Sorting nexin 5 mediates virus-induced autophagy and immunity.

Autophagy, a process of degradation that occurs via the lysosomal pathway, has an essential role in multiple aspects of immunity, including immune system development, regulation of innate and adaptive immune and inflammatory responses, selective degradation of intracellular microorganisms, and host protection against infectious diseases1,2. Autophagy is known to be induced by stimuli such as nutrient deprivation and suppression of mTOR, but little is known about how autophagosomal biogenesis is initiated in mammalian cells in response to viral infection. Here, using genome-wide short interfering RNA screens, we find that the endosomal protein sorting nexin 5 (SNX5)3,4 is essential for virus-induced, but not for basal, stress- or endosome-induced, autophagy. We show that SNX5 deletion increases cellular susceptibility to viral infection in vitro, and that Snx5 knockout in mice enhances lethality after infection with several human viruses. Mechanistically, SNX5 interacts with beclin 1 and ATG14-containing class III phosphatidylinositol-3-kinase (PI3KC3) complex 1 (PI3KC3-C1), increases the lipid kinase activity of purified PI3KC3-C1, and is required for endosomal generation of phosphatidylinositol-3-phosphate (PtdIns(3)P) and recruitment of the PtdIns(3)P-binding protein WIPI2 to virion-containing endosomes. These findings identify a context- and organelle-specific mechanism-SNX5-dependent PI3KC3-C1 activation at endosomes-for initiation of autophagy during viral infection.

Efficacy and safety of cilta-cel in patients with progressive multiple myeloma after exposure to other BCMA-targeting agents.

B-cell maturation antigen (BCMA)-targeting therapies, including bispecific antibodies (BsAbs) and antibody-drug conjugates (ADCs), are promising treatments for multiple myeloma (MM), but disease may progress after their use. CARTITUDE-2 is a phase 2, multicohort study evaluating the safety and efficacy of cilta-cel, an anti-BCMA chimeric antigen receptor T therapy, in various myeloma patient populations. Patients in cohort C progressed despite treatment with a proteasome inhibitor, immunomodulatory drug, anti-CD38 antibody, and noncellular anti-BCMA immunotherapy. A single cilta-cel infusion was given after lymphodepletion. The primary end point was minimal residual disease (MRD) negativity at 10-5. Overall, 20 patients were treated (13 ADC exposed; 7 BsAb exposed; 1 in the ADC group also had prior BsAb exposure). Sixteen (80%) were refractory to prior anti-BCMA therapy. At a median follow-up of 11.3 months (range, 0.6-16.0), 7 of 20 (35%) patients were MRD negative (7 of 10 [70.0%] in the MRD-evaluable subset). Overall response rate (95% confidence interval [CI]) was 60.0% (36.1-80.9). Median duration of response and progression-free survival (95% CI) were 11.5 (7.9-not estimable) and 9.1 (1.5-not estimable) months, respectively. The most common adverse events were hematologic. Cytokine release syndrome occurred in 12 (60%) patients (all grade 1-2); 4 had immune effector cell-associated neurotoxicity syndrome (2 had grade 3-4); none had parkinsonism. Seven (35%) patients died (3 of progressive disease, 4 of adverse events [1 treatment related, 3 unrelated]). Cilta-cel induced favorable responses in patients with relapsed/refractory MM and prior exposure to anti-BCMA treatment who had exhausted other therapies. This trial was registered at www.clinicaltrials.gov as NCT04133636.

Mechanical cloak via data-driven aperiodic metamaterial design.

SignificanceAn invisibility cloak to conceal objects from an outside observer has long been a subject of interest in metamaterial design. While cloaks have been manufactured for optical, thermal, and electric fields, limited progress has been made for mechanical cloaks. Most existing designs rely on mapping-based methods, which have so far been limited to special base cells and a narrow selection of voids with simple shapes. In this study, we develop a fundamentally different approach by exploiting data-driven designs to offer timely, customized solutions to mechanical cloaking that were previously difficult to obtain. Through simulations and experimental validations, we show that excellent cloaking performance can be achieved for various boundary conditions, shapes of voids, base cells, and even multiple voids.

Enabling Ultrafine Ru Nanoparticles with Tunable Electronic Structures via a Double-Shell Hollow Interlayer Confinement Strategy toward Enhanced Hydrogen Evolution Reaction Performance.

Engineering of the catalysts' structural stability and electronic structure could enable high-throughput H2 production over electrocatalytic water splitting. Herein, a double-shell interlayer confinement strategy is proposed to modulate the spatial position of Ru nanoparticles in hollow carbon nanoreactors for achieving tunable sizes and electronic structures toward enhanced H2 evolution. Specifically, the Ru can be anchored in either the inner layer (Ru-DSC-I) or the external shell (Ru-DSC-E) of double-shell nanoreactors, and the size of Ru is reduced from 2.2 to 0.9 nm because of the double-shell confinement effect. The electronic structures are efficiently optimized thereby stabilizing active sites and lowering the reaction barrier. According to finite element analysis results, the mesoscale mass diffusion can be promoted in the double-shell configuration. The Ru-DSC-I nanoreactor exhibits a much lower overpotential (η10 = 73.5 mV) and much higher stability (100 mA cm-2). Our work might shed light on the precise design of multishell catalysts with efficient refining electrostructures toward electrosynthesis applications.

Activation of the PI3K/AKT signaling pathway by ARNTL2 enhances cellular glycolysis and sensitizes pancreatic adenocarcinoma to erlotinib.

BACKGROUND: Pancreatic adenocarcinoma (PC) is an aggressive malignancy with limited treatment options. The poor prognosis primarily stems from late-stage diagnosis and when the disease has become therapeutically challenging. There is an urgent need to identify specific biomarkers for cancer subtyping and early detection to enhance both morbidity and mortality outcomes. The addition of the EGFR tyrosine kinase inhibitor (TKI), erlotinib, to gemcitabine chemotherapy for the first-line treatment of patients with advanced pancreatic cancer slightly improved outcomes. However, restricted clinical benefits may be linked to the absence of well-characterized criteria for stratification and dependable biomarkers for the prediction of treatment effectiveness. METHODS AND RESULTS: We examined the levels of various cancer hallmarks and identified glycolysis as the primary risk factor for overall survival in PC. Subsequently, we developed a glycolysis-related score (GRS) model to accurately distinguish PC patients with high GRS. Through in silico screening of 4398 compounds, we discovered that erlotinib had the strongest therapeutic benefits for high-GRS PC patients. Furthermore, we identified ARNTL2 as a novel prognostic biomarker and a predictive factor for erlotinib treatment responsiveness in patients with PC. Inhibition of ARNTL2 expression reduced the therapeutic efficacy, whereas increased expression of ARNTL2 improved PC cell sensitivity to erlotinib. Validation in vivo using patient-derived xenografts (PDX-PC) with varying ARNTL2 expression levels demonstrated that erlotinib monotherapy effectively halted tumor progression in PDX-PC models with high ARNTL2 expression. In contrast, PDX-PC models lacking ARNTL2 did not respond favorably to erlotinib treatment. Mechanistically, we demonstrated that the ARNTL2/E2F1 axis-mediated cellular glycolysis sensitizes PC cells to erlotinib treatment by activating the PI3K/AKT signaling pathway. CONCLUSIONS: Our investigations have identified ARNTL2 as a novel prognostic biomarker and predictive indicator of sensitivity. These results will help to identify erlotinib-responsive cases of PC and improve treatment outcomes. These findings contribute to the advancement of precision oncology, enabling more accurate and targeted therapeutic interventions.

The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.

OBJECTIVE: To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy. METHODS: 105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model. RESULT: The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively. CONCLUSION: The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.

The FLOWERING LOCUS T 5b positively regulates photoperiodic flowering and improves the geographical adaptation of soybean.

Plants can sense the photoperiod to flower at the right time. As a sensitive short-day crop, soybean (Glycine max) flowering varies greatly depending on photoperiods, affecting yields. Adaptive changes in soybeans rely on variable genetic loci such as E1 and FLOWERING LOCUS T orthologs. However, the precise coordination and control of these molecular components remain largely unknown. In this study, we demonstrate that GmFT5b functions as a crucial factor for soybean flowering. Overexpressed or mutated GmFT5b resulted in significantly early or later flowering, altering expression profiles for several downstream flowering-related genes under a long-day photoperiod. GmFT5b interacts with the transcription factor GmFDL15, suggesting transcriptional tuning of flowering time regulatory genes via the GmFT5b/GmFDL15 complex. Notably, GmFT5a partially compensated for GmFT5b function, as ft5a ft5b double mutants exhibited an enhanced late-flowering phenotype. Association mapping revealed that GmFT5b was associated with flowering time, maturity, and geographical distribution of soybean accessions, all associated with the E1 locus. Therefore, GmFT5b is a valuable target for enhancing regional adaptability. Natural variants or multiple mutants in this region can be utilized to generate optimized soybean varieties with precise flowering times.

A Three-Dimensional Non-Fullerene Acceptor with Contorted Hexabenzocoronene and Perylenediimide for Organic Solar Cells.

Although fullerene derivatives such as [6,6]-phenyl-C61/C71-butyric acid methyl ester (PC61BM/PC71BM) have dominated the the photoactive acceptor materials in bulk heterojunction organic solar cells (OSCs) for decades, they have several drawbacks such as weak absorption, limited structural tunability, prone to aggregation, and high costs of production. Constructing non-fullerene small molecules with three-dimensional (3D) molecular geometry is one of the strategies to replace fullerenes in OSCs. In this study, a 3D molecule, contorted hexa-cata-hexabenzocoronene tetra perylenediimide (HBC-4-PDI), was designed and synthesized. HBC-4-PDI shows a wide and strong light absorption in the whole UV-vis region as well as suitable energy levels as an acceptor for OSCs. More importantly, the 3D construction effectively reduced the self-aggregation of c-HBC, leading to an appropriate scale phase separation of the blend film morphology in OSCs. A preliminary power conversion efficiency of 2.70 % with a champion open-circuit voltage of 1.06 V was obtained in OSCs with HBC-4-PDI as the acceptor, which was the highest among the previously reported OSCs based on c-HBC derivatives. The results indicated that HBC-4-PDI may serve as a good non-fullerene acceptor for OSCs.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Adsorption Behavior of Ammonia on Strontium Bromide Surface: First-Principles Study.

Thermochemical heat storage based on a gas-solid interaction is an effective long-term energy storage technology and is considered as one of the important technologies for the recovery of industrial waste heat and renewable energy sources such as solar energy. There are many working pairs used for thermochemical heat storage, among which ammonium halides are widely trusted for their good thermodynamic properties. It has attracted a lot of attention in the past decade, but it is still in the laboratory-scale research stage. In this study, the adsorption behavior of strontium bromide surfaces on the atomic scale is investigated using density functional theory with SrBr2/NH3 as the working pair. The optimal adsorption location of ammonia molecules on the strontium bromide surface is determined. Meanwhile, different metal atoms were doped to explore the microscopic factors affecting the adsorption. The energy barrier of the SrBr2/NH3 reaction was 4.507 kcal/mol, which was reduced to 4.145 kcal/mol after doping Mg. The thermodynamics of the Ca atoms doped with SrBr2 were significantly improved, with a reduction in the energy barrier to 0.727 kcal/mol. Comparing the three energy barrier results, Ca doping has a significant optimization effect on the thermal storage process. The results could provide relevant information for the investigation of thermochemical adsorption heat storage, provide insight into the adsorption mechanism of ammonium molecules on strontium bromide, and also facilitate the design of efficient composite adsorbents.

Influence of increasing acclimation temperature on growth, digestion, antioxidant capacity, liver transcriptome and intestinal microflora of Ussruri whitefish Coregonus ussuriensis Berg.

For effective restoration, conservation of Ussruri whitefish Coregonus ussuriensis Berg and coping with global climate change, effects of environmental temperature on Ussruri whitefish urgently need to be explored. In current study, the effects of different acclimation temperatures on the growth, digestive physiology, antioxidant ability, liver transcriptional responses and intestinal microflora patterns of Ussruri whitefish were investigated. Ussruri whitefish (15.20 g ± 1.23 g) were reared for 42 days under different acclimation temperatures, i.e., 10, 13, 16, 19, 22 and 25 °C, respectively. Result first determined 28 °C as the semi-lethal temperature in order to design the temperature gradient test. Highest main gain rate (MGR) and specific growth rate (SGR) were observed in fish group having acclimation temperature of 19 °C. Significantly decrease (P < 0.05) in triglyceride (TG) content appeared at 19 °C as compared to the 10 °C and 13 °C temperature groups. 19 °C notablely increased protease activities of stomach and intestine and intestinal lipase and amylase activities. 19 °C group obtained the highest activities of chloramphnicol acetyltransferase (CAT) and total antioxidant capacity (T-AOC) and higher activities of superoxide dismutase (SOD). The intestinal microflora composition was most conducive to maintaining overall intestinal health when the temperature was 19 °C, compared to 10 °C and 25 °C. Ussruri whitefish exposed to 10 °C and 25 °C possessed the lower Lactobacillus abundance compared to exposure to 19 °C. Temperature down to 10 °C or up to 25 °C, respectively, triggered cold stress and heat stress, which leading to impairment in intestinal digestion, liver antioxidant capacity and intestinal microflora structure. Liver transcriptome response to 10 °C, 19 °C and 25 °C revealed that Ussruri whitefish might require the initiation of endoplasmic reticulum stress to correct protein damage from cold-temperature and high-temperature stress, and it was speculated that DNAJB11 could be regarded as a biomarker of cold stress response.Based on the quadratic regression analysis of MGR and SGR against temperature, the optimal acclamation temperature were, respectively, 18.0 °C and 18.1 °C. Our findings provide valuable theoretical insights for an in-depth understanding of temperature acclimation mechanisms and laid the foundation for conservation and development of Ussruri whitefish germplasm resources.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.

INTRODUCTION: Patients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients' functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. METHODS: FedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. RESULTS: ADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. CONCLUSION: NLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

Discovery of plant-derived anti-tumor natural products: Potential leads for anti-tumor drug discovery.

Natural products represent a paramount source of novel drugs. Numerous plant-derived natural products have demonstrated potent anti-tumor properties, thereby garnering considerable interest in their potential as anti-tumor drugs. This review compiles an overview of 242 recently discovered natural products, spanning the period from 2018 to the present. These natural products, which include 69 terpenoids, 42 alkaloids, 39 flavonoids, 21 steroids, 14 phenylpropanoids, 5 quinolines and 52 other compounds, are characterized by their respective chemical structures, anti-tumor activities, and mechanisms of action. By providing an essential reference and fresh insights, this review aims to support and inspire researchers engaged in the fields of natural products and anti-tumor drug discovery.

Effects of atmospheric particulate pollution on lung function of athletes.

There is a knowledge gap on how ground-level particulate pollution affects labor productivity in emerging nations due to a lack of study, especially when it comes to outdoor work settings like couriers in the express delivery industry. Combining findings from two research projects, this paper examines the socioeconomic consequences of particulate matter and ground-level particulate pollution. Special panel dataset from China's express delivery companies are used, we study how particulate pollution affects courier productivity. The instrumental variable of our analysis was built by particulate pollution data from upwind towns. Moreover, a comparable rise in particulate levels during the 30 days caused a significant 23.7% decline in worker productivity. This draws attention to a neglected area of the economic effects of particulate pollution, especially in underdeveloped countries. Our results also highlight the wider health hazards connected to outdoor activities in high-pollution locations, drawing comparisons on outdoor exercisers and particulate matter concentrations. The critical need for coordinated policy attention addressing both ground-level Particulate and particle matters in developing nations is highlighted by the increased risk of lung function impairment among outdoor exercisers owing to excessive particulate matter concentrations. The interrelated risks that air pollution poses to public health and economic productivity are clarified by this Comprehensive viewpoint.

Mitochondria-Translocated PGK1 Functions as a Protein Kinase to Coordinate Glycolysis and the TCA Cycle in Tumorigenesis.

It is unclear how the Warburg effect that exemplifies enhanced glycolysis in the cytosol is coordinated with suppressed mitochondrial pyruvate metabolism. We demonstrate here that hypoxia, EGFR activation, and expression of K-Ras G12V and B-Raf V600E induce mitochondrial translocation of phosphoglycerate kinase 1 (PGK1); this is mediated by ERK-dependent PGK1 S203 phosphorylation and subsequent PIN1-mediated cis-trans isomerization. Mitochondrial PGK1 acts as a protein kinase to phosphorylate pyruvate dehydrogenase kinase 1 (PDHK1) at T338, which activates PDHK1 to phosphorylate and inhibit the pyruvate dehydrogenase (PDH) complex. This reduces mitochondrial pyruvate utilization, suppresses reactive oxygen species production, increases lactate production, and promotes brain tumorigenesis. Furthermore, PGK1 S203 and PDHK1 T338 phosphorylation levels correlate with PDH S293 inactivating phosphorylation levels and poor prognosis in glioblastoma patients. This work highlights that PGK1 acts as a protein kinase in coordinating glycolysis and the tricarboxylic acid (TCA) cycle, which is instrumental in cancer metabolism and tumorigenesis.

A web-based dynamic predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic surgery.

OBJECTIVE: The aim of this study was to develop a web-based dynamic prediction model for postoperative nausea and vomiting (PONV) in patients undergoing gynecologic laparoscopic surgery. METHODS: The patients (N = 647) undergoing gynecologic laparoscopic surgery were included in this observational study. The candidate risk-factors related to PONV were included through literature search. Lasso regression was utilized to screen candidate risk-factors, and the variables with statistical significance were selected in multivariable logistic model building. The web-based dynamic Nomogram was used for model exhibition. Accuracy and validity of the experimental model (EM) were evaluated by generating receiver operating characteristic (ROC) curves and calibration curves. Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical practicability of the risk prediction model. RESULTS: Ultimately, a total of five predictors including patient-controlled analgesia (odds ratio [OR], 4.78; 95% confidence interval [CI], 1.98-12.44), motion sickness (OR, 4.80; 95% CI, 2.71-8.65), variation of blood pressure (OR, 4.30; 95% CI, 2.41-7.91), pregnancy vomiting history (OR, 2.21; 95% CI, 1.44-3.43), and pain response (OR, 1.64; 95% CI, 1.48-1.83) were selected in model building. Assessment of the model indicates the discriminating power of EM was adequate (ROC-areas under the curve, 93.0%; 95% CI, 90.7%-95.3%). EM showed better accuracy and goodness of fit based on the results of the calibration curve. The DCA curve of EM showed favorable clinical benefits. CONCLUSIONS: This dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.

Identification of the coexisting virus-derived siRNA in maize and rice infected by rice black-streaked dwarf virus.

Rice black-streaked dwarf virus is transmitted by small brown planthoppers, which causes maize rough dwarf disease and rice black-streaked dwarf disease. This virus leads to slow growth or death of the host plants. During the co-evolutionary arms race between viruses and plants, virus-derived small interfering RNAs challenge the plant's defense response and inhibit host immunity through the RNA silencing system. However, it is currently unknown if rice black-streaked dwarf virus can produce the same small interfering RNAs to mediate the RNA silencing in different infected species. In this study, four small RNA libraries and four degradome libraries were constructed by extracting total RNAs from the leaves of the maize (Zea mays) inbred line B73 and japonica rice (Oryza sativa) variety Nipponbare exposed to feeding by viruliferous and non-viruliferous small brown planthoppers. We analyzed the characteristics of small RNAs and explored virus-derived small interfering RNAs in small RNA libraries through high-throughput sequencing. On analyzing the characteristics of small RNA, we noted that the size distributions of small RNAs were mainly 24-nt (19.74%-62.00%), whereas those of virus-derived small interfering RNAs were mostly 21-nt (41.06%-41.87%) and 22-nt (39.72%-42.26%). The 5'-terminal nucleotides of virus-derived small interfering RNAs tended to be adenine or uracil. Exploring the distribution of virus-derived small interfering RNAs hot spots on the viral genome segments revealed that the frequency of hot spots in B73 was higher than those in Nipponbare. Meanwhile, hotspots in the S9 and S10 virus genome segments were distributed similarly in both hosts. In addition, the target genes of small RNA were explored by degradome sequencing. Analyses of the regulatory pathway of these target genes unveiled that viral infection affected the ribosome-related target genes in maize and target genes in metabolism and biosynthesis pathways in rice. Here, 562 and 703 virus-derived small interfering RNAs were separately obtained in maize and rice, and 73 virus-derived small interfering RNAs named as co-vsiRNAs were detected in both hosts. Stem-loop PCR and RT-qPCR confirmed that co-vsiRNA 3.1 and co-vsiRNA 3.5 derived from genome segment S3 simultaneously play a role in maize and rice and inhibited host gene expression. The study revealed that rice black-streaked dwarf virus can produce the same small interfering RNAs in different species and provides a new direction for developing the new antiviral strategies.

Prevention and Potential Treatment Strategies for Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) is a significant viral pathogen that causes respiratory infections in infants, the elderly, and immunocompromised individuals. RSV-related illnesses impose a substantial economic burden worldwide annually. The molecular structure, function, and in vivo interaction mechanisms of RSV have received more comprehensive attention in recent times, and significant progress has been made in developing inhibitors targeting various stages of the RSV replication cycle. These include fusion inhibitors, RSV polymerase inhibitors, and nucleoprotein inhibitors, as well as FDA-approved RSV prophylactic drugs palivizumab and nirsevimab. The research community is hopeful that these developments might provide easier access to knowledge and might spark new ideas for research programs.

Identification of hydrochemical fingerprints, quality and formation dynamics of groundwater in western high Himalayas.

Identifying hydrochemical fingerprints of groundwater is a challenge in areas with complex geological settings. This study takes the Gilgit-Baltistan, a complex geological area in west high Himalayas, Pakistan, as the study area to get insights into the hydrochemcial genesis and quality of groundwater in complex geological mountainous regions. A total of 53 samples were collected across the area to determine the hydrochemical characteristics and formation of groundwater. Results revealed groundwater there is characterized by slightly alkaline and soft fresh feature. Groundwater is dominated by the hydrochemical facies of HCO3·SO4-Ca·Mg type. The factor method yields three components (PCs) of principal component analysis, which together explain 75.71% of the total variances. The positive correlation of EC, TDS, Ca2+, SO42-, K+ in PC1, and NO3-, Cl- in PC2 indicate that a combination of natural and anthropogenic activities influences groundwater hydrochemistry. Water-rock interaction is the main mechanism governing the natural hydrochemistry of groundwater. The negative correlation of Cl-, SO42-, Ca2+, and Na+ with NDVI attributes to inorganic salt uptake by plant roots. Groundwater chemical composition is also affected by the type of land use. Groundwater is characterized as excellent and good water quality based on the entropy-weighted water quality index assessment, and is suitable for drinking purposes except for very few samples, while aqueous fluoride would pose potential health threats to water consumers in western high Himalayas, and infants are most at risk compared to other populations. This study will help to deepen the hydrochemial formation mechanism and exploitation suitability of groundwater resources in the mountainous areas that undergone the combined actions of nature and human activities, and provide insights into the characteristics of water environmental quality in western Himalayas area.

PAS-7 is Superior to APAIS for Assessing Preoperative Anxiety in the Chinese Population.

PURPOSE: The purpose of this study was to analyze the reliability and validity of the Perioperative Anxiety Scale-7 (PAS-7), which was created by Chinese medical professionals, by using the State-Trait Anxiety Scale (STAI-S) as the standard for the diagnosis of preoperative anxiety, and to compare whether there is a difference between the PAS-7 and the Amsterdam Preoperative Anxiety and Information Scale (APAIS) in the diagnosis of preoperative anxiety in the Chinese population. DESIGN: This study was an observational study. METHODS: The PAS-7, APAIS, and STAI-S were all completed the day before surgery. The internal consistency test was used to evaluate the scale's reliability, and exploratory factor analysis and confirmatory factor analysis were used to assess the scale's construct validity. Pearson correlation was used to analyze the correlation between PAS-7 and STAI-S, and APAIS. The area under the receiver operating characteristic (ROC) curve was used to compare the diagnostic value of PAS-7 and APAIS. FINDINGS: The PAS-7 Cronbach's α coefficient was 0.804. The indicators of the overall fitting coefficient were within the acceptable range. PAS-7 scores correlated well with STAI-S and APAIS scores (P < .01). The area under the ROC curve of PAS-7 was 0.808 (0.752-0.856), and the area under the ROC curve of APAIS was 0.674 (0.611-0.733). The difference between areas was 0.133 (0.0612-0.206), P < .001, and the diagnostic value of PAS-7 was higher than that of APAIS. CONCLUSIONS: The PAS-7 scale has high reliability and validity and can be used to assess preoperative anxiety in patients undergoing elective surgery. PAS-7 is superior to APAIS for assessing preoperative anxiety in the Chinese population.