Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: methods, applications and limitations

BACKGROUND

The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking.

OBJECTIVE

This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress.

METHODS

Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness.

RESULTS

Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT.

FUTURE DIRECTIONS

The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain.

CONCLUSION

Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.

Visual pollution real images benchmark dataset on the public roads

The term quality of life (QoL) refers to a wide range of multifaceted concepts that often involve subjective assessments of both positive and negative aspects of life. It is difficult to quantify QoL as the word has varied meanings in different academic areas and may have different connotations in different circumstances. The five sectors most commonly associated with QoL, however, are Health, Education, Environmental Quality, Personal Security, Civic Engagement, and Work-Life Balance. An emerging issue that falls under environmental quality is visual pollution (VP) which, as detailed in this study, refers to disruptive presences that limit visual ability in public roads with an emphasis on excavation barriers, potholes, and dilapidated sidewalks. Quantifying VP has always been difficult due to its subjective nature and lack of a consistent set of rules for systematic assessment of visual pollution. This emphasizes the need for research and module development that will allow government agencies to automatically predict and detect VP. Our dataset was collected from different regions in the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH) as a part of a VP campaign to improve Saudi Arabia's urban landscape. It consists of 34,460 RGB images separated into three distinct classes: excavation barriers, potholes, and dilapidated sidewalks. To annotate all images for detection (i.e., bounding box) and classification (i.e., classification label) tasks, the deep active learning strategy (DAL) is used where an initial 1,200 VP images (i.e., 400 images per class) are manually annotated by four experts. Images with more than one object increase the number of training object ROIs which are recorded to be 8,417 for excavation barriers, 25,975 for potholes, and 7,412 for dilapidated sidewalks. The MOMRAH dataset is publicly published to enrich the research domain with the new VP image dataset.

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health. This paper proposed a technique to predict brain strokes with high accuracy. The model was constructed using data related to brain strokes. The aim of this work is to use Multi Layer Perceptron (MLP) as a classification technique for stroke data and used multi-optimizers that include Adaptive moment estimation with Maximum (AdaMax), Root Mean Squared Propagation (RMSProp) and Adaptive learning rate method (Adadelta). The experiment shows RMSProp optimizer is best with a data training accuracy of 95.8% and a value for data testing accuracy of 94.9%. The novelty of work is to incorporate multiple optimizers alongside the MLP classifier which offers a comprehensive approach to stroke prediction, providing a more robust and accurate solution. The obtained results underscore the effectiveness of the proposed methodology in enhancing the accuracy of brain stroke detection, thereby paving the way for potential advancements in medical diagnosis and treatment.

Effect of membrane stabilizers on semen quality and sperm membrane protein expression during cryopreservation of goat semen

BACKGROUND

Semen cryopreservation is a complex process during which there is alteration in the expression of sperm and seminal plasma proteins, molecular weight of protein or loss of membrane proteins during the process. In order to compensate for these changes, different membrane stabilizers are used in freezing semen extenders. However, there is scarcity of such studies during cryopreservation of goat semen.

OBJECTIVE

To investigate the effect of membrane stabilizers on sperm membrane protein expression during cryopreservation of goat semen.

MATERIALS AND METHODS

A total of 36 semen ejaculates from nine Assam Hill Goat bucks aged 2 to 2.5 years was collected by artificial vagina method. Three membrane stabilizers, each at two different concentrations viz. 50 and 80 mM sucrose, 50 and 100 mM trehalose, and 100 and 150 ng per mL IGF-1 (insulin-like growth factor 1 protein) were added to Tris-citric acid fructose egg yolk glycerol (TCFEYG) extender and semen samples were cryopreserved. The sperm membrane protein profile was studied in fresh and cryopreserved semen by SDS-PAGE.

RESULTS

SDS- PAGE of sperm membrane extract of fresh semen revealed the presence of 24 protein bands with molecular weights ranging from 10 kDa to 240 kDa. Samples supplemented with 50 mM sucrose and 80 mM sucrose revealed 21 protein bands with molecular weights ranging from 10 kDa to 240 kDa. All the 21 protein bands were same as those observed in the sperm membrane of fresh spermatozoa, except that the 23 kDa, 29 kDa and 42 kDa bands were absent in frozen semen. Similarly, frozen semen extended with 50 mM trehalose and 100 mM trehalose revealed 22 protein bands with molecular weights ranging from 10 kDa to 240 kDa, but lacking the 29 kDa and 42 kDa bands. Proteins with molecular weights of 29 kDa, 130 kDa and 240 kDa were absent in frozen semen supplemented with 100 ng per mL IGF-1 and 150 ng per mL IGF-1.

CONCLUSION

The present study revealed that supplementation of tris basic extender with trehalose at 100 mM and or IGF-1 at 100 ng/mL or 150 ng per mL improves the post-thaw semen characteristics and protects certain fertility related sperm membrane proteins. Doi.org/10.54680/fr23510110612.

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence.

Multilayer perceptron-based self-care early prediction of children with disabilities

Early identification of children with self-care impairments is one of the key challenges professional therapists face due to the complex and time-consuming detection process using relevant self-care activities. Due to the complex nature of the problem, machine-learning methods have been widely applied in this area. In this study, a feed-forward artificial neural network (ANN)-based self-care prediction methodology, called multilayer perceptron (MLP)-progressive, has been proposed. The proposed methodology integrates unsupervised instance-based resampling and randomizing preprocessing techniques to MLP for improved early detection of self-care disabilities in children. Preprocessing of the dataset affects the MLP performance; hence, randomization and resampling of the dataset improves the performance of the MLP model. To confirm the usefulness of MLP-progressive, three experiments were conducted, including validating MLP-progressive methodology over multi-class and binary-class datasets, impact analysis of the proposed preprocessing filters on the model performance, and comparing the MLP-progressive results with state-of-the-art studies. The evaluation metrics accuracy, precision, recall, F-measure, TP rate, FP rate, and ROC were used to measure performance of the proposed disability detection model. The proposed MLP-progressive model outperforms existing methods and attains a classification accuracy of 97.14% and 98.57% on multi-class and binary-class datasets, respectively. Additionally, when evaluated on the multi-class dataset, significant improvements in accuracies ranging from 90.00% to 97.14% were observed when compared to state-of-the-art methods.

Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched. In this regard, this study represents one of the first studies in which a CDSS is proposed for clinicians who manage patients with end-stage renal disease undergoing maintenance hemodialysis, almost all of whom have some manifestation of chronic kidney disease-mineral and bone disorder (CKD-MBD). The primary objective of the system is to aid clinicians in dosage prescription by levering medical domain knowledge as well existing practices. The proposed CDSS is evaluated with a real-world hemodialysis patient dataset acquired from Kyung Hee University Hospital, South Korea. Our evaluation demonstrates overall high compliance based on the concordance metric between the proposed CKD-MBD CDSS recommendations and the routine clinical practice. The concordance rate of overall medication dosing selection is 78.27%. Furthermore, the usability aspects of the system are also evaluated through the User Experience Questionnaire method to highlight the appealing aspects of the system for clinicians. The overall user experience dimension scores for pragmatic, hedonic, and attractiveness are 1.53, 1.48, and 1.41, respectively. A service reliability for the Cronbach's alpha coefficient greater than 0.7 is achieved using the proposed system, whereas a dependability coefficient of the value 0.84 reveals a significant effect.

Balancing Complex Signals for Robust Predictive Modeling

Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models. Such models, however, may have poor predictive performance on the unseen testing data involving outliers. In modern machine learning, outliers are regarded as complex signals because of their significant role and are not suggested for removal from the training dataset. Models trained in modern regimes are interpolated (over trained) by increasing their complexity to treat outliers locally. However, such models become inefficient as they require more training due to the inclusion of outliers, and this also compromises the models’ accuracy. This work proposes a novel complex signal balancing technique that may be used during preprocessing to incorporate the maximum number of complex signals (outliers) in the training dataset. The proposed approach determines the optimal value for maximum possible inclusion of complex signals for training with the highest performance of the model in terms of accuracy, time, and complexity. The experimental results show that models trained after preprocessing with the proposed technique achieve higher predictive accuracy with improved execution time and low complexity as compared to traditional predictive modeling.

Impact of family-based selection on growth performance and immune response of Japanese quail

The present study was aimed to evaluate the growth performance and immune response of three genetic lines of Japanese quails. These lines i.e., selected for 4-week body-weight group (WBS), selected for egg number (EBS), and random-bred control (RBC), were selected for three consecutive generations from a base population of 1125 quails. In total, 2700 four-week-old quails from three selected groups were slaughtered in total of four generations (G0 to G3). Effects of selection and generations as well as their interactions were assessed for growth performance and immune response by applying a two-way analysis of variance. Significant means were compared with Duncan's Multiple Range Test. The statistical analysis showed a significant effect of selection and generations on most of the growth and immune response parameters. WBS in G3 presented significantly higher values of body weight, weight gain, and FCR than RBC and EBS. FCR was better in WBS during G3 than those of EBS and RBC. However, Livability% was highest in RBC while the lowest was noted in G3 of WBS line. Thymus% and spleen% were higher in EBS as compared to RBC and WBS. RBC presented a better B/S ratio and ND titer than those of EBS and WBS. The decreasing trend of ND titer in both lines of WBS and EBS as compared to RBC suggested a decrease in New Castle disease resistance in progressive generations of selection. It was concluded that selection for body weight and egg number has a positive impact on respective traits but negatively affects the immunity in later generations.

A practical approach towards causality mining in clinical text using active transfer learning

OBJECTIVE

Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge.

METHODS

A practical approach based on term expansion, phrase generation, BERT based phrase embedding and semantic matching, semantic enrichment, expert verification, and model evolution has been used to construct a comprehensive causality mining framework. This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text.

RESULTS

The multi-model transfer learning technique when applied over multiple iterations, gains substantial performance improvements. We also present a comparative analysis of the presented techniques with their common alternatives, which demonstrate the correctness of our approach and its ability to capture most causal relationships.

CONCLUSION

The presented framework has provided cutting-edge results in the healthcare domain. However, the framework can be tweaked to provide causality detection in other domains, as well.

SIGNIFICANCE

The presented framework is generic enough to be utilized in any domain, healthcare services can gain massive benefits due to the voluminous and various nature of its data. This causal knowledge extraction framework can be used to summarize clinical text, create personas, discover medical knowledge, and provide evidence to clinical decision making.

Effects of production system and feeding regimen on carcass and meat quality traits of Naked Neck chicken

To evaluate the effect of a production system and feeding regimen on meat quality attributes of Naked Neck chickens, a total of 150 cockerels at 18 weeks old (1625 ± 70 g) were collected from 10 treatment groups with five replicates of three birds. The factorial arrangement of treatments consisted of two production systems (intensive and free-range) and five nutritional regimens, namely 100% commercial feed; 75% commercial feed plus 25% kitchen waste; 50% commercial feed plus 50% kitchen waste; 25% commercial feed plus 75% kitchen waste; and 100% kitchen waste. Carcass traits, meat quality, and meat organoleptic were found to differ significantly among production systems, feeding regimens, and their interaction. Higher liver weight was observed in birds reared under an intensive system. Higher gizzard weight was noted in birds fed with 100% kitchen waste, whereas lower gizzard weight was observed in birds fed the commercial diet. The meat from cockerels fed with 75% kitchen waste was most yellow, whereas the meat from the birds fed with 100% kitchen waste was least yellow. At two hours after slaughter, pH of the meat was highest in birds fed 50% kitchen waste and lowest in birds fed 100% kitchen waste. The interaction of production system and feeding regimen was significant for overall acceptability score. In conclusion, Naked Neck chickens performed equally well under intensive and free-range systems, irrespective of the level of kitchen waste that they were fed.

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study

BACKGROUND

Since the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, many information resources have been created globally and are available for use. However, a defined structure to organize these resources into categories based on the nature and origin of the data is lacking.

OBJECTIVE

This study aims to organize COVID-19 information resources into a well-defined structure to facilitate the easy identification of a resource, tracking information workflows, and to provide a guide for a contextual dashboard design and development.

METHODS

A sequence of action research was performed that involved a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data were collected according to the defined structure of primary, secondary, and tertiary categories. Various techniques for descriptive statistical analysis were employed to gain insights into the data to help develop a conceptual framework to organize resources and track interactions between different resources.

RESULTS

Investigating diverse information at the primary, secondary, and tertiary levels enabled us to develop a conceptual framework for COVID-19-related efforts and initiatives. The framework of resource categorization provides a gateway to access global initiatives with enriched metadata, and assists users in tracking the workflow of tertiary, secondary, and primary resources with relationships between various fragments of information. The results demonstrated mapping initiatives at the tertiary level to secondary level and then to the primary level to reach firsthand data, research, and trials.

CONCLUSIONS

Adopting the proposed three-level structure allows for a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying future resources. This study is one of the earliest studies to introduce an infrastructure for locating and placing the right information at the right place. By implementing the proposed framework according to the stated guidelines, this study allows for the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 knowledge resources.

Behaviour, welfare, and tibia traits of fast- and slow-growing chickens reared in intensive and free range systems

The behaviour, welfare, and tibia characteristics of fast- and slow-growing chickens were evaluated in free-range and intensive production systems. A total of 720 birds of three strains were subjected to these systems after 21 days of rearing under the same intensive environment. Each treatment was replicated six times with 20 birds in each replicate. Data were collected for welfare aspects, including feather condition, footpad, hock joint and tonic immobility, behavioural features, especially feeding or foraging, sitting, walking, scratching, pecking and dust-bathing, and tibia characteristics, including weight, length, width, medullary canal diameter and robusticity index (4 - 8 weeks). Production system had significant effects on all welfare and behaviour aspects of the birds. However, tibia characteristics were not influenced by production system, except for medullary canal diameter. The strains differed significantly in welfare, tibia characteristics and behaviour. For example, the slow-growing strains had better feather condition,  footpad and hock joint scores. Significant interactions of strain and production system were noted for all characteristics. It was recommended that Rhode Island Red chickens could be raised under either production system without compromising their welfare or causing adverse effects on leg health.

Incidence of Cardiac Arrhythmias in Acute Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention and Associated Outcomes During the First 24 Hours

Background Acute myocardial infarction (AMI) is the most life-threatening manifestation of coronary artery diseases. The majority of deaths in AMI are due to arrhythmias. Therefore, the aim of this study was to evaluate the incidence and risk factors and outcomes of cardiac arrhythmias in AMI patients undergoing primary percutaneous coronary intervention (PCI) during the first 24 hours of the index hospitalization. Methodology This prospective observational study was conducted at the adult cardiology department of the National Institute of Cardiovascular Diseases (NICVD), Karachi, Pakistan. Patients undergoing primary PCI were included in this study. All the patients were kept under observation for the first 24 hours of AMI and monitored through telemetry system monitoring and the incidence of cardiac arrhythmias and the outcomes were recorded. Results A total of 110 patients were included; the mean age was 59.6±13.1 years. Most of them were male (70.9%). Arrhythmias were observed in 89.1% of the patients, with 169 episodes. The accelerated idioventricular rhythm was the most common type of arrhythmia (37.3%) followed by sinus tachycardia (36.4%), ventricular tachycardia (22.7%), and complete heart block (20.0%). Lethal arrhythmias were observed in 64.5% (71) of the patients. During the hospital course, 65.5% developed arrhythmias during arrival to balloon time, 30% during the procedure, and 53.6% within 24 hours of the procedure. The in-hospital mortality rate was 15.5% with a significant association with the development of lethal arrhythmias within 24 hours of the procedure (21.1% vs. 5.1%; p=0.026). Conclusions The incidence of arrhythmias within 24 hours of hospitalization is high in patients with ST-elevation myocardial infarction (STEMI) undergoing primary PCI, and it has been observed to be associated with an increased rate of in-hospital mortality.

Performance of Rhode Island Red, Black Australorp, and Naked Neck crossbreds under alternative production systems

The effects of the production system, breed cross, and their interaction on performance, egg quality, and hatching traits were evaluated. Rhode Island Red and Black Australorp were crossed with Naked Neck chickens (first generation RNN, and BNN, respectively). These crosses were mated among themselves and crossed to produce four crossbreds: RR (RNN x RNN), BB (BNN x BNN), RB (RNN x BNN), and BR (BNN x RNN). Thirty-six pullets and 9 cockerels from each crossbred were maintained in three production systems: the aviary system (AV), conventional cages (CC), and enriched cages (EC). Thus there were 48 pullets and 12 cockerels in each production system. Bodyweight, egg production percentage, and egg weight were highest in EC, followed by CC and AV. Higher egg weight, egg surface area, and egg volume were also observed in EC compared with CC and AV. Fertility and hatchability were higher and early embryonic mortality was lower in AV than in EC and CC. Bodyweight, egg production percentage, egg weight, egg volume, and surface area were higher for RB and BR than for BB and RR. Fertility and hatchability were similar for RB and BR. RR was similar to BR, but lower than RB. BB had the lowest fertility and hatchability. Thus, chickens in EC performed better than in the other systems, except that hatching traits were better in AV. RB and BR performed better than BB and RR.Key words: breed crosses, chicken, egg quality, hatchability

An Empirical Method of Automatic Pattern Extraction for Clinical Text Classification

Clinical text classification is an indispensable and extensively studied problem in medical text processing. Existing research primarily employs machine learning and pattern based approaches to address the stated problem. In general, pattern based approaches perform better than other methods. However, these approaches commonly require human intervention for pattern identification, which diminish their benefits and restrain their applications. In this study, we present a novel pattern extraction algorithm, which identifies and extracts patterns from clinical textual resources, automatically. The algorithm identifies the candidate concepts in the clinical text, finds the context of the concepts by discovering their context windows, and finally transforms each context window to a pattern. We evaluate our proposed algorithm on Hypertension, Rhinosinusitis, and Asthma guidelines. 70% of the hypertension guideline was used for pattern extraction while the remaining 30% and the other two guidelines were used for evaluations. The algorithm extracts 21 patterns that classify Hypertension, Rhinosinusitis, and Asthma guidelines sentences to the recommendation and non-recommendation sentences with 84.53%, 80.03%, and 84.62% accuracy, respectively. The initial results reveal the benefits and applicability of the algorithm for clinical text classification.

Sleep difficulties and alcohol use behaviors in adolescents and young adults: a systematic review

Background

Sleep disturbances and substance abuse are prevalent in adolescents, often associated with adverse behavioral outcomes and daytime dysfunction. While sleep problems are often seen as consequences to alcohol use, they are risk factors for alcohol misuse and related consequences. However, current evidence in adolescents and young adults (AYA) is mostly observational and is not well-documented in recent years. This systematic review aims to assess the literature surrounding sleep difficulties and their effects on subsequent alcohol misuse in AYA.

Methods

Medline, PsycINFO, and EMBASE were searched for observational studies assessing alcohol consumption, alcohol-related consequences, and self-reported sleep difficulties in AYA aged 16-25. Studies with clinical samples and randomized trials were excluded. Data on measurement tools, measures of association, and sample characteristics were recorded using a standardized form. Associations between sleep difficulties and alcohol misuse were qualitatively summarized by study type (cross-sectional and longitudinal studies). Risk of bias was assessed using CLARITY tools.

Results

Of 7706 records retrieved from databases, 24 studies met the inclusion criteria. Cross-sectional studies (n = 17) found insomnia-like symptoms (difficulties falling/staying asleep, frequent night-time awakenings) were significantly associated with alcohol use frequency and intensity (n = 16) and subsequent intoxication (n = 6). Among longitudinal studies (n = 7), persistent erratic sleep behaviors at baseline were associated with frequent binge-drinking (n = 2) and increased odds of interpersonal problems (n = 2) and risky behaviors at follow-up (n = 1).

Conclusions

The present review assesses the role of sleep difficulties in alcohol use behaviors and related consequences in AYA. Sleep inadequacies are involved in alcohol misuse, prompting investigations to clarify sleep's etiological role in alcohol use behaviors to prevent negative consequences in AYA.

Key messages

Sleep disturbances need to be investigated further as possible risk factors for incident alcohol misuse in young adults. Addressing sleep hygiene and promoting healthy sleep behaviors are important in evaluating the overarching public health issue of alcohol addiction and misuse in adolescents.

Night shift work, sleep quality and risk of endocrine-related cancer: a systematic review

Background

Cancer is a major public health concern as it is a leading cause of death worldwide. The risk of endocrine-related cancers may be associated with suboptimal sleep quality due to disruptions in circadian rhythm. However, the extent of this relationship is not clear and warrants a review summarizing recent research findings. This systematic review aims to provide an updated summary of the literature surrounding the association between night shift work, sleep quality, circadian rhythm disruption and the risk of endocrine-related cancers.

Methods

MEDLINE, EMBASE, CINAHL, and Web of Science databases have been searched for related cohort, case-control, and cross-sectional studies. The search was limited to studies published in English, after the year 2000. Non peer-reviewed literature and randomized control trials, were excluded. Reviewers will independently extract data on study characteristics, sleep quality measures, circadian rhythm measures, cancer diagnosis, and outcome measures. Risk of bias assessment will be performed using the CLARITY tools.

Results

Data synthesis is ongoing. Associations between sleep quality, night shift work, circadian disruption and risk of endocrine-related cancer will be summarized and risk estimates will be reported for four endocrine-related cancers: breast, prostate, thyroid, ovarian, and endometrial cancer. If data are available, subgroup analyses will be carried out by cancer type, study design, and age group.

Conclusions

This review qualitatively synthesizes current literature to determine the extent of the association between sleep quality, night shift work, circadian rhythm disruption and the risk of endocrine-related cancers. These findings can contribute to population-level cancer prevention efforts and aid public health promotion of healthier sleep-related behaviours.

Key messages

The relationship between sleep quality and endocrine-related cancers should be explored further in public health research due to inconclusive evidence from current literature. The findings of this project can help promote healthy sleep behaviours and population-level cancer prevention.

Evaluation of three fast- and slow-growing chicken strains reared in two production environments

The present study evaluated performance, physiological response and economics of commercial fast growing (CFG), commercial slow growing (CSG), and Rhode Island Red (RIR) chickens under intensive and free-range rearing environments. After 21 days of rearing under the same intensive environment 240 birds from each strain were subjected to free-range and intensive rearing until they were 56 days old. Each treatment was replicated six times with 20 birds in each replicate. Body surface and cloacal temperatures, respiration and heart rates, feed intake, bodyweight and weight gain, feed conversion efficiency, growth efficiency, and liveability were recorded. Significant differences among strains were detected in physiological response and growth performance (except liveability). Rearing environment also caused significant differences in physiological parameters (except body surface temperature) and growth performance (except liveability). Significant interactions of the strains and production systems were detected. The CFG strain grew most rapidly under the intensive system with differences between strains being reduced in the free-range system. The RSG and CSG strains had similar respiration rates under the two production systems but differed significantly from each other. However, the CFG strain had a significantly elevated respiration rate in the free-range system. Total input cost of rearing CFG under the intensive system was highest ($3.54) among the treatments, whereas CSG under a free-range environment generated the highest profit ($0.37 per bird). In conclusion, rearing CSG under free range was the most economic farming strategy in today’s scenario.Keywords: economics, fast-growing, free range, growth, intensive system, physiology, slow-growing rural chickens

On computing critical factors based healthy behavior index for behavior assessment

OBJECTIVE

Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.

MATERIALS AND METHODS

To study the combined impact of personal behaviors, we have proposed a methodology to derive the comprehensive Healthy Behavior Index (HBI) consisting of two major processes: (1) Behaviors' Weight-age Identification (BWI), and (2) Healthy Behavior Quantification and Index (HBQI) modeling. The BWI process identifies the high ranked contributing behaviors through life-expectancy based weight-age, whereas HBQI derives a mathematical model based on quantification and indexing of behavior using wellness guidelines.

RESULTS

The contributing behaviors are identified through text mining technique and verified by seven experts with a Kappa agreement level of 0.379. A real-world user-centric statistical evaluation is applied through User Experience Questionnaire (UEQ) method to evaluate the impact of HBI service. This HBI service is developed for the Mining Minds, a wellness management application. This study involves 103 registered participants (curious about the chronic disease) for a Korean wellness management organization. They used the HBI service over 12 weeks, the results for which were evaluated through UEQ and user feedback. The service reliability for the Cronbach's alpha coefficient greater than 0.7 was achieved using HBI service whereas the stimulation coefficient of the value 0.86 revealed significant effect. We observed an overall novelty of the value 0.88 showing the potential interest of participants.

CONCLUSIONS

The comprehensive HBI has demonstrated positive user experience concerning the stimulation for adapting the healthy behaviors. The HBI service is designed independently to work as a service, so any other wellness management service-enabled platform can consume it to evaluate the healthy behavior index of the person for recommendation generation, behavior indication, and behavior adaptation.

Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability

The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.

Exploring the dominant features of social media for depression detection

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.

Semantic preservation of standardized healthcare documents in big data

BACKGROUND

Standardized healthcare documents have a high adoption rate in today's hospital setup. This brings several challenges as processing the documents on a large scale takes a toll on the infrastructure. The complexity of these documents compounds the issue of handling them which is why applying big data techniques is necessary. The nature of big data techniques can trigger accuracy/semantic loss in health documents when they are partitioned for processing. This semantic loss is critical with respect to clinical use as well as insurance, or medical education.

METHODS

In this paper we propose a novel technique to avoid any semantic loss that happens during the conventional partitioning of healthcare documents in big data through a constraint model based on the conformance of clinical document standard and user based use cases. We used clinical document architecture (CDAR) datasets on Hadoop Distributed File System (HDFS) through uniquely configured setup. We identified the affected documents with respect to semantic loss after partitioning and separated them into two sets: conflict free documents and conflicted documents. The resolution for conflicted documents was done based on different resolution strategies that were mapped according to CDAR specification. The first part of the technique is focused in identifying the type of conflict in the blocks that arises after partitioning. The second part focuses on the resolution mapping of the conflicts based on the constraints applied depending on the validation and user scenario.

RESULTS

We used a publicly available dataset of CDAR documents, identified all conflicted documents and resolved all the them successfully to avoid any semantic loss. In our experiment we tested up to 87,000 CDAR documents and successfully identified the conflicts and resolved the semantic issues.

CONCLUSION

We have presented a novel study that focuses on the semantics of big data which did not compromise the performance and resolved the semantic issues risen during the processing of clinical documents.

Diagnosis of Avian Mycoplasmas: A Comparison between PCR and Culture Technique

Mycoplasma gallisepticum and Mycoplasma synoviae are the causative agents of avian mycoplasmosis in commercial poultry. Among the available tools, polymerase chain reaction (PCR) and culture are confirmatory tools for the diagnosis of mycoplasmosis after the initial serological screening of suspected birds. Overall, 181 samples were analyzed, 152 (84%) and 103 (57%) of which were found positive by PCR and culture, respectively. Further, 54 (92%) broiler samples were found positive for general avian mycoplasma. Among the total positive samples, MS positivity was as high as 72 (47%) by PCR, while it was 45 (44%) by culture. MG positivity was 23% and 25% in PCR- and culture-positive samples. MG grows more easily compared to MS. The agreement value between the tests was 67%. Overall, flock wise prevalence was not much varied. The prevalence of mycoplasmosis was higher during winter. Our study confirmed that PCR is the most sensitive and reliable tool for the diagnosis of avian mycoplasmosis in field samples.

Productive performance, egg quality, and hatching traits of Japanese quail reared under different levels of glycerin

This study evaluated subsequent effects of glycerin on productive performance, egg quality, and hatching traits in Japanese quail. A total of 200 birds was arranged according to a completely randomized design into 5 treatment groups having 5 replicates of 8 birds each (6 females and 2 males). Treatments consisted 5 levels of glycerin, i.e., 2.5, 5, 7.5, and 10% and the control group. Birds were fed with different levels of glycerin during a rearing period of 6 wk and their subsequent effects on productive performance, egg quality, and hatching traits were observed. Data were collected regarding productive performance for 16 wk; however, egg quality and hatching traits were recorded during pre-peak (at fourth wk), peak (at 12th wk), and post peak (at 16th wk) phase. Productive performance, egg quality, and hatching traits did not differ significantly throughout the experimental period. It was concluded that glycerin can be used as a replacement energy source, having no effect on productive and reproductive performance in Japanese quail.