Herbal therapy: a new pathway for the treatment of Alzheimer's disease

It has been a clinical challenge to treat Alzheimer's disease (AD). In the present commentary we discuss whether herbal therapy could be a novel treatment method for AD on the basis of results from clinical trials, and discuss the implications for potential therapy for AD pathophysiology. There is evidence to suggest that single herbs or herbal formulations may offer certain complementary cognitive benefits to the approved drugs. The current evidence supporting their use alone, however, is inconclusive or inadequate owing to many methodological limitations. Herbal mixtures may have advantages with multiple target regulation compared with the single-target antagonist in the view of traditional Chinese medicine. Several clinical trials using herbal mixtures are being conducted in China and will hopefully show promising results for treating AD in the near future

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However, when tuning publicly accessible, centralized LLMs with private instruction data, privacy concerns are inevitable. While direct transfer of parameterized modules between models is a plausible approach to address this, its implications and effectiveness need further exploration. This paper focuses on Offsite-Tuning (OFT), a representative technique that transfers transformer blocks between centralized LLMs and downstream emulators. Given the limited understanding of the underlying mechanism of OFT, we perform an empirical analysis on LLMs from the perspectives of representation and functional similarity. Interestingly, our findings reveal a unique modular structure within the layers of LLMs that appears to emerge as the model size expands. Simultaneously, we note subtle but potentially significant changes in representation and intermediate predictions across the layers. Inspired by these observations, we propose CRaSh, involving Clustering, Removing, and Sharing, a training-free strategy to derive improved emulators from LLMs. CRaSh significantly boosts performance of OFT with billions of parameters. Furthermore, we investigate the optimal solutions yielded by fine-tuning with and without full model through the lens of loss landscape. Our findings demonstrate a linear connectivity among these optima falling over the same basin, thereby highlighting the effectiveness of CRaSh and OFT. The source code is publicly available at https://github.com/TsinghuaC3I/CRaSh.Comment: Accepted to EMNLP 2023 (Main Conference

Analyzing the Study of Using Acupuncture in Delivery in the Past Ten Years in China

The use of acupuncture in inducing delivery has a long history in China. With progress over time, it has been applied in many aspects. For further study of acupuncture in delivery, this paper analyzed the papers using acupuncture in delivery in the past ten years in mainland China. 87 literatures were picked out by searching relevant electronic databases and bibliographies of relevant journals. The analysis showed randomized controlled trials that were the major type of research, while preclinical researches and literature reviews only account for around ten percent, respectively. Clinical researches indicated that acupuncture can relieve labor pain, promote maternal uterine contraction, shorten birth process, and treat postpartum disorders. Preclinical researches found that acupuncture can adjust certain hormones and improve uterus contraction of late-stage pregnant rats. However, due to lack of large multicenter randomized controlled clinical trials, standardized evaluations of clinical effects in clinical researches and detailed mechanism study in preclinical researches and unequivocal conclusions about the effectiveness, efficacy, and mechanisms of acupuncture in this field cannot be obtained from those researches yet. Further clinical and preclinical studies about the use of acupuncture in delivery with improved methodology is still needed

Analyzing the Study of Using Acupuncture in Delivery in the Past Ten Years in China

The use of acupuncture in inducing delivery has a long history in China. With progress over time, it has been applied in many aspects. For further study of acupuncture in delivery, this paper analyzed the papers using acupuncture in delivery in the past ten years in mainland China. 87 literatures were picked out by searching relevant electronic databases and bibliographies of relevant journals. The analysis showed randomized controlled trials that were the major type of research, while preclinical researches and literature reviews only account for around ten percent, respectively. Clinical researches indicated that acupuncture can relieve labor pain, promote maternal uterine contraction, shorten birth process, and treat postpartum disorders. Preclinical researches found that acupuncture can adjust certain hormones and improve uterus contraction of late-stage pregnant rats. However, due to lack of large multicenter randomized controlled clinical trials, standardized evaluations of clinical effects in clinical researches and detailed mechanism study in preclinical researches and unequivocal conclusions about the effectiveness, efficacy, and mechanisms of acupuncture in this field cannot be obtained from those researches yet. Further clinical and preclinical studies about the use of acupuncture in delivery with improved methodology is still needed

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach. Artificial intelligence methods, such as Convolutional Neural Network (CNN), are widely used to facilitate new drug discovery. Owing to the structural limitations of CNN, features extracted from this method are local patterns that lack global information. However, global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drug-target affinity. A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes. This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction (FingerDTA), which uses CNN to extract local patterns and utilize fingerprints to characterize global information. These fingerprints are generated on the basis of the whole sequence of drugs or targets. Furthermore, FingerDTA achieves comparable performance on Davis and KIBA data sets. In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019 (COVID-19), 7 of the top 10 drugs have been confirmed potential by literature. Ultimately, the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets. All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git

Identification and characterization of gonadotropin-releasing hormone (GnRH) in Zhikong scallop Chlamys farreri during gonadal development

Gonadotropin-releasing hormone (GnRH) controls synthesis of sex steroid hormones through hypothalamic-pituitary-gonadal (HPG) axis in vertebrates. But in mollusks, research on neuroendocrine control of gonadal function, such as the function of GnRH during gonadal development is limited. In this study, we investigated the morphology and structure of the nerve ganglia of Zhikong scallop Chlamys farreri by physiological and histological observations. We also cloned the ORF and studied the expression patterns of GnRH in the scallop. Tissue expression analysis showed that GnRH was highly expressed in parietovisceral ganglion (PVG). The in situ hybridization result further confirmed that GnRH mRNA only distributed in some good-sized neurons in the posterior lobe (PL) and some pint-sized neurons in the lateral lobe (LL). In addition, by examining the expression of GnRH during gonadal development in ganglia, we found GnRH displayed higher expression in the female scallops, and showed significant high expression at the growing stage of female scallops in PVG. This study would contribute to gaining insight into the mechanism underlying reproduction regulation by GnRH in the scallop and help to provide a better understanding of reproductive neuroendocrine in mollusks

Advances in reprogramming of energy metabolism in tumor T cells

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches