Identification of the novel HLA-DPA1*02:02:15 allele by next-generation sequencing.

The novel HLA-DPA1*02:02:15 allele differs from HLA-DPA1*02:02:02:01 by one nucleotide substitution in exon 1.

High-resolution KIR and HLA genotyping in three Chinese ethnic minorities reveals distinct origins.

Polymorphism of killer-cell immunoglobulin-like receptors (KIRs) and their HLA class I ligands impacts the effector activity of cytotoxic NK cell and T cell subsets. Therefore, understanding the extent and implications of KIR and HLA class I genetic polymorphism across various populations is important for immunological and medical research. In this study, we conducted a high-resolution investigation of KIR and HLA class I diversity in three distinct Chinese ethnic minority populations. We studied the She, Yugur, and Tajik, and compared them with the Zhejiang Han population (Zhe), which represents the majority Southern Han ethnicity. Our findings revealed that the Tajik population exhibited the most diverse KIR copy number, allele, and haplotype diversity among the four populations. This diversity aligns with their proposed ancestral origin, closely resembling that of Iranian populations, with a relatively higher presence of KIR-B genes, alleles, and haplotypes compared with the other Chinese populations. The Yugur population displayed KIR distributions similar to those of the Tibetans and Southeast Asians, whereas the She population resembled the Zhe and other East Asians, as confirmed by genetic distance analysis of KIR. Additionally, we identified 12.9% of individuals across the three minority populations as having KIR haplotypes characterized by specific gene block insertions or deletions. Genetic analysis based on HLA alleles yielded consistent results, even though there were extensive variations in HLA alleles. The observed variations in KIR interactions, such as higher numbers of 2DL1-C2 interactions in Tajik and Yugur populations and of 2DL3-C1 interactions in the She population, are likely shaped by demographic and evolutionary mechanisms specific to their local environments. Overall, our findings offer valuable insights into the distribution of KIR and HLA diversity among three distinct Chinese ethnic minority populations, which can inform future clinical and population studies.

Characterisation of the novel HLA-DPB1*1437:01 and HLA-DPB1*1438:01 alleles by next-generation sequencing.

The novel HLA-DPB1*1437:01 and HLA-DPB1*1438:01 alleles first identified in the Chinese individuals.

Detection of the HLA-C*07:04:29 allele in a Chinese individual.

HLA-C*07:04:29 differs from HLA-C*07:04:01:01 by a single substitution in exon 4.

Identification of two novel HLA-DRB1 alleles, HLA-DRB1*08:03:13 and HLA-DRB1*08:119.

Compared with HLA-DRB1*08:03:02:01, the alleles HLA-DRB1*08:03:13 and HLA-DRB1*08:119 each show one nucleotide substitution, respectively.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Reprogrammable Magnetic Soft Actuators with Microfluidic Functional Modules via Pixel-Assembly.

Magnetic soft actuators and robots have attracted considerable attention in biomedical applications due to their speedy response, programmability, and biocompatibility. Despite recent advancements, the fabrication process of magnetic actuators and the reprogramming approach of their magnetization profiles continue to pose challenges. Here, a facile fabrication strategy is reported based on arrangements and distributions of reusable magnetic pixels on silicone substrates, allowing for various magnetic actuators with customizable architectures, arbitrary magnetization profiles, and integration of microfluidic technology. This approach enables intricate configurations with decent deformability and programmability, as well as biomimetic movements involving grasping, swimming, and wriggling in response to magnetic actuation. Moreover, microfluidic functional modules are integrated for various purposes, such as on/off valve control, curvature adjustment, fluid mixing, dynamic microfluidic architecture, and liquid delivery robot. The proposed method fulfills the requirements of low-cost, rapid, and simplified preparation of magnetic actuators, since it eliminates the need to sustain pre-defined deformations during the magnetization process or to employ laser heating or other stimulation for reprogramming the magnetization profile. Consequently, it is envisioned that magnetic actuators fabricated via pixel-assembly will have broad prospects in microfluidics and biomedical applications.

Fungal community structure shifts in litter degradation along forest succession induced by pine wilt disease.

Fungi play a crucial role in decomposing litter and facilitating the energy flow between aboveground plants and underground soil in forest ecosystems. However, our understanding how the fungal community involved in litter decomposition responds during forest succession, particularly in disease-driven succession, is still limited. This study investigated the activity of degrading enzyme, fungal community, and predicted function in litter after one year of decomposition in different types of forests during a forest succession gradient from coniferous to deciduous forest, induced by pine wilt disease. The results showed that the weight loss of needles/leaves and twigs did not change along the succession process, but twigs degraded faster than needles/leaves in both pure pine forest and mixed forest. In pure pine forest, peak activities of enzymes involved in carbon degradation (ß-cellobiosidase, ß-glucosidase, ß-D-glucuronidase, ß-xylosidase), nitrogen degradation (N-acetyl-glucosamidase), and organic phosphorus degradation (phosphatase) were observed in needles, which subsequently declined. The fungal diversity and evenness (Shannon's diversity and Shannon's evenness) dropped in twig from coniferous forest to mixed forest during the succession. The dominant phyla in needle/leaf and twig litters were Ascomycota (46.9%) and Basidiomycota (38.9%), with Lambertella pruni and Chalara hughesii identified as the most abundant indicator species. Gymnopus and Desmazierella showed positively correlations with most measured enzyme activities. Functionally, saprotrophs constituted the main trophic mode (47.65%), followed by Pathotroph-Saprotroph-Symbiotroph (30.95%) and Saprotroph-Symbiotroph (10.57%). The fungal community and predicted functional structures in both litter types shifted among different forest types along the succession. These findings indicate that the fungal community in litter decomposition responds differently to disease-induced succession, leading to significant shifts in both the fungal community structure and function.

Description of two new HLA alleles: HLA-DRB1*14:234 and HLA-DRB1*14:240 in Chinese individuals.

Compared with HLA-DRB1*14:54:01:01, the alleles HLA-DRB1*14:234 and HLA-DRB1*14:240 each show one nucleotide substitution.

Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction.

Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.

Characterization of the novel HLA-B*40:01:02:48 allele by next-generation sequencing.

HLA-B*40:01:02:48 differs from HLA-B*40:01:02:01 by one nucleotide substitution C to A at position -138 in 5'UTR.

Short-term association of fine particulate matter and its constituents with oxidative stress, symptoms and quality of life in patients with allergic rhinitis: A panel study.

BACKGROUND: Short-term exposure to fine particulate matter (PM2.5) and its specific constituents might exacerbate allergic rhinitis (AR) conditions. However, the evidence is still inconclusive. METHOD: We conducted a panel study of 49 patients diagnosed with AR > 1 year prior to the study in Taiyuan, China, to investigate associations of individual exposure to PM2.5 and its constituents with oxidative parameters, symptoms, and quality of life among AR patients. All participants underwent repeated assessments of health and PM exposure at 4 time points in both the heating and nonheating seasons from June 2017 to January 2018. AR patients' oxidative parameters were assessed using nasal lavage, and their subjective symptoms and quality of life were determined through in-person interviews using a structured questionnaire. Short-term personal exposure to PM2.5 and its constituents was estimated using the time-microenvironment-activity pattern and data from the nearest air sampler, respectively. We applied mixed-effects regression models to estimate the short-term effects of PM2.5 and its constituents. RESULTS: The results showed that exposure to PM2.5 and its constituents, including BaP, PAHs, SO42-, NH4+, V, Cr, Cu, As, Se, Cd, and Pb, was significantly associated with increased oxidative stress, as indicated by an increase in the malondialdehyde (MDA) index. Exposure to PM2.5 and its components (V, Mn, Fe, Zn, As, and Se) was associated with decreased antioxidant activity, as indicated by a decrease in the superoxide dismutase (SOD) index. Additionally, increased visual analog scale (VAS) and rhinoconjunctivitis quality of life questionnaire (RQLQ) scores indicated that exposure to PM2.5 and its constituents exacerbated inflammatory symptoms and affected quality of life in AR patients. CONCLUSION: Exposure to PM2.5 and specific constituents, could exacerbate AR patients' inflammatory symptoms and adversely affect their quality of life in the heavily industrialized city of Taiyuan, China. These findings may have potential biological and policy implications.

Knockdown of lncRNA MALAT1 induces pyroptosis by regulating the miR­124/SIRT1 axis in cervical cancer cells.

The aim of the present study was to elucidate the role and downstream mechanism of long non­coding RNA (lncRNA) metastasis­associated lung adenocarcinoma transcript 1 (MALAT1) in the process of cervical cancer cell pyroptosis. The effect of inhibiting lncRNA MALAT1 on cervical cancer cells was determined using primary cells isolated from patients and U14 cervical tumor­bearing nude mice. The level of lncRNA MALAT1 expression and cell viability were determined for relationship analysis. Pyroptosis was then investigated in HeLa cells with lncRNA MALAT1 knockdown or overexpression with or without lipopolysaccharide (LPS) treatment. Bioinformatics tools were used to identify downstream factors of lncRNA MALAT1, which were subsequently verified by gain­ or loss­of­function analyses in the process of cervical cancer cell pyroptosis. It was observed that the level of lncRNA MALAT1 was markedly higher in cervical carcinoma cells compared with expression in paracarcinoma cells, and knockdown of lncRNA MALAT1 induced cervical cancer cell death through pyroptosis. By contrast, overexpression of lncRNA MALAT1 blocked LPS­induced pyroptosis. These results, combined with bioinformatics statistical tools, demonstrated that the microRNA (miR)­124/sirtuin 1 (SIRT1) axis may affect the progression of cervical cancer at least partly by mediating the effect of lncRNA MALAT1 on the pyroptosis of cervical cancer cells. In conclusion, the lncRNA MALAT1/miR­124/SIRT1 regulatory axis in cervical cancer cells may mediate pyroptosis and may provide potential targets against the progression of cervical cancer.

Antimycobacterial Activities of Hydroxamic Acids and Their Iron(II/III), Nickel(II), Copper(II) and Zinc(II) Complexes.

Hydroxamic acid (HA) derivatives display antibacterial and antifungal activities. HA with various numbers of carbon atoms (C2, C6, C8, C10, C12 and C17), complexed with different metal ions, including Fe(II/III), Ni(II), Cu(II) and Zn(II), were evaluated for their antimycobacterial activities and their anti-biofilm activities. Some derivatives showed antimycobacterial activities, especially in biofilm growth conditions. For example, 20-100 µM of HA10Fe2, HA10FeCl, HA10Fe3, HA10Ni2 or HA10Cu2 inhibited Mycobacterium tuberculosis, Mycobacterium bovis BCG and Mycobacterium marinum biofilm development. HA10Fe2, HA12Fe2 and HA12FeCl could even attack pre-formed Pseudomonas aeruginosa biofilms at higher concentrations (around 300 µM). The phthiocerol dimycocerosate (PDIM)-deficient Mycobacterium tuberculosis H37Ra was more sensitive to the ion complexes of HA compared to other mycobacterial strains. Furthermore, HA10FeCl could increase the susceptibility of Mycobacterium bovis BCG to vancomycin. Proteomic profiles showed that the potential targets of HA10FeCl were mainly related to mycobacterial stress adaptation, involving cell wall lipid biosynthesis, drug resistance and tolerance and siderophore metabolism. This study provides new insights regarding the antimycobacterial activities of HA and their complexes, especially about their potential anti-biofilm activities.

Characterization of the novel HLA-C*01:225 allele in a Chinese individual.

HLA-C*01:225 has one nucleotide change compared with HLA-C*01:02:01:01 in codon 110 of exon 3.

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph.

Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. Unlike 1D sequences for proteins or 2D graphs for ligands, the 3D graph of protein-ligand complex enables the more accurate representations of the protein-ligand interactions. Benchmark studies have shown that our fusion models FGNN can achieve more accurate prediction of binding affinity than any individual algorithm. The advantages of fusion strategies have been demonstrated in terms of expressive power of data, learning efficiency and model interpretability. Our fusion models show satisfactory performances on diverse data sets, demonstrating their generalization ability. Given the good performances in both binding affinity prediction and virtual screening, our fusion models are expected to be practically applied for drug screening and design. Our work highlights the potential of the fusion graph neural network algorithm in solving complex prediction problems in computational biology and chemistry. The fusion graph neural networks (FGNN) model is freely available in https://github.com/LinaDongXMU/FGNN.

Characterization of the novel HLA-B*35:546 allele by next-generation sequencing.

HLA-B*35:546 differs from HLA-B*35:03:01:01 by a single nucleotide substitution at position 397 C > T.

Hepatic cavernous hemangioma developed in non-small cell lung cancer patients after receiving Camrelizumab treatment: two case reports.

Purpose: To report two cases of hepatic cavernous hemangioma, a rare complication, in patients with locally advanced and advanced non-squamous non-small cell lung cancer (NSCLC) treated with PD-1 inhibitors. Additionally, to share clinical experiences related to the management of this condition. Methods: Two patients with locally advanced and advanced non-squamous non-small cell lung cancer (NSCLC) were enrolled in our hospital. Following the NCCN guidelines and expert consensus, both patients received standard treatment with Camrelizumab (PD-1 inhibitor). Subsequent abdominal CT scans revealed hepatic focal lesions that did not exhibit typical characteristics of metastatic tumors. Therefore, further systematic investigation was conducted to study the hepatic focal lesions. Results: (1) Ultrasound-guided percutaneous biopsy confirmed the diagnosis of hepatic cavernous hemangioma. A multidisciplinary consultation concluded that it was an adverse drug reaction to Camrelizumab. (2) Ten-gene testing for both patients did not reveal any driver gene mutations associated with lung cancer. Apart from the occurrence of hepatic cavernous hemangioma, there were no signs of disease progression or worsening. (3) Both patients had resolution of hepatic cavernous hemangioma after switching to alternative PD-1 inhibitors or discontinuing PD-1 inhibitor treatment. One patient experienced hemorrhage related to the hepatic hemangioma, which was managed with hemostasis and symptomatic treatment, resulting in improvement. (4) Clinical outcomes: The first patient achieved a progression-free survival (PFS) of 33 months in first-line treatment and had not reached the PFS endpoint in second-line treatment, with an overall survival exceeding 56 months. The second patient had not reached the PFS endpoint in first-line treatment, with an overall survival exceeding 31 months. Conclusion: Hepatic cavernous hemangioma is a rare and serious adverse reaction associated with PD-1 inhibitors. Camrelizumab may interact with the PD-1 molecule in a different manner compared to other PD-1 inhibitors, affecting the regulation of the VEGFR/ULBP2 signaling pathway. In future studies, next-generation sequencing may provide detailed molecular pathology information, which could help explain individual differences and provide a basis for the prevention or intervention of hepatic cavernous hemangioma.

Identification of the novel HLA-A*24:567N allele in a Chinese individual.

HLA-A*24:567N differs from HLA-A*24:02:01:01 by a single nucleotide deletion at position 149.

Collagen-Hyaluronic Acid Composite Hydrogels with Applications for Chronic Diabetic Wound Repair.

Chronic diabetic wounds have become a major healthcare challenge worldwide. Improper treatment may lead to serious complications. Current treatment methods including biological and physical methods and skin grafting have limitations and disadvantages, such as poor efficacy, inconvenience of use, and high cost. Therefore, developing a more effective and feasible treatment is of great significance for the repair of chronic diabetic wounds. Hydrogels can be designed to serve multiple functions to promote the repair of chronic diabetic wounds. Furthermore, 3D bioprinting enables hydrogel customization to fit chronic diabetic wounds, thus facilitating the healing process. This paper reports a study of 3D printing of a collagen-hyaluronic acid composite hydrogels with application for chronic diabetic wound repair. In situ printed hydrogels were developed by a macromolecular crosslinking network using methacrylated recombinant human collagen (RHCMA) and methacrylated hyaluronic acid (HAMA), both of which can respond to ultraviolet (UV) irradiation. The hydrogels were also loaded with silver nanoclusters (AgNCs) with ultra-small-size nanoparticles, which have the advantages of deep penetration ability and broad-spectrum high-efficiency antibacterial properties. The results of this study show that the developed RHCMA, HAMA, and AgNCs (RHAg) composite hydrogels present good UV responsiveness, porosity, mechanical properties, printability, and biocompatibility, all of which are beneficial to wound healing. The results of this study further show that the developed RHAg hydrogels not only effectively inhibited Staphylococcus aureus and Pseudomonas aeruginosa but also promoted the proliferation and migration of fibroblasts in vitro and tissue regeneration and collagen deposition in vivo, thus producing a desirable wound repair effect and can be used as an effective functional biomaterial to promote chronic diabetic wound repair.