Objective To understand the epidemiological and genotypic characteristics of norovirus (NoV) in Jingzhou area，and to design primers and probes covering the variant genomes in the NoV gene library. Methods A total of 556 fecal samples were collected from suspected NoV patients from the First People's Hospital of Jingzhou from January 2022 to May 2023. The positive rate of NoV nucleic acid in fecal samples was detected by commercial kits. The differences in positive rates among different seasons and five age groups were statistically analyzed. Primers covering the NoV variant genome were designed to genotype some positive specimens. Results The detection rate of NoV nucleic acid in the tested samples was 30.04% (167/556). The detection rate in spring and winter was higher than that in summer and autumn (χ²=20.411，P<0.01). There were statistical differences in the positive rates among the five age groups of <1 year, 1-5 years, 6-10 years, 11-19 years, and >19 years (χ²=17.192，P<0.01), and the positive rate in young children (1~5 years old) was the highest (39.29%, 88/224). In addition, all the positive samples were NoV GII. Conclusion The epidemic situation of NoV is serious in winter and spring in Jingzhou area, with a high infection rate in young children (1-5 years old), and NoV GII is the main prevalent genotype. The primers designed in this study can be used for genotyping of NoV GI and GII.

Negative logarithm of the acid dissociation constant(pKa)significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pKa prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pKa prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pKa values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pKa values,making it a valuable tool in the field of pKa prediction.

Objective To compare the filtration effects of different models of leukocyte filters on erythrocyte suspensions,so as to provide a reference for the selection of leukocyte filters in clinic.Methods The erythrocyte suspensions prepared by Department of Blood Transfusion of The First Affiliated Hospital of Naval Medical University were used for filtration.The test was categorized into three groups based on the model of leukocyte filters,namely,AKTT-type(group Ⅰ),STTB-type(group Ⅱ),and STTA-type(groupⅢ).Each group was randomly assigned 8 bags of erythrocyte suspensions(specification 2U)with hematocrit≤55%and 10 bags of erythrocyte suspensions(specification 2U)with hematocrit>55%,and leukapheresis was applied.The quality indexes of the blood were detected before and after filtration,and the experimental data were comprehensively analyzed to evaluate the leukocyte filtration effect of various filters.Results When the hematocrit of the filtered erythrocyte suspensions was≤55%,there were significant differences in the platelet count after filtration(F=49.94,P<0.001)and filtration time(F=73.45,P<0.001)between groups,and the two indexes in group Ⅰ were superior to those in groups Ⅱ and Ⅲ.When the hematocrit of the filtered erythrocyte suspensions was>55%,there were significant differences in the platelet count after filtration(F=160.69,P<0.000 1),filtration time(F=366.09,P<0.000 1),residual leukocytes(F=4.28,P<0.05),and hemolytic rate(F=8.16,P<0.01)between groups.The platelet count after filtration and filtration time in group I were superior to those in group II and III.The indexes of residual leukocyte and hemolytic rate in groups I and II were superior to those in group III.Conclusion In order to ensure the safety and effectiveness of erythrocyte suspension transfusion,AKTT-type filter can be chosen to perform leukocyte filtration,which can further lower the blood transfusion complications.

Objective: To analyze respiratory syncytial virus(RSV) outbreaks surveillance results and the epidemiological characteristics in kindergarten and school in Shenzhen during 2017-2023 , so as to provide a scientific reference for control and prevention of RSV. Methods: Epidemiological data and surveillance results of RSV outbreaks in kindergarten and school from 2017 to 2023 were collected for descriptive analyses. Results: A total of 31 RSV outbreaks were identified in kindergarten and school in 2017-2023 in Shenzhen, 346 cases were reported, the average incidence rate was 22.02%. The most annual RSV outbreaks were reported in 2020 with 14 outbreaks, followed by 8 outbreaks in 2023. A total of 64.52% of RSV outbreaks were identified in kindergarten with rest occurring in primary school or middle school. The greatest monthly count of outbreak was 18 (58.06%) in September, followed by 3 outbreaks (9.68%) in March and October. A total of 244 swab samples were collected, 169 samples were positive for respiratory viruses, the positive rate was 69.26%, 121 samples were positive for RSV,from 31 respiratory syncytical virus outbreaks 57 and samples were positive for other respiratory viruses(9 samples were positive for two respiratory viruses). A toral of 14(45.16%) outbreaks are caused by RSV alone, 17 outbreaks (54.84%) were caused by RSV and other respiratory viruses. Conclusions Most RSV outbreaks in kindergarten and school are reported after 2020 in Shenzhen, most RSV outbreaks occur in kindergarten, peak seasons of RSV outbreaks are autumn and spring.

Negative logarithm of the acid dissociation constant (pK _a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK _a, a pK _a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK _a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK _a values. The high reliability and interpretability of GraFpK _a ensure accurate pK _a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK _a values, making it a valuable tool in the field of pK _a prediction.

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

Traditionally, platelets, which are anucleate cell fragments derived from blood cells, have been primarily associated with their pivotal functions in hemostasis and thrombosis. However, recent research has elucidated their significant role in immune regulation, highlighting their expression of various immune receptors, involvement in numerous immune-related signaling pathways, and activation of diverse effector functions. This paper elaborates on the fundamental biological characteristics and immune functions of platelets, the involvement of activated platelets in immune regulation, and their prospective applications in clinical therapy. Furthermore, the paper discusses future directions in platelet immune research, as well as the prospects and developmental trends in immunotherapy, aiming to furnish a thorough reference for the investigation and clinical utilization of platelets within the domain of immune regulation.

Objective: To compare the biological characteristics of human induced pluripotent stem cell-derived platelet exosomes (hiPSC-Plt-Exos) with those of conventional apheresis platelet exosomes (Plt-Exos), specifically focusing on their differential abilities to enhance the proliferation and migration of human umbilical cord mesenchymal stem cells (hUC-MSCs). Methods: Exosomes were isolated from hiPSC-derived Plt and apheresis Plt concentrate using size exclusion chromatography. These exosomes were then characterized through nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and Western blotting. Co-culture experiments into hUC-MSCs were conducted with hiPSC-Plt-Exos and apheresis Plt-Exos, respectively. Their effects on the proliferation and migration of hUC-MSCs were assessed via cell proliferation assays and scratch tests. Results: hiPSC-Plt-Exos and apheresis Plt-Exos exhibited comparable particle sizes, morphological features (such as the characteristic cup-shaped structure), and surface markers (including CD9 and HSP70). Notably, hiPSC-Plt-Exos demonstrated a significantly greater ability to enhance the proliferation and migration of hUC-MSCs compared to apheresis Plt-Exos (P<0.05). These differences provide critical comparative data for their application in various clinical contexts. Conclusion: This study establishes a theoretical foundation for developing precise therapeutic strategies based on hiPSC-Plt-Exos. Furthermore, it underscores the necessity of selecting the appropriate type of exosomes according to the specific disease microenvironment to achieve optimal therapeutic outcomes.

OBJECTIVE: To observe the clinical efficacy of Daoqi (directing qi flowing) acupuncture technique in Huangdi Neijing (Yellow Emperor's Inner Classic) for moderate-to-severe obstructive sleep apnea-hypopnea syndrome (OSAHS). METHODS: Sixty patients with moderate-to-severe OSAHS were randomly divided into a Daoqi acupuncture group (30 cases) and a conventional acupuncture group (30 cases, 1 case dropped out). In the Daoqi acupuncture group, Daoqi acupuncture technique in Huangdi Neijing was applied at Shanglianquan (Extra), Fengfu (GV16), and bilateral Lieque (LU7), Zhaohai (KI6); in the conventional acupuncture group, conventional acupuncture was applied at Shanglianquan (Extra), Fengfu (GV16), Yamen (GV15), and bilateral Lieque (LU7), Zhaohai (KI6), Zusanli (ST36), Fenglong (ST40). The treatment was adopted once every other day, 3 times a week, 4 weeks as one course and 3 courses were required totally in both groups. Before and after treatment, the polysomnography (PSG) indexes [apnea-hypopnea index (AHI), hypopnea index (HI), apnea index (AI), longest apnea duration, lowest nocturnal SaO₂ (LSaO₂)], and scores of Epworth sleepiness scale (ESS), Pittsburgh sleep quality index (PSQI), World Health Organization quality of life-BREF (WHOQOL-BREF) were observed, and the clinical efficacy was evaluated after treatment in the two groups. RESULTS: After treatment, the AHI, HI, AI and longest apnea duration were reduced compared with those before treatment in the two groups (P<0.01), the LSaO₂ was increased in the Daoqi acupuncture group (P<0.01); in the Daoqi acupuncture group, the AHI, HI, AI and longest apnea duration were lower than those in the conventional acupuncture group (P<0.05), and the LSaO₂ was higher than that in the conventional acupuncture group (P<0.05). After treatment, the ESS and PSQI scores were decreased compared with those before treatment (P<0.01), while the WHOQOL-BREF scores were increased compared with those before treatment (P<0.01) in the two groups; in the Daoqi acupuncture group, the ESS and PSQI scores were lower than those in the conventional acupuncture group (P<0.05, P<0.01), and the WHOQOL-BREF score was higher than that in the conventional acupuncture group (P<0.05). The total effective rate was 93.3% (28/30) in the Daoqi acupuncture group, which was higher than 82.8% (24/29) in the conventional acupuncture group (P<0.01). CONCLUSION Daoqi acupuncture technique in Huangdi Neijing can effectively treat moderate-to-severe OSAHS patients, improve the clinical symptoms and quality of life, and has the advantages i.e. simpler acupoints selection and gentler stimulation.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Male ; Female ; Middle Aged ; Acupuncture Therapy ; Adult ; Acupuncture Points ; Treatment Outcome ; Aged ; Quality of Life