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4. Structure-based discovery of CFTR potentiators and inhibitors.

Author

Liu, Fangyu, Kaplan, Anat, Levring, Jesper, Einsiedel, Jürgen, Tiedt, Stephanie, Distler, Katharina, Omattage, Natalie, Kondratov, Ivan, Moroz, Yurii, Pietz, Harlan, Irwin, John, Gmeiner, Peter, Shoichet, Brian, and Chen, Jue

Subjects
ABC transporter, anion channel, inhibitors, large-scale docking, ligand discovery, potentiators, Cystic Fibrosis Transmembrane Conductance Regulator, Humans, Molecular Docking Simulation, Cystic Fibrosis, Aminophenols, Drug Discovery, Cryoelectron Microscopy, Quinolones, Allosteric Site, Animals, Ligands
Abstract

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

Published
2024

8. Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks

Author

Chen, Jue, Yuan, Huan, Tan, Jianchao, Chen, Bin, Song, Chengru, and Zhang, Di

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Multimedia
Abstract

Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such as neuromorphic chips. Most previous work focuses on SNNs training strategies to improve model performance and brings larger and deeper network architectures. It is difficult to deploy these complex networks on resource-limited edge devices directly. To meet such demand, people compress SNNs very cautiously to balance the performance and the computation efficiency. Existing compression methods either iteratively pruned SNNs using weights norm magnitude or formulated the problem as a sparse learning optimization. We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency. We also demonstrate that jointly applying compression and finetuning on SNNs is better than sequentially, especially for extreme compression ratios. The compressed SNN models achieved state-of-the-art (SOTA) performance on various benchmark datasets and architectures. Our code is available at https://github.com/chenjallen/Resource-Constrained-Compression-on-SNN., Comment: ACM MM 2023

Published
2023

9. Chinese Expert Consensus on the Diagnosis and Treatment of Binge Eating Disorder

Author

Eating Disorders Coordination Group of the Psychosomatic Medicine Society of the Chinese Medical Association, Eating Disorders Research Collaboration Group of the Psychiatric Medicine Society of the Chinese Medical Association, PENG Sufang, HE Qianqian, LIU Qiang, CHEN Yan, ZHANG Lan, LI Xueni, QIAO Huifen, KONG Qingmei, KUANG Guifang, CHEN Han, YU Yang, JIANG Jingjing, LIU Lanying, ZHANG Darong, LI Keqing, WANG Zhen, CHEN Jue

Subjects
binge-eating disorder, feeding and eating disorders, psychological treatment, drug therapy, expert consensus, Medicine
Abstract

Binge-eating disorder (BED) is a type of eating disorder characterized by recurrent episodes of binge eating. Patients with BED have recurrent impulsive and out-of-controlled binge eating behavior, which can lead to gastrointestinal disorders, obesity, and the resulting metabolic and functional disorders of various systems and other physical problems; and it is also often co-morbid with affective disorder, anxiety disorders, substance use disorders, and other mental disorders, which need to be taken seriously. However, the recognition of BED in China was late, and it is difficult to achieve early identification and diagnosis, resulting in a prolonged course of disease. Therefore, under the organization of the Eating Disorders Coordination Group of the Psychosomatic Medicine Society of the Chinese Medical Association, together with the Eating Disorders Research Collaboration Group of the Psychiatric Medicine Society of the Chinese Medical Association, an expert group composed of 15 psychiatrists and evidence-based medicine experts, and 2 internal medicine experts, formulated this expert consensus based on evidence-based medical evidence, domestic and foreign guidelines and expert consensus, and expert experiences. The aim of this study is to improve the recognition, diagnosis and treatment of BED by psychiatrists, internists and general practitioners in China.

Published
2024
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10. Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings

Author

Zhang, Yuan, Cao, Jian, Zhang, Ling, Chen, Jue, Sun, Wenyu, and Wang, Yuan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The event streams generated by dynamic vision sensors (DVS) are sparse and non-uniform in the spatial domain, while still dense and redundant in the temporal domain. Although spiking neural network (SNN), the event-driven neuromorphic model, has the potential to extract spatio-temporal features from the event streams, it is not effective and efficient. Based on the above, we propose an events sparsification spiking framework dubbed as Razor SNN, pruning pointless event frames progressively. Concretely, we extend the dynamic mechanism based on the global temporal embeddings, reconstruct the features, and emphasize the events effect adaptively at the training stage. During the inference stage, eliminate fruitless frames hierarchically according to a binary mask generated by the trained temporal embeddings. Comprehensive experiments demonstrate that our Razor SNN achieves competitive performance consistently on four events-based benchmarks: DVS 128 Gesture, N-Caltech 101, CIFAR10-DVS and SHD., Comment: Accepted by ICANN 2023 (Oral)

Published
2023

13. Oscillation stability analysis and mitigation method of photovoltaic field connected to the grid via VSC-HVDC

Author

DU Wenjuan, HAO Xiangkun, and CHEN Jue

Subjects
voltage source converter based high voltage direct current(vsc-hvdc), photovoltaic field, open-loop mode resonance, stability analysis, damping controller, dynamic interaction, Applications of electric power, TK4001-4102
Abstract

Photovoltaic field connected to the grid via voltage source converter based high voltage direct current (VSC-HVDC) transmission is an important trend in the future development of the power system, but there is a risk that the interaction between photovoltaic and VSC-HVDC system may trigger system oscillations and instabilities. So, the open-loop and closed-loop interconnection models of the photovoltaic field integrated into grid through VSC-HVDC are established. The dynamic interaction between the photovoltaic subsystem and the VSC-HVDC subsystem based on the open-loop mode resonance theory are analyzed. When a strong interaction between two subsystems occurs, it may cause the corresponding closed-loop oscillation mode to enter the right side of the complex plane and trigger system oscillation instability. The risk of interaction instability between the photovoltaic subsystem and the VSC-HVDC subsystem can be mitigated by adjusting the control parameters of the dominant link. If the control parameters can not be adjusted, an additional damping controller for the photovoltaic unit is proposed as an inhibitory measure, which destroys the mode resonance phenomenon by adjusting the open-loop oscillatory modes of the photovoltaic subsystem away from the open-loop oscillatory modes of the VSC-HVDC subsystem in the complex plane, so as to stabilise the closed-loop system. The correctness of the above theoretical analysis results and the superiority of the proposed damping controller are verified by the simulation examples.

Published
2024
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14. Inference and learning algorithms for wireless iterative receivers

Author

Chen, Jue, Maunder, Rob, and Ng, Soon Xin

Abstract

With the development of science and technology, more and more users and internet nodes are connected to the wireless network, resulting in a more complex communication environment and the requirement to process a large amount of data in the network. In this case, machine intelligence technology has been widely used in wireless network processing big data by virtue of its powerful big data computing capability. The key to machine intelligence is to design inference and decoding algorithms for different scenarios, and both Compressive Sensing (CS) and Deep Learning (DL) are the same ideas of machine intelligence, which emphasizes the learning and construction of features. Hence, this thesis may focus on designing inference and decoding algorithms, which may employ CS and DL in Wireless Sensor Networks (WSNs) and Multiple-Input Multiple-Output (MIMO) schemes, and all of them involve iterative decoding. The implementation of iterative decoding in WSNs and the MIMO scheme is designed for providing better service in 5G and beyond wireless communication networks. In WSNs, they may suffer from the constrained energy problem, CS is an impressive technique for the WSNs to overcome this problem, and support identification is an important point in the CS technique. A novel support identification technique based on factor graphs and belief propagation is proposed for CS-aided WSNs, which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. This thesis shows that the proposed factor graph-based approach outperforms the state-of-the-art relative frequency-based approach and Orthogonal Matching Pursuit (OMP) algorithm. This thesis also demonstrates that the proposed support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph-based approach, this thesis conceives a hybrid support identification technique for reducing communication between the sensor nodes and the Fusion Center (FC), while maintaining high-accuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction. Considering that WSNs suffer not only from constrained energy but also from unreliable channels, channel coding may be employed to protect the transmitted signals from unreliable channels. In this circumstance, the novel concept of joint CS and Low-Density Parity Check (LDPC) coding is conceived for Joint Source-Channel Coding (JSCC) in WSNs supporting a massive number of signals. More explicitly, this thesis demonstrates this concept for a specific scheme, which supports a massive number of signals simultaneously, using a small number of Sensor Nodes (SNs) based on the concept of CS. The compressed signals are LDPC coded in order to protect them from poor transmission channels. This thesis also proposes a new iterative joint source-channel decoding philosophy for exchanging soft extrinsic information, which combines CS decoding and LDPC decoding by merging their respective factor graphs. In this proposed iterative LDPC-CS decoding, an exhaustive Maximum A Posteriori (e-MAP) technique is employed in the CS decoding, which traverses all possible combinations of the connected signals. The BLock Error Rate (BLER) results show that the proposed iterative joint LDPC-CS decoding scheme attains about 1.5 dB gain at a BLER of 10−3 compared to a benchmarker, which employs separate CS and LDPC decoding. Naturally, this gain is achieved at the cost of approximately doubling the complexity of the proposed iterative joint LDPC-CS decoding scheme. The proposed e-MAP approach in the CS decoding of the JSCC scheme may suffer from high complexity according to the total number of combinations may be large. In order to solve the complexity problem of the e-MAP approach in the CS decoding, this thesis proposes two reduced complexity approaches based on the tree search, namely Soft-Input Soft-Output (SISO) Sphere Decoding (SD) approach and Hamming Distance (HD) approach. In the proposed SD and HD approaches, only the more likely combinations of source signals are tested for reducing the CS decoding complexity. More specifically, a tree search technique is used in the first step to finding the most likely combination of source signal values. Then, in the second step, the proposed SD continues the tree search to find a set of alternative hypotheses. This facilitates the generation of high-quality extrinsic information, which may be iteratively exchanged with the LDPC decoder. By contrast, in the HD approach, the second step obtains the alternative hypotheses within a certain HD of the most likely source signal combination. The proposed SD and HD approaches have been proven to reduce the complexity significantly while maintaining the performance of the e-MAP approach. In particular, this thesis shows that the e-MAP solution is about 56 times more complex than the SD approach and around 210 times more complex than the HD approach. Apart from the WSNs, the inference and decoding algorithms design is also important in the MIMO iterative receivers in order to improve decoding performance or reduce decoding complexity. In this case, this thesis proposes a DL-aided Logarithmic Likelihood Ratio (LLR) correction method, in order to improve the performance of Multiple-Input Multiple-Output (MIMO) receivers, where it is typical to adopt reduced-complexity algorithms for avoiding the excessive complexity of optimal full-search algorithms. These sub-optimal techniques typically express the probabilities of the detected bits using LLRs that often have values that are not consistent with their true reliability, either expressing too much confidence or not enough confidence in the value of the corresponding bits, leading to performance degradation. To circumvent this problem, a Deep Neural Network (DNN) is trained for detecting and correcting both over-confident and under-confident LLRs. It is demonstrated that the complexity of employing the DL-aided technique is relatively low compared to the popular reduced-complexity receiver detector techniques since it only depends on a small number of real-valued inputs. Furthermore, the proposed approach is applicable to a wide variety of iterative receivers as demonstrated in the context of an iterative detection and decoding-aided MIMO system, which uses a low-complexity Smart Ordering Candidate Adding (SOCA) scheme for MIMO detection and LDPC codes for channel coding. Extrinsic Information Transfer (EXIT) charts are adopted for quantifying the Mutual Information (MI) and show that the proposed DL method significantly improves the BLER. Explicitly, it is demonstrated that about 0.9 dB gain can be achieved at a BLER of 10−3 by employing the proposed DL-aided LLR correction method, at the modest cost of increasing the complexity by 16% compared to a benchmarker dispensing with LLR correction. Considering the complex communication environment of the 5G and beyond communication systems, the interference problem becomes vital. The demodulation reference signal in the 5G Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) wave has been designed to enable Minimum Mean-Square Error-Interference Rejection Combining (MMSEIRC) equalization, which becomes state-of-the-art. By contrast, in the 4G LTE system, the state-of-the-art non-linear turbo equalization techniques, where the nonlinearity afforded superior performance, compared to linear equalization. The reversion of the state-of-the-art from the non-linear technique in 4G to the linear technique in 5G represents an opportunity for nonlinear techniques to be introduced into the 5G systems, which preserves the IRC capability but additionally benefits from the performance enhancement of non-linear processing. A novel technique is proposed in the MIMO-OFDM system with interference, where a linear MMSE-IRC equalizer plus a non-linear equalizer, which makes the system non-linear is employed for interference mitigation and signal detection. The MMSE-IRC is a conventional linear algorithm, which is designed to not only enhance the transmitted signal but also attenuate the noise and the crucial interference as much as possible. This is in contrast to for example MMSE equalizers, which treat interference as noise and attempt only to attenuate this noise without the advantage of considering the particular nature of the interference. However, the MMSE-IRC may fail to detect the signals when all degrees of the freedom of the MIMO channel are occupied by the desired users, for example, when the transmitter adopts a high number of transmission layers. The linear MMSE-IRC equalizer is modified, which is compatible with the inputs of a non-linear equalizer. More specifically, the inputs need to be equivalent channel information, covariance matrix, and equivalent received signals.

Published
2023

15. Chinese Expert Consensus on the Diagnosis and Treatment of Anorexia Nervosa

Author

Eating Disorders Coordination Group of the Psychosomatic Medicine Society of the Chinese Medical Association, Eating Disorders Research Collaboration Group of the Psychiatric Medicine Society of the Chinese Medical Association, CHEN Han, CHEN Yan, HAN Huiqin, LI Xueni, ZHANG Lan, KONG Qingmei, QIAO Huifen, KUANG Guifang, SUN Jianqin, YU Yang, JIN Furui, JIANG Jingjing, ZHANG Darong, LI Keqing, WANG Zhen, CHEN Jue

Subjects
anorexia nervosa, nutrition therapy, psychotherapy, pharmacotherapy, expert consensus, Medicine
Abstract

Anorexia nervosa (AN) is a type of eating disorders characterized by self-starvation, significant loss of body mass, and malnutrition. AN has the highest mortality rate among all psychiatric disorders, leading to severe psychopathologic symptoms and life-threatening medical complications. However, AN cannot be recognized and diagnosed early in China, resulting in a prolonged course of the disease. Therefore, under the organization of the Eating Disorders Coordination Group of the Psychosomatic Medicine Society of the Chinese Medical Association, together with the Eating Disorders Research Collaboration Group of the Psychiatric Medicine Society of the Chinese Medical Association, this expert consensus was formulated by 16 medical experts based on evidence-based medical evidence, domestic and international guidelines and expert consensus, expert experience, etc., in order to improve the recognition, diagnosis and treatment of AN among domestic professionals.

Published
2024
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16. Boosting Pruned Networks with Linear Over-parameterization

Author

Qian, Yu, Cao, Jian, Li, Xiaoshuang, Zhang, Jie, Li, Hufei, and Chen, Jue

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Structured pruning compresses neural networks by reducing channels (filters) for fast inference and low footprint at run-time. To restore accuracy after pruning, fine-tuning is usually applied to pruned networks. However, too few remaining parameters in pruned networks inevitably bring a great challenge to fine-tuning to restore accuracy. To address this challenge, we propose a novel method that first linearly over-parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters and then re-parameterizes them to the original layers after fine-tuning. Specifically, we equivalently expand the convolution/linear layer with several consecutive convolution/linear layers that do not alter the current output feature maps. Furthermore, we utilize similarity-preserving knowledge distillation that encourages the over-parameterized block to learn the immediate data-to-data similarities of the corresponding dense layer to maintain its feature learning ability. The proposed method is comprehensively evaluated on CIFAR-10 and ImageNet which significantly outperforms the vanilla fine-tuning strategy, especially for large pruning ratio.

Published
2022

18. Chinese Expert Consensus on the Diagnosis and Treatment of Bulimia Nervosa

Author

Eating Disorders Coordination Group of the Psychosomatic Medicine Society of the Chinese Medical Association, Eating Disorders Research Collaboration Group of the Psychiatric Medicine Society of the Chinese Medical Association, CHEN Yan, CHEN Han, LIU Lanying, KONG Qingmei, QIAO Huifen, ZHANG Lan, LI Xueni, KUANG Guifang, ZHANG Darong, LI Keqing, WANG Zhen, CHEN Jue

Subjects
bulimia nervosa, psychological treatment, drug treatment, diagnostic and treatment guideline, guidelines, Medicine
Abstract

Bulimia nervosa is a type of eating disorders characterized by recurrent binge eating and compensatory behaviors to prevent weight gain, as well as excessive concern about body shape and mass. The patients with bulimia nervosa present with disturbed eating behavior, resulting in electrolyte abnormalities, gastrointestinal disorders, metabolic and endocrine disorders and other physical problems; secondly, the bulimia nervosa patients are often co-morbid with a variety of mental disorders such as bipolar disorder, depression, substance use disorders, which need to be taken seriously. However, bulimia nervosa and even eating disorders cannot be recognized and diagnosed at an early stage in China, which leads to a prolonged course of the disease. Therefore, under the organization of the Eating Disorders Coordination Group of the Psychosomatic Medicine Society of the Chinese Medical Association, in conjunction with the Eating Disorders Research Collaboration Group of the Psychiatric Medicine Society of the Chinese Medical Association, this expert consensus was formulated by 12 experts in the field of psychiatry and evidence-based medicine based on evidence-based medicine, domestic and foreign guidelines and expert consensus and expert experience, in order to improve the recognition, diagnosis and treatment of bulimia nervosa by psychiatric professionals and general practitioners in China.

Published
2023
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19. Stability analysis of grid-connected photovoltaic generation control system under DC voltage timescale

Author

GONG Ziwei, WANG Haifeng, CHEN Jue, and DU Wenjuan

Subjects
grid-connected photovoltaic power generation system, dc voltage outer loop, phase-locked loop, dc voltage timescale, stability criterion, small-disturbance stability, Applications of electric power, TK4001-4102
Abstract

With the expansion of grid-connected photovoltaic power generation, the problem of small-disturbance stability caused by the dynamic characteristics of its converter control system becomes increasingly prominent. Due to the small capacity of a single photovoltaic power generation unit, the grid-connected photovoltaic power generation system often contains a large number of photovoltaic power generation units. As a result, the model of the system is high-order. And the large numbel of photovolatic power generation units greatly increases the complexity of stability analysis. Therefore, in order to propose a relatively simple and effective stability discrimination method, a reduced-order model of small-disturbance stability analysis of grid-connected photovoltaic power generation system under DC voltage control timescale through the timescale decomposition method is established in this paper. Then the small-disturbance stability criterion of the system is deduced by applying the Routh-Hurwitz criterion on the basis of this model. From the criterion, the key factors affecting the small-disturbance stability of the system under DC voltage control timescale are analyzed. The main unfavorable affecting factors are heavy loading condition, condition of weak grid connection, improper setting of parameters of converter control systems. The correctness and effectiveness of the proposed stability criterion are verified by simulation examples. The results show that the stability criterion can effectively evaluate the small-disturbance stability of the grid-connected photovoltaic power generation system under DC voltage timescale. The evaluation method does not need to establish a complex high-order model of the system, which greatly simplifies the calculation and analysis process.

Published
2023
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27. Impact of COVID-19 Lockdown in Eating Disorders: A Multicentre Collaborative International Study

Author

Baenas, Isabel, Etxandi, Mikel, Munguía, Lucero, Granero, Roser, Mestre-Bach, Gemma, Sánchez, Isabel, Ortega, Emilio, Andreu, Alba, Moize, Violeta L, Fernández-Real, Jose-Manuel, Tinahones, Francisco J, Diéguez, Carlos, Frühbeck, Gema, Le Grange, Daniel, Tchanturia, Kate, Karwautz, Andreas, Zeiler, Michael, Imgart, Hartmut, Zanko, Annika, Favaro, Angela, Claes, Laurence, Shekriladze, Ia, Serrano-Troncoso, Eduardo, Cecilia-Costa, Raquel, Rangil, Teresa, Loran-Meler, Maria Eulalia, Soriano-Pacheco, José, Carceller-Sindreu, Mar, Navarrete, Rosa, Lozano, Meritxell, Linares, Raquel, Gudiol, Carlota, Carratala, Jordi, Plana, Maria T, Graell, Montserrat, González-Parra, David, Barrio, José A Gómez-del, Sepúlveda, Ana R, Sánchez-González, Jéssica, Machado, Paulo PP, Håkansson, Anders, Túry, Ferenc, Pászthy, Bea, Stein, Daniel, Papezová, Hana, Gricova, Jana, Bax, Brigita, Borisenkov, Mikhail F, Popov, Sergey V, Gubin, Denis G, Petrov, Ivan M, Isakova, Dilara, Mustafina, Svetlana V, Kim, Youl-Ri, Nakazato, Michiko, Godart, Nathalie, van Voren, Robert, Ilnytska, Tetiana, Chen, Jue, Rowlands, Katie, Voderholzer, Ulrich, Monteleone, Alessio M, Treasure, Janet, Jiménez-Murcia, Susana, and Fernández-Aranda, Fernando

Subjects
Eating Disorders, Health Services, Behavioral and Social Science, Nutrition, Clinical Research, Mental Health, Prevention, Brain Disorders, Mental health, Good Health and Well Being, Adolescent, Adult, Asia, COVID-19, Child, Europe, Feeding and Eating Disorders, Female, Humans, Internationality, Longitudinal Studies, Male, Quarantine, SARS-CoV-2, Social Isolation, Young Adult, eating disorders, COVID-19 lockdown, COVID-19 Isolation Eating Scale, eating symptoms, psychological impact, Food Sciences, Nutrition and Dietetics
Abstract

The COVID-19 lockdown has had a significant impact on mental health. Patients with eating disorders (ED) have been particularly vulnerable. (1) To explore changes in eating-related symptoms and general psychopathology during lockdown in patients with an ED from various European and Asian countries; and (2) to assess differences related to diagnostic ED subtypes, age, and geography. The sample comprised 829 participants, diagnosed with an ED according to DSM-5 criteria from specialized ED units in Europe and Asia. Participants were assessed using the COVID-19 Isolation Scale (CIES). Patients with binge eating disorder (BED) experienced the highest impact on weight and ED symptoms in comparison with other ED subtypes during lockdown, whereas individuals with other specified feeding and eating disorders (OFSED) had greater deterioration in general psychological functioning than subjects with other ED subtypes. Finally, Asian and younger individuals appeared to be more resilient. The psychopathological changes in ED patients during the COVID-19 lockdown varied by cultural context and individual variation in age and ED diagnosis. Clinical services may need to target preventive measures and adapt therapeutic approaches for the most vulnerable patients.

Published
2022

28. CRNN-SA: A Network Intrusion Detection Method Based on Deep Learning

Author

Liu, Wanxiao, Chen, Jue, Qiu, Xihe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Xiaochun, editor, Suhartanto, Heru, editor, Wang, Guoren, editor, Wang, Bin, editor, Jiang, Jing, editor, Li, Bing, editor, Zhu, Huaijie, editor, and Cui, Ningning, editor

Published
2023
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29. Feasibility, Safety and Timing of Secondary Percutaneous Coronary Intervention via Distal Transradial Artery Approach

Author

LIU Minghao, WANG Pan, GAO Lijian, XU Shuqing, WANG Huanhuan, ZHAO Guangxian, CHEN Jue, QIAO Shubin, XU Bo, YUAN Jinqing

Subjects
coronary disease, coronary artery disease, percutaneous coronary intervention, radial artery, distal radial artery, reoperation, treatment outcome, safety, Medicine
Abstract

Background Percutaneous coronary intervention (PCI) via distal transradial artery approach (dTRA) is effective in reducing the incidence of radial artery occlusion, however, there are no reports on the feasibility, safety and timing of secondary PCI via dTRA domestically. Objective To explore the feasibility, safety and timing of secondary PCI via dTRA. Methods A total of 70 patients who were to undergo secondary PCI via dTRA in Fuwai Hospital, CAMS&PUMC from July 2021 to July 2022 were consecutively included and divided into ≤30 d group (n=33) and >30 d group (n=37) according to the time interval since the last PCI via dTRA. The general clinical data, operation-related indicators, coronary artery lesions and radial artery inner diameters at 5, 10 and 15 cm proximal to the radial styloid process before and 24 h after PCI were compared between the two groups. Results The dTRA puncture and catheterization were successfully conducted in 69 of 70 patients, with a success rate of 98.6% (69/70) . Second PCI via dTRA was successfully completed in 66 of 67 patients assessed for secondary PCI, with a success rate of 98.5% (66/67) . There were significantly difference in age, platelet count, incidence of diabetes, number of lesioned vessels and ACC/AHA coronary artery lesions typing (P30 d group before and 24 h after PCI when comparing between the two groups, respectively (P30 d group (P30 days from the first dTRA PCI) , and the timing of secondary PCI via dTRA should be determined according to the patient's condition and puncture site.

Published
2023
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45. COVID Isolation Eating Scale (CIES): Analysis of the impact of confinement in eating disorders and obesity—A collaborative international study

Author

Fernández‐Aranda, Fernando, Munguía, Lucero, Mestre‐Bach, Gemma, Steward, Trevor, Etxandi, Mikel, Baenas, Isabel, Granero, Roser, Sánchez, Isabel, Ortega, Emilio, Andreu, Alba, Moize, Violeta L, Fernández‐Real, Jose M, Tinahones, Francisco J, Diegüez, Carlos, Frühbeck, Gema, Le Grange, Daniel, Tchanturia, Kate, Karwautz, Andreas, Zeiler, Michael, Favaro, Angela, Claes, Laurence, Luyckx, Koen, Shekriladze, Ia, Serrano‐Troncoso, Eduardo, Rangil, Teresa, Meler, Maria Eulalia Loran, Soriano‐Pacheco, Jose, Carceller‐Sindreu, Mar, Bujalance‐Arguijo, Sara, Lozano, Meritxell, Linares, Raquel, Gudiol, Carlota, Carratala, Jordi, Sanchez‐Gonzalez, Jessica, Machado, Paulo PP, Håkansson, Anders, Túry, Ferenc, Pászthy, Bea, Stein, Daniel, Papezová, Hana, Bax, Brigita, Borisenkov, Mikhail F, Popov, Sergey V, Kim, Youl‐Ri, Nakazato, Michiko, Godart, Nathalie, Voren, Robert, Ilnytska, Tetiana, Chen, Jue, Rowlands, Katie, Treasure, Janet, and Jiménez‐Murcia, Susana

Subjects
Clinical Research, Anorexia, Behavioral and Social Science, Eating Disorders, Nutrition, Brain Disorders, Mental Health, Mental health, Good Health and Well Being, Adolescent, Adult, Aged, COVID-19, Factor Analysis, Statistical, Feeding and Eating Disorders, Female, Humans, Male, Middle Aged, Obesity, Psychiatric Status Rating Scales, Psychometrics, Social Isolation, Spain, Young Adult, COVID Isolation Eating Scale, eating disorders, obesity, Clinical Sciences, Psychology, Clinical Psychology
Abstract

Confinement during the COVID-19 pandemic is expected to have a serious and complex impact on the mental health of patients with an eating disorder (ED) and of patients with obesity. The present manuscript has the following aims: (1) to analyse the psychometric properties of the COVID Isolation Eating Scale (CIES), (2) to explore changes that occurred due to confinement in eating symptomatology; and (3) to explore the general acceptation of the use of telemedicine during confinement. The sample comprised 121 participants (87 ED patients and 34 patients with obesity) recruited from six different centres. Confirmatory Factor Analyses (CFA) tested the rational-theoretical structure of the CIES. Adequate goodness-of-fit was obtained for the confirmatory factor analysis, and Cronbach alpha values ranged from good to excellent. Regarding the effects of confinement, positive and negative impacts of the confinement depends of the eating disorder subtype. Patients with anorexia nervosa (AN) and with obesity endorsed a positive response to treatment during confinement, no significant changes were found in bulimia nervosa (BN) patients, whereas Other Specified Feeding or Eating Disorder (OSFED) patients endorsed an increase in eating symptomatology and in psychopathology. Furthermore, AN patients expressed the greatest dissatisfaction and accommodation difficulty with remote therapy when compared with the previously provided face-to-face therapy. The present study provides empirical evidence on the psychometric robustness of the CIES tool and shows that a negative confinement impact was associated with ED subtype, whereas OSFED patients showed the highest impairment in eating symptomatology and in psychopathology.

Published
2020

48. The crystal structure of 2-bromo-1,3-phenylene bis(4-methylbenzenesulfonate), C20H17BrO6S2

Author

Chen Jue-Yuan, Zou Long-Sheng, Lai Ni, Yin Zi-Yi, Yuan Lin, and Li Zhong-Yan

Subjects
2244691, Physics, QC1-999, Crystallography, QD901-999
Abstract

C20H17BrO6S2, triclinic, P1‾ $P\overline{1}$ (no. 2), a = 8.173(2) Å, b = 8.256(2) Å, c = 16.514(5) Å, α = 89.947(3)°, β = 83.105(3)°, γ = 72.077(3)°, V = 1051.8(5) Å3, Z = 2, Rgt (F) = 0.0334, wRref (F 2) = 0.0889, T = 296(2) K.

Published
2023
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