Antitumorale Wirkung von AFP-beladenen und CD40L-exprimierenden Dendritischen Zellen in etablierten subkutanen hepatozellulären Tumoren <em>in vivo</em>

Das HCC ist das häufigste primär hepatische Malignom. Trotz verbessertem Screening und neuen Therapieansätzen haben Patienten mit HCC weiterhin eine sehr schlechte Prognose. Die meisten Malignome, so auch das HCC, besitzen allerdings ein immunogenes Potenzial. Das Immunsystem ist in der Lage initial eine spezifische zelluläre Immunantwort gegen TAA, wie dem AFP beim HCC aufzubauen. AFP wird in über 50 % der HCC-Patienten reexprimiert und wurde bereits für gezielte Vakzinierungstherapien genutzt. Jedoch konnte mangels potenter Kostimulation kein klinischer Benefit erzielt werden. Ursache hierfür ist das stark immunsuppressive Tumormilieu, welches u.a. DC in ihrer Maturation und Antigenpräsentation behindert. Das CD40L, ein potentes kostimulatorisches Molekül, konnte in Vorarbeiten der eigenen Arbeitsgruppe im HCC-Modell erfolgreich eingesetzt werden. In dieser Arbeit wurde eine HCC-spezifische Immunantwort durch eine Vakzinierung mit AFP-gepulsten DC induziert und mit einer i.t. Injektion von CD40L-exprimierenden DC im s.c. HCC-Modell in C3H-Mäusen kombiniert. Untersucht wurden mögliche synergistisch-antitumorale Effekte in Bezug auf das Tumorwachstum und das Überleben der Mäuse sowie immunologische Effekte im Tumor über Quantifizierung von CD4+- und CD8+-T-Zellen mittels Durchflusszytometrie und Analyse des Zytokinmilieus auf IL-12, IFN-gamma und IL-10 nach abgeschlossener Therapie. Zudem erfolgte eine Analyse des apoptotischen Effektes von CD40L als weiteren Mechanismus seiner antitumoralen Wirksamkeit. Die Kombinatiosntherapie führte zu einer kompletten Tumorregression in mehr als 60 % der Mäuse und zu einem signifikant verlängerten Überleben. Als Zeichen der induzierten Proinflammation konnte in den mit der Kombinationstherapie behandelten Tumoren eine starke Rekrutierung von CD4+- und CD8+-T-Zellen sowie eine erhöhte i.t. Konzentration an den proinflammatorischen Zytokinen IL-12 und IFN-gamma, und ein Trend zur verminderten Expression des antiinflammatorischen IL-10 mittels ELISA verzeichnet werden. Unsere Ergebnisse zeigen, dass eine i.t.-Applikation von CD40L-exprimierenden DC ein starkes proinflammatorisches Tumormilieu, sowie Tumorzellapoptose bewirkt und dies in Kombination mit einer AFP-DC-Vakzinierung einen synergistisch-antitumoralen Effekt gegen AFP-positive s.c. HCC-Zellen in vivo erzielen kann. Diese Ergebnisse scheinen eine vielversprechende DC-basierte Strategie zur Therapie des HCC zu eröffnen, die weiter am Menschen untersucht und ggf. in klinischen Studien getestet werden sollte

INFLUENCE OF A POOR LIFESTYLE ON THE DEVELOPMENT OF CYSTS IN WOMEN

Poor lifestyle choices, such as frequently consuming fatty fast food, consuming sugary beverages, living a sedentary lifestyle, and not getting enough exercise, may hasten the ovarian hyperthecosis progression. A recurrent hormonal disorder that affects women of reproductive age (15-30) known as polycystic ovarian disease (PCOD), where the ovaries contain an abundance of tiny cysts. It might interfere with their menstrual cycles and complicate attempts to conceive. PCOD is a serious health issue since those who have it have a higher risk factors include endometrial hyperplasia, endometrial cancer, obesity, cardiovascular disease, diabetes mellitus, obstructive sleep apnea, depression, and nonalcoholic fatty liver disease. Endometrial cancer may be brought on by aberrant, unopposed endogenous estrogenic stimulation brought on by feminizing ovarian tumors and polycystic ovarian syndrome. Patients with polycystic ovarian disease support the finding that endometrial carcinoma with a concurrent endogenous estrogenic stimulation has a better prognosis (P0.01) than endometrial carcinoma alone. A mature follicle, which is also a cystic structure, develops throughout a typical menstrual cycle with ovulation. The symptoms and potential problems of PCOD appear to be well managed by lifestyle changes. Depending on the condition, medication or lifestyle adjustments may be necessary. Getting regular exercise, losing weight, and increasing your daily activity can all help treat or perhaps prevent insulin resistance and lower testosterone levels. Synthetic medications can be used to treat PCOD. This paper contains the correlation between the poor life style and the occurrence of PCOD in women and its cur

Dual water choices: the assessment of the influential factors on water sources choices using unsupervised machine learning market basket analysis

An unsupervised machine learning model of association rule known as market basket analysis is proposed in this study to analyze the influence of various socio-economic factors on the choice of the water source. Data of 51 socio-economic factors collected from 295 individuals living in 65 households in Ambo city in the Oromia region of Ethiopians were used for this purpose. The results revealed (i) 64% of the family preferred multiple water sources (i.e., public tap and river water), (ii) the water was collected females in 92% of the households, and (iii) majority of people preferred bathing and laundering in the river (support = 32% and confidence = 87%). Direct utilization of river water is not a preferable choice for the user since it may lead to severe health issues and cause water pollution from bathing and laundering. Education and monthly income have a significant impact on the choices of water sources. Local management authorities can improve sanitation and public health management using the results obtained in the study. The paper only gives a glimpse of the important factors that should be considered for improving the way of life for the underdeveloped areas of the world using advanced machine learning techniques

Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system (DENFIS), hybrid neural-fuzzy inference system (HyFIS), and adaptive neurofuzzy inference system (ANFIS) for AT prediction. Modelling was done for three stations in North Dakota (ND), USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point (DP), total solar radiation (TSR), and negative correlation with average wind speed (WS). At the Robinson station, DENFIS performed the best with a coefficient of determination (R$^{2}$) of 0.96 and a modified index of agreement (md) of 0.92, followed by ANFIS with R$^{2}$ of 0.94 and md of 0.89, and HyFIS with R$^{2}$ of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R$^{2}$: 0.953/0.960, md: 0.903/0.912, then ANFIS with R$^{2}$: 0.943/0.942, md: 0.888/0.890, and HyFIS with R$^{2}$ 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure

Tumoral PD-L1 expression defines a subgroup of poor-prognosis vulvar carcinomas with non-viral etiology

Vulvar cancer is rare but incidence rates are increasing due to an aging population and higher frequencies of young women being affected. In locally advanced, metastatic or recurrent disease prognosis is poor and new treatment modalities are needed. Immune checkpoint blockade of the PD-1/PD-L1 pathway is one of the most important advancements in cancer therapy in the last years. The clinical relevance of PD-L1 expression in vulvar cancer, however, has not been studied so far. We determined PD-L1 expression, numbers of CD3+ T cells, CD20+ B cells, CD68+ monocytes/macrophages, Foxp3+ regulatory T cells and CD163+ tumor-associated macrophages by immunohistochemistry in 103 patients. Correlation analysis with clinicopathological parameters was undertaken; the cause-specific outcome was modeled with competing risk analysis; multivariate Cox regression was used to determine independent predictors of survival. Membranous PD-L1 was expressed in a minority of tumors, defined by HPV-negativity. Its presence geographically correlated with immunocyte-rich regions of cancer islets and was an independent prognostic factor for poor outcome. Our data support the notion that vulvar cancer is an immunomodulatory tumor that harnesses the PD-1/PD-L1 pathway to induce tolerance. Accordingly, immunotherapeutic approaches might have the potential to improve outcome in patients with vulvar cancer and could complement conventional cancer treatment

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay’s ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and Tavg oC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb predictio

Evaluating Physical and Fiscal Water Leakage in Water Distribution System

With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549,627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.Validerad;2019;Nivå 2;2019-10-15 (johcin)</p

State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations

Dam and powerhouse operation sustainability is a major concern from the hydraulic engineering perspective. Powerhouse operation is one of the main sources of vibrations in the dam structure and hydropower plant; thus, the evaluation of turbine performance at different water pressures is important for determining the sustainability of the dam body. Draft tube turbines run under high pressure and suffer from connection problems, such as vibrations and pressure fluctuation. Reducing the pressure fluctuation and minimizing the principal stress caused by undesired components of water in the draft tube turbine are ongoing problems that must be resolved. Here, we conducted a comprehensive review of studies performed on dams, powerhouses, and turbine vibration, focusing on the vibration of two turbine units: Kaplan and Francis turbine units. The survey covered several aspects of dam types (e.g., rock and concrete dams), powerhouse analysis, turbine vibrations, and the relationship between dam and hydropower plant sustainability and operation. The current review covers the related research on the fluid mechanism in turbine units of hydropower plants, providing a perspective on better control of vibrations. Thus, the risks and failures can be better managed and reduced, which in turn will reduce hydropower plant operation costs and simultaneously increase the economical sustainability. Several research gaps were found, and the literature was assessed to provide more insightful details on the studies surveyed. Numerous future research directions are recommended.Validerad;2020;Nivå 2;2020-04-16 (alebob)</p

Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5&amp;gt; Grid-RF-M5&amp;gt; Grid-RF-M4&amp;gt; ANN-M4&amp;gt; SVM-M4&amp;gt; SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay. © 2021 Elsevier Lt