Wavefunction matching for solving quantum many-body problems.

Ab initio calculations have an essential role in our fundamental understanding of quantum many-body systems across many subfields, from strongly correlated fermions1-3 to quantum chemistry4-6 and from atomic and molecular systems7-9 to nuclear physics10-14. One of the primary challenges is to perform accurate calculations for systems where the interactions may be complicated and difficult for the chosen computational method to handle. Here we address the problem by introducing an approach called wavefunction matching. Wavefunction matching transforms the interaction between particles so that the wavefunctions up to some finite range match that of an easily computable interaction. This allows for calculations of systems that would otherwise be impossible owing to problems such as Monte Carlo sign cancellations. We apply the method to lattice Monte Carlo simulations15,16 of light nuclei, medium-mass nuclei, neutron matter and nuclear matter. We use high-fidelity chiral effective field theory interactions17,18 and find good agreement with empirical data. These results are accompanied by insights on the nuclear interactions that may help to resolve long-standing challenges in accurately reproducing nuclear binding energies, charge radii and nuclear-matter saturation in ab initio calculations19,20.

Structure Factors for Hot Neutron Matter from Ab Initio Lattice Simulations with High-Fidelity Chiral Interactions.

We present the first ab initio lattice calculations of spin and density correlations in hot neutron matter using high-fidelity interactions at next-to-next-to-next-to-leading order in chiral effective field theory. These correlations have a large impact on neutrino heating and shock revival in core-collapse supernovae and are encapsulated in functions called structure factors. Unfortunately, calculations of structure factors using high-fidelity chiral interactions were well out of reach using existing computational methods. In this Letter, we solve the problem using a computational approach called the rank-one operator (RO) method. The RO method is a general technique with broad applications to simulations of fermionic many-body systems. It solves the problem of exponential scaling of computational effort when using perturbation theory for higher-body operators and higher-order corrections. Using the RO method, we compute the vector and axial static structure factors for hot neutron matter as a function of temperature and density. The ab initio lattice results are in good agreement with virial expansion calculations at low densities but are more reliable at higher densities. Random phase approximation codes used to estimate neutrino opacity in core-collapse supernovae simulations can now be calibrated with ab initio lattice calculations.

Amniotic miracle: Investigating the unique development and applications of amniotic membrane in wound healing.

BACKGROUND: The perfect repair of damaged skin has always been a constant goal for scientists; however, the repair and reconstruction of skin is still a major problem and challenge in injury and burns medicine. Human amniotic membrane (hAM), with its good mechanical properties and anti-inflammatory, antioxidant and antimicrobial benefits, containing growth factors that promote wound healing, has evolved over the last few decades from simple skin sheets to high-tech dressings, such as being made into nanocomposites, hydrogels, powders, and electrostatically spun scaffolds. This paper aims to explore the historical development, applications, trends, and research hotspots of hAM in wound healing. METHODS: We examined 2660 publications indexed in the Web of Science Core Collection (WoSCC) from January 1, 1975 to July 12, 2023. Utilizing bibliometric methods, we employed VOSviewer, CiteSpace, and R-bibliometrix to characterize general information, identify development trends, and highlight research hotspots. Subsequently, we identified a collection of high-quality English articles focusing on the roles of human amniotic epithelial stem cells (hAESCs), human amniotic mesenchymal stem cells (hAMSCs), and amniotic membrane (AM) scaffolds in regenerative medicine and tissue engineering. RESULTS: Bibliometric analysis identified Udice-French Research Universities as the most productive affiliation and Tseng S.C.G. as the most prolific author. Keyword analysis, historical direct quotations network, and thematic analysis helped us review the historical and major themes in this field. Our examination included the knowledge structure, global status, trends, and research hotspots regarding the application of hAM in wound healing. Our findings indicate that contemporary research emphasizes the preparation and application of products derived from hAM. Notably, both hAM and the cells isolated from it - hADSCs and hAESCs are prominent and promising areas of research in regenerative medicine and tissue engineering. CONCLUSION: This research delivers a comprehensive understanding of the knowledge frameworks, global dynamics, emerging patterns, and primary research foci in the realm of hAM applications for wound healing. The field is rapidly evolving, and our findings offer valuable insights for researchers. Future research outcomes are anticipated to be applied in clinical practice, enhancing methods for disease prevention, diagnosis, and treatment.

A multicenter cross-sectional study in China revealing the intrinsic relationship between medical students' grade and their perceptions of the learning environment.

BACKGROUND: Medical school learning environment (MSLE) has a holistic impact on students' psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student's grade in the perception of MSLE. METHODS: Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student's grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch's ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student's grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. RESULTS: A significant difference between two JHLES categories among students in different grades was observed (p < 0.001), with the proportion of the high JHLES group dominating in grade 1, 5, and the graduate subgroups (p < 0.001). The mean JHLES score declined especially in the third and fourth graders compared to freshmen (p < 0.001), while the mean score among the fifth graders had a remarkable rebound from the third graders (p < 0.001). Most imperatively, identified by multivariable logistic regression analysis, students in grade 3 (OR = 1.470, 95% CI = 1.265-1.709, p < 0.001) and 4 (OR = 1.578, 95% CI = 1.326-1.878, p < 0.001) perceived more negatively than freshmen. The constructed nomogram provided a promising prediction model for student's low JHLES score probability, with accuracy, accordance, and discrimination (area under the curve (AUC) = 0.627). CONCLUSION: The student's grade was a significant influencing factor in medical students' perception of MSLE. The perceptions among the third and fourth graders got worse, probably due to the worrying changes in various aspects of MSLE during that period. The relevant and appropriate interventions to improve medical students' perceptions are urgently needed.

Identification of prognostic stemness-related genes in kidney renal papillary cell carcinoma.

BACKGROUND: Kidney renal papillary cell carcinoma (KIRP) is the second most prevalent malignant cancer originating from the renal epithelium. Nowadays, cancer stem cells and stemness-related genes (SRGs) are revealed to play important roles in the carcinogenesis and metastasis of various tumors. Consequently, we aim to investigate the underlying mechanisms of SRGs in KIRP. METHODS: RNA-seq profiles of 141 KIRP samples were downloaded from the TCGA database, based on which we calculated the mRNA expression-based stemness index (mRNAsi). Next, we selected the differentially expressed genes (DEGs) between low- and high-mRNAsi groups. Then, we utilized weighted gene correlation network analysis (WGCNA) and univariate Cox analysis to identify prognostic SRGs. Afterwards, SRGs were included in the multivariate Cox regression analysis to establish a prognostic model. In addition, a regulatory network was constructed by Pearson correlation analysis, incorporating key genes, upstream transcription factors (TFs), and downstream signaling pathways. Finally, we used Connectivity map analysis to identify the potential inhibitors. RESULTS: In total, 1124 genes were characterized as DEGs between low- and high-RNAsi groups. Based on six prognostic SRGs (CCKBR, GPR50, GDNF, SPOCK3, KC877982.1, and MYO15A), a prediction model was established with an area under curve of 0.861. Furthermore, among the TFs, genes, and signaling pathways that had significant correlations, the CBX2-ASPH-Notch signaling pathway was the most significantly correlated. Finally, resveratrol might be a potential inhibitor for KIRP. CONCLUSIONS: We suggested that CBX2 could regulate ASPH through activation of the Notch signaling pathway, which might be correlated with the carcinogenesis, development, and unfavorable prognosis of KIRP.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications.

Background: Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field. Methods: We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field. Results: 3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field. Conclusion: Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

Unveiling the unique role of TSPAN7 across tumors: a pan-cancer study incorporating retrospective clinical research and bioinformatic analysis.

BACKGROUND: TSPAN7 is an important factor in tumor progression. However, the precise function of TSPAN7 and its role in pan-cancer are not clear. METHODS: Based on Xinhua cohort incorporating 370 patients with kidney neoplasm, we conducted differential expression analysis by immunohistochemistry between tumor and normal tissues, and explored correlations of TSPAN7 with patients' survival. Subsequently, we conducted a pan-cancer study, and successively employed differential expression analysis, competing endogenous RNA (ceRNA) analysis, protein-protein interaction (PPI) analysis, correlation analysis of TSPAN7 with clinical characteristics, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. Last but not least, gene set enrichment analysis was applied to identify enriched pathways of TSPAN7. RESULTS: In Xinhua cohort, TSPAN7 expression was significantly up-regulated (P-value = 0.0019) in tumor tissues of kidney neoplasm patients. High TSPAN7 expression was associated with decreases in overall survival (OS) (P-value = 0.009) and progression-free survival (P-value = 0.009), and it was further revealed as an independent risk factor for OS (P-value = 0.0326, HR = 5.66, 95%CI = 1.155-27.8). In pan-cancer analysis, TSPAN7 expression was down-regulated in most tumors, and it was associated with patients' survival, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. The ceRNA network and PPI network of TSPAN7 were also constructed. Last but not least, the top five enriched pathways of TSPAN7 in various tumors were identified. CONCLUSION: TSPAN7 served as a promising biomarker of various tumors, especially kidney neoplasms, and it was closely associated with tumor purity, tumor genomics, tumor immunology, and drug sensitivity in pan-cancer level.

A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma.

BACKGROUND: Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. METHOD: Three databases, including NCBI-GEO, TCGA, and MET-500, separately provide single-cell RNA sequencing data, bulk RNA sequencing profiles of MESO, and RNA sequencing information on bone metastatic tumors. Dimensionality reduction and clustering analysis were leveraged to acquire fibroblast subtypes in the MESO microenvironment. The fibroblast differentiation-related genes (FDGs), which were associated with survival and subsequently utilized to generate the MESO categorization and prognostic prediction model, were selected in combination with pseudotime analysis and survival information from the TCGA database. Then, regulatory network was constructed for each MESO subtype, and candidate inhibitors were predicted. Clinical specimens were collected for further validation. RESULT: A total of six fibroblast subtypes, three differentiation states, and 39 FDGs were identified. Based on the expression level of FDGs, three MESO subtypes were distinguished in the fibroblast differentiation-based classification (FDBC). In the multivariate prognostic prediction model, the risk score that was dependent on the expression level of several important FDGs, was verified to be an independently effective prognostic factor and worked well in internal cohorts. Finally, we predicted 24 potential drugs for the treatment of MESO. Moreover, immunohistochemical staining and statistical analysis provided further validation. CONCLUSION: Fibroblast differentiation-related genes (FDGs), especially those in low-differentiation states, might participate in the proliferation and invasion of MESO. Hopefully, the raised clinical subtyping of MESO would provide references for clinical practitioners.

A 10-year mono-center study on patients with burns ≥70% TBSA: prediction model construction and multi-center validation: retrospective cohort.

BACKGROUND: Burn injuries with ≥70% total body surface area (TBSA) are especially acute and life-threatening, leading to severe complications and terrible prognosis, while a powerful model for prediction of overall survival (OS) is lacked. The objective of this study is to identify prognostic factors for the OS of patients with burn injury ≥70% TBSA, construct and validate a feasible predictive model. MATERIALS AND METHODS: Patients diagnosed with burns ≥70% TBSA admitted and treated between 2010 and 2020 in our hospital were included. A cohort of the patients from the Kunshan explosion were assigned as the validation set. The Chi-square test and K-M survival analysis were conducted to identify potential predictors for OS. Then, multi-variate Cox regression analysis was performed to identify the independent factors. Afterwards, we constructed a nomogram to predict OS probability. Finally, the Kunshan cohort was applied as an external validation set. RESULTS: Gender, the percentage of third- and fourth-degree burn as well as organ dysfunction were identified as significant independent factors. A nomogram only based on the factors of the individuals was built and evidenced to have promising predictive accuracy, accordance, and discrimination by both internal and external validation. CONCLUSIONS: This study recognized significant influencing factors for the OS of patients with burns ≥70% TBSA. Furthermore, our nomogram proved to be an effective tool for doctors to quickly evaluate patients' outcomes and make appropriate clinical decisions at an early stage of treatment.

Good learning environment of medical schools is an independent predictor for medical students' study engagement.

Background: Study engagement is regarded important to medical students' physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement. Methods: We collected 10,901 valid questionnaires from 12 medical universities in China, and UWES-S was utilized to assess the study engagement levels. Then Pearson Chi-Square test and Welch's ANOVA test were conducted to find the relationship between study engagement and learning environment, and subgroup analysis was used to eradicate possible influence of confounding factors. After that, a multivariate analysis was performed to prove learning environment was an independent factor, and we constructed a nomogram as a predictive model. Results: With Pearson Chi-Square test (p < 0.001) and Welch's ANOVA test (p < 0.001), it proved that a good learning environment contributed to a higher mean of UWES scores. Subgroup analysis also showed statistical significance (p < 0.001). In the multivariate analysis, we could find that, taking "Good" as reference, "Excellent" (OR = 0.329, 95%CI = 0.295-0.366, p < 0.001) learning environment was conducive to one's study engagement, while "Common" (OR = 2.206, 95%CI = 1.989-2.446, p < 0.001), "Bad" (OR = 2.349, 95%CI = 1.597-3.454, p < 0.001), and "Terrible" (OR = 1.696, 95%CI = 1.015-2.834, p = 0.044) learning environment only resulted into relatively bad study engagement. Depending on the result, a nomogram was drawn, which had predictive discrimination and accuracy (AUC = 0.680). Conclusion: We concluded that learning environment of school was an independent factor of medical student's study engagement. A higher level of learning environment of medical school came with a higher level of medical students' study engagement. The nomogram could serve as a predictive reference for the educators and researchers.