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Abstract Bladder urothelial carcinoma (BLCA) presents a persistent challenge in clinical management. Despite recent advancements demonstrating the BLCA efficacy of immune checkpoint inhibitors (ICI) in BLCA patients, there remains a critical need to identify and expand the subset of individuals who benefit from this treatment. Mitochondria, as pivotal regulators of various cell death pathways in eukaryotic cells, exert significant influence over tumor cell fate and survival. In this study, our objective was to investigate biomarkers centered around mitochondrial function and cell death mechanisms to facilitate prognostic prediction and guide therapeutic decision-making in BLCA. Utilizing ssGSEA and LASSO regression, we developed a prognostic signature termed mitochondrial function and cell death (mtPCD). Subsequently, we evaluated the associations between mtPCD score and diverse clinical outcomes, including prognosis, functional pathway enrichment, immune cell infiltration, immunotherapy response analysis and drug sensitivity, within high- and low-risk subgroups. Additionally, we employed single-cell level functional assays, RT-qPCR, and immunohistochemistry to validate the differential expression of genes comprising the mtPCD signature. The mtPCD signature comprises a panel of 10 highly influential genes, strongly correlated with survival outcomes in BLCA patients and exhibiting robust predictive capabilities. Importantly, individuals classified as high-risk according to mtPCD score displayed a subdued overall immune response, characterized by diminished immunotherapeutic efficacy. In summary, our findings highlight the development of a novel prognostic signature, which not only holds promise as a biomarker for BLCA prognosis but also offers insights into the immune landscape of BLCA. This paradigm may pave the way for personalized treatment strategies in BLCA management.

Abstract Currently, there is a lack of relevant research on the efficacy difference between SHD combined with IBG and PVIBGT in the treatment of osteonecrosis of the femoral head(ONFH). Firstly, this study intends to compare the effectiveness of surgical hip dislocation combined with impacting bone grafts (SHD-IBG) and pedicled vascularised iliac bone graft transfer (PVIBGT) in treating ONFH. And the study investigates patients who suffered from hip preservation failures from both groups to better comprehend failure reasons. 30 patients (34 hips) with ARCO stage IIIA femoral head necrosis were selected between January 2012 and July 2022. They were divided into group A(SHD-IBG) and group B (PVIBGT) according to different surgical methods. Firstly, compared the 1-year effect between SHD-IBG and PVIBGT at 1 year postoperatively; Secondly, assessed the medium and long-term efficacy of SHD-IBG hip preservation treatment; Lastly, based on study of the femoral head removed from patients with hip preservation failure in the two groups, the reasons for the failure of hip preservation were comprehensively analyzed in the two groups. Group A: 11 males (13 hips), 4 females (4 hips);Group B: 9 males (11 hips), 6 females (6 hips).Firstly, the average Harris scores of the two groups at 1 year after surgery: preoperative: 70.7, 1 year after surgery: 78.9 in group A; preoperative: 69.5, 1 year after surgery: 81.5 in group B. The differences were statistically significant (P

Abstract Background Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time‐consuming and expensive high‐throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self‐supervised attention‐based multiple instance learning (SSL‐ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin‐stained histopathological images. Methods We obtained whole slide images (WSIs) and somatic mutation data of 350 ccRCC patients from The Cancer Genome Atlas for developing SSL‐ABMIL model. In parallel, 163 ccRCC patients from Clinical Proteomic Tumor Analysis Consortium cohort was used as independent external validation set. We systematically compared three different models (Wang‐ABMIL, Ciga‐ABMIL, and ImageNet‐MIL) for their ability to predict TMB and VHL alterations. Results We first identified two groups of populations with high‐ and low‐TMB (cut‐off point = 0.9). In two independent cohorts, the Wang‐ABMIL model achieved the highest performance with decent generalization performance (AUROC = 0.83 ± 0.02 and 0.8 ± 0.04 in predicting TMB and VHL, respectively). Attention heatmaps revealed that the Wang‐ABMIL model paid the highest attention to tumor regions in high‐TMB patients, while in VHL mutation prediction, non‐tumor regions were also assigned high attention, particularly the stromal regions infiltrated by lymphocytes. Conclusions Our results indicated that SSL‐ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology.

The incorporation of steel fibers in concrete imparts strain-hardening characteristics, significantly elevating the tensile toughness of the concrete mixture. However, this enhancement often comes at the expense of reduced workability and strength, posing challenges in achieving optimal densification in practical engineering applications. Moreover, the improvement of the performance of steel fiber-reinforced concrete (SFRC) hinges on the establishment of interfacial transition zone (ITZ) between steel fibers and the concrete paste. It has been established that the introduction of fly ash and silica fume to concrete mixtures can increase fluidity and strength. Consequently, this study investigates the impact of fly ash and silica fume on the performance enhancement and workability of SFRC mixtures, scrutinizing both macroscopic and microscopic aspects. Two sets of high-flowable steel fiber-reinforced concretes (HF-SFRC) incorporating silica fume or fly ash were prepared and subjected to testing. The assessment covered mechanical properties, including compressive strength, compressive toughness, and flexural toughness, along with the microstructure. The microstructure provides evidence that fly ash and silica fume reduced the voids in the concrete matrix to different degrees and that the fully hydrated dense matrix contributed to reinforcing the bond between steel fibers and the cement matrix. The synergistic effect among fly ash or silica fume, steel fibers, and cement in the mixture resulted in enhanced flowability and improved mechanical properties in HF-SFRC.

Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by presenting the accuracies of applying transfer learning algorithms to our previous method. Second, we improve the signal-to-noise ratio by proposing the supervised spectral factorization method that learns the amplitude and phase mappings between around-ear signals and the signals of articulated facial muscles. Third, we leverage the time continuity of commands and introduce the time decay into confidence rejection. Finally, extensive experiments have been conducted to evaluate the feasibility and improvements. The results indicate an average accuracy of 92.38% which is significantly larger than solely using transfer learning algorithms. And a comparable accuracy can be achieved with significantly reduced data of new users. The overall performance shows the framework can significantly improve the accuracy of user adaptations. The work would aid a further step toward commercial products for silent command recognition and inspire the solution to the user adaptation challenge of wearable human-computer interactions.

Abstract Renal fibrosis is considered to be the ultimate pathway for various chronic kidney disease, with a complex etiology and great therapeutic challenges. Tripartite motif-containing (TRIM) family proteins have been shown to be involved in fibrotic diseases, but whether TRIM39 plays a role in renal fibrosis remain unexplored. In this study, we investigated the role of TRIM39 in renal fibrosis and its molecular mechanism. TRIM39 expression was analyzed in patients’ specimens, HK-2 cells and unilateral ureteral obstruction (UUO) mice were used for functional and mechanistic studies. We found an upregulated expression of TRIM39 in renal fibrosis human specimens and models. In addition, TRIM39 knockdown was found efficient for alleviating renal fibrosis in both UUO mice and HK-2 cells. Mechanistically, we demonstrated that TRIM39 interacted with PRDX3 directly and induced ubiquitination degradation of PRDX3 at K73 and K149 through the K48 chain, which resulted in ROS accumulation and increased inflammatory cytokine generation, and further aggravated renal fibrosis. It provided an emerging potential target for the therapies of renal fibrosis.

Abstract Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of only RNA-seq and ChIP-seq data, allows for the ready generalization of CREaTor to a broad range of cell types.

Abstract Background Bladder urothelial carcinoma (BLCA) is the most common genitourinary cancer and the prognosis of patients is often poor. However, studies of basement membrane-related genes (BM-related genes) in BLCA are less reported. Therefore, we established a BM-related genes signature to explore their functional and prognostic value in BLCA. Methods In this study, a BM-related genes signature was constructed by LASSO-Cox regression analysis, and then a series of bioinformatics methods was used to assess the accuracy and validity of the signature. We constructed a nomogram for clinical application and also screened for possible therapeutic drugs. To investigate the functions and pathways affected by BM-related genes in BLCA, we performed functional enrichment analyses. In addition, we analyzed the immune cell infiltration landscape and immune checkpoint-related genes in the high and low-risk groups. Finally, we confirmed the prognostic value of BM-related genes in BLCA in vitro. Results Combining multiple bioinformatics approaches, we identified a seven-gene signature. The accuracy and validity of this signature in predicting BLCA patients were confirmed by the test cohort. In addition, the risk score was strongly correlated with prognosis, immune checkpoint genes, drug sensitivity, and immune cell infiltration landscape. The risk score is an independent prognostic factor for BLCA patients. Further experiments revealed that all seven signature genes were differentially expressed between BLCA cell lines and normal bladder cells. Finally, overexpression of LAMA2 inhibited the migration and invasion ability of BLCA cell lines. Conclusions In summary, the BM-related genes signature was able to predict the prognosis of BLCA patients accurately, indicating that the BM-related genes possess great clinical value in the diagnosis and treatment of BLCA. Moreover, LAMA2 could be a potential therapeutic target, which provides new insights into the application of the BM-related genes in BLCA patients.

Abstract Background Epidemiological trends of lower respiratory infections (LRIs) attributable to secondhand smoke (SHS) among children under 5 years since smoking bans have been increasingly applied globally remain unclear. Here, we aimed to estimate the spatiotemporal trends of the global, regional, and national burden of LRIs attributable to SHS among children under 5 years old between 2010 and 2019. Methods Data on the deaths, and disability adjusted life years (DALYs) of the disease burden was retrieved from the Global Burden of Disease (GBD) 2019 for 204 countries and territories between 2010 and 2019. The rates per 100,000 population, along with 95% uncertainty intervals, as well as population-attributable fraction (PAF) was presented for each estimate. Results In 2019, an estimated 6.94% (3.80–10.12%) of under-5 LRIs deaths were attributable to SHS globally, with an under-5 mortality rate of 7.02 per 100,000, a decrease of 5.77% since 2010. Similarly, 6.95% (3.81–10.13%) of LRIs DALYs were due to SHS among children under 5 years, with a rate in under-5s of 619.36 DALYs per 100,000, and also a 5.77% decrease since 2010. Azerbaijan, Turkmenistan, and Papua New Guinea showed the highest under-5 mortality and DALYs burden rates of LRIs attributable to SHS in 2019. In contrast, the PAF was stagnant over the past ten years and there is even a year-on-year upward trend in South Asia. Nationally, in 2019, Bosnia and Herzegovina, Armenia, and Montenegro showed the highest PAFSHS of LRIs burden among children under 5 years of age. In addition, the burden was heavier in children under 1 year of age and was significantly negatively associated with sociodemographic index. Conclusions SHS remains a risk factor that cannot be ignored for LRIs burden worldwide. Hence, governments and health systems should continue to take steps to reduce SHS pollution among young children to mitigate this burden.

To rigorously analyze the effects of high-salt environments on dimension lumber and provide scientific and reliable data to facilitate the advancement of light-frame construction in such environments, this study subjected dimension lumber to salt solution treatment. The study investigated the trend of nail-holding power variations across the radial, tangential, and cross-sections of spruce–pine–fir (SPF) dimension lumber under varying salt concentrations and treatment durations. The experimental results exhibited a significant influence of salt on the nail-holding power across all sections of the SPF dimension lumber. As the concentration of salt solution increased, the holding power gradually decreased across all directions, exhibiting considerable differences across salinity gradients. Specifically, the radial and tangential sections exhibited a 15%–20% higher nail-holding power compared to the cross-section. An increase in the salt solution concentration above 3% corresponded to an approximate 1% decrement in nail-holding power per section for every 0.5% rise in concentration. Additionally, prolonged salt treatment initially resulted in an increase, followed by a subsequent decrease in nail-holding power, demonstrating a consistent pattern across all variations. Post hoc analyses confirmed that the differences between individual salt concentrations, including between 3.5%, 4%, and 4.5%, were statistically significant. These findings provide valuable data for understanding the degradation of timber connectors in high-salt environments, contributing to the development of more durable and resilient wood-frame buildings in such conditions.

Epidermal growth factor receptor 2 (HER2) has been widely recognized as one of the targets for bladder cancer immunotherapy. The key to implementing personalized treatment for bladder cancer patients lies in achieving rapid and accurate diagnosis. To tackle this challenge, we have pioneered the application of deep learning techniques to predict HER2 expression status from H&E-stained pathological images of bladder cancer, bypassing the need for intricate IHC staining or high-throughput sequencing methods. Our model, when subjected to rigorous testing within the cohort from the People’s Hospital of Wuhan University, which encompasses 106 cases, has exhibited commendable performance on both the validation and test datasets. Specifically, the validation set yielded an AUC of 0.92, an accuracy of 0.86, a sensitivity of 0.87, a specificity of 0.83, and an F1 score of 86.7%. The corresponding metrics for the test set were 0.88 for AUC, 0.67 for accuracy, 0.56 for sensitivity, 0.75 for specificity, and 77.8% for F1 score. Additionally, in a direct comparison with pathologists, our model demonstrated statistically superior performance, with a p-value less than 0.05, highlighting its potential as a powerful diagnostic tool.

All vertebrates organisms produce erythroferrone, a secretory hormone with structure-related functions during iron homeostasis. However, limited knowledge exists regarding the effect of this hormone on the occurrence and progression of cancer. To systematically and comprehensively identify the diverse implications of Erythroferrone (ERFE) in various malignant tumors, we conducted an in-depth analysis of multiple datasets, including the expression levels of oncogenes and target proteins, biological functions, and molecular characteristics. This analysis aimed to assess the diagnostic and prognostic value of ERFE in pan-cancer. Our findings revealed a significant elevation in ERFE expression across 20 distinct cancer types, with notable increases in gastrointestinal cancers. Utilizing the Cytoscape and STRING databases, we identified 35 ERFE-targeted binding proteins. Survival prognosis studies, particularly gastrointestinal cancers indicated by Colon adenocarcinoma (COAD), demonstrated a poor prognosis in patients with high ERFE expression (p 0.9). Understanding the roles and interactions of ERFE in biological processes can also be aided by examining the genes co-expressed with ERFE in the coat and ranking the top 50 positive and negative genes. In the correlation analysis between the ERFE gene and different immune cells in COAD, we discovered that the expression of ERFE was positively correlated with Th1 cells, cytotoxic cells, and activated DC (aDC) abundance, and negatively correlated with Tcm (T central memory) abundance (P

Summary: In mammals, many retrotransposons are de-repressed during zygotic genome activation (ZGA). However, their functions in early development remain elusive largely due to the challenge to simultaneously manipulate thousands of retrotransposon insertions in embryos. Here, we applied CRISPR interference (CRISPRi) to perturb the long terminal repeat (LTR) MT2_Mm, a well-known ZGA and totipotency marker that exists in ∼2,667 insertions throughout the mouse genome. CRISPRi robustly perturbed 2,485 (∼93%) MT2_Mm insertions and 1,090 (∼55%) insertions of the closely related MT2C_Mm in 2-cell embryos. Remarkably, such perturbation caused downregulation of hundreds of ZGA genes and embryonic arrest mostly at the morula stage. Mechanistically, MT2 LTRs are globally enriched for open chromatin and H3K27ac and function as promoters/enhancers downstream of OBOX/DUX proteins. Thus, we not only provide direct evidence to support the functional importance of MT2 activation in development but also systematically define cis-regulatory function of MT2 in embryos by integrating functional perturbation and multi-omic analyses.

Palm oil mill effluent (POME) treatment is an anthropogenic activity contributing to global warming through methane emission. The inability to address this issue would deem true the catastrophic impacts of global temperatures exceeding 2 °C as was predicted by the Intergovernmental Panel on Climate Change (IPCC) in 2015. Little research and development exist on GHGs monitoring and methane emissions in POME treatment facilities as opposed to research on improving biogas production. A methane emission prediction tool based on machine learning models and tools can address this problem and consequently facilitate the development of efficient carbon neutrality approaches in POME treatment plants. In this study, six regression models were explored alongside their kernels using eight predictors, linking towards methane emission volume. The best model found was support vector machine (SVM), producing performance metrics for R2 and RMSE with values of 0.45 and 0.749, respectively. HIGHLIGHTS Selection and utilisation of a suitable machine learning algorithm for the prediction of CH4 emissions.; Obtaining raw data on POME treatment for database development and understanding the nature and quality of the dataset using available data from pre-processing methods.; Integrating the developed database into the CH4 emission tool.; Comparing factors influencing CH4 emissions.; Determining the highest influencing parameters.;

Esketamine is a widely used intravenous general anesthetic. However, its safety, particularly its effects on the heart, is not fully understood. In this study, we investigated the effects of esketamine exposure on zebrafish embryonic heart development. Zebrafish embryos were exposed to esketamine at concentrations of 1, 10, and 100 mg/L from 48 h post-fertilization (hpf) to 72 hpf. We found that after exposure, zebrafish embryos had an increased hatching rate, decreased heart rate, stroke volume, and cardiac output. When we exposed transgenic zebrafish of the Tg(cmlc2:EGFP) strain to esketamine, we observed ventricular dilation and thickening of atrial walls in developing embryos. Additionally, we further discovered the abnormal expression of genes associated with cardiac development, including nkx2.5, gata4, tbx5, and myh6, calcium signaling pathways, namely ryr2a, ryr2b, atp2a2a, atp2a2b, slc8a3, slc8a4a, and cacna1aa, as well as an increase in acetylcholine concentration. In conclusion, our findings suggest that esketamine may impair zebrafish larvae’s cardiac development and function by affecting acetylcholine concentration, resulting in weakened cardiac neural regulation and subsequent effects on cardiac function. The insights garnered from this research advocate for a comprehensive safety assessment of esketamine in clinical applications.

Eectrochemical glucose sensors is crucial for both environmental and human health, requiring rational nanoarchitectures with high electrochemical performance for glucose oxidation. Ni(OH)2/Ni(DMG)2 composite nanotubes were synthesized by etching nickel-dimethylglyoxime (DMG) nanorods with OH−, using Ni(DMG)2 as a partially sacrificial template. The optimal Ni(OH)2/Ni(DMG)2 nonenzymatic glucose sensor was evaluated on both conventional and portable electrochemical workstations, showing high sensitivity and a low detection limit. The optimal Ni(OH)2/Ni(DMG)2 glucose sensors integrated into smartphones demonstrated a low detection limit of 3.3 µM (M = mol L−1), a wide linear range (10 µM–8 mM), and a sensitivity of 262.80 µA mM−1 cm−2 for glucose detection. The sensors also exhibited favorable stability and reproducibility, along with preferable resistance to interference in the presence of uric acid, gluconate, proline, NaCl, valine, and lysine. Moreover, the portable sensor also demonstrated satisfactory glucose recovery (97.25–104 %) in serum samples, indicating its potential for future applications in real samples analysis.

Abstract Background In light of previous studies that profiled breed-specific traits or used genome-wide association studies to refine loci associated with characteristic morphological features in dogs, the field has gained tremendous genetic insights for known dog traits observed among breeds. Here we aim to address the question from a reserve perspective: whether there are breed-specific genotypes that may underlie currently unknown phenotypes. This study provides a complete set of breed-specific genetic signatures (BSGS). Several novel BSGS with significant protein-altering effects were highlighted and validated. Results Using the next generation whole-genome sequencing technology coupled with unsupervised machine learning for pattern recognitions, we constructed and analyzed a high-resolution sequence map for 76 breeds of 412 dogs. Genomic structures including novel single nucleotide polymorphisms (SNPs), SNP clusters, insertions, deletions (INDELs) and short tandem repeats (STRs) were uncovered mutually exclusively among breeds. We also partially validated some novel nonsense variants by Sanger sequencing with additional dogs. Four novel nonsense BSGS were found in the Bernese Mountain Dog, Samoyed, Bull Terrier, and Basset Hound, respectively. Four INDELs resulting in either frame-shift or codon disruptions were found in the Norwich Terrier, Airedale Terrier, Chow Chow and Bernese Mountain Dog, respectively. A total of 15 genomic regions containing three types of BSGS (SNP-clusters, INDELs and STRs) were identified in the Akita, Alaskan Malamute, Chow Chow, Field Spaniel, Keeshond, Shetland Sheepdog and Sussex Spaniel, in which Keeshond and Sussex Spaniel each carried one amino-acid changing BSGS in such regions. Conclusion Given the strong relationship between human and dog breed-specific traits, this study might be of considerable interest to researchers and all. Novel genetic signatures that can differentiate dog breeds were uncovered. Several functional genetic signatures might indicate potentially breed-specific unknown phenotypic traits or disease predispositions. These results open the door for further investigations. Importantly, the computational tools we developed can be applied to any dog breeds as well as other species. This study will stimulate new thinking, as the results of breed-specific genetic signatures may offer an overarching relevance of the animal models to human health and disease.

Five-day biochemical oxygen demand (BOD5) is a vital wastewater contamination strength indicator. The process of measuring BOD5 is to measure the mass of molecular oxygen consumed in 1 L of water at 20 °C over a 5-day incubation period. It is a time-consuming process and often too late for water management agencies to make a timely reaction if the result of measurement shows a water body is seriously polluted. Biosensors can simplify the process of BOD5 measurement; however, the measurement results often deviate significantly from the measured BOD5 values. The main aim of this research is to identify a machine learning model, which could predict BOD5 value from historical data and make it easier to detect water pollution in advance and timely adopt treatment measures. Three machine learning techniques, linear regression, support vector regression (SVR) and multi-layer perceptron (MLP) and two optimization processes have been studied in this research. Four main steps, preprocessing (one-time only), model training, model evaluation (testing) and analysis have been implemented in the experiments. With three feature selection strategies, the results of the experiment showed that SVR with genetic algorithm (GA) optimizer achieved the best performance with R2 of 0.694 and the lowest MAE of 0.109. HIGHLIGHTS Genetic algorithm-based support vector regression has been proposed to predict the biochemical oxygen demand values from simple variables that are easily measured.; Comparison experiments have been conducted among three popular machine learning techniques with two optimization processes.; The best model SVR with genetic algorithm optimizer achieved the best performance with R2 of 0.694 and the lowest MAE of 0.109.;

Disclosed herein is a novel Rh/Ag co-catalyzed SNH directed C–H activation and C–H/N–H bond functionalization protocol of free NH-sulfoximines with hypervalent iodonium ylides. With the aid of AgOTf, these C–H functionalization/cyclization sequences could be achieved at room temperature conditions. The reaction employed EtOH as a “green” solvent and low catalyst loading was required under an oxygen/water-insensitive condition. Under this mild protocol, a wide range of polyheterocyclic sulfoximines bearing fused saturated carbo(hetero)cycles are readily prepared, even toward a complex pharmaceutical Folliculin analog.

Introduction: With the mounting number of cancer survivors, the complications following cancer treatment become novel conundrums and starve for countermeasures. Intravenous immunoglobulin (IVIg) is a purified preparation for immune-deficient and autoimmune conditions. Objectives: Here, we investigated whether IVIg could be employed to fight against radiation injuries and explored the underlying mechanism. Methods: Hematopoietic or gastrointestinal (GI) tract toxicity was induced by total body or abdominal local irradiation. High-throughput sequencing was performed to analyze the gut microbiota configurations and gene expression profile of small intestine. The untargeted metabolomics of gut microbiome was assessed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analyses. Hydrodynamic-based gene delivery was used to knockdown the target genes in vivo. Results: Intravenous injection of IVIg protected against radiation-induced hematopoietic and GI tract toxicity in female mice but not in males. IVIg structured sex-characteristic gut microbiota configurations in abdominal irradiated mice. The irradiation enriched gut Lachnospiraceae in female mice but reduced those in males. IVIg injection combined with oral gavage of Lachnospiraceae or its metabolite hypoxanthine, alleviated radiation toxicity in male mice however, Lachnospiraceae or hypoxanthine alone failed to ameliorate the injuries. Abdominal local irradiation drove sex-distinct gene expression signatures in small intestine. Mechanistic investigation showed that replenishment of Lachnospiraceae or hypoxanthine offset abdominal radiation-reduced PLD1 expression in male mice. In females, irradiation elevated PLD1 expression. Deletion of PLD1 in GI tract of female mice erased the radioprotective effects of IVIg. Conclusion: IVIg battles against radiation injuries in a sex-specific, gut microbiome-dependent way through Lachnospiraceae/hypoxanthine/PLD1 axis. Our findings provide a sex-precise therapeutic avenue to improve the prognosis of cancer patients with radiotherapy in pre-clinical settings.

For the general population, increasing potassium intake can reduce the incidence of cardiovascular and cerebrovascular diseases. However, since hyperkalemia is a common and life-threatening complication in maintenance hemodialysis patients, which can increase the risk of malignant arrhythmia and sudden death, the current mainstream of management for hemodialysis patients is dietary potassium restriction in order to prevent hyperkalemia. Hemodialysis patients are usually advised to reduce dietary potassium intake and limit potassium-rich fruits and vegetables, but there is limited evidence to support this approach can reduce mortality and improve quality of life. There is still no consistent conclusion on the association between dietary potassium intake and serum potassium and survival in hemodialysis patients. According to the current small observational studies, there was little or even no association between dietary potassium intake and serum potassium in hemodialysis patients when assurance of adequate dialysis and specific dietary patterns (such as the plant-based diet mentioned in the article) are being followed, and excessive dietary potassium restriction may not benefit the survival of hemodialysis patients. Additionally, when assessing the effect of diet on serum potassium, researchers should not only focus on the potassium content of foods, but also consider the type of food and the content of other nutrients. However, more large-scale, multi-center clinical trials are required to provide high-quality evidence support. Besides, further research is also needed to determine the optimal daily potassium intake and beneficial dietary patterns for hemodialysis patients.

Due to their potential for high energy density, low cost, and environmental sustainability, zinc-ion batteries (ZIBs) have emerged as a promising energy storage technology. The performance, safety, and overall efficiency of ZIBs are significantly impacted by the properties of the electrolyte, such as ionic conductivity, electrochemical stability window, viscosity, and compatibility with other battery components. The use of ionic liquids (ILs) in ZIBs has gained extensive attention in recent years due to their desirable properties, such as high thermal stability, low volatility, wide electrochemical window, and tunable physicochemical properties. Therefore, this paper provides a bibliometric analysis of recent advances in the use of ILs as electrolytes in ZIBs. Current research trends, authorship patterns, and publications of ILs in ZIBs are analyzed. Our review reveals a growing interest in the use of ILs as electrolytes in ZIBs, and the development of novel ILs with tailored properties to meet the specific requirements of ZIBs is of a specific focus. This paper provides insights into the recent advancements and future research directions in the field of ILs as electrolytes for ZIBs.

Abstract Compared with a conventional three‐levelconverter, a reduced switch counts three‐level neutral point clamped (RSC‐3LNPC) inverter uses only 10 switching devices to achieve three‐leveloutput at a lower cost. However, in the RSC‐3LNPC inverter, the common modevoltage (CMV) reduction and neutral point voltage (NPV) balance are mutuallycoupled, and the buck switching module eliminates medium vectors, making conventional modulation methods no longer applicable. To address this problem, this paperdevelops a generation mechanism of the voltage and switching vectors for RSC‐3LNPC inverter and proposes a switching decomposition pulse width modulation (SDPWM) scheme. First, a virtual medium vector with two large vectors having the samedwell time is introduced. Then, the small and zero vectors with low CMV, along with the large and virtual medium vectors, are adopted, and by adjusting their dwell time, the NPV is balanced while reducing CMV. In addition, to reduce computational burden, the switching vector isdecomposed into a buck switching vector and a two‐level switching vector. Both switching vector sequences with transformed duty cycles are carefully designed to achieve only three switching transactions per switching period. Finally, validation and correctness of the proposed SDPWM scheme for RSC‐3LNPC inverterare verified by simulation and experiments.

Objectives: To quantify the seasonal and antigenic characteristics of influenza to help understand influenza activity and inform vaccine recommendations. Methods: We employed a generalized linear model with harmonic terms to quantify the seasonal pattern of influenza in China from 2005-2017, including amplitude (circulatory intensity), semiannual periodicity (given two peaks a year), annual peak time, and epidemic duration. The antigenic differences were distinguished as antigenic similarity between 2009 and 2020. We categorized regions above 33° N, between 27° N and 33° N, and below 27° N as the north, central, and south regions, respectively. Results: We estimated that the amplitude in the north region (median: 0.019, 95% CI: 0.018-0.021) was significantly higher than that in the central region (median: 0.011, 95% CI: 0.01-0.012, P

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches.

In previous work, we demonstrated that machine-learning techniques based on mixture density networks (MDNs) are successful in inferring the interior structure of rocky exoplanets with large compositional diversity. In this study, we compare the performance of a well-trained MDN model with the conventional Bayesian inversion method based on the Markov chain Monte Carlo (MCMC) method, under the same observable constraints. Considering that MCMC inversion is generally performed with the prior knowledge of planetary mass, radius, and bulk molar ratios of Fe/Mg and Si/Mg, we regenerate a substantial data set of interior structure data for rocky exoplanets and train a new MDN model with inputs of planetary mass, radius, Fe/Mg, and Si/Mg. It has been found that the well-trained MDN model has comparable performance to that of the MCMC method but requires significantly less computation time. The MDN model presents a practical alternative to the traditional MCMC method, surpassing the latter with minimal requirements for specialized knowledge, faster prediction, and greater adaptability. The developed MDN model is made publicly available on GitHub for the broader scientific community’s utilization. With the advent of the James Webb Space Telescope, we are ushering in a new epoch in exoplanetary explorations. In this evolving landscape, the MDN model stands out as a valuable asset, particularly for its ability to rapidly assimilate and interpret new data, thereby substantially advancing our understanding of the interior and habitability of exoplanetary systems.

The physics potential of detecting ^8 B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of ^13 C nuclei in the liquid scintillator detectors and the expected low background level, ^8 B solar neutrinos are observable in the CC and NC interactions on ^13 C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the ^8 B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1 σ precision levels of 5%, 8%, and 20% for the ^8 B neutrino flux, ${\sin }^{2}{\theta }_{12}$ , and ${\rm{\Delta }}{m}_{21}^{2}$ , respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the ^8 B neutrino flux.

Turbidity currents are important carriers for transporting terrestrial sediment into the deep sea, facilitating the transfer of matter and energy between land and the deep sea. Previous studies have suggested that turbidity currents can exhibit high velocities during their movement in submarine canyons. However, the maximum vertical descent velocity of high-concentration turbid water simulating turbidity currents does not exceed 1 m/s, which does not support the understanding that turbidity currents can reach speeds of over twenty meters per second in submarine canyons. During their movement, turbidity currents can compress and push the water ahead, generating propagating waves. These waves, known as excitation waves, exert a force on the seafloor, resuspending bottom sediments and potentially leading to the generation of secondary turbidity currents downstream. Therefore, the propagation distance of excitation waves is not the same as the initial journey of the turbidity currents, and the velocity of excitation waves within this journey has been mistakenly regarded as the velocity of the turbidity currents. Research on the propagation velocity of excitation waves is of great significance for understanding the sediment supply patterns of turbidity currents and the transport patterns of deep-sea sediments. In this study, numerical simulations were conducted to investigate the velocity of excitation waves induced by turbidity currents and to explore the factors that can affect their propagation velocity and amplitude. The relationship between the velocity and amplitude of excitation waves and different influencing factors was determined. The results indicate that the propagation velocity of excitation waves induced by turbidity currents is primarily determined by the water depth, and an expression (v2 = 0.63gh) for the propagation velocity of excitation waves is provided.

In order to manufacture high-speed rubber tracks, it is urgent to develop natural rubber composites with excellent thermal and mechanical properties. In this paper, natural rubber was filled with milled carbon fiber (CF) which was synergistically modified by dopamine and silane coupling agent (SCA), including KH560, KH570 and KH590. Firstly, the polydopamine initial platform was formed on CF surface through self-polymerization of dopamine. Subsequently, SCA was grafted to improve the interface properties between the filler and the matrix. Eventually, the co-modified CF filled natural rubber (CF/NR) was fabricated by mechanical mixing method. The modification process was confirmed by scanning electron microscopy and other characterization methods. The heat build-up, thermal conductivity and mechanical properties of CF/NR were compared. The results showed that SCA could effectively graft on the polydopamine platform, and the thermal and mechanical properties of the three silane functionalized carbon fiber filled natural rubber composites were improved in different degrees. Among them, the carbon fiber grafted with KH590 filled natural rubber had the best comprehensive properties. Concretely, the thermal conductivity increased by 71.3% compared with the unfilled natural rubber, and the tensile strength increased by 73.3%. The improvement of the interfacial interaction of the modified fiber and rubber matrix was considered to be one of the essential reasons.

Introduction: Cancer patients commonly experience high levels of psychological stress, which poses significant risks to their well-being. Radiotherapy is a primary treatment modality for cancer; however, it often leads to intestinal injuries in these patients. Nevertheless, the impact of mental stress on radiotherapy-intertwined complications remains unclear. Methods: To induce intestinal injury, we employed total abdominal irradiation in our experimental model. We conducted high-throughput sequencing to analyze the expression profile of miRNAs in the hippocampus. Results: We observed that mice with depression exhibited more severe intestinal injuries following total abdominal irradiation. Remarkably, oral administration of Marasmius androsaceus not only alleviated the depressive phenotype but also mitigated radiation-induced intestinal toxicity. Notably, this radioprotective effect was not observed in mice without depression. Depression disrupted the hippocampal miRNA expression profile in mice subjected to local irradiation of the abdomen, leading to the accumulation of miR-139–5p and miR-184–3p in the hippocampus, serum, and small intestine tissues. However, treatment with Marasmius androsaceus reprogrammed the miRNA expression signature in mice with depression. Furthermore, intravenous injection of antagomirs targeting miR-139–5p and miR-184–3p ameliorated depression, up-regulated Spn expression, reduced radiation enteritis, and improved the integrity of the small intestine in irradiated mice. Conclusion: Our findings demonstrate the efficacy of Marasmius androsaceus, a small mushroom, in alleviating depression-aggravated intestinal toxicity following radiotherapy by reprogramming hippocampal miRNA expression.

Renal ischemia reperfusion injury (RIRI) is an inevitable complication during renal surgery. Histone deacetylase 6 (HDAC6), a key member of the histone deacetylase family, is associated with multiple pathologies, including renal diseases. However, whether HDAC6 could become a potential therapeutic target for clinical application of RIRI remained to be proven. Here, we found that HDAC6 expression was abnormally enhanced by the transcription factor OSR2 in RIRI. Moreover, we were the first to validate that a selective HDAC6 degrader, proteolysis-targeting chimeras (PROTAC) NP8, could significantly improve RIRI. Further in vivo and in vitro mechanism studies have found that the reduction of HDAC6 alleviated RIRI by inhibiting ROS mediated apoptosis. Remarkably, a renal protective protein, Klotho, has been proven to be a target of HDAC6, and the degradation of HDAC6 restored KL expression, thereby ameliorating ROS mediated apoptosis. Overall, our results illustrated that the degradation of HDAC6 restrained ROS mediated apoptosis by restoring Klotho expression during RIRI. PROTAC-NP8 might be a potential therapeutic strategy for clinical prevention of RIRI.

Ion-driven magnetic nozzles (Ti > Te) are designed as intrinsic parts of cutting-edge propulsive technologies such as variable specific impulse magnetoplasma rockets (VASIMRs) and applied-field magnetoplasmadynamic thrusters. Employing a two-dimensional axisymmetric particle-in-cell (PIC) code, in the ion-driven magnetic nozzle, the compositions and distributions of azimuthal currents in different axial regions are investigated under various inlet ion temperatures Ti0 and found to differ dramatically from that in the electron-driven magnetic nozzles. Previously reported to be all paramagnetic and vanishing under a high magnetic field, the azimuthal currents resulting from the E × B drift are shown to turn diamagnetic and sustain a considerable magnitude when Ti0 is considered. The previously reported profile of diamagnetic drift current is altered by the introduction of inlet ion temperature, and the paramagnetic part is significantly suppressed. Moreover, a wide range of paramagnetic currents appear downstream due to the inward detachment of ions, which can also be reduced by increasing inlet ion temperature. Albeit considered in this paper, the azimuthal currents resulting from grad-B and curvature drift are still negligible in all cases of interest. The magnitude of diamagnetic azimuthal currents increases with amplifying Ti0, indicating a clear physical image of energy transformation from ion thermal energy to the directed kinetic energy through electromagnetic processes in the magnetic nozzle. Additionally, the magnetic inductive strength also has noticeable impacts on the azimuthal currents, the current magnitude tends to decrease as the magnetic field increases, and over-increment of it may result in larger divergence angles and lower nozzle efficiency.

Abstract Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20 kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3% at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

The preparation of chiral sulfones featuring quaternary carbon stereocenters remains challenging. Here the authors report the synthesis of such compounds via a multicomponent reaction involving a photoinduced radical sulfur dioxide insertion followed by an asymmetric Truce–Smiles rearrangement.

Abstract Background A major hand-foot-and-mouth disease (HFMD) pathogen, coxsackievirus A16 (CVA16), has predominated in several of the last 10 years and caused the largest number of HFMD outbreaks between 2011 and 2018 in China. We evaluated the efficacy of maternal anti-CVA16 antibody transfer via the placenta and explored the dynamics of maternal and natural infection-induced neutralizing antibodies in children. Methods Two population-based longitudinal cohorts in southern China were studied during 2013–2018. Participants were enrolled in autumn 2013, including 2475 children aged 1–9 years old and 1066 mother-neonate pairs, and followed for 3 years. Blood/cord samples were collected for CVA16-neutralizing antibody detection. The maternal antibody transfer efficacy, age-specific seroprevalence, geometric mean titre (GMT) and immune response kinetics were estimated. Results The average maternal antibody transfer ratio was 0.88 (95% CI 0.80–0.96). Transferred maternal antibody levels declined rapidly (half-life: 2.0 months, 95% CI 1.9–2.2 months). The GMT decayed below the positive threshold (8) by 1.5 months of age. Due to natural infections, it increased above 8 after 1.4 years and reached 32 by 5 years of age, thereafter dropping slightly. Although the average duration of maternal antibody-mediated protection was < 3 months, the duration extended to 6 months on average for mothers with titres ≥ 64. Conclusions Anti-CVA16 maternal antibodies are efficiently transferred to neonates, but their levels decline quickly. Children aged 0–5 years are the main susceptible population and should be protected by CVA16 vaccination, with the optimal vaccination time between 1.5 months and 1 year of age.

The promotion of distributed photovoltaics (DPV) is significant to prompt the energy structure transformation. There are many participants in the DPV project, and their strategies directly affect the construction and operation quality of the DPV plant and determine whether the DPV promotion can be carried out smoothly. This paper establishes an evolutionary game model among governments, enterprises, and households considering the impact of strategic choices of the three parties on the operational efficiency of DPV power plants. The Chinese DPV incentive policies and the actual situation of the industry are used for numerical simulation to analyze the strategies of stakeholders for DPV promotion. The results show that participants can reach eight stable states under different situations. The effect estimation results show that increasing the initial willingness, financial subsidy, punishment taxes, and operating efficiency coefficient have positive effects on encouraging participants to actively promote DPVs, while the additional costs and investment costs have negative effects. Recommendations are made for the formulation of investment strategies and DPV promotion policies.

As the only provincial-level scientific research institution specializing in rice research, the Rice Research Institute (RRI) of Guangdong Academy of Agricultural Sciences has made great achievements in the researched of rice breeding, such as breeding for dwarf rice, high-quality conventional rice breeding, hybrid rice breeding and super rice breeding. It has cultivated numerous rice varieties approved through all levels of inspection, including a batch of important parent materials widely applied by the seed industry peers, and a number of outstanding indica rice varieties widely used in southern China. It has made major contributions to the development of the rice seed industry in southern rice regions and even the whole country and the national food security, and has established the status of"China Indica Rice, Guangdong Seed Core". Since 2011, with the implementation of the Opinions of The State Council on Accelerating the Development of Modern Crop Seed Industry (state issue〔2011〕No. 8) and other documents, scientific research institutes and universities have gradually been restricted in commercial breeding researches, such as regional test indicators for hybrid rice varieties. In a bid to adapt to the new situation, the RRI strengthens the cooperation between science and technology and enterprise through the transfer of varieties, entrusted breeding and technical services so as to seek collaboration with seed enterprises which owns strong promotion ability and is close to the market, and these enterprises play their respective advantages to jointly carry out variety transformation and promotion, and good results are achieved. It has fostered and developed a number of seed enterprises, promoted the large-scale application of a batch of varieties in production, and enhanced the scientific and technological innovation of rice and the development of the seed industry. In addition, the main direction of rice breeding and the cooperation between science and enterprise in the future is analyzed.

Disulfidptosis is a newly discovered mode of cell death induced by disulfide stress. However, the prognostic value of disulfidptosis-related genes (DRGs) in renal cell carcinoma (RCC) remains to be further elucidated. In this study, consistent cluster analysis was used to classify 571 RCC samples into three DRG-related subtypes based on changes in DRGs expression. Through univariate regression analysis and LASSO-Cox regression analysis of differentially expressed genes (DEGs) among three subtypes, we constructed and validated a DRG risk score to predict the prognosis of patients with RCC, while also identifying three gene subtypes. Analysis of DRG risk score, clinical characteristics, tumor microenvironment (TME), somatic cell mutations, and immunotherapy sensitivity revealed significant correlations between them. A series of studies have shown that MSH3 can be a potential biomarker of RCC, and its low expression is associated with poor prognosis in patients with RCC. Last but not least, overexpression of MSH3 promotes cell death in two RCC cell lines under glucose starvation conditions, indicating that MSH3 is a key gene in the process of cell disulfidptosis. In summary, we identify potential mechanism of RCC progression through DRGs -related tumor microenvironment remodeling. In addition, this study has successfully established a new disulfidptosis-related genes prediction model and discovered a key gene MSH3. They may be new prognostic biomarkers for RCC patients, provide new insights for the treatment of RCC patients, and may inspire new methods for the diagnosis and treatment of RCC patients.

IκBζ (encoded by NFKBIZ) is the most recently identified IkappaB family protein. As an atypical member of the IkappaB protein family, NFKBIZ has been the focus of recent studies because of its role in inflammation. Specifically, it is a key gene in the regulation of a variety of inflammatory factors in the NF-KB pathway, thereby affecting the progression of related diseases. In recent years, investigations into NFKBIZ have led to greater understanding of this gene. In this review, we summarize the induction of NFKBIZ and then elucidate its transcription, translation, molecular mechanism and physiological function. Finally, the roles played by NFKBIZ in psoriasis, cancer, kidney injury, autoimmune diseases and other diseases are described. NFKBIZ functions are universal and bidirectional, and therefore, this gene may exert a great influence on the regulation of inflammation and inflammation-related diseases.

BackgroundThe ability of cancer-associated fibroblasts (CAFs) to encourage angiogenesis, tumor cell spread, and increase treatment resistance makes them pro-tumorigenic. We aimed to investigate the CAF signature in Bladder urothelial carcinoma (BLCA) and, for clinical application, to build a CAF-based risk signature to decipher the immune landscape and screen for suitable treatment BLCA samples.MethodsCAF-related genes were discovered by superimposing CAF marker genes discovered from single-cell RNA-seq (scRNA-seq) data taken from the GEO database with CAF module genes discovered by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data from TCGA. After identifying prognostic genes related with CAF using univariate Cox regression, Lasso regression was used to build a risk signature. With microarray data from the GEO database, prognostic characteristics were externally verified. For high and low CAF-risk categories, immune cells and immunotherapy responses were analyzed. Finally, a nomogram model based on the risk signature and prospective chemotherapeutic drugs were examined.ResultsCombining scRNA-seq and bulk-seq data analysis yielded a total of 124 CAF-related genes. LRP1, ANXA5, SERPINE2, ECM1, RBP1, GJA1, and FKBP10 were the seven BLCA prognostic genes that remained after univariate Cox regression and LASSO regression analyses. Then, based on these genes, prognostic characteristics were created and validated to predict survival in BLCA patients. Additionally, risk signature had a strong correlation with known CAF scores, stromal scores, and certain immune cells. The CAF-risk signature was identified as an independent prognostic factor for BLCA using multifactorial analysis, and its usefulness in predicting immunotherapy response was confirmed. Based on risk classification, we projected six highly sensitive anticancer medicines for the high-risk group.ConclusionThe prognosis of BLCA may be accurately predicted using CAF-based risk signature. With a thorough understanding of the BLCA CAF-signature, it might be able to explain the BLCA patients’ response to immunotherapy and identify a potential target for BLCA treatment.

The yield of total alkaloids from Artemisia argyi leaves was used as the index, and the optimal range conditions of solid-liquid ratio, compound enzyme addition amount, enzymolysis time, enzymolysis pH, ultrasonic time, ultrasonic power, ethanol concentration and ultrasonic temperature were obtained through single factor experiments. The Plackett-Burman method was employed to screen out the factors that had significant effects on the yield of total alkaloids from Artemisia argyi leaves. The Box-Behnken method was used to optimize the extraction process to obtain the optimun extraction conditions. Finally, the inhibitory effect and the minimum inhibitory concentration of the total alkaloid extract of Artemisia argyi leaves against Escherichia coli and Staphylococcus aureus were investigated. The results showed that the important factors affecting the yield of total alkaloids from Artemisia argyi leaves were ultrasonic time, compound enzyme addition amount and enzymolysis time. The optimum extraction conditions were as follows: Ultrasonic time 40 min, compound enzyme addition 1.60%, enzymolysis time 1.5 h, solid-liquid ratio 1:25 g/mL, enzymolysis pH6.0, ultrasonic power 160 W, ethanol concentration 80%, ultrasonic temperature 60 ℃. Under the optimized conditions, the highest yield of total alkaloids was 0.720±0.05 mg/g. The total alkaloids from Artemisia argyi leaves had antibacterial activity against Escherichia coli and Staphylococcus aureus, and their minimum inhibitory concentrations were 3.2 and 1.6 mg/mL, respectively. The actual value of the extraction process and the predicted value had a high degree of fit, which was reliable for prediction of the extraction of the total alkaloids from Artemisia argyi leaves, and the obtained total alkaloids from Artemisia argyi leaves had a certain antibacterial activity.

Abstract Background Hundreds of millions of doses of coronavirus disease 2019 (COVID-19) vaccines have been administered globally, but progress on vaccination varies considerably between countries. We aimed to provide an overall picture of COVID-19 vaccination campaigns, including policy, coverage, and demand of COVID-19 vaccines. Methods We conducted a descriptive study of vaccination policy and doses administered data obtained from multiple public sources as of 8 February 2022. We used these data to develop coverage indicators and explore associations of vaccine coverage with socioeconomic and healthcare-related factors. We estimated vaccine demand as numbers of doses required to complete vaccination of countries’ target populations according to their national immunization program policies. Results Messenger RNA and adenovirus vectored vaccines were the most commonly used COVID-19 vaccines in high-income countries, while adenovirus vectored vaccines were the most widely used vaccines worldwide (180 countries). One hundred ninety-two countries have authorized vaccines for the general public, with 40.1% (77/192) targeting individuals over 12 years and 32.3% (62/192) targeting those ≥ 5 years. Forty-eight and 151 countries have started additional-dose and booster-dose vaccination programs, respectively. Globally, there have been 162.1 doses administered per 100 individuals in target populations, with marked inter-region and inter-country heterogeneity. Completed vaccination series coverage ranged from 0.1% to more than 95.0% of country target populations, and numbers of doses administered per 100 individuals in target populations ranged from 0.2 to 308.6. Doses administered per 100 individuals in whole populations correlated with healthcare access and quality index (R 2 = 0.59), socio-demographic index (R 2 = 0.52), and gross domestic product per capita (R 2 = 0.61). At least 6.4 billion doses will be required to complete interim vaccination programs—3.3 billion for primary immunization and 3.1 billion for additional/booster programs. Globally, 0.53 and 0.74 doses per individual in target populations are needed for primary immunization and additional/booster dose programs, respectively. Conclusions There is wide country-level disparity and inequity in COVID-19 vaccines rollout, suggesting large gaps in immunity, especially in low-income countries.

BackgroundLaparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms.MethodsPeripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m2 in the Department of General Surgery of Kunming First People’s Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-Seq data were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases.ResultsWe identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P.ConclusionThis study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.

In order to further improve the computing power of the information aggregation operator in the q-rung orthopair probabilistic hesitant fuzzy environment, this paper proposes a multi-attribute decision-making method based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator. Firstly, the algorithm of q-rung orthopair probabilistic hesitant fuzzy set is improved based on the Schweizer-Sklar T-norm. In order to better reflect the degree of hesitation of decision-making experts, a new q-rung orthopair probabilistic hesitant fuzzy distance measure is proposed, which provides a basis for subsequent power weighted calculations. Furthermore, considering the correlation between attributes and the influence of data extremes, some information aggregation operators and their power weighted forms are proposed. Finally, a multi-attribute decision-making model based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator is established, and the reliability and validity of the research content in this paper are verified through decision-making examples and comparative analysis.

Abstract Background To allow a return to a pre-COVID-19 lifestyle, virtually every country has initiated a vaccination program to mitigate severe disease burden and control transmission. However, it remains to be seen whether herd immunity will be within reach of these programs. Methods We developed a compartmental model of SARS-CoV-2 transmission for China, a population with low prior immunity from natural infection. Two vaccination programs were tested and model-based estimates of the immunity level in the population were provided. Results We found that it is unlikely to reach herd immunity for the Delta variant given the relatively low efficacy of the vaccines used in China throughout 2021 and the lack of prior natural immunity. We estimated that, assuming a vaccine efficacy of 90% against the infection, vaccine-induced herd immunity would require a coverage of 93% or higher of the Chinese population. However, even when vaccine-induced herd immunity is not reached, we estimated that vaccination programs can reduce SARS-CoV-2 infections by 50–62% in case of an all-or-nothing vaccine model and an epidemic starts to unfold on December 1, 2021. Conclusions Efforts should be taken to increase population’s confidence and willingness to be vaccinated and to develop highly efficacious vaccines for a wide age range.

Biological oxygen demand (BOD5) is an indicator used to monitor water quality. However, the standard process of measuring BOD5 is time consuming and could delay crucial mitigation works in the event of pollution. To solve this problem, this study employed multiple machine learning (ML) methods such as random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP) to train a best model that can accurately predict the BOD5 values in water samples based on other physical and chemical properties of the water. The training parameters were optimized using genetic algorithm (GA) and feature selection was made using the sequential feature selection (SFS) method. The proposed machine learning framework was first tested on a public dataset (Waterbase). The MLP method produced the best model, with an R2 score of 0.7672791942775417, relative mean squared error (MSE) and relative mean absolute error (MAE) of approximately 15%. Feature importance calculations indicated that chemical oxygen demand (CODCr), ammonium and nitrate are features that highly correlate to BOD5. In the field study with a small private dataset consisting of water samples collected from two different lakes in Jiangsu Province of China, the trained model was found to have a similar range of prediction error (around 15%), a similar relative MAE (around 14%) and achieved about 6% better relative RMSE. Highlights Multiple machine learning (ML) methods used to train a best model to predict the BOD5 values in water samples.; Permutation feature importance (PFI) values calculated indicate that CODCr, ammonium and nitrate are features with the highest correlation with BOD5.; The best model trained using MLP yielded the best performance with an R2 score of 0.7672791942775417 together with relative RMSE and relative MAE of approximately 15%.;