Prediction of Solubility of Proteins in Escherichia coli Based on Functional and Structural Features Using Machine Learning Methods.

Protein solubility is a critical parameter that determines the stability, activity, and functionality of proteins, with broad and far-reaching implications in biotechnology and biochemistry. Accurate prediction and control of protein solubility are essential for successful protein expression and purification in research and industrial settings. This study gathered information on soluble and insoluble proteins. In characterizing the proteins, they were mapped to STRING and characterized by functional and structural features. All functional/structural features were integrated to create a 5768-dimensional binary vector to encode proteins. Seven feature-ranking algorithms were employed to analyze the functional/structural features, yielding seven feature lists. These lists were subjected to the incremental feature selection, incorporating four classification algorithms, one by one to build effective classification models and identify functional/structural features with classification-related importance. Some essential functional/structural features used to differentiate between soluble and insoluble proteins were identified, including GO:0009987 (intercellular communication) and GO:0022613 (ribonucleoprotein complex biogenesis). The best classification model using support vector machine as the classification algorithm and 295 optimized functional/structural features generated the F1 score of 0.825, which can be a powerful tool to differentiate soluble proteins from insoluble proteins.

Development of Highly Sensitive Immunochromatography Using Time-Resolved Fluorescence Microspheres for Amantadine Detection.

Amantadine (AMA), commonly used to treat viral infections in livestock and poultry, has been banned owing to its potential hazards to human well-being. To detect unauthorized AMA usage in livestock, we developed a polyclonal antibody with a high affinity for the specific recognition of AMA through a rational design based on a structure similar to AMA and revealed the availability of the hapten design by computational chemistry analysis. Using this antibody, we established a highly responsive time-resolved fluorescence immunochromatographic assay (TRFICA). The visual detection limit of the assay is 0.6 µg/kg, and the quantitative detection limit is 0.05 µg/kg. The TRFICA also showed good recovery rates ranging from 94.5 to 109.9%, with variability coefficients not exceeding 10%. The outcomes of undisclosed sample examinations aligned with those of HPLC-MS/MS analyses, indicating that this approach can function as an ideal screening and monitoring tool for detecting illegal AMA in chicken muscle.

A recombinant N2 neuraminidase-based CpG 1018® adjuvanted vaccine provides protection against challenge with heterologous influenza viruses in mice and hamsters.

Recombinant influenza virus neuraminidase (NA) is a promising broadly protective influenza vaccine candidate. However, the recombinant protein alone is not sufficient to induce durable and protective immune responses and requires the coadministration of immunostimulatory molecules. Here, we evaluated the immunogenicity and cross-protective potential of a recombinant influenza virus N2 neuraminidase vaccine construct, adjuvanted with a toll-like receptor 9 (TLR9) agonist (CpG 1018® adjuvant), and alum. The combination of CpG 1018 adjuvant and alum induced a balanced and robust humoral and T-cellular immune response against the NA, which provided protection and reduced morbidity against homologous and heterologous viral challenges in mouse and hamster models. This study supports Syrian hamsters as a useful complementary animal model to mice for pre-clinical evaluation of influenza virus vaccines.

Identification of proliferative hepatocellular carcinoma using the SMARS score and implications for microwave ablation.

OBJECTIVE: To compare therapeutic outcomes of predicted proliferative and nonproliferative hepatocellular carcinoma (HCC) after microwave ablation (MWA) using a previously developed imaging-based predictive model, the SMARS score. METHODS: This multicenter retrospective study included consecutive 635 patients with unresectable HCC who underwent MWA between August 2013 and September 2020. Patients were stratified into predicted proliferative and nonproliferative phenotypes according to the SMARS score. Overall survival (OS) and recurrence-free survival (RFS) were compared between the predicted proliferative and nonproliferative HCCs before and after propensity score matching (PSM). OS and RFS were also compared between the two groups in subgroups of tumor size smaller than 30 mm and tumor size 30-50 mm. RESULTS: The SMARS score classified 127 and 508 patients into predicted proliferative and nonproliferative HCCs, respectively. The predicted proliferative HCCs exhibited worse RFS but equivalent OS when compared with nonproliferative HCCs before (p < 0.001 for RFS; p = 0.166 for OS) and after (p < 0.001 for RFS; p = 0.456 for OS) matching. Regarding subgroups of tumor size smaller than 30 mm (p = 0.098) and tumor size 30-50 mm (p = 0.680), the OSs were similar between the two groups. However, predicted proliferative HCCs had worse RFS compared to nonproliferative HCCs in the subgroup of tumor size 30-50 mm (p < 0.001), while the RFS did not differ in the subgroup of tumor size smaller than 30 mm (p = 0.141). CONCLUSION: Predicted proliferative HCCs have worse RFS than nonproliferative ones after MWA, especially in tumor size larger than 30 mm. However, the phenotype of the tumor may not affect the OS. CRITICAL RELEVANCE STATEMENT: Before performing microwave ablation for hepatocellular carcinoma, the tumor phenotype should be considered because it may affect the therapeutic outcome. KEY POINTS: Proliferative hepatocellular carcinoma (HCC) may be identified using the SMARS score, an imaging-based predictive model. SMARS predicted proliferative HCCs have worse recurrence-free and equivalent overall survival compared to nonproliferative HCC after microwave ablation. Tumor phenotype should be considered before performing microwave ablation.

Improving Antifreeze Proteins Prediction with Protein Language Models and Hybrid Feature Extraction Networks.

Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs prediction, the complex and diverse nature of AFPs limits the prediction performance of existing methods. In this study, we propose AFP-Deep, a new deep learning method to predict antifreeze proteins by integrating embedding from protein sequences with pre-trained protein language models and evolutionary contexts with hybrid feature extraction networks. The experimental results demonstrated that the main advantage of AFP-Deep is its utilization of pre-trained protein language models, which can extract discriminative global contextual features from protein sequences. Additionally, the hybrid deep neural networks designed for protein language models and evolutionary context feature extraction enhance the correlation between embeddings and antifreeze pattern. The performance evaluation results show that AFP-Deep achieves superior performance compared to state-of-the-art models on benchmark datasets, achieving an AUPRC of 0.724 and 0.924, respectively.

Characteristics of anti-contactin1 antibody positive autoimmune nodopathies combined with membranous nephropathy.

BACKGROUND: Autoimmune nodopathy (AN) is a very rare new disease entity, especially when combined with membranous nephropathy (MN). METHODS: Antibodies against nodal-paranodal cell adhesion molecules in the serum were detected using cell-based assays. Antibody subtypes against contactin-1 (CNTN1) were confirmed. Cases of anti-CNTN1 antibody-positive AN with and without MN were retrieved through a literature search to compare clinical and electrophysiological characteristics. RESULTS: A 65-year-old male patient with MN developed limb numbness and weakness, along with walking instability. Serum CNTN1 antibodies were positive, primarily those of the IgG4 subtype. Electromyography showed prominent demyelination patterns in both the proximal and distal segments of the nerves compared to the middle nerve trunk. Magnetic resonance imaging revealed enlargement of the bilateral brachial and lumbosacral plexuses and local hyperintensity of the right C5-C6 nerve roots. Thirty-five cases with anti-CNTN1 antibody-positive AN with MN and 51 cases with anti-CNTN1 antibody-positive AN without MN were compared. Furthermore, the proportion of patients with MN combined with AN presenting with acute or subacute onset was higher than that observed in the MN without AN group. Nevertheless, no substantial differences were noted between the two groups concerning the clinical and electrophysiological characteristics, which were mainly elderly men, manifested as sensory ataxia, IgG4 antibody subtype, electrophysiological demyelination, and a certain effect on immunotherapy. CONCLUSION: In cases of electrophysiological manifestation of demyelinating peripheral neuropathy, especially in distal and poximal segments of nerves, AN should be considered, and further screening for renal function should be performed. Concomitant MN does not aggravate or alleviate peripheral nerve symptoms.

Structure-based design of a soluble human cytomegalovirus glycoprotein B antigen stabilized in a prefusion-like conformation.

Human cytomegalovirus (HCMV) glycoprotein B (gB) is a class III membrane fusion protein required for viral entry. HCMV vaccine candidates containing gB have demonstrated moderate clinical efficacy, but no HCMV vaccine has been approved. Here, we used structure-based design to identify and characterize amino acid substitutions that stabilize gB in its metastable prefusion conformation. One variant containing two engineered interprotomer disulfide bonds and two cavity-filling substitutions (gB-C7), displayed increased expression and thermostability. A 2.8 Å resolution cryoelectron microscopy structure shows that gB-C7 adopts a prefusion-like conformation, revealing additional structural elements at the membrane-distal apex. Unlike previous observations for several class I viral fusion proteins, mice immunized with postfusion or prefusion-stabilized forms of soluble gB protein displayed similar neutralizing antibody titers, here specifically against an HCMV laboratory strain on fibroblasts. Collectively, these results identify initial strategies to stabilize class III viral fusion proteins and provide tools to probe gB-directed antibody responses.

CD36-mediated accumulation of MDSCs exerts abscopal immunosuppressive responses in hepatocellular carcinoma after insufficient microwave ablation.

The immune landscape of distant unablated tumors following insufficient microwave ablation (iMWA) in hepatocellular carcinoma (HCC) remains to be clarified. The objective of this study is to define the abscopal immune landscape in distant unablated tumor before and after iMWA for HCC. Two treatment-naive patients were recruited for tumor tissue sampling, of each with two HCC lesions. Tumor samples were obtained at before and after microwave ablation in distant unablated sites for single-cell RNA sequencing (scRNA-seq). Mouse model with bilateral hepatoma tumors were developed, and distant unablated tumors were analyzed using multicolor immunofluorescence, RNA sequencing and flow cytometry. The scRNA-seq revealed that a reduced proportion of CD8+ T cells and an increased proportion of myeloid-derived suppressor cells (MDSCs) were observed in the distant unablated tumor microenvironment (TME). A notable disruption was observed in the lipid metabolism of tumor-associated immune cells, accompanied by an upregulated expression of CD36 in tumor-infiltrating immune cells in distant unablated tumor. The administration of a CD36 inhibitor has been demonstrated to ameliorate the adverse effects induced by iMWA, primarily by reinstating the anti-tumor responses of T cells in distant unablated tumor. These findings explain the recurrence and progression of tumors after iMWA and provide a new target of immunotherapy for HCC.

Unravelling oncosis: morphological and molecular insights into a unique cell death pathway.

Programmed cell death (PCD) is a fundamental biological process for maintaining cellular equilibrium and regulating development, health, and disease across all living organisms. Among the various types of PCD, apoptosis plays a pivotal role in numerous diseases, notably cancer. Cancer cells frequently develop mechanisms to evade apoptosis, increasing resistance to standard chemotherapy treatments. This resistance has prompted extensive research into alternative mechanisms of programmed cell death. One such pathway is oncosis, characterized by significant energy consumption, cell swelling, dilation of the endoplasmic reticulum, mitochondrial swelling, and nuclear chromatin aggregation. Recent research suggests that oncosis can impact conditions such as chemotherapeutic cardiotoxicity, myocardial ischemic injury, stroke, and cancer, mediated by specific oncosis-related proteins. In this review, we provide a detailed examination of the morphological and molecular features of oncosis and discuss various natural or small molecule compounds that can induce this type of cell death. Additionally, we summarize the current understanding of the molecular mechanisms underlying oncosis and its role in both normal physiology and pathological conditions. These insights aim to illuminate future research directions and propose innovative strategies for leveraging oncosis as a therapeutic tool against human diseases and cancer resistance.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.

Identification of a nomogram predicting overall survival based on ADAP2-related apoptosis genes in gliomas.

BACKGROUND: Apoptosis continues to be a pivotal area of investigation in glioma research. ADAP2 mediates the malignant progression of gliomas through the inhibition of apoptosis and predicts the overall survival(OS) of glioma patients based on prognostic modeling of the apoptotic gene set. METHODS: The study encompassed 686 glioma patients, with 413 allocated to the training group and 273 to the validation group. Differential expression of ADAP2 across various glioma subtypes was assessed through bioinformatics analysis and Western blotting. The correlation between ADAP2 and apoptosis was examined using Gene Set Enrichment Analysis (GSEA). Multivariate Cox regression analysis and LASSO dimension reduction analysis were employed to identify apoptosis-related genes with prognostic significance in glioma patients and to construct a nomogram. Biological functions and mechanisms associated with risk scores were explored via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA analyses, with validation through Western blotting, flow cytometry, and AM/PI staining. RESULTS: ADAP2 was found to be enriched in more aggressive glioma subtypes and was closely linked to glioma cell apoptosis, modulating this process via the NF-κB and P53 signaling pathway. A nomogram for OS in glioma patients was constructed using thirteen apoptosis-related genes. Additionally, ROC curves, calibration curves, and C-indices confirmed the robust applicability of the nomogram. CONCLUSION: ADAP2 functions as a prognostic biomarker for glioma patients, regulating glioma cell apoptosis through the NF-κB and P53 signaling pathway. Moreover, prognostic models based on apoptosis-related genes can accurately predict OS for glioma patients at 1, 2, 3, 5, and 10 years.

Psoriasis as a Predictor of Neovascular Age-Related Macular Degeneration in Type 2 Diabetes Mellitus: A Nationwide Cohort Study.

PURPOSE: The objective of this study is to evaluate the relationship of psoriasis and neovascular Age-related Macular Degeneration (AMD) in diabetic mellitus (DM) population. DESIGN: Nationwide, population-based, retrospective cohort study. METHODS: Records of patients who had been diagnosed with type 2 diabetes mellitus over 40 years of age from January 2009 to December 2012 were analyzed. The incidence of neovascular AMD was observed from the index year to December 2018 in all subjects. We compared the incidence rate of neovascular AMD among the psoriasis group and control group. Covariates include age, sex, BMI, income level, smoking status, drinking status, regular exercise habits, hypertension, dyslipidemia, end-stage renal disease, diabetic retinopathy, glucose level, the prescription of more than three oral hypoglycemic agents, and a history of diabetes mellitus exceeding five years. RESULTS: Of 2,245,358 type 2 DM patients, 20,853 patients were classified in the psoriasis group, and the other 2,224,505 individuals in the control group. A total of 105 neovascular AMD cases occurred in the psoriasis group and 7,459 cases in the control group. According to multivariable Cox proportional hazard models, individuals with psoriasis had a significantly higher risk for neovascular AMD compared to controls (HR=1.329, 95% confidence interval: 1.096-1.612) after adjustments for covariates. CONCLUSION: This study demonstrated that psoriasis was an independent risk factor for developing neovascular AMD in DM patients. Therefore, physicians should be alert to the development of neovascular AMD in DM patients who also have psoriasis.

Experiences and prospects for national survey of terrestrial wildlife resources in China.

Wildlife resources are strategic resources of a country, and the investigation of which is a key task for effective management in protection and utilization. Since the 1990s, two national surveys of terrestrial wildlife resources have been carried out in China, and the situation of wildlife resources has been known to a certain extent. Due to the complexity and difficulty of national wildlife survey, we are still not able to grasp the background and dynamics of wildlife resources as a whole promptly and effectively. The results and effectiveness of wildlife resources investigation will directly affect the decision-making related in wildlife protection. According to Law of the People's Republic of China on the Protection of Wildlife and Regulations of the People's Republic of China for the Implementation of the Protection of Terrestrial Wildlife, it is imperative to carry out the third national survey of terrestrial wildlife resources, and to be integrated with the national strategy of ecological civilization construction. The aims of this review were to summarize the earlier experiences in time, to further improve the investigation scheme and technical methods, to serve the third national survey of terrestrial wildlife resources, in addition to obtain more comprehensive and reliable data of wildlife resources, grasp the development trend of domestic wildlife resources, and provide more effective supports for the wildlife conservation in China.

Maintenance of delay-period activity in working memory task is modulated by local network structure.

Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.

Pipeline of New Drug Treatment for Non-alcoholic Fatty Liver Disease/Metabolic Dysfunction-associated Steatotic Liver Disease.

Given the global prevalence and rising incidence of metabolic dysfunction-associated steatotic liver disease (MASLD), the absence of licensed medications is striking. A deeper understanding of the heterogeneous nature of MASLD has recently contributed to the discovery of novel groups of agents and the potential repurposing of currently available medications. MASLD therapies center on four major pathways. Considering the close relationship between MASLD and type 2 diabetes, the first approach involves antidiabetic medications, including incretins, thiazolidinedione insulin sensitizers, and sodium-glucose cotransporter 2 inhibitors. The second approach targets hepatic lipid accumulation and the resultant metabolic stress. Agents in this group include peroxisome proliferator-activated receptor agonists (e.g., pioglitazone, elafibranor, saroglitazar), bile acid-farnesoid X receptor axis regulators (obeticholic acid), de novo lipogenesis inhibitors (aramchol, NDI-010976), and fibroblast growth factor 21/19 analogs. The third approach focuses on targeting oxidative stress, inflammation, and fibrosis. Agents in this group include antioxidants (vitamin E), tumor necrosis factor α pathway regulators (emricasan, pentoxifylline, ZSP1601), and immune modulators (cenicriviroc, belapectin). The final group targets the gut (IMM-124e, solithromycin). Combination therapies targeting different pathogenetic pathways may provide an alternative to MASLD treatment with higher efficacy and fewer side effects. This review aimed to provide an update on these medications.

An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value.

APMG: 3D Molecule Generation Driven by Atomic Chemical Properties.

Recently, mask-fill-based 3D Molecular Generation (MG) methods have become very popular in virtual drug design. However, the existing MG methods ignore the chemical properties of atoms and contain inappropriate atomic position training data, which limits their generation capability. To mitigate the above issues, this paper presents a novel mask-fill-based 3D molecule generation model driven by atomic chemical properties (APMG). Specifically, we construct a new attention-MPNN-based encoder and introduce the electronic information into atom representations to enrich chemical properties. Also, a multi-functional classifier is designed to predict the electronic information of each generated atom, guiding the type prediction of elements and bonds. By design, the proposed method uses the chemical properties of atoms and their correlations for high-quality molecule generation. Second, to optimize the atomic position training data, we propose a novel atomic training position generation approach using the Chi-Square distribution. We evaluate our APMG method on the CrossDocked dataset and visualize the docking states of the pockets and generated molecules. The obtained results demonstrate the superiority and merits of APMG over the state-of-the-art approaches. The dataset and codes will be available on the project homepage: https://github.com/JU-HuaY/APMG.

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory syndrome coronavirus 2 infection on the homeostatic immune status and secondary immune response in recovered patients remains unclear. To investigate these persistent alterations, we employed five feature-ranking algorithms (LASSO, MCFS, RF, CATBoost, and XGBoost), incremental feature selection, synthetic minority oversampling technique and two classification algorithms (decision tree and k-nearest neighbors) to analyze multi-omics data (surface proteins and transcriptome) from coronavirus disease 2019 (COVID-19) recovered patients and healthy controls post-influenza vaccination. The single-cell multi-omics dataset was divided into five subsets corresponding to five immune cell subtypes: B cells, CD4+ T cells, CD8+ T cells, Monocytes, and Natural Killer cells. Each cell was represented by 28,402 scRNA-seq (RNA) features, 3 Hash Tag Oligo (HTO) features, 138 Cellular indexing of transcriptomes and epitopes by sequencing (CITE) features and 23,569 Single Cell Transform (SCT) features. Some multi-omics markers were identified and effective classifiers were constructed. Our findings indicate a distinct immune status in COVID-19 recovered patients, characterized by low expression of ribosomal protein (RPS26) and high expression of immune cell surface proteins (CD33, CD48). Notably, TMEM176B, a membrane protein, was highly expressed in monocytes of COVID-19 convalescent patients. These observations aid in discerning molecular differences among immune cell subtypes and contribute to understanding the prolonged effects of COVID-19 on the immune system, which is valuable for treating infectious diseases like COVID-19.

Controlled interconversion of macrocyclic atropisomers via defined intermediates.

Macrocyclic conformations play a crucial role in regulating their properties. Our understanding of the determinants to control macrocyclic conformation interconversion is still in its infancy. Here we present a macrocycle, octamethyl cyclo[4](1,3-(4,6)-dimethylbenzene)[4]((4,6-benzene)(1,3-dicarboxylate) (OC-4), that can exist at 298 K as two stable atropisomers with C2v and C4v symmetry denoted as C2v-OC-4 and C4v-OC-4, respectively. Heating induces the efficient stepwise conversion of C2v- to C4v-OC-4 via a Cs-symmetric intermediate (Cs-OC-4). It differs from the typical transition state-mediated processes of simple C-C single bond rotations. Hydrolysis and further esterification with a countercation dependence promote the generation of C2v- and Cs-OC-4 from C4v-OC-4. In contrast to C2v-OC-4, C4v-OC-4 can bind linear guests to form pseudo-rotaxans, or bind C60 or C70 efficiently. The present study highlights the differences in recognition behavior that can result from conformational interconversion, as well as providing insights into the basic parameters that govern coupled molecular rotations.

Bacteria, Fungi, and Scalp Psoriasis: Understanding the Role of the Microbiome in Disease Severity.

Background: Psoriasis is a chronic skin condition affected by genetic and environmental factors. Changes in the skin microbiome may affect the immune system and skin barrier functions, thereby contributing to the development and progression of psoriasis. The scalp, which is a common site for psoriasis, is often resistant to therapy. Although several studies have investigated the scalp microbiome, analyses focusing on both bacteria and fungi remain scarce. Methods: We examined the scalp microbiomes of 11 patients with psoriasis complicated with scalp lesions and categorized them according to their Psoriasis Area Severity Index (PASI) scores. The bacterial and fungal data were analyzed using QIIME2 pipeline version 2021.04 and the UNITE database version 8.3, respectively. Results: The Shannon indices for mild (2 patients), moderate (4 patients), and severe (5 patients) groups were 0.97, 1.38, and 1.88, respectively. A significant correlation was observed between increased mycobiome diversity and disease severity (p = 4.07 × 10-5, Spearman's correlation: 0.9269). Compared with the mild and moderate groups, the severe group exhibited a higher abundance of Malassezia globosa. Pseudomonas and Staphylococcus were, respectively, more prevalent in the moderate and severe groups than in the mild group. Conclusions: This study highlights the potential role of increased fungal diversity and specific microbial compositions in the severity of scalp psoriasis, suggesting a possible avenue for targeted therapeutic interventions.