Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis.

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain's active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

Brief cognitive behavior therapy for stigmatization, depression, quality of life, social support and adherence to treatment among patients with HIV/AIDS: a randomized control trial.

OBJECTIVE: Individuals living with HIV/AIDs are at a high risk of many problems like depression, stigma, quality of life, decreased adherence to treatment, and lack of social support. The present study aimed to investigate the impact of brief-cognitive behavior therapy (B-CBT) on reducing depression and stigma and improving treatment adherence, quality of life, and social support among patients with HIV/AIDS attending antiretroviral therapy (ART). MATERIALS AND METHODS: This randomized clinical trial was conducted at ART Clinic in the Tehsil Headquarters Hospital Shahkot Nankana Sahib from July 2021 to October 2021. After baseline screening, 126 patients met the eligibility criteria and 63 were allocated to the experimental group (EXPg = 63) and 63 to waitlist-control group (WLCg = 63). Participants' age range was from 20 to 55 years. Participants who were taking ART treatment were enrolled for the CBT treatment. Before this, all the participants completed a baseline assessment to ensure a level of severity and diagnosis. A total of eight CBT based therapeutic sessions were conducted individually with EXPg. To assess the outcomes among patients receiving ART, we used Demographic form, Patient health questionnaire, HIV stigma scale, General medication adherence scale, Multidimensional scale of perceived social support, and WHOQOL BREF scale. RESULTS: Findings suggest that B-CBT significantly reduced the level of depression (i.e. F (1, 78) = 101.38, p < .000, η2 = .599), and social stigma (i.e. F (1, 78) = 208.47, p < .000, η2 = .787) among patients with HIV/AIDS. Furthermore, CBT substantially improved the level of adherence to treatment (i.e. F(1,78) = 24.75, p < .000, η2 = .503), social support (i.e. F (1, 78) = 128.33, p < .000, η2 = .606), and quality of life (i.e. F (1, 78) = 373.39, p < .000, η2 = .837) among patients with HIV/AIDS. Significant mean difference M(SD) on PHQ at post-analysis in the EXPg vs. WLCg was seen 1.22(0.47) vs. 2.30(0.68) and similarly, on MPSS at a post-analysis in the EXPg vs. WLCg 2.85(0.36) vs. 1.70(0.51) which indicates sound therapeutic outcomes. CONCLUSIONS: Cognitive behavioral therapy effectively decreases the level of depression and stigma and enhances the level of social support, quality of life, and adherence to treatment among HIV/AIDS patients. It is concluded that cognitive behavior therapy is an effective treatment approach for patients with HIV/AIDS. TRIAL REGISTRATION: Thai clinical trial registry (i.e. TCTR = TCTR20210702002 ).

Potential use of iPSCs for disease modeling, drug screening, and cell-based therapy for Alzheimer's disease.

Alzheimer's disease (AD) is a chronic illness marked by increasing cognitive decline and nervous system deterioration. At this time, there is no known medication that will stop the course of Alzheimer's disease; instead, most symptoms are treated. Clinical trial failure rates for new drugs remain high, highlighting the urgent need for improved AD modeling for improving understanding of the underlying pathophysiology of disease and improving drug development. The development of induced pluripotent stem cells (iPSCs) has made it possible to model neurological diseases like AD, giving access to an infinite number of patient-derived cells capable of differentiating neuronal fates. This advance will accelerate Alzheimer's disease research and provide an opportunity to create more accurate patient-specific models of Alzheimer's disease to support pathophysiological research, drug development, and the potential application of stem cell-based therapeutics. This review article provides a complete summary of research done to date on the potential use of iPSCs from AD patients for disease modeling, drug discovery, and cell-based therapeutics. Current technological developments in AD research including 3D modeling, genome editing, gene therapy for AD, and research on familial (FAD) and sporadic (SAD) forms of the disease are discussed. Finally, we outline the issues that need to be elucidated and future directions for iPSC modeling in AD.

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals.

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings.

Distress tolerance, anxiety-related symptoms, stress, and depression among dropout and non-dropout university students: a mediation analysis.

This study explores the relationship between distress tolerance and depression with mediating role of anxiety-related symptoms and stress among dropout and non-dropout university students. This cross-sectional study was conducted from October 2019 to December 2020. Participants' age ranged between 20-40 years. Data was collected using the Distress Tolerance Scale and Depression Anxiety and Stress Scale. Descriptive, t-test, and mediation analyses were performed to calculate the results. A sample of 500 respondents was recruited. A significant difference was noted between dropout and non-dropout students on CGPA (p<0.001), depression (p<.001), anxiety (p<0.001), stress (p<0.001), tolerance (p<.001), absorption (p<0.001), appraisal (p<0.001), and distress tolerance (p<0.001). Mediation analysis reveals that stress and anxiety significantly mediates between distress tolerance and depressive symptoms (i.e., F (498) = 31.14, p<0.001; F (498) =34.14, p<0.001; F (496) = 161.21, p<0.001). It is concluded that low distress tolerance increases stress and anxiety, triggering depressive symptoms.

The Leading Role Of Evidence-Based Practices In The Treatment Of Patients With Substance Use Disorders: A Systematic Review.

Objective: To explore the effectiveness of motivational interviewing, motivational enhancement therapy, and cognitive behaviour therapy for patients with substance use disorders, and to estimate the effect of such comparison in patient care setting. METHODS: The systematic review was conducted from September 2021 to February 2022, and comprised search on MEDLINE, EMBASE, Web of Science, PsycINFO, Google Scholar, Science Direct, PubMed, Clinical Trials.gov and OvidSP databases for experimental studies and randomised controlled trials related to substance use disorders published in peer-reviewed English-language journals between 2001 and 2021. Quality of the studies was assessed using the Modified Cochrane Collaboration risk of the bias assessment criteria. RESULTS: Of the 314 studies initially identified, 41(13%) were subjected to full-text assessment, and, of them, 16(39%) were reviewed and analysed. There were 8(50%) studies done is the United States, 4(25%) in the United Kingdom, and 1(6.25%) each in Germany, Australia, South Korea and South Africa. All the 16(100%) studies were intervention-based, with 6(37.5%) being randomised controlled trials. There were 8(50%) studies using motivational interviewing and cognitive behaviour therapy, 5(31.25%) had significant results with a combination of motivational enhancement therapy and cognitive behaviour therapy, 3(18.75%) supported motivational enhancement therapy and cognitive behaviour therapy in combination, and 2(12.5%) studies combined motivational interviewing, motivational enhancement therapy and cognitive behaviour therapy, reporting significant results while simultaneously addressing multiple patient variables. Conclusion: All studies were heterogeneous. Motivational interviewing produced short-term treatment outcomes and played a supportive role in sustaining motivation. Motivational enhancement therapy was an effective therapeutic intervention that significantly addressed inadequate causes, and enhanced motivation for treatment. Cognitive behaviour therapy had a short-term impact and remained influential in the long term as well in handling cognitive and behavioural setbacks.

A clinical trial of cognitive behavior therapy for psychiatric comorbidity and quality of life with Cancer Patients during Chemotherapy (CPdC).

BACKGROUND: Cancer is a common worldwide illness; it evokes psychological distress at different stages, during chemotherapy patient perceives a variety of psychiatric symptoms due to various medication side-effects and psychological distress. Studies have shown a significant impact of cognitive behavior therapy (CBT) in the management of psychiatric symptoms during chemotherapy. This study aims to investigate the effectiveness of CBT for depression, anxiety, stress, death anxiety, satisfaction with life, and self-esteem among cancer patients during chemotherapy (CPdC). METHODS: Place and duration of the study: Department of Applied Psychology, Government College University Faisalabad in collaboration with Department of Oncology, Allied Hospital Faisalabad from November 20, 2020 and July 31, 2021. A total of 90 cancer patients were enrolled. 70 out of 90 met the eligibility criteria and 60 participants fulfilled all requirements. Participants were randomly allocated to four different groups. The pre-assessment screening was started along with the first trial of chemotherapy. The CBT-based treatment plan was formulated and one session per week was given to each patient for 3 to 4 months. Participants' age range was 18-65 years (M ± SD = 47.51 ± 12.36. Demographic form, Depression Anxiety and Stress Scale (DASS), Death Anxiety Scale (DAS), Satisfaction with Life Scale (SWLS), and Rosenberg Self-Esteem Scale (RSES) were administered. Descriptive, t-test, and repeated measure ANOVA statistics were used to investigate the findings. RESULTS: Results indicated significant mean difference on the variable of depression, anxiety and stress across four conditions (i.e. F(2, 56) = 39.55, p < .000, η2 = .679; F(2,56) = 73.32, p < .000, η2 = .797; F(2,56) = 119.77, p < .000, η2 = .865 respectively). On death anxiety significant difference across four conditions was found (F(2,56) = 22.71, p < .000, η2 = .549) with large effect size. Furthermore, findings indicated significant mean difference on the variable of satisfaction with life and self-esteem across four conditions was found (F(2,56) = 22.05, p < .000, η2 = .542; F(2,56) = 36.19, p < .000, η2 = .660) with large effect size. CONCLUSION: It is concluded that CBT played a very effective role to reduce depression, anxiety, and stress-related psychiatric symptoms. CBT reduces the level of death anxiety and improving the quality of life and level of self-esteem among CPdC. TRIAL REGISTRATION: The study trial was registered in the Thai Clinical Trial Registry-TCTR ( TCTR20201113002 ).

Transcriptome Analysis of the Responses of Rice Leaves to Chilling and Subsequent Recovery.

Improving chilling tolerance at the seedling stage in rice is essential for agricultural research. We combined a physiological analysis with transcriptomics in a variety Dular subjected to chilling followed by recovery at normal temperature to better understand the chilling tolerance mechanisms of rice. Chilling inhibited the synthesis of chlorophyll and non-structural carbohydrate (NSC) and disrupted the ion balance of the plant, resulting in the impaired function of rice leaves. The recovery treatment can effectively reverse the chilling-related injury. Transcriptome results displayed that 21,970 genes were identified at three different temperatures, and 11,732 genes were differentially expressed. According to KEGG analysis, functional categories for differentially expressed genes (DEGs) mainly included ribosome (8.72%), photosynthesis-antenna proteins (7.38%), phenylpropanoid biosynthesis (11.41%), and linoleic acid metabolism (10.07%). The subcellular localization demonstrated that most proteins were located in the chloroplasts (29.30%), cytosol (10.19%), and nucleus (10.19%). We proposed that some genes involved in photosynthesis, ribosome, phenylpropanoid biosynthesis, and linoleic acid metabolism may play key roles in enhancing rice adaptation to chilling stress and their recovery capacity. These findings provide a foundation for future research into rice chilling tolerance mechanisms.

Relationship of Muslim Religiosity and Death Anxiety with the Mediating Effect of Optimism and Depression Among Cancer Patients in Pakistan.

Optimism and the practice of any religion are known to reduce depression and anxiety in cancer patients. In the present study, the specific role of Muslim religiosity, optimism, depression, and death anxiety in cancer patients has been explored. The sample of this study consisted of 200 cancer patients from different hospitals of the districts Faisalabad and Lahore (Pakistan). The sample's average age was 26.6 years. Parallel mediation findings show that optimism and depression are significant mediators between Muslim religiosity and death anxiety among cancer patients. Muslim religiosity is positively associated with optimism, and it helps to decrease the level of death anxiety, while depression is negatively associated with Muslim religiosity, and high depression increases the death anxiety level of cancer patients. In conclusion, Muslim religiosity and optimism play significant roles in managing depressive symptoms and death anxiety among cancer patients.

Proteomic analysis reveals a role of ADP-glucose pyrophosphorylase in the asynchronous filling of rice superior and inferior spikelets.

The poor grain filling of inferior spikelets (IS) situated on the lower secondary rachis branch leads to a remarkable decrease in rice yield and quality. The AGPase small subunit 2 (AGPS2) encodes a small subunit of adenosine diphosphate-glucose pyrophosphorylase (AGPase) enzyme, which plays an important role in sucrose-starch conversion and starch biosynthesis in the grain filling of rice. In the present study, qPCR analysis showed low expression abundance of AGPS2 in IS, compared to the superior spikelets (SS), which was consistent with the lower grain weight of IS. To evaluate the molecular mechanism of AGPS2, we first identified the AGPS2 interaction network through Co-immunoprecipitation (Co-IP). In total, 29 proteins of AGPS2 interaction network were characterized by LC-MS/MS. Bioinformatics analysis revealed that, the characterized proteins in the interaction network are likely to be related to starch synthesis, sugar conversion, energy pathway, and folding/modification, and most of them were involved in the grain filling of rice. The sequent Co-IP analysis showed that AGPS2 can bind to starch branching enzyme (SBE), pullulanase (PUL) and starch debranching enzyme (DBE) and assemble into starch synthesizing protein complex (SSPC). In addition, the 14-3-3 protein GF14e was also found to interact with AGPS2. Further analysis by qPCR showed that the expression of GF14e was much higher on IS than on SS. The qPCR results also showed that the expression of GF14e was relatively stable in SS, but changed significantly in IS under alternate wetting and moderate soil drying (WMD), which is consistent with the AGPS2 expression pattern. Our present work provides direct molecular evidence for the different expression patterns of AGPS2 in SS and IS, which could be greatly helpful for the molecular amelioration of the poor grain filling of IS in rice.

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme.

Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

Field Decorrelation and Isolation Improvement in an MIMO Antenna Using an All-Dielectric Device Based on Transformation Electromagnetics.

This work presents a new technique for enhancing the performance of a multiple-input multiple-output (MIMO) antenna by improving its correlation coefficient ρ. A broadband dielectric structure is designed using the transformation electromagnetics (TE) concept to decorrelate the fields of closely placed radiating elements of an MIMO antenna, thereby decreasing ρ and mutual coupling. The desired properties of the broadband dielectric wave tilting structure (DWTS) are determined by using quasi-conformal transformation electromagnetics (QCTE). Next, the permittivity profile of the DWTS is realized by employing air-hole technology, which is based on the effective medium theory, and the DWTS is fabricated using the additive manufacturing (3D printing) technique. The effectiveness of the proposed technique is verified by designing two-element patch-based MIMO antenna prototypes operating at 3 GHz, 5 GHz, and 7 GHz, respectively. The proposed technique helped to reduce the correlation coefficient ρ in the range of 37% to 99% in the respective operating bandwidth of each MIMO antenna, thereby, in each case, improving the isolation between antenna elements by better than 3 dB, which is an excellent performance.

The Role of Membrane Transporters in Plant Growth and Development, and Abiotic Stress Tolerance.

The proteins of membrane transporters (MTs) are embedded within membrane-bounded organelles and are the prime targets for improvements in the efficiency of water and nutrient transportation. Their function is to maintain cellular homeostasis by controlling ionic movements across cellular channels from roots to upper plant parts, xylem loading and remobilization of sugar molecules from photosynthesis tissues in the leaf (source) to roots, stem and seeds (sink) via phloem loading. The plant's entire source-to-sink relationship is regulated by multiple transporting proteins in a highly sophisticated manner and driven based on different stages of plant growth and development (PG&D) and environmental changes. The MTs play a pivotal role in PG&D in terms of increased plant height, branches/tiller numbers, enhanced numbers, length and filled panicles per plant, seed yield and grain quality. Dynamic climatic changes disturbed ionic balance (salt, drought and heavy metals) and sugar supply (cold and heat stress) in plants. Due to poor selectivity, some of the MTs also uptake toxic elements in roots negatively impact PG&D and are later on also exported to upper parts where they deteriorate grain quality. As an adaptive strategy, in response to salt and heavy metals, plants activate plasma membranes and vacuolar membrane-localized MTs that export toxic elements into vacuole and also translocate in the root's tips and shoot. However, in case of drought, cold and heat stresses, MTs increased water and sugar supplies to all organs. In this review, we mainly review recent literature from Arabidopsis, halophytes and major field crops such as rice, wheat, maize and oilseed rape in order to argue the global role of MTs in PG&D, and abiotic stress tolerance. We also discussed gene expression level changes and genomic variations within a species as well as within a family in response to developmental and environmental cues.

Recent Advances in Biopolymeric Composite Materials for Tissue Engineering and Regenerative Medicines: A Review.

The polymeric composite material with desirable features can be gained by selecting suitable biopolymers with selected additives to get polymer-filler interaction. Several parameters can be modified according to the design requirements, such as chemical structure, degradation kinetics, and biopolymer composites' mechanical properties. The interfacial interactions between the biopolymer and the nanofiller have substantial control over biopolymer composites' mechanical characteristics. This review focuses on different applications of biopolymeric composites in controlled drug release, tissue engineering, and wound healing with considerable properties. The biopolymeric composite materials are required with advanced and multifunctional properties in the biomedical field and regenerative medicines with a complete analysis of routine biomaterials with enhanced biomedical engineering characteristics. Several studies in the literature on tissue engineering, drug delivery, and wound dressing have been mentioned. These results need to be reviewed for possible development and analysis, which makes an essential study.

Development of Antibacterial, Degradable and pH-Responsive Chitosan/Guar Gum/Polyvinyl Alcohol Blended Hydrogels for Wound Dressing.

The present research is based on the fabrication preparation of CS/PVA/GG blended hydrogel with nontoxic tetra orthosilicate (TEOS) for sustained paracetamol release. Different TEOS percentages were used because of their nontoxic behavior to study newly designed hydrogels' crosslinking and physicochemical properties. These hydrogels were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and wetting to determine the functional, surface morphology, hydrophilic, or hydrophobic properties. The swelling analysis in different media, degradation in PBS, and drug release kinetics were conducted to observe their response against corresponding media. The FTIR analysis confirmed the components added and crosslinking between them, and surface morphology confirmed different surface and wetting behavior due to different crosslinking. In various solvents, including water, buffer, and electrolyte solutions, the swelling behaviour of hydrogel was investigated and observed that TEOS amount caused less hydrogel swelling. In acidic pH, hydrogels swell the most, while they swell the least at pH 7 or higher. These hydrogels are pH-sensitive and appropriate for controlled drug release. These hydrogels demonstrated that, as the ionic concentration was increased, swelling decreased due to decreased osmotic pressure in various electrolyte solutions. The antimicrobial analysis revealed that these hydrogels are highly antibacterial against Gram-positive (Staphylococcus aureus and Bacillus cereus) and Gram negative (Pseudomonas aeruginosa and Escherichia coli) bacterial strains. The drug release mechanism was 98% in phosphate buffer saline (PBS) media at pH 7.4 in 140 min. To analyze drug release behaviour, the drug release kinetics was assessed against different mathematical models (such as zero and first order, Higuchi, Baker-Lonsdale, Hixson, and Peppas). It was found that hydrogel (CPG2) follows the Peppas model with the highest value of regression (R2 = 0.98509). Hence, from the results, these hydrogels could be a potential biomaterial for wound dressing in biomedical applications.

Role of religiosity, optimism, demographic characteristics and mental health problems among cancer patients.

OBJECTIVE: To investigate the role of religiosity, optimism, depression, death anxiety and differences in demographic characteristics among cancer patients. METHODS: The cross-sectional study was conducted from July 2018 to July 2019 in three different hospitals of Lahore and Faisalabad, Pakistan, and comprised patients with diagnosed stage 1 and 2 cancer. Non-cancer subjects were enrolled as the control group. Data was collected using the Short Muslim Practice and Brief Scale, the Siddiqui-Shah Depression Scale, Death Anxiety Scale and the revised version of Life Orientation Test. One-way analysis of variance and other tests were used for data analyses. RESULTS: Of the 400 subjects, 200(50%) each were cases and controls. Among the cases, 100(50%) each were males and females. There was a significant difference between cancer and non-cancer subjects on the variables of religiosity, optimism, depression and death anxiety (p<0.05). Significant gender differences were found on the variables of religiosity, depression and death anxiety (p<0.05), while the difference on the construct of optimism was non-significant among cancer patients (p>0.05). Cancer patients of rural and urban areas were significantly different on the variables of religiosity, depression and death anxiety (p<0.05), but the difference was non-significant on the optimism scale (p>0.05). Also, the differences on death anxiety scale were significantly related to the type of cancer (p<0.05). CONCLUSIONS: There was a greater role of religiosity and optimism in controlling the level of depression and fear of death among cancer patients. Also, the role of gender, residential area and type of cancer was significant.

Unsupervised deep learning for depth estimation with offset pixels.

Offset Pixel Aperture (OPA) camera has been recently proposed to estimate disparity of a scene with a single shot. Disparity is obtained in the image by offsetting the pixels by a fixed distance. Previously, correspondence matching schemes have been used for disparity estimation with OPA. To improve disparity estimation we use a data-oriented approach. Specifically, we use unsupervised deep learning to estimate the disparity in OPA images. We propose a simple modification to the training strategy which solves the vanishing gradients problem with the very small baseline of the OPA camera. Training degenerates to poor disparity maps if the OPA images are used directly for left-right consistency check. By using images obtained from displaced cameras at training, accurate disparity maps are obtained. The performance of the OPA camera is significantly improved compared to previously proposed single-shot cameras and unsupervised disparity estimation methods. The approach provides 8 frames per second on a single Nvidia 1080 GPU with 1024×512 OPA images. Unlike conventional approaches, which are evaluated in controlled environments, our paper shows the utility of deep learning for disparity estimation with real life sensors and low quality images. By combining OPA with deep learning, we obtain a small depth sensor capable of providing accurate disparity at usable frame rates. Also the ideas in this work can be used in small-baseline stereo systems for short-range depth estimation and multi-baseline stereo to increase the depth range.

Q-SNARE protein FgSyn8 plays important role in growth, DON production and pathogenicity of Fusarium graminearum.

SNAREs (Soluble N-ethylmaleimide-sensitive factor attachment protein receptors) help intracellular vesicle trafficking and membrane fusion among eukaryotes. They are vital for growth and development of phyto-pathogenic fungi such as Fusarium graminearum which causes Fusarium Head Blight (FHB) of wheat and barley. The SNARE protein Syn8 and its homologues play many roles among different organisms. Here, we have characterized FgSyn8 in F. graminearum as a homologue of Syn8. We have integrated biochemical, microbiological and molecular genetic approaches to investigate the roles of this protein. Our results reveal that FgSyn8 is indispensable for normal vegetative growth, conidiation, conidial morphology and pathogenicity of F. graminearum. Deoxynivalenol (DON) biochemical assay reveals active participation of this protein in DON production of F. graminearum. This has further been confirmed by the production of bulbous structures among the intercalary hyphae. FgSyn8 mutant strain produced defects in perithecia formation which portrays its role in sexual reproduction. In summary, our results support that the SNARE protein FgSyn8 is required for vegetative growth, sexual reproduction, DON production and pathogenicity of F. graminearum.

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features.

Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

Subjective quality of multiple choice questions used in undergraduate courses in orthopedics and other specialties.

BACKGROUND AND OBJECTIVE: Multiple Choice Questions (MCQs) can sample broad domains of knowledge efficiently and reliably. The MCQs of lower order C1(Cognitive Level1=Recall of knowledge) do not fulfill this purpose and those of higher cognitive order C2 (Cognitive Level2=Interpret) &C3(Cognitive Level3=Analyze) are better at assessing the problem solving capabilities of the student. Every good educational activity must be supported by quality examination to complete the objectives of a curriculum. The objective of the study was to evaluate MCQs presently being used in internal examinations of medical colleges in Lahore. METHODS: Papers consisting of MCQs from Orthopedics other specialties were collected in June 2019 from different medical colleges of Lahore and reviewed by a senior medical teacher without blinding and without his knowing the scores the students had been awarded before. Question statement, clinical scenarios, options and other mistakes were assessed in each item on predetermined criteria. Cognitive level of the item was determined if it was asking for a recall/identify/ analyze response. The results were tabulated and compared in two groups i.e. Miscellaneous and Orthopedics. RESULTS: Most of the items(total=589) in both groups were of C1 cognitive level though Orthopedics (229) were slightly better (χ2 = 49.882 P-Value = 0.000 (Statistically Significant). Miscellaneous group (360) was better in quality in making clinical scenarios (χ2 = 29.952 P-Value = 0.000 (Statistically Significant) and writing a question statement without confusion. Options were better written in both groups. A good percentage of items needed to be corrected for mistakes in spellings, grammar and segregation into under graduate level. CONCLUSIONS: The cognitive level of assessment tool s MCQs is quiet low in both groups especially clinical scenario construction can be improved. Mistakes in spellings, grammar and conceptual mediocrity is common in both groups.