Overcoming chemoresistance and radio resistance in prostate cancer: The emergent role of non-coding RNAs.

Prostate cancer (PCa) continues to be a major health concern worldwide, with its resistance to chemotherapy and radiation therapy presenting major hurdles in successful treatment. While patients with localized prostate cancer generally have a good survival rate, those with metastatic prostate cancer often face a grim prognosis, even with aggressive treatments using various methods. The high mortality rate in severe cases is largely due to the lack of treatment options that can offer lasting results, especially considering the significant genetic diversity found in tumors at the genomic level. This comprehensive review examines the intricate molecular mechanisms governing resistance in PCa, emphasising the pivotal contributions of non-coding RNAs (ncRNAs). We delve into the diverse roles of microRNAs, long ncRNAs, and other non-coding elements as critical regulators of key cellular processes involved in CR & RR. The review emphasizes the diagnostic potential of ncRNAs as predictive biomarkers for treatment response, offering insights into patient stratification and personalized therapeutic approaches. Additionally, we explore the therapeutic implications of targeting ncRNAs to overcome CR & RR, highlighting innovative strategies to restore treatment sensitivity. By synthesizing current knowledge, this review not only provides a comprehension of the chemical basis of resistance in PCa but also identifies gaps in knowledge, paving the way for future research directions. Ultimately, this exploration of ncRNA perspectives offers a roadmap for advancing precision medicine in PCa, potentially transforming therapeutic paradigms and improving outcomes for patients facing the challenges of treatment resistance.

Natural regulatory T cells increase significantly in pediatric patients with parasitic infections: Flow cytometry study.

Background: The most accepted definition of regulatory T cells (Tregs) relies on the expression of several biomarkers, including CD4, CD25, and transcription factor, Foxp3. The Tregs maintain tolerance to self-antigens and prevent autoimmune diseases. Aim: The purpose of this study was to determine the difference in natural Treg levels in Entamoeba histolytica, Schistosoma mansoni, Giardia lamblia, Enterobius vermicularis, and Hymenolepis nana infected patients. Setting and Design: Fifty-one pediatric subjects (29 males and 22 females) were recruited from a tertiary care hospital, and were divided into infected and non-infected (control) groups. The mean age of the subjects was 8.7 years. Materials and Methods: Blood samples were collected from infected and non-infected groups, and change in the level of Tregs in these subjects was investigated by flow cytometry. Statistical Analysis Used: The statistical analysis of data was performed by SPSS software. Quantitative data used in this study included mean and standard deviation. Data from the two groups were compared by the Student's t-test. The age of the patient and infection status were used for multivariate logistic regression analysis. Odds ratios (ORs) were estimated within a 95% confidence interval, and a P value of <0.05 was considered significant. Results and Conclusions: The levels of natural regulatory T cells, indicated by the biomarkers, CD4+, CD25+, and Foxp3+, increase significantly in patients infected by Entamoeba histolytica, Schistosoma mansoni, Giardia lamblia, Enterobius vermicularis, and Hymenolepis nana as compared to controls. They also increase in cases of mixed infection as compared to infection by a single parasite.

A CAD System for Lung Cancer Detection Using Hybrid Deep Learning Techniques.

Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models.

Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica.

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

Preparation of stimuli responsive microgel with silver nanoparticles for biosensing and catalytic reduction of water pollutants.

Herein, we report poly(N-isopropylacrylamide/2-acrylamido-2-methylpropane sulfonic acid) microgel fabricated with silver nanoparticles. The identification of copolymerization and functional groups in the bare microgel and those fabricated with silver nanoparticles was examined by Fourier transform infrared spectroscopy. The pH and temperature sensitivity of microgels was studied using dynamic light scattering. Thermogravimetric analysis was carried out to study the thermal stability. X-Ray diffraction patterns indicated the amorphous nature of bare microgel and crystalline nature of those containing silver nanoparticles. A bathochromic shift was found in the surface plasmon resonance of silver nanoparticles present in microgel with increase in pH of the medium. Moreover, the microgel containing silver nanoparticles served as an effective catalyst for reducing the toxic nitroaromatic pollutants and carcinogenic dyes. The microgel containing silver nanoparticles also showed good capability to serve as biosensor for the detection of hydrogen peroxide.

Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers.

Breast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the death rate. The identification and classification of breast cancer are challenging tasks. The most commonly known procedure of breast cancer detection is performed by using mammographic images. Recently implemented algorithms suffer from generating accuracy below expectations, and their computational complexity is high. To resolve these issues, this paper proposes a fully automated biomedical diagnosis system of breast cancer using an AlexNet, a type of Convolutional Neural Network (CNN), and multiple classifiers to identify and classify breast cancer. This system utilizes a neuro-fuzzy method, a segmentation algorithm, and various classifiers to reach a higher accuracy than other systems have achieved. Numerous features are extracted to detect and categorize breast cancer. Three datasets from Kaggle were tested to validate the proposed system. The performance evaluation is performed with quantitative and qualitative accuracy, precision, recall, specificity, and F-score. In addition, a comparative assessment is performed between the proposed system and some works of literature. This assessment shows that the presented algorithm provides better classification results and outperforms other systems in all parameters. Its average accuracy is over 98.6%, while other metrics are more than 98%. This research indicates that this approach can be applied to assist doctors in diagnosing breast cancer correctly.

A Deep Convolutional Neural Network for the Early Detection of Heart Disease.

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment.

Impact of interpregnancy intervals on perinatal and neonatal outcomes in a multiethnic Pakistani population.

BACKGROUND: Short birth intervals (SBIs) and long birth intervals (LBIs) have been shown to have serious implications for health of both mothers and their children. This study was aimed to investigate the determinants and reproductive outcome of SBI and LBI in a multiethnic Pakistani population. METHODS: In a cross-sectional prospective study design, 2798 women admitted in a tertiary-care hospital in Islamabad for delivery were recruited and data on second or higher birth order deliveries were collected. Birth intervals were defined as short (<24 months) and long (>36 months). The reproductive outcome was defined in terms of perinatal and neonatal mortalities, and neonatal complications. Univariate and multivariate logistic regression analyses were performed. RESULTS: Pregnancies with SBI and LBI were observed in 20% and 24% of 2798 women, respectively. Women with SBI had increased odds of perinatal death [adjusted odd ratio (AOR): 1.50] and neonatal death (AOR: 1.47) as compared to women with optimal birth intervals, while women with LBI had slightly lower odds of perinatal deaths (AOR: 0.96), but increased odds of neonatal deaths (AOR: 1.12). Further, the pregnancies with both SBI and LBI were associated with increased odds of short body length, low birth weight, small head circumference and low APGAR score. CONCLUSION: Nearly half of all pregnancies do not have optimal birth spacing albeit there is wide heterogeneity in the distribution of BI in various Pakistani ethnicities. Pregnancies with SBI and LBI had high risk of adverse reproductive outcome. Intervention programs for maternal and child health need to emphasize optimal birth spacing.

Birth interval (BI) or interpregnancy interval is the length of time between a birth and conception of the next pregnancy. Short birth intervals (SBIs) as well as long birth intervals (LBIs) have been shown to have serious implications for health of both mothers and their children. WHO recommendation for optimal spacing between 3 and 5 years. In this study, we aimed to investigate the effect of SBI and LBI on pregnancy outcome in the Pakistani population. A total of 2798 pregnant women admitted in a tertiary-care hospital in Islamabad for delivery were recruited and data on BI and pregnancy outcomes, i.e. perinatal and neonatal mortalities, and neonatal complications, were obtained. Results revealed that pregnancies with SBI and LBI were 20% and 24% of the total pregnancies, respectively. Women with SBI had higher likelihood of perinatal and neonatal death as compared to women with optimal birth intervals. Similarly, the women with LBI had higher likelihood of neonatal deaths. Furthermore, the pregnancies with both SBI and LBI were associated neonatal complications like short body length, low birth weight, small head circumference and low APGAR score. In conclusion, nearly half of all pregnancies do not have optimal birth spacing. Intervention programs for maternal and child health need to emphasize optimal birth spacing.

An insight into the discovery, clinical studies, compositions, and patents of macozinone: A drug targeting the DprE1 enzyme of Mycobacterium tuberculosis.

Decaprenyl-phosphoryl-ribose 2'-epimerase (DprE1) inhibitors are an innovative and futuristic orally active group of antituberculosis agents. A few DprE1 inhibitors are in the clinical trial for tuberculosis (TB), including macozinone. This review highlights the discovery, developmental status, clinical studies, patents, and prospects of macozinone (MCZ). The patent and non-patent literature search was done by entering keywords such as macozinone; MCZ; PBTZ169; PBTZ-169 in Pubmed, Espacenet, Patentscope, and the USPTO databases. However, data on Sci-Finder was searched using CAS registry number: 1377239-83-2. MCZ clinical trial studies were retrieved from the clinicaltrials.gov database using the exact keywords. The chemical structure of MCZ was disclosed in 2009. Accordingly, patents/patent applications published from 2009 to June 12, 2022, have been discussed herein. MCZ and MCZ hydrochloride salt patents were granted in 2014 and 2019, respectively, in the USA. The patent literature and the clinical trial studies suggest capsule, tablet, and suspension formulations of crystalline MCZ and its hydrochloride salt as the possible and prospective dosage forms to treat TB. Some combinations of MCZ with other drugs (chloroquine, telacebec, tafenoquine, TBI-166, and sanfetrinem) with improved anti-TB efficacy have been documented. Based on the literature covered in this review article on the clinical studies and patents applied/granted to MCZ, it can be inferred that MCZ seems to be a promising DprE1 inhibitor and could help to tackle the emerging dilemma of drug-resistant either as a monotherapy or in combination with additional anti-TB agents. Furthermore, the authors anticipate the development of new combinations, salts, and polymorphs of MCZ as anti-TB agents shortly. This review article might prove beneficial to the scientific community as it summarizes chemistry, pharmacology and provides an update on the clinical studies and patents/patent applications of one of the emerging anti-TB drugs in one place.

Deverra triradiata Hochst. ex Boiss. from the Northern Region of Saudi Arabia: Essential Oil Profiling, Plant Extracts and Biological Activities.

Devrra triradiata Hochst. ex Boiss is an occasional plant species in the Northern region of Saudi Arabia. The shrub is favored on sandy desert wadis, gypsaceous substrate, and sandy gravel desert. In folk medicine, the plant is used for many purposes; to relieve stomach pains, against intestinal parasites, and for the regulation of menstruation. The present study describes the chemical composition of the essential oils (EOs) of different plant parts of D. triradiata. In vivo and in vitro biological activities of plant extracts and essential oils were also studied. Phenylpropanoids, elemicin (flowers: 100%), dillapiole (Stems: 82.33%; and seeds: 82.61%), and apiol (roots: 72.16%) were identified as the major compounds. The highest antioxidant activity was recorded for the EOs of roots and stems (IC50 = 0.282 µg/mL and 0.706 µg/mL, respectively). For plant extracts, ethyl acetate showed the highest antioxidant activities (IC50 = 2.47 and 3.18 µg/mL). EOs showed high antifungal activity against yeasts with low azole susceptibilities (i.e., Malassezia spp. and Candida krusei). The MIC values of EOs ranged between 3.4 mg/mL and 56.4 mg/mL. The obtained results also showed phytotoxic potential of plant extracts both on the germination features of Triticum aestivum seeds and the vegetative growth of seedlings.

Nigella sativa L. and COVID-19: A Glance at The Anti-COVID-19 Chemical Constituents, Clinical Trials, Inventions, and Patent Literature.

COVID-19 has had an impact on human quality of life and economics. Scientists have been identifying remedies for its prevention and treatment from all possible sources, including plants. Nigella sativa L. (NS) is an important medicinal plant of Islamic value. This review highlights the anti-COVID-19 potential, clinical trials, inventions, and patent literature related to NS and its major chemical constituents, like thymoquinone. The literature was collected from different databases, including Pubmed, Espacenet, and Patentscope. The literature supports the efficacy of NS, NS oil (NSO), and its chemical constituents against COVID-19. The clinical data imply that NS and NSO can prevent and treat COVID-19 patients with a faster recovery rate. Several inventions comprising NS and NSO have been claimed in patent applications to prevent/treat COVID-19. The patent literature cites NS as an immunomodulator, antioxidant, anti-inflammatory, a source of anti-SARS-CoV-2 compounds, and a plant having protective effects on the lungs. The available facts indicate that NS, NSO, and its various compositions have all the attributes to be used as a promising remedy to prevent, manage, and treat COVID-19 among high-risk people as well as for the therapy of COVID-19 patients of all age groups as a monotherapy or a combination therapy. Many compositions of NS in combination with countless medicinal herbs and medicines are still unexplored. Accordingly, the authors foresee a bright scope in developing NS-based anti-COVID-19 composition for clinical use in the future.

Development of Therapeutic and Prophylactic Zinc Compositions for Use against COVID-19: A Glimpse of the Trends, Inventions, and Patents.

Zinc is an essential nutrient for human health; it is involved in the catalytic, structural, and regulatory functions of the human cellular system. Different compositions of zinc, as well as its pharmaceutically acceptable salts, are available on the market. Recent studies have demonstrated the role of zinc in combating COVID-19. It has been determined that zinc prevents the entry of SARS-CoV-2 into cells by lowering the expression of ACE-2 receptors and inhibiting the RNA-dependent RNA polymerase of SARS-CoV-2. Zinc also prevents the cytokine storm that takes place after the entry of SARS-CoV-2 into the cell, via its anti-inflammatory activity. The authors believe that no study has yet been published that has reviewed the trends, inventions, and patent literature of zinc compositions to treat/prevent COVID-19. Accordingly, this review has been written in order to fill this gap in the literature. The information about the clinical studies and the published patents/patent applications was retrieved from different databases. This review covers patent literature on zinc compositions up to 31 January 2022. Many important patents/patent applications for zinc-based compositions filed by innovative universities and industries were identified. The patent literature revealed zinc compositions in combination with zinc ionophores, antioxidants, antivirals, antibiotics, hydroxychloroquine, heparin, ivermectin, and copper. Most of these studies were supported by clinical trials. The patent literature supports the potential of zinc and its pharmaceutical compositions as possible treatments for COVID-19. The authors believe that countless zinc-based compositions are still unexplored, and there is an immense opportunity to evaluate a considerable number of the zinc-based compositions for use against COVID-19.

Deverra tortuosa (Desf.) DC from Saudi Arabia as a new source of marmin and furanocoumarins derivatives with α-glucosidase, antibacterial and cytotoxic activities.

Deverra tortuosa (Desf.) DC (Syn. Pituranthos tortusus (Desf.) Benth. & Hook.f.) is a species belonging to the Apiaceae family that is common in the Northern Region of Saudi Arabia. The plant is well known in traditional medicine along the Arabian ecoregion. In the framework of the present study, the crude extract of n-hexane and ethyl acetate of the seeds were fractionated to purify major bioactive secondary metabolites. Five compounds were identified for the first time from the seeds of D. tortuosa: Marmin 1, Pituranthoside 2, Isoimperatorin 3, Bergapten 4 and Isopimpinellin 5. Their structures were elucidated using 1D and 2D NMR, (ESI)-MS and IR spectroscopic analyses. The cytotoxic, α-glucosidase and antibacterial activities of the pure phytochemicals were also evaluated.