Miracle-dz/UniFAR-FTUs huggingface dataset:
Health-Related Quality of Life in Very Long-Term Cancer Survivors 14–24 Years Post-Diagnosis Compared to Population Controls: A Population-Based Study

Simple Summary Little is known about the health-related quality of life in very long-term cancer survivors 10 and more years post-diagnosis. Therefore, we compared the health-related quality of life of survivors of breast, colorectal, and prostate cancer (14–24 years post-diagnosis) with that of same-aged non-cancer controls, according to age, sex, and disease status (disease-free vs. stage IV, recurrence, metastasis, or second cancer). We found that the overall global health status/quality of life of cancer survivors more than a decade after diagnosis was slightly higher than that of population controls of the same age, but more symptoms and lower functioning were reported. Differences were small but statistically significant. Results differed by age, sex, and disease status. The findings point out the need for a comprehensive survivorship care program in order to monitor and treat potential late and long-term effects after the diagnosis and treatment of cancer. Survivorship care should be risk-adapted to survivors’ needs according to sociodemographic and clinical factors. Abstract (1) Background: Little is known about the health-related quality of life (HRQoL) in very long-term cancer survivors (VLTCS) 10 and more years post-diagnosis. The objective was to compare cancer survivors’ HRQoL 14–24 years post-diagnosis with that of same-aged non-cancer controls, stratified by age, sex, and disease status (disease-free vs. stage IV, recurrence, metastasis, or second cancer). (2) Methods: We recruited 2704 very long-term survivors of breast, colorectal and prostate cancer, and 1765 controls in German multi-regional population-based studies. The HRQoL was assessed by the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). Differences in the HRQoL were estimated with multiple regression, controlling for age, sex (where appropriate), and education. (3) Results: The overall global health status/quality of life of VLTCS more than a decade after diagnosis was slightly higher than that of population controls of the same age, but more symptoms and lower functioning were reported. Differences were small but statistically significant. Results differed by age, sex, and disease status. (4) Conclusions: The findings point out the need for a comprehensive survivorship care program in order to monitor and treat potential late and long-term effects after the diagnosis and treatment of cancer. Survivorship care should be risk-adapted to survivors’ needs according to sociodemographic and clinical factors.
Effect of Salt Stress on the Expression and Promoter Methylation of the Genes Encoding the Mitochondrial and Cytosolic Forms of Aconitase and Fumarase in Maize

The influence of salt stress on gene expression, promoter methylation, and enzymatic activity of the mitochondrial and cytosolic forms of aconitase and fumarase has been investigated in maize (Zea mays L.) seedlings. The incubation of maize seedlings in 150-mM NaCl solution resulted in a several-fold increase of the mitochondrial activities of aconitase and fumarase that peaked at 6 h of NaCl treatment, while the cytosolic activity of aconitase and fumarase decreased. This corresponded to the decrease in promoter methylation of the genes Aco1 and Fum1 encoding the mitochondrial forms of these enzymes and the increase in promoter methylation of the genes Aco2 and Fum2 encoding the cytosolic forms. The pattern of expression of the genes encoding the mitochondrial forms of aconitase and fumarase corresponded to the profile of the increase of the stress marker gene ZmCOI6.1. It is concluded that the mitochondrial and cytosolic forms of aconitase and fumarase are regulated via the epigenetic mechanism of promoter methylation of their genes in the opposite ways in response to salt stress. The role of the mitochondrial isoforms of aconitase and fumarase in the elevation of respiration under salt stress is discussed.
Proofing Direct-Seeded Rice with Better Root Plasticity and Architecture

The underground reserve (root) has been an uncharted research territory with its untapped genetic variation yet to be exploited. Identifying ideal traits and breeding new rice varieties with efficient root system architecture (RSA) has great potential to increase resource-use efficiency and grain yield, especially under direct-seeded rice, by adapting to aerobic soil conditions. In this review, we tried to mine the available research information on the direct-seeded rice (DSR) root system to highlight the requirements of different root traits such as root architecture, length, number, density, thickness, diameter, and angle that play a pivotal role in determining the uptake of nutrients and moisture at different stages of plant growth. RSA also faces several stresses, due to excess or deficiency of moisture and nutrients, low or high temperature, or saline conditions. To counteract these hindrances, adaptation in response to stress becomes essential. Candidate genes such as early root growth enhancer PSTOL1, surface rooting QTL qSOR1, deep rooting gene DRO1, and numerous transporters for their respective nutrients and stress-responsive factors have been identified and validated under different circumstances. Identifying the desired QTLs and transporters underlying these traits and then designing an ideal root architecture can help in developing a suitable DSR cultivar and aid in further advancement in this direction.
Polymer Film Blend of Polyvinyl Alcohol, Trichloroethylene and Cresol Red for Gamma Radiation Dosimetry

This study investigated the polymer film composite of polyvinyl alcohol (PVA), trichlorethylene (TCE) and cresol red (CR) dye irradiated with gamma (γ) rays for potential application as radiation dosimetry. The film was prepared via the solvent-casting method with varying concentrations of TCE. Film samples were exposed to radiation from a γ-rays radiation source of 60Cobalt isotope. Color changes before and after γ-rays irradiation were observed, and the optical properties of the polymer films were investigated by spectrophotometry. Results show that increasing the radiation dose physically changed the color of the polymer film, from purple (pH > 8.8) without radiation (0 kGy) to yellow (almost transparent) (2.8 < pH < 7.2) at the highest dose (12 kGy). The concentration of acid formed due to irradiation increased with the increase in irradiation doses and at higher TCE content. The critical doses of PVA-TCE composites decreased linearly with the increase of TCE composition, facilitating an easy calibration process. The dose response at 438 nm increased exponentially with increasing radiation dose, but showed an opposite trend at the 575 nm band. An increase in the TCA concentration indicated a decrease in the absorption edge and an increase in activation energy, but both decreased for all TCE concentrations at higher doses. The energy gap for the direct and the indirect transitions decreased with increasing TCE concentration and γ-rays radiation dose. The results of this study demonstrated the potential application of PVA-TCE-CR polymer film as γ-rays irradiation dosimetry in a useful dose range of 0–12 kGy.
Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance.
Functional Coupling between the P2X7 Receptor and Pannexin-1 Channel in Rat Trigeminal Ganglion Neurons

The ionotropic P2X receptor, P2X7, is believed to regulate and/or generate nociceptive pain, and pain in several neuropathological diseases. Although there is a known relationship between P2X7 receptor activity and pain sensing, its detailed functional properties in trigeminal ganglion (TG) neurons remains unclear. We examined the electrophysiological and pharmacological characteristics of the P2X7 receptor and its functional coupling with other P2X receptors and pannexin-1 (PANX1) channels in primary cultured rat TG neurons, using whole-cell patch-clamp recordings. Application of ATP and Bz-ATP induced long-lasting biphasic inward currents that were more sensitive to extracellular Bz-ATP than ATP, indicating that the current was carried by P2X7 receptors. While the biphasic current densities of the first and second components were increased by Bz-ATP in a concentration dependent manner; current duration was only affected in the second component. These currents were significantly inhibited by P2X7 receptor antagonists, while only the second component was inhibited by P2X1, 3, and 4 receptor antagonists, PANX1 channel inhibitors, and extracellular ATPase. Taken together, our data suggests that autocrine or paracrine signaling via the P2X7-PANX1-P2X receptor/channel complex may play important roles in several pain sensing pathways via long-lasting neuronal activity driven by extracellular high-concentration ATP following tissue damage in the orofacial area.
A Review on Cancer Immunotherapy and Applications of Nanotechnology to Chemoimmunotherapy of Different Cancers

Clinically, different approaches are adopted worldwide for the treatment of cancer, which still ranks second among all causes of death. Immunotherapy for cancer treatment has been the focus of attention in recent years, aiming for an eventual antitumoral effect through the immune system response to cancer cells both prophylactically and therapeutically. The application of nanoparticulate delivery systems for cancer immunotherapy, which is defined as the use of immune system features in cancer treatment, is currently the focus of research. Nanomedicines and nanoparticulate macromolecule delivery for cancer therapy is believed to facilitate selective cytotoxicity based on passive or active targeting to tumors resulting in improved therapeutic efficacy and reduced side effects. Today, with more than 55 different nanomedicines in the market, it is possible to provide more effective cancer diagnosis and treatment by using nanotechnology. Cancer immunotherapy uses the body’s immune system to respond to cancer cells; however, this may lead to increased immune response and immunogenicity. Selectivity and targeting to cancer cells and tumors may lead the way to safer immunotherapy and nanotechnology-based delivery approaches that can help achieve the desired success in cancer treatment.
Help-Seeking from a National Youth Helpline in Australia: An Analysis of Kids Helpline Contacts

Counselling helplines or hotlines are key support services for young people with mental health concerns or in suicide and self-harm crises. We aimed to describe young peoples’ use of a national youth helpline (Kids Helpline, Australia, KHL) to understand how usage changed over time. A descriptive analysis was conducted on 1,415,228 answered contacts between 2012–2018. We described the trend of service usage over the observed period, the types of youth who used the service, and the problems young people contacted the service about. Phone (APC = −9.1, KHL: −10.4 to −7.8, p < 0.001) and email (APC = −13.7, 95%CI: −17.1 to −10.2, p < 0.001) contacts decreased over time whereas webchat contacts increased (APC = 16.7, 95%CI: 11.7 to 22.0, p < 0.001). With this increase in webchat contacts, there was an associated increase in total webchat contact duration. Concerns raised in contacts to the service were primarily related to emotional wellbeing and mental health concerns (53.2% phone, 57.3% webchat, 58.2% email) followed by social relationship issues (20.4% phone, 20.3% webchat, 16.8% email) and family relationships (19.4% phone, 17.2% webchat, 21.8% email). The increased preference for online text-based information and counselling services can help inform development of services for young people and allocation of staff/service training and resources.
A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Simple Summary Breast cancer is one of the most commonly diagnosed diseases in females around the world. The most threatening is when cancer spreads uncontrollably to other parts of the body and can cause death. Early detection of breast cancer lowers the risk of death among patients and enables appropriate treatments to control the progression of cancer. To diagnose breast cancer, high complex visuals of the breast tissue can be collected through histopathology images that provide informative details which validate the stage of the cancer. The aim of this study is to investigate techniques applied in histopathology images in diagnosing breast cancer. Abstract A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.
Estimating Service Demand for Intermediary Care at a Community Integrated Intermediary Care Center among Family Caregivers of Older Adults Residing in Chiang Mai, Northern Thailand

Background: Thailand’s population is currently the third most rapidly aging in the world, with an estimated 20 million ageing population by 2050. Sustainability of the family based long-term care model is challenged by the chronic burden on family caregivers and by smaller family sizes. We aimed to introduce a new service model, Community Integrated Intermediary Care (CIIC), TCTR20190412004, including free of charge intermediary care services at CIIC centers in the local community, to help older adults whose caregivers are temporarily unable to sustain care at home. Since Thai society upholds values of gratefulness, it is better to estimate willingness to use such an intermediary care service first, before introducing the service. Methods: A total of 867 pairs of senior citizens and their family caregivers were interviewed with structured-questionnaires in 2019. Descriptive analysis and binary logistic regression were applied to determine the predictors of family caregivers’ willingness to use the CIIC service, guided by Anderson’s model of health services use. Results: About 26.8% of elderly participants and 24.0% of family caregivers were willing to use an intermediary care service. The family caregiver determinants of predisposing factors (kinship: spouse caregivers, other relatives, maid or friends; job types: own business and private company staff), enabling factors (original community residents and monthly income ≤9000 baht), and need factors (caregiver burden total scores ≥24, taking leave for caregiving, and having diabetes), were found to be significantly associated with willingness to use the CIIC service. Conclusions: The baseline survey data noted that caregivers’ sociodemographic factors and burden determined their willingness to use the intermediary care service, although the dependency of care recipients was low in this study. This, nonetheless, indicated that there is need for a backup respite care to strengthen current family based long-term aging care in Thailand.
AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.
Effect of Particle Morphology on Stiffness, Strength and Volumetric Behavior of Rounded and Angular Natural Sand

Stress–strain and volume change behavior for clean sands which have distinct particle shape (rounded and angular) with very similar chemical (mineralogical) composition, size, and texture in one-dimensional (1D) compression and drained triaxial compression are presented. The effect of particle morphology on the crushing behavior in one-dimensional loading is explored using laser light diffraction technique which is suitable for particle crushing because of its high resolution and small specimen volume capability. Particle size distribution in both volume/mass and number distributions are considered for improved understanding associated with the process of comminution. Number distributions present a clearer picture of particle crushing. It is argued that particle crushing in granular assemblies initiates in larger particles, rather than in smaller particle. It was found that rounded sand specimens showed greater crushing than angular sand specimens with higher uniformity coefficient. In 1D compression, loose specimens compress approximately 10% more than dense specimens irrespective of particle shape. Densification of angular sand results in improvement in stiffness (approximately 40%) and is comparable to that of loose rounded sand. In general, density has a greater influence on the behavior of granular materials than particle morphology. The effect of particle shape was found to be greater in loose specimens than in dense specimens. The effect of grain shape on critical state friction angle is also quantified.
Post-COVID-19 Pulmonary Fibrosis: Novel Sequelae of the Current Pandemic

Since the initial identification of the novel coronavirus SARS-CoV-2 in December 2019, the COVID-19 pandemic has become a leading cause of morbidity and mortality worldwide. As effective vaccines and treatments begin to emerge, it will become increasingly important to identify and proactively manage the long-term respiratory complications of severe disease. The patterns of imaging abnormalities coupled with data from prior coronavirus outbreaks suggest that patients with severe COVID-19 pneumonia are likely at an increased risk of progression to interstitial lung disease (ILD) and chronic pulmonary vascular disease. In this paper, we briefly review the definition, classification, and underlying pathophysiology of interstitial lung disease (ILD). We then review the current literature on the proposed mechanisms of lung injury in severe COVID-19 infection, and outline potential viral- and immune-mediated processes implicated in the development of post-COVID-19 pulmonary fibrosis (PCPF). Finally, we address patient-specific and iatrogenic risk factors that could lead to PCPF and discuss strategies for reducing risk of pulmonary complications/sequelae.
Identification of 3-Methoxycarpachromene and Masticadienonic Acid as New Target Inhibitors against Trypanothione Reductase from Leishmania Infantum Using Molecular Docking and ADMET Prediction

Polyphenolic and Terpenoids are potent natural antiparasitic compounds. This study aimed to identify new drug against Leishmania parasites, leishmaniasis’s causal agent. A new in silico analysis was accomplished using molecular docking, with the Autodock vina program, to find the binding affinity of two important phytochemical compounds, Masticadienonic acid and the 3-Methoxycarpachromene, towards the trypanothione reductase as target drugs, responsible for the defense mechanism against oxidative stress and virulence of these parasites. There were exciting and new positive results: the molecular docking results show as elective binding profile for ligands inside the active site of this crucial enzyme. The ADMET study suggests that the 3-Methoxycarpachromene has the highest probability of human intestinal absorption. Through this work, 3-Methoxycarpachromene and Masticadienonic acid are shown to be potentially significant in drug discovery, especially in treating leishmaniasis. Hence, drug development should be completed with promising results.