bluuebunny/crossref_metadata_2025 huggingface dataset:
Epidemiology of Injuries during Judo Tournaments
Authors: Mooren Jeroen , von Gerhardt Amber L. , Hendriks Irene T. J. , Tol Johannes L. , Koëter Sander
Published: February 2023

Objective. To determine the injury incidence proportion, distribution of injuries by anatomical location; injury type; injury severity, time loss; mechanism and situations of injuries; and the relative risk of injuries by gender, age, and weight categories during judo tournaments. Study Design. It is a systematic review. Data Sources. A systematic review of the literature was conducted via searches in PubMed, EMBASE, Web of Science, CINAHL, SPORTDiscus, Google Scholar, and PEDro. Eligibility Criteria. All original studies on the incidence of injuries during judo tournaments were included. Results. Twenty-five studies were included out of the 1979 studies. Using the modified AXIS tool score for quality assessment, seven were rated as having good quality, nine were rated as having fair quality, and four were rated as having poor quality. The injury incidence proportion during tournaments ranged from 2.5% to 72.5% for injuries requiring medical evaluation and 1.1% to 4.1% for injuries causing time loss (i.e., inability to continue game participation). The most commonly reported injury location was the head, followed by the hand, knee, elbow, and shoulder. The most frequent types of injury were sprains, followed by contusions, skin lacerations, strains, and fractures. In judo tournaments, injuries were more often sustained during standing fights (tachi-waza) than in ground fights (ne-waza). Conclusion. The tournament injury incidence proportion ranged from 2.5% to 72.5% for injuries requiring medical attention and 1.1% to 4.1% for injuries causing time loss. The head was the most frequently injured body part, and sprain was the most frequent injury type. However, current reports on injuries during judo tournaments are heterogeneous and inconsistent, limiting our understanding of in-match injury risks. Future studies should utilize the guidelines of the International Olympic Committee consensus meeting statement on the methodological approach to injury reporting. We recommend a judo-specific extension of this statement to fit the unique features of judo sports practice.
A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories
Authors: Zhang Guanghao , Xu Xiaolong , Zhu Luojiang , Li Sisi , Chen Rundong , Lv Nan , Li Zifu , Wang Jing , Li Qiang , Zhou Wang , Yang Pengfei , Liu Jianmin
Published: February 2023

The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of “glioblastoma, IDH-wild type.” Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.
Deconstruction of Related Technologies of Ground Image Processing Based on High-Resolution Satellite Remote Sensing Images
Authors: Feng Kai , Wu Yi , Zhang Rui
Published: February 2023

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world’s major scientific and technological powers to develop. Although China’s satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world’s advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN’s recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments.
A Level‐Based Learning Swarm Optimizer with Stochastic Fractal Search for Parameters Identification of Solar Photovoltaic Models
Authors: Zhang Qingsong , He Yibo , Shu Meng , Zhang Weizheng , Yang Daojian , Song Jinhua , Li Guanhua , Zheng Yanan , Yang Yang , Tie Jinxin , Li Jie , Li Meng
Published: February 2023

As the most popular renewable energy, solar energy could be converted into electricity by photovoltaic (PV) systems directly. To maximize the effectiveness of the conversion, it is critical to find the precise and accurate parameters of the PV model. In this paper, we propose a level‐based learning swarm optimizer with stochastic fractal search (LLSOF) to tackle the parameter estimation of several kinds of solar PV models. The population is separated into multiple levels according to their fitness at first. The individuals at the lower levels evolve through learning from the individuals at the higher levels. Benefiting from the interactive learning among levels, the population could approach the multiple optimal regions rapidly. To enhance the local search ability, stochastic fractal search is introduced to locate the optima accurately. Combination of both, the proposed LLSOF could achieve a good balance on both exploration and exploitation. To evaluate the performance of LLSOF, it is used to obtain the parameters of three PV models and compared with nine well‐established algorithms. Comparative results validate the excellent performance of LLSOF. Moreover, the application manufactory’s data sheets report the superior efficiency and effectiveness of LLSOF for the parameter estimation of PV systems.
An Updated Review on Efficiency of Penthorum chinense Pursh in Traditional Uses, Toxicology, and Clinical Trials
Authors: Nabi Fazul , Ahmed Jameel , Tao Weilai , Lu Qin , Bhutto Zohaib Ahmed , Qadir Ayaz , Hameed Abdul , Ahmed Iftikhar , Ahmed Nazeer , Essa Muhammad , Liu Juan
Published: February 2023

Traditional Chinese medicines (TCM) play an important role in the control and treatment of several animal diseases. Penthorum chinense Pursh (PCP) is a famous plant for its use in traditional medication practice and therapeutic effects in numerous pathological conditions. In China, PCP is utilized for both food and medication due to numerous bioactivities. PCP is widely administered in prevention and treatment of traumatic injury, edema, and liver diseases with functions of reducing swelling, support diuresis, blood stasis, and mitigation symptoms of excessive alcohol intake. Recently, PCP highlighted for research trials in various fields including pharmacology, pharmacognosy, cosmeceuticals, nutraceuticals, and pharmaceuticals due to medicinal significance with less toxicity and an effective ethnomedicine in veterinary practice. PCP contains diverse important ingredients such as flavonoids, organic acids, coumarins, lignans, polyphenols, and sterols that are important bioactive constituents of PCP exerting the therapeutic benefits and organ‐protecting effects. In veterinary, PCP extract, compound, and phytochemicals/biomolecules significantly reversed the liver and kidney injuries, via antioxidation, oxidative stress, apoptosis, mitochondrial signaling pathways, and related genes. PCP water extract and compounds also proved in animal and humans’ clinical trial for their hepatoprotective, antiaging, nephroprotective, anti‐inflammatory, antidiabetic, antibacterial, antiapoptotic, immune regulation, and antioxidative stress pathways. This updated review spotlighted the current information on efficiency and application of PCP by compiling and reviewing recent publications on animal research. In addition, this review discussed the toxicology, traditional use, comparative, and clinical application of PCP in veterinary practices to authenticate and find out new perspectives on the research and development of this herbal medicine.
Effects of Land-Use Dynamics on Soil Organic Carbon and Total Nitrogen Stock, Western Ethiopia
Authors: Leul Yitayh , Assen Mohammed , Damene Shimeles , Legass Asmamaw
Published: February 2023

Soil organic carbon (SOC) and total nitrogen (TN) stock are key indicators of soil quality in tropical regions; however, their status is often degraded, especially due to massive deforestation in natural forest areas associated with extensive agricultural land use. The aim of this study was to investigate the dynamics of SOC and TN stock in different land-use systems in the Abobo woreda, Western Ethiopia. To analyze their status, 80 disturbed (composite) and 45 undisturbed soil samples were collected from the top 20 cm of soil in five major land-use types: natural forestlands, grasslands, recently developed commercial farmlands, old commercial farmlands, and small-scale cultivated lands. The results showed that SOC stock varied significantly across the different land-use types, with mean stock ranging from 32.23 Mg·ha−1 in recently developed commercial farmlands to 54.54 Mg·ha−1 in natural forestlands. The mean TN stock ranged from 2.54 Mg·ha−1 in recently developed commercial farmlands to 4.63 Mg·ha−1 in natural forestlands. With natural forestlands as a baseline and the duration ranging in age from 15 to 45 years since land-use conversion, the mean annual absolute rates of change in SOC and TN stock loss were 0.49, 1.49, 0.39, and 0.45 Mg·ha−1·yr−1 and 0.05, 0.14, 0.03, and 0.04 Mg·ha−1·yr−1 for grasslands, recently developed commercial farmlands, old commercial farmlands, and small-scale cultivated lands, respectively. The results of this study revealed that soil disturbance during forestland conversion to tillage enhanced the decomposition rate of organic matter in recently developed commercial farmlands. Nevertheless, after agricultural abandonment and vegetation restoration, the SOC and TN stock capacities were enriched in the old commercial farmlands. It is, therefore, important to effectively restore vegetation and implement sustainable land-use management practices.
Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer
Authors: Jiang Zhenxing , Yan Lizhao , Deng Shenghe , Gu Junnan , Qin Le , Mao Fuwei , Xue Yifan , Cai Wentai , Nie Xiu , Liu Hongli , Shang Fumei , Tao Kaixiong , Wang Jiliang , Wu Ke , Cao Yinghao , Cai Kailin
Published: February 2023

Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test,

P

value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.
Cytotoxicity, Antimicrobial, Antioxidant, Anthelmintic, and Anti-Inflammatory Activities and FTIR Analysis of Combretum nioroense Stem Bark
Authors: Osei Akoto Clement , Enyonam Adzimah Matilda , Yeboah Forkuoh Anthony , Obedi Patrick , Oguns Jacob , Owusu Boahen Felix
Published: February 2023

Combretum nioroense is widely used in the folkloric treatment of a variety of bacterial and helminthic infections. The decoction of its leaves is traditionally fed to newborn babies in some rural parts of Ghana. The study focused on identifying the prime components of petroleum ether and ethanolic extracts of the stem bark using standard phytochemical screening protocols and chromatographic and spectroscopic techniques. The activities (cytotoxicity, antimicrobial, antioxidant, anthelmintic, and anti-inflammatory) of the extracts of C. nioroense were also investigated. Preliminary phytochemical analysis of the extracts revealed the presence of glycosides, saponins, phenols, coumarins, alkaloids, flavonoids, tannins, steroids, phytosterols, flavanols, terpenoids, and cardiac glycosides. The action of the extracts of C. nioroense on Milsonia ghanensis worms was concentration-dependent, with the least concentration (0.75 mg/mL) paralyzing and killing M. ghanensis after the maximal exposure time. The IC50 values for petroleum ether and ethanol extracts in the DPPH assay were >100.0 and 27.940 ± 1.005 μg/mL and those of the H2O2 assay were 400.900 ± 3.400 and 322.500 ± 1.005 μg/mL, respectively. The total antioxidant capacities (TACs) for petroleum ether and ethanol extracts were 47.197 ± 0.533 and 57.968 ± 0.560 gAAE/100 g, respectively. The IC50 value for ethanol extract in the cytotoxicity studies was 115.4 ± 1.332 μg/mL. The MICs of the extracts against the test organisms were within the range of 0.0122–25.0 mg/mL. The extracts (petroleum ether and ethanol) showed a concentration-dependent increase in anti-inflammatory activity with IC50 values of 31.254 ± 0.359 and 24.402 ± 0.569 μg/mL, respectively. Chromatographic separations of the ethanol extract gave three fractions. FTIR analysis of the extracts and purified fractions revealed the presence of functional groups, confirming the presence of the phytochemicals identified in the screening test. The results indicate that both extracts of C. nioroense exhibit cytotoxicity, antimicrobial, antioxidant, anthelmintic, and anti-inflammatory activities, thereby proving the folkloric use to treat ailments caused by worms and microorganisms.
Integrating Fuzzy Multiobjective Programming and System Dynamics to Develop an Approach for Talent Retention Policy Selection: Case on Health‐Care Industry
Authors: Lo Chia-Lun , Hu Ya-Han
Published: February 2023

The demand for medical services has been increasing yearly in aging countries. Medical institutions must hire a large number of staff members to provide efficient and effective health‐care services. Because of high workload and pressure, high turnover rates exist among health‐care staff members, especially those in nonurban areas, which are characterized by limited resources and a predominance of elderly people. Turnover in health‐care institutions is influenced by complex factors, and high turnover rates result in considerable direct and indirect costs for such institutions (Lo and Tseng 2019). Therefore, health‐care institutions must adopt appropriate strategies for talent retention. Because institutions cannot determine the most effective talent retention strategy, many of them simply passively adopt a single human resource (HR) policy and make minor adjustments to the selected policy. In the present study, system dynamics modeling was combined with fuzzy multiobjective programming to develop a method for simulating HR planning systems and evaluating the suitability of different HR policies in an institution. We also considered the external insurance policy to be the parameter for the developed multiobjective decision‐making model. The simulation results indicated that reducing the turnover rate of new employees in their trial period is the most effective policy for talent retention. The developed procedure is more efficient, effective, and cheaper than the traditional trial‐and‐error approaches for HR policy selection.
An ENet Semantic Segmentation Method Combined with Attention Mechanism
Authors: Bai Wei
Published: February 2023

Image semantic segmentation is one of the core tasks for computer vision. It is widely used in fields such as unmanned driving, medical image processing, geographic information systems, and intelligent robots. Aiming at the problem that the existing semantic segmentation algorithm ignores the different channel and location features of the feature map and the simple method when the feature map is fused, this paper designs a semantic segmentation algorithm that combines the attention mechanism. First, dilated convolution is used, and a smaller downsampling factor is used to maintain the resolution of the image and to obtain its detailed information. Secondly, the attention mechanism module is introduced to assign weights to different parts of the feature map, which reduces the accuracy loss. The design feature fusion module assigns weights to the feature maps of different receptive fields obtained by the two paths and merges them together to obtain the final segmentation result. Finally, through experiments, it was verified on the Camvid, Cityscapes, and PASCAL VOC2012 data sets. Mean intersection over union (MIoU) and mean pixel accuracy (MPA) are used as metrics. The method in this paper can make up for the loss of accuracy caused by downsampling while ensuring the receptive field and improving the resolution, which can better guide the model learning. And the proposed feature fusion module can better integrate the features of different receptive fields. Therefore, the proposed method can significantly improve the segmentation performance compared to the traditional method.
Bifurcation Analysis of a Discrete‐Time Chemostat Model
Authors: Al-Basyouni Khalil Salem Nemer , Khan Abdul Qadeer
Published: February 2023

In this article, a discretized two‐dimensional chemostat model is investigated. By using the algebraic technique, it is proved that the discrete chemostat model has semitrivial equilibrium solution for all involved parametric values, but it has an interior equilibrium solution under certain parametric conditions. We have studied the local dynamics with topological classifications about the semitrivial and interior equilibrium solution on the basis of the theory of the discrete dynamical system. By bifurcation theory, we studied the existence of bifurcations for the understudied discrete chemostat model and proved that at a semitrivial equilibrium solution, it did not undergo flip bifurcation, but it undergoes flip bifurcation at interior equilibrium solution, and no other bifurcation occurs at this point. It has also explored the existence of periodic points and chaos control by the state feedback method. Finally, with the help of MATLAB software, flip bifurcation diagrams and corresponding maximum Lyapunov exponents were also presented. These numerical simulations not only show the correctness of theoretical results but also reveal the complex dynamics such as the orbits of period −7, −8, −13, and −16.
Cyber Security against Intrusion Detection Using Ensemble-Based Approaches
Authors: Alatawi Mohammed Naif , Alsubaie Najah , Ullah Khan Habib , Sadad Tariq , Alwageed Hathal Salamah , Ali Shaukat , Zada Islam
Published: February 2023

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.
Resistance Sources for Powdery Mildew and Breeding Strategies for Improvement in Fenugreek
Authors: Demelash Asaye
Published: February 2023

Fenugreek is a multiuse and very valuable commercial spice crop farmed around the world, notably in Ethiopia, where it is valued for its seeds, tender shoots, and fresh leaves as well as its significant economic contribution. The yield of fenugreek crops adversely impacted by powdery mildew is the most damaging disease that exerts a substantial impact on the entire plant components and loss of its output. Appropriate breeding strategies such as resistance breeding, collection of germplasm, evaluation and conservation, mutation breeding, tissue culture techniques, and marker-assisted selection are therefore worthy sources of resistance to fenugreek powdery mildew disease as well as need to be implemented and applied immediately to minimize yield losses due to the incidence of complex crop pathogen. For culture, ovule culture, micropropagation, in vitro selection, and somaclonal variations, for example, are biotechnological methods that can be used in fenugreek breeding programmes to get varieties free from powdery mildew disease. New tools to open up new research avenues to create new genotypes with unlimited potential for the treatment of powdery mildew disease are also being used to improve and supplement conventional programmes for fenugreek enhancement. The application of resistance breeding, collection of germplasm, evaluation and conservation, mutation breeding, tissue culture techniques, and marker-assisted selection have an immense role in the production of fenugreek crops by reducing yield limiting factors in the crop, primarily for powdery mildew disease.
Malignant Melanoma Presenting as Spinal Cord and Pleural Lesions
Authors: Albastaki Aysha , Ahmed Sharifa , Khan Asher , Farhan Abeer , Almayman Talal
Published: February 2023

Primary spinal cord melanoma (PSCM) and primary pleural melanoma (PPM) are extremely rare entities with scarce cases reported in the literature. We present a case of a 54-year-old male diagnosed with possible primary pleural melanoma and primary spinal melanoma, managed with partial surgical resection, postoperative radiotherapy, and chemotherapy consisting of Ipilimumab, nivolumab, and temozolomide. This leads to decreased symptoms and improved quality of life of the patient. In this case report, we review the literature on PSCM and PPM in detail, addressing the pertinent clinical aspects as well as current and upcoming therapeutic options.
Addressing Tumor Heterogeneity by Sensitizing Resistant Cancer Cells to T cell–Secreted Cytokines
Authors: Ito Yoshinaga , Pan Deng , Zhang Wubing , Zhang Xixi , Juan Tiffany Y. , Pyrdol Jason W. , Kyrysyuk Oleksandr , Doench John G. , Liu X. Shirley , Wucherpfennig Kai W.
Published: February 2023

Tumor heterogeneity is a major barrier to cancer therapy, including immunotherapy. Activated T cells can efficiently kill tumor cells following recognition of MHC class I (MHC-I)–bound peptides, but this selection pressure favors outgrowth of MHC-I–deficient tumor cells. We performed a genome-scale screen to discover alternative pathways for T cell–mediated killing of MHC-I–deficient tumor cells. Autophagy and TNF signaling emerged as top pathways, and inactivation of Rnf31 (TNF signaling) and Atg5 (autophagy) sensitized MHC-I–deficient tumor cells to apoptosis by T cell–derived cytokines. Mechanistic studies demonstrated that inhibition of autophagy amplified proapoptotic effects of cytokines in tumor cells. Antigens from apoptotic MHC-I–deficient tumor cells were efficiently cross-presented by dendritic cells, resulting in heightened tumor infiltration by IFNγ-and TNFα-producing T cells. Tumors with a substantial population of MHC-I–deficient cancer cells could be controlled by T cells when both pathways were targeted using genetic or pharmacologic approaches.

Significance:

Tumor heterogeneity is a major barrier to immunotherapy. We show that MHC-I–deficient tumor cells are forced into apoptosis by T cell–derived cytokines when TNF signaling and autophagy pathways are targeted. This approach enables T cell–mediated elimination of tumors with a substantial population of resistant, MHC-I–deficient tumor cells.

This article is highlighted in the In This Issue feature, p. 1027
Impaired Decision-Making and Skin Conductance Responses Are Associated with Reward and Punishment Sensitivity in Individuals with Severe Alcohol Use Disorder
Authors: Logge Warren B. , Morley Kirsten C. , Haber Paul S. , Baillie Andrew J.
Published: February 2023

Introduction: Individuals with alcohol use disorder (AUD) have difficulties regulating alcohol consumption, despite adverse drinking-related consequences. This may be due to incapacity incorporating previous negative feedback from drinking, resulting in impaired decision-making. Methods: We assessed whether decision-making is impaired in participants with AUD related to severity of AUD, indexed by severe negative drinking consequences using the Drinkers Inventory of Consequences (DrInC) and reward and punishment sensitivity with the Behavioural Inhibition System Behavioural Activation System (BIS BAS) scales. 36 treatment-seeking alcohol-dependent participants completed the Iowa gambling task (IGT) with skin conductance responses (SCRs) measured continuously as an index of somatic autonomic arousal to evaluate impaired expectancy of negative outcomes. Results: Two-thirds of the sample showed behavioural impairment during the IGT, with greater AUD severity related to worse performance. BIS moderated IGT performance according to severity of AUD, with increased anticipatory SCRs for those with fewer reported DrInC severe consequences. Participants with more DrInC severe consequences showed IGT deficits and reduced SCRs regardless of BIS scores. BAS-Reward was associated with increased anticipatory SCRs to disadvantageous deck choices among those with lower AUD severity, while SCRs did not differ related to AUD severity for reward outcomes. Discussion: Effective decision-making in the IGT and adaptive somatic responses were moderated by punishment sensitivity contingent on severity of AUD in these drinkers, with impairments in expectancy to negative outcomes from risky choices, including reduced somatic responses, resulting in poor decision-making processes that may help explain impaired drinking and worse drinking-related consequences.
Geriatrisches Assessment: Warum es für die Therapieentscheidung so wichtig ist
Authors: Leischker Andreas
Published: February 2023

Background: Older patients with metastatic pancreatic cancer may suffer increased toxicity from intensive chemotherapy. Treatment individualization by geriatric assessment (GA) might improve functional outcome. Methods: We performed a multicenter, phase IV, open label trial in patients ≥70 years with metastatic pancreatic adenocarcinoma. Patients underwent GA and were assigned to one of three categories based on their scores: Go-Go, Slow-Go, or Frail. These categories were intended to guide physician's treatment decisions when choosing to treat patients with nab-paclitaxel/gemcitabine (arm A), gemcitabine (arm B), or best supportive care (arm C). Primary objective was a stable (loss of five points or less) Barthel's Activities of Daily Living (ADL) score during chemotherapy; secondary endpoints included GA scores during therapy, safety, quality of life, response and survival rates. Results: Thirty-two patients were enrolled in the trial in six centers in Germany (out of 135 planned), resulting in termination due to low recruitment. Fifteen patients were allocated to nab-paclitaxel/gemcitabine, fifteen to gemcitabine, and two to best supportive care by their physicians, although according to their GA scores 29 patients (91%) were categorized as Slow-Go and three (9%) as Go-Go. Thus, fifteen of 32 (47%) patients were misclassified and given a course of treatment inconsistent with their GA scores. Median progression-free survival (PFS) were 3.3 months and 9.1 months and median time to quality-of-life deterioration 13 days and 29 days in the nab-paclitaxel/gemcitabine and gemcitabine monotherapy arms, respectively. Serious adverse events were reported in 11 (78.6%) patients in the nab-paclitaxel/gemcitabine and 8 (53.3%) patients in the gemcitabine arm. Conclusions: Clinical evaluations by investigators differed markedly from geriatric assessments, leading to potential overtreatment. In our modest sample size study, those patients undergoing more intensive therapy had a less favorable course.
Uveamelanom: Neue Therapieoption zugelassen
Authors: Klespe Kai-Christian
Published: February 2023

In der IMCgp100-202 Studie wurden zuvor unbehandelte HLA-A*02:01-positive Patienten mit metastasiertem Aderhautmelanom im Verhältnis 2:1 nach dem Zufallsprinzip entweder mit Tebentafusp (Tebentafusp-Gruppe) oder einer Therapie nach Wahl des Prüfarztes mit Pembrolizumab, Ipilimumab oder Dacarbazin (Kontrollgruppe) behandelt, stratifiziert nach dem Laktatdehydrogenase-Spiegel. Der primäre Endpunkt war das Gesamtüberleben. Die Gesamtüberlebensrate nach einem Jahr betrug 73% in der Tebentafusp-Gruppe und 59 %in der Kontrollgruppe (Hazard Ratio für Tod 0,51; 95% Konfidenzintervall [CI] 0,37 bis 0,71; P<0,001) in der Intention-to-Treat-Population. Das progressionsfreie Überleben war in der Tebentafusp-Gruppe ebenfalls signifikant höher als in der Kontrollgruppe (31% gegenüber 19% nach 6 Monaten; Hazard Ratio für Krankheitsprogression oder Tod, 0,73; 95% CI, 0,58 bis 0,94; P = 0,01). Die häufigsten behandlungsbedingten unerwünschten Ereignisse in der Tebentafusp-Gruppe waren zytokinvermittelte Ereignisse (aufgrund von T-Zell-Aktivierung) und hautbezogene Ereignisse (aufgrund von Glykoprotein-100-positiven Melanozyten), einschließlich Hautausschlag (83%), Pyrexie (76%) und Pruritus (69%). Die Häufigkeit und der Schweregrad dieser unerwünschten Ereignisse nahmen nach den ersten 3 oder 4 Dosen ab und führten nur in seltenen Fällen zum Abbruch der Studienbehandlung (2%). Es wurden keine behandlungsbedingten Todesfälle gemeldet.
Distributed resource allocation model with presence of multiple jammer for underwater wireless sensor networks
Authors: Bagali Sheetal , Sundaraguru Ramakrishnan
Published: February 2023

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.
Covariance absolute values spectrum sensing method based on two adaptive thresholds
Authors: Hashim Bushra T. , Ziboon Hadi T. , Abdulsatar Sinan M.
Published: February 2023

Cognitive radio is a modern wireless communication methodology that deals with the issue of spectrum untapped. Cognitive users can continually perceive the licensed spectrum to hunt for unoccupied spots. The essential technology in cognitive radio (CR) for primary user detection is spectrum sensing. Due to signal to noise ratio (SNR), noise uncertainty in spectrum sensing would make the detection unreliable. In this paper, the two adaptive thresholds based on_covariance absolute values (TATCAV) are proposed to increase detection performance in the presence of noise uncertainty. According to the computer simulations using MATLAB 2021b, the value of the probability of detection is Pd=98.1% Compared with the results of two thresholds based on_covariance absolute values (TTCAV) Pd= 95.3% at SNR=-18, noise uncertainty Nu=1.761 dB, and using quadrature amplitude modulation (QAM). And the error rate for the proposed approach is Pe=12.1% under the same circumstances. The proposed approach results, according to the simulations, are considerably better than the results of the fixed two-threshold approach.