excluded from the study. Each consecutive multiple-choice question was entered in separate chat-rooms. In almost every response, ChatGPT discussed the topic of the submitted question, and then selected an option. If ChatGPT did not choose any of the provided answers, investigators resubmitted the question in a new chat, adding the prompt “You must choose one” after the question mark, to force a definite choice.Upon submitting the multiple-choice option selected by ChatGPT in the self-assessment test, the correct answer is revealed. Results for humans were obtained from past completed examinations stored in the AAO website, rather than through direct engagement of human participants: after answering each question, the percentage of humans who chose each distinct option are revealed on the website. Regrettably, the AAO does not provide any information about the number of human respondents, their level of education or degree of proficiency in ophthalmology. Questions answered incorrectly by more than 50% of human users were categorized as difficult, while those answered correctly by at least 95% of humans were considered easy.AAO questions, ChatGPT outputs and chosen answers, along with the percentage of humans who selected each multiple-choice option, were recorded. As secondary outcome, the word counts for questions and for responses by ChatGPT were calculated to assess (1) AI verbosity, (2) word count response differences for correct or incorrect answers and (3) correlation between the length of the questions and the length of the discussion provided by the AI.All data were entered into an electronic database via Microsoft Office Excel 365  and analyzed with IBM SPSS Statistics . Ethical approval and informed consent were not required, since the study did not involve human participants.P value < 0.05 was considered statistically significant.The number of correct answers from humans, GPT-4.0 and GPT-3.5 was compared by Chi-square  exempted this study from the requirement for ethical approval.https://www.aao.org/education/self-assessments included 1073 multiple-choice questions; 50 (4.7%) were excluded from the analysis because they incorporated images. In total, 1023 questions were submitted to both GPT-4.0 and GPT-3.5. Table As of June 2023, the self-assessment test at 9. The analysis indicated that to achieve 0.80 power at a 0.05 significance level, the study should include a sample size of 252 questions for comparisons between GPT-4.0 and GPT-3.5, 404 questions for GPT-4.0 vs humans, and a notably larger sample of 5566 questions for GPT-3.5 vs humans.To ensure that our study was adequately powered to detect meaningful differences between groups of responders, we conducted a power analysis based on the percentage of questions answered correctly by GPT-4.0 (84.3%), GPT-3.5 (69.5%) and humans (72.9%) in a previous study by Lin et al., who also employed the multiple-choice questions of the AAO BCSC programP = 0.25). Upon resubmitting the questions with the additional prompt “You must choose one”, a multiple-choice option was always selected.In some cases, ChatGPT did not select an answer on the first query  and GPT-3.5 (P < 0.0001). Conversely, GPT-3.5 compared unfavorably to humans (P < 0.0001).Overall, GPT-4.0 and GPT-3.5 answered correctly to 843 (82.4%) and 674 (65.9%) questions respectively, while the mean percentage of questions answered correctly by humans was 75.7 ± 17.2%, corresponding to 774 ± 176  questions. The number of correct answers by GPT-4.0 was significantly higher than both human users (P = 0.0002). In particular, GPT-3.5 obtained the highest percentage of correct answers in Ocular Pathology/Oncology  and the lowest in Pediatric Ophthalmology/Strabismus . Conversely, GPT-4.0 and humans showed more consistent results, with no significant difference across subspecialty groups (P = 0.15 and P = 0.96 respectively).GPT-3.5 performance was significantly variable across the 10 different subspecialties (P = 0.02) and Oculoplastics/Orbit (P = 0.04). A post-hoc power analysis was performed to check whether the lack of significant differences in other subspecialties could be determined by an insufficient sample size. The power analysis confirmed this suspicion, showing low statistical power for the comparisons in most subspecialty groups.Despite the fact that GPT-4.0 performed better than humans in all subspecialties, the difference was statistically significant only in Glaucoma (P = 0.002), Glaucoma (P = 0.02), Neuro-Ophthalmology/Orbit (P = 0.003) and Pediatric Ophthalmology/Strabismus (P = 0.0004). Table GPT-3.5 showed worse performance, obtaining significantly lower scores than humans in Cataract/Anterior Segment (entclass1pt{minimaP = 0.03 and P = 0.04 respectively). GPT-4.0 and GPT-3.5 obtained the best scores in Diagnostics/Clinics  and the worst in Surgery . Conversely, human users achieved better consistency, with no significant difference across practice areas (P = 0.86). More specifically, GPT-4.0 performed better than humans in Diagnostics/Clinics (P = 0.0002) and Medical treatment (P = 0.08), while GPT-3.5 obtained significantly lower scores than humans in all practice areas . Table GPT-4.0 and GPT-3.5 showed significant difference in performance among practice areas (entclass1pt{minimaentclass1pt{minimaP > 0.05). Difference between GPT-4.0 and GPT-3.5 was also not significant (P = 0.46).In total, 92 out of 1023 (9.0%) questions were answered incorrectly by more than 50% of human users. The majority of these questions were included in the Refractive  and the Ocular Pathology/Oncology  subspecialties. In these difficult cases, the mean percentage of correct answers for humans was 40.1 ± 7.5%. Both GPT-4.0 and GPT-3.5 performed better than humans in these questions , but without reaching significancy , however the difference between GPT-4.0 and GPT-3.5 almost reached a significant level (P = 0.059). Figure Overall, 120 out of 1023 (11.7%) questions were answered correctly by 95% or more humans. To evaluate major mistakes, the number of times ChatGPT chose the wrong answer to these questions was tracked. The mean percentage of correct answers for humans in this subset was 96.4 ± 1.2%; only 2 questions were answered incorrectly by GPT-4.0 (98.3%), and 9 by GPT-3.5 (92.5%). There was no significant difference between both GPT models and humans (P < 0.0001). Furthermore, there was a significant increase in word count in responses generated for questions that were answered incorrectly compared to those that were answered correctly, for both GPT-4.0  and GPT-3.5 . According to univariate analyses, the word count for responses of both GPT-4.0 and GPT-3.5 was positively correlated to the word count of the questions .On average, the word count for answers provided by GPT-4.0 was significantly lower than those produced by GPT-3.5 .This study compared the performance of humans, GPT-4.0 and GPT-3.5 in a large ophthalmology test based on multiple-choice questions. Instead of running a simulation inside the AAO BCSC web app, which includes 260 random questions extracted from a database of 4500 + entries, the more comprehensive self-assessment test available at The difference in correct answers among the three study groups was statistically significant: GPT-4.0 achieved the best score (82.4%), followed by humans (75.7%) and GPT-3.5 (65.9%). Interestingly, both GPT-4.0 and GPT-3.5 showed less consistent results across different practice areas compared to humans. Diagnostics/Clinics was the easiest practice area (83.7% and 68.1% respectively), with Medical treatment following closely (83.4% and 65.1%). Instead, Surgery was the hardest area for both models (74.6% and 57.0%). It is possible to hypothesize that answers for clinical questions are easier to deduct from web resources, while surgery may involve highly specialized techniques and protocols that may not be well-represented in the data AI language models were trained on. Likewise, surgical knowledge often relies on images or videos, which were not included in the training database. Concerning subspecialties, GPT-3.5 also demonstrated significant inconsistencies, whereas GPT-4.0 achieved a degree of consistency much closer to human performance.The performance of GPT-4.0 and GPT-3.5 was further analyzed by considering only difficult questions, defined as questions answered incorrectly by more than 50% of human users. Remarkably, both models performed better than humans in this challenging subset, but the difference was not statistically significant . The outcomes for the easiest questions, defined as those answered correctly by 95% or more humans, was comparable among AI models and human users .Although in multiple-choice questions the word count of ChatGPT responses has no impact on the selection of a correct answer, succinctness still plays a significant role in user experience, especially in the healthcare environment. Clear and concise answers are generally preferred, as long as the complexity of the topic is respected. Overall, the length of ChatGPT answers was directly proportional to word count of the questions, with GPT-4.0 outputs being significantly more succinct than GPT-3.5 (160 ± 56 vs. 206 ± 77 words). On the other hand, responses were significantly longer for questions answered incorrectly in both language models. Although explaining the technical reasons behind this behavior goes beyond the scope of this study, it was observed that excessive verbosity was often associated with evasive and generic responses, hinting at potential uncertainties in the resolution of the questions.9.Other authors have performed similar research to assess the potential of ChatGPT in ophthalmology. Lin et al. compared the performance of GPT-4.0, GPT-3.5 and humans in the setting of the AAO BCSC Self-Assessment Program Simulated Exam (260 questions). The three groups of respondents scored 84.3%, 69.5% and 72.9% respectively. These results are consistent with our findings ; however, Lin et al. marked non-answers as incorrect, while we included the additional prompt “You must choose one” to force an answer. Such difference in design limits the relatability of the comparison between the two studies. Furthermore, Lin et al. took an alternative approach to assess the difficulty of each question: instead of evaluating difficulty based on the percentage of human users who answered correctly to the questions, a masked investigator classified questions as first-order  or higher-order  problem-solving12. A limitation of this study lies in the fact that the AAO self-assessment test exclusively reveals the percentage of humans who selected a certain multiple-choice option, without disclosing the number of respondents and their degree of knowledge. Additionally, the reproducibility of ChatGPT answers was not assessed herein, although a previous study by Antaki et al. reported almost perfect repeatability6.More recently, Moshirfar et al. evaluated the performance of GPT-4.0, GPT-3.5 and humans in answering 467 questions from the StatPearls question bank, respectively scoring 73%, 55% and 58%. While a direct comparison is not completely appropriate, since two different datasets of questions were used, scores for all respondents were lower than what we found . In this study, the difficulty score, ranging from 1 to 4, was provided by StatPearls. Results for humans were not affected by difficulty grading, while GPT-3.5 showed a notable decline in performance as the level of difficulty increased. This trend was much less evident for GPT-4.013. Low quality data may introduce algorithmic bias, possibly reinforcing old, outdated knowledge. Finally, AI can generate hallucinations, consisting of completely fabricated information, factual inaccuracies, logical inconsistencies or nonsensical responses14. Malevolent uses of ChatGPT are also conceivable, posing a serious threat for the integrity of scientific research. For instance, many authors reported how ChatGPT could produce seemingly authentic scientific manuscripts or abstracts, avoiding plagiarism checks18.Despite the great potential of generative AI, many challenges and outstanding issues are emerging. First, the source of the massive amount of data used to train LLMs was not subjected to rigorous quality control, with high risk of false or misleading information. In this regard, it is crucial to understand that ChatGPT does not really “understand” questions; instead, it answers based on probability correlations of text found within the training datasets and statistical patterns5, as well as assisting ophthalmologists in clinical decision-making, research and administration20. Acknowledging ChatGPT shortcomings, such as hallucinations, and potential abuses, will be crucial for the successful integration of this technology in the medical setting21.In conclusion, GPT-4.0 represented a substantial improvement over GPT-3.5, achieving higher performance compared to humans in the self-assessment tests for the American Board of Ophthalmology examination. However, ChatGPT is still limited by performance inconsistency across different practice areas, especially when it comes to surgical/practical knowledge. Currently, the utility of ChatGPT extends beyond that of a comprehensive repository of ophthalmological knowledge. AI based language models demonstrated remarkable capabilities for real-time data interpretation, with the potential to revolutionize teleophthalmology services by providing almost instant responses to patient queries


Caenorhabditis eleganshas been instrumental in studying the underlying mechanisms of TDP-43 pathology. Here, we extend the possibilities of previous studies by examining aC. elegansmodel expressing human wild-typeTDP-43(hTDP-43) pan-neuronally. We show that disease-related (hyper)phosphorylation and cytosolic localisation of hTDP-43 are present in hTDP-43 worms and that these features can be enhanced by adjusting the environmental temperature.Inclusions consisting of transactive response DNA-binding protein 43 (TDP-43) are a characteristic feature of amyotrophic lateral sclerosis (ALS). Caenorhabditis elegansis a suitable model to phenocopy ALS and FTD. Especially the overexpression of mutated forms of TDP-43 inC. eleganshas been shown to govern cellular toxicity and to capture several molecular hallmarks seen in TDP-43C. elegansmodels. However, these molecular features are either absent or less pronounced inC. elegans models expressing wild-type TDP-43C. elegansmodel overexpressing wild-typeTDP-43when the environmental temperature is raised.Transactive response DNA binding-protein 43 (TDP-43) is a highly conserved RNA/DNA-binding protein with a pivotal role in RNA metabolism and homeostasisC. elegansmodel expressing humanTDP-43in a pan-neuronal fashion under the promotor ofsnb-1(hereafter referred to as hTDP-43 worms)Pmtl-2::GFP), worms expressing the same marker were used as controls). The effect of temperature on TDP-43 aggregation has previously been established inC. elegans. Switching the environmental temperature of hTDP-43 worms from 20 °C to 25 °C increases the aggregation propensity of TDP-43 and results in the accumulation of the protein in aggregates).To broadly investigate the proteotoxicity of wild-type TDP-43 at the molecular level, we used a). The increased hTDP-43 levels were, however, mirrored at the transcriptional level for bothhTDP-43 and its promotor,snb-1. Therefore, increased protein levels of hTDP-43 can at least partially be attributed to the temperature-sensitivity of thesnb-1promotor.First, we established the effect of temperature on the levels of hTDP-43. Raising the growth temperature of hTDP-43 worms from 20 °C to 25 °C substantially increased hTDP-43 protein levels . However, when the temperature was raised to 25 °C, hTDP-43 localisation shifted towards the cytosolic compartment. Furthermore, exposing worms to an environmental temperature of 25 °C increased hTDP-43 phosphorylation of residues S409/S410 , a hTDP-43 feature that was nearly absent at 20 °C. The relative insolubility of hTDP-43, on the other hand, was not significantly altered by different growth temperatures . Noteworthy, while hTDP-43 turned more insoluble at 25 °C in absolute measurements  the total proteome solubility was not affected when compared to control conditions . Since TDP-43 has previously been reported to mainly aggregate in the cytosol, the extent of observed TDP-43 aggregation may be underestimated by our whole-cell extract fractionation procedure.Given the notion that cytosolic localisation, phosphorylation, and insolubility are all characteristic hallmarks of TDP-43-proteinopathiesC. eleganscaptures the characteristic molecular hallmarks of TDP-43 proteinopathies especially at higher growth temperatures.Taking everything together, our results show that neuronal expression of wild-type hTDP-43 inStrains and maintenanceC. eleganspropagation at 20 °COP50before being tested, unless stated differently.Standard conditions were used forWestern blotting/SDS-PAGEWorms were collected in PBS supplemented with Complete Protease Inhibitor  and then sonicated for 15 cycles: 30 sec on, 15 sec off. Worm debris was spun down and the protein concentration was determined using the PierceTM BCA Protein Assay Kit  according to manufacturer’s protocol. 20-30µg protein was dissolved in 5x laemmli buffer and boiled at 95 °C for 10 minutes. Subsequently, samples were loaded on a 12% Tris-Glycine acrylamide gel. Gels were transferred to a 0.2µm nitrocellulose membrane  or an activated 0.2µm PVDF membrane . Membranes were blocked with 5% milk in PBS-T (0.1%) for 1 hour. For protein detection the following antibodies were used: anti-tdp-43 ; 1:1000 in 5% milk, anti-tubulin ; 1:10.000 in 5% milk, anti-phospho tdp-43 (S409/410) ; 1:500 in 3% BSA and anti-lmn-1 ; 1:1000 in 5% milk. Membranes were incubated with primary antibodies overnight at 4°C. Membranes were incubated with the secondary antibody (1:10.000) for 1 hour at room temperature (goat anti- mouse IgG (H+L)-HRP conjugate  or goat anti- Rabbit IgG (H+L)-HRP conjugate ). Antibody binding was visualized using Amersham ECL Prime Western Blotting Detection Reagent  and imaged with the ImageQuant LAS4000 Imaging unit .Fractionation2, 0.1 mM EDTA, 350 mM Sucrose, 1 mM DTT, supplemented with protease inhibitors cocktail . Worms were subsequently homogenized using a pellet pestle by applying 5-7 strokes of 1 min followed by 1 min rest on ice between the strokes. Next, worm debris was removed by centrifugation at 500gfor 5 min at 4 °C. The supernatant was collected as used as input of the cell fractionation (25 μl was collected in a separate tube for immunoblotting). Nuclei were separated by spinning the supernatant at 4000gfor 5 min at 4 °C. The supernatant (containing the cytosolic fraction) was removed and subsequently centrifuged at 17.000gfor 30 min at 4 °C. to remove further contaminants. The nuclear pellet was washed two times with hypotonic buffer (and centrifuged at 500g for 5 min at 4 °C) before resuspending it in hypertonic buffer ) – in 1/3 of the volume of the cytosolic fraction (enriching the nuclear fraction 3 times). Finally, the nuclear fraction was treated with 25 U/μl Benzonase (Millipore) for 30 min at 4 °CD1 worms were resuspended in hypotonic buffer . Protein content was measured and equalized to 400 μg using the PierceTM BCA Protein Assay Kit  according to manufacturer’s protocol. A whole cell extract fraction  was taken and subsequently the rest of sample was pelleted by high-speed centrifugation (20.000gfor 25 minutes at 4 °C). The supernatant was set apart  and the pellet was resuspended in lysis buffer and pelleted again as an extra washing step (the supernatant was discarded). Then, the pellet was resuspended in the same volume of lysis buffer with 1% SDS and placed at heating block  for 30 minutes. When the pellet was completely dissolved, the proteins were pelleted by high-speed centrifugation (20.000gfor 25 minutes at 4 °C) to yield a supernatant containing the SDS-soluble fraction. A small fraction was set apart  and the pellet was resuspended and pelleted again as an extra washing step (the supernatant was discarded). Finally, the pellet with SDS-insoluble proteins was solubilized in the same volume of urea buffer  first 1 hour at 60 °C and subsequently overnight at RT in a Thermomixer R (Eppendorf) at 1200 rpm . The I, A, S-fractions were mixed with sample buffer and boiled for 10 minutes at 95 °C. The same volumes of all fractions were separated using SDS-PAGE and visualized with coomassie staining.Worms (D1) were resuspended in lysis buffer ), homogenized by 5-7 20s cycles with a FastPrep-24  at 4 m/s, and clarified by low-speed centrifugation  according to manufacturer’s protocol. The RNA quality and concentration were assessed with a NanoDrop 2000 spectrophotometer (Thermo Scientific). From 1 µg total RNA, cDNA was made using the RevertAid H Minus First Strand cDNA Synthesis kit , using random hexamer primers. The quantitative real-time PCR was performed with a Roche LightCycler 480 Instrument II (Roche diagnostics), using 2 µl of 1:10 cDNA dilution. SYBR green dye  was used for detection of cDNA amplification. Relative transcript levels were quantitated using a standard curve of pooled cDNA samples. Expression levels were normalized to endogenous reference genepmp-3.To assess the gene expression levels, total RNA from Day 1 adultTable 1: Strains usedTable 2: Primers


Multi-parameter control of light is a key functionality to modulate optical signals in photonic integrated circuits for various applications. However, the traditional optical modulators can only control one or two properties of light at the same time. Herein, we propose a hybrid structure which can modulate the amplitude, wavelength and phase of surface plasmon polaritons (SPPs) simultaneously to overcome these limitations. The numerical results show that when the Fermi level of graphene changes from 0.3 to 0.9 eV, the variation of optical transmission, wavelength and phase are 32.7 dB, 428 nm and 306°, respectively. The demonstrated structure triggers an approach for the realization of ultracompact modulation and has potential applications in the fields of optical switches, communications and photo-detection. Effectively manipulating of these parameters at micro/nanoscales is of great significance and has been widely exploited in optical communication8, optical sensing11, photo-detection12 and so on. Surface plasmon polaritons (SPPs), originating from the interaction between free electrons of metal and the electromagnetic waves, hold a more potential applications at micro/nanoscales because they can break the conventional diffraction limit14. Herein, the active control of SPPs has attracted great attention of researchers16.As the essential property of electromagnetic field, polarization, wavelength, amplitude and phase reflect a wealth of applications in optical perception and operation19. For example, Zhuang Ren’s group demonstrated an active and smart electro-optic THz modulator, which is based on a strongly correlated electron oxide vanadium dioxide (VO2). With milliampere current excitation on the VO2 thin film, the transmission, reflection, absorption and phase of THz waves can be modulated efficiently. In particular, the antireflection condition can be actively achieved and the modulation depth reaches 99.9%, accompanied by a 180° phase switching20. Ali Forouzmand’s group proposed an electrically tunable amplitude and phase modulators, which are designed by the hybridization of indium tin oxide (ITO) into a guided-mode resonance mirror. A gate-tunable amplitude modulator with a modulation depth as high as ∼ 0.80 is realized, and the phase variation of ∼ 210° is accomplished when the applied bias voltage alters from − 15 V to + 24 V21. However, these proposed modulators can only control one or two parameter of light despite its superior performance, which greatly hinders their widely application23. Herein, a modulator which can control more properties of light will attract much attention of experts.Currently, much work has been done in multi-parameter modulation to meet the increasing demand for various application scenarios25.In this paper, a hybrid silicon-dielectric-graphene-grating structure is proposed, which can modulate the amplitude, wavelength and phase of SPPs simultaneously. The SPPs is stimulated by the grating and propagating on the graphene, the Fermi level of the graphene is controlled by the voltage applied between silicon substrate and graphene. The numerical results show that when the Fermi level changes from 0.3 to 0.9 eV, the variation of optical transmission, wavelength and phase are 32.7 dB, 428 nm and 306°, respectively. This structure has the potential application in the fields of optical switches, communications and photo-detection2) exhibit an excellent performance such as low leakage current, high dielectric strength and low amounts of defects, we select the CaF2 as the dielectric layer, which could improve the performance of modulator effectively26.In Fig. 0 and ɛr are the charge constant, the permittivity of vacuum and the relative permittivity of CaF2 respectively, V and d are the voltage and thickness of CaF2. The relation between Fermi level and the carrier density is presented in Eq. , and the two-dimensional 2D) simulation model is shown in Fig. D simulatFor the model of anisotropic graphene, its out-of-plane permittivity is set to 2.5, and the in-plane conductivity can be obtained from random-phase approximation, including the effect of finite temperature (T = 300 K):g and kB are the conductivity of graphene and the Boltzmann’s constant. Ef and ɷ are the Fermi energy level and radian frequency, respectively. The carrier relaxation time τ = μEf/evf2, where the Fermi velocity vf = 106 m/s and carrier mobility μ = 10,000 cm2/(V s). The effective permittivity of graphene ɛg can be described by means of the following expression:0 and tg = 0.33 nm represent the vacuum permittivity and thickness of graphene respectively27.Here σFigure 5 V/m, while it is 1.37 × 105 V/m when the Fermi level increases to 0.6 eV. The electric field intensity reaches 1.53 × 105 V/m as the Fermi level increases to 0.8 eV, and it has changed about 23.3%. It can be concluded that with the increase of the Fermi level, the amplitude of the SPPs increases gradually.Figure Figure 32. The wavelength changes by 428 nm when the Fermi level of graphene increase from 0.3 to 0.9 eV (226 nm at 0.3 eV while 654 nm at 0.9 eV). The results verify that the wavelength of the SPPs can also be modulated by the demonstrated structure effectively.Figure Figure d is 1.6 μm and 1.8 μm, respectively (the operating wavelength is fixed at 8 μm). The phase is − 139° when the Fermi level of graphene is 0.3 eV, and the phase is 167° when the Fermi level increases to 0.5 eV at the propagation length of 1.6 μm. The phase changes at right port changes can reach to 306°. . Thus, the amplitude, wavelength and phase of propagating SPPs in our model can be simultaneously controlled through the Fermi level of graphene by changing the applied voltage, which will have a potential application in modern integrated optics and communication33.The phase of SPPs can be affected by both the refractive index and the propagation length of graphene due to the change of optical path. Figure In conclusion, we have demonstrated a silicon-dielectric-graphene-grating hybrid structure. The amplitude, wavelength and phase of SPPs can be modulated simultaneously when the SPPs is propagating along the graphene. The numerical results show that when the Fermi level of graphene changes from 0.3 to 0.9 eV, the optical transmission, wavelength and phase are changed by 32.7 dB, 428 nm and 306°, respectively. The realization of multi-parameter modulation in SPPs will have a promising application in the field of optical communication, sensor and photo-detection.2 is set as 6.76. The mesh size is 1/5 of one wavelength.The simulation has been performed using the commercial finite element method (FEM), trial version of software “COMSOL Multiphysics 5.5”. In simulation, the scattering boundary condition and user-defined port are used. The module of radio frequency is used to investigate the relationship between voltage and optical transmission, wavelength and phase. The surface conductivity model and the transitional boundary condition are used in the model of graphene. In addition, the permittivity of CaF


Inflammatory breast cancer (IBC) is one of the most lethal subtypes of breast cancer (BC), accounting for approximately 1–5% of all cases of BC. Challenges in IBC include accurate and early diagnosis and the development of effective targeted therapies. Our previous studies identified the overexpression of metadherin (MTDH) in the plasma membrane of IBC cells, further confirmed in patient tissues. MTDH has been found to play a role in signaling pathways related to cancer. However, its mechanism of action in the progression of IBC remains unknown. To evaluate the function of MTDH, SUM-149 and SUM-190 IBC cells were edited with CRISPR/Cas9 vectors for in vitro characterization studies and used in mouse IBC xenografts. Our results demonstrate that the absence of MTDH significantly reduces IBC cell migration, proliferation, tumor spheroid formation, and the expression of NF-κB and STAT3 signaling molecules, which are crucial oncogenic pathways in IBC. Furthermore, IBC xenografts showed significant differences in tumor growth patterns, and lung tissue revealed epithelial-like cells in 43% of wild-type (WT) compared to 29% of CRISPR xenografts. Our study emphasizes the role of MTDH as a potential therapeutic target for the progression of IBC. Inflammatory breast cancer (IBC) is a very aggressive and rare subtype of breast cancer (BC) with a poor prognosis and accounts for 1–5% of all cases of BC. The 5-year survival rate for people with IBC is 41% compared to non-IBC (nIBC) ,3. HowevPreviously, our group characterized the cell surface proteome of IBC. We identified metadherin (MTDH) overexpression in IBC cell lines vs. non-cancerous mammary epithelial cells, and nIBC cell lines . SubsequMetastasis is the leading cause of death in BC patients . IBC patThis study investigates the role of MTDH in IBC by assessing its function in cell proliferation, migration, tumor spheroid formation, signaling of oncogenic pathways, tumor progression, and metastasis. We develop isogenic models of the SUM-149 and SUM-190 IBC cell lines using MTDH-CRISPR/Cas9 base knockout for in vitro and in vivo studies. To validate the oncogenic role of MTDH, we also overexpressed MTDH in the non-cancerous mammary epithelial cell line MCF-10A. Our findings show that the absence of MTDH significantly decreases IBC cell proliferation, migration, invasion, and tumor spheroid formation and decreases the expression of various signaling molecules. In addition, we demonstrate that MTDH affects invasion and metastasis. To our knowledge, this study is the first to assess the role of MTDH in the invasion and metastasis of IBC. Thus, this study provides significant new evidence for the field of IBC, contributing to studying possible new targets involved in IBC progression.In a previous study, we demonstrated that MTDH is overexpressed in IBC cells and patient tumor tissues compared to normal tissues and in the tumor emboli. Furthermore, we reported that MTDH overexpression was found in the plasma membrane of IBC cells, specifically in SUM-149 cells, compared to the non-cancerous mammary epithelial cell line, MCF-10A . MoreoveSeveral publications have highlighted MTDH as an important target in the treatment of BC, including its role in cancer onset and progression ,32. TherPrevious studies have shown that IBC cells can spontaneously form 3D tumorspheres in vitro ,34,35. TMTDH overexpression has been associated with increased cancer cell metastasis . TherefoSTAT3 and NFKB1 genes in both IBC cell models. For SUM-149, there is a trend (p = 0.10) of STAT3 downregulation (−2.00 f.ch.) in sg-MTDH cells. Moreover, the expression of the NFKB1 gene is significantly reduced by −2.11 f.ch.  and MCF-10A cells (Ctrl and OE-MTDH) cells C. DensitMTDH has been correlated with BC progression and poor overall survival in patients , but itsFirst, the results showed that the health of the mice was not compromised by the absence of MTDH A. A reduTo validate that SUM-149 sg-MTDH cells remained with a low expression of MTDH after 10 weeks of study, we performed a qPCR for MTDH gene expression. A significant reduction (−5.52 f.ch.) was observed E in mousHigh expression of MTDH has been associated with tumor metastasis, decreased survival outcomes, and higher mortality in female reproductive cancers such as breast, ovarian, and cervical . We prevOur results demonstrated that MTDH depletion decreased the proliferation of the TN and HER2+ IBC models and their ability to form colonies. At the same time, MTDH overexpression promoted the proliferation of non-cancerous mammary cells. Although MTDH has been correlated with BC proliferation by showing high levels of Ki-67 in tissues, previous studies demonstrated that knocking down of MTDH did not affect the proliferation of MDA-MB-231-LM2 cells, a subcell line of nIBC with a high propensity to lung metastasis ,46,47, iHerein, we also established that depletion of MTDH decreased the migration and invasion of IBC cells while overexpression of MTDH promoted the migration of non-cancerous cells. Studies demonstrate that MTDH is relevant in cancer cell invasion, since Matrigel invasion assays have shown that different cancer cells, such as hepatocellular carcinoma and glioma cells, display an increased invasive ability through MTDH overexpression . AdditioNFKB1) and the expression of NF-κB, AKT, ERK, and STAT3 in a TN IBC model. In a HER2+ IBC model, no changes in gene expression were observed and only a down-regulation of NF-κB, AKT, and ERK proteins was detected. The contrasting results in gene expression between the IBC models could be explained by previous studies which have demonstrated that the amplification or overexpression of HER2 promotes the activation of NF-κB and STAT3 in contrast to TNBCs  as described in [Female severe combined immunodeficient mice (SCID)  between 21 and 28 days of age were housed under specific pathogen-free conditions. Mice received an irradiated AIN 76-A phytoestrogen-free diet  and water ad libitum. To test the effects of MTDH silencing in IBC tumor formation and progression, we injected 1.5 × 10ribed in ,73. Grouribed in . At the TM, General Data Company Inc., Cincinnati, OH, USA). Sectioning was performed using a Leica CM 1860 cryostat  by cutting 8 μm sections of the left and right lung of mice injected with WT SUM-149 or MTDH-edited cells (n = 7/group). The sections were mounted on slides with a subbing solution as described in [Sectioning: The excised lungs were left in 10% neutral-buffered formalin (NBF) overnight and then immersed in a solution of 20% sucrose in 1X PBS. The lungs were then placed in optimal cutting temperature compound (OCT) . A pathologist analyzed H&E-stained slides and the micrographs were captured at a magnification of 20×. Hematoxylin & Eosin (H & E) staining: First, tissues were stained with Mayer hematoxylin solution (Sigma-Aldrich) for 10 min at RT and then rinsed with running tap water for 10 min until the water was colorless. The tissues were then rinsed in 95% alcohol for 10 s. Then, the staining was performed with Eosin Y with Phloxine B (Sigma-Aldrich) solution for 30 s and rinsed in ascending series of ethanol . Finally, tissues were processed in xylene 1 min twice and mounted using VectaMountp-value for the in vitro studies was calculated using Student’s t-tests, ordinary-one way or two-way analysis of variance (ANOVA) with the Bonferroni multiple comparison test. Gene expression studies for each cell line or tumor were evaluated using the 2−ΔΔCt formula by comparing their relative gene expression to the reference genes. Data are expressed as Mean ± S.E.M. Each experiment was carried out in three or more independent biological replicates. Statistical analyses were performed using Graph Pad Prism v.9.0 , and differences were considered significant when p < 0.05.The In vivo studies: Summary statistics were performed to describe the tumor volume and weight. The normality evaluation was performed using the Shapiro–Wilk test. The presence of outliers was verified using a generalized extreme studentized deviation (ESD) test. Mean changes were evaluated over time using a general linear model with repeated measures. Model diagnostics were performed as well, i.e., Cooks distances and DF betas were ascertained to access influential values. A pairwise comparison was performed to determine the difference between the groups and each week. Only if the data did not follow a normal distribution, Wilcoxon–Mann–Whitney tests were performed. The significance level (α) was set to 0.05. Analyses were performed using the statistical software R version 4.1.0.Our findings identify for the first time a potential role of MTDH in promoting IBC proliferation, migration, invasion, and metastasis by regulating NF-κB and STAT3. Furthermore, we showed that MTDH plays a role in the invasive capacity of IBC promoting metastasis to the lung. Altogether, these findings open the possibility of a new target to better understand the molecular biology of IBC. MTDH has the potential to serve as a diagnostic target to diagnose and treat the progression of this deadly disease in IBC patients.


Transactive response element DNA/RNA-binding protein 43 kDa (TDP-43) is the causative protein of amyotrophic lateral sclerosis (ALS); several ALS-associated mutants of TDP-43 have been identified. TDP-43 has several domains: an N-terminal domain, two RNA/DNA-recognition motifs, and a C-terminal intrinsically disordered region (IDR). Its structures have been partially determined, but the whole structure remains elusive. In this study, we investigate the possible end-to-end distance between the N- and C-termini of TDP-43, its alterations due to ALS-associated mutations in the IDR, and its apparent molecular shape in live cells using Förster resonance energy transfer (FRET) and fluorescence correlation spectroscopy (FCS). Furthermore, the interaction between ALS-associated TDP-43 and heteronuclear ribonucleoprotein A1 (hnRNP A1) is slightly stronger than that of wild-type TDP-43. Our findings provide insights into the structure of wild-type and ALS-associated mutants of TDP-43 in a cell. Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by motor neuron dysfunction and muscle atrophy and is thus classified as a motor neuron disease (MND). A common feature of ALS is the formation of inclusion bodies (IBs) containing proteins and often RNA in the cytoplasm and nucleus of motor neurons . VariousTARDBP) that cause amino acid substitutions have been identified [TDP-43 is the major disease protein in ALS as well as frontotemporal dementia (FTD) [entified ,6. Theseentified ,7. TDP43entified . TDP43 hentified ,10,11. Tentified ,13; howeentified ,15. Furtentified . HoweverFörster resonance energy transfer (FRET) is a physical mechanism that describes the energy transfer from the excited state of a donor fluorophore to the unexcited state of an acceptor fluorophore without the emission of donor fluorescence. The FRET efficiency mainly depends on three conditions: (i) displacement between the donor and acceptor fluorophores (within ~10 nm); (ii) spectral overlap between the donor fluorescence spectra and acceptor excitation spectra; and (iii) transition dipole moments of the respective fluorophores . FRET isFluorescence correlation spectroscopy (FCS) has been widely used to determine the diffusion coefficient of fluorescent molecules, not only in solution but also in live cells with single-molecule sensitivity ,23,24. TIn this study, we describe the structural basis of TDP-43 using FRET and FCS in live cells. The ALS-associated mutants of TDP-43 have a different conformation than their wild-type. The typical distance between the N- and C-termini and the molecular shape of TDP-43 in live cells was assessed. Furthermore, the interaction between ALS-associated TDP-43 and hnRNP A1 may be stronger than that with wild-type TDP-43.Clavularia-derived monomeric teal fluorescent protein (mTFP1) as a FRET donor was tagged with TDP-43 at the N-termini, and a monomeric yellow fluorescent protein (Venus) as a FRET acceptor was tagged at its C-termini  (EFRET) between the N- and C-terminal fluorophores in the nucleoplasm was measured by the fluorescence recovery of the donor fluorophore after acceptor photobleaching. To minimize the influence of intermolecular FRET of condensed TDP-43 in the nuclear bodies, the regions excluding the nuclear bodies and nucleoplasm were measured. EFRET between the independently diffused mTFP1 and Venus monomers as a negative control was not observed (EFRET of the wild-type and ALS-associated mutants (A315T and Q331K) of T43V, T43V173, and T43V195 were statistically positive .To examine the structural basis of TDP-43, FRET was used, which was altered by the relative distance and orientation between the donor and acceptor fluorophores. A ed T43V) b. To chaed T43V) a,b. The ed T43V) c. T43V wobserved d, lane 1observed , two freEFRET obtained in this study ranged from approximately 1.5% to 7.8%  is 0.67 [dT43V) was calculated as 9.6 nm using its EFRET  (Equation (4) in Materials and Methods). The assumption for the κ2 is not precise here because the EFRET was changed when substituting the circularly permutated acceptor  have been determined [dNC using the full-length TDP-43 structure predicted by AlphaFold2 as a reference, while the structure of the IDR is most likely not highly precise. In this structure, the dNC value of TDP-43 was determined to be 6.8 nm. Since fluorescent proteins are tagged in T43V, their tagging effects on distance must be considered. From the crystal structures of mTFP1 and Venus [dNC of TDP-43, the distance between the fluorophores is estimated to be 11.1 nm; the actual distance between the fluorophores is likely to be shorter than this value because the N- and C-termini of fluorescent proteins are unlikely to be in the most distant (opposite) orientation. In contrast, if the distance between the fluorophores is assumed to be 6.8 nm , the κ2 is calculated as 0.08 when the EFRET is 5.3%. This κ2 is so close to zero, which is probably not supposed to be the case because this occurs only when the orientation between the fluorophores is highly restricted by strong steric hindrance between the N- and C-termini, such as in membranous proteins with no dramatic structural changes. The EFRET changed according to the relative dipole orientation of the fluorophores by substituting the circularly permutated acceptors  is a fully extended linear chain, its length would reach ~50 nm. This is much longer than the longest distance between these fluorophores estimated from the EFRET when the κ2 is 4 (12.9 nm), suggesting that the IDR of TDP-43 is not a stretched structure in the cells. On the other hand, the AlphaFold2-predicted dNC of the N-terminal domains of TDP-43, including NTD and RRM1&2 , was 7.2 nm. This was longer than the dNC of an intact TDP-43, as predicted by AlphaFold2 (6.8 nm). However, if the IDR adopts a conformation that turns from the C-terminus of NTD262 and is close to the N-terminus, the dNC of a full-length TDP-43 can be shorter than that of NTD262. Therefore, the EFRET observed here is not an inconsequential value, and dT43V could be estimated in the range of approximately 6.8–12.9 nm . Furthermore, the distance between the fluorophores of the T43V mutants associated with ALS (A315T and Q331K) would not be significantly different from that of the wild-type because the EFRET values were similar.The positive  to 7.8% d. To det is 0.67 ,29. Usinacceptor d; howevetermined ,30,31,32nd Venus ,34, the cceptors d; moreov2/s, respectively  carrying the A206K mutation (pTDP-43WT-eGFP), eGFP monomers (pTKbX-meGFP), iRFP713-tagged Histon H2B (pBOS-H2B-iRFP), and pCAGGS were described previously . The pla5 N2a cells were transferred to a glass-bottom dish  for live cell imaging and FCS in the live cells, or a 35 mm plastic dish  for cell lysis. Plasmid DNAs were transfected using Lipofectamine 2000 (Thermo Fisher Scientific), according to the manufacturer’s protocol. To express the fluorescent protein-tagged TDP-43, a plasmid mixture with 0.3 mg of the one coding fluorescent protein-tagged TDP-43 and 0.7 mg of pCAGGS as a mock vector was used for the transfection. When iRFP-tagged H2B was co-expressed, 0.7 mg of pBOS-H2B-iRFP was added instead of pCAGGS to the plasmid mixture. To express monomeric fluorescent proteins, a plasmid mixture with 0.1 mg of the plasmid coding monomeric fluorescent proteins, and 0.9 mg of pCAGGS was used. To express GFP-tagged TDP-43 (43G) and mCherry-tagged hnRNPA1 (RA1), a plasmid mixture with 0.75 mg of pTDP-43WT-eGFP, 0.15 mg of pmCherry-hnRNPA1, and 0.1 mg of pCAGGS was used. To express the eGFP-tagged 274–414 amino acids of TDP-43 and mCherry-tagged hnRNPA1, a plasmid mixture with 0.8 mg of peGFP-C1-C274, 0.15 mg of pmCherry-hnRNPA1, and 0.05 mg of pCAGGS was used. Following overnight incubation, the transfection medium was replaced with the same medium that was used just before the observation for live cell imaging and FCS measurements in the live cells.The murine neuroblastoma Neuro-2a (N2a) cells were obtained from the American Type Culture Collection  and maintained in DMEM , which was supplemented with 10% FBS , 100 U/mL penicillin G (Sigma-Aldrich), and 0.1 mg/mL streptomycin (Sigma-Aldrich) as previously described ,39. One Confocal images were acquired using a laser scanning microscope system LSM 510 META + ConfoCor 3 (Carl Zeiss) equipped with a C-Apochromat 40×/1.2NA W Korr. UV-VIS-IR water immersion objective. To observe the nuclear localization of both T43V and H2B-iRFP, mTFP1, Venus, and mCherry were sequentially excited at 458, 514, and 594 nm, respectively. The excitation and emission waves were separated using the main dichroic beam splitter HFT458/514 for mTFP1 and Venus, and HFT488/594 for iRFP. To spectrally separate the fluorescence of mTFP1 and Venus, a secondary dichroic mirror NFT515 was used to split the fluorescence emission at 515 nm. mTFP1 fluorescence was detected after passing through a 470–495 nm band-pass filter (BP470-495), Venus fluorescence was detected after passing through a 530–575 nm band-pass filter (BP530-570), and iRFP fluorescence was detected after passing through a 655 nm long pass filter (LP655). The pinhole size was set as 92 mm. Fluorescence was detected using an avalanche photodiode (APD) detector. To observe colocalization between 43G and RA1, eGFP and mCherry were sequentially excited at 488 and 594 nm, respectively. The excitation and emission lights were separated using the main dichroic beam splitter (HFT488/594). To spectrally separate the fluorescence of eGFP and mCherry, a secondary dichroic mirror NFT600 was used to split the fluorescence emission at 600 nm. eGFP fluorescence was detected after passing through a 505–540 nm band-pass filter (BP505-540), and mCherry fluorescence was detected after passing through a 655 nm long-pass filter (LP655). The pinhole size was set as 71 mm. Fluorescence was detected using APD detectors.Confocal images were acquired using a laser scanning microscope system LSM 510 META (Carl Zeiss) equipped with a C-Apochromat 40×/1.2NA W Korr. UV-VIS-IR water immersion objective. mTFP1 and Venus were sequentially excited at 458 and 514 nm, respectively. The excitation and emission lights were separated using a main dichroic beam splitter (HFT458/514). Fluorescence was detected using a spectrometric multichannel detector; a range of 462–483 nm was collected for mTFP1 and 516–537 nm for Venus. The pinhole size was set as 248 mm. Fluorescence images were acquired by repeatedly irradiating a 514 nm laser at the maximum power after acquiring a single frame of the images. The FRET efficiency was calculated using the following equation:EFRET is the FRET efficiency; In Equation (1), R0) was calculated using the following equation:R0 is the distance at which the energy transfer efficiency is 50% [QD is the fluorescence quantum yield of the donor in the absence of an acceptor, n is the refractive index of water, NA is the Avogadro constant, and J is the spectral overlap integral calculated using the following equation:The Förster distance between mTFP1 and Venus , dT43V) was calculated using the following equation:The distance between the fluorophores of wild-type T43V  and a protein data bank. The end-to-end distance (dNC) was the alpha carbon of the first methionine and the 414th methionine or 262nd proline in the predicted structure of TDP-43. The distance between the N- or C-termini and the fluorophore in the fluorescent proteins was between the fluorophores and the alpha carbon of the 6th glycine at the N-terminus and the 221st asparagine at the C-terminus of mTFP1 (PDF#2hqk) or the 1st methionine at the N-terminus and the 230th tyrosine at the C-terminus of Venus (PDB#1myw).PyMol Ver. 2.5.0  was used to display and determine the distance between specific atoms in the models. The structure coordination files of TDP-43 and the fluorescent proteins were obtained from the AlphaFold2 server  equipped with a C-Apochromat 40×/1.2NA W Korr. UV-VIS-IR water immersion objective, multi-line argon ion laser, and APD detectors. GFP and Venus were excited using the 488 nm laser line of the argon ion laser. Fluorescence was detected after passing through a 505 nm long-pass filter (LP505) using an APD detector. The pinhole was set to 70 mm. Autocorrelation functions were calculated for different lag times (t) and plotted as a function of the lag time to derive the corresponding autocorrelation curves. The autocorrelation curves were further analyzed by fitting a theoretical function for the three-dimensional diffusion of one component with blinking due only to equilibrium between the dark and bright states (Equation (5)) using the ZEN (Carl Zeiss) software:G(τ) is the cross-correlation function; FFast and FSlow are the fractions of the fast and slow components, respectively ; N is the average number of fluorescent molecules in the effective detection volume; s is the structure parameter representing the ratio of the radial (w0) and axial (z0) 1/e2 radii of the effective volume; T is the exponential relaxation fraction; and s) were determined using 0.1 mM ATTO488 (A488) as a standard. The diffusion coefficient of eGFP was calculated using the following equation:D is the diffusion coefficient of the samples, DA488 is the diffusion coefficient of A488 (400 2/s), and To measure the diffusion coefficient in the live cells and cell lysates, FCS measurements were performed. For live cell measurements, the N2a cells transiently expressing 43G, T43V, or eGFP monomers were measured at 37 °C with 5% COIn Equation (7), DTheo), using the estimated molecular weight from the amino acid composition, was calculated using the following equation:The theoretical diffusion coefficient (In Equation (8), sing FCS .To obtain the cell lysates, N2a cells transiently expressing Venus, wild-type T43V, and A315T/Q331K mutants of T43V were washed in PBS and thereafter lysed in a buffer containing 25 mM HEPES-KOH (pH 7.5), 500 mM NaCl, 1% NP-40, 1% sodium deoxycholate, 1% SDS, 0.01 U/mL benzonase, and 1% protease inhibitor cocktail (Sigma-Aldrich) (Buffer H). FCS measurements of Venus and Venus-tagged TDP-43 in the cell lysates were performed using an LSM510 META ConfoCor2 system  equipped with a C-Apochromat 40×/1.2NA W Korr. UV-VIS-IR water immersion objective, multi-line argon ion laser, and APD detectors. Venus was excited using the 514 nm laser line of an argon ion laser. Fluorescence was detected after passing through a 530–610 nm band-pass filter (BP530-610) using an APD detector. The pinhole was set to 70 mm. The autocorrelation functions were analyzed in the same manner as those of live cells.To obtain the cell lysates, N2a cells transiently expressing fluorescent proteins were washed in PBS and lysed in a buffer containing 50 mM HEPES-KOH (pH 7.5), 150 mM NaCl, 0.1% NP-40, and 1% protease inhibitor cocktail (Sigma-Aldrich). The interaction in highly concentrated salt was measured after adding high concentrations of NaCl . RNase A  using an APD detector. The mCherry fluorescence was detected after passing through a 655 nm long-pass filter (LP655) using an APD detector. The pinhole was set to 70 mm. The auto- and cross-correlation curves were further analyzed by fitting a theoretical function (Equation (5)) using the ZEN (Carl Zeiss) software. The RCA was calculated according to a previously reported procedure .t-tests were calculated using Microsoft Excel.The Student’s In this study, we estimated the molecular shape of TDP-43 in live cells by using diffusion states and FRET measurements. The FRET analysis showed that the end-to-end distance of wild-type TDP-43 was approximately 10 nm. This estimation was not very different in the ALS-associated mutants (A315T and Q331K); however, the relative orientation of the dipole moment between the fluorophores suggests that the A315T and Q331K mutants of TDP-43 may have different conformations in live cells. Consequently, the different conformations of the ALS-associated mutants of TDP-43 may be due to a conformational change in the IDR-carrying mutation. Furthermore, the difference in IDR-mediated interactions of TDP-43 with cellular proteins, such as hnRNP A1, may be involved in the N- and C-terminal conformational changes in the ALS-associated mutants of TDP-43.


We analyzed the X-ray photoelectron spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly accurate spectral decomposition of the XPS spectra, we proposed a framework for discovering physical constraints from the absence of prior quantified physical knowledge, in which we designed the prior probabilities based on the found constraints and the physically required conditions. This suppresses the exchange of peak components during replica exchange Monte Carlo iterations and makes possible to decompose XPS in the case where a reliable structure model or a presumable number of components is not known. As a result, we have successfully decomposed XPS of one monolayer (1ML), two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene samples deposited on SiC substrates with the meV order precision of the binding energy, in which the posterior probability distributions of the binding energies were obtained distinguishably between the different components of buffer layer even though they are observed as hump and shoulder structures because of their overlapping. Especially, due to the short escape depth of photoelectrons excited by soft X-rays, XPS has been applied to various surface and interface analysis such as film thickness3, chemical states at surfaces or interfaces6, and atomic distortion at the interface7. The elemental information measured by XPS is revealed by line-shape analysis for the obtained spectrum. Although the regression analysis of XPS spectra is a non-linear regression, the least squares method has been used to minimize the fitting error until now. In an analysis using the least-squares method, it is not always possible to obtain a globally optimal solution, and its reproducibility is frequently low because it depends on the initial values. It is also difficult to incorporate known physical property information such as constraints on the parameters, and it is impossible to evaluate the accuracy of the parameter estimation. In the analysis of XPS, it is essential to develop a highly reproducible analysis method that properly incorporates known physical property information9. In this paper, to solve these problems, we apply Bayesian spectroscopy to analyze the XPS spectra of one monolayer (1ML), two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene layers grown on SiC substrates and attempt to extract changes in the chemical states of the graphene layers by chemical modification at the interface.X-ray core-level photoelectron spectroscopy (XPS) is a popular and powerful tool for investigating the elemental composition of materials5. Riedl et al. proposed that graphene C 1s spectra have four peaks, in addition to SiC and graphene (Gr), two additional components S1 and S25. S1 comes from the C atoms bound to one Si atom on the surface of SiC(0001) and to three C atoms in the buffer layer. S2 comes from the remaining sp10, O12, Ge13, Si14, Au15 and Bi16 have been intercalated between the buffer layer and SiC. When atoms are intercalated beneath the buffer layer, dangling bonds of Si are terminated by intercalated atoms to break covalent bonds between the buffer layer and SiC. Since the charge transfer into the graphene layer is also modified by the intercalation, the charge neutrality level can be controlled around the Dirac point artificially. However, the transport properties are often degraded after the interface modification. To enhance the transport property of graphene, precise control and characterization of the chemical states should be performed at the intercalated interface. The bonding at the modified interface would differ depending on the intercalated atoms and on the treatment conditions.High-quality, large-scale graphene can be formed by thermal decomposition of SiC at elevated temperatures. It is known that buffer layer are formed between graphene and the SiC substrate and have an equivalent structure to graphene, although the buffer layer adhere strongly to the SiC substrate, while dangling bonds remain with the SiC substrate due to lattice mismatchIn such cases, the reliable structure model for the intercalated interface would often be lacking. Thus, an alternative method is strongly required to decompose core-level photoelectron spectra even in the case where a reliable structure model or a presumable number of components is not known. However, we have been forced to analyze the lineshape with the number of components and the constraint parameters assumed to validate a plausible structure and the experimental setting. Thus, there might be a concern of containing subjective and empirical arbitrariness in the fitting results in the XPS spectra. In addition, if the line-shape analysis was treated as a black-box tool due to its complexity, that would yield incorrect results. Therefore, a reproducible and reliable approach without arbitrariness is required for XPS spectral analysis.17. 3d core-level XPS spectra of 17. In Japan, efforts to realize spectral decomposition based on Bayesian inference have begun in synchrotron radiation facilities18.Recently, the result of Bayesian spectral deconvolution for core-level XPS has been reported to realize automatic analysis of core-level XPS spectra by incorporating the effective Hamiltonian into the stochastic model of spectral deconvolution9. However, even in such a situation, scientists can achieve high-precision spectral analysis by the following scenario. They first analyze the spectral data without assuming prior knowledge. By reviewing the results of this analysis, they quantify physical constraints that were previously unnoticed or unquantifiable. Utilizing such knowledge, for example, by constraining the range of regression parameters, they can achieve the analysis of spectral data with the desired accuracy. The purpose of this study is to propose a framework for incorporating this natural flow of spectral data analysis conducted by scientists into Bayesian spectroscopy20.The difficulty in performing such Bayesian inference is in designing the prior probability distribution. Since prior probability distributions can restrict the range of parameters, if the constrain condition for their parameters is known in advance, it can be incorporated into the prior probability distribution. Otherwise, a distribution that does not affect parameter estimation, such as a wide uniform distribution, is used as the prior probability distribution. In that case, the posterior probability distribution may exhibit multimodality due to exchange among spectral components. As a result, parameter estimates are not precise, are poorly reproducible, and may not be physically reasonable. When analyzing the XPS spectra, it is important to overcome these difficulties and quantify knowledge of the target materialIn this article, core-level spectra in both pristine and oxygen-intercalated graphenes grown in SiC(0001) have been analyzed by Bayesian spectroscopy with constraints on the values of the spectral parameters based on knowledge of the physical properties.n-doped 6H-SiC(0001) using the face-to-face method21, where two SiC substrates were placed one on top of the other with a gap of 20 22. The core-level and valence-band photoelectron spectra were measured using a photon energy of 680 and 40 eV, respectively. The Fermi energy and the energy resolution were confirmed by measurements for the Fermi level of the gold reference. The overall energy resolutions were estimated to be 0.69 and 0.04 eV for core-level and valence band measurements, respectively.Graphene layers were grown on 23 for the 1ML and 2ML samples, which are shown in red for the SiC substrate, blue for graphene (Gr), green for S1 and magenta for S2, respectively. Although the peak positions are shifted about 0.2 eV comparing the vertical dashed lines with the peak positions observed in Fig. Figure 23) gives the strongest peak at 283.8 eV, and the peak structures of the graphene (284.67 eV23) and of the two components S1 (285.04 eV23) and S2 (285.53 eV23) for buffer layer atoms are observed as broad peaks from 284.5 to 286.0 eV without separation. In the 2ML sample shown in Fig. 23) gives a second intense peak at 283.3 eV, which is the almost same as that of 1ML. However, the spectral structure in the high-binding energy region changes markedly, giving a dominant peak at 284.8 eV. Although this peak is considered to be chiefly attributed to graphene (284.56 eV23), and also includes the components of the buffer layer S1 (285.01 eV23) and S2 (285.50 eV23) because it has a shoulder structure on the high energy side.In the 1ML sample depicted in Fig. 24.The surface cleanliness and the thickness of graphene were checked by the core-level and valence-band photoemission measurements. No contamination-related features such as an O 1s peak were found in the core-level photoemission spectra. Angle-resolved photoemission spectra (ARPES) showed clear dispersions originating from the 26, it is not known whether the Si dangling bonds remain in the sample qfs-2ML after annealing. The XPS spectrum in the qfs-2ML sample is similar to that in the 2ML sample, as seen in Fig. 24 and the objective is to confirm the validity of our proposed framework.The buffer layer at the interface between SiC and graphenes consists of a carbon layer in a graphene-like honeycomb arrangement that bonds covalently to the Si-terminated substrate partially. For the 1ML and 2ML samples, not all Si atoms can bond to carbon atoms due to the different lattice constants between SiC and graphene and due to the 27. However, applying uniform distribution of the prior probability of energy E, the width of the red component is large and the obtained solution is not reasonable as a result of XPS as shown in the left of Fig. E is inhibited because the posterior distribution of the red component obtained by the exhaustive search becomes two-modal and one distributed near the blue component as shown in Fig. E. When we have constrain conditions for E: each energy difference is greater than Figure entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaK is a subscript for model identification. Based on Bayesian spectroscopy is a spectral decomposition analysis method that incorporates a Bayesian inference framework. Let 29, the posterior probability distribution b is the quasi-inverse temperature27 defined as an inverse variance i according to a normal distribution with zero mean and variance Z19 is obtained by marginalizing the numerator of Eq. (19 (BFE) is defined as F, the estimation of the noise intensity 30 method according to Eq.  and Gaussian G(x) shapes, with their intensity A, binding energy E, spectral width w at full width at half maximum (FWHM) and a mixing ratio C in Eq.  and estimation of the binding energies has been achieved with high precision of the order of meV. From their binding energies, we estimated the existence ratio of the buffer layers S1 for S2 with its standard deviation, which is consistent with previous studies. We performed model selection to determine the number of components in the XPS spectrum of the qfs-2ML sample. The four-peak model is selected, however, the contribution of S2 is quite small and this is probably considered due to the heterogeneity of the qfs-2ML graphene sample. These results demonstrate that the appropriate design of the prior probability distributions based on the information of physical properties and the insights gained from the exhaustive search is effective to perform the spectral decomposition with high precision.


Artificial Intelligence chatbots such as ChatGPT are increasingly being utilized for medical information by the public as well as by medical professionals, including researchers. We investigated the accuracy of information and citations generated by this search engine for a common medical condition, UTI.This was a qualitative pilot evaluation of an artificial intelligence chatbot for answering and referencing clinical questions on UTI. We created 5 clinical questions (Table) related to UTI diagnosis and treatment. Two clinical infectious diseases experts graded the responses on a Likert scale as to accuracy of the response. The gold standard for responses was derived from IDSA guidelines and/or evidence based electronic resources (e.g. UpToDate). Citations were checked for accuracy using PubMed searches, Google Scholar and also, in one case, direct communication with the purported author.ChatGPT Performance on UTI QuestionsChatGPT answered 3 of the 5 questions mostly correctly . The response to one question was rated as only half correct . One question was answered 100% correctly. Incorrect information was primarily related to diagnostic approaches to urine cultures for both asymptomatic and symptomatic patients (Table), and did not reflect answers found in current guidelines.When prompted for supportive evidence of answers, ChatGPT created plausible, yet fabricated studies. The authors were real and well-known experts in the field, but the combinations of authors, journal title and paper title were often false. Citations were incorrect for all 5 questions and fabricated in 12 of 24 provided citations.ChatGPT performed surprisingly well on correctly answering basic diagnosis and management questions on UTI, a commonly encountered condition with a large body of literature to train from. The lack of current information from guidelines hampered accuracy and could be a barrier to appropriate use of this technology for real-time learning or clinical decision-making. The inaccuracy of citations limits its use for supporting evidence-based research and publications.Kalpana Gupta, MD MPH, GSK: Advisor/Consultant|Iterum Therapeutics Limited: Advisor/Consultant|Qiagen, Inc.: Advisor/Consultant|UpToDate Wolters Kluwer Health: Royalties