Correction: A tutorial on asymmetric electrocatalysis.

Correction for 'A tutorial on asymmetric electrocatalysis' by Jonas Rein et al., Chem. Soc. Rev., 2023, https://doi.org/10.1039/D3CS00511A.

α,ß-Desaturation and Formal ß-C(sp3)-H Fluorination of N-Substituted Amines: A Late-Stage Functionalization Strategy Enabled by Electrochemistry.

Incorporation of C(sp3)-F bonds in biologically active compounds is a common strategy employed in medicinal and agricultural chemistry to tune pharmacokinetic and pharmacodynamic properties. Due to the limited number of robust strategies for C(sp3)-H fluorination of complex molecules, time-consuming de novo syntheses of such fluorinated analogs are typically required, representing a major bottleneck in the drug discovery process. In this work, we present a general and operationally simple strategy for site-specific ß-C(sp3)-H fluorination of amine derivatives including carbamates, amides, and sulfonamides, which is compatible with a wide range of functional groups including N-heteroarenes. In this approach, an improved electrochemical Shono oxidation is used to set the site of functionalization via net α,ß-desaturation to access enamine derivatives. We further developed a series of new transformations of these enamine intermediates to synthesize a variety of ß-fluoro-α-functionalized structures, allowing efficient access to pertinent targets to accelerate drug discovery campaigns.

A tutorial on asymmetric electrocatalysis.

Electrochemistry has emerged as a powerful means to enable redox transformations in modern chemical synthesis. This tutorial review delves into the unique advantages of electrochemistry in the context of asymmetric catalysis. While electrochemistry has historically been used as a green and mild alternative for established enantioselective transformations, in recent years asymmetric electrocatalysis has been increasingly employed in the discovery of novel asymmetric methodologies based on reaction mechanisms unique to electrochemistry. This tutorial review first provides a brief tutorial introduction to electrosynthesis, then explores case studies on homogenous small molecule asymmetric electrocatalysis. Each case study serves to highlight a key advance in the field, starting with the historic electrification of known asymmetric transformations and culminating with modern methods relying on unique electrochemical mechanistic sequences. Finally, we highlight case studies in the emerging reasearch areas at the interface of asymmetric electrocatalysis with biocatalysis and heterogeneous catalysis.

Exploring Electrochemical C(sp3)-H Oxidation for the Late-Stage Methylation of Complex Molecules.

The "magic methyl" effect, a dramatic boost in the potency of biologically active compounds from the incorporation of a single methyl group, provides a simple yet powerful strategy employed by medicinal chemists in the drug discovery process. Despite significant advances, methodologies that enable the selective C(sp3)-H methylation of structurally complex medicinal agents remain very limited. In this work, we disclose a modular, efficient, and selective strategy for the α-methylation of protected amines (i.e., amides, carbamates, and sulfonamides) by means of electrochemical oxidation. Mechanistic analysis guided our development of an improved electrochemical protocol on the basis of the classic Shono oxidation reaction, which features broad reaction scope, high functional group compatibility, and operational simplicity. Importantly, this reaction system is amenable to the late-stage functionalization of complex targets containing basic nitrogen groups that are prevalent in medicinally active agents. When combined with organozinc-mediated C-C bond formation, our protocol enabled the direct methylation of a myriad of amine derivatives including those that have previously been explored for the "magic methyl" effect. This synthesis strategy thus circumvents multistep de novo synthesis that is currently necessary to access such compounds and has the potential to accelerate drug discovery efforts.

Iron(III) Nitrate/TEMPO-Catalyzed Aerobic Alcohol Oxidation: Distinguishing between Serial versus Integrated Redox Cooperativity.

Aerobic alcohol oxidations catalyzed by transition metal salts and aminoxyls are prominent examples of cooperative catalysis. Cu/aminoxyl catalysts have been studied previously and feature "integrated cooperativity", in which CuII and the aminoxyl participate together to mediate alcohol oxidation. Here we investigate a complementary Fe/aminoxyl catalyst system and provide evidence for "serial cooperativity", involving a redox cascade wherein the alcohol is oxidized by an in situ-generated oxoammonium species, which is directly detected in the catalytic reaction mixture by cyclic step chronoamperometry. The mechanistic difference between the Cu- and Fe-based catalysts arises from the use iron(III) nitrate, which initiates a NOx-based redox cycle for oxidation of aminoxyl/hydroxylamine to oxoammonium. The different mechanisms for the Cu- and Fe-based catalyst systems are manifested in different alcohol oxidation chemoselectivity and functional group compatibility.

A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video.

Depression is one of the most common mental disorders, and rates of depression in individuals increase each year. Traditional diagnostic methods are primarily based on professional judgment, which is prone to individual bias. Therefore, it is crucial to design an effective and robust diagnostic method for automated depression detection. Current artificial intelligence approaches are limited in their abilities to extract features from long sentences. In addition, current models are not as robust with large input dimensions. To solve these concerns, a multimodal fusion model comprised of text, audio, and video for both depression detection and assessment tasks was developed. In the text modality, pre-trained sentence embedding was utilized to extract semantic representation along with Bidirectional long short-term memory (BiLSTM) to predict depression. This study also used Principal component analysis (PCA) to reduce the dimensionality of the input feature space and Support vector machine (SVM) to predict depression based on audio modality. In the video modality, Extreme gradient boosting (XGBoost) was employed to conduct both feature selection and depression detection. The final predictions were given by outputs of the different modalities with an ensemble voting algorithm. Experiments on the Distress analysis interview corpus wizard-of-Oz (DAIC-WOZ) dataset showed a great improvement of performance, with a weighted F1 score of 0.85, a Root mean square error (RMSE) of 5.57, and a Mean absolute error (MAE) of 4.48. Our proposed model outperforms the baseline in both depression detection and assessment tasks, and was shown to perform better than other existing state-of-the-art depression detection methods. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00152-8.

CALLM: Enhancing Clinical Interview Analysis through Data Augmentation with Large Language Models.

The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges for researchers, limiting the effectiveness of machine learning algorithms. To cope with this issue, this paper aims to introduce a novel clinical transcript data augmentation framework by leveraging large language models (CALLM). The framework follows a "patient-doctor role-playing" intuition to generate realistic synthetic data. In addition, our study introduces a unique "Textbook-Assignment-Application" (T-A-A) partitioning approach to offer a systematic means of crafting synthetic clinical interview datasets. Concurrently, we have also developed a "Response-Reason" prompt engineering paradigm to generate highly authentic and diagnostically valuable transcripts. By leveraging a fine-tuned DistilBERT model on the E-DAIC PTSD dataset, we achieved a balanced accuracy of 0.77, an F1-score of 0.70, and an AUC of 0.78 during test set evaluations, which showcase robust adaptability in both Zero-Shot Learning (ZSL) and Few-Shot Learning (FSL) scenarios. We further compare the CALLM framework with other data augmentation methods and PTSD diagnostic works and demonstrates consistent improvements. Compared to conventional data collection methods, our synthetic dataset not only demonstrates superior performance but also incurs less than 1% of the associated costs.

A systematic review on automated clinical depression diagnosis.

Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.

Analysis of Automated Clinical Depression Diagnosis in a Chinese Corpus.

Depression clinical interview corpora are essential for advancing automated depression diagnosis. While previous studies have used written speech material in controlled settings, these materials do not accurately represent spontaneous conversational speech. Additionally, self-reported measures of depression are subject to bias, making the data unreliable for training models for real-world scenarios. This study introduces a new corpus of depression clinical interviews collected directly from a psychiatric hospital, containing 113 recordings with 52 healthy and 61 depressive patients. The subjects were examined using the Montgomery-Asberg Depression Rating Scale (MADRS) in Chinese. Their final diagnosis was based on medical evaluations through a clinical interview conducted by a psychiatry specialist. All interviews were audio-recorded and transcribed verbatim, and annotated by experienced physicians. This dataset is a valuable resource for automated depression detection research and is expected to advance the field of psychology. Baseline models for detecting and predicting depression presence and level were built, and descriptive statistics of audio and text features were calculated. The decision-making process of the model was also investigated and illustrated. To the best of our knowledge, this is the first study to collect a depression clinical interview corpus in Chinese and train machine learning models to diagnose depression patients.

Bioimpedance Measurement of Knee Injuries Using Bipolar Electrode Configuration.

Currently, there is no suitable solution for the point-of-care diagnosis of knee injuries. A potential portable and low-cost technique for accessing and monitoring knee injuries is bioimpedance measurement. This study validated the feasibility of the bipolar electrode configuration for knee bioimpedance measurement with two electrodes placed on a fixed pair of knee acupuncture locations called Xiyan. Then, the study collected 76 valid samples to investigate the relationship between bioimpedance and knee injuries, among whom 39 patients have unilateral knee injuries, and 37 individuals have healthy knees. The self-contrast results indicated that knee injuries caused a reduction of bioimpedance of the knee by about 5% on average, which was detectable at around 100 kHz (p ≈ 0.001). Furthermore, the results analyzed by principal component analysis and support vector machines show that the detection sensitivity can reach 87.18% using the leave-one-out cross-validation. We also proposed a low-cost and portable bioimpedance measurement device that meets the needs for measuring knee joint bioimpedance.

High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis.

Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.