Three-dimensional atomic structure and local chemical order of medium- and high-entropy nanoalloys.

Medium- and high-entropy alloys (M/HEAs) mix several principal elements with near-equiatomic composition and represent a model-shift strategy for designing previously unknown materials in metallurgy1-8, catalysis9-14 and other fields15-18. One of the core hypotheses of M/HEAs is lattice distortion5,19,20, which has been investigated by different numerical and experimental techniques21-26. However, determining the three-dimensional (3D) lattice distortion in M/HEAs remains a challenge. Moreover, the presumed random elemental mixing in M/HEAs has been questioned by X-ray and neutron studies27, atomistic simulations28-30, energy dispersive spectroscopy31,32 and electron diffraction33,34, which suggest the existence of local chemical order in M/HEAs. However, direct experimental observation of the 3D local chemical order has been difficult because energy dispersive spectroscopy integrates the composition of atomic columns along the zone axes7,32,34 and diffuse electron reflections may originate from planar defects instead of local chemical order35. Here we determine the 3D atomic positions of M/HEA nanoparticles using atomic electron tomography36 and quantitatively characterize the local lattice distortion, strain tensor, twin boundaries, dislocation cores and chemical short-range order (CSRO). We find that the high-entropy alloys have larger local lattice distortion and more heterogeneous strain than the medium-entropy alloys and that strain is correlated to CSRO. We also observe CSRO-mediated twinning in the medium-entropy alloys, that is, twinning occurs in energetically unfavoured CSRO regions but not in energetically favoured CSRO ones, which represents, to our knowledge, the first experimental observation of correlating local chemical order with structural defects in any material. We expect that this work will not only expand our fundamental understanding of this important class of materials but also provide the foundation for tailoring M/HEA properties through engineering lattice distortion and local chemical order.

Single-cell analysis of CD4+ tissue residency memory cells (TRMs) in adult atopic dermatitis: A new potential mechanism.

The pathophysiology of atopic dermatitis (AD) is complex. CD4+ T cells play an essential role in the development of lesions in AD. However, the underlying mechanism remains unclear. In the present study, we investigated the differentially expressed genes (DEGs) between adult AD lesioned and non-lesioned skin using two datasets from the Gene Expression Omnibus (GEO) database. 62 DEGs were shown to be related to cytokine response. Compared to non-lesioned skin, lesioned skin showed immune infiltration with increased numbers of activated natural killer (NK) cells and CD4+ T memory cells (p < 0.01). We then identified 13 hub genes with a strong association with CD4+ T cells using weighted correlation network analysis. Single-cell analysis of AD detected a novel CD4+ T subcluster, CD4+ tissue residency memory cells (TRMs), which were verified through immunohistochemistry (IHC) to be increased in the dermal area of AD. The significant relationship between CD4+ TRM and AD was assessed through further analyses. FOXO1 and SBNO2, two of the 13 hub genes, were characteristically expressed in the CD4+ TRM, but down-regulated in IFN-γ/TNF-α-induced HaCaT cells, as shown using quantitative polymerase chain reaction (qPCR). Moreover, SBNO2 expression was associated with increased Th1 infiltration in AD (p < 0.05). In addition, genes filtered using Mendelian randomization were positively correlated with CD4+ TRM and were highly expressed in IFN-γ/TNF-α-induced HaCaT cells, as determined using qPCR and western blotting. Collectively, our results revealed that the newly identified CD4+ TRM may be involved in the pathogenesis of adult AD.

Enhanced High Thermal Conductivity Cellulose Filaments via Hydrodynamic Focusing.

Nanocellulose is regarded as a green and renewable nanomaterial that has attracted increased attention. In this study, we demonstrate that nanocellulose materials can exhibit high thermal conductivity when their nanofibrils are highly aligned and bonded in the form of filaments. The thermal conductivity of individual filaments, consisting of highly aligned cellulose nanofibrils, fabricated by the flow-focusing method is measured in dried condition using a T-type measurement technique. The maximum thermal conductivity of the nanocellulose filaments obtained is 14.5 W/m-K, which is approximately five times higher than those of cellulose nanopaper and cellulose nanocrystals. Structural investigations suggest that the crystallinity of the filament remarkably influence their thermal conductivity. Smaller diameter filaments with higher crystallinity, that is, more internanofibril hydrogen bonds and less intrananofibril disorder, tend to have higher thermal conductivity. Temperature-dependence measurements also reveal that the filaments exhibit phonon transport at effective dimension between 2D and 3D.

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

TIMM8A is associated with dysfunction of immune cell in BRCA and UCEC for predicting anti-PD-L1 therapy efficacy.

BACKGROUND: TIMM8A is a protein-coding gene located on the X chromosome. There is evidence that TIMM8A plays an important role in mitochondrial morphology and fission. Studies have shown that mitophagy and fission could affect the function of immune cells. However, there is currently no research on this gene's role in cancer occurrence and progression. METHODS: TIMM8A expression was analyzed via the Tumor Immune Estimation Resource (TIMER) site and UALCAN database. We evaluated the influence of TIMM8A on clinical prognosis using Kaplan-Meier plotter, the PrognoScan database, and Human Protein Atlas (HPA). The correlations between TIMM8A and cancer immune infiltrates were investigated via TIMER. Tumor Immune Dysfunction and Exclusion (TIDE) was used to evaluate the potential of tumor immune evasion. Functions of TIMM8A mutations and 50 genes significantly associated with TIMM8A mutations in breast cancer (BRCA) and uterine corpus endometrial cancer (UCEC) were analyzed by GO and KEGG in LinkedOmics database. RESULTS: We investigated the role of TIMM8A in multiple cancers and found that it was significantly associated with poor prognosis in BRCA and UCEC. After analyzing the effect of TIMM8A on immune infiltration, we found Th2 CD4+ T cells might be a common pathway by which TIMM8A contributed to poor prognosis in BRCA and UCEC. Our results suggested that myeloid-derived suppressor cells (MDSC) and tumor-associated M2 macrophages (TAM M2) might be important factors in immune evasion through T cell rejection in both cancers, and considered TIMM8A as a biomarker to predict the efficacy of this therapy in BRCA and UCEC. The results of TIMM8A enrichment analysis showed us that abnormally expressed TIMM8A might affect the mitochondrial protein in BRCA and UCEC. CONCLUSIONS: Contributed to illustrating the value of TIMM8A as a prognostic biomarker, our findings suggested that TIMM8A was correlated with prognosis and immune infiltration, including CD8+ T cells, Th2 CD4+ T cells, and macrophages in BRCA and UCEC. In addition, TIMM8A might affect immune infiltration and prognosis in BRCA and UCEC by affecting mitophagy. We believed it could also be a biomarker to predict the efficacy of anti-PD-L1 therapy and proposed to improve the efficacy by eliminating MDSC and TAM M2.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network.

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Anisotropic Thermal Conductivity Enhancement of the Aligned Metal-Organic Framework under Water Vapor Adsorption.

Metal-organic frameworks (MOFs) exhibit high adsorption and catalytic activities for various gas species. Because gas adsorption can cause a temperature increase in the MOF, which decreases the capacity and adsorption rate, a strict evaluation of its effect on the thermal conductivity of MOFs is essential. In this study, the thermal conductivity measurement of the MOF under water vapor adsorption was performed using an oriented film of copper tetrakis(4-carboxyphenyl)porphyrin (Cu-TCPP) MOF. A recently developed bidirectional 3ω method enabled the anisotropic thermal conductivity measurement of layered Cu-TCPP while maintaining its ordered structure. The water adsorption was found to increase the thermal conductivity in both in-plane and cross-plane directions with different trends and magnitudes, owing to the structural anisotropy. Molecular dynamics simulations suggest that additional vibrational modes provided by the adsorbed water molecules were the reason for the thermal conductivity enhancement.

The correlation between parents' education attainment and humanistic literacy of eight-year medical students.

Objectives: To explore the content, ways, and methods of family education in cultivating students' humanistic literacy. Methods: We used a cross-sectional study and collected questionnaire data from 616 eight-year clinical medical students of Central South University by a convenience sampling survey. To determine the influence of parents' educational attainment on children's humanistic literacy, the students were mainly divided into two groups including parents' education attainment was college or above (Group B) and parents' education attainment below college (Group A). Non-parametric tests are used to test the differences between the two groups in humanistic spirit, interpersonal communication, humanistic knowledge and ability, and development planning. Results: Group B had better social morality and a sense of social responsibility than group A (P=0.024, P=0.001). Compared to group A, students in group B could better integrate into the new environment, communicate with students from different institutes, and take an active part in activities (P=0.001). In a nutshell, students in group B had more excellent humanistic knowledge and ability and could consult medical literature and write in Chinese or English more proficiently than group A (P=0.0001, P=0.0001). Conclusions: We found that the eight-year medical students whose parents' highest education attainment is college or above almost mastered a higher level of humanistic literacy. It demonstrated family humanistic literacy education is irreplaceable. We recommend systematic efforts to build a reasonable and effective family humanistic literacy education platform and form an educational synergy with school education to make the cultivation of humanistic literacy among students more efficient.

Animal experiments on respiratory viruses and analogous studies of infection factors for interpersonal transmission.

Air pollution caused by SARS-CoV-2 and other viruses in human settlements will have a great impact on human health, but also a great risk of transmission. The transmission power of the virus can be represented by quanta number in the Wells-Riley model. In order to solve the problem of different dynamic transmission scenarios, only a single influencing factor is considered when predicting the infection rate, which leads to large differences in quanta calculated in the same space. In this paper, an analog model is established to define the indoor air cleaning index RL and the space ratio parameter. Based on infection data analysis and rule summary in animal experiments, factors affecting quanta in interpersonal communication were explored. Finally, by analogy, the factors affecting person-to-person transmission mainly include viral load of infected person, distance between individuals, etc., the more severe the symptoms, the closer the number of days of illness to the peak, and the closer the distance to the quanta. In summary, there are many factors that affect the infection rate of susceptible people in the human settlement environment. This study provides reference indicators for environmental governance under the COVID-19 epidemic, provides reference opinions for healthy interpersonal communication and human behavior, and provides some reference for accurately judging the trend of epidemic spread and responding to the epidemic.

Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning.

Feature extraction is the most fundamental step when analyzing liquid chromatography-mass spectrometry (LC-MS) datasets. However, traditional methods require optimal parameter selections and re-optimization for different datasets, thus hindering efficient and objective large-scale data analysis. Pure ion chromatogram (PIC) is widely used because it avoids the peak splitting problem of the extracted ion chromatogram (EIC) and regions of interest (ROIs). Here, we developed a deep learning-based pure ion chromatogram method (DeepPIC) to find PICs using a customized U-Net from centroid mode data of LC-MS directly and automatically. A model was trained, validated, and tested on the Arabidopsis thaliana dataset with 200 input-label pairs. DeepPIC was integrated into KPIC2. The combination enables the entire processing pipeline from raw data to discriminant models for metabolomics datasets. The KPIC2 with DeepPIC was compared against other competing methods (XCMS, FeatureFinderMetabo, and peakonly) on the MM48, simulated MM48, and quantitative datasets. These comparisons showed that DeepPIC outperforms XCMS, FeatureFinderMetabo, and peakonly in recall rates and correlation with sample concentrations. Five datasets of different instruments and samples were used to evaluate the quality of PICs and the universal applicability of DeepPIC, and 95.12% of the found PICs could precisely match their manually labeled PICs. Therefore, KPIC2+DeepPIC is an automatic, practical, and off-the-shelf method to extract features from raw data directly, exceeding traditional methods with careful parameter tuning. It is publicly available at https://github.com/yuxuanliao/DeepPIC.

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis.

Introduction: Gastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly. Methods: HP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model. Results and discussion: In this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.

Highly accurate and large-scale collision cross sections prediction with graph neural networks.

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.

Origami-Inspired Soft Twisting Actuator.

Soft actuators have shown great advantages in compliance and morphology matched for manipulation of delicate objects and inspection in a confined space. There is an unmet need for a soft actuator that can provide torsional motion to, for example, enlarge working space and increase degrees of freedom. Toward this goal, we present origami-inspired soft pneumatic actuators (OSPAs) made from silicone. The prototype can output a rotation of more than one revolution (up to 435°), more significant than its counterparts. Its rotation ratio ( = rotation angle/aspect ratio) is more than 136°, about twice the largest one in other literature. We describe the design and fabrication method, build the analytical model and simulation model, and analyze and optimize the parameters. Finally, we demonstrate the potentially extensive utility of the OSPAs through their integration into a gripper capable of simultaneously grasping and lifting fragile or flat objects, a versatile robot arm capable of picking and placing items at the right angle with the twisting actuators, and a soft snake robot capable of changing attitude and directions by torsion of the twisting actuators.

From the West to the East: an evidence-based educational reform for modern medical students in traditional Chinese medicine learning.

Introduction: Generally, Traditional Chinese Medicine (TCM) courses are now given to modern medicine students without proper course scheduling, resulting in poor teaching results. Methods: To analyze the main factors affecting TCM learning, we surveyed the medical students and TCM teachers from Xiangya School of Medicine of Central South University via online questionnaires. The questionnaire comprised two parts, the students' part included the basic information, the subjective cognition in TCM, the attitude toward TCM course arrangements, and the attitude toward curriculum content and the design of TCM. The teachers' part included the basic information, the attitudes and opinions on TCM course arrangements, and suggestions and views on TCM teaching reform. The related data were collected from 187 medical students divided into two groups, namely, clinical medical students and non-clinical medical students. Results: We found a more positive attitude toward TCM [including "Scientific nature of TCM" (P = 0.03) and "Necessity for modern medicine students to learn TCM" (P = 0.037)] in clinical medical students compared with non-clinical medical students, clinical and non-clinical medical students tended to find TCM courses difficult, and the students prefer clinical training to be better than theoretical teaching, while the teachers believe that lecture-based education should have a more significant proportion. Discussion: Hence, to optimize the current TCM teaching, we conducted education reform, including differentiated teaching, hybrid teaching, and selective teaching.

Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis.

Background: Metastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood. Methods: Thirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman's analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype. Result: Arecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein-protein interaction network construction, and Spearman's correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis. Conclusion: We found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.

The Impact of Undergraduate Tutor System in Chinese 8-Year Medical Students in Scientific Research.

Purpose: This study aimed to evaluate the effect and influence of the undergraduate tutor system on the undergraduate stage of Chinese 8-year medical program students in scientific research. Methods: We collected related data from 194 medical students in the Xiangya Medical School of Central South University. The questionnaire was composed of three parts, namely, eight questions for basic information about individual and undergraduate tutor system, five questions for the subjective feeling impact of the undergraduate tutor system, and 22 questions for accessing the scientific research ability and academic results. The students were mainly divided into three groups to compare different kinds of undergraduate tutor systems, namely, single tutor for multiple students' system (group A), multiple tutors for multiple students' system (group B), and no tutor system for comparison (group C). Results: The type of tutorial system, the frequency of guidance, and the way of guidance were independent influence factors of the view of 8-year medical students on scientific research. Group B behaved better than group C in literature processing (P = 0.012), experimental operation (P < 0.001), statistical analysis (P < 0.001), and manuscript producing (P = 0.019). Group A and B joined in more National college students' innovation and entrepreneurship training programs (P = 0.003, P < 0.001). The most popular types of articles published by students were bioinformatics, meta-analysis, and reviews. Conclusion: Undergraduate tutor system has made tremendous achievements in cultivating students' scientific research capacity; however, implement improvement should be considered to better educate students.

A cross-sectional study on the response abilities of clinical and preventive medical students in public health emergency.

Inconsistent training programs for public health emergency (PHE) have been criticized as a contributing factor in PHE's managerial weak points. In response, to analyze the relevant discrepancies among the medical students in the class of 2021 from Xiangya School of Medicine of Central South University, the present study conducted an online questionnaire survey using convenience sampling. The questionnaire comprised four sections, including the basic information, the subjective cognition in PHE, the rescue knowledge and capabilities of PHE, and the mastery of PHE regulations and psychological intervention abilities. To compare the abovementioned aspects, related data were collected from 235 medical students divided into two groups, namely, clinical medical students (Group A) and preventive medical students (Group B). We found a more positive attitude in PHE (P = 0.014) and a better grasp of the PHE classification (P = 0.027) and the reporting system in group B compared with group A. In addition, even if group B showed the same response capability in communicable diseases as group A, the former had less access to clinical practice, resulting in poorer performance in the noncommunicable diseases during a fire, flood, and traffic accidents (P = 0.002, P = 0.018, P = 0.002). The different emphasis of each training program contributed to the uneven distribution of abilities and cognition. Meanwhile, the lack of an integrated PHE curriculum led to unsystematic expertise. Hence, to optimize the PHE management system, equal attention should be paid to medical students with diverse majors along with a complete integrated PHE curriculum.

Identification and validation of four photodynamic therapy related genes inhibiting MAPK and inducing cell cycle alteration in squamous cell carcinoma.

Introduction: Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer, and photodynamic therapy (PDT) is a promising modality against cSCC. This study investigated the impact of PDT on the MAPK pathway and cell cycle alternation of cSCC as well as the related molecular mechanisms. Method: Expressing mRNA profile data sets GSE98767, GSE45216, and GSE84758 were acquired from the GEO database. The functions of differently expressed genes (DEGs) were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Least absolute shrinkage and selection operator (Lasso) analysis were used to establish a diagnosis model based on GSE98767. A correlation analysis and a protein-protein interaction (PPI) network were used to evaluate the relationship between cSCC-PDT-related genes and the MAPK pathway. Single-sample gene set enrichment analysis (ssGSEA) was performed on GSE98767 to estimate MAPK activation and cell cycle activity. Finally, the effect of MAPK activation on the cell cycle was explored in vitro. Result: Four cSCC-PDT-related genes, DUSP6, EFNB2, DNAJB1, and CCNL1, were identified as diagnostic markers of cSCC, which were upregulated in cSCC or LC50 PDT-protocol treatment and negatively correlated with the MAPK promoter. Despite having a smaller MAPK activation score, cSCC showed higher cell cycle activity. The PDT treatment suppressed the G1 to G2/M phase in JNK overexpressed A431 cells. Conclusion: CCNL1, DNAJB1, DUSP6, and EFNB2 were identified as potential PDT target genes in cSCC treatment, whose potential therapeutic mechanism was inhibiting the MAPK pathway and inducing cell cycle alternation.

TMED2/9/10 Serve as Biomarkers for Poor Prognosis in Head and Neck Squamous Carcinoma.

Background: Head and neck squamous carcinoma (HNSC) is one of the most common malignant tumors with high incidence and poor prognosis. Transmembrane emp24 structural domain (TMED) proteins are involved in protein transport and vesicle budding processes, which have implicated various malignancies' progression. However, the roles of TMEDs in HNSC, especially in terms of development and prognosis, have not been fully elucidated. Methods: We applied TIMER 2.0, UALCAN, GEPIA 2, Kaplan-Meier plotter, GEO, The Human Protein Atlas (HPA), cBioPortal, Linkedomics, Metascape, GRNdb, STRING, and Cytoscape to investigate the roles of TMED family members in HNSC. Results: Compared with normal tissues, the mRNA expression levels of TMED1/2/4/5/7/8/9/10 were significantly increased in the TCGA HNSC dataset. And we combined GEPIA 2 and Kaplan-Meier Plotter to select TMED2/9/10 with prognostic value. Then we detected the levels of mRNA in the GEO HNSC database and the protein expression in HPA. It was found that the mRNA and protein expression levels of TMED2/9/10 were increased in HNSC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that TMED2/9/10 and their co-expressed genes promoted the malignant behavior of tumors by participating in biological processes such as intracellular transferase complex, protein transport, focal adhesion, intracellular protein processing. Single-cell analysis and immune infiltration analysis suggested that immune responses of cancer-associated fibroblasts and endothelial cells might be associated with prognosis. Finally, the transcription factors-genes network and protein-protein functional interaction network pointed to genes such as X-box binding protein 1 (XBP1) and TMED7, which might cooperate with TMED2/9/10 to change the progression of HNSC. Conclusions: Our study implied that TMED2/9/10 and related genes mightjointly affect the prognosis of HNSC, providing specific clues for further experimental research, personalized diagnosis strategies, and targeted clinical therapy for HNSC.