b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT.

OBJECTIVE: Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach. we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations. Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests,which demonstrate b-MAR improvements of > 1.4131 dB in PSNR, > 0.3473 HU decrements in RMSE, and > 0.0025 promotion in SSIM over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively. Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation.

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. And they only learn the posterior distribution of latent normal-dose CT (NDCT) images conditioned on observed LDCT images in the traditional maximum a posteriori (MAP) framework, while ignoring the noise generation process of LDCT images. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is constructed to integrate the LDCT denoising and simulation tasks into a unified optimization framework by learning the joint distribution of LDCT and NDCT data. We approximate the joint distribution of continuous dose level data by training DDT-Net with discrete dose data, which can be generalized to denoising and simulation of unseen dose data. In particular, the mixed-dose training strategy adopted by DDT-Net can promote the denoising performance of lower-dose data. The paired dataset simulated by DDT-Net can be used for data augmentation to further restore the tissue texture of LDCT images. Experimental results on synthetic data and clinical data show that the proposed DDT-Net outperforms competing methods in terms of denoising and generalization performance at unseen dose data, and it also provides a simulation tool that can quickly simulate realistic LDCT images at arbitrary dose levels.

A fusion protein of vimentin with Fc fragment inhibits Japanese encephalitis virus replication.

Japanese encephalitis virus (JEV), a member of the Flaviviridae family and a flavivirus, is known to induce acute encephalitis. Vimentin protein has been identified as a potential receptor for JEV, engaging in interactions with the viral membrane protein. The Fc fragment, an integral constituent of immunoglobulins, plays a crucial role in antigen recognition by dendritic cells (DCs) or phagocytes, leading to subsequent antigen presentation, cytotoxicity, or phagocytosis. In this study, we fused the receptor of JEV vimentin with the Fc fragment of IgG and expressed the resulting vimentin-Fc fusion protein in Escherichia coli. Pull-down experiments demonstrated the binding ability of the vimentin-Fc fusion protein to JEV virion in vitro. Additionally, we conducted inhibition assays at the cellular level, revealing the ability of vimentin-Fc protein suppressing JEV replication, it may be a promising passive immunotherapy agent for JEV. These findings pave the way for potential therapeutic strategies against JEV.

Facile Synthesis of Ag-Doped Urchin-like MnO2 on Carbon Cloth for Supercapacitors.

Based on MnO2/carbon cloth (CC) composite materials, an Ag-doped MnO2 nanowire, self-assembled, urchin-like structure was synthesized in situ on the surface of CC using a simple method, and a novel and efficient flexible electrode material for supercapacitors was developed. The morphology, structure, elemental distribution, and pore distribution of the material were analyzed using SEM, TEM, XRD, XPS, and BET. The electrochemical performance was tested using cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD). In the three-electrode system, GCD testing showed that the specific capacitance of the material reached 520.8 F/g at 0.5 A/g. After 2000 cycles at a current density of 1 A/g, the capacitance retention rate was 90.6%, demonstrating its enormous potential in the application of supercapacitor electrode materials.

Activation of polyamine catabolism promotes glutamine metabolism and creates a targetable vulnerability in lung cancer.

Polyamines are a class of small polycationic alkylamines that play essential roles in both normal and cancer cell growth. Polyamine metabolism is frequently dysregulated and considered a therapeutic target in cancer. However, targeting polyamine metabolism as monotherapy often exhibits limited efficacy, and the underlying mechanisms are incompletely understood. Here we report that activation of polyamine catabolism promotes glutamine metabolism, leading to a targetable vulnerability in lung cancer. Genetic and pharmacological activation of spermidine/spermine N1-acetyltransferase 1 (SAT1), the rate-limiting enzyme of polyamine catabolism, enhances the conversion of glutamine to glutamate and subsequent glutathione (GSH) synthesis. This metabolic rewiring ameliorates oxidative stress to support lung cancer cell proliferation and survival. Simultaneous glutamine limitation and SAT1 activation result in ROS accumulation, growth inhibition, and cell death. Importantly, pharmacological inhibition of either one of glutamine transport, glutaminase, or GSH biosynthesis in combination with activation of polyamine catabolism synergistically suppresses lung cancer cell growth and xenograft tumor formation. Together, this study unveils a previously unappreciated functional interconnection between polyamine catabolism and glutamine metabolism and establishes cotargeting strategies as potential therapeutics in lung cancer.

A comparative analysis of energy expenditure and substrate metabolism in male university students with overweight/obesity: Tabata vs HIIT and MICT.

Introduction: Exploring the energy expenditure and substrate metabolism data during exercise, 10-minute recovery, and 20-minute recovery phases in Tabata, HIIT(High-Intensity Interval Training), and MICT(Moderate-Intensity Continuous Training). This study explores the scientific aspects of weight reduction strategies, examining energy expenditure and substrate metabolism from various training perspectives. The aim is to establish a theoretical foundation for tailoring targeted exercise plans for individuals within the population with overweight/obesity. Methods: This study used an experimental design with fifteen male university students with overweight/obesity. Participants underwent random testing with Tabata, HIIT, and MICT. Tabata involved eight sets of 20 seconds exercise and 10 seconds rest, totaling 4 minutes. HIIT included four sets of power cycling: 3 minutes at 80% VO2max intensity followed by 2 minutes at 20% VO2max. MICT comprised 30 minutes of exercise at 50% VO2max intensity. Gas metabolism indices were continuously measured. Subsequently, fat and glucose oxidation rates, along with energy expenditure, were calculated for each exercise type. Results: During both the exercise and recovery phases, the Tabata group exhibited a significantly higher fat oxidation rate of (0.27 ± 0.03 g/min) compared to the HIIT group (0.20 ± 0.04 g/min, p<0.05) and the MICT group (0.20 ± 0.03g/min, p<0.001). No significant difference was observed between the HIIT and MICT groups (p=0.854). In terms of energy expenditure rate, the Tabata group maintained a substantially elevated level at 5.76 ± 0.74kcal/min compared to the HIIT group (4.81 ± 0.25kcal/min, p<0.01) and the MICT group (3.45 ± 0.25kcal/min, p<0.001). Additionally, the energy expenditure rate of the HIIT group surpassed that of the MICT group significantly (p<0.001). Conclusion: The study finds that male college students with overweight/obesity in both exercise and recovery, Tabata group has lower fat and glucose oxidation rates, and energy expenditure compared to HIIT and MICT groups. However, over the entire process, Tabata still exhibits significantly higher rates in these aspects than HIIT and MICT. Despite a shorter exercise duration, Tabata shows a noticeable "time-efficiency" advantage. Tabata can be used as an efficient short-term weight loss exercise program for male college students with overweight/obesity.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

Multicolor emissive carbon dot-based fluorometric analysis platform for rapid quantification and discrimination of nitroimidazole antibiotic residues.

The development of efficient, rapid, portable, and accurate analysis of veterinary drug residues in food matrices is in great demand for food safety assessment. Here, we have developed a smartphone-integrated platform for fluorometric quantification of metronidazole (MNZ) residues and constructed a sensor array for discrimination of different nitroimidazole antibiotics (NIIMs). Multicolor CDs (B-CDs, C-CDs, Y-CDs, and R-CD) were prepared and showed different fluorescence response to MNZ. The fluorescence of C-CDs was quenched Because of the inner filter effect (IFE) between the C-CDs and MNZ, while that of R-CDs was enhanced due to the passivation of surface defects by MNZ. Based on the response pattern, the fluorometric quantification of MNZ based on the fluorescence images of C-CD + R-CD system (R/G values) was achieved with a low detection limit of 0.45 µM. By designing a smartphone-integrated platform, the analysis can be completed within 20 min. In addition, a fluorescence sensor array based C-CDs and R-CDs was also developed. The unique fingerprint of each NIIMs was obtained by linear discriminant analysis (LDA) of the response patterns, indicating an effective discrimination of five NIIMs. Moreover, the platform was used for quantification of MNZ in food samples and the recoveries were within 84.0-106.3 % with relative standard deviations 1.2-10.2 %. Therefore, the proposed method shows great potential as a universal platform for rapid detection of veterinary drug residues.

Three-century climate variations recovered from Lake Heilongtan in southwest China and potential Anthropogenic evidences.

Under increasing influences of human activities on earth surface system, the concept of Anthropocene has been proposed and widely investigated to represent such a human-dominated geological epoch. To acquire further details about the Anthropocene, investigations on high-resolution continuous records are essentially necessary, especially for regions under notable human impacts. Here, a continuous sediment record covering the past three centuries was collected from Lake Heilongtan, a closed basin lake located in the Hengduan Mountains, in southwest China. High-resolution sedimentary proxies were examined to reconstruct past climate and environment changes, including grain size distribution, geochemical element composition, and organic matter content. The results indicated that water levels were relatively higher under generally warm and wet conditions between 1717 and 1800 CE, while a decline in regional moisture after 1800 CE caused serious shrinkage of the lake level. Comparisons with regional paleoclimate records revealed that solar activity played a significant role in promoting climate variations in southwest China. After 1910 CE, the sedimentary proxies revealed an out-of-phase with regional climate changes, especially the progressive increase after 1950 CE. With the expansion of regional population, the intensified human activities have possibly affected the catchment erosion and sedimentation processes, accounting for the deviation from natural climate changes. Accordingly, the reconstructed sedimentation history in Lake Heilongtan experienced a possible transition from natural-driven to human-dominant status during the past three centuries, revealing potential evidence for the Anthropocene in southwest China.

Local Injection of Rapamycin-Loaded Pcl-Peg Nanoparticles for Enhanced Tendon Healing in Rotator Cuff Tears via Simultaneously Reducing Fatty Infiltration and Drug Toxicity.

As a common cause of shoulder pain, rotator cuff tears (RCTs) are difficult to treat clinically because of their unsatisfactory prognosis due to the fatty infiltration caused by muscle-derived stem cells (MDSCs). Previous studies have found that rapamycin (RAPA) can inhibit fatty infiltration. However, systemic administration of RAPA may cause complications such as infection and nausea, while local administration of RAPA may lead to the cytotoxicity of tendon cells, affecting the healing of rotator cuffs. In this study, biocompatible and clinically approved polycaprolactone-polyethylene glycol (PCL-PEG) is formulated into an injectable nanoparticle for the sustained release of RAPA. The results indicate that the RAPA/PCL-PEG nanoparticles (NPs) can efficiently prolong the release of RAPA and significantly reduce the cytotoxicity of tendon cells caused by RAPA. The study of the fatty infiltration model in rats with delayed rotator cuff repair shows that weekly intraarticular injection of RAPA/PCL-PEG NPs can more effectively reduce the fatty infiltration and muscle atrophy of rat rotator cuffs and leads to better mechanical properties and gait improvements than a daily intraarticular injection of RAPA. These findings imply that local injection of RAPA/PCL-PEG NPs in the shoulder joints can be a potential clinical option for RCTs patients with fatty infiltration.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution.

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

A Simplified Method for the Preparation of Highly Conductive and Flexible Silk Nanofibrils/MXene Membrane.

Silk nanofibers (SNF) have great applications in high-performance functional nanocomposites due to their excellent mechanical properties, biocompatibility, and degradability. However, the preparation of SNF by traditional methods often requires the use of some environmentally harmful or toxic reagents, limiting its application in green chemistry. In this paper, we successfully prepared SNF using natural silk as raw material and solvent stripping technology by adjusting the solvent concentration and solution ratio (the diameter of about 120 nm). Using the above SNFs as raw materials, SNF membranes were prepared by vacuum filtration technology. In addition, we prepared an SNF/MXene nanocomposite material with excellent humidity sensitivity by simply coating MXene nanosheets with silk fibers. The conductivity of the material can approach 1400.6 S m-1 with excellent mechanical strength (51.34 MPa). The SNF/MXene nanocomposite material with high mechanical properties, high conductivity, and green degradability can be potentially applied in the field of electromagnetic interference (EMI) shielding, providing a feasible approach for the development of functional nanocomposite materials.

RNA-binding motif protein 10 inactivates c-Myc by partnering with ribosomal proteins uL18 and uL5.

RNA-binding motif protein 10 (RBM10) is a frequently mutated tumor suppressor in lung adenocarcinoma (LUAD). Yet, it remains unknown whether cancer-derived mutant RBM10 compromises its tumor suppression function and, if so, the molecular insight of the underlying mechanisms. Here, we show that wild-type RBM10 suppresses lung cancer cell growth and proliferation by inactivating c-Myc that is essential for cancer cell survival. RBM10 directly binds to c-Myc and promotes c-Myc's ubiquitin-dependent degradation, while RBM10 knockdown leads to the induction of c-Myc level and activity. This negative action on c-Myc is further boosted by ribosomal proteins (RPs) uL18 (RPL5) and uL5 (RPL11) via their direct binding to RBM10. Cancer-derived mutant RBM10-I316F fails to bind to uL18 and uL5 and to inactivate c-Myc, thus incapable of suppressing tumorigenesis. Our findings uncover RBM10 as a pivotal c-Myc repressor by cooperating with uL18 and uL5 in lung cancer cells, as its failure to do so upon mutation favors tumorigenesis.

Analysis of the histidine kinase gene family and the role of GhHK8 in response to drought tolerance in cotton.

As an important member of the two-component system (TCS), histidine kinases (HKs) play important roles in various plant developmental processes and signal transduction in response to a wide range of biotic and abiotic stresses. So far, the HK gene family has not been investigated in Gossypium. In this study, a total of 177 HK gene family members were identified in cotton. They were further divided into seven groups, and the protein characteristics, genetic relationship, gene structure, chromosome location, collinearity, and cis-elements identification were comprehensively analyzed. Whole genome duplication (WGD) / segmental duplication may be the reason why the number of HK genes doubled in tetraploid Gossypium species. Expression analysis revealed that most cotton HK genes were mainly expressed in the reproductive organs and the fiber at initial stage. Gene expression analysis revealed that HK family genes are involved in cotton abiotic stress, especially drought stress and salt stress. In addition, gene interaction networks showed that HKs were involved in the regulation of cotton abiotic stress, especially drought stress. VIGS experiments have shown that GhHK8 is a negative regulatory factor in response to drought stress. Our systematic analysis provided insights into the characteristics of the HK genes in cotton and laid a foundation for further exploring their potential in drought stress resistance in cotton.

Targeting dysregulated splicing factor in cancer: lessons learned from RBM10 deficiency.

RNA splicing dysregulation is considered as a molecular hallmark and important therapeutic target of cancer. While targeting splicing has been intensively pursued for cancer therapy, specific modulators for dysregulated splicing factors are generally lacking. RNA binding motif protein 10 (RBM10) is frequently mutated in multiple cancers, in particular lung adenocarcinoma. Increasing evidence demonstrates that RBM10 deficiency due to loss-of-function mutation or aberrant expression contributes to cancer development, progression and therapy response. Here we summarize the functional consequences and new therapeutic implications of RBM10 deficiency in cancer. Using RBM10 as a representative example, we highlight the main strategies and discuss the considerations of targeting dysregulated splicing factors for cancer therapy. Those strategies alone or in combination with currently approved therapies may hold great promise in cancer treatment.

PUF60 promotes cell cycle and lung cancer progression by regulating alternative splicing of CDC25C.

Alternative splicing (AS) has been implicated in cell cycle regulation and cancer, but the underlying mechanisms are poorly understood. The poly(U)-binding splicing factor 60 (PUF60) is essential for embryonic development and is overexpressed in multiple types of cancer. Here, we report that PUF60 promotes mitotic cell cycle and lung cancer progression by controlling AS of the cell division cycle 25C (CDC25C). Systematic analysis of splicing factors deregulated in lung adenocarcinoma (LUAD) identifies that elevated copy number and expression of PUF60 correlate with poor prognosis. PUF60 depletion inhibits LUAD cell-cycle G2/M transition, cell proliferation, and tumor development. Mechanistically, PUF60 knockdown leads to exon skipping enriched in mitotic cell cycle genes, including CDC25C. Exon 3 skipping in the full-length CDC25C results in nonsense-mediated mRNA decay and a decrease of CDC25C protein, thereby inhibiting cell proliferation. This study establishes PUF60 as a cell cycle regulator and an oncogenic splicing factor in lung cancer.

Identification of novel candidate loci and genes for seed vigor-related traits in upland cotton (Gossypium hirsutum L.) via GWAS.

Seed vigor (SV) is a crucial trait determining the quality of crop seeds. Currently, over 80% of China's cotton-planting area is in Xinjiang Province, where a fully mechanized planting model is adopted, accounting for more than 90% of the total fiber production. Therefore, identifying SV-related loci and genes is crucial for improving cotton yield in Xinjiang. In this study, three seed vigor-related traits, including germination potential, germination rate, and germination index, were investigated across three environments in a panel of 355 diverse accessions based on 2,261,854 high-quality single-nucleotide polymorphisms (SNPs). A total of 26 significant SNPs were detected and divided into six quantitative trait locus regions, including 121 predicted candidate genes. By combining gene expression, gene annotation, and haplotype analysis, two novel candidate genes (Ghir_A09G002730 and Ghir_D03G009280) within qGR-A09-1 and qGI/GP/GR-D03-3 were associated with vigor-related traits, and Ghir_A09G002730 was found to be involved in artificial selection during cotton breeding by population genetic analysis. Thus, understanding the genetic mechanisms underlying seed vigor-related traits in cotton could help increase the efficiency of direct seeding by molecular marker-assisted selection breeding.

Research on the development methodology for clinical practice guidelines for organic integration of traditional Chinese and Western medicine.

Integrated traditional Chinese medicine (TCM) and Western medicine (WM) is a new medical science grounded in the knowledge bases of both TCM and WM, which then forms a unique modern medical system in China. Integrated TCM and WM has a long history in China, and has made important achievements in the process of clinical diagnosis and treatment. However, the methodological defects in currently published clinical practice guidelines limit its development. The organic integration of TCM and WM is a deeper integration of TCM and WM. To realize the progression of "integration" to "organic integration", a targeted and standardized guideline development methodology is needed. Therefore, the purpose of this study is to establish a standardized development procedure for clinical practice guidelines for the organic integration of TCM and WM to promote the systematic integration of TCM and WM research results into clinical practice guidelines in order to achieve optimal results as the whole is greater than the sum of the parts.

Genome-wide association study reveals genomic loci of sex differentiation and gonadal development in Plectropomus leopardus.

Introduction: Plectropomus leopardus, a commercially significant marine fish, is primarily found in the Western Pacific regions and along the coast of Southeast Asia. A thorough analysis of the molecular mechanisms involved in sex differentiation is crucial for gaining a comprehensive understanding of gonadal development and improving sex control breeding. However, the relevant fundamental studies of P. leopardus are relatively lacking. Methods: In this study, a genome-wide association study (GWAS) was conducted to investigate the genetic basis mechanism of sex differentiation and gonadal developmental traits in P. leopardus utilizing about 6,850,000 high-quality single-nucleotide polymorphisms (SNPs) derived from 168 individuals (including 126 females and 42 males) by the genome-wide efficient mixed-model association (GEMMA) algorithm. Results: The results of these single-trait GWASs showed that 46 SNP loci (-log10 p > 7) significantly associated with sex differentiation, and gonadal development traits were distributed in multiple different chromosomes, which suggested the analyzed traits were all complex traits under multi-locus control. A total of 1,838 potential candidate genes were obtained by considering a less-stringent threshold (-log10 p > 6) and ±100 kb regions surrounding the significant genomic loci. Moreover, 31 candidate genes were identified through a comprehensive analysis of significant GWAS peaks, gene ontology (GO) annotations, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, including taf7, ddx6, apoeb, sgk1, a2m, usf1, hsd3b7, dll4, xbp1, tet3, esr1, and gli3. These trait-associated genes have been shown to be involved in germline development, male sex differentiation, gonad morphogenesis, hormone receptor binding, oocyte development, male gonad development, steroidogenesis, estrogen-synthetic pathway, etc. Discussion: In the present study, multiple genomic loci of P. leopardus associated with sex differentiation and gonadal development traits were identified for the first time by using GWAS, providing a valuable resource for further research on the molecular genetic mechanism and sex control in P. leopardus. Our results also can contribute to understanding the genetic basis of the sex differentiation mechanism and gonadal development process in grouper fish.