This study presents a comprehensive DNA barcoding analysis of the family Elateridae of Türkiye, aiming to address taxonomic challenges associated with morphologically similar or cryptic species in this group. DNA barcoding was conducted on 59 different species, and for the first time, sequences of the Cytochrome Oxidase I (COI) gene were successfully obtained for 28 of those species.The COI sequences were compared against the National Center for Biotechnology Information (NCBI) and Barcode of Life Data System (BOLD) databases to assess species identification accuracy and systematic alignment. Results revealed both congruence and discrepancies between morphological and molecular classifications, with some species showing high genetic similarity despite morphological distinctions. These findings highlight the value of DNA barcoding for enhancing the resolution of taxonomic identifications and addressing systematic ambiguities within the Elateridae family. This study contributes important molecular data for Türkiye’s Elateridae species and underscores the need for ongoing molecular studies to improve species classification in this diverse beetle family.

Title: Surgical Management of Renal Duplication and Ureteropelvic Junction Obstruction in a Pediatric Patient: A Case ReportHasan Haydar a,+ ,Mouhammed Sleiaya, Lyne Barakat a , Abdulrahim Hamad Alokla a, Ahmad Buz a , Mohammed ali Abshi a , Mohammed Alqreea b , Hasan Alsmoudi a , Mohamad Yasin Lutfi c.a Faculty of Medicine, Hama University, Hama, Syrian Arab Republic.b Orthopedic surgery Department ; Faculty of medicine ; Damascus University ;Damascus ;Syria.c Hama National Hospital,Urology Department, Hama, Syrian Arab Republic.*Corresponding author: Hasan Haydar, Faculty of Medicine, Hama University, Hama, Syrian Arab Republic. email: hasanhaydar458@gmail.com+ Hasan Haydar ,Mouhammed Sleiay, Lyne Barakat , Abdulrahim Hamad Alokla, Ahmad Buz , Mohammed ali Abshi , Mohammed Alqreea , Hasan Alsmoudi , Mohamad Yasin Lutfi are co-first authors. All contributed equally in this paper.Emails & ORCID:Hasan Haydar:hasanhaydar458@gmail.comORCID : 0000-0002-7335-2096Mouhammed Sleiay:abdmouh1234mouhmouh@gmail.comORCID: 0009-0003-8887-0739Lyne Barakat :Lynebarakat1@gmail.comORCID : 0009-0005-5135-6554Abdulrahim Hamad Alokla :abdulrahimhamadalokla@gmail.comAhmad Buz :AhmadIbraheemBuz@hotmail.comMohammed ali Abshi :saemnaem11@gmail.comORCID : 0009-0005-0037-076XMohammed Alqreea :abokhaledqreea@gmail.comHasan Alsmoudi :dr.hasanalsmoudi@gmail.comORCID : 0000-0001-6121-3948Mohamad Yasin Lutfi:Doctor85yasen@gmail.comKey Clinical Message:In pediatric patients with recurrent UTIs and a history of ureteral surgery, clinicians should consider renal duplication with ureteropelvic junction obstruction. Timely imaging and surgical correction can preserve renal function and improve outcomes, emphasizing the need for comprehensive evaluation beyond routine voiding cystourethrogram.

Main Agroecological Structures (MAS) have been demonstrated to play a critical role in pollinator conservation, agroecological resilience, and the provisioning of essential ecosystem services such as pollination. The present study aims to evaluate the scientific development and understanding of MAS and their impact on beekeeping and associated biodiversity, particularly under land degradation and climate change pressures. A bibliometric analysis and systematic review of 73 peer-reviewed articles retrieved from the Web of Science (WoS) was conducted. The Tree of Science was utilised to categorise the extant literature into three fundamental perspectives: classical (roots), structural (trunk), and recent (branches and leaves). The findings indicate that spatial heterogeneity and ecological connectivity of landscapes substantially impact the stability and functionality of plant–pollinator networks. While agroforestry systems have been shown to enhance pollinator richness, they frequently fail to replicate the biodiversity levels found in native ecosystems. The review also identifies key threats to sustainability, including agricultural intensification, overuse of agrochemicals, and habitat loss. These factors are exacerbated by climate change and compromise the sustainability of apicultural systems. In response, the study presents a series of pragmatic recommendations for agroecological management and environmental policy-making, encompassing the establishment of biological corridors, the promotion of diversified agricultural practices, and the development of multifunctional landscapes that support pollinators and other beneficial species. There are critical research gaps that require attention. These include the necessity for long-term studies on pollinator dynamics in fragmented landscapes and the impact of agrochemical reduction. This work provides a robust foundation to guide climate-resilient agroforestry strategies that advance biodiversity conservation, sustainable land management, and SDG 15 targets.

Objective: To study the otoscopic, tympanometric changes and curative effect of intranasal pulmonary surfactant (PS) drops on secretory otitis media (OME) in guinea pigs. Methods: An OME model was established by injecting Haemophilus influenzae solution into the tympanic cavity. Ten animals were assigned as the normal control group, 10 as the model control group, 10 as the saline treatment group, and 11 as the PS treatment group. Changes in the tympanic membrane and middle ear pressure were recorded via otoscopy and tympanometry. Results: The tympanogram was type-A in the normal group and this changed to type-C in the model-control group and in the saline-treated group. The tympanogram changed to type-B in the PS-treated group. Statistically significant differences in middle ear pressure and peak acoustic admittance were observed among all groups except between the model control and saline treatment groups. Conclusion: The study demonstrated that administration of intranasal pulmonary surfactant drops plays a significant role in reducing effusion and correcting negative middle ear pressure in guinea pigs with otitis media with effusion.

OBJECTIVES To measure the degree of tongue tie presenting to a dedicated clinic in a tertiary paediatric university hospital, to report the variety and severity of issues encountered by carers, to measure a single surgeons practice by pre and post carer survey and to report complications. METHODS All patients who underwent tongue tie release at a single surgeons’ practice over one year were included. Tongue tie was assessed and scored by a single operator on a 4 point scale. Carers filled in a questionnaire pre and post operatively at an interval of 1 month for comparison. Students paired t test was used to analyse the difference between groups. RESULTS Fifty four patients underwent tongue tie release. The mean age at procedure was 2 months. The most common type of tongue tie was type 3 (27), followed by type 2 (19). The mean score of overall problem pre procedure was 6.92 (SD 1.92) and post procedure was 2.52 (SD 2.45), the mean difference was 4.46 points (SD 3.10). The difference in pre and post procedure scores was significant (p<0.001, 95% CI, 3.56 – 5.36). Two patients experienced mild self limited bleeding post operatively that was managed conservatively. CONCLUSION Tongue tie release is a safe and effective procedure when performed in the correct setting with the appropriate experience. The greatest effect is seen in type 2 and 3 tongue tie groups. Improvements are noted on subjective feeding scales as well as visual analogue scales.

Can face image with partially or heavily occlusion be inpainted? In this paper, it is considered as a decision-making issue. An adaptive gating boosts multi-scale self-attention face image inpainting network is proposed to address the issues; it focuses on the task of inpainting large-area occluded face images in complex backgrounds including insufficient fine-grained texture synthesis, inaccurate color inpainting, and semantic dissonance. Multi-level dilated convolution group is constructed to capture local details and long-range contextual information with the help of a dual adaptive gating mechanism which works as: (1) multi-layer convolution and batch normalization to achieve spatially adaptive feature selection, replacing the traditional fixed fusion method of residual connections; (2) multi-scale self attention mechanism explicitly models the global pixel dependency relationship, solving the problems of structural coherence and fine-grained synthesis in large-scale defect inpainting. A large number of experiment results show that this method improves the PSNR and SSIM metrics by an average of 0 .284dB and 0 .0042 on the FFHQ dataset, and improves the FID by an average of 8 .265%. Especially in scenarios with large areas of occlusion ( >50%), the FID decreases by 3 .009, significantly improving the quality of facial image inpainting on complex backgrounds. The adaptive gating boosts multi-scale self-attention work strategy can provide reference for other decision-making issues.

Background: Secondary acute myeloid leukemia (s-AML) is a subtype of acute myeloid leukemia AML that arises in the bone marrow and primarily affects white blood cells. It is associated with prior exposure to cytotoxic agents, including chemotherapy and radiotherapy. The risk of the disease increases with age and treatment intensity. Although s-AML represents 25–30% of all AML cases, its occurrence following radiotherapy is relatively rare. Diagnosis is confirmed via bone marrow biopsy, with differential diagnoses including other leukemias, lymphomas, and myelodysplastic syndromes. Prognosis is generally poor. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains the most effective curative option, though elderly patients often have limited eligibility due to comorbidities and poor performance status. Case: Five years after treatment for stage IIA rectal cancer, including radiotherapy, chemotherapy, and surgery, a 77-year-old male smoker presented with fatigue, weight loss, progressive dyspnea, headache, dizziness, blurred vision, and melena. Physical examination revealed pallor, purpura, skin crusts, and pustules. Laboratory findings showed anemia, thrombocytopenia, and circulating blasts. Bone marrow biopsy confirmed lymphoid infiltration. He was diagnosed with secondary acute myeloid leukemia (s-AML), likely therapy-related. Despite receiving three cycles of azacitidine, he died of septic shock seven months later. Conclusion: This case presents a rare and multifactorial association between prior chemotherapy, radiation therapy, and the development of therapy-related acute myeloid leukemia (t-AML). It highlights the importance of long-term hematologic surveillance in cancer survivors, particularly those exposed to alkylating agents and radiation, given their increased risk for secondary hematologic malignancies.

Objective To compare the efficacy and safety of different neoadjuvant treatment options in locally advanced head and neck squamous cell carcinoma (LA-HNSCC). Method We conducted a comprehensive literature search across four major databases (PubMed, Web of Science [WOS], Embase, and Cochrane Library) from their inception through August 2024. The primary outcome measures included objective response rate (ORR), overall survival (OS), progression-free survival (PFS), and serious adverse events (SAEs). Result This analysis included 23 studies (19 randomized controlled trials [RCTs] and 3 non-RCTs) involving 4,052 patients. Network meta-analysis (NMA) revealed the following findings: Regarding efficacy, thermochemotherapy demonstrated superior outcomes in both objective response rate (ORR) and overall survival (OS), followed by immunochemotherapy. Thermochemotherapy showed significantly better ORR compared to targeted therapy + chemotherapy, TP, PF, T, and single-agent immunotherapy ( P < 0.05). Similarly, immunochemotherapy outperformed all other therapies except thermochemotherapy in ORR (P < 0.05). For OS, both thermochemotherapy and TPF were significantly more effective than PF and TP ( P < 0.05). In progression-free survival (PFS), immunochemotherapy showed the best results, followed by targeted therapy plus chemotherapy, with immunochemotherapy being significantly superior to PF ( P < 0.05). Regarding safety, thermochemotherapy showed the poorest safety profile, followed by TPF. Specifically, T and TP demonstrated significantly better safety than thermochemotherapy ( P < 0.05). Conclusion In the neoadjuvant treatment of locally advanced head and neck squamous cell carcinoma (LA-HNSCC), both thermochemotherapy and immunochemotherapy regimens have demonstrated promising therapeutic efficacy; however, their safety profiles require further comprehensive evaluation. Register:PROSPERO CRD42024571174

Background: USP5, a deubiquitinating enzyme, is linked to various cancers. However, its relationship with immune infiltration and its prognostic significance in non–small-cell lung cancer (NSCLC) remains to be determined. Methods: USP5 expression patterns in NSCLC were analyzed using data sourced from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Functional enrichment analyses were performed to predict the role of USP5 in NSCLC development, and Hub genes were identified through a protein–protein interaction (PPI) network. Immune cell infiltration was assessed via single-sample gene set enrichment analysis, while the prognostic significance of USP5 was evaluated using the Kaplan–Meier method and Cox regression analysis. To facilitate survival rate predictions at different time points, a prognostic model was developed. Previous findings were validated using real-time PCR and in vitro functional assays in NSCLC cell lines. Results: USP5 expression was found to be markedly elevated in NSCLC tissues when compared to normal tissues. Functional enrichment analysis revealed the involvement of USP5 in regulating key pathways linked to lung adenocarcinoma development. PPI network analysis revealed several potential interactions contributing to NSCLC progression. A correlation was observed between higher USP5 levels and the reduced presence of immune cells (e.g., macrophages, CD8+T cells, NK cells, and iDCs) within the tumor microenvironment. ROC curves confirmed the prognostic value of USP5 for NSCLC. Functional studies in NSCLC cell lines confirmed the molecular effects of USP5 on NSCLC development. Conclusions: USP5 appears to be a reliable marker for diagnosing NSCLC and predicting its prognosis. Further investigation into the role of USP5 in immune responses may aid in the development of immunotherapies for NSCLC.

As organizations transition from on-premises infrastructure to cloud environments, accurately predicting and managing cloud costs becomes a critical concern. Traditional methods of cloud cost modeling and budget forecasting are often based on static rules and historical usage data, leading to inaccuracies and inefficiencies. To address these challenges, AI-assisted cloud cost modeling and budget forecasting have emerged as transformative solutions, offering enhanced precision and adaptability both before and after migration to the cloud. This paper explores the role of artificial intelligence (AI) in improving cloud cost management, with a specific focus on pre-and post-migration scenarios. The AI models discussed leverage machine learning (ML) and data analytics to analyze large sets of data, including historical consumption patterns, resource utilization, and usage trends. By predicting future cloud resource needs based on these insights, AI-driven models help organizations develop more accurate pre-migration cost estimates, enabling better decision-making in the early stages of the cloud adoption journey. In the pre-migration phase, AI-assisted tools analyze the on-premises infrastructure, identify workloads suitable for the cloud, and model cost scenarios based on cloud provider pricing structures (IaaS, PaaS, SaaS). These models also assist in optimizing resource allocation, allowing for the creation of tailored migration strategies that consider both cost-efficiency and performance. Post-migration, AI-based cost forecasting tools continue to offer significant value by providing dynamic, real-time tracking and forecasting of cloud expenses. AI-powered algorithms take into account various factors such as changing usage patterns, fluctuating demand, and pricing changes by cloud providers, delivering accurate cost predictions that help avoid budget overruns. Furthermore, AI-driven optimization strategies allow for continuous monitoring and adjustment of resources, ensuring that organizations maintain optimal cloud spending while maximizing performance. Key benefits of AI-assisted cloud cost modeling and budget forecasting include enhanced accuracy, proactive cost management, the ability to scale resources dynamically, and the reduction of unforeseen expenditures. By leveraging AI in both pre-and post-migration phases, organizations are equipped with the tools necessary to manage their cloud environments efficiently, optimize resource usage, and maintain budgetary control throughout the cloud lifecycle. This research highlights several case studies that demonstrate the practical application of AI-driven cloud cost forecasting tools and the measurable impact on cost reduction, efficiency, and resource optimization post-migration. Furthermore, the paper outlines the key challenges, including data privacy concerns, AI model transparency, and the need for continuous learning and adaptation as cloud environments evolve. Overall, AI-assisted cloud cost modeling and budget forecasting offer organizations a robust framework for achieving better financial control over their cloud environments, thereby empowering more sustainable and scalable cloud adoption strategies.

Legacy systems, while foundational to many enterprise operations, present significant challenges in the context of modern software requirements such as scalability, maintainability, and cloud-native capabilities. The process of modernizing these systems is typically labor-intensive, error-prone, and time-consuming. Recent advancements in artificial intelligence (AI) have introduced novel opportunities to automate and accelerate legacy system transformation. This paper explores the application of AI techniques to facilitate the end-to-end modernization of legacy systems, specifically targeting cloud readiness. We propose a comprehensive framework that leverages machine learning (ML), natural language processing (NLP), and knowledge-based systems to automate key aspects of legacy modernization. This includes code analysis and comprehension, dependency mapping, automated refactoring, service extraction, and cloud deployment recommendations. Legacy codebases, often written in outdated programming languages and lacking documentation, are analyzed using NLP and code embeddings to extract business logic and identify modular components suitable for transformation into microservices or serverless functions. The framework incorporates deep learning models trained on large code repositories to suggest cloud-compatible architectures and optimize deployment strategies. Reinforcement learning agents are employed to iteratively improve modernization recommendations based on performance metrics and feedback from cloud-based test environments. Additionally, the use of AI-driven static and dynamic code analysis tools enhances security and ensures compliance with modern software standards. Case studies are presented showcasing the application of the proposed AI-driven modernization process on real-world legacy systems. Results indicate significant reductions in manual effort, improved code quality, and enhanced alignment with cloud-native design principles. The paper concludes by discussing the limitations of current AI techniques, potential risks, and future research directions for fully autonomous legacy system modernization.

As organizations increasingly rely on cloud infrastructure to drive digital transformation, the need for intelligent, data-driven cloud resource planning and cost management has become more critical than ever. Predictive analytics-a discipline that leverages statistical algorithms, machine learning models, and historical data-has emerged as a powerful enabler for enhancing decision-making in cloud environments. This paper explores the application of predictive analytics in cloud resource planning and cost forecasting, highlighting its potential to transform reactive resource management into a proactive and strategic function. The abstract introduces key predictive techniques such as time series analysis, regression models, and neural networks, which are used to forecast workload demands, storage utilization, and budgetary trends. It discusses how these models can analyze vast volumes of historical usage and billing data to predict future cloud consumption with high accuracy. Through these predictions, organizations can automate capacity planning, optimize infrastructure investments, and reduce the risk of over-provisioning or underutilization. Furthermore, predictive cost forecasting helps finance and DevOps teams collaborate effectively by providing granular visibility into expected expenses, enabling better budgeting and reducing financial surprises. The abstract also addresses challenges in implementing predictive analytics, such as data quality issues, model drift, and the complexity of multi-cloud environments. Additionally, it reviews emerging trends, including the integration of AI-driven anomaly detection, real-time predictive dashboards, and FinOps alignment strategies. By enabling smarter cloud governance, predictive analytics empowers businesses to achieve cost efficiency, scalability, and performance optimization. This paper concludes that predictive analytics is not merely a tactical tool for cloud management, but a strategic asset that can deliver competitive advantage in an era of agile, cloud-first enterprises.

Semi-Direct Posterior Resin Composite Restorations: The Silicone Die Approach

This beginner’s guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant -omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype-phenotype relationships. In this paper, we introduce networks and compare network-building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED-II, and STRING. AraNet, and GeneMANIA are functional association networks, ATTED-II is a gene coexpression database, and STRING is a protein-protein interaction database. AraNet, and ATTED-II are plant-specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks for Arabidopsis thaliana and non-plant species, and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of the A. thaliana Nuclear Factor-Y (NF-Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.

A document by Yasin Ergen. Click on the document to view its contents.

”Exceptional Complete Pathological Response in Stage IIIB Non-Small Cell Lung Cancer: A Rare Outcome with Neoadjuvant Chemotherapy Alone, case report and review of the literature”Authores:Nidal Jebrini (1+2) , Husein A. Sarahneh(1) , Mohanad Jaber(1+5), Motaz Natsheh(3), Yousef Abu Asabeh(6) ,, Raghad S. Razem(1),Mohannad Samaheen(7), Malak T. Etkaidek (1)..(1) Faculty of Medicine- Palestine Polytechnic University(2) MD. Medical oncology at al-Ahli hospital(3) Pathologist, MD at al-Ahli hospital(5) (M.D, Higher Specialization on Forensic Medicine).(6)Consultant thoracic surgeon at AL-Ahli hospital(7) Faculty of Medicine- Al-Quds university

Abstract Mindset – the attitudes, beliefs, and expectations an individual holds – plays a critical yet often underappreciated role in managing food allergies, especially during oral immunotherapy (OIT). Emotional states such as stress and anxiety can amplify allergic responses and shape how patients experience OIT, influencing adherence and perceived risk. Experimental evidence from behavioural conditioning, neuroimmunology, and placebo research demonstrates that psychological factors can modulate immune function, even in the absence of allergens, through learned associations and expectation-driven physiological responses. Symptom framing also matters. Describing mild OIT symptoms as “positive signals” of progress, rather than “side effects,” may improve emotional responses and support treatment retention. Patient education requires recognition of the patient and parent mindset as emphasised in the Preparing Patients for Oral Immunotherapy (PPOINT) expert consensus guidelines. This acknowledges the mental challenges that OIT can bring and helps to empower patients and their families. Additionally, stress-induced changes to gut barrier integrity and immune activation underline the biological plausibility of mindset-driven effects. Notably, emerging research also shows that immune sensing of allergens can directly shape behaviour, with allergic sensitisation prompting avoidance even in the absence of anaphylaxis. Collectively, these findings support the integration of mindset, emotional regulation, and communication strategies into OIT protocols to reduce adverse events and enhance long-term outcomes. This perspective advocates for a broader application of behavioural science in allergy management, emphasising its feasibility, safety, and clinical relevance.

Energy is considered as a limited resource in wireless sensor networks as it is difficult to supply more energy sources after the first supply runs out. Extending network lifespan requires efficient energy use, even when energy harvesting is feasible. Most energy in a WSN is consumed during data transmission for remote object detection, so we have focused on data compression techniques to enable energy-efficient transmission. Here, we employ machine learning methods such as Principal Component Analysis (PCA), encoder, and Recursive Feature Elimination (RFE) to reduce the number of selected sensors, effectively decreasing the dimensionality of the sensor data. Additionally, various detection models, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XG-Boost), and Adaptive Boosting (Ada-Boost), are used to detect original data from compressed representations. We evaluate the effectiveness of the proposed approach on three benchmark datasets and compare it with existing methods. The evaluation metrics for performance comparison include accuracy, precision, recall, F1 score, and area under the Receiver Operating Characteristic (ROC) curve. Experimental results show that PCA with RF performs well across all three benchmark datasets, outperforming other models. Furthermore, PCA with RF demonstrates superiority over state-of-the-art approaches in terms of accuracy.

Objective: It was aimed to analyze the speech-FFR of Turkish-speaking young adults and middle-aged adults with normal hearing thresholds and to compare the values according to age and gender. Methods: Thirty-one young adults with normal hearing thresholds between the ages of 19 to 25 years and thirty healthy middle-aged adults between the ages of 40 to 57 years participated in this study. The 40 ms speech syllable /da/ was used to record speech-FFR waveforms with Intelligent Hearing Systems at 80 dB SPL from each participant’s right ear. Results: In young adults, females generally exhibited shorter latencies than males in all peaks, while middle-aged females had shorter latencies in A and D peaks compared to males. Young females showed shorter latencies in most peaks compared to middle-aged females, except for the A peak. Young adult males had shorter D and O peak latencies compared to middle-aged males. Amplitude differences were observed only in the V peak for age, with no significant gender-related differences in any peak amplitudes. Overall, there were no significant differences between groups and subgroups in spectral peak amplitudes concerning age and gender. Interpretation: The study revealed the effect of gender and age on neural processing of speech sounds in the brainstem. Although the older participants were in the middle age group, the study revealed that aging began to disrupt the coding of speech and integration in the brainstem. Speech-FFR latency, amplitude and spectral characteristics were determined for young and middle-aged Turkish-speaking individuals.

Twenty-four novel 14N-substituted tetrandrine (Tet) were synthesized and investigated for their effects on anti-proliferative activities against human cancer cell lines HepG2 and A549 by the MTT method. Compound 17 exhibited the strongest cytotoxic effect against HepG2 cells with an IC 50 value of 2.09 μM and satisfactory SI value of 11.5, which was 5.3- and 6.4-fold than the activity of parental tetrandrine and adriamycin, respectively. Further in vitro experiments (flow cytometry assay, transwell assay, and scratching assay) were designed to validate the significant therapeutic effect of representative compound 17 on HCC. Network pharmacology was used to examine the mechanism of 17 in HCC treatment and the AKT signaling pathway was identified as the key pathway. Molecular docking, cellular thermal shift assay (CETSA) and ADP-Glo™ kinase enzyme inhibition assay experiment further demonstrated that 17 could specifically bind to AKT and inhibit its phosphorylation, then activated the proapoptotic protein BAX, inhibited the anti-apoptosis protein Bcl-2, with activating the caspase 3, so that to induce apoptosis. Moreover, it indicating a potent in vivo killing effect against liver cancers orthotopically transplanted HCC in AKT1-dependent manner with safety profile. Taken together, these results highlighted that compound 17 induced apoptosis.

1) Baccharis × intermedia (Asteraceae), found in central Chile, is a naturally occurring hybrid derived from the parent species B. macraei and B. linearis. It represents an extraordinary example of admixture with additive plant chemistry; however, the genetic structure of the hybrid complex and its evolution are still unclear. 2) Intensive field sampling and Genotyping-by-Sequencing (GBS) was used to clarify the structure of the B. × intermedia hybrid complex. In addition, B. vernalis, another species that resembles the morphology of B. macraei, was subjected to analysis to ascertain its role in the hybridization process. 3) A total of 3724 SNPs and 378 individuals were analyzed using clustering, PCA, Treemix, Patterson’s D- and f-statistics. Furthermore, other genetic indicators, such as levels of heterozygosity, Tajima’s D and nucleotide diversity (π) also provided further insight into the hybrid complex. 4) Our results show that B. × intermedia consists of mainly F1-hybrids with 18 % backcrossing to both parental species. B. vernalis was not involved into recent hybridization with B. macraei and B. linearis. Additionally, a recent introgression into the Quintay population of B. macraei from B. linearis was detected during the analysis. 5) Altogether, our extensive field sampling combined with genetic analyses have provided deeper insights into the genetic structure and evolution of the B. × intermedia hybrid complex in Chile.

Saving energy or converting it to different forms requires a continuous technique to improve the applications’ thermal performance. The present investigation analyzed the thermal performance and temperature distribution of the Phase Change Material (PCM) discharging method numerically and experimentally inside an annulus of concentric tube. The inlet temperature of heat transfer fluid flow in the inner tube of the annulus was maintained to be lower than the solidification points of the PCM. The computational domain has been simulated as a 3D model for the coupled system. Under various flow rates, the local temperature is measured and computed. It is found that the computational predictions in a good matching with the acquired experimental data. Different mass flow rates have been considered varying in range of (0.0134-0.0952) kg/s. The experimental results showed that there is an inverse proportional relationship between the amount of mass flow rates and the time required for the solidification of paraffin wax. The optimum times (275, 220, 165, and 75) minutes are reached to obtain hot water for domestic use compared to the previously mentioned mass flow rates during the discharge process.

Objective What are pregnancy outcomes following screening for asymptomatic bacteriuria, comparing significant growth, mixed growth and non-significant growth against no growth? Design Retrospective cohort study of pregnant women booking for birth. Setting Tertiary UK hospital Population or Sample All patients screened for bacteriuria before twenty weeks’ gestation for whom pregnancy outcome data was available. Methods We used routinely collected data obtained using an automated extract from the hospital electronic patient records database (EPIC, EPIC Systems Corporation) incorporating all patient data including obstetric, neonatal, microbiology and pathology results. Main Outcome Measures Primary outcomes were preterm birth before 37 weeks and pyelonephritis, with pre-planned secondary outcomes including antimicrobial resistance and subgroup analyses. Results 6095 pregnant women met inclusion criteria (6095/10678, 57.1% of all women booked for care in that period). Preterm birth (<37 weeks’ gestation) was more common in women with non-significant growth (<10 5 colony forming units per ml (CFU/ml), 203/2942, 6.9%, p<0.001) and significant growth (≥10 5 CFU/ml, 41/604, 6.8%, p<0.05) compared with no growth (95/2116, 4.5%) on booking mid-stream urine sample. Following adjustment for risk factors, preterm birth remained more common in those with non-significant growth (OR 1.91, 95%CI 1.13-3.26). Pyelonephritis occurred in 5/6095 (0.1%), more commonly in significant growth (3/604, 0.5%, p<0.05). Conclusions Bacteriuria of any kind was associated with subsequent preterm birth in this large single centre cohort study of booking mid-stream urine culture. Pyelonephritis, although rare, was more common with significant growth, with no cases occurring following no growth.

Purpose: We report a case of a 69-year-old female patient who developed a lipid droplet-like foreign body in the anterior chamber 8 months after undergoing phacoemulsification cataract surgery with intraocular lens implantation. Location: Cataract Center, Tianjin Eye Hospital, Tianjin, China. Methods: The lipid droplet was surgically removed from the anterior chamber and sent to Tianjin Medical University School of Pharmacy for analysis, confirming the presence of dexamethasone in the foreign body. Results: The patient experienced a significant decrease in corneal endothelial cell density compared to preoperative levels and her vision remained stable after the removal of the ointment. Conclusion: The presence of tobramycin-dexamethasone ointment in the anterior chamber post-surgery was identified as the primary cause of corneal endothelial cell density loss, attributed mainly to white petroleum and mineral oil. Surgical intervention to remove the ointment from the anterior chamber should be performed promptly. During surgery, mydriasis should be avoided to prevent contamination of the intraocular lens.