ABE-ultramax for high-efficiency biallelic adenine base editing in zebrafish.

Advancements in CRISPR technology, particularly the development of base editors, revolutionize genetic variant research. When combined with model organisms like zebrafish, base editors significantly accelerate and refine in vivo analysis of genetic variations. However, base editors are restricted by protospacer adjacent motif (PAM) sequences and specific editing windows, hindering their applicability to a broad spectrum of genetic variants. Additionally, base editors can introduce unintended mutations and often exhibit reduced efficiency in living organisms compared to cultured cell lines. Here, we engineer a suite of adenine base editors (ABEs) called ABE-Ultramax (Umax), demonstrating high editing efficiency and low rates of insertions and deletions (indels) in zebrafish. The ABE-Umax suite of editors includes ABEs with shifted, narrowed, or broadened editing windows, reduced bystander mutation frequency, and highly flexible PAM sequence requirements. These advancements have the potential to address previous challenges in disease modeling and advance gene therapy applications.

Diterpenoids from the Aerial Parts of Isodon serra with Selective Cytotoxic Activity.

Four new diterpenoids, isodosins A-D (1-4), together with nine known compounds (5-13) were isolated and identified from the aerial parts of Isodon serra (Maxim.) Hara. The structures of the new diterpenoids were elucidated based on the analysis of HR-ESI-MS data, 1D/2D-NMR-spectroscopic data, and electronic circular dichroism (ECD) calculations. Cytotoxicities of compounds 2, 3, 5, 6, and 9 against the HepG2 and H1975 cell lines were evaluated with the MTT assay. As a result, compounds 2, 3, and 6 revealed higher levels of cytotoxicity against HepG2 cells than against H1975 cells. Moreover, compund 6 demonstrated the most efficacy in inhibiting the proliferation of HepG2 cells, with an IC50 value of 41.13 ± 3.49 µM. This effect was achieved by inducing apoptosis in a dose-dependent manner. Furthermore, the relationships between the structures and activities of these compounds are briefly discussed.

METTL3-induced circ_0008345 contributes to the progression of colorectal cancer via the microRNA-182-5p/CYP1A2 pathway.

BACKGROUND: Circular RNA (circRNAs) have been found to play major roles in the progression of colorectal cancer (CRC). However, the functions of circ_0008345 (transcribed by PTK2) in regulating CRC development remain undefined. In this study, we aimed to explore the roles and underlying mechanisms of circ_0008345 in CRC. METHODS: RNase R-treated total cellular RNA was used to verify the circular structure of circ_0008345, and a subcellular fractionation assay was performed to detect the subcellular localization of circ_0008345. RNA pull-down and dual-luciferase assays were used to verify the binding relation between microRNA (miR)-182-5p and circ_0008345 and/or CYP1A2. Colony formation assay, EdU, and Transwell assays were performed to detect the biological behavior of CRC cells in vitro, and CRC cells were injected into mice to observe the tumor formation. m6A immunoprecipitation was used to detect the m6A modification of circ_0008345 in CRC cells. RESULTS: Circ_0008345, upregulated in CRC tissues and cells, was mainly present in the cytoplasm. Circ_0008345 bound to miR-182-5p, and miR-182-5p targeted CYP1A2, an oncogene in CRC. The colony formation, mobility, EdU-positive cell rate in vitro, and tumor growth in mice were inhibited after the knockdown of circ_0008345. However, the suppressing effects of sh-circ_0008345 on CRC and CYP1A2 expression were significantly reversed after further knockdown of miR-182-5p. METTL3 was the m6A modifier mediating circ_0008345 expression, and the suppression of METTL3 reduced the expression of circ_0008345. CONCLUSIONS: METTL3-dependent m6A methylation upregulated circ_0008345, which blocked the inhibitory effect of miR-182-5p on CYP1A2, thereby exacerbating the malignant phenotype of CRC cells.

Systematic identification and functional characterization of the CFEM proteins in fishscale bamboo rhombic-spot pathogen Neostagonosporella sichuanensis.

Fungal effectors play a crucial role in the interaction between pathogenic fungi and their hosts. These interactions directly influence the invasion and spread of pathogens, and the development of diseases. Common in fungal extracellular membrane (CFEM) effectors are closely associated with the pathogenicity, cell wall stability, and pathogenic processes of pathogenic fungi. The aim of this study was to investigate the role of CFEM proteins in Neostagonosporella sichuanensis in pathogen-host interactions. We retrieved 19 proteins containing CFEM structural domains from the genome of N. sichuanensis. By systematic analysis, five NsCFEM proteins had signal peptides but lacked transmembrane structural domains, and thus were considered as potential effectors. Among them, NsCFEM1 and NsCFEM2 were successfully cloned and their functions were further investigated. The validation results show that NsCFEM1 was localized in the cell membrane and nucleus, whereas NsCFEM2 was exclusively observed in the cell membrane. Both were identified as secreted proteins. Additionally, NsCFEM1 inhibited Bax-induced programmed cell death in Nicotiana benthamiana, whereas NsCFEM2 did not induce or inhibit this response. NsCFEM1 was implicated as a virulence factor that contributes to fungal growth, development, stress response, and pathogenicity. NsCFEM2 was implicated in maintenance of cell wall stability. This study lays a foundation for elucidating the role of CFEM proteins in the pathogen of fishscale bamboo rhombic-spot caused by N. sichuanensis. In particular, the functional studies of NsCFEM1 and NsCFEM2 revealed their potential roles in the interaction between N. sichuanensis and the host Phyllostachys heteroclada.

White root rot of Bletilla striata: the pathogen, biological characterization, and fungicide screening.

Bletilla striata is an endangered traditional medicinal herb in China. In May 2020, the emergence of white root rot severely impacted the quality and yield of B. striata, affecting about 5% of the plants at plant nurseries of the Chengdu Academy of Agricultural and Forestry Sciences. Through a series of experiments and evaluations, the pathogen was identified as Fusarium solani. This is the first report of B. striata white root rot caused by F. solani in Sichuan, China. To better understand this disease and provide data support for its control, a combination of morphological, molecular characterisation and pathogenicity determination was used in this study for assessment. Meanwhile, the effects of different carbon and nitrogen sources, culture medium, temperature, photoperiod and pH on mycelial growth and spore production of F. solani were investigated. In addition, effective fungicides were screened and the concentration ratios of fungicides were optimized using response surface methodology (RSM). The experimental results showed that sucrose was the optimum carbon source for the pathogen, and the optimum temperature and pH were 25°C and pH 7, respectively, while light did no significant effect. Effective fungicides were screened, among which difenoconazole showed the strongest inhibition with EC50 of 142.773 µg/mL. The optimum fungicide concentration scheme (difenoconazole, pyraclostrobin, and thiophanate-methyl at 395.42, 781.03, and 561.11 µg/mL, respectively) was obtained using response surface methodology (RSM) to improve the inhibition rate of 92.24 ± 0.34%. This study provides basic data for the pathogen characterization of B. striata white root rot and its potential fungicides in Sichuan, China. In addition, the optimal fungicide concentration ratios were obtained through response surface methodology (RSM) optimization, which significantly enhanced the fungicidal effect and provided a scientific basis for the future control of B. striata white root rot.

Downregulation of circ-RAPGEF5 inhibits colorectal cancer progression by reducing the expression of polypeptide N-acetylgalactosaminyltransferase 3 (GALNT3).

BACKGROUND: Circular RNA (circRNA) plays a crucial role in the pathogenesis and progression of colorectal cancer (CRC). However, the current understanding of the emerging function and mechanism of circ-RAPGEF5 in CRC remains poorly understood. METHODS: We first evaluated the expression level of circ-RAPGEF5 in CRC tissues and cells by quantitative real-time polymerase chain reaction (qRT-PCR). Then, we analyzed cell proliferation (EdU and colony formation assay), migration (cell wound healing assay), invasion (transwell assay), and apoptosis (flow cytometry assay). To further elucidate the mechanism of circ-RAPGEF5 in CRC, bioinformatics tools, Dual-luciferase reporter assay, Ago2 RNA immunoprecipitation assay, and RNA pull-down assay were employed. Moreover, we established a CRC transplantation tumor model to evaluate the effect of circ-RAPGEF5 on tumor growth in vivo. RESULTS: circ-RAPGEF5 was significantly upregulated in CRC tissues and CRC cells. Furthermore, the downregulation of circ-RAPGEF5 restrained CRC cell proliferation, migration, and invasion, and promoted cell apoptosis in vitro. Mechanistically, circ-RAPGEF5 accelerated the malignant behaviors of CRC cells by sponging miR-545-5p, which targeted polypeptide N-acetylgalactosaminyltransferase 3 (GALNT3). In addition, we revealed that circ-RAPGEF5 silence curbed tumor growth in vivo. CONCLUSION: These findings revealed that circ-RAPGEF5 played an oncogenic role through the miR-545-5p/GALNT3 axis in CRC progression, providing potential therapeutic targets for the treatment of CRC.

Differentiation of bone marrow mesenchymal stem cells into Leydig-like cells with testicular extract liquid in vitro.

Differentiation of Leydig cells plays a key role in male reproductive function. Bone marrow mesenchymal stem cells (BMSCs) have emerged as a potential cell source for generating Leydig-like cells due to their multipotent differentiation capacity and accessibility. This study aimed to investigate the morphological and genetic expression changes of BMSCs during differentiation into Leydig-like cells. Testicular extract liquid, which simulates the microenvironment in vivo, induced the third passage BMSCs differentiated into Leydig-like cells. Changes in cell morphology were observed by microscopy, the formation of lipid droplets of androgen precursor was identified by Oil Red Staining, and the expression of testicular specific genes 3ß-HSD and SF-1 in testicular stromal cells was detected by RT-qPCR. BMSCs isolated from the bone marrow of Sprague-Dawley (SD) rats were cultured for 3 generations and identified as qualified BMSCs in terms of morphology and cell surface markers. After 14 days of induction with testicular tissue lysate, lipid droplets appeared in the cytoplasm of P3 BMSCs by Oil Red O staining. RT-qPCR detection was performed on BMSCs on the 3rd, 7th, 14th, and 21st day after induction. Relative expression levels of 3ß-HSD mRNA significantly increased after 14 days of induction, while the relative expression of SF-1 mRNA increased after 14 days of induction but was not significant. BMSCs can differentiate into testicular interstitial cells with reserve androgen precursor lipid droplets after induction by testicular tissue lysate. The differentiation ability of BMSCs provides the potential to reconstruct the testicular microenvironment and is expected to fundamentally improve testicular function and provide new treatment options for abnormal spermatogenesis diseases.

Recent advances and challenges in protein complex model accuracy estimation.

Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.

BACKGROUND: Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. OBJECTIVE: We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. METHODS: Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps. RESULTS: For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO2). CONCLUSIONS: We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

Preparation of PLCL/ECM nerve conduits by electrostatic spinning technique and evaluationin vitroandin vivo.

Objective. Artificial nerve scaffolds composed of polymers have attracted great attention as an alternative for autologous nerve grafts recently. Due to their poor bioactivity, satisfactory nerve repair could not be achieved. To solve this problem, we introduced extracellular matrix (ECM) to optimize the materials.Approach.In this study, the ECM extracted from porcine nerves was mixed with Poly(L-Lactide-co-ϵ-caprolactone) (PLCL), and the innovative PLCL/ECM nerve repair conduits were prepared by electrostatic spinning technology. The novel conduits were characterized by scanning electron microscopy (SEM), tensile properties, and suture retention strength test for micromorphology and mechanical strength. The biosafety and biocompatibility of PLCL/ECM nerve conduits were evaluated by cytotoxicity assay with Mouse fibroblast cells and cell adhesion assay with RSC 96 cells, and the effects of PLCL/ECM nerve conduits on the gene expression in Schwann cells was analyzed by real-time polymerase chain reaction (RT-PCR). Moreover, a 10 mm rat (Male Wistar rat) sciatic defect was bridged with a PLCL/ECM nerve conduit, and nerve regeneration was evaluated by walking track, mid-shank circumference, electrophysiology, and histomorphology analyses.Main results.The results showed that PLCL/ECM conduits have similar microstructure and mechanical strength compared with PLCL conduits. The cytotoxicity assay demonstrates better biosafety and biocompatibility of PLCL/ECM nerve conduits. And the cell adhesion assay further verifies that the addition of ECM is more beneficial to cell adhesion and proliferation. RT-PCR showed that the PLCL/ECM nerve conduit was more favorable to the gene expression of functional proteins of Schwann cells. Thein vivoresults indicated that PLCL/ECM nerve conduits possess excellent biocompatibility and exhibit a superior capacity to promote peripheral nerve repair.Significance.The addition of ECM significantly improved the biocompatibility and bioactivity of PLCL, while the PLCL/ECM nerve conduit gained the appropriate mechanical strength from PLCL, which has great potential for clinical repair of peripheral nerve injuries.

Mechanism of salt tolerance in the endangered semi-mangrove plant Barringtonia racemosa: anatomical structure and photosynthetic and fluorescence characteristics.

To elucidate the mechanisms governing the salt tolerance of the endangered semi-mangrove plant Barringtonia racemosa, the biomass, photosynthetic and fluorescent characteristics, and anatomical structure of B. racemosa were studied under low, medium and high salt stress. The results showed that the stem dry weight, net photosynthetic rate, intercellular CO2 concentration, Fv/Fm, and ΦPSI of B. racemosa decreased under high salt stress, which led to a significant reduction in total dry weight. Stem dry weight was significantly positively correlated with the thickness of palisade tissue and significantly negatively correlated with the thickness of the epidermis of roots and xylem of stems. Therefore, a stable net photosynthetic rate and intercellular CO2 concentration, an increase in Fv/Fm and ΦPSI, an increase in or stable palisade tissue and spongy mesophyll of leaves and an increase in xylem thickness of the stem and epidermis, outer cortex, and stele diameter of roots could contribute to the salt tolerance of B. racemosa.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data.

Background: Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. Objective: We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). Methods: The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. Results: Ten critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better. Conclusion: Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Enhancing neuroinduction activity of PLCL-based nerve conduits through native epineurium integration.

Autologous nerve grafts have been considered the gold standard for peripheral nerve grafts. However, due to drawbacks such as functional loss in the donor area and a shortage of donor sources, nerve conduits are increasingly being considered as an alternative approach. Polymer materials have been widely studied as nerve repair materials due to their excellent processing performance. However, their limited biocompatibility has restricted further clinical applications. The epineurium is a natural extra-neural wrapping structure. After undergoing decellularization, the epineurium not only reduces immune rejection but also retains certain bioactive components. In this study, decellularized epineurium (DEP) derived from the sciatic nerve of mammals was prepared, and a bilayer nerve conduit was created by electrospinning a poly (l-lactide-co-ε-caprolactone) (PLCL) membrane layer onto the outer surface of the DEP. Components of the DEP were examined; the physical properties and biosafety of the bilayer nerve conduit were evaluated; and the functionality of the nerve conduit was evaluated in rats. The results demonstrate that the developed bilayer nerve conduit exhibits excellent biocompatibility and mechanical properties. Furthermore, this bilayer nerve conduit shows significantly superior therapeutic effects for sciatic nerve defects in rats compared to the pure PLCL nerve conduit. In conclusion, this research provides a novel strategy for the design of nerve regeneration materials and holds promising potential for further clinical translation.

Refining the 8th edition TNM classification for EBV related nasopharyngeal carcinoma.

The AJCC/UICC TNM classification describes anatomic extent of tumor progression and guides treatment decisions. Our comprehensive analysis of 8,834 newly diagnosed patients with non-metastatic Epstein-Barr virus related nasopharyngeal carcinoma (NPC) from six Chinese centers indicates certain limitations in the current staging system. The 8th edition of the AJCC/UICC TNM classification inadequately differentiates patient outcomes, particularly between T2 and T3 categories and within the N classification. We propose reclassifying cases of T3 NPC with early skull-base invasion as T2, and elevating N1-N2 cases with grade 3 image-identified extranodal extension (ENE) to N3. Additionally, we suggest combining T2N0 with T1N0 into a single stage IA. For de novo metastatic (M1) NPC, we propose subdivisions of M1a, defined by 1-3 metastatic lesions without liver involvement, and M1b, characterized by >3 metastatic lesions or liver involvement. This proposal better reflects responses of NPC patients to the up-to-date treatments and their evolving risk profiles.

5-methylcytosine-mediated upregulation of circular RNA 0102913 augments malignant properties of colorectal cancer cells through a microRNA-571/Rac family small GTPase 2 axis.

Circular RNAs (circRNAs) are a class of stable non-coding RNAs that have emerged as key regulators in human diseases including cancer. This study investigates the role of circRNA_0102913 (circ_0102913) in malignant behavior of colorectal cancer (CRC) cells and the underpinning mechanisms. By analyzing CRC-related GSE197991, GSE159669, and GSE223001 datasets, we obtained circ_0102913 as an aberrantly upregulated circRNA in CRC. Increased circ_0102913 expression was detected in CRC tissues and cells. By querying multiple bioinformatics systems (circBank, Circular RNA Interactome, TargetScan, miRDIP, miRwalk, and miRDB), we identified microRNA-571 (miR-571) as a target of circ_0102913 and Rac family small GTPase 2 (RAC2) mRNA as a target of miR-571. Biotinylated-RNA pull-down and/or luciferase assays showed that circ_0102913 bound to miR-571 to restore the expression of RAC2 mRNA. Circ_0102913 silencing or miR-571 overexpression repressed proliferation, migration and invasion, and in vivo tumorigenesis abilities of CRC cells. However, the malignant properties of cells were restored by RAC2 overexpression. The increased circ_0102913 expression in CRC cells was attributed to increased 5-methylcytosine (m5C) modification levels. Silencing of NOP2/Sun RNA methyltransferase 5 reduced the m5C level and therefore reduced stability and expression of circ_0102913 expression in CRC cells. In conclusion, this study demonstrates that m5C-mediated upregulation of circ_0102913 augments malignant properties of CRC cells through a miR-571/RAC2 axis.

Meta-Analysis and Network Analysis Differentially Detect Various Pro-Inflammatory Mediators and Risk Factors for Type 2 Diabetes in the Elderly.

Type 2 diabetes (T2D) has a pathophysiological component that includes inflammation. Inflammation-sensitive marker measurement may be helpful in determining the risk of complications for both older T2D patients and the public. This study aimed to investigate the association between blood pro-inflammatory mediators and the characteristics of elderly patients with T2D using meta and network analyses. The Web of Science, Scopus, PubMed, and Cochrane Library databases were selected as study methodology. The Quality in Prognosis Studies (QUIPS) tool in the meta-analysis assessed the studies' methodological quality. The selected studies were statistically analyzed using the META-MAR tool based on the standardized mean difference (SMD). The selected studies included nine examinations involving 6399 old people [+>+55 years old, 65.9+±+4.09 (mean+±+SD)]. The meta-analysis showed that pro-inflammatory mediators (SMD 0.82) and patient-related variables [risk factors (SMD 0.71)] were significantly associated with T2D (p+<+0.05). Subgroup analysis revealed that tumor necrosis factor alpha (TNF-α; SMD 1.08), body mass index (SMD 0.64), high-density lipoprotein (HDL; SMD -0.61), body weight (SMD 0.50), and blood pressure (SMD 1.11) were factors significantly associated with T2D (p+<+0.05). Network analysis revealed that among patient characteristics, diastolic blood pressure and, among inflammatory mediators, leptin were the most closely associated factors with T2D in older adults. Moreover, network analysis showed that TNF-α and systolic blood pressure were most closely associated with leptin. Overall, alternate techniques, such as meta-analysis and network analysis, might identify different markers for T2D in older people. A therapeutic decision-making process needs to consider these differences in advance.

Recent Advances and Challenges in Protein Structure Prediction.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Acupuncture Combined with Voice Training Improved Voice Function in Patients with Primary Muscular Tension Dysphonia (MTD): A Randomized, Three-Armed, Open-Label Clinical Trial.

Objective: This randomized controlled trial aimed to evaluate the clinical efficacy of acupuncture combined with voice training for treating patients with primary muscular tension dysphonia (MTD) (Qi stagnation and blood stasis type in traditional Chinese medicine). Methods: A total of 108 patients with primary MTD (Qi stagnation and blood stasis type) were recruited in this study. The participants were randomly divided into 3 equal groups: a test group and two control groups (control groups 1 and 2). An additional 38 participants without primary MTD were recruited as the healthy group. Control group 1 received acupuncture sessions 3 times per week on alternate days on the Hegu (LI 4), Taichong (LR 3), Open Voice No. 1 point, and Open Voice No. 2 points. Control group 2 received a 40-minute voice training session once weekly. The test group received both treatments. The total treatment course for all groups was 30 days. The healthy participants did not receive any interventions. The physiological and functional voice improvements after treatment were compared between all 3 MTD groups and healthy participants. The Voice Handicap Index (VHI-10), the VHI-10 emotional score, the Chinese Medicine Symptom Score Scale (TCM), and the Grade Roughness Breathiness Asthenia Strain (GRBAS) were used to evaluate the changes in the voice after treatment. A laryngeal muscle blood oxygen monitoring was used to measure the blood oxygen saturation (SO2) of the suprahyoid and infrahyoid muscles, and a stroboscopic laryngoscopy was used to measure the dysphonia severity index (DSI). Acoustic voice analysis was used to measure the maximum phonation time (MPT), the jitter, and the shimmer. Result: The SO2 levels of the laryngeal muscle were significantly higher in the healthy subjects than in pretreatment MTD patients and were correlated with the VHI-10 and DSI scores. A significant improvement in the physiological and functional scores, the total VHI-10, the GRBAS score, the voice acoustic analysis indices, MPT, jitter, shimmer, and DSI, was noted after treatment in all 3 MTD groups (P < 0.05). However, the posttreatment scores in the test group were significantly higher compared to control group 2, whose score were superior than that in control group 1 (P < 0.05). Both the test group and control group 1 showed a significant increase in the SO2 levels of the laryngeal muscles after treatment, where the test group had a higher elevation than control group 1. No significant difference was noted in the posttreatment SO2 of the laryngeal muscles in control group 2 (P > 0.05). Conclusion: Acupuncture monotherapy or in combination with voice training can reduce the anxiety emotion, relieve MTD-associated systemic symptoms, and increase the SO2 levels of the laryngeal muscle. This combination is a promising approach for the treatment of MTD. This trial is registered with ChiCTR2200061469.

The chromosome-level genome assembly of Fraxinus americana provides insights into the evolution of Oleaceae plants.

White ash (Fraxinus americana linn.) originates from the southeastern United States. It is a tall and fast-growing tree species with strong salt-alkali resistance and cold tolerance, making it an important reforestation species and widely planted worldwide. Here, we completed the chromosome-level reference genome assembly of F. americana based on Illumina, PacBio, and Hi-C reads, with a genome size of 878.98 Mb, an N50 of 3.27 Mb, and a heterozygosity rate of 0.3 %. Based on de novo prediction, transcriptome prediction, and homology-based protein prediction, we obtained 39,538 genes. Approximately 843.21 Mb of the assembly genome was composed of 37,928 annotated protein-coding genes, with a gene function annotation rate of 95.93 %. 99.94 % of the overlap clusters (877.44 Mb) were anchored to 23 chromosomes. Synteny analysis of F. americana and other Oleaceae plants showed that F. americana underwent frequent chromosome rearrangements. The amplification of the Ale transposons effectively promoted the genome size of F. americana. Compared with other Oleaceae plants, the Glutathione S-transferase (GST) gene family in the F. americana genome has undergone significant expansion, which may help F. americana cope with adverse natural environments. Furthermore, we found that key enzyme-coding gene families related to lignin biosynthesis were expanded and highly expressed in F. americana leaves. These key genes drive lignin synthesis and benefit F. americana in fast-growing, as well as resisting biotic and abiotic stress. Overall, the F. americana genome assembly provides insights into the evolution of Oleaceae plants and provides abundant resources for breeding and germplasm conservation of white ash.

Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.