Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model. Robots that can interact with humans are becoming commonplace in everyday life. For example, robots that are capable of task-oriented dialogues, such as responding to inquiries in different circumstances such as corporate and hotel receptions and restaurant reservations ,3,4,5,6,However, since the topics of general conversations are more diverse than those of task-based dialogues, it is very difficult to understand the intention of an utterance and to generate a response accordingly. For this reason, in conventional research on continuing chatting, when the content and the intention associated with the utterance cannot be understood, many dialogue models return safe responses that can be applied in any situation.In addition, an objective evaluation of the generated responses is difficult to achieve in general. In particular, in daily conversations between people who have close relationships, there are dialogues in which subjects, objects, abbreviations, etc., are frequently used and can be established only when there are close relationships, such as between family and friends. For example, consider the sentence “I had a delicious sandwich in front of the station the other day.” If the dialogue robot is unaware of the user’s daily activities, it may not be able to provide an appropriate response. It is difficult for task-based systems and existing dialogue models that focus only on continuing a conversation to appropriately deal with these utterances. Since these dialogues between closely related individuals cannot be understood by a third party on the basis that there is a relation between the utterance and the response, it is difficult to grasp the intention associated with the utterance.In this study, we focus on interview-style dialogues. We assume that the dialogue system plays the role of the interviewer, and another user (human) serves as the interviewee. In an interview dialogue, the interviewer can control the dialogue, and even though the topic is broad, the dialogue can be facilitated by limiting the questions to those tailored to the other person. The interviewer first understands the user’s speech intention. In addition, the interviewer generates responses to additional questions and topics based on the understood speech intentions. Based on this process, the dialogue system can take the initiative while allowing the user to speak freely. Moreover, by not limiting the scope of the topic, the system can obtain information other than what it wants to ask the user in advance, which has the effect of continuing the dialogue.Furthermore, regarding the problem of a disinterested user, if the information acquired in the previous interview is kept in the dialogue’s history and can be referred to in a later conversation, the same topics can be avoided as much as possible, and the system can take the initiative to ask new questions and discover new interests. In this manner, the interview dialogue format can elicit the other party’s actions, thoughts, and experiences more efficiently. Moreover, the intention associated with the utterance, which is important for smooth dialogue, can be grasped more accurately.The purpose of this study is to identify topic breaks in dialogues during an interview. If the dialogue can be segmented into topics, it will be easier for the dialogue system, which is the interviewer, to delve deeper into the topic. We also considered that by referring to past topic units, it would be easier to control the flow of the dialogue by avoiding similar topics as much as possible, successfully diverting past topics and so on. In this study, we used an interview dialogue corpus in which the intentions of the interviewer or interviewee were manually tagged, and each utterance was assigned an originally created utterance intention tag. By targeting this corpus and determining the occurrence of the utterance intention tags and their relationship to the flow of the utterance, it was possible to detect breaks in the topic. We also believe that it can be used as a reference for interviewers to intentionally change topics. Most existing studies have attempted to detect topic boundaries using topic modeling or word association. However, in a dialogue wherein the topics are related to each other and transition smoothly, such as in an interview dialogue, it is difficult to identify the transition point of the topic by using methods such as topic modeling based on word occurrence probability.The proposed method uses distributed representations of sentences (sentence embedding) that can account for context, such as BERT, as features for detecting topic breaks. In order to extend features, we propose a model that considers speech intention and a model that uses sensory features. This method enables the dialogue system to determine the appropriate topic break when a topic is presented to the interviewee in an interview dialogue and for the identification of topic breaks by the interviewee. This enables the system to conduct interview dialogues seamlessly and continuously and to alert the interviewees of their potential through dialogue.In This section presents examples of related research on dialogue systems and previous research on topic segmentation and describes the differences when compared to our research.The MAD proposed by Zhang et al.  is a newhttps://github.com/microsoft/botframework-sdk, accessed on 16 January 2022, https://developers.facebook.com/docs/messenger-platform/, accessed on 16 January 2022, https://docs.sebastien.ai/ accessed on 16 January 2022) are currently focusing on research and development involving chat dialogue AI.In addition, there is ongoing research on dialogue systems, and many researchers ,31,32,33In order to realize a non-task-oriented chatting dialogue, it is necessary to consider not only speech intention and semantic understanding, but also contextual understanding and world knowledge. Many studies have attempted to generate utterances or to understand the topic of an utterance. In our study, we propose a method for detecting topic breaks in system-driven interview dialogues.In previous studies, Miyamura et al.  proposedIn addition, Tomiyama et al.  proposedTanaka  detectedArguello et al.  proposedTopic segmentation for dialogue text is more difficult than for general documents. Existing neural models for topic segmentation are usually built on recurrent neural networks (RNNs) and convolutional neural networks, which work well when applied to written text but perform poorly for interactive text . Therefore, Zhang et al.  formulatSheikh et al.  proposedKimura et al.  proposedFaycal et al.  proposedIn their method, features from pre-trained BERT are used, and complex networks such as Bi-LSTM, highway network layer, attention layer, and multihop attention layer are trained to perform binary classification of topic separation. The prediction target is binary, which is the topic break, but using a network that is too complex may lead to overtraining of the model. In addition, the target is an article such as Wikipedia, which is considered to be more prone to topic separation than dialogue data. In our research, we aimed to find the most suitable features and algorithms for topic segmentation by using multiple conditions without making the network structure too complex.Solbiati et al.  proposedOther studies include the determination of dialogue boundaries , a methoThis section describes the interview dialogue corpus used to apply the proposed method. Specifically, the details of the data collection, annotation of the speech intention tags, and corpus analysis are explained.Sentences uttered by interviewers and guests (interviewees) were extracted from the subtitled text of dialogues in the TV program “Tetsuko’s Room.” A speech intention tag was assigned to each utterance by determining the intention associated with the utterance. The first advantage of using data from dialogue programs is the high reliability of the content, since the subtitles are created by a specialized company. Furthermore, in this program, the direction of the topic is decided in detail in advance, and the interviewee is familiar with the dialogue due to the hosting of the same program for many years. Therefore, it is easy for a third party to understand. Finally, the size of the dataset is large because “Tetsuko’s room” is a long-running program.In a study by Sasayama et al. ,47,48,49The total number of the eight main tags assigned by the annotator in the interview dialogue corpus by the interviewer and interviewee is shown in The topic breaks to be predicted in this study were manually assigned to all 30 dialogues. One worker assigned the topic breaks, and two researchers checked the annotations and made decisions. Specifically, only those tagged by the annotators and approved by the two evaluators were adopted as official topic breaks. The breakdown of topic breaks for each interviewer and interviewee is as shown in Here, we investigate how the similarity between adjacent topics can be obtained by documenting the set of intra-topic utterances based on tf-idf for a corpus of interview dialogues with topic breaks.d. Equation (2) shows the formula for calculating reverse document frequency d. t in a topic interval d. N is the total number of topic intervals. The base of the logarithms was 10.Equation (1) shows the formula for calculating importance We create a bag-of-words vector (tf-idf vector) for each topic section, wherein the dimension is the word ID, and the value is the importance ascribed by tf-idf. Given that the total number of words in the interview dialogue corpus is 6662, the number of dimensions becomes a vector of 6662 dimensions.xT and yT in a topic is given by Equation (3). The cosine similarity between the tf-idf vectors The distributions of similarity between tf-idf vectors and all tf-idf vectors in adjacent topic segments are shown in When similarity is relatively high, there is a large percentage of multiple occurrences of the same word. In this research study, we performed topic segmentation based on the features of each uttered sentence. In the past, topic analysis has often been based on probabilistic methods such as latent Dirichlet allocation (LDA), which classifies or clusters documents into topics based on word co-occurrence or the probability of occurrence based on a large amount of text data. In the case of LDA, documents are classified into topics or clustered based on word co-occurrence or the probability of occurrence based on large amounts of text data. However, in interview dialogues, which are the subject of this study, the amount of data in a single utterance is usually not sufficient for obtaining the probability of word occurrence from the utterances of the interviewer and the interviewee. As such, it is expected that topic analysis using LDA will be difficult. Therefore, in this study, we considered that by transforming sentences into vector representations that capture context and meaning, we could efficiently and accurately grasp the relationships between utterances in a dialogue.Traditionally, methods such as bag-of-words and contextual co-occurrence vectors have been used to convert sentences into semantic vectors. However, in recent years, distributed representations of words and sentences have often been used. In the case of distributed representation, words and sentences can be represented as dense real-valued vectors by embedding them in a high-dimensional real-valued vector space, and relationships (such as similarity) can be expressed numerically. The most famous distributed representations of words are Word2vec , fastTexIn the following subsections, we describe the vectorization of utterances in dialogues using BERT and its improved model and the topic segmentation method based on this approach.In this study, we used a pre-trained model of BERT, its improved version DistilBERT , and thehttps://nlp.ist.i.kyoto-u.ac.jp/?ku_bert_japanese, accessed on 16 January 2022), Inui Laboratory, at Tohoku University , NICT , and others. In this study, we used the pre-trained model published by NICT and 768-dimensional CLS tokens just before the final layer as feature vectors. This pre-trained BERT model is distributed using SentencePiece tokenization and byte pair encoding (BPE) to reduce the vocabulary size. In this study, the model based on BPE was used for the feature extraction of utterances. For Sentence BERT, we used a pre-trained model published by Nittetsu Solution (NSSOL) . For Sentence BERT, we use the pre-trained model published by NSSOL (Nittetsu Solution), and for Distill BERT, we use Laboro DistilBERT Japanese .The results of some BERTs trained on Japanese corpora have been published by the Kurohashi Laboratory at Kyoto University , there will be insufficient training data to determine the segmentation point for each pattern, and overtraining will occur.It is also possible to use recurrent neural networks to learn each context using N vectors of utterances before the target utterance. However, in such a case, if the context contains multiple topic breaks, the prediction may become more difficult. For this reason, we use only one input context and train the topic break labels of the context together to increase the amount of information that can be used as cues.A dialogue system that plays the role of an interviewer can at least determine in advance the speech intentions to which its utterances correspond. Some of the intentions are important cues in determining a topic break, such as a question to change the topic. For this reason, the proposed model improves the accuracy of topic break estimation by providing the interviewer’s speech intention tag as an input only when the input utterance contains the interviewer’s utterance.As previously indicated, there are three levels of speech intention tags: 8, 11, and 3. Each of these vectors is used as a one-hot vector, and the three vectors are concatenated to form a 22-dimensional speech intention vector. This vector is not only used as the input, but also as a prediction target in the interviewee’s speech, which is expected to improve learning efficiency. In the model with two sentences, only the interviewer’s speech intention vector is used as the input. However, the interviewee is a 22-dimensional vector of zeros that is used to specify one of the two sentences. In other words, the input is a 44-dimensional vector consisting of speech intention vectors of the two sentences concatenated back-to-back.In the case of binary classification, a vector series of embedding of the input dialogue sentences The paired sequence of two sentences in a binary classification can be represented as If we add a vector U representing the speaker type (interviewee or interviewer), we obtain Y can be expressed as Y is represented in two ways: t, n, and speech intention tag) and nt, tn, nn, speech intention tag, and speaker type).The prediction target t be the label of a topic break utterance, and n be the label for a non-topic-break utterance. In the case of three-valued classification, there are three possible labels, nt, tn, and nn, depending on the order of t and n. Situation tt, wherein there are consecutive topic breaks, is not included in this study because it does not exist in the actual corpus and is inappropriate as a break. In the architecture shown in this figure, layers 1, 2, and 3 have dropout rates of 0.4, 0.1, and 0.1, respectively.In multi-task learning, a shared layer facilitates the sharing of weights of relevant features between each prediction task. Although multi-task learning is sometimes used in machine learning algorithms other than neural networks, the advantage of “sharing weights” is more apparent in neural networks. In our method, the sentence vector, intention vector, and speaker vector used as inputs are all interrelated elements. Therefore, we place a shared layer just before the output layer in the proposed model and trained a classification model efficiently and accurately by concatenating the features. Although it is possible to construct a network with concatenated features, the number of dimensions of each feature is too large; thus, the model is designed with multiple inputs.https://www.tensorflow.org/api_docs/python/tf/keras, accessed on 16 January 2022)) was used to build and train the neural network.The Keras module of Tensorflow , triple-tasking , and single-tasking). As a baseline method, we compared our approach with SVM, logistic regression, and random forest. In addition to BERT(CLS), DistilBERT, and Sentence BERT, we also used the TF-IDF weighted bag-of-words vector (tf-idf vector) as features for comparison.The parameters for training the machine learning models, such as neural networks and SVMs, are summarized in https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html, accessed on 16 January 2022). Training and testing were performed using a five-part cross-validation method. In the training step, the training data and validation data were split at a ratio of 8:2. Two types of evaluations were used: accuracy and F1-score of topic-separated labels. Given that there is a large bias in the number of data depending on the class, we used random undersampling to adjust the number of labels in the training and test data such that they were almost the same.SVM, logistic regression, and random forest were performed using scikit-learn’s class library, and TF-IDF was calculated using scikit-learn’s TFIDFvectorizer (C can be represented as The expression for determining accuracy is shown in Equation (4). The set of classes ccM indicates the number of cases of class c that is correctly predicted as class c by the prediction model. n is the number of cases, and ijM is the sum of the number of cases of class i that is predicted as class j by the predictive model, that is, the total number of cases. t labels for binary classification, nt, and tn labels for three-valued classification). Equation (7) shows the formula for the F1-score. cTP is the number of cases of class c that is correctly predicted by the prediction model. cFP is the number of non-class c cases that are incorrectly predicted as class c by the prediction model. cFN represents the number of non-class c cases that are correctly predicted as classes other than c by the prediction model.Equations (5) and (6) show the formulas for recall and precision, respectively, for positive examples  for bin and ternary break tags  for “tri.” “t” is a topic break utterance, and “n” represents other utterances. In the case of task, “triple” represents the learning of three types of predictive tags. Specifically, it represents the topic break tag, speech intention tag, and speaker type (interviewer or interviewer). “multi” represents multi-task learning with topic break tags and speech intention tags as prediction targets. “single” indicates single-task learning, wherein only topic breaks are used for prediction.In the proposed method, when the speech intention tag is not used as a feature, the performance of the multitask model is higher compared to that of the existing single-task model. The performance of the proposed method was higher for binary classification. In terms of the F-score, the multi-task model with binary classification showed the highest accuracy of 0.84 when DistilBERT was used as a feature and the speech intention tag was added.For three-valued classification, without the speech intention tag, all methods achieved an accuracy of nearly 70%, and with the speech intention tag, an accuracy of 0.75 was obtained.However, SVM, random forest, and logistic regression, which were used as baseline methods, also achieved the same maximum accuracy (0.81) using speech intention tags. It can be concluded that the advantages of neural networks and advanced sentence embedding, such as BERT, were not fully exploited.In an interview dialogue system, the intention of the interviewee is known; thus, there are no issues associated with the use of the intention tag as a feature. However, when the intention tag is not known in advance, such as when the interviewer does not participate in the conversation, a multi-task model can be useful because it can detect topic segmentation with higher accuracy than a single-task model. When topic segmentation is performed on dialogue history data from different dialogue systems, it is highly likely that information on speech intentions will not be available; thus, models based on multi-task learning can be used.In this study, undersampling was used to reduce the bias of the training data in evaluation experiments, which may have resulted in insufficient training. Random oversampling and synthetic minority oversampling technique (SMOTE)  are two In addition, in the case of multi-task learning, the combination pattern of output vectors becomes large, and it becomes difficult to adjust the imbalance between classes. There are several methods of data expansion in natural language processing, such as replacing words in sentences with similar words. One problem with this method is that sentence meaning is not necessarily established after the substitution of similar words. Word substitution is also possible using BERT’s mask language model, but it is thought to be plagued by the same problem as a similar word substitution.Here, we analyzed which words are effective features for topic segmentation when tf-idf is used as a feature by using recursive feature elimination (RFE) for feature selection. When the maximum of 100 features was selected by RFE, Bold letters in the table are commonly selected features among the models. From the results, we can observe that, as word features, many expressions are frequently used by interviewers. However, features such as <s> and <par> are effective speech intention tags. Using the features of the utterance labels, we obtained a maximum accuracy of 83% when predicting using only the features selected by RFE with random forest. This accuracy is comparable to that of a multi-task neural network with DistilBERT and speech intention tags. Given that the accuracy was improved by reducing the number of features to 100 dimensions by feature selection, it can be assumed that the unknown words had little effect on accuracy reduction.In many cases, the topic break is caused by the interviewer who takes the initiative in the dialogue. This indicates that topic breaks can be detected 80% of the time, because we were able to identify the interviewer and examine intrinsic features (such as speech intention tag).In this report, we proposed a topic segmentation method for interview dialogue to realize an interview dialogue system that can efficiently and flexibly elicit information from a dialogue partner. However, the topic of the dialogue changes gradually; thus, it is difficult to predict from back and forth relations. The proposed method focuses on whether the speaker is an interviewer or not and uses the speech intention tags of the interviewer’s utterance as a feature. This method can be applied to two consecutive utterances. The accuracy of this method is higher than that of three-valued classifying pairs of two consecutive utterances as features. Thus, it was possible to clarify the effectiveness of speech intention tags in an interview dialogue.By using this method, it is easy to detect topic breaks in dialogue when the interviewer takes the initiative. The application to dialogue systems can be used to understand the topic transition status and topic content.In future work, we plan to create a model for estimating topic vectors from topic-delimited dialogue segments by improving existing methods and to construct a model for generating speech from similar topics. In addition, we intend to further expand the corpus and use the trained model to detect similar topic segments to realize an interview dialogue system that can discern the user’s interest and enhance the user’s charm and characteristics.


Background: The use of non-steroidal anti-inflammatory drugs (NSAIDs) during the third trimester of pregnancy can cause premature constriction of the ductus arteriosus. This report describes a case of in utero narrowing of the ductus arteriosus (DA) diagnosed postnatally in a baby with Persistent Pulmonary Hypertension of the Newborn (PPHN), after maternal use of Diclofenac-Epolamine 140 mg patch during the second and third trimester.Case Presentation: A fetal ultrasounds revealed an enlarged hypertrophic right ventricle at 32 weeks of gestation. Detailed questioning of the mother highlighted that topical Diclofenac (FLECTOR®) had been used at 26 and at 31 weeks of gestation. An echocardiography performed 8 h postnatally showed supra-systemic pulmonary hypertension, a restrictive ductus arteriosus and a dilated right ventricle. The newborn was treated by inhaled nitric oxide and oral Sildenafil and was discharged from hospital on day 24. He had a complete normalization of his pulmonary vascular resistance on day 48.Conclusion: This case illustrates the potential fetal and neonatal complications associated with maternal topical Diclofenac medication during pregnancy resulting in antenatal closure of the DA. It has also been classified as one of the most potent inducers of ductus arteriosus constriction in rats  during the third trimester of pregnancy can cause premature constriction of the ductus arteriosus by inhibiting cyclo-oxygenase 2 (COX-2) . Diclofe in rats . Diclofe in rats . The pla in rats . This caWe report the case of a newborn infant with persistent pulmonary hypertension of the newborn (PPHN) resulting from antenatal narrowing of the ductus arteriosus related to maternal application of topical Diclofenac during the second trimester of pregnancy. The infant was born to a 35-year-old mother both the course of the pregnancy–the mother’s second–and antenatal blood tests were unremarkable. As recommended by the French National technical Committee on Prenatal Ultrasound Screening, three antenatal echography were performed during the first, second were described as normal. The third revealed cardiac ventricular asymmetry and a right ventricle cardiac hypertrophy .2 supplementation (from 30 to 100% FiO2). The newborn was admitted to the Neonatal Intensive Care Unit where an echocardiography showed supra-systemic pulmonary hypertension with tricuspid regurgitation blood flow velocities of 60 mmHg  was delivered at 39 weeks of gestation by a planned cesarean section for breech presentation. Apgar scores were 10 at both 1 and 5 min. A pediatrician examined the baby 8 h after birth for moderate dyspnea. Cardiac auscultation revealed a systolic ejection murmur loudest over the pulmonary valve Pre-ductal oxygen saturation was 88% in room air, which increased to 98% with high flow nasal cannula at 2 L/Kg/min and O 60 mmHg , whereas- a premature closure of the ductus arteriosus during pregnancy,- a premature closure of the foramen ovale,- or an alveolar capillary dysplasia.Our hypotheses were therefore:A CT scan with contrast was performed 5 days after birth, showing an aneurysm of the inter-atrial septum without shunting, a dilated right ventricle and a markedly dilated main pulmonary artery.−1 kg.min−1) and high-flow oxygen supplementation through nasal cannulas for 18 days. Oral Sildenafil was started on day 3 due to the persistence of pulmonary hypertension. Symptoms improved on day 17, the ductus arteriosus was closed at day 20 and the infant was discharged from hospital on day 24 with normalization of pulmonary pressures , alprostadil infusion  on her lumbar area for back pain at 26 and 31 weeks of gestational age. In order to further reduce the pain, a hot water bottle was also applied to the area at the same time as the patch.Rigorous and repeated anamnestic determined that the mother applied FlectorThis case reveals the risk of a sustained narrowing of the ductus arteriosus resulting in prolonged persistent pulmonary hypertension in the postnatal period after maternal use of topical diclofenac during the second trimester.Besides NSAIDs, other causes of fetal ductal constriction and PPHN have previously been reported. Acetaminophen may increase the risk of prenatal ductus arteriosus constriction, through inhibition of prostaglandin G2 synthesis. Growing body of evidence suggests a role of maternal consumption of polyphenols-rich food , which interfere with prostaglandin metabolism, in prenatal ductus arteriosus narrowing . After rAntenatal narrowing of the DA results in increased pulmonary artery pressure, which in turn mediates a remodeling of the vascular wall through sustained elevation of vascular stretch stress, leading to structural pulmonary hypertension .Previous studies clearly showed that DA sensitivity to constricting factors increases during the last trimester of pregnancy . The risOnly one previous case of antenatal DA closure has been reported after maternal topical use of diclofenac during the third trimester of pregnancy. In that case, diclofenac has been applied at 35 weeks gestational age and the constriction of the ductus arteriosus was rapidly reversible at birth . In thisThere are no reports on the occurrence of systemic side effects related to a single topical NSAIDs application, when applied in conformity with recommendations of us, in current literature. The risk of occurrence is often related to misuse; in our case, the application of heat to the same area as the patch led to an increase of the drug’s absorption and the modification of the delivery profile of diclofenac.®) in the occurrence of pulmonary hypertension in newborn infants cannot be excluded. Furthermore, it is likely that the use of a hot water bottle for back pain may have promoted skin absorbance of the drug (®) on pulmonary hypertension in newborns in must be highly considered.Regarding these elements, the implication of the DI-EP patch . They differ in the salt of diclofenac they contain and must be responsible for an equivalent risk of DA narrowing. Patches containing diclofenac salts are over-the-counter medications in many countries and their use does not require any medical prescription, enhancing the risk of misuse in pregnant women.Cases of antenatal closure of the DA after oral use of diclofenac during the third trimester of pregnancy have more commonly been reported . AccordiOur findings suggest that maternal educational programs should include information pertaining to topical treatments in order to prevent potentially harmful self-medication in pregnant women.


By varying X = H, F, Cl, Br, I in junctions with S(CH2)(10-18)X, current densities (J) increase >4 orders of magnitude, creating molecular conductors via reduction of β from 0.75 to 0.25 Å−1. Impedance measurements show tripled dielectric constants (εr) with X = I, reduced HOMO-LUMO gaps and tunnelling-barrier heights, and 5-times reduced contact resistance. These effects alone cannot explain the large change in β. Density-functional theory shows highly localized, X-dependent potential drops at the S(CH2)nX//electrode interface that modifies the tunnelling barrier shape. Commonly-used tunnelling models neglect localized potential drops and changes in εr. Here, we demonstrate experimentally that We present an efficient strategy to modulate tunnelling in molecular junctions by changing the tunnelling decay coefficient,  In molecular junctions, where a molecule is placed between two electrodes, the current passed decays exponentially as a function of length. Here, Chen et al. show that this exponentially attenuation can be controlled by changing a single atom at the end of the molecular wire. It is well-known that the tunnelling current density (J in A/cm2) decreases exponentially with the length of the molecular wire (d in Å) given by the general tunnelling equationJ0 is a pre-exponential factor and the tunnelling decay coefficient (β in Å−1) determines how quickly the measured current decays with d7. In this context, unsaturated molecules with conjugated π-bonds are usually thought of as “molecular conductors” with low values of β (0.1–0.4 Å−1)9 and saturated molecular wires with localized σ-bonds provide “molecular insulators” with large values of β (0.8–1.2 Å−1)11. This rule of thumb stands in sharp contrast with the high tunnelling rates established for various biomolecules13, molecular wires of oligo-peptides15, and oligosilanes16. These all have saturated molecular backbones yet they exhibit low values of β (0.1–0.5 Å−1), and support long-range tunnelling over remarkably large distances of up to tens of nanometres17.Significant effort has been dedicated to study and manipulate tunnelling rates across molecular wires, which serve as model systems to improve our understanding of the mechanisms of charge transport across molecules which, in turn, play a central role in, e.g., biological processes, catalysis, and energy conversionβ in experiments, and this difficulty is also reflected in various established mechanisms of charge transport across molecular wires. Often, coherent tunnelling is assumed , as 18. This explains why conjugated molecules, which often have frontier orbitals aligned close to EF, have lower values of β than saturated molecules, which have frontier orbitals further from EF. Conversely, in the McConnell superexchange model, charge carriers tunnel via virtual states defined by the repeat units of the molecular wire; here, the tunnelling rate depends on the interaction strength between the repeat units of the molecular bridge21. This model has been used to explain low β values (0.2–0.5 Å−1) measured across tunnel junctions with self-assembled monolayers (SAMs) that have σ-bond backbones of oligoglycines14, oligoprolines22, and oligoglycols15. Furthermore, for very long molecules , a flickering resonance model has been proposed to explain long-range tunnelling and low β values24. Finally, different types of hopping models have been proposed to explain low β values of, for instance, bacterial nanowires23, DNA25, proteins26, and long conjugated molecular wires28; here the value of β also depends on the coupling strength between the repeat units, but these models predict a thermally activated component28. To summarize, all previous models suggest the necessity of tuning the chemical nature of the molecular wire to change the value of β.So far, it has been challenging to engineer umed Eq. , where ββ also depends, besides the chemical nature of the molecular backbone15, on the coupling strength between the molecules and electrodes (Γ) that is naturally related to 30. For molecular wires, where −1)32 and even negative β values have been reported35. Such low β values are also a signature of incoherent hopping and these junctions, in particular those containing redox centres, may operate in this hopping regime 33. Lambert and co-workers36 were able to tune the β value between 0.06 and 0.39 Å−1 in Au–S(CH2)nFG(CH2)nS–Au junctions with a functional group FG = α-terthiophene, phenyl, or viologen. They found that changing the anchoring group from dithiol to dithiolmethyl for FG = phenyl resulted in an increase of the β value from 0.14 to 0.50 Å−1 from which they concluded that localized states on the Au–S bond are involved in tunnelling along the FG units. In contrast, Frisbie and co-workers37 found that β values are similar for Au–S(CH2)nCH3//Au and Au–S(CH2)nS–Au junctions, implying that localized states on the Au–S bond are not important for tuning β (but note that they still significantly affect the contact resistance). Frisbie and co-workers38,39 suggested that Stark effects are important to consider as they can cancel the potential effects of localized anchoring group-electrode states. Indeed, strong Au–S interaction results in severe broadening of the molecular states and therefore the Au–S states only occur as weak features in valence band spectra of aliphatic SAMs38 , highlighting the need to optimize the Γ such that the molecular states remain localized in the molecule. Recently, Chen and co-workers40 reported a method using bimetallic electrodes to enhance the conductance of HO2C(CH2)nCO2H single-molecule junctions via the surface d-band. They improved the interfacial interactions between molecules and transition metal electrodes, promoting interfacial electron transport. Here, we use junctions of the form Ag–S(CH2)nX//EGaIn  where the weak interaction between the top electrode and the SAM allows us to investigate in detail how the terminal group X affects the tunnelling rates across the junctions.We note that the value of β has been largely ignored. One way of quantifying trends in the electrostatics of various systems is by studying the static dielectric constant (εr) of molecular junctions, a macroscopic observable that can be measured via impedance spectroscopy41. Previous work focused mainly on π-conjugated systems and established that in densely packed SAMs, εr hardly changes when the polarizability of the molecules of a SAM, α, is tuned due to depolarization effects 44. However, it is not known how α affects the tunnelling behaviour of junctions in which depolarization effects are reduced to a minimum. In addition, the molecular ionization potential directly relates to α and, consequently, changing α affects molecular frontier orbital energies45 and the energy level alignment of molecule–electrode interfaces46, but it is disputed whether an increase in α changes the conductance of the junction47. Also not currently understood is how α affects the relationship between εr and β. In principle, polarizable groups screen applied electric fields48 or result in an induced dipole and, therefore, also affect the potential drop profile inside junctions46. Thus polarizable atoms or moieties are expected to have a large effect on the measured tunnelling rates, but so far experimental examples are rare and conflicting47. For instance, Whitesides and co-workers reported that the charge transport rates in metal-S(CH2)nFG//EGaIn junctions with aliphatic SAMs are independent of FG with FG being terminal aromatic groups49, polar groups50, ionic and/or hydrogen bonding groups51, or halogen atoms47, and concluded that changes in terminal group does not affect the charge transport rates. In these studies they used large junction areas of >1000 µm2, but we have shown that such large junctions are prone to defects masking molecular effects and that, for EGaIn-based methods, stable junctions that are dominated by molecular effects should have an area of 300–500 µm2 (ref. 52). Indeed, the Whitesides’ group could reproduce our results and also found a factor of 600 in the charge transport rates when X = H was replaced with X = Br when small junctions were used47.So far, the influence of electrostatic effects in molecular tunnelling junctions on β of molecular wires with an alkyl chain backbone can be reduced from 0.75 to 0.25 Å−1, in effect turning them from insulators into conductors without changing the chemical structure of the backbone of the molecular wire, by introducing one distal polarizable atom at one end of the molecular wire of the form HS(CH2)nX. Changing X from H to I in the long S(CH2)18X molecular wire gives a factor of 10≈5 increase in J. For S(CH2)10X, the currents change by a factor of 10≈2. As we will discuss below, these observations cannot be explained by changes in the molecule–electrode interfaces, or contact resistances, alone. While we have shown before that the halide group affects the current and εr in Ag–S(CH2)11X//GaOx/EGaIn junctions45, here we demonstrate that the value of β can be controlled by changing X without the need to modify the chemical structure of the molecular backbone. This change in β explains why the largest change in current is found for the longest molecules studied in this work. On the basis of experimental and theoretical data, we discuss how introducing this polarizable atom changes the electrostatic potential profile of the tunnelling barrier, the εr of the junction, and the contact resistance (or Γ), which are important to consider when modifying tunnelling efficiency across molecular wires.Here, we show that the value of 2)nX//GaOx/EGaIn junctions, and indicates how the coupling and energy level alignment  change with X as discussed in detail below. The schematic also includes the equivalent circuit consisting of the contact resistance  in series with a parallel combination of the SAM resistance  and the capacitance of the SAM  in the junction. The equivalent circuit and the associated physical meaning of each circuit component has been explicitly discussed in our previous work41  with associated resistance (RSAM) as expressed in Eqs. . The SAMs were formed on template-stripped Ag electrodes using well-established methods and the junctions were completed with cone-shaped GaOx/EGaIn top contacts53 (Supplementary Sections 2)nX SAMs (there only n = 11 was studied) the measured current increased by three orders of magnitude and the value of εr increased by a factor of 4, when X was changed along the halogen series from F to I45. However, the evolution of J and ɛr with increasing molecular length and the corresponding β values for different X have so far not been studied. Here we address whether this increase in current is caused by changes in RC or by changes from coherent tunnelling to incoherent process . Changing the value of n for each X allows us to investigate in detail how and why β changes as a function of X while keeping the nature of the molecule–electrode interfaces and the molecular backbone the same.Figure  in Eqs. . It highn = 14 for X = H, F, Cl, Br, or I, and n = 10, 14, or 18 for X = Br with angle-resolved X-ray photoelectron spectroscopy (ARXPS) and molecular dynamics (MD) simulations  and all results are summarized in Table 2)14I SAM with computed molecule heights in excellent agreement with film thicknesses dSAM measured by XPS 14I precursor readily forms dense layers with all molecules in a fully-upright position. We determined the relative values of surface coverage (ΨSAM) with XPS, which confirms that all SAMs have indistinguishable packing densities , which is in close agreement with the MD value of 1.3 ± 0.1 Å per carbon . Figure Emol,MD, in eV) and per methylene CH2 unit  extracted from the MD calculations. The values of Emol,MD and Emeth,MD improve slightly as X shifts from H (−1.8 ± 0.1 eV per molecule) to Br (−2.4 ± 0.2 eV per molecule), which is due to the increasing intermolecular van der Waals interaction. For SAMs with X = I, the packing energies weaken slightly due to small competing effects caused by mild steric repulsion between the large I headgroups. These observations confirm that the halogen functionality does not significantly disrupt the supramolecular structure of the SAM. Finally, we determined the energy level alignment of the SAMs on Ag using ultra-violet photoemission spectroscopy in Supplementary Section We characterized the SAMs on Ag with ies Fig.  within eXPS Fig. . Figure n using J(V) measurements and impedance spectroscopy. The SAMs were contacted with cone-shaped GaOx/EGaIn electrodes following a previously reported method53. To minimize leakage currents and to ensure that molecular effects dominate the junction characteristics, we used junctions with a small contact area of ~350 μm2, as large junctions suffer from leakage currents across defective sites52. We recorded statistically large numbers of J(V) curves to determine the Gaussian log-average J(V) curves, <log10|J| > G, and associated Gaussian log-standard deviations  which are plotted in Fig. n = 10–18  curves and histograms of the log10 |J| at ±0.5 V are given in Supplementary Section 10|J| > G at –0.5 V as a function of dSAM,MD  characteristics as a function of temperature, T in K, of Ag–S(CH2)14X//GaOx/EGaIn junctions for all X using top electrode of EGaIn confined in a microfluidic network in polydimethylsiloxane following a previous reported method41  vs. frequency (f) plots along with the fits to the equivalent circuit , which was mainly driven by a large increase in Γ, with changes in β playing only a minor role.To characterize the dielectric response of the junctions, we conducted impedance spectroscopy using a sinusoidal voltage perturbation with an amplitude of 30 mV around 0 V in the frequency range of 100 Hz to 1.00 MHz and the data were fitted to the equivalent circuit shown in Fig. f n Fig. . This chJ(V) and impedance measurements, we determined the value of β from the impedance measurements for junctions with X = Br. The value of RSAM increases exponentially with n . The value of RSAM,0 is essentially equivalent to J0 . The contribution of RC is minor since RC is a parallel circuit element, but it is included in J0. The β and RSAM,0 values are, within error, the same as the values determined with the J(V) measurements.To confirm the consistency between the th n Eq. 2\documenmentclass2pt{minimεr as a function of X and nε0 is the vacuum permittivity and Ageo is the geometrical area of the junction. Figure εr increases by a factor of 3 when changing X from H or F to I, yet εr is independent of n  and are dynamic in nature60.Figure = F Fig. . We tent2)14X. All systems show a feature at ~1.5 eV 14X, which confirms that these new occupied and unoccupied states are due to the halogen functionalization. For both the occupied and unoccupied parts of the DOS, these halogen-derived states do partially overlap in energy with other features but are localized primarily at the tail of the SAM 61.Figure εr as a function of X for the free-standing and hydrogen-terminated HS(CH2)14X SAMs using previously reported protocols62 14X SAMs.Finally, we determined 43, a competition between suppression of in-plane polarization and enhancement of out-of-plane polarization occurs in SAMs. The suppression dominates for densely-packed SAMs and, thus, the substituent X should not affect the calculated εr of the SAM in sharp contrast to our herin reported experimental findings. Our calculations as well as previous theoretical studies, however, only probe the intrinsic dielectric properties of the isolated highly organized SAM without contacts. The interaction between the SAM and the top electrode that is naturally present in the experimental determination of εr could affect the dielectric behaviour of the junction considerably, which would be consistent with the experimentally recorded trends for the RC shown in Fig. 64. We note that previously reported DFT calculations of εr of the HS(CH2)11X SAMs45 were incorrect due to simulation artefacts of uncompensated dipoles in the unit cell, which created a spurious correlation with experimentally measured εr values.As pointed out by Natan et al.2)nX molecular junctions. The single-level Landauer model is frequently used to model the current flowing across molecular tunnel junctions65. Here we modelled the current using the following expression:65In the following, we discuss our results in the context of commonly used models to interpret charge transport through the S14X//GaOx/EGaIn (Supplementary Table 2)14X molecule. Figure Γ), defined as We accounted for the behaviour of a group of molecules by setting the number of such molecules fixed at mentclasspt{minimar–I Fig. , and thence Fig.  and accoβ, which agrees with commonly used coherent tunnelling models18 including the Simmons model which also accounts for εr. However, the Simmons model also predicts a decrease of the tunnelling rates with increasing εr due to a reduction of the image charge effects in the electrodes due to screening within the SAM66. This reduction of image charge in effect increases δEME and, consequently, β, but we observe the opposite trend. Using the same model, a reduction of the effective electron mass could also account for an increase in tunnelling rates, but it is not clear how the effective electron mass would change as a function of X with the essentially localized features derived from the HOMO and LUMO. Furthermore, Vilan67 argued that changes in the electron mass are equivalent to changes in the Figure 30), i.e., the coupling of the molecules with the electrode we have determined above 52 junctions. The largest effects of X on J are found in the longest molecules of S(CH2)18X where the J increases by a factor of 10≈5 when X changes from H to I. In contrast, for the shortest molecule S(CH2)10X, J increases by a factor of 10≈2, indicating that the observed changes in J, and the corresponding values of β, are driven by more than just changes in the interfaces, which has been discussed previously by Frisbie and co-workers38. Combining experiment with DFT and Landauer charge transport models, we established three factors that contribute to the dramatic change in β of these aliphatic halogenated junctions with varying X and associated increase in α and εr: (1) The HOMO-LUMO gap45 and associated δEME is reduced which lowers β, (2) the shape of the tunnelling barrier is modified at the SAM-top electrode interface, resulting in larger potential drops at this interface, and (3) the electronic coupling Γ of the molecular orbitals with the electrodes increases .This work shows that substitution of a single highly polarizable atom can have a pronounced effect on the energy level alignment, charge transport rate, and dielectric response of molecular junctions. We were able to tune εr, resulting in lowering of the tunnelling rates67, which is in sharp contrast to what we find. Superexchange models21 also fail to explain our observations, since the coupling between the molecular repeat unit  was not changed here. Conversely, the Landauer model65 could explain our results at least qualitatively, but not quantitatively. This is because it does not treat electrostatic effects in the junctions explicitly and self-consistently which resulted in our case in a large increase in the values of Γ (29 times) even though the 68 that is based on charge traps provides a hypothesis for how the value of β could decrease with increasing εr. Although the physical interpretation differs as the tunnelling behaviour is explained in terms of charge traps rather than the electrostatic response of the SAM inside the junction, this line of thought stimulates further theoretical and experimental testing of the presence of “impurities”—here in the form of polarizable atoms—as charge carriers that move across the energy band profiles. Although our findings suggest a correlation between β and εr, the increase of εr as a function of X could not be reproduced in our DFT calculations, which may be because the calculations do not take the SAM–metal interface into consideration, and perhaps other factors are important such as  charging of highly polarizable molecules during charge transport inside the junctions. To summarize, our work proposes an effective way of tuning the tunnelling rates across molecular junctions without chemically altering the backbone of the molecules and highlights the importance of understanding dielectric effects in these junctions. We hope that our findings will stimulate further experimental and theoretical investigations towards establishing improved transport mechanisms for junctions in their in situ physicochemical environment and electronic states inside working devices.In a broad context of widely used charge tunnelling mechanisms, our findings point out their limitations highlighting the need for improved models that take dielectric (or collective) effects of the junctions into consideration. Specifically, the popular Simmons model predicts that image charge effects in the electrodes are reduced with increasing Supplementary InformationPeer Review File


This lack of translation between preclinical and clinical efficacy suggests a systematic failure in current methods that determine efficacy in preclinical rodent models. In this review, we critically evaluate rodent models and behavioural tests used to determine preclinical efficacy, and look to clinical research to provide a roadmap for developing improved translational measures. We highlight the dependence of preclinical models and tests on dopamine-centric theories of dysfunction and how this has contributed towards a self-reinforcing loop away from clinically meaningful predictions of efficacy. We review recent clinical findings of distinct dopamine-mediated dysfunction of corticostriatal circuits in patients with treatment-resistant vs. non-treatment-resistant schizophrenia and suggest criteria for establishing rodent models to reflect such differences, with a focus on objective, translational measures. Finally, we review current schizophrenia drug discovery and propose a framework where preclinical models are validated against objective, clinically informed measures and preclinical tests of efficacy map onto those used clinically.Despite 50+ years of drug discovery, current antipsychotics have limited efficacy against negative and cognitive symptoms of schizophrenia, and are ineffective—with the exception of clozapine—against any symptom domain for patients who are treatment resistant. Novel therapeutics with diverse non-dopamine D Schizophrenia is a syndrome commonly associated with symptoms that are classified into positive , negative , and cognitive  domains . Since i2 receptor and numerous issues relating to efficacy. Drugs are effective against positive symptoms in only ~70% of patients . The resee ref. –8. It issee ref. –12. Despsee ref. . The fie2 mechanisms have been sought for more than 30 years and include targets as diverse as the receptors, transporters and signalling pathways of dopamine, glutamate, glycine, serotonin, acetylcholine, oxytocin, histamine, opioids and neurosteroids [2 mechanisms concluded “…we cannot confidently state that any of the mechanistically novel experimental treatments covered in this review are definitely effective for the treatment of schizophrenia and ready for clinical use.” [Therapeutics with novel, non-Dsteroids . Compounal use.” .While the large number of failed clinical trials may be attributed to shortcomings at multiple stages in the drug discovery process, there is a clear and systematic disconnect between preclinical and clinical efficacy—particularly with respect to negative and cognitive symptoms , 14. Thi2 candidates has failed to translate in late-stage clinical trials, then highlight key issues in preclinical development limiting accurate predictions of clinical efficacy. Finally, we consider a revised framework whereby neuroimaging could be used as a translational tool to improve preclinical predictions of clinical outcomes.In an industry that increasingly strives to test new drugs in a “fail fast” approach, the inability to reliably predict clinical outcomes poses a significant and costly challenge . Without2 therapeutics have been sought with the idea that they will treat all symptom domains and be effective in TRS patients. Disappointingly, there has been an overwhelming failure to convert promising preclinical data, and in some cases encouraging early clinical findings, into successes in phase 3 and new drug approvals for the treatment of symptom domains of schizophrenia. Inhibitors of PDE10 (e.g. PF-02545920 and TAK-063), that act downstream of dopamine signalling, have failed to show a significant improvement in PANSS total score in Phase II clinical trials despite showing D2 antagonist-like activity in preclinical models [It is clear that current antipsychotics have limited utility in treating the broad spectrum of symptoms in all schizophrenia patients, thus novel non-Dl models , 19. Siml models . Alpha-7l models , 25.The aetiology of these failures is unclear; collectively therapeutics targeting solely the glutamatergic system alone appear to have fared poorly in later-stage clinical trials. However, both glycine transporter type-1 inhibitors  and AMPAkines  remain in active mid-late-stage clinical development for different symptom domains in schizophrenia.AMPAkines are positive allosteric modulators of AMPA receptors and act to enhance long-term potentiation, a critical process involved in learning and memory formation . The firFurthermore, the endogenous glutamate tone will likely play a key role in any clinical efficacy. While TRS patients exhibit normal levels of striatal dopamine compared to treatment-sensitive patients, they express a higher concentration of cortical glutamate; therefore it is difficult to predict the extent of AMPA receptor potentiation by an AMPAkine . It is a2 receptor antagonists in preclinical studies, multiple PDE10 inhibitors failed due to lack of efficacy in clinical development. Recent speculation attributes failures of such inhibitors to their only modulating indirect spiny projection neurons in the striatum and / or the additional effects on cyclic GMP with respect to modulation of cyclic AMP by dopamine D2 receptor antagonists [With respect to non-glutamatergic approaches, despite many similarities with the phenotype of dopamine Dagonists .2 receptor-based medicines.Irrespective, the overall picture is one of a disconnect between preclinical testing and clinical evaluation. Thus, it is with cautious optimism that recent positive phase 2 clinical data with KarXT  and ulotaront  provides potential hope for alternative, non-dopamine DIn a four-week phase 2 trial in patients with acute exacerbation of schizophrenia, the TAAR1 agonist, ulotaront, significantly improved PANSS total scores with respect to placebo control, with a generally favourable side effect profile (including a lack of metabolic effects) . It rema4 receptor agonist activity (rather than the M1 activity) that engenders the antipsychotic effect, which would be consistent with the recently reported antipsychotic activity of CVL-231, a subtype selective positive allosteric modulator of the M4 receptor, in a phase 1b trial [KarXT also recently completed a five-week phase 2 trial in inpatients with established schizophrenia, improving PANSS total, positive and negative scores with respect to placebo control . These d1b trial –42.4 receptor on cortical glutamatergic afferent terminals in the striatum , presents the enticing theory that activation of the M4 receptor might be an effective treatment in TRS. Whereas there is unchanged striatal presynaptic dopamine content in TRS , there are elevated anterior cingulate cortical glutamate levels, which may increase corticostriatal drive, and be normalised by M4 receptor activation. Furthermore, both M1 and M4 receptor activation could yield pro-cognitive efficacy; thus the clinical benefit of KaXT and those compounds in its wake will be interesting to follow [The presynaptic expression of the inhibitory Mo follow , 44.Despite disappointing results in humans, each novel therapeutic discussed in the previous section showed promise in preclinical development. Preclinical assessments of efficacy can be broken down into two components: models and tests. Models refer to the methods by which schizophrenia-relevant behaviours are induced in laboratory animals, while tests quantify the magnitude and robustness of schizophrenia-relevant changes in these models, and thereafter the extent to which a novel therapeutic can reverse them. The scope of available animal models and tests has been reviewed extensively elsewhere (see ref. ); this sSchizophrenia-relevant models can be produced by a variety of interventions, commonly in mice and rats, broadly characterised as genetic, pharmacological, and neurodevelopmental models . While sFace validity: model reflects rodent correlates of positive, negative and cognitive symptomsConstruct validity: behavioural dysfunction is a result of neurochemical and structural alterations similar to those present in the brains of schizophrenia patientsPredictive validity: therapeutic effects in the model are predictive of efficacy in humans.To assess the validity of each model with respect to human illness, a set of criteria have been established :Face valNo current rodent model meets the criteria for face, construct, and predictive validity. Schizophrenia is an exclusively human disorder that is highly heterogeneous with respect to symptoms, pathogenesis and aetiology; therefore, it is highly unlikely that an animal model meeting all criteria for face, construct and predictive validity will ever be established. As such, preclinical testing is often spread across a variety of models to ensure varying aspects of face and construct validity are appropriately met.2 receptor, for which their affinity is highly correlated with clinical efficacy [Early animal models of schizophrenia were based on the observation that dopamine enhancing drugs, such as amphetamine, produce psychotic symptoms in healthy individuals and worsen positive symptoms in schizophrenia patients , 51. Dopefficacy . While defficacy .Early theories of dopamine dysfunction in schizophrenia are further embedded in schizophrenia drug discovery with the use of current antipsychotics to validate rodent models. The limitations of this approach are twofold: First, selecting models based on the efficacy of drugs with a common mechanism of action severely limits the model’s ability to identify drugs with distinct mechanisms of action. This is largely problematic as current drugs are ineffective, with the exception of clozapine, in ~30% of patients . Second,Finally, it is important to note the reciprocal relationship between preclinical models and the behavioural tests used to validate such. The basic tenet is that disrupted behaviours in various animal models are more likely relevant to schizophrenia than behaviours that are unaffected by such interventions . As such2 antagonists [As alluded to in the previous section, the reliance on behavioural paradigms to assess human symptoms presents a significant limitation of preclinical testing. Rodents are not prone to perceptual disturbances such as hallucinations and delusions, therefore positive symptoms are generally assessed by an increase in locomotor activity and stimulant sensitivity . Althougagonists . Howeveragonists . The latagonists , 57.A similar disconnect is also apparent in the preclinical tests used to assess negative and cognitive symptoms; however, these methods are not predictive for discriminating drugs with human efficacy. While the serendipitous discovery of chlorpromazine provided a prototype for drugs that reverse positive symptoms, and therefore a behavioural profile in rodents indicative of clinical efficacy, there is yet to be a drug that produces meaningful improvements in negative and cognitive symptoms. As such, there is no benchmark for clinically relevant changes to correlates of negative and cognitive symptoms in rodents.Negative symptoms are typically assessed in rodents by reduced social interaction and reduced preference or motivation for palatable substances . AlthougA α2/α3 partial agonists, and DAT and NET inhibitors reverse cognitive dysfunction in a putatively schizophrenia-relevant rodent model as determined by the novel object recognition task, yet do not improve cognition, as determined by a range of rating scales, in clinical trials of schizophrenia patients  in the striatum, where it is converted to 18F-dopamine by aromatic L-amino acid decarboxylase, therefore providing a complex measure of presynaptic dopamine function [2 receptor availability can be assessed at baseline or following dopamine release or depletion using D2 PET tracers [Positron emission tomography (PET) has proven to be a versatile tool for the assessment of multiple aspects of dopamine function in vivo, including dopamine receptor and transporter availability, and capacity for dopamine synthesis and release . While sfunction , 101. Ad tracers .Meta-analyses of striatal dopamine synthesis capacity or presynaptic dopamine function more broadly have found both to be significantly greater in schizophrenia patients than healthy controls, with large effect sizes (d’) of 0.867 and 0.79, respectively , 102. Movide supra) will be interesting to follow as they both move towards late-stage development.Together with converging lines of evidence from studies of dopamine metabolites and density of striatal dopaminergic synapses, it has been proposed that schizophrenia may be further classified into subtypes based on the status of dopamine function . Namely,Consequences of elevated or normal presynaptic dopamine function in non-TRS and TRS, respectively, have been observed more broadly with respect to changes in corticostriatal functional connectivity. The idea of dysconnectivity in schizophrenia has been implicit since conception, but it was not until the advent of non-invasive neuroimaging techniques in the 1980s and 1990s that there was clear evidence to support pathophysiological disconnection of widespread neural systems –108. FunInterestingly, patients often show disrupted functional connectivity within corticostriatal systems known to be influenced by dopamine, and it appears that presynaptic dopamine function, corticostriatal connectivity, and treatment response are inextricably linked , 111. InWhile not investigating dopamine-related changes directly, divergent patterns of functional connectivity in TRS and non-TRS patients provides further support for subtype-dependent changes to corticostriatal circuitry , 114. Wi2 receptors and increased sensitivity of D2 receptors to dopamine [Given that TRS patients have normal presynaptic dopamine function, it stands to reason that symptoms and corticostriatal dysconnectivity arise from dysfunction elsewhere. It has been proposed that maladaptations may be present at other levels of the dopaminergic system, including increased expression of Ddopamine , 116. Bedopamine . Althougdopamine , 118–121dopamine , 123. Hodopamine , 125.A vast number of neurodevelopmental, pharmacological, and genetic rodent models exist, each with varying degrees of relevance to human disorder. Clinical neuroimaging research has provided strong evidence to suggest that treatment response in schizophrenia patients, or lack thereof, is due to distinct neurochemical profiles Fig.  12, 104104. ThesBy the proposed criteria, TRS and non-TRS models are distinguished by presynaptic dopamine function, anterior cingulate glutamate concentration and corticostriatal functional connectivity Table . It may A particular strength of the proposed criteria is the highly translational measures by which TRS and non-TRS models are defined. While rodent models are limited by the fact that schizophrenia is an exclusively human disorder, the disparate measures by which symptoms, and a reduction thereof, are assessed in preclinical models vs. patients presents an additional hurdle for translating preclinical to clinical efficacy. Clinical neuroimaging modalities such as PET, MRS and fMRI are increasingly used to investigate rodent models of brain disorders, with the recognition that these techniques may act as objective translators of pathophysiology between species –130. AltHere, the application of neuroimaging in preclinical discovery is reconciled in a way that serves an objective goal: to establish TRS and non-TRS models of schizophrenia in order to improve predictions of clinical outcomes in each subtype. We propose a two-step application of the criteria: first, to revalidate existing models as models for TRS and non-TRS, rather than the umbrella schizophrenia. Second, where existing models fall short, new models should be developed specifically to meet the translational criteria for TRS and non-TRS. The EDiPS model provides a recent example of this, which has been developed specifically to recapitulate elevated presynaptic dopamine function found in non-TRS patients . Behavio2 mechanism of action [2 mechanisms of action. While the above is certainly promising for non-TRS patients, a critical next step will be the application of this strategy in the development of novel treatments for TRS.As history has shown, identifying and defining disease subtypes has a significant impact on treatment outcomes –135. Thef action . Notablyf action , 127. ItDespite the progress to be made from reverse translating the clinical neuroimaging findings discussed above, a number of caveats follow. First, models that meet the proposed criteria for TRS may only be representative of the 70–84% of patients who are deemed TRS from illness onset, but not those who develop TRS over the course of illness . With reFinally, it is unclear the extent to which the translational power of these techniques will be bound by fundamental differences between rodent and human brains. A recent fMRI study in both mice and humans has begun to address this overarching question, identifying similar patterns of striatal functional connectivity in regions corresponding with limbic and sensorimotor, but not associative circuitry . This fiThe past 50+ years of schizophrenia drug discovery has seen little in the way of treatments that effectively manage the disorder: current therapeutics have limited efficacy against negative and cognitive symptoms, and are ineffective against any symptom domain, with the exception of clozapine, in approximately 30% of patients , 104. WhEquipped with a growing number of translational techniques and an understanding of the pathophysiology underlying treatment response and non-response, we now see a pivotal opportunity to redefine preclinical drug discovery for the twenty-first century. Here, we outline fundamental principles guiding a roadmap forward. First, rodent models should be validated against objective, clinically informed measures with a greater emphasis on construct validity. Given the distinct neurochemical profiles of TRS and non-TRS patients, validation should also seek to distinguish TRS and non-TRS rodent models. Key experiments will include the re-validation of commonly used rodent models with respect to presynaptic dopamine function, ACC glutamate content and corticostriatal connectivity; in addition to cognitive fingerprinting with a battery of touchscreen-based tasks.Second, preclinical tests of efficacy should resemble, to the extent permitted by species differences, clinical tests of efficacy. Where clinical tests are unsuitable for rodent adaptation , theragnostic biomarkers may offer more robust measures of efficacy compared to current behavioural-based tasks. A key checkpoint in developing translational measures of efficacy—particularly those for cognition—will be to understand the false-positive rate, i.e., how many drugs without pro-cognitive efficacy in humans are misclassified as pro-cognitive using these tasks? Given this indication will be model-dependent, it is imperative that models are first refined to reflect the current understanding of schizophrenia pathophysiology before judgements about predictive validity are made. Once established, these frameworks must be benchmarked using clinically relevant drugs to test their validity, i.e., medicines that have no nootropic activity must also fail to ameliorate cognitive deficits in a cognition-based task.We acknowledge the road forward will be long and not without challenges, but the weight of unmet need and a history of rare success signifies it is certainly one worth exploring. Insights from clinical research coupled with a growing toolbox of translational techniques now provide means with which to redefine preclinical development of schizophrenia therapeutics for the twenty-first century.


The so-grown aligned PLGA nanofibers exhibited remarkable adhesion to different substrates , even when immersed in PBS solution and kept at physiological temperature (37 °C) for up to two weeks. Finally, the Directional ASB-SANS technique allowed us to grow PLGA fibers also on highly heterogeneous substrates such as polyimide-based, gold-coated flexible electrodes. These results suggest the viability of Directional ASB-SANS method for realizing biocompatible/bioresorbable, nanostructured coatings, potentially suitable for neural interface systems.The development of aligned nanofibers as useful scaffolds for tissue engineering is an actively sought-for research objective. Here, we propose a novel improvement of an existing self-assembly-based nanofabrication technique (ASB-SANS). This improvement, which we termed Directional ASB-SANS, allows one to produce cm Numerous processes have been developed for producing nanofibers, such as electrospinning ,2, phaseAnother interesting nanofibers fabrication technique is electrospinning, which allows one to obtain indefinitely long single nanofibers in an uninterrupted way from various polymers, potentially on an industrial scale. With a proper instrumental setup, electrospinning can also deliver aligned fibers over considerably large surfaces, up to tens of square centimeters . HoweverSelf-assembly-based methods have the potential to solve these problems. In fact, self-assembly is the worldwide most diffused nanofabrication process, as it has been chosen by nature for producing living organisms of any size and type, as well as many mineral objects characterized by surprisingly ordered nanostructures ,9,10. HoRecently, we proposed a novel self-assembling technique named ASB-SANS (Auxiliary Solvent-Based Sublimation-Aided Nanostructuring), and we showed it to be effective in delivering large-area, hierarchically organized nanofibers, and aligned nanodots ,14. The Since the whole process is carried out using small amounts of TM , after the evaporation of the AS and the consequent solidification the SS maintains its crystalline structure, and the resulting SS crystallites play the role of templates for the TM dissolved in them, leading to well-ordered nanostructures. For higher TM concentrations, the crystalline structure of the SS is disrupted, leading to the formation of disordered TM nanostructures. However, even with appropriately low TM concentrations, the SS crystallizes in randomly oriented polycrystalline domains  and fast  and can be carried out with common glassware. Moreover, the technique is very versatile in terms of materials, allowing one to produce nanostructures out of different polymers, such as poly (P3HT), a semiconducting polymer ,16,17,18Due to this versatility, ASB-SANS-generated nanostructures can be used for a wide range of applications, among which gaseous acetone sensors ,16,17,18Despite this applicative potential, ASB-SANS still suffers from the aforementioned lack of control over the uniform and long-range orientation of the generated nanostructures, which is a highly desirable property in view of growing biological tissues sensitive to the topographic characteristics of the underlying substrate .2. These results are obtained using a seed SS crystal as a multiepitaxial growth template for the solidifying TM-SS layer. In more detail, the seed crystal is put in physical contact with the metastable TM-SS liquid mixture immediately after the AS evaporation, so as to impart a preferential growth direction to the growing polycrystalline layer. Albeit carried out using manual procedures, the here-presented process can be easily automated, opening up interesting opportunities toward the controlled generation of highly aligned and macroscopically developed nanofibers in an inexpensive, fast, and versatile fashion.To fill this gap, we present here a variation of the ASB-SANS technique able to generate uniformly oriented and aligned nanofibers out of PLGA, achieving alignment length exceeding 5 cm and overall aligned nanofibers domains larger than 10 cm3) (≥99%+), para-dichlorobenzene (PDCB) (≥99%), (−)-menthol (≥99%) were purchased from Sigma-Aldrich  and used without further purification. PLGA was used as Target Material (TM), Chloroform as Auxiliary Solvent (AS), and PDCB as Sublimating Substance (SS).Poly(lactic-co-glycolic Acid) (PLGA) , chloroform (CHClPolyimide resin (PI 2610) was purchased from HD Microsystems . Common laboratory glassware/plasticware was used for the solutions preparation.3 concentration. In total, 100 μL of mother solution was added to various quantities of Sublimating Substance (SS), as detailed in w/w) ratios variable from 400 to 50.To prepare the ASB-SANS solutions, at first a TM/AS mother solution was prepared, with a 1 mg/1 mL PLGA/CHClThe chosen solution, once properly mixed, was deposited by careful drop-casting on the selected substrate (vide infra). Preliminary tests were carried out with PDCB as SS but were discarded upon non-satisfactory results, briefly discussed in the For menthol as SS, the standard ASB-SANS method involved the deposition on the substrate of the ternary solution at room temperature (25 °C), and the simple waiting for the AS to evaporate and the SS to sublimate  °C, on a custom-made thermostated plate with an attached digital thermometer. After the ternary solution deposition and the almost-complete evaporation of the AS , Each developed nanostructure sample was examined by optical microscope ) and, whenever deemed opportune, by Scanning Electron Microscope .In some cases, the generated nanostructures were also characterized with an ATR FT-IR , after having been placed for 24 to 48 h in the water pump vacuum, to check for possible residual menthol left in the PLGA nanopatterns.-x wafer chips  were washed with acetone and isopropanol, followed by a nitrogen gas drying step; they were used for SEM imaging of the developed nanostructures.SiO-(a)pristine polyimide surface(b)2, 300 mTorrsamples treated with oxygen plasma , Italy): 30 s, 150 W, 15 sccm of ORound glass slides (d = 8 mm) coated with a polyimide layer (about 400 nm thick), obtained using a thin-film technology protocol adapted from . The sliThe nanofibers growth was carried out on different substrates, suited for different purposes:The (a)-type surfaces of these samples were used as controls, in order to evaluate the effect of the surface treatment over the substrate wettability against the ASB-SANS solution.The outcome of the ASB-SANS procedure is based on a delicate equilibrium between the three solution components: the Auxiliary Solvent (AS), the Target Material (TM), and the Sublimating Substance (SS). The interplay between these three components, and its role in determining the topology and size of the developed nanostructures, were already investigated in detail  and are PLGA is a copolymer formed by glycolic acid and lactic acid as comonomers, in variable proportions. It was chosen as TM due to its useful properties in terms of cell compatibility and tunable (upon modification of the ratio between the comonomers) bioresorbability. In fact, this polymer has repeatedly shown good ability to promote cell adhesion and proliferation. Thanks to these features, it is considered a reference material in the field of scaffold fabrication ,23,24.Chloroform was chosen as AS, thanks to its relatively low boiling point , which allows the resulting solution to be easily handled and yet produce a solid film in a few minutes from the deposition. Previous infrared studies did not detect any residual presence of chloroform after the ASB-SANS procedure in the formed nanostructures, even when poly(lactic acid) (PLLA), which is structurally very similar to PLGA, was used as TM . This isWe first attempted to obtain a directional growth of the nanofibers using para-dichlorobenzene PDCB, b, which This resulted in the production of very discontinuous, though remarkably aligned, nanostructures . NanostrAlso known as mint camphor, (−)-menthol  is a chiral cyclic monoterpene alcohol, whose chemical nature allows for enhanced PLGA solubility, due to its alcoholic functional group and its alkylic frame c. It hasRegarding its possible applications in scaffolds for cell growth, menthol is classified as safe by the U.S. Food and Drug Administration (FDA) , and sevUpon the aforementioned properties, we deemed menthol as a suitable SS for our studies, especially thanks to its complete solubility in chloroform, its low melting temperature, its crystalline habit (very favorable for the growth of fibrillar nanopatterns), and its wide and consolidated use in the biomedical field. Due to possible adverse effects on cell viability and growth, after the ASB-SANS process, we dedicated special care to unambiguously rule out the presence of possible menthol residues in the developed nanostructures.x wafer or polyimide-coated glass). FT-IR spectrum analysis did not evidence detectable menthol residues The use of menthol as SS in the ASB-SANS procedure resulted in PLGA fibers with a homogeneous morphology and a width varying from 300 nm to about 4 µm, depending on the initial PLGA concentration: the higher the concentration, the larger the features, in line with previous studies ,15 Figu. All theAiming at developing nanofibers suitable for tissue engineering and biomedical applications , we identified the M200 as the most suitable composition among the tested ones see . The pat2). Instead, relatively disordered and branched assemblies of nanofibers were obtained.The obtained PLGA nanofibers, even at the optimized SS/TM ratio of 200, were not uniformly oriented along large surfaces  crystal domains. These polycrystalline zones developed into disordered nanofibers domains  led to a fast evaporation of chloroform, causing unwanted secondary nucleation centers. In turn, these secondary nucleation centers provided extended seeding, which originated differently oriented and small (a few hundreds of μm domains a.2) were disrupted and sparse, likely due to the slow nucleation and precipitation dynamics of PLGA trapped within the menthol crystals [Our explanation of the observed findings was that secondary nucleation centers were originated by unwanted and uncontrolled local temperature minima, due to spatially uneven chloroform evaporation rates. To overcome this issue, the AS evaporation step of the ASB-SANS process was controlled by keeping the system within a closed system (a closed Petri dish couple), allowing the chloroform vapors to saturate the local atmosphere and considerably slowing down the chloroform evaporation. This approach resulted in an appreciable decrease in menthol nucleation, which in turn delivered an overall more uniform orientation of the fibers, as well as a significant reduction of the branching b. Howevecrystals .In order to further improve the outcome of the procedure in terms of domain sizes and orientation, we decided to minimize the temperature variations possibly occurring upon uneven heat subtraction operated by the evaporating chloroform. To achieve this result, the initial steps of the ASB-SANS process were carried out at a controlled temperature, from the ternary solution deposition to the starting of the menthol crystallization. In detail, we kept the substrate at around 38 °C, a temperature slightly lower than that of menthol crystallization (about 42–45 °C). This procedure allowed the deposited mixture to remain in liquid phase even after the chloroform was almost completely evaporated , and to We hence decreased the temperature to 36 °C, and we observed a slow and homogeneous formation of radially oriented menthol/PLGA crystals, with large domains on each considered substrate a.The so-developed fiber topology  was only partly in line with our goals. Therefore, we further modified the protocol by placing a seed menthol crystal in direct contact with the metastable solution .The crystal seed, as expected, stimulated the nucleation in the metastable solution, triggering concurrent epitaxial crystallization of menthol at multiple crystal sites . This phenomenon occurred at a slow rate and generated beautiful, regular, and straight cm-long acicular crystals b. The crSuch an ordered polycrystalline structure obtained with the ASB-SANS procedure provided extremely long and well-ordered PLGA nanofibers c. The SEOverall, the thermal regulation conferred robustness to the growth protocol, allowing reproducibility and granting a reliable control over the directionality of the crystals, and consequently of the nanofibers. With this technique, we were indeed able to obtain menthol crystals with an average lengths >3.5 cm e,f and, x chips (no surface treatment) showed significant substrate adhesion properties: PLGA nanofibers suitable for biological and/or biomedical applications require strong adhesion properties to the targeted substrate, even in the presence of biological fluids. Therefore, we first assessed their adhesion to the substrate by manually scratching the surface of the nanofibers-coated samples with a metallic spatula. Nanofibers grown on Si/SiOTo test more demanding adhesion conditions, we subjected the developed nanofibers to the so-called “tape test”, which consisted of at first pressing on the surface of the sample and then peeling off a strip of common adhesive tape. This test was carried out using the M100 solutions, in order to evaluate the impact on higher density fibers and hence to stress as much as possible their adhesion behavior. Surprisingly, SEM analysis revealed that the tape actually removed only an outer layer of fibers, and that a notable amount of them, though often damaged, remained attached to the substrate b,c, confFinally, we assessed the ability of the fibers to withstand physiological conditions by immersing supported fibrous samples in phosphate-buffered saline solution (PBS), kept at 37 °C for periods as long as two weeks.We carried out the first tests of this type on glass/PI substrates, since the PI surface is largely used to develop implantable devices  and regenerative electrodes ,37,38,39To counter this problem, we modified the PI surface making it more hydrophilic, so as to increase its interactions with the polar PLGA fibers. In more detail, we treated the PI-coated slides with UV irradiation, followed by oxygen plasma, since it is known that this type of process appreciably increases the amount of hydroxylic groups on PI surfaces ,41.We hence grew aligned PLGA nanofibers on the so-treated PI-coated slides, preparing two samples that were immersed in the same PBS solution at 37 °C, and a third one that was used as a reference (not immersed in PBS). The fibers grown on this reference sample were robustly adherent to the oxygen plasma-treated PI, as the tape test resulted in the detachment of the whole PI layer from the underlying glass, rather than in the detachment of the PLGA nanofibers from the PI. After 96 h (4 days) of immersion, the PLGA nanofibers evidenced slight structural modifications under optical microscopy imaging , but thex. These samples were tested following the same procedure of immersion in PBS at 37 °C used for the glass/PI substrates.In order to obtain a more accurate morphological characterization of the PLGA nanofibers behavior upon prolonged PBS immersion, we prepared, using the same protocol adopted for the glass/PI substrates, three more samples of Directional ASB-SANS-grown PLGA nanofibers on a substrate more suitable for SEM imaging, i.e., Si/SiOThe results of these tests are shown in These morphological changes are attributed to PLGA swelling consequent to water insertion (absorption) within the fibers and to bulk autocatalytic degradation of the PLGA. In fact, the progressive diffusion of the PBS solution inside the material causes local hydrolysis phenomena, which in turn lead to an accumulation of degradation compounds (mainly glycolic and lactic acid) . These rNeural interfaces are devices able to provide an electrical bridge between the nervous system and an artificial device to restore sensory-motor functions or bioelectronic therapies ,45,46. HIn this frame, the aligned PLGA fibers produced by directional ASB-SANS can contribute to support and promote neurons growth and axonal sprouting. In fact, aligned fibers are well known to favor the growth and development of neural cells, creating an ideal surface for effective neuronal growth and nerve regeneration ,55,56,57We hence explored the possibility to use Directional ASB-SANS for directly producing aligned PLGA nanofibers onto flexible neural interfaces. In particular, we targeted flexible neural interfaces of the TIME type , designeDespite the apparent unfavorable heterogeneous surface, we decided anyway to carry out the Directional ASB-SANS protocol on the aforementioned neural interfaces. Surprisingly, the PLGA nanofibers did develop very well all over the heterogeneous surface a,b, with2 and nanofiber lengths in the range of several cm. The directional growth was obtained thanks to an improvement of an already-known and simple technique, called ASB-SANS, which is based on a ternary solution that is deposited onto a given substrate. The ternary solution includes a Sublimating Substance -menthol) able to crystallize, PLGA, and an Auxiliary Solvent (AS) to keep the system at the liquid state. The here-demonstrated modification of the ASB-SANS method consists of adding a menthol crystal seed to the ternary solution deposited on the substrate immediately after the AS evaporation. The whole system is hence kept at a temperature slightly lower that of the solidification of the Sublimating Substance. Using this procedure, the seed crystal triggers the uniform and directional crystallization of the Sublimating Substance via concurrent epitaxial growth at multiple nucleation sites.In this work, we showed that it is possible to easily, inexpensively, and rapidly produce well-aligned PLGA nanofibers over remarkably vast areas, achieving uniformly coated surfaces of many cmx), and to oxygen-plasma-treated polyimide-coated glass, even when immersed in PBS solution and kept at physiological temperature (37 °C).The so-grown aligned PLGA nanofibers showed significant adhesion to different substrates  substrates, keeping a constant alignment, morphology, and topology.These results pave the way for practical applications of Directional ASB-SANS in oriented, nanostructured coatings for neural interface systems. Tests of neural cell growth on these substrates have already been started and will be reported soon.