ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings

Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 11 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.Comment: 9 pages, 4 figures, AAAI-202

Effects of a Cardiotonic Medicine Danshen Pills, on Cognitive Ability and Expression of PSD-95 in a Vascular Dementia Rat Model

A widely used Chinese cardiotonic proprietary medicine, compound Danshen dripping pills (CDDP, Fufang Danshen Diwan) has also begun to be used for treatment of vascular dementia (VaD). We tried to explore the mechanism of CDDP action in this case. A VaD experimental model was built in rats by bilateral ligation of the common carotid arteries. The cognitive ability of experimental animals was evaluated in the Morris water maze test. Synaptic ultrastructural changes in the hippocampus were detected by transmission electron microscopy; expression of PSD-95 mRNA in the hippocampus was examined using hybridization in situ. The latter index (mRNA expression) in the VaD group was significantly lower than those in the CDDP and shamoperated groups (P < 0.05). CDDP treatment considerably improved disturbed ultrastructural synaptic characteristics in the hippocampus of VaD rats. The mean escape latency in the Morris water maze test was significantly shorter in CDDP-treated VaD rats, compared with that those of the VD group (P < 0.05). In the CDDP group compared to the VaD one, escape strategies improved from edge and random searches to more linear swim pathway (P < 0.05). Thus, decreasing expression of PSD-95 plays an important role in the pathogenesis of VaD. CDDP treatment improves the learning and memory ability of VaD rats by improving neural synaptic ultrastructural characteristics and increasing expression of PSD-95 mRNA in the hippocampus.Широко вживаний у Китаї патентований кардіотонічний засіб «складні пілюлі Даншен» (CDDP) почав також використовуватися для лікування васкулярної деменції (ВД). Ми досліджували можливі механізми дії цього засобу в даному аспекті. ВД моделювали у щурів, застосовуючи білатеральну перев’язку загальних сонних артерій. Когнітивні здатності експериментальних тварин оцінювали в тесті водного лабіринту Морріса. Ультраструктурні зміни синаптичних утворень у гіпокампі спостерігали, використовуючи трансмісійну електронну мікроскопію. Експресію мРНК білка PSD-95 у гіпокампі оцінювали із застосуванням методики гібридизації in situ. Останній показник (експресія мРНК) у щурів групи ВД був вірогідно нижчим, ніж у тварин контрольної групи та щурів із ВД, лікованих за допомогою CDDP. Середня затримка реакції уникання у тварин групи ВД істотно перевищувала відповідне значення в групі CDDP (P < 0.05). Стратегії уникання в останній групі були вірогідно кращими, ніж у групі ВД (збільшувалася пропорція лінійних маршрутів порівняно з «крайовими» та випадковими; P < 0.05). Зроблено висновок, що зниження експресії PSD-95 відіграє важливу роль у патогенезі ВД. Лікувальний ефект CDDP забезпечує покращення пам’яті та здатності до навчання у щурів з ВД; цей ефект опосередковується покращенням ультраструктурних показників синаптичних структур та збільшенням експресії мРНК білка PSD-95 у гіпокампі

CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines

Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data. Although existing methods, like rule-based approaches and generative adversarial networks (GANs), generate synthetic data that resembles real-world EHR data, these methods often use a tabular format, disregarding temporal dependencies in patient histories and limiting data replication. Recently, there has been a growing interest in leveraging Generative Pre-trained Transformers (GPT) for EHR data. This enables applications like disease progression analysis, population estimation, counterfactual reasoning, and synthetic data generation. In this work, we focus on synthetic data generation and demonstrate the capability of training a GPT model using a particular patient representation derived from CEHR-BERT, enabling us to generate patient sequences that can be seamlessly converted to the Observational Medical Outcomes Partnership (OMOP) data format

Pioneering nanomedicine in orthopedic treatment care: a review of current research and practices

A developing use of nanotechnology in medicine involves using nanoparticles to administer drugs, genes, biologicals, or other materials to targeted cell types, such as cancer cells. In healthcare, nanotechnology has brought about revolutionary changes in the treatment of various medical and surgical conditions, including in orthopedic. Its clinical applications in surgery range from developing surgical instruments and suture materials to enhancing imaging techniques, targeted drug delivery, visualization methods, and wound healing procedures. Notably, nanotechnology plays a significant role in preventing, diagnosing, and treating orthopedic disorders, which is crucial for patients’ functional rehabilitation. The integration of nanotechnology improves standards of patient care, fuels research endeavors, facilitates clinical trials, and eventually improves the patient’s quality of life. Looking ahead, nanotechnology holds promise for achieving sustained success in numerous surgical disciplines, including orthopedic surgery, in the years to come. This review aims to focus on the application of nanotechnology in orthopedic surgery, highlighting the recent development and future perspective to bridge the bridge for clinical translation

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes

Targeting the miR-6734-3p/ZEB2 axis hampers development of non-small cell lung cancer (NSCLC) and increases susceptibility of cancer cells to cisplatin treatment

The unclear pathogenesis mechanisms and resistance of cancer cells to chemical drugs serious limits the development of effective treatment strategies for non-small cell lung cancer (NSCLC). In this study, we managed to investigate this issue, and identify potential cancer associated biomarkers for NSCLC diagnosis, prognosis and treatment. This study found that miR-6734-3p was downregulated in both NSCLC clinical specimens (tissues and serum) and cells, compared to the normal tissues and cells. Next, upregulation of miR-6734-3p inhibited cancer formation and progression in NSCLC cells in vitro and in vivo. Conversely, miR-6734-3p ablation had opposite effects and facilitated NSCLC development. In addition, miR-6734-3p bound to the 3ʹ untranslated region (3ʹUTR) of zinc finger E-box binding homeobox 2 (ZEB2) mRNA to suppress its expressions in NSCLC cells. Interestingly, the inhibiting effects of miR-6734-3p overexpression on NSCLC progression were abrogated by upregulating ZEB2. Furthermore, both upregulated miR-6734-3p and silencing of ZEB2 increased cisplatin-sensitivity in cisplatin-resistant NSCLC (CR-NSCLC) cells. Taken together, miR-6734-3p played an anti-tumor role to hinder cancer development and enhanced the cytotoxic effects of cisplatin treatment on NSCLC cells by downregulating ZEB2

Effect of aromatase inhibitors for preventing ovarian hyperstimulation syndrome in infertile patients undergoing in vitro fertilization: a systematic review and meta-analysis

Abstract Purpose To summarize the findings of relevant randomized controlled trials (RCTs) and conduct a meta-analysis to investigate the potential effect of aromatase inhibitors on preventing moderate to severe ovarian hyperstimulation syndrome (OHSS) in infertile women undergoing in vitro fertilization (IVF). Methods We searched for relevant RCTs in electronic databases, including MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov (from inception to August 2023). In addition, we manually searched the related reviews and reference lists of included studies for further relevant studies. We included RCTs where aromatase inhibitors prescribed either during controlled ovarian stimulation (COS) or in early luteal phase. The meta-analysis was performed using RevMan 5.4.1 software. The primary outcome was the incidence of moderate to severe OHSS. A descriptive analysis was conducted in cases where a meta-analysis was not feasible due to heterogeneity or lack of comparable data. Results 2858 records were retrieved and 12 RCTs were finally included. Letrozole was administered in the treatment group during COS in seven RCTs, whereas in the early luteal phase in five RCTs. Compared with the control group, the risk of moderate to severe OHSS significantly reduced by 55% in the letrozole group (RR 0.45, 95% CI 0.32 to 0.64, I 2  = 0%, 5 RCTs, 494 patients). Moreover, serum estradiol (E2) levels on hCG trigger day significantly decreased with the administration of letrozole during COS (MD -847.23, 95% CI -1398.00 to -296.47, I 2  = 93%, 5 RCTs, 374 patients). And serum E2 levels on the 4th, 5th and 7th to 10th day after hCG trigger were also significantly lower than those in the control group when letrozole was administered in the early luteal phase. Conclusions Patients with high risk of OHSS probably benefit from letrozole, which has been revealed to reduce the incidence of moderate to severe OHSS by this systematic review. However, the very limited number of participants and the quality of the included studies does not allow to recommend letrozole for the prevention of severe OHSS

Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of preference drift. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 9 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec

"Cell-addictive" dual-target traceable nanodrug for Parkinson's disease treatment via flotillins pathway

alpha-synclein (aS) aggregation is a representative molecular feature of the pathogenesis of Parkinson&#39;s disease (PD). Epigallocatechin gallate (EGCG) can prevent alpha S aggregation in vitro. However, the in vivo effects of PD treatment are poor due to the obstacles of EGCG accumulation in dopaminergic neurons, such as the blood brain barrier and high binding affinities between EGCG and membrane proteins. Therefore, the key to PD treatment lies in visual examination of EGCG accumulation in dopaminergic neurons. Methods: DSPE-PEG-B6, DSPE-PEG-MA, DSPE-PEG-phenylboronic acid, and superparamagnetic iron oxide nanocubes were self-assembled into tracing nanoparticles (NPs). EGCG was then conjugated on the surface of the NPs through the formation of boronate ester bonds to form a &quot;cell-addictive&quot; dual-target traceable nanodrug (B6ME-NPs). B6ME-NPs were then used for PD treatment via intravenous injection. Results: After treatment with B6ME-NPs, the PD-like characteristics was alleviated significantly. First, the amount of EGCG accumulation in PD lesions was markedly enhanced and traced via magnetic resonance imaging. Further, alpha S aggregation was greatly inhibited. Finally, the dopaminergic neurons were considerably increased. Conclusion: Due to their low price, simple preparation, safety, and excellent therapeutic effect on PD, B6ME-NPs are expected to have potential application in PD treatment.</p