Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening.

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as "fingerprint", in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

The complete mitogenome of Argas vulgaris (Filippova, 1961) and its phylogenetic status in subgenus Argas (Acari: Argasidae).

Soft ticks (Ixodida: Argasidae) are ectoparasites of terrestrial vertebrates with worldwide distributions. As one representative group of Argasidae, the genus Argas has an important vectorial role in transmitting zoonotic diseases. However, our knowledge of the subgenus Argas in China is still limited, as most literature only lists occurrence records or describes specific case reports without providing detailed morphological characteristics and further molecular data. This study aims to characterize Argas vulgaris through complete mitochondrial sequencing and morphological diagnostic techniques based on a batch of adult specimens collected from Ningxia Hui Autonomous Regions (NXHAR), North China. The morphology and microstructures of Ar. vulgaris and other lectotypes of argasid ticks in the subgenus Argas were also observed using a stereomicroscope. Following DNA extraction and sequencing, a complete mitochondrial sequence of Ar. vulgaris was assembled and analyzed within a phylogenetic context. The 14,479 bp mitogenome of Ar. vulgaris consists of 37 genes, including 13 genes for protein coding, two for ribosomal RNA, 22 for transfer RNA, and one for control region (D-loops). Phylogenetic analysis of Ar. vulgaris showed 98.27%-100% nucleotide identity with Ar. japonicus, indicating a close relationship between the two tick species. The morphological diagnostic features to differentiate Ar. vulgaris from other ticks within the subgenus Argas included the location of the anus and setae on the anterior lip of the female genital aperture. This study provided high-resolution scanning electron microscope images of female Ar. vulgaris and corresponding molecular data, representing valuable resources for future accurate species identification.

Virome diversity shaped by genetic evolution and ecological landscape of Haemaphysalis longicornis.

BACKGROUND: Haemaphysalis longicornis is drawing attentions for its geographic invasion, extending population, and emerging disease threat. However, there are still substantial gaps in our knowledge of viral composition in relation to genetic diversity of H. longicornis and ecological factors, which are important for us to understand interactions between virus and vector, as well as between vector and ecological elements. RESULTS: We conducted the meta-transcriptomic sequencing of 136 pools of H. longicornis and identified 508 RNA viruses of 48 viral species, 22 of which have never been reported. Phylogenetic analysis of mitochondrion sequences divided the ticks into two genetic clades, each of which was geographically clustered and significantly associated with ecological factors, including altitude, precipitation, and normalized difference vegetation index. The two clades showed significant difference in virome diversity and shared about one fifth number of viral species that might have evolved to "generalists." Notably, Bandavirus dabieense, the pathogen of severe fever with thrombocytopenia syndrome was only detected in ticks of clade 1, and half number of clade 2-specific viruses were aquatic-animal-associated. CONCLUSIONS: These findings highlight that the virome diversity is shaped by internal genetic evolution and external ecological landscape of H. longicornis and provide the new foundation for promoting the studies on virus-vector-ecology interaction and eventually for evaluating the risk of H. longicornis for transmitting the viruses to humans and animals. Video Abstract.

CsCuAO1 Associated with CsAMADH1 Confers Drought Tolerance by Modulating GABA Levels in Tea Plants.

Our previous study showed that COPPER-CONTAINING AMINE OXIDASE (CuAO) and AMINOALDEHYDE DEHYDROGENASE (AMADH) could regulate the accumulation of γ-aminobutyric acid (GABA) in tea through the polyamine degradation pathway. However, their biological function in drought tolerance has not been determined. In this study, Camellia sinensis (Cs) CsCuAO1 associated with CsAMADH1 conferred drought tolerance, which modulated GABA levels in tea plants. The results showed that exogenous GABA spraying effectively alleviated the drought-induced physical damage. Arabidopsis lines overexpressing CsCuAO1 and CsAMADH1 exhibited enhanced resistance to drought, which promoted the synthesis of GABA and putrescine by stimulating reactive oxygen species' scavenging capacity and stomatal movement. However, the suppression of CsCuAO1 or CsAMADH1 in tea plants resulted in increased sensitivity to drought treatment. Moreover, co-overexpressing plants increased GABA accumulation both in an Agrobacterium-mediated Nicotiana benthamiana transient assay and transgenic Arabidopsis plants. In addition, a GABA transporter gene, CsGAT1, was identified, whose expression was strongly correlated with GABA accumulation levels in different tissues under drought stress. Taken together, CsCuAO1 and CsAMADH1 were involved in the response to drought stress through a dynamic GABA-putrescine balance. Our data will contribute to the characterization of GABA's biological functions in response to environmental stresses in plants.

[Spatiotemporal Occurrence of Organophosphate Esters in the Surface Water and Sediment of Taihu Lake and Relevant Risk Assessment].

Aiming to explore the spatiotemporal occurrence of organophosphate esters (OPEs) in the aquatic environment of Taihu Lake and to assess the relevant ecological risk, monomeric and oligomeric OPEs in the surface water and sediment of Taihu Lake were determined using solid phase extraction and ultra-high performance liquid chromatography-tandem mass spectrometry. The target monomeric OPEs included chlorinated OPEs, alkyl OPEs, and aryl OPEs. There was no significant difference in the spatial distribution of OPEs in water and sediment between the pollutant-impacted and less-impacted zones. The average concentrations of OPEs in summer and winter were, respectively, 752.7 and 498.5 ng·L-1 in water and 124.0 and 54.5 ng·g-1 in sediment, indicating an obvious seasonal difference, especially for the monomeric OPEs. The OPEs levels in both the water and sediment were ranked in the order of chlorinated OPEs > alkyl OPEs > aryl OPEs≈oligomeric OPEs. The pseudo-partitioning coefficients (Kd) of OPEs between the sediment and surface water of Taihu Lake were much higher in summer (0.05-4.17 L·g-1) compared to those in winter (0.02-3.47 L·g-1) and were significantly positively correlated with the lg Kow values of OPEs. Based on the median concentrations of OPEs in the water of Taihu Lake, the ecological risk of OPEs was assessed by risk quotient (RQ) values, which indicated a medium risk at an RQ level of 0.34 during winter and 0.35 during summer. The assessment results showed that the risk ranking of OPEs was consistent with that of their concentrations, and the monomeric OPEs posed a higher ecological risk in summer compared to that in winter. Although the ecological risk of oligomeric OPEs in this study was not serious and was lower than that of monomeric OPEs, it is an urgent requirement to conduct ecotoxicology studies on oligomeric OPEs in the future since available data is highly limited at present.

Increasing evolution, prevalence, and outbreaks for rift valley fever virus in the process of breaking geographical barriers.

BACKGROUND: Rift valley fever (RVF) is listed as one of prioritized diseases by WHO. This study aims to describe RVF virus' landscape distribution globally, and to insight dynamics change of its evolution, prevalence, and outbreaks in the process of breaking geographical barriers. METHODS: A systematic literature review and meta-analyses was conducted to estimate RVF prevalence by hosts using a random-effect model. Molecular clock-based phylogenetic analyses were performed to estimate RVF virus nucleotide substitution rates using nucleotide sequences in NCBI database. RVF virus prevalence, nucleotide substitution rates, and outbreaks were compared before and after breaking geographical barriers twice, respectively. RESULTS: RVF virus was reported from 26 kinds of hosts covering 48 countries from 1930 to 2022. Since RVF broke geographical barriers, (1) nucleotide substitution rates significantly increased after firstly spreading out of Africa in 2000, (2) prevalence in humans significantly increased from 1.92 % (95 % CI: 0.86-3.25 %) to 3.03 % (95 % CI: 2.09-4.12 %) after it broke Sahara Desert geographical barriers in 1977, and to 5.24 % (95 % CI: 3.81-6.82 %) after 2000, (3) RVF outbreaks in humans and the number of wildlife hosts presented increasing trends. RVF virus spillover may exist between bats and humans, and accelerate viral substitution rates in humans. During outbreaks, the RVF virus substitution rates accelerated in humans. 60.00 % RVF outbreaks occurred 0-2 months after floods and (or) heavy rainfall. CONCLUSION: RVF has the increasing risk to cause pandemics, and global collaboration on "One Health" is needed to prevent potential pandemics.

Salvianolic acid B-loaded polydopamine-modified hollow mesoporous organic silica nanoparticles for treatment of breast cancer metastasis via suppressing cancer-associated fibroblasts.

OBJECTIVE: Drug Delivery System was constructed using dopamine-coated organic-inorganic hybrid hollow mesoporous organic silica nanoparticles (HMON-PDA) as drug carriers and salvianolic acid B (SAB) as a model drug. Then, we further investigated whether it can inhibit lung metastasis of breast cancer by inhibiting cancer-associated fibroblasts (CAFs). METHODS: The organic-inorganic hybrid hollow mesoporous organic silica nanoparticles (HMON) were prepared. The particle size, zeta potential, and polydispersion coefficient were characterized. High-performance liquid chromatography was used to determine the effect of different feed ratios of HMON and SAB on drug loading rate. Then, SAB-loaded HMON were modified by polydopamine, which is called SAB@HMON-PDA. Cell viability was detected by MTT assay. The migration of 4T1 cells was investigated by wound healing experiment, and the invasion of 4T1 cells was detected by the transwell method. Finally, the mouse breast cancer lung metastasis models were used to explore whether SAB@HMON-PDA can inhibit lung metastasis of breast cancer by inhibiting CAFs. RESULTS: The obtained nanoparticles have hollow spherical structure. The average particle sizes of HMON, SAB@HMON, and SAB@HMON-PDA were 143.5 ± 0.03, 138.3 ± 0.02, and 172.3 ± 0.18 nm, respectively. The zeta potentials were -44.33±0.15, -41.4 ± 1.30, and -24.13±0.47 mV, respectively. When the ratio of HMON to SAB was 2:1, the drug loading rate reached (18.37±0.04)%. In addition, the prepared SAB@HMON-PDA responded to release SAB under acidic and GSH conditions. The prepared SAB@HMON-PDA could inhibit the migration and invasion of 4T1 cells. The results showed that SAB@HMON-PDA and SAB could inhibit lung metastasis of breast cancer in mice, and SAB@HMON-PDA had a more significant inhibitory effect than SAB. CONCLUSION: We successfully prepared SAB@HMON-PDA with the dual response of pH and GSH. SAB@HMON-PDA can inhibit the migration and invasion of 4T1 cells, and the effect is more significant than free SAB. This inhibitory effect may be related to the inhibition of CAFs. In vivo experiments demonstrated that SAB@HMON-PDA can inhibit lung metastasis of breast cancer by inhibiting CAFs, and its effect was more significant than that of free SAB.

Helicobacter pylori vacA affects the expression of COX-2 in the duodenal mucosa of patients with duodenitis.

Duodenitis refers to inflammation that occurs in the duodenum. Helicobacter pylori (Hp) is a known risk factor for duodenitis. This paper attempted to analyze the correlation between Hp virulence genotypes and the initiation and development of duodenal bulbar inflammation (DBI) to lay the foundation for the management of duodenitis induced by Hp infection. Total RNA was extracted from duodenal samples of 156 Hp-positive patients [70 with DBI and 86 with duodenal bulbar ulcer (DBU)] and 80 Hp-free DBI patients, followed by RT-qPCR detection of COX-2 mRNA expression and the presence of virulence factors. The cagA positive (62.2%), vacAs1 (21.79%), vacAm2 (23.72%), vacAs1m2 (19.87%) and iceA1 (55.80%) genotypes were dominant in 156 Hp-positive samples. Statistical difference was observed in vacAs and vacA mixtures between DBI and DBU patients. Gastric metaplasia had an association with vacA allelotypes, and its occurrence had strong correlations with vacAs1 and vacAs1m2 genotypes. The vacAs1 and vacAs1m2 genotypes were correlated with gastric metaplasia occurrence (all p<0.05). There were significant correlations between vacAs and vacA mixtures with cagA genotypes, and between iceA genotypes with vacA mixtures (all p<0.05). COX-2 was strongly expressed in Hp-infected duodenal mucosa and showed correlations with vacA genotype. COX-2 was differentially expressed in vacAs1- and vacAs2-positive patients. COX-2 was more highly upregulated in vacAs1m1- and vacAs1m2-positive patients than vacAs2m2-positive patients. Overall, Hp virulence genotype vacA was correlated with DBI and DBU initiation and development.

Deep Learning-Based Quantitative Blastocyst Assessment.

Selecting the single best blastocyst based on morphological appearance for implantation is a crucial part of in vitro fertilization (IVF). Various deep learning and computer vision-based methods have recently been applied for assessing blastocyst quality. However, to the best of our knowledge, most previous works utilize classification networks to give a qualitative evaluation. It would be challenging to rank blastocyst quality with the same qualitative result. Thus, this paper proposes a regression network combined with a soft attention mechanism for quantitatively evaluating blastocyst quality. The network outputs a continuous score to represent blastocyst quality precisely rather than some categories. As to the soft attention mechanism, the attention module in the network outputs an activation map (attention map) localizing the regions of interest (ROI, i.e., inner cell mass (ICM)) of microscopic blastocyst images. The generated activation map guides the entire network to predict ICM quality more accurately. The experimental results demonstrate that the proposed method is superior to traditional classification-based networks. Moreover, the visualized activation map makes the proposed network decision more reliable.

Identification and analysis of differential miRNA-mRNA interactions in coronary heart disease: an experimental screening approach.

Objective: This aim of this study is to screen the differential molecules of kidney deficiency and blood stasis (KDBS) syndrome in coronary heart disease by high-throughput sequencing. In addition, the study aims to verify the alterations in the expression levels of miR-4685-3p and its regulated downstream, namely, C1QC, C4, and C5, using quantitative polymerase chain reaction (qPCR) and enzyme-linked immunosorbent assay (ELISA), and to determine whether the complement and coagulation cascade pathway is the specific pathogenic pathway. Methods: Patients diagnosed with unstable angina pectoris with KDBS syndrome, patients with non-kidney deficiency blood stasis (NKDBS) syndrome, and a Normal group were recruited. The clinical symptoms of each group were further analyzed. Illumina's NextSeq 2000 sequencing platform and FastQC software were used for RNA sequencing and quality control. DESeq software was used for differential gene expression (DGE) analysis. qPCR and ELISA verification were performed on DGE analysis. Results: The DGE profiles of 77 miRNA and 331 mRNA were selected. The GO enrichment analysis comprised 43 biological processes, 49 cell components, and 42 molecular functions. The KEGG enrichment results included 40 KEGG pathways. The PCR results showed that, compared with the Normal group, the miR-4685-3p levels decreased in the CHD_KDBS group (P = 0.001), and were found to be lower than those observed in the CHD_NKDBS group. The downstream mRNA C1 regulated by miR-4685-3p showed an increasing trend in the CHD_KDBS group, which was higher than that in the Normal group (P = 0.0019). The mRNA C4 and C5 in the CHD_KDBS group showed an upward trend, but the difference was not statistically significant. ELISA was utilized for the detection of proteins associated with the complement and coagulation cascade pathway. It was found that the expression level of C1 was significantly upregulated in the CHD_KDBS group compared with the Normal group (P < 0.0001), which was seen to be higher than that in the CHD_NKDBS group (P < 0.0001). The expression levels of C4 and C5 in the CHD_KDBS group were significantly lower than the Normal group, and were lower than that in the CHD_NKDBS group (P < 0.0001). Conclusion: The occurrence of CHD_KDBS might be related to the activation of the complement and coagulation cascade pathway, which is demonstrated by the observed decrease in miR-4685-3p and the subsequent upregulation of its downstream C1QC. In addition, the expression levels of complement C4 and C5 were found to be decreased, which provided a research basis for the prevention and treatment of this disease.

Value of Pretreatment Inflammation-nutrition Score to Predict Non-response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer.

Objective: To investigate the value of pretreatment inflammatory-nutritional biomarkers in predicting the pathological response of locally advanced rectal cancer (LARC) after neoadjuvant chemotherapy (nCT). Methods: This retrospective study included eligible participants who underwent nCT followed by radical surgery. Pretreatment inflammatory nutritional biomarkers were calculated within one week prior to nCT. Correlations between biomarkers and pathological responses were analyzed. The cut-off values of the pretreatment biomarkers for predicting non-response were determined using receiver operating characteristic (ROC) curve analysis. The inflammation-nutrition score was calculated using the lymphocyte level, neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index (PNI). Results: A total of 235 patients were retrospectively recruited between January 2017 and September 2022. Lower lymphocyte levels, lymphocyte monocyte ratio (LMR), and PNI, and higher NLR and platelet-to-lymphocyte ratio (PLR) were observed in patients without response. Multivariate logistic regression analysis revealed that NLR could independently predict non-response to nCT in patients with LARC. The sensitivity and specificity of the inflammation-nutrition score for predicting nonresponse were 71.2% and 61.7%, respectively. Conclusion: The pretreatment inflammation-nutrition score is a practical parameter for predicting non-response to nCT in patients with LARC. Patients with high scores were more likely to respond poorly to nCT.

Prioritized experience replay in path planning via multi-dimensional transition priority fusion.

Introduction: Deep deterministic policy gradient (DDPG)-based path planning algorithms for intelligent robots struggle to discern the value of experience transitions during training due to their reliance on a random experience replay. This can lead to inappropriate sampling of experience transitions and overemphasis on edge experience transitions. As a result, the algorithm's convergence becomes slower, and the success rate of path planning diminishes. Methods: We comprehensively examines the impacts of immediate reward, temporal-difference error (TD-error), and Actor network loss function on the training process. It calculates experience transition priorities based on these three factors. Subsequently, using information entropy as a weight, the three calculated priorities are merged to determine the final priority of the experience transition. In addition, we introduce a method for adaptively adjusting the priority of positive experience transitions to focus on positive experience transitions and maintain a balanced distribution. Finally, the sampling probability of each experience transition is derived from its respective priority. Results: The experimental results showed that the test time of our method is shorter than that of PER algorithm, and the number of collisions with obstacles is less. It indicated that the determined experience transition priority accurately gauges the significance of distinct experience transitions for path planning algorithm training. Discussion: This method enhances the utilization rate of transition conversion and the convergence speed of the algorithm and also improves the success rate of path planning.

Air-ventilated normothermic machine perfusion alleviates hepatic injury from DCD rat through CYP1A2.

BACKGROUND: Normothermic machine perfusion (NMP) could provide protection to organs from donation after circulatory death (DCD) before transplantation, and its molecular mechanism remains unclear. Our previous study discovered that the air-ventilated NMP confers a better DCD liver recovery than oxygen-ventilated NMP. The purpose in the current study was to investigate the protective mechanism of air-ventilated NMP in a rat model of DCD liver by metabolomics, and to select biomarker to predict liver function recovery. MATERIALS AND METHODS: Peroxisome proliferator activator receptor-α (PPARα) agonist or antagonist was administered via the perfusion circuit in the air-ventilated NMP. Perfusate samples were taken for measurements of aminotransferases using standard biochemical methods, tumor necrosis factor-alpha and interleukin-6. Liver biopsies were allocated for detection of metabolomics, PPARα and cytochrome P450 1A2 (CYP1A2). RESULTS: Metabolomics analysis revealed the significant increased γ-linolenic acid and decreased adrenic acid during the air-ventilated NMP, indicating linoleic acid metabolism pathway was associated with a better DCD liver recovery; as a major enzyme involved in linolenic acid metabolism, CYP1A2 was found correlated with a less inflammation and better liver function with the air-ventilated NMP; PPARα agonist could increase CYP1A2 expression and activity, decrease inflammation response, and improve liver function with the air-ventilated NMP, while PPARα antagonist played the opposite. CONCLUSION: Air-ventilated NMP confers a better liver recovery from DCD rats through the activated linoleic acid metabolism and CYP1A2 upregulation; CYP1A2 expression and activity might function as biomarker to predict DCD liver function recovery with NMP.

miR-552 promotes the proliferation and metastasis of intrahepatic cholangiocarcinoma by targeting FOXO1.

Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly malignant cancer. Few effective systemic targeted therapies are available for patients with unresectable ICC, but there exists an urgent need to explore mechanisms underlying the initiation and progression of ICC. MicroRNA (miRNA) plays vital roles in the initiation, progression, and drug resistance of different cancers. Recently, the biological function of a novel miRNA, miR-552, has been widely analyzed in hepatocellular carcinoma and colorectal, cervical, gastric, and other cancers. However, its role in ICC has not yet been elucidated. In this study, we found that miR-552 expression was upregulated in ICC and that miR-552 predicted poor prognosis. Using functional studies, we found that miR-552 enhanced the proliferation and invasion ability of ICC cells. Mechanistic research identified that forkhead box O1 (FOXO1) is the target of miR-552 in ICC. Moreover, the combined panels of miR-552 and FOXO1 exhibited a better prognostic value for ICC patients than did miR-552 alone. In conclusion, these findings demonstrated that the miR-552/FOXO1 axis drove ICC progression, further suggesting that targeting this axis could be a novel therapeutic strategy for ICC.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.

The application of nanomedicine in clinical settings.

As nanotechnology develops in the fields of mechanical engineering, electrical engineering, information and communication, and medical care, it has shown great promises. In recent years, medical nanorobots have made significant progress in terms of the selection of materials, fabrication methods, driving force sources, and clinical applications, such as nanomedicine. It involves bypassing biological tissues and delivering drugs directly to lesions and target cells using nanorobots, thus increasing concentration. It has also proved useful for monitoring disease progression, complementary diagnosis, and minimally invasive surgery. Also, we examine the development of nanomedicine and its applications in medicine, focusing on the use of nanomedicine in the treatment of various major diseases, including how they are generalized and how they are modified. The purpose of this review is to provide a summary and discussion of current research for the future development in nanomedicine.

Understanding the role of NLRP3-mediated pyroptosis in allergic rhinitis: A review.

Allergic rhinitis (AR) is a chronic, inflammatory disease of the nasal mucosa, caused by the immunoglobulin E-mediated immune response. The annual incidence rate of AR is on the rise, exerting a significant impact on individuals' physical and mental wellbeing. The treatment effect in some patients is still not ideal, as the pathogenesis of AR is complex and diverse. Recent studies have shown that NLRP3 inflammasome-mediated pyroptosis is widely involved in the occurrence and development of AR through various pathways. This article reviews the mechanism of pyroptosis and its research progress in the field of AR, and puts forward possible therapeutic targets to offer innovative approaches for its management.

Translation and validation study of the Chinese version of the service user technology acceptability questionnaire.

Objective: This study aimed to assess the reliability and validity of the translated Chinese version of the Service User Technology Acceptability Questionnaire (C-SUTAQ). Methods: Patients with cancer (n â€‹= â€‹554) from a tertiary hospital in China completed the C-SUTAQ. Item analysis, content and construct validity test, internal consistency test, and test-retest reliability analysis were conducted on the instrument to test its applicability. Results: The critical ratio of each item of the C-SUTAQ ranged from 11.869 to 29.656; the correlation of each item and subscale ranged from 0.736 to 0.929. The Cronbach's α value for each subscale ranged from 0.659 to 0.941, and the test-retest reliability ranged from 0.859 to 0.966. The content validity index of the scale level and the item level content validity index of the instrument were both 1. Exploratory factor analysis indicated it was reasonable that the C-SUTAQ consists of six subscales after rotation. Confirmatory factor analysis demonstrated good construct validity (χ2/df â€‹= â€‹2.459, comparative fit index â€‹= â€‹0.922, incremental fit index â€‹= â€‹0.907, standardized root mean square residual â€‹= â€‹0.060, root-mean-square error of approximation â€‹= â€‹0.073, goodness of fit index â€‹= â€‹0.875, normed fit index â€‹= â€‹0.876. Conclusions: The C-SUTAQ had good reliability and validity and may be useful to assess Chinese patients' acceptability of telecare. However, the small sample size limited generalization and there is a need to expand the sample to include persons with other diseases. Further studies are required using the translated questionnaire.

Continuous evolution and emerging lineage of seasonal human coronaviruses: A multicenter surveillance study.

The seasonal human coronaviruses (HCoVs) have zoonotic origins, repeated infections, and global transmission. The objectives of this study are to elaborate the epidemiological and evolutionary characteristics of HCoVs from patients with acute respiratory illness. We conducted a multicenter surveillance at 36 sentinel hospitals of Beijing Metropolis, China, during 2016-2019. Patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) were included, and submitted respiratory samples for screening HCoVs by multiplex real-time reverse transcription-polymerase chain reaction assays. All the positive samples were used for metatranscriptomic sequencing to get whole genomes of HCoVs for genetical and evolutionary analyses. Totally, 321 of 15 677 patients with ILI or SARI were found to be positive for HCoVs, with an infection rate of 2.0% (95% confidence interval, 1.8%-2.3%). HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1 infections accounted for 18.7%, 38.3%, 40.5%, and 2.5%, respectively. In comparison to ILI cases, SARI cases were significantly older, more likely caused by HCoV-229E and HCoV-OC43, and more often co-infected with other respiratory pathogens. A total of 179 full genome sequences of HCoVs were obtained from 321 positive patients. The phylogenetical analyses revealed that HCoV-229E, HCoV-NL63 and HCoV-OC43 continuously yielded novel lineages, respectively. The nonsynonymous to synonymous ratio of all key genes in each HCoV was less than one, indicating that all four HCoVs were under negative selection pressure. Multiple substitution modes were observed in spike glycoprotein among the four HCoVs. Our findings highlight the importance of enhancing surveillance on HCoVs, and imply that more variants might occur in the future.

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia.

Objective: To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors. Methods: The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool. Results: The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877-0.926), 0.901 (95% CI: 0.874-0.923), 0.902 (95% CI: 0.876-0.924), and 0.955 (95% CI: 0.935-0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5). Conclusion: Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.