Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large corpus of training data to train the models. Labelling requires human annotators from different backgrounds to annotate key frames in videos which is not only expensive and time consuming but also prone to subjective errors and inconsistencies between the labelers. To overcome these problems, we propose an automatic self-supervised method for detecting key frames in a video. Our method comprises a two-stream ConvNet and a novel automatic annotation architecture able to reliably annotate key frames in a video for self-supervised learning of the ConvNet. The proposed ConvNet learns deep appearance and motion features to detect frames that are unique. The trained network is then able to detect key frames in test videos. Extensive experiments on UCF101 human action and video summarization VSUMM datasets demonstrates the effectiveness of our proposed method. Videos normally contain 30 frames per second and more information than is actually required for many computer vision tasks. Typical applications of key frame detection are video summarization, action recognition and visual simultaneous localization and mapping. To process all frames requires extensive memory and computational resources. In many applications, a few and sometimes even one, key frames may be sufficient to achieve the desired results e.g., we can recognize some actions from one frame. Similarly, video summarization itself is the task of finding key frames in a video to summarize the entire video content.In this paper, we address the problem of automatically annotating and detecting key frames in a video. A video is represented as a sequence of continuous frames and the aim is to automatically annotate a set of frames of interest. Here we define “interest” is an abstract concept that denotes the frames can be representative of the video content, meanwhile, diverse to reduce the redundancy . We assuA typical approach to address this problem is to invite human subjects to watch a video and annotate key frames. Due to the inherent subjectivity of the task, it is not possible to obtain a consensus on the key frames. The videos with labelled key frames can then be used to learn models for automatic detection of key frames in previously unseen videos. In this case, the annotated key frames are treated as gold standard. While the users are often well instructed on the annotation task, discrepancies are still expected due to many uncontrollable individual factors such as idiosyncratic viewing preferences and whether the person was attentive etc. Another approach is to use deep neural networks which can deliver state-of-art performance for many visual tasks including key frame detection. However, deep models require large datasets for training, which are time-consuming to annotate by humans.To push forward on high-level analysis and understanding of various tasks in videos, we argue that the development of automatic key frame detection is invaluable. To this end, we propose a deep self-supervised two-stream ConvNet for automatically annotating key frames in a video. We particularly target the case of analyzing human action videos to annotate key frames. Our work is based on the two-stream convolutional network architecture . This arTo train the proposed automatic deep key frame detection network, we require a large labeled dataset of videos and their corresponding keyframes. To address this problem, we draw support from the powerful feature representation ability of Convolutional Neural Networks (CNNs) for images and the class separation and dimensionality reduction ability of Linear Discriminant Analysis (LDA) . We deviExisting key frame annotation approaches for various applications rely on learning from manually annotated data. For example, the most popular key frame based video summarization approaches leverage the key frames that are annotated by several human subjects from different backgrounds. To the best of our knowledge, there is no unsupervised learning method for automatic detection of key frames in videos. Instead, the bulk of related works in a broader sense comes from video annotation and key frame detection.The process of ground truth annotation has become a fundamental task in the development of machine learning based computer vision applications. Due to its time-consuming nature, various tools and strategies have emerged to facilitate the annotation task. For object detection in videos and related tasks, many popular annotation tools have been exploited such as ViPER , LabelMeConventional Methods: Many earlier approaches in this domain rely on using a segmentation based pipline. Such methods typically extract optical flow and SIFT features [features ,37,38,39Another class of conventional methods rely on clustering the features, such as color histograms, of video frames. These methods determine the key frames in a video by detecting a representative frame from each cluster e.g., the frame whose features are nearest to the cluster mean. Zhuang et al.  proposedDeep Learning Methods: To overcome the limitations of conventional methods, more recent works focus on designing deep learning models to tackle the problem of key frame detection. Several supervised and unsupervised models have been proposed for key frame detection in videos which significantly boost the performance of various downstream tasks [am tasks ,54,55,56am tasks  first inam tasks . Mahasseam tasks  adopted am tasks  also proam tasks  introducam tasks  proposedGiven a test video, our goal is to devise an effective strategy for annotating key frames in the video and subsequently develop a key frame detection algorithm. To this end, we introduce a deep regression ConvNet with two-streams to learn and fuse both spatial and temporal features for exploiting spatio-temporal information in videos. Spatio-temporal features have been demonstrated to be very effective in video based classification and recognition tasks in the computer vision field ,61,62,63V be represented as a collection of frames T is the total number of frames in video V and i-th frame. Hence, the key frame annotation set Key frame detection is a pre-requisite for video summarization and other video understanding tasks. These methods represent a video containing a large number of sequential frames by only a few (key) frames which can still well represent the original video. If we could obtain the key frame annotations (labels) for a training set of videos, these could be used to learn various models to perform the above tasks. Let video In this paper, we propose a self supervised method that learns to automatically detect key frames in videos. The proposed method has two main parts. In the first part, frame-level video labelling is performed using the Linear Discriminant Analysis (LDA). Spatial and temporal features are extracted using two CNNs and LDA is used to find a projection that minimizes the distance between same action videos and maximizes the distance between different action videos. We use a human action video dataset for this where the action labels are provided. After projection on the LDA space, the distance of a frame from the class mean is used as a uniqueness measure for the frame. The more unique a frame is, the higher the chances that it is a key frame. In the second step, a two stream CNN is trained to regress video frames on the uniqueness score obtained from the LDA projection of the first step. Once the CNN is trained, it is ready to output the uniqueness score of frames in a video and the frames corresponding to the peaks are selected as key frames.To learn our supervised key frame automatic annotation model, we need the ground-truth (labelled key frames) in videos. Since manual labelling is expensive, we use an alternate approach to obtain labels automatically. We first obtain the frame-level features by fusing the video’s appearance and motion information. Appearance features are extracted with a pretrained CNN which outputs a feature vector of an RGB frame. Motion features are obtained from optical flow. Optical flow output is a two dimensional vector for each pixel. Using the direction of the vector and a circular 2D colormap, we convert each directional vectors to an RGB pixel. Hence, the optical flow output becomes a pseudo RGB image which is also passed through a pretrained CNN to get a feature vector.X belonging to labelled class c, k-th RGB frame and its corresponding optical flow image. Similarly, we can obtain the RGB CNN features and the corresponding optical flow image CNN features of each training video N denotes the number of training videos, n-th training video. Specifically, The process of frame-level video labelling is illustrated in tion See . Assume A as a matrix C projection matrices. For each class, its LDA projection matrix minimizes the within class distance (class A) and maximizes the between class distance (class B).The appearance and motion feature vectors of each frame are fused to form a combined representation enhancing the frame representation capability. The fused features can be written as is (LDA)  techniquGiven A:i-th frame of the m-th video of class A and Then we use class A:fi,m=Fi,mNow that we have a label  network . The majV is passed through the fully trained network described above and the prediction outputs are denoted as smooth spline function. From Equation . Note that there is no overlap between training and test videos.In order to verify our proposed key frame detection method, we also perform experiments on the video dataset VSUMM , which hTo demonstrate the efficacy of our approach, we compare it with several state-of-the-art approaches. 1) Traditional approaches ,68,70,71 TraditioTo the best of our knowledge, literature is devoid of benchmarks on key frame annotation. There are indeed works that have used key frames (self defined) to detect actions but have not benchmarked the “accuracy” or “meaningfulness” of these key frames. This makes our task of comparing our results with other methods very challenging. Before we evaluate our experimental results, we need to define some meaningful evaluation metrics as our objective is to match the ground truth. One direct measure is to evaluate the number of key frames detected by our method as compared to the key frames annotated by the self supervised key frame annotation model. The second measure is to evaluate the temporal distance between the location of the detected key frames compared with the ground truth. To obtain this information for the test videos we separately pass them through our automatic key frame annotation model to get a sense of “ground truth” and then test them on our two-stream key frame detection network. Since we have both the number and location information of the key frames, in the following, we derive the two types of evaluation metrics accordingly.Key frame number match error: Annotating too many irrelevant frames as key frames in a video would undermine the main objective of video summarization, action recognition. It will also affect subsequent processing in terms of more computations and less accuracy due to insignificant key frames. Thus, we propose a key frame number match error Key frame location match error: The temporal location matching of key frames is very important in key frame detection, since it directly illustrates whether the annotated frame is indeed a key frame i.e., important for video summarization, action recognition and video retrieval. We assume that the ground truth key frame locations are F.Furthermore, we also consider F-Score, which is widely used in video summarization tasks, for evaluation ,68,74, tResults on the UCF 101 Dataset: (1) Next we qualitatively report the output of our key frame annotation and detection models by visually analysing the detected key frames. Furthermore, from Results on the VSUMM Dataset: (2) To better visualize the characteristics of our method, we presents two different kinds of key frame detection results using our method on the VSUMM dataset in We presented a key frame detection deep ConvNets framework which can automatically annotate the key frames in human action videos. Such frames can correctly represent a human action shot with less redundancy, which is helpful for video analysis task such as video summarization and subsequent action recognition. The method used to address this problem learns to dynamically detect key frames of different videos. We trained training a deep ConvNet for key frame detection by combining the RGB video frames and their corresponding optical flow images. Since there is no benchmark dataset for evaluating key frame detection, we used the popular action recognition dataset UCF101 to perform our experiments. However, this dataset lacks frame-level labels. To mitigate this issue, we employed a self supervised learning method to automatically annotate frame-level labels by combining the CNN features and LDA. We verified our method on the UCF101 dataset and obtained encouraging results. Furthermore, to demonstrate the efficacy of our network trained on dataset UCF101, we also performed experiments on the video dataset VSUMM and obtained state-of-the-art results. To the best of our knowledge, this work is the first to report results for automatic key frame annotation in human action videos via deep learning.


Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing the rapid collection of large amounts of data. However, due to the laborious and time-consuming manual analysis procedure, only a small part of the information stored in the archives of underwater images is retrieved. Emerging novel deep learning methods open up the opportunity for more effective, accurate and rapid analysis of seabed images than ever before.We present annotated images of the bottom macrofauna obtained from underwater video recorded in Spitsbergen island's European Arctic waters, Svalbard Archipelago. Our videos were filmed in both the photic and aphotic zones of polar waters, often influenced by melting glaciers. We used artificial lighting and shot close to the seabed (<1 m) to preserve natural colours and avoid the distorting effect of muddy water. The underwater video footage was captured using a remotely operated vehicle (ROV) and a drop-down camera. The footage was converted to 2D mosaic images of the seabed. 2D mosaics were manually annotated by several experts using the Labelbox tool and co-annotations were refined using the SurveyJS platform.A set of carefully annotated underwater images associated with the original videos can be used by marine biologists as a biological atlas, as well as practitioners in the fields of machine vision, pattern recognition, and deep learning as training materials for the development of various tools for automatic analysis of underwater imagery. The dataset can be useful both as a biological atlas and training material for automatic segmentation solutions. Seabed imagery data can be used for multiple purposes in marine biology and environmental sciences, for example: benthic habitat classification and mapping, marine environmental monitoring, impact assessment, biogeographical reconstructions in the context of climate change, etc.•A set of carefully annotated underwater images, linked to source videos, is of great value for both marine biologists and researchers as well as practitioners working in the fields of Machine Vision, Pattern Recognition, Machine Learning, and Deep Learning.•The data will be used for the development of methods and tools for automatic identification of biological categories in underwater imagery, semantic image segmentation, object detection, and automatic characterization of the seabed. The data might be used for validation of various machine vision applications , educational purposes  and other tasks.•Posidonia oceanica meadows.There is a lack of annotated underwater imagery datasets with just a few recently published cases •Acropora palmata, Orbicella spp., Siderastrea, Porites astreoides, Gorgonia ventalina, sea plume, sea rod, algae, rubble, sand) were prepared. More coral reef transects exist The most similar dataset to ours in terms of biological accuracy and seabed aspect is the coral reef study by King et al. •Our video was captured in both the photic and aphotic zones of polar waters, often in the vicinity of melting glaciers. We used artificial lighting and shot close to the seabed to preserve natural colours and avoid the distorting effect of turbid waters.1a)Video samples. In total, 22 min 51 s of video footage was filmed and split into 10–30 s segments, resulting in 47 video samples; frame rate was reduced to 3–5 fps.b)Laminaria sp., benthic trachymedusa Ptychogastria polaris, burrowing anemone Halcampa sp., tube anemone Ceriantharia sp., tube-dwelling Polychaeta, spider crab Hyas sp., Shrimps, brittle stars Ophiuroidea, sea star Urasterias lincki, sea squirts Tunicata, snailfishes Liparidae and flatfishes Pleuronectiformes.2D mosaics. All video samples were converted into still images (video mosaics), that were manually analysed by marine biologists – specialists in the Arctic biota, who identified visible biological objects at the lowest possible taxonomic level. Twelve taxons were targeted for annotation see : brown ac)Annotations. The annotation process, where four experts performed manual pixel-wise segmentation see  and maskWe present visual data of bottom macrofauna filmed in the sublittoral of European Arctic – Svalbard. Some of the areas  are in the vicinity of melting glaciers; others are in ice-free areas . The dataset B1_0332_30 s.jpg, where B stands for Borebukta bay , 1 – transect number, 0332 – 3 min 32 s start time from a raw video, 30 s – length of video sample from which this mosaic file was made.The labels of files indicate site, transect, a part of raw video from which video sample was cropped and the duration of a video sample. I.e., file named 22.1Underwater video data were acquired with a ROV, equipped with a low-resolution analogue camera on a tilted unit for navigation, and a primary camera. The primary camera was mounted vertically, has 3 CCD, Full HD (1920 × 1080) resolution, high-quality Leica Dicomar lenses and 10x optical zoom. The primary camera lighting system consists of 16 bright LED in 4 × 4 stations. The ROV was used in Borebukta, Dahlbrebukta, Eidembukta, Gipsvika, St. Johnsfjord and in Trygghamna. A Drop-down camera was equipped with an analogue camera of 700 TV lines (TVL) resolution for live view and a digital camera (Panasonic HX-A500) that recorded the material at high resolution (1280  ×  720 px) on a memory card. The drop-down camera was used in Adriabukta and Burgerbukta. During the filming, camera speed was about 1 knot to avoid motion blur, and camera altitude over the seabed was 0.4–0.5 m, to ensure optimal lighting conditions. Stations near glaciers had very turbid water because of the inflow of glacial meltwater. At those stations, colours were slightly washed out due to light scattering on the suspended particles, but the imagery was still useful.2.2Video mosaicking is a process of converting a video sample into a single still image containing overlapping video frames. For the pre-mosaicking process raw videos were divided into 10–30 s video segments. Frame size was reduced, and the frame rate was lowered to 3–5 fps to shorten computing time. Each frame was enhanced for more accurate pair-wise registration and video mosaics were produced using original non-enhanced video footage and pair-wise registration data. Algorithms for video mosaicking have been developed by Rzhanov et al. 2.3LabelboxPrepared 47 large 2D mosaics were uploaded to the online collaborative annotation platform 2.4Labelbox, were later processed using the R language script to form a survey for all masks (both overlapping between experts and unique) in .json format. Correctly formated .json survey was uploaded to the SurveyJS platform Expert annotations, downloaded from 1.Find objects segmented only by a single expert.2.Find objects segmented by several experts simultaneously. Create a new synthetic mask using a union of two masks with the highest overlap.3.For each object cut-out its view from mosaic to get a background image.4.For each mask of the object create an overlay to get overlayed images.5.imguR.Upload background and overlayed images to a free image hosting service 6.For each object make a survey question using image picker and dropdown see .7.SurveyJS platform for survey serving  or choosing between the two selected masks at random.Andrius Šiaulys: Writing- Original draft preparation, Data collection, Annotation, Data curation, Survey; Evaldas Vaičiukynas: Writing- Reviewing and Editing, Methodology, Mask refinement; Saulė Medelytė: Selection of taxons, Mosaicking, Annotation, Survey; Sergej Olenin: Data collection, Annotation, Conceptualization, Survey; Aleksej Šaškov: Data collection, Annotation, Survey; Kazimieras Buškus: Editing, Data curation; Antanas Verikas: Conceptualization, Supervision.None.


Compounds 2 and 4 showed interestingly efficient and selective antitumor (hCA IX and hCA XII) inhibitor activities . Compounds 4 and 5 showed selective hCA IX inhibitory activity over hCA I , hCA IX/hCA II  and selective hCA XII inhibitory activity over hCA I , hCA XII/hCA II,  respectively compared to AAZ. Compounds 12–17, and 19–20 showed selective inhibitory activity towards hCA IX over hCA I and hCA II, with selectivity ranges of 27–195 and 3.2–19, respectively, while compounds 12, 14–17, and 19 exhibited selective inhibition towards hCA XII over hCA I and hCA II, with selectivity ratios of 48–158 and 5.4–31 respectively, compared to AAZ. Molecular docking analysis was carried out to investigate the selective interactions among the most active derivatives, 17 and 20 and hCAs isoenzymes. Compounds 17 and 20, which are highly selective CA IX and XII inhibitors, exhibited excellent interaction within the putative binding site of both enzymes, comparable to the co-crystallized inhibitors.Quinazoline-linked ethylbenzenesulfonamides inhibiting CA were synthesised.The new molecules potently inhibited the hCA isoforms I, II, IV, and IX.4 and 5 were found to be selective hCA IX/hCA I and hCA IX/hCA II inhibitors.Compounds 4 and 5 were found to be selective hCA XII/hCA I and hCA XII/hCA II inhibitors.Compounds 12–17, 19, and 20 were found to be selective hCA IX/hCA I and hCA IX/hCA II inhibitors.Compounds 12, 14–17, 19 were found to be selective hCA XII/hCA I and hCA XII/hCA II inhibitors.Compounds 4 and 5 are selective hCA IX and XII inhibitors over hCA I  and hCA II . Compounds 12–17, and 19–20 are selective hCA IX inhibitors over hCA I (selectivity ratios of 27-195) and hCA II (selectivity ratios of 3.2-19). Compounds 12, 14–17 and 19 are also selective hCA XII inhibitors over hCA I (selectivity ratios of 48-158) and hCA II (selectivity ratios of 5.4-31).Compounds We evaluated the hCA  inhibitory activity of novel 4-(2-(2-substituted-thio-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamides (compounds  CAs are classified into eight genetically distinct families, named α-, β-, γ-, δ-, ζ-, η-, ɵ- and ι-CAs,,,,,A, ,N-(4-sulfamoylphenethyl)anildes  constitute the superfamily of metalloenzymes that catalyse the COildes B,  also shoes B, C,  showed sC, 2–20, Here, we studied 2-mercaptoquinazolinone, C,  a slight2.2.1.1H and 13C NMR) spectra were recorded with Bruker 700 MHz spectrometers . Micro-analytical data  were obtained using a Perkin-Elmer 240 analyser  and agreed with the proposed structures within ±0.4% of the theoretical values. Mass spectra were recorded on a Varian TQ 320 GC/MS/MS mass spectrometer . Thione 1 and compounds 8–20 were prepared as described earlier,Melting points were recorded on a Barnstead 9100 electrothermal melting point apparatus (UK). IR spectra (KBr) were recorded on a FT-IR Perkin-Elmer spectrometer . NMR  and potassium carbonate  in 6 ml acetone were stirred at room temperature for one hour. Appropriate phenacyl bromide (1 mmol) was added and the reaction mixture was stirred at room temperature for 9–12 h, filtered, and the crude solid was washed with water, dried and recrystallized from ethanol .A mixture of thione 4-(2-(4-Oxo-2-((2-oxo-2-phenylethyl)thio)quinazolin-3(4H)-yl)ethyl)benzenesulfonamide (2): m.p 246–247°; 94% yield; IR  ν: 3284, 3237 (NH), 1665 (C=O), 1342, 1151 (O=S=O); 1H NMR : δ 8.14 , 8.04 , 7.82 , 7.74 , 7.66 , 7.62 , 7.52 , 7.41 , 7.37 , 6.98 , 4.92 , 4.33 , 3.14 ; 13C NMR : δ 194.04, 160.76, 156.10, 146.92, 143.11, 142.29, 136.90, 135.19, 134.01, 129.67, 129.29, 128.79, 126.92, 126.45, 125.87, 119.08, 45.69, 39.38, 33.67; Ms; m/z (479).4-(2-(2-((2-(4-Bromophenyl)-2-oxoethyl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamide (3): m.p 248–248°; 95% yield; IR  ν: 3280, 3236 (NH), 1686 (C=O), 1340, 1153 (O=S=O); 1H NMR : δ 8.07 , 8.04 , 8.85 , 7.81 , 7.68 , 7.52 , 7.41 , 7.37 , 7.00 , 4.89 , 4.32 , 3.13 ; 13 C NMR : δ 193.41, 160.74, 156.02, 146.88, 143.12, 142.27, 135.9266, 135.26, 132.38, 130.81, 129.67, 128.10, 126.93, 126.55, 126.52, 126.45, 125.87, 119.08, 45.72, 39.27, 33.67; Ms; 558.0; Ms; .4-(2-(2-((2-(4-Chlorophenyl)-2-oxoethyl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamide (4): m.p 250–251°; 93% yield; IR  ν: 3281, 3239 (NH), 1684 (C=O), 1345, 1159 (O=S=O); 1H NMR : δ 8.15 , 8.04 , 7.82 , 7.70 , 7.67 , 7.52 , 7.41 , 7.37 , 6.99 , 4.89 , 4.32 , 3.13 ; 13 C NMR : δ 193.20, 160.74, 156.03, 146.89, 143.12, 142.28, 138.89, 135.60, 135.25, 130.73, 129.67, 129.43, 126.93, 126.53, 125.86, 119.08, 45.72, 39.28, 33.67; Ms; 514; Ms; .4-(2-(2-((2-(4-Fluorophenyl)-2-oxoethyl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamide (5): m.p 249–250°; 92% yield; IR  ν: 3278, 3238(NH), 1666 (C=O), 1342, 1152 (O=S=O); 1H NMR : δ 8.23 , 8.04 , 7 82 , 7.68 , 7.52 , 7.46 , 7.41 , 7.37 , 6.99 , 4.90 , 4.33 , 3.14 ; 13 C NMR : δ 192.73, 166.36, 164.93, 160.7571, 156.07, 146.89, 143.12, 142.28, 135.24, 133.65, 133.64, 131.88, 131.83, 129.67, 126.92, 126.52, 125.87, 119.08, 116.41, 116.29, 45.70, 39.26, 33.67; Ms; m/z (497).4-(2-(4-Oxo-2-((2-oxo-2-(p-tolyl)ethyl)thio)quinazolin-3(4H)-yl)ethyl)benzenesulfonamide (6): m.p 257–258°; 92% yield; IR  ν: 3281, 3237 (NH), 1665 (C=O), 1339, 1150 (O=S=O); 1H NMR : δ 8.04 , 7.82 , 7.68 , 7.52 , 7.42 , 7.37 , 7.04 , 4.90 , 4.33 , 3.14 , 2.44 ; 13 C NMR : δ 193.40, 160.78, 156.12, 146.94, 144.46, 143.12, 142.30, 135.21, 134.33, 129.83, 129.67, 128.93, 126.91, 126.52, 125.95, 119.09, 45.65, 39.41, 33.67, 21.73; Ms; m/z (493).4-(2-(4-Oxo-2-((1-oxo-1-phenylpropan-2-yl)thio)quinazolin-3(4H)-yl)ethyl)benzenesulfonamide (7): m.p 245–246°; 90% yield; IR  ν: 3279, 3237 (NH), 1668 (C=O), 1347, 1154 (O=S=O); 1H NMR : δ 8.16 , 8.29 , 7.80 , 7.73 , 7.65–7.61 , 7.84 , 7.39 , 7.37 , 6.78 , 5.75 , 4.29–4.19 , 3.07 , 1.57 ; 13 C NMR : δ 198.20, 160.67, 155.81, 146.87, 143.12, 142.23, 135.98, 135.15, 134.01, 129.69, 129.34, 128.95, 126.92, 126.57, 126.48, 125.46, 119.13, 46.23, 45.76, 33.60, 16.44; Ms; 493.00; Ms; m/z (493).2.2.,The hCA I, II, IX, and XII isoenzyme inhibition assays were performed according to the reported method using the SX.18 MV-R stopped-flow instrument 2.3.,,,,,The molecular docking protocol was conducted according to the reported methods3.3.1.H)-yl)ethyl)benzenesulfonamide (1) was obtained via the reaction of 4-(2-isothiocyanatoethyl)benzenesulfonamide, triethylamine and 2-aminobenzoic acid in boiling ethanol,1 with potassium carbonate in acetone and different phenacyl bromides produced the corresponding 4-(2-(2-((2-(4-substituted-phenyl)-2-oxoethyl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamides 2–7 with 90–95% yield. Various spectroscopic studies were conducted to validate the structures of the newly synthesised compounds, 2–7. The target compounds, 2–6, were validated by the diminishing of the thioamidic proton (NH–C=S) at 13.03 ppm and that of the thione moiety (NH–C=S) at 175.29 ppm, as well as by the presence of the phenacyl carbonyl group (SCH2COAr) at 194.04–192.73 ppm, with singlet peaks at 4.92–4.89 ppm and 39.41–39.26 ppm due to the phenacyl (S2CHCOAr) moiety, in the 1H and 13C NMR spectra, respectively. Additionally, 4-(2-(4-oxo-2-((1-oxo-1-phenylpropan-2-yl)thio)quinazolin-3(4H)-yl)ethyl)benzenesulfonamide (7) was confirmed by presence of the carbonyl group of (S(CH)CH3COAr) at 198.20 ppm in the 13C NMR spectrum, as well as by the quartette (S(CH)CH3COAr) and doublet (S(CH)3CHCOAr) peaks at 5.75 and 1.57 ppm respectively in the 1H NMR spectrum, together with the characteristic peaks (S(CH)CH3COAr) at 46.23 and (S(CH)3CHCOAr) at 16.44 ppm in the 13C NMR spectrum. The ethylbenzenesulfonamide amino group (NH2) (in compounds 2–7) was long-established by the presence of a typical singlet peak at 7.37 ppm in the 1H NMR spectrum. The tails of aliphatic ethylbenzenesulfonamide moiety were fixed by triplet peaks at 4.33–4.32 and 3.15–3.12 ppm in the 1H NMR spectrum and distinctive peaks at 45.72–45.65 and 33.67 ppm in 13C NMR spectrum, respectively. 2-Substituted mercapto-4(3H)-quinazolinones (8–20) were prepared in 90–96% yield by mixing compound 1 and 2-chloro-N-substitutedamide in acetone at room temperature in the presence of potassium carbonate4- towards hCA I, II, IV, and IX isoforms was measured and compared to acetazolamide (AAZ), a typical sulphonamide inhibitor. hCA I was effectively inhibited by compounds 2 and 4–13 with the inhibition-constant (KI) values ranging from 114.5–938.3 nM (AAZ: KI = 250.0 nM). Compounds 3 and 16 showed moderate activity with KI values of 1447.0 and 1697.0 nM, respectively, while compounds 14–15 and 17–20 showed weak activity with KI values ranging from 2048–5467 nM. Compounds 5, 8, 9, 11, 12, and 20 were verified to be effective hCA II inhibitors, with KI values of 25.4–95.4 nM (AAZ: KI = 12.0 nM). Compounds 2, 3, 4, 6, 7, 10, 14, and 16 showed modest hCA II inhibitory activity with KI values ranging between 116.2 and 266.1 nM, whereas compounds 13 and 15 showed a weak inhibitory activity with KI values of 304.6 and 1099.0 nM, respectively. Compounds 2–17 and 20 displayed potent hCA IX inhibitory activity with KI values ranging from 8.0 to 100.4 nM, which were greater than or nearly identical to that of AAZ (KI = 25.0 nM), whereas compounds 18 and 19 showed modest hCA IX inhibitory activity with KI values ranging between 256.4 and 145.1 nM, respectively. 4-(2-(2-(Substituted-thio)-4(3H)-quinazolinon-3-yl)ethyl)benzenesulfonamide derivatives 2, 4, 5, 8, 9, 11, 12, 13, 14, 16 and 17 showed potent hCA XII inhibitory activity with KI values of 2.4–49.1 nM compared to AAZ (KI = 5.7 nM), whereas compounds 3, 6, 7, 10, 15, 18, 19, and 20 exerted moderate hCA XII inhibitory activities with KI values of 59.7–113.4 nM (2 and 4 showed characteristic effective and selective antitumor (hCA IX and hCA XII) carbonic anhydrase inhibitory activity with KI values  and KI values  compared with AAZ . 4-(2-(2-((2-(4-Substituted-phenyl)-2-oxoethyl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamides (compounds 2–7) showed high selectivity in the inhibition of hCA IX over hCA I and hCA II , as well as selectivity in the inhibition of hCA XII over hCA I and hCA II  (The CA inhibitory activity of 4-(2-(2-(substituted-thio)-4(3H)-quinazolinon-3-yl)ethyl)benzenesulfonamides (compounds 113.4 nM . On the ctively) .4 and 5 showed high selectivity in the inhibition of hCA IX over hCA I and hCA II, with selectivity ratios of 95.0 and 23.0, respectively for compound 4, and those of 24.0 and 5.8, respectively, for compound 5, compared with AAZ selectivity ratios of 10.0 and 0.5, respectively. Additionally, compounds 4 and 5 showed selective inhibition of hCA XII over hCA I and hCA II with selectivity ratios of 70.0 and 17.0, respectively, for compound 4, and 44.0 and 10.0, respectively, for compound 5, compared with AAZ selectivity ratios of 44.0 and 2.1, respectively. N-(substituted)-2-((4-oxo-3-(4-sulfamoylphenethyl)-3,4-dihydroquinazolin-2-yl)thio)amides (compounds 8–20) showed high selectivity in the inhibition of hCA IX over hCA I and hCA II, with selectivity ratios in the range 3.3–195.0 and 0.7–8.3 respectively, compared with AAZ selectivity ratios of 10.0 and 0.5 respectively, and that of hCA XII over hCA I and hCA II, respectively with selectivity ratios in the range 11.0–158.0 and 1.8–31.0, compared with AAZ selectivity ratios of 44.0 and 2.1 respectively. Compounds 12–17, and 19–20 showed selective inhibition of hCA IX over hCA I and hCA II, with selectivity ratios of 23.0–195.0 and 3.2–19.0, respectively, compared with AAZ selectivity ratios of 10.0 and 0.5. Additionally, Compounds 12, 14–17, and 19 showed selective inhibition of hCA XII over hCA I and hCA II, with selectivity ratios of 48.0–158.0 and 5.4–31.0, respectively, compared with AAZ selectivity ratios of 44.0 and 2.1 respectively.Compounds 3.3.2–20) were potent inhibitors of the hCA isoforms.Several synthesised quinazolinone derivatives (compounds 3.3.1.2), with a KI value of 592.7 nM, was more potent than 4-(2-(2-((1-(4-substituted-phenyl)-1-oxopropan-2-yl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamides 3–4 and 4-(2-(4-oxo-2-((1-oxo-1-phenylpropan-2-yl)thio)quinazolin-3(4H)-yl)ethyl)benzenesulfonamide 7, with KI values of 758.7–1447 nM, but less potent than 4-(2-(2-((1-(4-flouro/4-methyl-phenyl)-1-oxopropan-2-yl)thio)-4-oxoquinazolin-3(4H)-yl)ethyl)benzenesulfonamide 5 and 6 with KI values of 399.5–471.0 nM. (2) Unsubstituted-N-acetamide 8  was more active than the corresponding N-phenylacetamide 9  Substitution of the phenyl ring of N-phenylacetamide 9  resulted in substituted-N-phenylacetamides 10–17 and N-phenylpropanamide 20 with significantly decreased CA inhibitory activity . (4) The hCA I inhibitory activity of N-(4-fluorophenyl)-2-((4-oxo-3-(4-sulfamoylphenethyl)-3,4-dihydroquinazolin-2-yl)thio)acetamide (12), with a KI value of 548.6 nM, was more stronger than the corresponding N-(4-fluorobenzyl)-2-((4-oxo-3-(4-sulfamoylphenethyl)-3,4-dihydroquinazolin-2-yl)thio)acetamide (18) KI of 2048 nM. (5) hCA I inhibition of N-acetamide 11, with a KI value of 726.4 nM, was more powerful than the corresponding N-propanamide 20 with a KI value of 3628 nM.SAR analysis of hCA I inhibition indicated revealed several key features. (1) 4–(2-(4-Oxo-2-((2-oxo-2-phenylethyl)thio)quinazolin-3(4H)-yl)ethyl)benzenesulfonamide (3.3.2.H)-yl)ethyl)benzenesulfonamides 5 and 6 with KI values of 95.4–116.2 nM were more effective than unsubstituted phenyl and other substituted phenyl derivatives, such as compounds 2–4 and 7 with KI values of 140.8–202.6 nM. (2) hCA II inhibition of 2-((2-oxo-2-phenylethyl)thio)quinazolinone 2, with a KI value of 140.8 µM, was stronger than the corresponding 2-((1-oxo-1-phenylpropan-2-yl)thio)quinazolinone 7 with a KI value of 202.6 nM. (3) N-Phenylacetamide 9 with a KI value of 69.7 nM was less potent than unsubstituted-N-acetamide 8 . (4) Substitution of the phenyl ring of N-phenylacetamide 9  resulted in substituted-N-phenylacetamides 10–17 and N-phenylpropanamide 20 with considerably diminished CA II inhibitory activity ; (5) The hCA II inhibitory effect of N-acetamide 11  was less potent than the corresponding N-propanamide 20 . (6) The hCA II inhibitory activity of N-(4-fluorophenyl)acetamide 12, with a KI value of 87.6 nM, was stronger than the corresponding N-(4-fluorobenzyl)acetamide 18 (KI of 2048 nM).The SAR analysis for hCA II inhibition revealed several key features. (1) 4-(2-(2-((1-(4-Fluorophenyl/4-methylphenyl)-1-oxopropan-2-yl)thio)-4-oxoquinazolin-3thio)quinazolinone 7 with KI value of 63.2 nM. (2) The induction of the activating group, such as the 4-methyl group on the phenyl ring of compound 2  led to compound 6, with an increased hCA IX inhibitory activity . (3) The introduction of the deactivating group on phenyl ring of compound 2, such as the 4-bromo group, resulted in compound 3 with diminished hCA IX inhibition activity ; in contrast, the introduction of 4-fluoro/4-chloro groups produced compounds 4–5 with boosted the inhibitory potency of the hCA IX . (4) N-propanamide 20, with a KI value of 18.6 nM, was powerful than the corresponding N-acetamide 11 with a KI value of 66.8 nM. (5) The introduction of activating/deactivating groups on the phenyl ring of compound 2  yielded compounds 10–17 with reduced inhibitory activity , except for compounds 12 and 16, which had improved hCA IX inhibitory potency . (6) Substitution of the phenyl group of compound 12  with a benzyl moiety resulted in compound 18, which had significantly reduced hCA IX inhibitory activity .SAR analysis of hCA IX inhibition revealed several key factors. (1) The 2-((2-oxo-2-phenylethyl)thio)quinazolinone 3.3.4.2, with a KI value of 13.0 nM, was more potent than 2-((1-oxo-1-phenylpropan-2-yl)thio)quinazolinone 7 with a KI value of 76.8 nM. (2) The introduction of a chloro/fluoro group at the phenyl ring, such as in compounds 4 and 5 , improved the hCA XII inhibition activity and was similar to that of compound 2 . (3) The unsubstituted N-acetamide, compound 8,  resulted in more powerful hCA XII inhibition than N-substituted amides, compounds 9–20, . (4) hCA XII inhibition of N-acetamide 11, with a KI value of 31.6 nM, was more powerful than that of the corresponding N-propanamide 20 with a KI value of 66.7 nM. (5) The substitution of the phenyl group of N-(4-fluorophenyl)acetamide 12  with a benzyl moiety resulted in the N-(4-fluorobenzyl)acetamide 18, with sharply reduced CA inhibitory activity .SAR analysis for hCA XII inhibition revealed several key factors. (1) 2-((2-Oxo-2-phenylethyl)thio)quinazolinone 3.4.3.4.1.17 and 20 with the hCAs targets, we performed docking simulations into the binding pockets of the hCA isoforms, IX and XII, using the MOE SuiteTo further investigate the interactions between the selected active compounds 17 and 20 were shown to directly interact with the zinc ion of CA IX and CA XII isoenzymes, via the sulphonamide anion of the active sites of both enzymes. However, the contributions of the quinazoline scaffold and the terminal bulky thioether fragments interaction are different, based on the CA isoform. In CA IX, the quinazoline ring of compound 20 interacts with the Gln71 residue through a stable hydrogen bond, and gets accommodated in the hydrophobic pocket lined by the Val121, Val130, Leu134, and Leu91 residues, thereby stabilising the binding  towards the hCA I, II, IV, and IX isoforms was assessed and compared with acetazolamide (AAZ), a typical sulphonamide inhibitor. Of the different hCA isoforms, hCA I was effectively inhibited by the compounds 2 and 4–13, with inhibition constant (KI) values in the range of 114.5–938.3 nM , while compounds 3 and 14–20 showed moderate to weak CA inhibitory activity with KI values of 1447.0–5467 nM. Compounds 5, 8, 9, 11, 12, and 20 were revealed to be effective hCA II inhibitors, with KI values of 25.4–95.4 nM . Compounds 2, 3, 4, 6, 7, 10, 13, 14, 15, and 16 showed modest to weak hCA II inhibitory activity with KI values ranging between 116.2 and 1099.0 nM. Compounds 2–17 and 20 displayed potent hCA IX inhibitory activity with KI values ranging from 8.0 to 100.4 nM compared to AAZ , whereas compounds 18 and 19 showed modest hCA IX inhibitory activity with KI values ranging between 256.4 and 145.1 nM, respectively. Ethylbenzenesulfonamide derivatives, 2, 4, 5, 8, 9, 11, 12, 13, 14, 16, and 17 showed potent hCA XII inhibitory activities with KI values of 2.4–49.1 nM compared to AAZ , whereas compounds 3, 6, 7, 10, 15, 18, 19, and 20 showed moderate hCA XII inhibitory activities with KI values of 59.7–113.4 nM. Compounds 2 and 4 showed characteristic effective and selective antitumor (hCA IX and hCA XII) carbonic anhydrase inhibitory activity with KI values  and KI values . Compounds 2–7 showed high selectivity ratios for the inhibition of hCA IX over hCA I (15.0–95.0) and hCA IX over hCA II (2.3–23.0), while selectivity ratios of hCA XII over hCA I (5.5–70.0) and hCA XII over hCA II (1.4–17.0). Compounds 4 and 5 displayed selective inhibitory activity towards hCA IX over hCA I with selectivity ratios of 95.0 and 24.0 respectively, and hCA IX over hCA II with selectivity ratios of 23.0 and 5.8 respectively, as well as, selective inhibitory activity for hCA XII over hCA I and hCA XII over hCA II . Compounds 12–17, and 19–20 exhibited selective inhibitory activities towards hCA IX over hCA I and hCA IX over hCA II . In addition, compounds 8, 12, 14–17, and 19 showed selective inhibitory activity towards hCA XII over hCA I and hCA XII over hCA II . Docking study of the selective derivatives, compounds 17 and 20, with the hCAs revealed consistent interactions, particularly selectivity-oriented hydrophobic and aromatic interactions through the S-alkyl substituent.The CA inhibitory activity of 4-(2-(2-(substituted-thio)-4(3H)-quinazolinon-3-yl)ethyl)benzenesulfonamides (compounds Click here for additional data file.


Playing video games, a form of sedentary behaviour, is associated with poor well-being and increased risk of morbidity due to chronic disease. However, the association between health behaviours and video gaming is poorly understood. The purpose of this population-based study was to reveal the differences in dietary habits and physical activity for adolescent men with high amount of video games on weekdays, as compared to their peers who play less often.Seven hundred ninety-six adolescent men  attended compulsory conscription for military service in 2013 and completed a questionnaire regarding the amount and frequency of their video gaming. They also participated in a medical examination and underwent physiological measurements. The participants who played video games more than 3 h/d on weekdays were compared with those who played 3h/d or less. The association between health behaviours and the amount of playing video gaming was analysed using multivariable logistic regression analysis.n = 192) of the participants reported video gaming in excess of 3 h/d. This group had higher incidence of having low physical fitness, having poor eating habits, and being obese. No differences were found in smoking or alcohol drinking habits. Other factors, including low leisure-time physical activity , low consumption of vegetables and fruits , high consumption of sweetened soft drinks  and high amount of sitting time , explained one-fourth of the difference.24.1% (In this population-based study, adolescent men who played video games a lot on weekdays had lower physical fitness, were more often obese, and had poorer dietary habits, as compared to their peers who played less often. Because playing video games typically adds to a person’s total sedentary time, this activity may be associated with adverse health outcomes at a very young age— especially in combination with poor health behaviours. The results of this study can be utilized to promote health interventions targeted at adolescent men so as to raise their awareness of the disadvantages of excessive video gaming. According to the current evidence, regular physical activity in adolescence is associated with many health benefits . In addiTraditional television watching has declined in the past 10 years, and it has been replaced by other screen-based activities . In theisedentary behaviour is not a synonym for physical inactivity, which refers to physical activity levels that do not meet recommendations ) . Weekly in [90]) . The resin [90]) .The amount of leisure time spent sitting each day was determined with the following question: “Approximately how much time do you spend sitting per day, outside of school and work ?” The participants reported this in hours, and the results were classified as a two-class variable (nonsedentary vs. sedentary) with a cut-off point of 5 h/d , and sitsignificantly poorer) through 5 (significantly better). The values of 1 and 2 were combined as poorer, and the values of 3–5 were combined as similar or better, resulting in a two-class variable. Similarly, self-rated health was investigated with the following question: “What is the state of your health?” The response options were on a 5-point scale from 1 (good) through 5 (poor). Again, the values from 1 to 3 were combined (good or average), as were the values of 4 and 5 (poor).Physical fitness was evaluated with the question “How fit are you, as compared to your peers?” The response options were on a 5-point scale from 1 (never), 2 (1–2 days per week), 3 (3–5 days per week), and 4 (6–7 days per week), and the frequency was recoded using the following conversion: never = 0 d/wk., 1–2 days per week = 1.5 d/wk., 3–5 days per week = 4 d/wk., and 6–7 days per week = 6.5 d/wk. [Salty foods were defined as including “pizza and kebabs,” “savoury pastries ,” and “salty snacks .” Sweet foods were defined as including “sweet pastries ” and “chocolate and other sweets.” The mean consumption of sweetened soft drinks was based on the average of the frequencies for sugary or artificially sweetened soft drinks and energy drinks. The participants also answered whether they usually eat breakfast (yes or no).Dietary habits were determined with the following question about the mean consumption of various foods: “How often during the preceding week did you consume the following foods or drinks?” The answers were 1  through 6 . A two-grade variable was formed to measure binge drinking (defined as 6 servings or more in a day at least once per week) [Alcohol intake was measured with this question: “How often do you drink six or more servings of alcohol at once?” The response options were on a 6-point scale from 1 (er week) , 28. The. Similarly, waist circumference was measured (to the nearest 1 cm) along the midline between the lowest rib and the iliac crest. Body composition was measured using weight (with 0.1-kg accuracy), fat-free mass (at 0.1-kg accuracy), and percentage body fat; the latter was measured with direct segmental multifrequency bioelectric impedance analysis . During the measurements, the subjects stood without shoes or socks and in light indoor clothing. This method has been validated using whole-body dual X-ray absorptiometry. Each participant’s percentage of fat-free mass was then calculated by dividing muscle mass (kg) by whole-body mass (kg), and each participant’s BMI was calculated by dividing weight (kg) by height squared (m2).Each participant’s height was measured (with 0.5-cm precision) using a wall-mounted measuring tapeEach participant’s grip strength was measured using a hand dynamometer . During the test, each subject stood with legs apart and elbows at a 90° angle, gripping the dynamometer with maximum force. After two attempts using each hand, the best result for each hand was recorded, and the mean of these results was used in the analyses. Aerobic fitness was evaluated using the Polar Fitness Test , which was conducted while the subject was at rest for 5 min. This test predicts maximal oxygen uptake (mL/min/kg) using resting heart rate, heart-rate variability, sex, age, height, body weight, and self-assessed physical activity . The Polt test for continuous variables was used to analyse the statistical significance of the mean differences with 95% confidence intervals between the PV > 3 and PV ≤ 3 groups. Due to the skewed distributions of MET min values, the medians of these values were used, as were the results of a Mann-Whitney U test. Cross-tabulation and the chi squared test were used for the categorical variables. All variables that were significantly associated with video gaming in the univariate analysis were then entered into the binary logistic regression analysis. Video game playing was a dependent variable with categories: 0 = playing ≤ 3 h/d on weekdays and 1 = playing > 3 h/d on weekdays. Because playing video games is a type of sedentary behaviour, leisure-time sitting (as a continuous variable) was entered into the block one with enter selection, and the other associated variables in the second block with forward stepwise (Likelihood Ratio) selection. The level of significance was set at P < .05.Statistical analyses were performed using IBM SPSS Statistics for Windows . An independent-sample The study population comprised 796 adolescent men with mean age 17.9 (age range 17–22). The PV > 3 group included 192 (24.1%) individuals, who played video games, on average, 5.5 (SD = 2.0) h/d on weekdays and 7.3 (SD 3.9) h/d on weekends and days off. The PV ≤ 3 group included 604 individuals, who played, on average, 1.3 (SD 1.1) h/d on weekdays and 2.4 (SD 2.2) h/d on weekends and days off. The difference on playing on weekends and days off was statistically significant between the groups (mean difference 4.9 (95% CI 4.3–5.5).P = .007), but there was no difference in the number of underweight individuals. In addition, those in the PV > 3 group had, on average, 3.4% higher body fat , 3.1 cm larger waist circumference , and 2.1% less muscle mass , as compared to those in the PV ≤ 3 group. The participants in the PV > 3 group had poorer physical fitness, on average: 1.8 kg weaker grip strength  and 3.2 mL/min/kg higher maximal oxygen uptake . They also had less physical activity, as measured in MET min . In the PV > 3 group, 44% rated their physical fitness as poorer than that of their peers, but in the PV ≤ 3 group, only 18% did so (P < .001). In the PV > 3 group, 12.8% rated their health as poorer than that of their peers, but in the PV ≤ 3 group, only 5.4% did so (P = .001). Those who played a lot of video games also sat a lot; in the PV > 3 group, 69% of the participants sat for 5 h/d or more during their leisure time, but in the PV ≤ 3 group, only 25% did so (P < .001). Eating habits were also poorer in the PV > 3 group; during the preceding week, those in this group ate vegetables and fruits less frequently  and drank sweetened soft drinks and energy drinks more often . In addition, those in the PV > 3 group skipped breakfast more often than did those in the PV ≤ 3 group (P < .001).The characteristics of the study population are described in Table In the binary logistic regression analysis Table , playingThis population-based cross-sectional study of adolescent men mainly 17–18 years old related to the associations that both physical activity and dietary habits have with the amount of video gaming on weekdays. Having low physical fitness, being obese, and having poor eating habits were more frequent among those who played video games more than 3 h/d on weekdays. No difference was found in smoking and alcohol drinking. Low leisure-time physical activity, low frequency of consumption of vegetables and fruits, high consumption of sweetened soft drinks and energy drinks, and high amount of leisure sitting time explained one-fourth of the results.High screen time is also associated with insufficient physical activity and higher sitting time, according to studies of 13- to 18-year-old boys and girls; in these studies, screen time included watching television, using a computer, playing video games, and using smartphones , 32. VidPoor eating habits are also associated with playing video games. The results of a previous study of fourth-grade children show a positive association between video gaming and consumption of high-calorie and low-nutrient foods . AnotherThis study’s results indicate no association between alcohol consumption and the amount of video gaming, which supports the previous findings –44. The P < .001) and had lower self-rated fitness , as compared to their peers.The strengths of this research are its population-based design, which contained an entire age cohort of adolescent men at approximately 18 years old; its substantial data collection ; and its high compliance rate due to the data being collected during compulsory conscription. In addition, the measurements of body composition and physical performance were taken under controlled conditions. Those who did not complete the study questionnaire did not differ from the study participants with regards to perceived weight, self-rated health, sedentary behaviour, or anthropometric measures. However, those who did not complete the questionnaire were less likely to be students (76% vs. 86%, One limitation of this study is the lack of objective measurements of physical activity and time spent playing video games. In this study, we used self-reported measures, which may have led to overestimates of the participants’ amount of overall physical activity , 47 or oIn this population-based study of approximately 18 years old men, those who played video games more than 3 hours per day on weekdays, as compared to their peers, had lower levels of physical activity and were more likely to be obese. They also consumed less vegetables and fruits, and more sweetened soft drinks. Video gaming typically increases daily sitting time, as was also in this study, which affect harmful for health with other poor health behaviours. These preliminary results of this study fortify that video gaming is associated with detrimental health behaviour, which would be worth to take account with adolescent men, who play a lot on weekdays. Because physically less active 17–18 years old men seemed to spend more time playing video games, it could be studied in the future whether video games could be used in physical activation of adolescent men. Prospective studies are also needed in order to investigate the causality of the correlation between video gaming and health behaviours in adolescent men.


Recent data suggest that excessive alcohol use is increasing among women and older adults. Such trends are concerning, as women are more vulnerable to alcohol-related health consequences, and such health problems may be exacerbated with age. Furthermore, there are sex-specific factors that may influence alcohol consumption among women, including the hormonal changes associated with the menopausal transition and negative affect. The present study sought to investigate transitions in excessive drinking among women across the menopausal transition and included exploration of sex hormones  and depression.The present study utilized publicly available data from the Study of Women Across the Nation (SWAN) and included 3302 women (42–52 years old at baseline), who completed 10 years of annual assessments. National Institute on Alcohol Abuse and Alcoholism (NIAAA) criteria were used as guidance when defining excessive drinking within the present dataset. At year 1, 170 women were identified as drinking excessively. Random-effect logistic regressions were used to examine transitions in excessive drinking.ORs range = 3.71–5.11), while women were more likely to transition from non-excessive to excessive drinking during the early peri- and postmenopausal stages . Higher testosterone levels were associated with a decreased likelihood of transitioning to non-excessive drinking (OR = 0.59). Depression and estradiol levels were not related to transitions in drinking.Women identified as excessive drinkers were more likely to transition to non-excessive drinking across all menopausal transition stages (The present study demonstrates that the menopausal transition marks a period of instability in alcohol use among women. Further research is warranted to understand factors related to transitioning in and out of excessive drinking. Historically, alcohol use disorders (AUDs) have been observed in greater rates among males; however, recent data demonstrate that there has been an increase in excessive drinking among women . In factRecent evidence suggests that alcohol use affects reproductive function, menstrual cyclicity, and hormonal levels across the lifespan, including in postmenopausal women , 9. DuriIn addition to the hormonal changes associated with the menopausal transition, another important factor in women’s alcohol consumption is negative affect, as women are generally more likely to drink for negative reinforcement  . While oThe present study sought to investigate the effect of the menopausal transition, as well as the effect of sex hormones (estradiol and testosterone) and negative affect (depressive symptoms), on excessive drinking patterns utilizing publicly available data from the Study of Women Across the Nation (SWAN). Unfortunately, progesterone levels were not included as part of the publicly available dataset and thus were unable to be included in the present analyses. Overall, it was hypothesized that the stages of late peri-menopause and postmenopause would be associated with an increased likelihood of transition to quitting excessive drinking and a decreased likelihood of onset of excessive drinking patterns. It was hypothesized that increasing levels of estradiol, as well as increased self-reported depressive symptoms, would be related to a decreased likelihood of transitioning from excessive to non-excessive drinking behaviors and increased likelihood of transitioning from non-excessive to excessive drinking behaviors. It was hypothesized that testosterone would be unrelated to transitions in excessive drinking behaviors across the menopausal transition.The present study utilized publicly available data from the nationally representative SWAN dataset. SWAN enrolled 3302 women aged 42–52 years old from 1996 to 1997 across seven, geographically representative locations across the USA. All women enrolled in SWAN reported being premenopausal or early perimenopausal , denied taking hormonal medications within the past 3 months, and similarly denied history of total hysterectomies at baseline. SWAN participants in the publicly available dataset were followed annually after completing the baseline visit. Participants attended in-person examinations which assessed for a variety of biopsychosocial variables via both survey and biological measurements  each year .pre-, early peri-, late peri-, and postmenopausal) was identified in each annual dataset. For the purpose of the current analyses, once categorized, participants were not able to be categorized “backwards” in subsequent years. Specifically, women were identified as premenopausal if they reported regularly occurring menses within the past 3 months. Early peri-menopause was categorized as women who had menses within the past 3 months, but menses did not occur regularly. Women who endorsed no menses for the past 3–11 months were categorized as late peri-menopausal, whereas women with no menses for over the past 12 months were identified as postmenopausal. Of note, women taking hormonal therapies, as well as those who endorsed having hysterectomies/ovariectomies over the course of the study, were identified as “unknown”, and their data was not included for the particular year. Finally, in years during which women reported pregnancy and/or breastfeeding, women were excluded from the present analyses.SWAN datasets including each year’s data were merged, and all variables were matched based upon a SWAN-derived identification variable. Participants’ stage in the menopausal transition  from variables identifying the amount of beer, wine, and liquor consumed per past month. Only information on drinking quantity was available. This dichotomous variable was created following guidance from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) criteria for hazardous drinking in women (exceeding 7 drinks per week) . Excessiearly peri- to postmenopausal status). Additionally, measures of estradiol and testosterone, which were assessed annually, were included in the two models. These hormonal assay results did not have normal distributions and thus, in accordance with previously published SWAN hormonal data results [Random-effects logistic regression models with robust standard errors were used to examine transitions in excessive drinking behavior . Specifically, the first model examined the transition from excessive to non-excessive drinking, and the second model explored the transition from non-excessive drinking to excessive drinking. Data was not censored at transition from/to excessive drinking in order to best capture transitions across the 10-year period. All models included baseline age (in years), age at postmenopause, and length of menopausal transition  had a mean age of 45.85 years old (SD = 2.69) at baseline. The majority of the women included in SWAN were Caucasian . At visit 1, 170 women were identified as excessive drinkers, as they reported consuming over 30 drinks per month. Similarly at visit 10, 166 women were identified as excessive drinkers using this same criteria.Women (ORs range = 3.71–5.11) to transition to non-excessive drinking during the early/late peri- and postmenopausal stages, when compared to pre-menopausal stage. Additionally, increased age at postmenopause status categorization was associated with a greater likelihood to transition to non-excessive drinking, whereas higher testosterone levels were associated with a decrease in likelihood of transition to consuming less than 30 drinks per month. There were no significant associations between estradiol levels and depressive symptoms and this transition  to transition to excessive drinking, as compared to the pre-menopausal stage. Being older at baseline and having a longer length of the transition were associated with a decreased likelihood of transitioning to excessive drinking. Sex hormones and depression were not associated with changes to excessive drinking patterns , this represents shifts among the women who are excessively drinking as they age. These results replicate previous research findings that alcohol use is related to onset of menopause and the menopausal transition. The present study demonstrated that increased age at postmenopausal status was associated with a 15% increase in the likelihood of transitioning to non-excessive drinking, and there was a 13% increase in the likelihood of those transitioning to excessive drinking. Being older at baseline and having a longer length of menopausal transition were also associated with a decreased likelihood of being categorized as an excessive drinker later in the study. These results illustrate the connection between excessive alcohol consumption and the menopausal transition. Previous literature has established that heavy or moderate drinking patterns are related to later onset of menopause; however, additional research is warranted to explore if later onset of menopause is directly attributable to alcohol consumption, or is representative of other biopsychosocial variables, such as demographics [The present results demonstrate that women, who were identified as excessive drinkers, were more likely to transition to non-excessive drinking status during all three stages of the menopausal transition when compared to pre-menopause, while women who were identified as non-excessive drinkers were more likely to transition to excessive drinking during early peri- and postmenopausal transition stages. Given that there was little difference between the number of women identified as excessive drinkers between visit 1 and visit 10  that were evaluated at only one time point annually. Additionally, the menopausal status categorization did not rely on biological confirmation. Thus, further studies with confirmatory biological measures are warranted. The present publicly available data did not include annual measures of progesterone, thus future studies should explore the role of progesterone fluctuations within these transitions. The publicly available datasets also did not include more in-depth variables to explore psychiatric diagnoses and specific alcohol-related variables . Future studies would be strengthened by including such variables. In spite of such limitations, the present study contributes to the current understanding of transitions in alcohol use across menopausal stages.The present study demonstrates that the menopausal transition is a period of instability and change regarding excessive drinking behaviors. Women may newly transition to excessive alcohol consumption during the menopausal transition, and other women who have existing excessive alcohol consumption may cease to be excessive drinkers. These results indicate the need for additional research to explore how factors may contribute to changes in alcohol consumption among women.


Those classified as having a low malnutrition risk were younger (p = 0.004), heavier (p < 0.001) and taller (p = 0.009) than their counterparts with a moderate risk. On the other hand, patients in the moderate risk group were more likely pubertal (p = 0.034), with a reduced mid-upper arm fat area (p = 0.011), and worse pulmonary function (p < 0.001). Interestingly, none of the children attaining ideal body weight were classified as having a moderate malnutrition. risk, whereas 37.5% of the patients allocated at the moderate risk group exhibited physiological lung function. In this cohort of outpatients with CF that were predominantly well-nourished and attained physiological lung function, malnutrition risk was identified only in small proportions of the sample. Our data support that patients that are older, pubertal, or have diminished fat mass are at greater risk for malnutrition.Malnutrition prevails in considerable proportions of children with Cystic Fibrosis (CF), and is often associated with adverse outcomes. For this, routine screening for malnutrition is pivotal. In the present cross-sectional study, we aimed to assess the risk for malnutrition in pediatric outpatients with CF. A total of 76 outpatients  were recruited and anthropometric, clinical, dietary and respiratory measures were collected. All outpatients were screened for malnutrition risk with a validated disease-specific instrument. Most children exhibited a low risk for malnutrition (78.9%), whereas none of the participants were characterized as having a high malnutrition risk. In the total sample, malnutrition risk was positively associated with age ( Cystic fibrosis (CF) is a chronic condition characterized by an increased risk of malnutrition. Poor nutritional intake due to increased energy loss and demands, inadequate caloric intake, malabsorption and maldigestion, exacerbations of the disease, genetic susceptibility and the often underlying infections, increase the risk of malnutrition among patients with CF ,3,4,5,6.Although several research projects have revealed a high degree of malnourishment among children with CF ,9,10, eaWith regards to CF however, disease-specific screening tools for malnutrition are limited, especially for pediatric patients, with only two existing in the literature to date, the first being published during 2008  and the With this in mind, the aim of the present study was to assess the risk for malnutrition among pediatric patients with CF, using a disease-specific tool.The GreeCF study is an observational study, aiming to assess the relationship between growth indices, clinical and dietary parameters among school-aged children with CF. The study was conducted during the last quarter of 2015, in the outpatient Clinic for CF, situated in Aghia Sophia Children’s Hospital, in metropolitan Athens, Greece. Details concerning the recruitment have been previously reported ,16. All The parents/guardians of all patients provided informed written consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committees of both Aghia Sophia Children’s Hospital, and University of West Attica (reference number 16084/14-07-15).Anthropometry was conducted during morning hours, with patients wearing light clothing and having bare feet, by the same experienced dietician (DP). Body weight was measured at the nearest 0.1 kg  and height to the nearest 0.5 cm , and then body mass index (BMI) was computed. Height-for-age (HAZ) and BMI-for age (BAZ) z-scores were calculated for each patient, based on the Centers for Disease Control (CDC) growth charts . AccordiOn the left side of the body, mid-upper arm circumference (MUAC) was measured at the mid-point between the olecranon process and the acromium, while triceps’ skinfold thickness (TSF) values were taken three times  in order to attain a median value. MUAC, TSF, mid upper arm muscle (MUAMA) and fat area (MUAFA) z-scores were calculated according to the NHANES III survey .Dietary intake was assessed at the day of the visit, through a multiple-pass, 24-h dietary recall, by the same experienced dietician (D.P.). For children aged below 9 years old, parental consensus during the interview was sought. Recall data were then analyzed for total energy intake (TEI) using the Food Processor software . TEI was then converted in % of the recommended daily intake.Pseudomonas Aeruginosa, methicillin resistant Staphylococcus Aureus, Burkholderia cepacia), enteral feeding, and serum albumin levels. Pulmonary function was assessed during the day of each patient’s visit to the clinic, through the Forced Expiratory Volume at 1 s (FEV1). Pulmonary function was compared against predicted values (FEV1%) using the equations suggested by Wang et al. [Individual medical records were searched for age at diagnosis and diagnostic criteria, presence of pancreatic insufficiency (PI), CF-related diabetes (CFRD), intermittent and/or chronic infection by pathogens  by the team of medical doctors; no specific data regarding the pubertal stages of the patients were recorded.1%, and (x) low serum albumin levels. A composite score is then computed by summing up the ratings of each risk factor. The score ranges from 0 to 14, with higher values indicative of higher nutritional risk. Furthermore, ratings between 0–3 indicate low malnutrition risk, ratings of 4–7 suggest medium risk, whereas a score ≥ 8 is indicative of increased risk for malnutrition [Malnutrition Risk was assessed by the validated CF-specific tool, developed by dos Santos Simon and associates . In moreutrition .t-test (or Mann–Whitney test for age at diagnosis). Chi-square or the Fisher’s exact test was employed to explore differences between categorical variables. Association between continuous variables was examined through Spearman’s r correlation co-efficient. The level of significance was set at α = 0.05. All analyses were performed with SPSS version 25.0 . Data distribution was visually explored with Q-Q plots; all continuous variables but age at diagnosis (months) were normally distributed. Therefore, continuous variables are presented as means ± standard deviation, whereas age at diagnosis is presented as a median with its respective interquartile ranges (1st and 3rd IQR). Categorical variables are presented as relative frequencies and percentages. Differences between continuous variables were assessed with independent A total of 32 boys and 44 girls were recruited for the purposes of this study . Their mean BAZ was 0.05 ± 1.13, whereas their mean HAZ was −0.28 ± 1.09. Referring to their pulmonary function, patients exhibited a mean FEV1% of 98.0 ± 19.8%.p > 0.05 for all comparisons, data not shown).Risk factors for malnutrition, and malnutrition risk are presented in r = 0.369, p = 0.001). Malnutrition risk was inversely associated with BAZ , HAZ , and FEV1% . Referring to arm anthropometrics, malnutrition risk was not associated with TSF, MUAC, MUAMA or MUAFA z-scores . When the aforementioned indices were examined according to sex, similar results were revealed, with the exception of age. In further detail, malnutrition risk did not significantly associate with the age of boys (data not shown).In the total sample, a moderate positive association was noted between malnutrition risk and age (p = 0.004), heavier (p < 0.001) and taller (p = 0.009) than their moderate-risk counterparts. Significantly more patients in the medium-risk group were in puberty (p = 0.034), whereas patients in the moderate-risk group exhibited smaller mean mid upper arm fat area (p = 0.011), and overall worse pulmonary function (p < 0.001).When patients were stratified according to malnutrition risk, several differences between the groups were identified . PatientNone of the other clinical parameters were associated with any of the nutritional risk strata. Interestingly, among the children attaining ideal body weight, none were categorized as being of moderate risk for malnutrition. On the other hand, 37.5% of the participants stratified as being at moderate risk for malnutrition exhibited physiological lung function.1 > 90% being allocated in the moderate risk for malnutrition group.In the present study, participants with CF were predominantly well-nourished and exhibited adequate physiological lung function, with a moderate risk for malnutrition prevailing only in small proportions (1/5) of the sample, and none of the patients were classified as being of high malnutrition risk. Malnutrition risk was inversely associated with patients’ anthropometry and lung function. Although none of the patients attaining ideal body weight for CF were at nutritional risk, this was not the case for physiological lung function, with 38% of the children with FEVThroughout the literature, BMI z-scores have been extensively employed to identify malnutrition in pediatric CF patients, using a variety of cut-offs . Most ofIn the present analysis, patients at risk for malnutrition were shorter and exhibited a lower BMI than their counterparts with low-malnutrition risk. While malnutrition is a known effector impeding linear growth, weight and height measures have also been critiqued for not providing early signs of malnutrition in children with CF .1. Consistent with the existing literature [1 ≥ 90%). At the same time, 1/5 of the children with low malnutrition risk demonstrated impaired lung function (predicted FEV1 < 90%). These observations may indicate that in response to malnutrition, lung function is delayed, or that FEV1 measures may not be sensitive enough during the early phase and signs of malnutrition . For this reason, triage for malnutrition using CF-specific tools is required in all cases and one should not rely on anthropometric indices or pulmonary function alone. It may also be that these measures are more sensitive in identifying malnutrition risk when their secular trends are taken into account, such as when using CF-specific screening tools for malnutrition, like in the present study.A similar case might also apply for FEVterature , patientOther risk factors for malnutrition identified in the present study include ascending age, puberty, and diminished fat mass. In our sample, age was positively correlated with malnutrition risk, a finding more profound among girl participants. Regardless of sex, BMI has been previously shown to decline with age , while, With regards to puberty, it remains a grey area in CF-research. Girls with CF have been reported to enter puberty with a two year delay as compared to their healthy counterparts ,36. In p1 and frequency of exacerbations in both children and adults with CF [With regards to the relationship between low fat mass and malnutrition risk, very few studies have assessed body composition among children with CF. Although a variety of tools have been proposed for the assessment of body composition in CF , the maj with CF ,43,44. AThe limitations of the present study include its cross-sectional design, not allowing for the establishment of causality in the observed relationships. The sample might appear as relatively small, however, sampling was performed in the largest CF center in Greece, located in the capital of Greece providing CF-care to a large population with CF. The inclusion of younger children (aged < 6 years old) in our sample may have provided more robust results, given that early-life malnutrition impairs lung function, and this impairment is persistent for over a decade . AlthougIn this cohort of predominantly well-nourished patients with CF, 1/5 of the patients were at risk for malnutrition, with those that were older, pubertal or with diminished fat-mass being at higher risk. Screening for malnutrition risk in pediatric patients with CF is important, although often neglected. Even in populations with a low prevalence of malnutrition, the risk for malnutrition might be elevated, highlighting the need for more frequent assessments and nutrition interventions.