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Abstract Objective Long non-coding RNAs (lncRNAs) LINC02466 is an lncRNA newly linked to the adverse outcomes in primary liver cancer patients, and its crucial involvement in the disease's escalation. Decoding the specific role of LINC02466 in HCC progression is of great significance to provide a potential therapeutic target for HCC. Methods RT-qPCR and Western Blot techniques was used to analyze the expression levels of LINC02466 in both malignant and surrounding healthy liver tissues. CCK8 assays and colony formation experiments indicates the LINC02466's effect on the proliferation rates of liver cancer cells. Flow cytometry was pivotal in revealing its significant influence on the cell cycle of these cells. Kaplan–Meier survival analysis with log-rank tests were employed. Results The suppression of LINC02466 markedly reduces the stemness attributes of liver cancer cells, indicating a potential therapeutic target. LINC02466 overexpression significantly increased tumor growth rates and final volumes. Further research indicated that LINC02466 significantly influences liver cancer progression through regulating the mTOR signaling pathway. Conclusion LINC02466 regulating cell proliferation, the cell cycle, and stemness characteristics via the mTOR pathway, suggesting LINC02466 as a potential therapeutic target for primary liver cancer.

Abstract Inactivated suppressor of mothers against decapentaplegic homolog (SMAD) 4 significantly affects cancer development in pancreatic ductal adenocarcinoma (PDAC). However, the contribution of smad4 loss to drug resistance in PDAC is largely undetermined. In the present study, we reported that the loss of SMAD4 endows PDAC cells the ability to drug resistance through upregulating histone lysine demethylase, Lysine-Specific Demethylase 5B (KDM5B, also known as JARID1B or PLU1). Upregulated KDM5B was found in PDAC, associated with poor prognosis and recurrence of PDAC patients. Upregulated KDM5B promotes PDAC tumor malignancy, i.e. cancer cells stemness and drug resistance in vitro and in vivo, while KDM5B knockout exerts opposite effects. Mechanistically, loss of Smad4-mediated upregulation of KDM5B promotes drug resistance through inhibiting the discs-large homolog 1 (DLG1), thereby facilitating nuclear translocation of YAP to induce de novo lipogenesis. Moreover, m6A demethylase FTO is involved in the upregulation of KDM5B by maintaining KDM5B mRNA stability. Collectively, the present study suggested FTO-mediated KDM5B stabilization in the context of loss of Smad4 activate DLG1/YAP1 pathway to promote tumorigenesis by reprogramming lipid accumulation in PDAC. Our study confirmed that the KDM5B-DLG1-YAP1 pathway axis plays a crucial role in the genesis and progression of PDAC, and KDM5B was expected to become a target for the treatment of PDAC. The schematic diagram of KDM5B-DLG1-YAP pathway axis in regulating drug resistance of PDAC to gemcitabine (GEM). In the context of SMAD4 loss PDAC cells, FTO-mediated stabilization and upregulation of KDM5B promotes drug resistance through directly targeting DLG1 to promote YAP1 translocation to nucleus to induce de novo lipogenesis (DNL).

Abstract Background The traditional surgical procedures for upper lumbar disc herniation (ULDH) usually lead to frequent complications. We aim to investigate the clinical efficacy of the unilateral biportal endoscopy (UBE) technique in treating upper lumbar disc herniation (ULDH). Methods From January 2020 to December 2021, the clinical data of 28 patients with ULDH treated with the UBE technique were collected and analyzed for surgery time under UBE, postsurgical drainage, postsurgical hospital stay, and complications. The clinical efficacy was evaluated according to the modified MacNab score, Oswestry disability index (ODI), and visual analogue scale (VAS) of low back pain and lower limb pain before the surgery; one week, one month, and three months after the surgery; and at the last follow-up. Results All patients underwent the UBE surgery successfully. The surgery time under UBE for non-fusion cases was 47.50 ± 11.84 min (monosegment) and 75.00 ± 20.66 min (two segments), while that for fusion cases was 77.50 ± 21.02 min. The postsurgical drainage for non-fusion cases was 25.00 ± 13.94 mL (monosegment) and 38.00 ± 11.83 mL (two segments), while that for fusion cases was 71.25 ± 31.72 mL. The postsurgical hospital stay was 8.28 ± 4.22 days. The follow-up time was 15.82 ± 4.54 months. The VAS score for each time period after the surgery was significantly lower (P

Abstract Background Meningiomas are common intradural extramedullary spinal tumors, which arise from arachnoid cap cells in the leptomeninges surrounding the brain or spinal cord. Sensory and motor dysfunction as well as pain were the most common presenting symptoms. Surgical resection remains the primary treatment for spinal intradural extramedullary meningiomas. Traditionally, spinal meningiomas excision requires longer skin incision, bilateral subperiosteal muscle stripping, and total laminectomy. We report a new technique for the treatment of thoracic spinal intradural extramedullary meningioma, which involves the use of unilateral biportal endoscopy (UBE) technique to resect the tumor, and reviewed and analyzed relevant literature. Case presentation A 69-year-old female patient presented with back pain accompanied by slowly progressive lower limb paresis, and severe pain in the right lower limb. Magnetic resonance imaging suggests a thoracic spinal intradural extramedullary meningioma. She underwent meningioma resection using UBE technique with complete recovery at the follow-up examination 3 months after surgery. Conclusion This case confirmed the UBE technique can be a good choice for resection of spinal intradural extramedullary meningioma.

Abstract Tuning CO2 hydrogenation product distribution to obtain high-selectivity target products is of great significance. However, due to the imprecise regulation of chain propagation and hydrogenation reactions, the oriented synthesis of a single product is challenging. Herein, we report an approach to controlling multiple sites with graphene fence engineering that enables direct conversion of CO2/H2 mixtures into different types of hydrocarbons. Fe-Co active sites on the graphene fence surface present 50.1% light olefin selectivity, while the spatial Fe-Co nanoparticles separated by graphene fences achieve liquefied petroleum gas of 43.6%. With the assistance of graphene fences, iron carbides and metallic cobalt can efficiently regulate C-C coupling and olefin secondary hydrogenation reactions to achieve product-selective switching between light olefins and liquefied petroleum gas. Furthermore, it also creates a precedent for CO2 direct hydrogenation to liquefied petroleum gas via a Fischer-Tropsch pathway with the highest space-time yields compared to other reported composite catalysts.

Synthetic aperture LiDAR can generate point cloud data, which is widely used in 3-D scene reconstruction. However, existing point cloud object recognition methods are vulnerable to adversarial attacks, and such attacks are difficult to transfer to the physical world. Even if adversarial perturbations are added to physical objects, they are easily detectable by other sensors. Our proposed method includes two modules, R-D and D-R, which generate more concealed adversarial point cloud samples by modifying digital and physical features. The R-D module maps real-world entities to point cloud data in the digital world and generates adversarial samples by modifying signal amplitude values. The D-R module constructs adversarial objects by modifying the surface diffuse reflectance of the target object based on ray tracing and correspondences between digital and physical features. Our method is evaluated through experiments on attack effectiveness, robustness after subsampling and transferability, demonstrating its effectiveness, and achieving new state-of-the-art performance.

In American men, the incidence of prostate cancer (PC) is the highest among all types of cancer, making it the second leading cause of mortality associated with cancer. For advanced or metastatic PC, antiandrogen therapies are standard treatment options. The administration of these treatments unfortunately carries the potential risk of inducing neuroendocrine prostate cancer (NEPC). Neuroendocrine differentiation (NED) serves as a crucial indicator of prostate cancer development, encompassing various factors such as phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR), Yes-associated protein 1 (YAP1), AMP-activated protein kinase (AMPK), miRNA. The processes of autophagy and ferroptosis (an iron-dependent form of programmed cell death) play pivotal roles in the regulation of various types of cancers. Clinical trials and preclinical investigations have been conducted on many signaling pathways during the development of NEPC, with the deepening of research, autophagy and ferroptosis appear to be the potential target for regulating NEPC. Due to the dual nature of autophagy and ferroptosis in cancer, gaining a deeper understanding of the developmental programs associated with achieving autophagy and ferroptosis may enhance risk stratification and treatment efficacy for patients with NEPC.

Abstract Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.

BackgroundIn 2020, the Coronavirus disease (COVID-19) triggered the latest wave of anti-Asian discrimination. During the first year of the pandemic, symptoms of depression and anxiety increased seven-fold within Asian Pacific Islander Desi American (APIDA) communities. Among this population, APIDA college students were at particularly high risk for mental health challenges due to COVID-19-related racial discrimination. This study examined the association between COVID-19-related racial discrimination and the mental health of APIDA college students, conceptualizing ethnic identity as a moderator in the association.MethodsSecondary analysis was conducted on data from 2,559 APIDA college students aged 18 to 29 who participated in the Fall and Winter/Spring Cohorts of the 2020-2021 Healthy Minds Study (HMS), a non-probability web-based survey administered to students in higher education in the United States. Descriptive statistics, comparative analysis (e.g., Chi-square and t-test), and multivariable linear regression were conducted using STATA 17.1 (StataCorp LLC, College Station, TX). Survey weights were applied in all analyses.ResultsThere were significant positive associations between COVID-19-related racial discrimination and symptoms of depression (b = 2.15, p < 0.001) and anxiety (b = 1.81, p < 0.001) among the overall sample. Furthermore, a greater sense of ethnic identity was associated with lower symptoms of depression (b = -0.15, p< 0.001) among the overall sample. Finally, ethnic identity buffered the association between COVID-19-related racial discrimination and symptoms of anxiety among East Asian students and symptoms of both depression and anxiety among Native Hawaiian and Pacific Islander students. In contrast, ethnic identity intensified the association between COVID-19-related racial discrimination and symptoms of depression among Filipino students.ConclusionsThe research found that COVID-19-related racial discrimination was associated with increased symptoms of depression and anxiety among the full sample of APIDA college students during the first year of the pandemic. Additionally, higher levels of ethnic identity were associated with decreased depression among the entire group. The striking results on the moderating role of ethnic identity among subgroups call for further research on the ethnic identity development of APIDA college students, to help mitigate the effects of racial discrimination within a variety of systemic, complex, and dynamic sociocultural contexts.

Summary: The development of castration-resistant prostate cancer (CRPC) is driven by intricate genetic and epigenetic mechanisms. Traf2- and Nck-interacting kinase (TNIK) has been reported as a serine/threonine kinase associated with tumor cell proliferation or unfavorable cancer behavior. The microarray approach revealed a substantial upregulation of TNIK expression levels, enabling us to investigate the functional behaviors of the TNIK gene in CRPC. Specifically, we discovered that AR suppresses TNIK gene transcription in LNCaP and C4-2 cells by forming a complex with H3K27me3. Following the reduction of AR levels induced by androgen deprivation therapy (ADT), TNIK is recruited to activate EGFR signaling through phosphorylation in C4-2 cells, thereby promoting CRPC progression. Our findings unveil a regulatory role of AR as a repressor for TNIK while also highlighting how TNIK activates the EGFR pathway via phosphorylation to drive CRPC progression. Consequently, targeting TNIK may represent an appealing therapeutic strategy for CRPC.

Abstract Prostate cancer is a major disease that threatens men’s health. Its rapid progression, easy metastasis, and late castration resistance have brought obstacles to treatment. It is necessary to find new effective anticancer methods. Ferroptosis is a novel iron-dependent programmed cell death that plays a role in various cancers. Understanding how ferroptosis is regulated in prostate cancer will help us to use it as a new way to kill cancer cells. In this review, we summarize the regulation and role of ferroptosis in prostate cancer and the relationship with AR from the perspective of metabolism and molecular pathways. We also discuss the feasibility of ferroptosis in prostate cancer treatment and describe current limitations and prospects, providing a reference for future research and clinical application of ferroptosis.

The emergence of castration-resistant prostate cancer (CRPC) following androgen deprivation therapy (ADT) is associated with increased malignancy and limited treatment options. This study aims to investigate potential connections between immune cell infiltration and inflammatory cytokines with the YAP1/AR/PSA axis by exploring their interactions with autophagy. Our research reveals heightened levels of Yes-associated protein 1 (YAP1) expression in CRPC tissues compared with tissues from androgen-dependent prostate cancer (ADPC) and benign prostate hyperplasia (BPH). Additionally, a correlation was observed between YAP1 and PSA expressions in CRPC tissues, suggesting that YAP1 may exert a regulatory influence on PSA expression within CRPC. Enhanced YAP1 expression in C4-2 cells resulted in the upregulation of androgen receptor (AR) nuclear translocation and intracellular prostate-specific antigen (PSA) levels. Conversely, the suppression of YAP1 led to a decrease in PSA expression, suggesting that YAP1 may positively regulate the PSA in castration-resistant prostate cancer (CRPC) by facilitating AR nuclear import. The modulation of the autophagy activity exerts a significant impact on the expression levels of YAP1, the AR, and the PSA. Moreover, recent advancements in immunity and inflammation studies present promising avenues for potential therapies targeting prostate cancer (PC).

BackgroundAs a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.MethodsFirst, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.ResultsEight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.ConclusionsThis study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.

Abstract A common stage of advanced prostate cancer is castration-resistant prostate cancer (CRPC), greater understanding of which is required in order to address and solve the clinically difficult challenge. Cathepsin K (CTSK) is a cysteine protease that usually has a strong activity of degrading extracellular matrix and is related to osteoclast-mediated bone destruction. However, the mechanism of CTSK-regulation in CRPC is still unclear to us. The current study aimed to analyze the expression of differentially expressed genes (DEGs) in patient samples (from localized PC and CRPC). Interestingly, we found that CTSK to be significantly up-regulated in CRPC. Through further signal pathway enrichment analysis, we found that the IL-17 signaling pathway to be highly correlated with CTSK. The oncogenic functions of CTSK and IL-17 in CRPC were proven by a series of in vivo and in vitro experiments. Possible downstream molecules of CTSK were investigated, which could serve as control elements to regulate the expression of EMT, thereby facilitating the metastasis and excessive proliferation of PC cells. Expression of CTSK was related to high concentration of M2 tumor-associated macrophages (TAMs) M2 in CRPC. A CTSK-mediated feedback circuit between TAMs and CRPC tissues was indicated in the process of transfer, proving the possibility of CTSK could be use as an available therapeutic target for CRPC.

Abstract Particle image velocimetry (PIV) is an essential method in experimental fluid dynamics. In recent years, the development of deep learning‐based methods has inspired new approaches to tackle the PIV problem, which considerably improves the accuracy of PIV. However, the supervised learning of PIV is driven by large volumes of data with ground truth information. Therefore, the authors consider unsupervised PIV methods. There has been some work on unsupervised PIV, but they are not nearly as effective as supervised learning PIV. The authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical constraints. In this paper, the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint, which obtains better performance than other unsupervised PIV methods. The authors compare some classical PIV methods and some deep learning methods, such as LiteFlowNet, LiteFlowNet‐en, and UnLiteFlowNet with the authors’ model on the synthetic dataset. Besides, the authors contrast the results of LiteFlowNet, UnLiteFlowNet and the authors’ model on experimental particle images. As a result, the authors’ model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.

The transmission spectrum of a narrow-band interference filter is crucial and highly influenced by factors such as the temperature and angle, thus requiring precise and online measurements. The traditional method of measuring the transmission spectrum of an interference filter involves the use of a spectrometer, but the accuracy of this method is limited. Moreover, placing a narrow-band interference filter inside a spectrometer hinders real-time online measurements. To address this issue, there is demand for high-precision online spectral testing methods. In response to this demand, we propose and experimentally validate a fine spectral characterization method for narrow-band interference filters. This method uses a narrow-linewidth tunable laser, achieving a spectral resolution in the MHz range for online testing. Two types of narrow-band interference filters were tested using the constructed laser spectroscopy experimental system, obtaining a transmission spectrum with a spectral resolution of 318 MHz. In comparison to spectrometer-based methods, our proposed method demonstrates higher spectral accuracy, enables online measurements, and provides more accurate measurements for special spectral interference filters. This approach has significant application value and promising development prospects.

The components of water balance (WBC) that involve precipitation, evapotranspiration, runoff, irrigation, and groundwater recharge are critical for understanding the hydrological cycle and water management of resources in semi-arid and arid areas. This paper assesses temporal and spatial distributions of surface runoff, actual evapotranspiration, and groundwater recharge upstream of the New Assiut Barrage (NAB) in the Nile Valley, Upper Egypt, using the WetSpass-M model for the period 2012–2020. Moreover, this study evaluates the effect of land cover/land use (LULC) alterations in the study period on the WBC of the NAB. The data provided as input for the WetSpass-M model in the structure of raster maps using the Arc-GIS tool. Monthly meteorological factors (e.g., temperature, rainfall, and wind speed), a digital elevation model (DEM), slope, land cover, irrigation cover, a soil map, and depth to groundwater are included. The long-term temporal and spatial mean monthly irrigation and precipitation (127 mm) is distributed as 49% (62 mm) actual evapotranspiration, 15% (19 mm) groundwater recharge, and 36% (46 mm) surface runoff. The replacement of cropland by built-up areas was recognized as the primary factor responsible for the major decrease in groundwater, an increase in evapotranspiration and an increase in surface runoff between LCLU in 2012 and 2020. The integration of the WetSpass model with GIS has shown its effectiveness as a powerful approach for assessing WBC. Results were more accurate and reliable when hydrological modeling and spatial analysis were combined. The results of this research can help make well-informed decisions about land use planning and sustainable management of water resources in the upstream area of the NAB.

BackgroundTelehealth has gained substantial attention during the COVID-19 pandemic, and reimbursement policies in health care settings have increased access to remote modes of care delivery. Telehealth has the potential to mitigate care concerns for people living with dementia and their family caregivers. There is a paucity of knowledge on the performance of telehealth services and user experiences, especially among caregiving dyads during the pandemic. ObjectiveThis study aims to describe the implementation, effectiveness, user experience, and barriers to accessing and using telehealth services for people living with dementia and their caregivers during the COVID-19 pandemic. MethodsFollowing the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, we searched 7 databases (PubMed, PsycINFO, AgeLine, CINAHL, Social Services Abstracts, Web of Science, and Scopus) and a web-based search engine (Google Scholar). The inclusion criteria for peer-reviewed English publications from March 2020 to August 2022 consisted of studies related to telehealth services for people living with dementia and their family caregivers and studies conducted during the COVID-19 pandemic. ResultsA total of 24 articles (10 quantitative and 14 qualitative studies) from 10 different countries were included. The major findings of the reviewed articles were extracted and organized into the following 4 themes: study design characteristics—strategies were adopted to improve the accessibility and experience of people living with dementia-caregiver dyads; efficacy outcomes of telehealth services—robust evidence is lacking on the comparative effectiveness of in-person services; perceived experiences of people living with dementia and caregivers—most reviewed studies reported positive experiences of using telehealth services and perceived personal and social benefits from their participants; and barriers to accessing and using telehealth services—several barriers related to individuals, infrastructure, and telehealth environments were identified. ConclusionsAlthough evidence of its effectiveness is still limited, telehealth is widely accepted as a viable alternative to in-person care for high-risk groups, such as people living with dementia and their caregivers. Future research should include expanding digital access for those with limited resources and low technology literacy, adopting randomized controlled trial designs to establish the comparative effectiveness of different modes of service delivery, and increasing the sample diversity.

IntroductionDementia is characterized by significant declines in cognitive, physical, social, and behavioral functioning, and includes multiple subtypes that differ in etiology. There is limited evidence of the influence of psychiatric and substance use history on the risk of dementia subtypes among older underrepresented racial/ethnic minorities in the United States. Our study explored the role of psychiatric and substance use history on the risk of etiology-specific dementias: Alzheimer’s disease (AD) and vascular dementia (VaD), in the context of a racially and ethnically diverse sample based on national data.MethodsWe conducted secondary data analyses based on the National Alzheimer’s Coordinating Center Uniform Data Set (N = 17,592) which is comprised a large, racially, and ethnically diverse cohort of adult research participants in the network of US Alzheimer Disease Research Centers (ADRCs). From 2005 to 2019, participants were assessed for history of five psychiatric and substance use disorders (depression, traumatic brain injury, other psychiatric disorders, alcohol use, and other substance use). Cox proportional hazard models were used to examine the influence of psychiatric and substance use history on the risk of AD and VaD subtypes, and the interactions between psychiatric and substance use history and race/ethnicity with adjustment for demographic and health-related factors.ResultsIn addition to other substance use, having any one type of psychiatric and substance use history increased the risk of developing AD by 22–51% and VaD by 22–53%. The risk of other psychiatric disorders on AD and VaD risk varied by race/ethnicity. For non-Hispanic White people, history of other psychiatric disorders increased AD risk by 27%, and VaD risk by 116%. For African Americans, AD risk increased by 28% and VaD risk increased by 108% when other psychiatric disorder history was present.ConclusionThe findings indicate that having psychiatric and substance use history increases the risk of developing AD and VaD in later life. Preventing the onset and recurrence of such disorders may prevent or delay the onset of AD and VaD dementia subtypes. Prevention efforts should pay particular attention to non-Hispanic White and African American older adults who have history of other psychiatric disorders.Future research should address diagnostic shortcomings in the measurement of such disorders in ADRCs, especially with regard to diverse racial and ethnic groups.

In recent years, as AI technology has advanced, online monitoring of dams has garnered increasing interest. In addition, surrogate model technology is a crucial component of online monitoring. As a result, developing a high-quality surrogate model has become one of the pillars of dam online monitoring. This work proposes a local radial basis function based on sensitivity modification to address the deficiencies of the current radial basis function. In addition, a benchmark function is utilized to validate the method’s viability. Comparisons with BP neural network and RBF demonstrate the usefulness of the proposed strategy. The analysis demonstrates that the proposed strategy for constructing a surrogate model of the dam’s structural behavior is possible and accurate. This paper aims to establish a high-quality surrogate model to provide technical support for dam online monitoring.

Abstract Lung cancer is the malignancy most commonly seen worldwide. Emerging evidences indicated that lncRNAs may serve as a prognosis marker and play important role in NSCLC tumor biology. In this work, we analyzed the prognosis value of RP11-10A14.5 using TCGA and GEPIA database and expression profiles using PCR and FISH assay. The biological roles of RP11-10A14.5 in cell growth and invasion were determined by in vitro and in vivo experiments. Expression of RP11-10A14.5 is correlated with increased clinical stage and poor survival prognosis. In vitro experiments revealed that RP11-10A14.5 was widely expressed in lung cancer cell lines and mainly distributed in the cytoplasm and enhanced the growth, invasion and migration ability of NSCLC cell lines. Immunofluorescence assay suggested that RP11-10A14.5 may promote EMT by downregulating E-cadherin and upregulating N-cadherin and Vimentin. Flow cytometry results suggested that RP11-10A14.5 did not significantly affect cell cycle function, but could significantly inhibit apoptosis which may further enhance metastasis cell survival. In conclusion, RP11-10A14.5 is associated with clinical stage and poor survival outcome, may serve as a diagnosis and prognosis predictor for LUAD. Further, RP11-10A14.5 could promote LUAD cell growth and metastasis.

Abstract Background Lung adenocarcinoma (LUAD) is the most prevalent non-small cell lung cancer (NSCLC). Patients with LUAD have a poor 5-year survival rate. The use of immune checkpoint inhibitors (ICIs) for the treatment of LUAD has been on the rise in the past decade. This study explored the prognostic role of butyrophilin-like 9 (BTNL9) in LUAD. Methods Gene expression profile of buytrophilins (BTNs) was determined using the GEPIA database. The effect of BTNL9 on the survival of LUAD patients was assessed using Kaplan-Meier plotter and OncoLnc. Correlation between BTNL9 expression and tumor-infiltrating immune cells (TILs) was explored using TIMER and GEPIA databases. Further, the relationship between BTNL9 expression and drug response was evaluated using CARE. Besides, construction and evaluation of nomogram based on BTNL9 expression and TNM stage. Results BTNL9 expression was downregulated in LUAD and was associated with a poor probability of 1, 3, 5-years overall survival (OS). In addition, BTNL9 expression was regulated at epigenetic and post-transcriptional modification levels. Moreover, BTNL9 expression was significantly positively correlated with ImmuneScore and ESTIMATEScore. Furthermore, BTNL9 expression was positively associated with infiltration levels of B cells, CD4+ T cells, and macrophages. Kaplan-Meier analysis showed that BTNL9 expression in B cells and dendritic cells (DCs) was significantly associated with OS. BTNL9 expression was significantly positively correlated with CARE scores. Conclusions These findings show that BTNL9 is a potential prognostic biomarker for LUAD. Low BTNL9 expression levels associated with low infiltration levels of naïve B cells, and DCs in the tumor microenvironment are unfavorable for OS in LUAD patients.

Objective: Neuroendocrine prostate cancer (NEPC) is an aggressive variant of prostate cancer (PC) that may arise de novo or in patients previously treated with hormonal therapies for prostate adenocarcinoma as a mechanism of resistance. In our investigation, there appeared to be a strong correlation between neuroendocrine differentiation prostate cancer (NEDPC) and NEPC. The objectives of this study included exploring whether NEDPC is an intermediate stage in the progression of high-risk prostate cancer (HRPC) to NEPC and identifying risk factors and new targets associated with survival in the treatment of NEPC.Methods: The selected prostate cancer patients were progressed to high-risk and characterized by neuroendocrine. We collected the clinical data and characteristics of patients with three types of cancer: the incidence of metastasis, site and time of metastasis, recurrence rate, related treatment methods, etc. The similarity and differences of the three groups were compared through experiment and database.Results: By analyzing the clinical data and immunohistochemical results, we found that there seems to be a clinical feature of neuroendocrine differentiation (NED) status in between when patients progress from PC to NEPC. Finding novel treatment targets would therefore be beneficial by taking into account NEDPC as the stage of PC progression prior to NEPC. The metastasis-free survival curve and the immunohistochemical results are informing us that NEDPC can be a pre-state for diagnosing NEPC.Conclusion: NEPC is a late PC symptom that is frequently disregarded and has a bad prognosis. Finding novel treatment targets would therefore be beneficial by taking into account NEDPC as the stage of PC progression prior to NEPC.

Semantic segmentation is increasingly being applied on mobile devices due to advancements in mobile chipsets, particularly in low-power consumption scenarios. However, the lightweight design of mobile devices poses limitations on the receptive field, which is crucial for dense prediction problems. Existing approaches have attempted to balance lightweight designs and high accuracy by downsampling features in the backbone. However, this downsampling may result in the loss of local details at each network stage. To address this challenge, this paper presents a novel solution in the form of a compact and efficient convolutional neural network (CNN) for real-time applications: our proposed model, local spatial perception convolution (LSPConv). Furthermore, the effectiveness of our architecture is demonstrated on the Cityscapes dataset. The results show that our model achieves an impressive balance between accuracy and inference speed. Specifically, our LightSeg, which does not rely on ImageNet pretraining, achieves an mIoU of 76.1 at a speed of 61 FPS on the Cityscapes validation set, utilizing an RTX 2080Ti GPU with mixed precision. Additionally, it achieves a speed of 115.7 FPS on the Jetson NX with int8 precision.

BackgroundParkinson’s disease (PD) is the second most common progressive neurodegenerative disorder and the leading cause of disability in the daily activities. In the management of PD, accurate and specific biomarkers in blood for the early diagnosis of PD are urgently needed. DNA methylation is one of the main epigenetic mechanisms and associated with the gene expression and disease initiation of PD. We aimed to construct a methylation signature for the diagnosis of PD patients, and explore the potential value of DNA methylation in therapeutic options.Materials and methodsWhole blood DNA methylation and gene expression data of PD patients as well as healthy controls were extracted from Gene Expression Omnibus database. Next, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between PD patients and healthy controls were identified. Least absolute shrinkage and selection operator cox regression analysis was carried out to construct a diagnostic signature based on the overlapped genes. And, the receiver operating characteristic (ROC) curves were drawn and the area under the curve (AUC) was used to assess the diagnostic performance of the signature in both the training and testing datasets. Finally, gene ontology and gene set enrichment analysis were subsequently carried out to explore the underlying mechanisms.ResultsWe obtained a total of 9,596 DMGs, 1,058 DEGs, and 237 overlapped genes in the whole blood between PD patients and healthy controls. Eight methylation-driven genes (HIST1H4L, CDC42EP3, KIT, GNLY, SLC22A1, GCM1, INO80B, and ARHGAP26) were identified to construct the gene expression signature. The AUCs in predicting PD patients were 0.84 and 0.76 in training dataset and testing dataset, respectively. Additionally, eight methylation-altered CpGs were also identified to construct the CpGs signature which showed a similarly robust diagnostic capability, with AUCs of 0.8 and 0.73 in training dataset and testing dataset, respectively.ConclusionWe conducted an integrated analysis of the gene expression and DNA methylation data, and constructed a methylation-driven genes signature and a methylation-altered CpGs signature to distinguish the patients with PD from healthy controls. Both of them had a robust prediction power and provide a new insight into personalized diagnostic and therapeutic strategies for PD.

BackgroundAcute myeloid leukemia (AML) is a highly aggressive hematological malignancy characterized by extensive genetic abnormalities that might affect the prognosis and provide potential drug targets for treatment. Reprogramming of lipid metabolism plays important roles in tumorigenesis and progression and has been newly recognized a new hallmark of malignancy, and some related molecules in the signal pathways could be prognostic biomarkers and potential therapeutic targets for cancer treatment. However, the clinical value of lipid metabolism reprogramming in AML has not been systematically explored. In this study, we aim to explore the clinical value of lipid metabolism reprogramming and develop a prognostic risk signature for AML.MethodsWe implemented univariate Cox regression analysis to identify the prognosis-related lipid metabolism genes, and then performed LASSO analysis to develop the risk signature with six lipid metabolism-related genes (LDLRAP1, PNPLA6, DGKA, PLA2G4A, CBR1, and EBP). The risk scores of samples were calculated and divided into low- and high-risk groups by the median risk score.ResultsSurvival analysis showed the high-risk group hold the significantly poorer outcomes than the low-risk group. The signature was validated in the GEO datasets and displayed a robust prognostic value in the stratification analysis. Multivariate analysis revealed the signature was an independent prognostic factor for AML patients and could serve as a potential prognostic biomarker in clinical evaluation. Furthermore, the risk signature was also found to be closely related to immune landscape and immunotherapy response in AML.ConclusionsOverall, we conducted a comprehensive analysis of lipid metabolism in AML and constructed a risk signature with six genes related to lipid metabolism for the malignancy, prognosis, and immune landscape of AML, and our study might contribute to better understanding in the use of metabolites and metabolic pathways as the potential prognostic biomarkers and therapeutic targets for AML.

Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is the main cause of cancer-related death worldwide. Reprogramming of glucose metabolism plays a crucial role in tumorigenesis and progression. However, the regulation of glucose metabolism is still being explored in LUAD. Determining the underlying clinical value of glucose metabolism will contribute in increasing clinical interventions. Our study aimed to conduct a comprehensive analysis of the landscape of glucose metabolism-related genes in LUAD and develop a prognostic risk signature.Methods: We extracted the RNA-seq data and relevant clinical variants from The Cancer Genome Atlas (TCGA) database and identified glucose metabolism-related genes associated with the outcome by correlation analysis. To generate a prognostic signature, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed.Results: Finally, ten genes with expression status were identified to generate the risk signature, including FBP2, ADH6, DHDH, PRKCB, INPP5J, ABAT, HK2, GNPNAT1, PLCB3, and ACAT2. Survival analysis indicated that the patients in the high-risk group had a worse survival than those in the low-risk group, which is consistent with the results in validated cohorts. And receiver operating characteristic (ROC) curve analysis further validated the prognostic value and predictive performance of the signature. In addition, the two risk groups had significantly different clinicopathological characteristics and immune cell infiltration status. Notably, the low-risk group is more likely to respond to immunotherapy.Conclusion: Overall, this study systematically explored the prognostic value of glucose metabolism and generated a prognostic risk signature with favorable efficacy and accuracy, which help select candidate patients and explore potential therapeutic approaches targeting the reprogrammed glucose metabolism in LUAD.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype in renal cell carcinoma with relatively poor clinical outcomes DNA damage repair genes (DDRGs) as potential biomarkers are rarely reported in predicting immunotherapy response and clinical prognosis for ccRCC.Methods: RNA-seq and clinical data of ccRCC cohort were collected form TCGA database. Univariate Cox regression and LASSO analysis were performed to construct a DDRG risk signature. Functional enrichment analysis was performed to explore latently enriched pathways associated with DDRG signature. Immune cell infiltration level was estimated using gene set enrichment analysis, and immune response of ccRCC was predicted by tumor immune dysfunction and exclusion (TIDE) algorithm. To predict 1-, 3-, and 5-years overall survival (OS), a nomogram was constructed based on independent prognostic factors, whose performance would be evaluated by calibration curve.Results: A total of 47 DNA damage repair related genes (DDRGs) with significant prognostic value were identified in the ccRCC cohort (n = 519). A DDRG risk signature comprising six DRRGs (MSH3, RAD54L, RAD50, EME1, UNG, and NEIL3) were constructed by the LASSO analysis. ccRCC patients were then divided into low- and high-risk groups based on the risk score. Survival analysis revealed that patients in high-risk groups exhibited significantly poorer OS and progression-free survival (PFS), as was confirmed by the testing dataset. Functional enrichment analysis indicated that differentially expressed genes (DEGs) between high- and low-risk groups were mainly associated with immune-related biological processes in ccRCC, among which the immunodeficiency pathway was significantly enriched in the high-risk group. Though the risk signature was significantly correlated with the immune cell infiltration, PD-1 and PD-L1 were less expressed in the DDRG signature, which might indicate the poor response to immunotherapy in the high-risk group. Furthermore, the Cox regression analysis indicated that the DDRG signature can be served as an independent prognostic predictor when compared to clinical characteristics. Based on the independent prognostic predictors, we constructed a nomogram with excellent predictive ability in OS prediction for ccRCC patients.Conclusion: We developed a reliable DDRG risk signature that can independently predict the OS and PFS of ccRCC, which is also promising for predicting immunotherapeutic responses in ccRCC patients.

Background: Acute myeloid leukemia (AML) remains the most common type of hematopoietic malignancy in adults and has an unfavorable outcome. Herein, we aimed to construct an N6-methylandenosine (m6A)-related long noncoding RNAs (lncRNAs) signature to accurately predict the prognosis of patients with AML using the data downloaded from The Cancer Genome Atlas (TCGA) database.Methods: The RNA-seq and clinical data were obtained from the TCGA AML cohort. First, Pearson correlation analysis was performed to identify the m6A-related lncRNAs. Next, univariate Cox regression analysis was used to determine the candidate lncRNAs with prognostic value. Then, feature selection was carried out by Least absolute shrinkage and selection operator (LASSO) analysis, and seven eligible m6A-related lncRNAs were included to construct the prognostic risk signature. Kaplan–Meier and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive capacity of the risk signature both in the training and testing datasets. A nomogram was used to predict 1-year, 2-year, and 3-year overall survival (OS) of AML patients. Next, the expression levels of lncRNAs in the signature were validated in AML samples by qRT-PCR. Functional enrichment analyses were carried out to identify probable biological processes and cellular pathways. The ceRNA network was developed to explore the downstream targets and mechanisms of m6A-related lncRNAs in AML.Results: Seven m6A-related lncRNAs were identified as a prognostic signature. The low-risk group hold significantly prolonged OS. The nomogram showed excellent accuracy of the signature for predicting 1-year, 2-year and 3-year OS (AUC = 0.769, 0.820, and 0.800, respectively). Moreover, the risk scores were significantly correlated with enrichment in cancer hallmark- and malignancy-related pathways and immunotherapy response in AML patients.Conclusion: We developed and validated a novel risk signature with m6A-related lncRNAs which could predict prognosis accurately and reflect the immunotherapy response in AML patients.

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed.Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package “limma,” and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package “clusterProfiler.” Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves.Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations.Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

Background: Bladder cancer (BLCA) is a common malignant tumor of the genitourinary system, and there is a lack of specific, reliable, and non-invasive tumor biomarker tests for diagnosis and prognosis evaluation. Homeobox genes play a vital role in BLCA tumorigenesis and development, but few studies have focused on the prognostic value of homeobox genes in BLCA. In this study, we aim to develop a prognostic signature associated with the homeobox gene family for BLCA.Methods: The RNA sequencing data, clinical data, and probe annotation files of BLCA patients were downloaded from the Gene Expression Omnibus database and the University of California, Santa Cruz (UCSC), Xena Browser. First, differentially expressed homeobox gene screening between tumor and normal samples was performed using the “limma” and robust rank aggregation (RRA) methods. The mutation data were obtained with the “TCGAmutation” package and visualized with the “maftools” package. Kaplan–Meier curves were plotted with the “survminer” package. Then, a signature was constructed by logistic regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using “clusterProfiler.” Furthermore, the infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, the performance of the signature was evaluated by receiver-operating characteristic (ROC) curve and calibration curve analyses.Results: Six genes were selected to construct this prognostic model: TSHZ3, ZFHX4, ZEB2, MEIS1, ISL1, and HOXC4. We divided the BLCA cohort into high- and low-risk groups based on the median risk score calculated with the novel signature. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The infiltration levels of almost all immune cells were significantly higher in the high-risk group than in the low-risk group. The average risk score for the group that responded to immunotherapy was significantly lower than that of the group that did not.Conclusion: We constructed a risk prediction signature with six homeobox genes, which showed good accuracy and consistency in predicting the patient’s prognosis and response to immunotherapy. Therefore, this signature can be a potential biomarker and treatment target for BLCA patients.

Abstract Background Following trauma, the elbow is the most susceptible to restricted motion among all joints. Open arthrolysis is often performed for post-traumatic elbow stiffness if that stiffness does not improve with non-operative management. However, the optimal timing for performing an open arthrolysis remains controversial. The purpose of this study was to compare the outcome (elbow motion and function) and the rate of complications among patients who had undergone early, median and late release procedures to establish an optimal time interval following the injury, after which, an effective open arthrolysis can be performed. Methods In this retrospective cohort study, we included total 133 patients, who had undergone open arthrolysis for post-traumatic elbow stiffness. The subjects were divided into 3 groups, with 31 patients in the early release group (arthrolysis performed at 6–10 months after injury), 78 patients in the median release group (at 11–20 months), and 24 patients in the late release group (at > 20 months). The release procedure in all patients was performed by the same surgeon, using the same technique. The general data, functional performance, and complications, if any, were retrospectively documented for all patients and statistically analysed. Results The demographic data and disease characteristics of all patients were comparable at baseline. Postoperatively, no significant differences were found among the three groups with respect to the range of motion (p = 0.067), Mayo Elbow Performance Score (p = 0.350) and its ratings (p = 0.329), visual analog scale score for pain (p = 0.227), Dellon classification for ulnar nerve symptoms (p = 0.497), and each discrete complication (all p values > 0.05). Conclusions At the final follow-up, our results showed no significant difference in the postoperative elbow motion capacities, functional scores and the rates of complications among patients who had undergone an early, median, and late release. Therefore, we have recommended that an early arthrolysis would be preferable due to its multiple advantages, and the conventionally observed interval of > 1 year after the injury, could be shortened. Level of evidence Level III; Retrospective Cohort Design; Therapeutic Study.

Tomographic particle image velocimetry (Tomo-PIV) has been successfully applied in measuring three-dimensional (3D) flow field in recent years. Such technology highly relies on the reconstruction technique which provides the spatial particle distribution by using images from multiple cameras at different viewing angles. As the most popular reconstruction method, the multiplicative algebraic reconstruction technique (MART) has advantages in high computational speed and high accuracy for low particle seeding reconstruction. However, the accuracy is not satisfactory in the case of dense particle distributions to be reconstructed. To overcome this problem, a symmetric encode–decoder fully convolutional network is proposed in this paper to improve the reconstruction quality of MART. The input of the neural network is the particle field reconstructed by the MART approach, while the output is the regenerated image with the same resolution. Numerical evaluations indicate that those blurred or irregular particles can be significantly refined by the trained neural network. Most of the ghost particles can also be removed by this filtering method. The reconstruction accuracy can be improved by more than 10% without increasing the computational cost. Experimental evaluations indicate that the trained neural network can also provide similar satisfactory reconstruction and improved velocity fields.

In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach.

Objectives: To explore the transfer effects of cognitive training on working memory among older Chinese adults with mild cognitive impairment (MCI).Methods: Sixty-two MCI participants aged more than 60 years old were recruited by holding recruitment sessions in communities in China [33 for cognitive training, and 29 for mental leisure activities (MLA) control]. Cognitive functions, including working memory, execution function, reasoning ability, verbal ability, ability of daily living, were measured at three time-points (baseline, post-training and 3 months after training).Results: Compared to the MLA control, the cognitive training group showed significant effects in both the trained (working memory) and untrained (execution function and ability of daily living) domains. The effects of cognitive training on overall cognitive function, working memory and daily life ability of daily living of MCI could be maintained for at least 3 months, even without the cognitive training. Besides, complete mediating effects of cognitive training were found in executive function through working memory and working memory in ability of daily living though executive function, which suggests the presence of transfer effect of cognitive training.Conclusions: The present study supported that cognitive training could effectively improve working memory in elders with MCI. The training effects on working memory could transfer to other untrained areas (such as executive function), which also improved the comprehensive ability (ability of daily living). And the effects of training could largely persist for 3 months.

Flexible pressure sensors with a high sensitivity in the lower zone of a subtle-pressure regime has shown great potential in the fields of electronic skin, human−computer interaction, wearable devices, intelligent prosthesis, and medical health. Adding microstructures on the dielectric layer on a capacitive pressure sensor has become a common and effective approach to enhance the performance of flexible pressure sensors. Here, we propose a method to further dramatically increase the sensitivity by adding elastic pyramidal microstructures on one side of the electrode and using a thin layer of a dielectric in a capacitive sensor. The sensitivity of the proposed device has been improved from 3.1 to 70.6 kPa−1 compared to capacitive sensors having pyramidal microstructures in the same dimension on the dielectric layer. Moreover, a detection limit of 1 Pa was achieved. The finite element analysis performed based on electromechanical sequential coupling simulation for hyperelastic materials indicates that the microstructures on electrode are critical to achieve high sensitivity. The influence of the duty ratio of the micro-pyramids on the sensitivity of the sensor is analyzed by both simulation and experiment. The durability and robustness of the device was also demonstrated by pressure testing for 2000 cycles.

ABSTRACT Human immunodeficiency virus (HIV) infection persists despite decades of active antiretroviral therapy (ART), effectively preventing viral eradication. Treatment decreases plasma viral RNA, but viral DNA persists, mostly integrated within the genome of nucleated blood cells. Viral DNA blood levels correlate with comorbidities and the rapidity of viral rebound following treatment interruption. To date, no intervention aiming at decreasing HIV DNA levels below those attained through ART has been successful. This includes use of some integrase inhibitors either as part of ART or in treatment intensification studies. We have argued that using the integrase inhibitor dolutegravir (DTG) in similar studies may yield better results, but this remains to be studied. In treatment-experienced individuals, the most frequent substitution associated with failure with dolutegravir is R263K in integrase. R263K decreases integration both in cell-free and tissue culture assays. We investigated here how integrated DNA levels evolve over time during prolonged infections with R263K viruses. To investigate a potential defect in reverse transcription with R263K, the levels of reverse transcripts were measured by quantitative PCR. We measured HIV type 1 (HIV-1) integration in Jurkat cells over the course of 4-week infections using Alu-mediated quantitative PCR. The results show that R263K did not decrease reverse transcription. Prolonged infections with R263K mutant viruses led to less HIV-1 integrated DNA over time compared to wild-type viruses. These tissue culture results help to explain the absence of the R263K substitution in most individuals experiencing failure with DTG and support studies aiming at longitudinally measuring the levels of integrated DNA in individuals treated with this drug. IMPORTANCE Antiretroviral treatment decreases plasma viral RNA, but HIV DNA persists for decades within infected cells. Studies of nonhuman primates have suggested that reducing retroviral DNA levels might represent a path to eradication. The integrase inhibitor dolutegravir is less susceptible than any other anti-HIV drug to the emergence of resistance in treatment-naive individuals. In treatment-experienced individuals, in contrast, rare cases of treatment failure were commonly associated with emergence of an R263K integrase substitution that confers low-level resistance to dolutegravir. It is unclear why this substitution is not more common in individuals experiencing failure with dolutegravir. We report here that R263K progressively diminishes the levels of integrated HIV-1 DNA in tissue culture over multiple cycles of infection. Our results help to explain aspects of the clinical efficacy of dolutegravir and suggest that this drug may be able to reduce HIV DNA levels within infected individuals compared to other drugs.

Enterococcus faecalis is becoming an increasingly important opportunistic pathogen worldwide, especially because it can cause life-threatening nosocomial infections. Treating E. faecalis infections has become increasingly difficult because of the prevalence of multidrug-resistant E. faecalis strains. Because bacteriophages show specificity for their bacterial hosts, there has been a growth in interest in using phage therapies to combat the rising incidence of multidrug-resistant bacterial infections. In this study, we isolated a new lytic phage, EF-P29, which showed high efficiency and a broad host range against E. faecalis strains, including vancomycin-resistant strains. The EF-P29 genome contains 58,984 bp (39.97% G+C), including 101 open reading frames, and lacks known putative virulence factors, integration-related proteins or antibiotic resistance determinants. In murine experiments, the administration of a single intraperitoneal injection of EF-P29 (4 × 105 PFU) at 1 h after challenge was sufficient to protect all mice against bacteremia caused by infection with a vancomycin-resistant E. faecalis strain (2 × 109 CFU/mouse). E. faecalis colony counts were more quickly eliminated in the blood of EF-P29-protected mice than in unprotected mice. We also found that exogenous E. faecalis challenge resulted in enrichment of members of the genus Enterococcus (family Enterococcaceae) in the guts of the mice, suggesting that it can enter the gut and colonize there. The phage EF-P29 reduced the number of colonies of genus Enterococcus and alleviated the gut microbiota imbalance that was caused by E. faecalis challenge. These data indicate that the phage EF-P29 shows great potential as a therapeutic treatment for systemic VREF infection. Thus, phage therapies that are aimed at treating opportunistic pathogens are also feasible. The dose of phage should be controlled and used at the appropriate level to avoid causing imbalance in the gut microbiota.