Biochemical Perspectives on Natural Products in Wound Healing: Exploring Phytochemicals in Tissue Repair and Scar Prevention.

Wound healing is a critical process in tissue repair following injury, and traditional herbal therapies have long been utilized to facilitate this process. This review delves into the mechanistic understanding of the significant contribution of pharmacologically demonstrated natural products in wound healing. Natural products, often perceived as complex yet safely consumed compared to synthetic chemicals, play a crucial role in enhancing the wound-healing process. Drawing upon a comprehensive search strategy utilizing databases such as PubMed, Scopus, Web of Science, and Google Scholar, this review synthesizes evidence on the role of natural products in wound healing. While the exact pharmacological mechanisms of secondary metabolites in wound healing remain to be fully elucidated, compounds from alkaloids, phenols, terpenes, and other sources are explored here to delineate their specific roles in wound repair. Each phytochemical group exerts distinct actions in tissue repair, with some displaying multifaceted roles in various pathways, potentially enhancing their therapeutic value, supported by reported safety profiles. Additionally, these compounds exhibit promise in the prevention of keloids and scars. Their potential alongside economic feasibility may propel them towards pharmaceutical product development. Several isolated compounds, from natural sources, are undergoing investigation in clinical trials, with many reaching advanced stages.

In vitro exploration of Acinetobacter strain (SG-5) for antioxidative potential and phytohormone biosynthesis in maize (Zea mays L.) cultivars differing in cadmium tolerance.

Cadmium (Cd) poses serious threats to plant growth and development, whereas the use of plant growth-promoting rhizobacteria (PGPR) has emerged a promising approach to diminish Cd retention in crops. A pot experiment was conducted to evaluate the effect of Cd tolerant strain Acinetobacter sp. SG-5 on growth, phytohormonal response, and Cd uptake of two maize cultivars (3062 and 31P41) under various Cd stress levels (0, 5, 12, 18, 26, and 30 µM CdCl2). The results revealed that CdCl2 treatment significantly suppressed the seed germination and growth together with higher Cd retention in maize cultivars in a dose-dependent and cultivar-specific manner with pronounced negative effect in 31P41. However, SG-5 strain exerted positive impact by up-regulating seed germination traits, plant biomass, photosynthetic pigments, enzymatic and non-enzymatic antioxidants, endogenous hormone level indole-3-acetic acid (IAA), abscisic acid (ABA), and sustained optimal nutrient's levels in both cultivars but predominantly in Cd-sensitive one (31P41). Further, Cd-resistant PGPR decreased the formation of reactive oxygen species in terms of malondialdehyde (MDA) and hydrogen peroxide (H2O2) verified through 3, 3'-diaminobenzidine (DAB) and nitroblue tetrazolium (NBT) analysis in conjunction with reduced Cd uptake and translocation in maize root and shoots in comparison to controls, advocating its sufficiency for bacterial-assisted Cd bioremediation. In conclusion, both SG-5 inoculated cultivars exhibited maximum Cd tolerance but substantial Cd tolerance was acquired by Cd susceptible cultivar-31P41 than Cd-tolerant one (3062). Current work recommended SG-5 strain as a promising candidate for plant growth promotion and bacterial-assisted phytomanagement of metal-polluted agricultural soils.

Unique regulatory network of dragon fruit simultaneously mitigates the effect of vanadium pollutant and environmental factors.

Under changing climatic conditions, plants are simultaneously facing conflicting stresses in nature. Plants can sense different stresses, induce systematic ROS signals, and regulate transcriptomic, hormonal, and stomatal responses. We performed transcriptome analysis to reveal the integrative stress response regulatory mechanism underlying heavy metal stress alone or in combination with heat and drought conditions in pitaya (dragon fruit). A total of 70 genes were identified from 31,130 transcripts with conserved differential expression. Furthermore, weighted gene co-expression network analysis (WGCNA) identified trait-associated modules. By integrating information from three modules and protein-protein interaction (PPI) networks, we identified 10 interconnected genes associated with the multifaceted defense mechanism employed by pitaya against co-occurring stresses. To further confirm the reliability of the results, we performed a comparative analysis of 350 genes identified by three trait modules and 70 conserved genes exhibiting their dynamic expression under all treatments. Differential expression pattern of genes and comparative analysis, have proven instrumental in identifying ten putative structural genes. These ten genes were annotated as PLAT/LH2, CAT, MLP, HSP, PB1, PLA, NAC, HMA, and CER1 transcription factors involved in antioxidant activity, defense response, MAPK signaling, detoxification of metals and regulating the crosstalk between the complex pathways. Predictive analysis of putative candidate genes, potentially governing single, double, and multifactorial stress response, by several signaling systems and molecular patterns. These findings represent a valuable resource for pitaya breeding programs, offering the potential to develop resilient "super pitaya" plants.

Segmented X-ray image data for diagnosing dental periapical diseases using deep learning.

The study presents a segmented dataset comprising dental periapical X-ray images from both healthy and diseased patients. The ability to differentiate between normal and abnormal dental periapical X-rays is pivotal for accurate diagnosis of dental pathology. These X-rays contain crucial information, offering in- sights into the physiological and pathological conditions of teeth and surrounding structures. The dataset outlined in this article encompasses dental periapical X-ray images obtained during routine examinations and treatment procedures of patients at the oral and dental health department of a local government hos- pital in North Jordan. Comprising a total of 929 high-quality X-ray images, the dataset includes subjects of varying ages with a spectrum of dental and pulpal diseases, bone loss, periapical diseases, and other abnormalities. Employing an advanced image segmentation approach, the collected dataset is categorized into healthy and diseased dental patients. This labelled dataset serves as a foundation for the development of an automated system capable of detecting dental pathologies, including caries and pulpal diseases, and distinguishing between normal and abnormal cases. Notably, recent advancements in deep learning artificial intelligence have significantly contributed to the creation of advanced dental models for diverse applications. This technology has demonstrated remarkable accuracy in the development of diagnostic and detection tools for various dental problems.

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans.

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

Soluble and insoluble expression of recombinant human interleukin-2 protein using pET expression vector in Escherichia coli.

Interleukin-2 has emerged as a potent protein-based drug to treat various cancers, AIDS, and autoimmune diseases. Despite its immense requirement, the production procedures are inefficient to meet the demand. Therefore, efficient production procedures must be adopted to improve protein yield and decrease procedural loss. This study analyzed cytoplasmic and periplasmic IL-2 expression for increased protein yield and significant biological activity. The study is focused on cloning IL-2 into a pET-SUMO and pET-28a vector that expresses IL-2 in soluble form and inclusion bodies, respectively. Both constructs were expressed into different E. coli expression strains, but the periplasmic and cytoplasmic expression of IL-2 was highest in overnight culture in Rosetta 2 (DE3). Therefore, E. coli Rosetta 2 (DE3) was selected for large-scale production and purification. Purified IL-2 was characterized by SDS-PAGE and western blotting, while its biological activity was determined using MTT bioassay. The results depict that the periplasmic and cytoplasmic IL-2 achieved adequate purification, yielding 0.86 and 0.51 mg/mL, respectively, with significant cytotoxic activity of periplasmic and cytoplasmic IL-2. Periplasmic IL-2 has shown better yield and significant biological activity in vitro which describes its attainment of native protein structure and function.

Investigating the chemo-preventive role of noscapine in lung carcinoma via therapeutic targeting of human aurora kinase B.

Lung carcinoma is the major contributor to global cancer incidence and one of the leading causes of cancer-related mortality worldwide. Irregularities in signal transduction events, genetic alterations, and mutated regulatory genes trigger cancer development and progression. Selective targeting of molecular modulators has substantially revolutionized cancer treatment strategies with improvised efficacy. The aurora kinase B (AURKB) is a critical component of the chromosomal passenger complex and is primarily involved in lung cancer pathogenesis. Since AURKB is an important therapeutic target, the design and development of its potential inhibitors are attractive strategies. In this study, noscapine was selected and validated as a possible inhibitor of AURKB using integrated computational, spectroscopic, and cell-based assays. Molecular docking analysis showed noscapine occupies the substrate-binding pocket of AURKB with strong binding affinity. Subsequently, MD simulation studies confirmed the formation of a stable AURKB-noscapine complex with non-significant alteration in various trajectories, including RMSD, RMSF, Rg, and SASA. These findings were further experimentally validated through fluorescence binding studies. In addition, dose-dependent noscapine treatment significantly attenuated recombinant AURKB activity with an IC50 value of 26.6 µM. Cell viability studies conducted on A549 cells and HEK293 cells revealed significant cytotoxic features of noscapine on A549 cells. Furthermore, Annexin-PI staining validated that noscapine triggered apoptosis in lung cancer cells, possibly via an intrinsic pathway. Our findings indicate that noscapine-based AURKB inhibition can be implicated as a potential therapeutic strategy in lung cancer treatment and can also provide a novel scaffold for developing next-generation AURKB-specific inhibitors.

Novel artificial intelligence assisted Landsat-8 imagery analysis for mango orchard detection and area mapping.

The mango fruit plays a crucial role in providing essential nutrients to the human body and Pakistani mangoes are highly coveted worldwide. The escalating demand for agricultural products necessitates enhanced methods for monitoring and managing agricultural resources. Traditional field surveys are labour-intensive and time-consuming whereas remote sensing offers a comprehensive and efficient alternative. The field of remote sensing has witnessed substantial growth over time with satellite technology proving instrumental in monitoring crops on a large scale throughout their growth stages. In this study, we utilize novel data collected from a mango farm employing Landsat-8 satellite imagery and machine learning to detect mango orchards. We collected a total of 2,150 mango tree samples from a farm over six months in the province of Punjab, Pakistan. Then, we analyzed each sample using seven multispectral bands. The Landsat-8 framework provides high-resolution land surface imagery for detecting mango orchards. This research relies on independent data, offering an advantage for training more advanced machine learning models and yielding reliable findings with high accuracy. Our proposed optimized CART approach outperformed existing methods, achieving a remarkable 99% accuracy score while the k-Fold validation score also reached 99%. This research paves the way for advancements in agricultural remote sensing, offering potential benefits for crop management yield estimation and the broader field of precision agriculture.

Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation.

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu-O, Mg-O, and Zn-O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10-3 min-1, for CuO/MgO (2.04 × 10-3 min-1), for CuO/ZnO (1.82 × 10-3 min-1) and CuO/MgO/ZnO was found to be 2.00 × 10-3 min-1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention.

Comparative Probiogenomics Analysis of Limosilactobacillus fermentum 3872.

Limosilactobacillus fermentum is an important member of the lactic acid bacteria group and holds immense potential for probiotic properties in human health and relevant industries. In this study, a comparative probiogenomic approach was applied to analyze the genome sequence of L. fermentum 3872, which was extracted from a commercially available yogurt sample, along with 20 different publicly available strains. Results indicate that the genome size of the characterized L. fermentum 3892 strain is 2,057,839 bp, with a single- and circular-type chromosome possessing a G + C content of 51.69%. The genome of L. fermentum 3892 strain comprises a total of 2120 open reading frames (ORFs), two genes encoding rRNAs, and 53 genes encoding tRNAs. Upon comparative probiogenomic analysis, two plasmid sequences were detected among the study strains, including one for the L. fermentum 3872 genome, which was found between position 1,288,203 and 1,289,237 with an identity of 80.98. The whole-genome alignment revealed 2223 identical sites and a pairwise identity of 98.9%, indicating a significant difference of 1.1% among genome strains. Comparison of amino acid encoding genes among strains included in this study suggests that the strain 3872 exhibited the highest degree of amino acids present, including glutamine, glutamate, aspartate, asparagine, lysine, threonine, methionine, and cysteine. The comparative antibiotic resistome profiling revealed that strain 3872 exhibited a high resistant capacity only to ciprofloxacin antibiotics as compared to other strains. This study provides a genomic-based evaluation approach for comparative probiotic strain analysis in commercial foods and their significance to human health.

Evaluating the Efficacy of Antioxidant Therapy in Enhancing the Quality of Life of Chronic Pancreatitis Patients: A Systematic Review.

Chronic pancreatitis (CP), an inflammatory disease characterized by irreversible pancreatic changes and progressive fibrosis, significantly impairs patients' quality of life. This systematic review aims to assess the efficacy of antioxidant therapy in enhancing the quality of life of CP patients. Focusing on the role of oxidative stress in CP pathogenesis, we explored several databases for studies evaluating the impact of antioxidant supplementation. The review included randomized controlled trials and cohort studies reporting pain frequency, intensity, and overall quality of life measures. Findings from these studies present a mixed view of the efficacy of antioxidants in CP, with some suggesting benefits in symptom management, while others show inconsistency in improving patient outcomes. The review concludes that while antioxidant therapy holds potential, especially in symptom alleviation, there is a need for more rigorous, larger-scale studies to confirm its effectiveness in CP management and to establish standardized treatment protocols. The incorporation of antioxidants into CP treatment plans should be approached with personalized care, considering the varied responses observed in different patient populations.

Multiwaves, breathers, lump and other solutions for the Heimburg model in biomembranes and nerves.

In this manuscript, a mathematical model known as the Heimburg model is investigated analytically to get the soliton solutions. Both biomembranes and nerves can be studied using this model. The cell membrane's lipid bilayer is regarded by the model as a substance that experiences phase transitions. It implies that the membrane responds to electrical disruptions in a nonlinear way. The importance of ionic conductance in nerve impulse propagation is shown by Heimburg's model. The dynamics of the electromechanical pulse in a nerve are analytically investigated using the Hirota Bilinear method. The various types of solitons are investigates, such as homoclinic breather waves, interaction via double exponents, lump waves, multi-wave, mixed type solutions, and periodic cross kink solutions. The electromechanical pulse's ensuing three-dimensional and contour shapes offer crucial insight into how nerves function and may one day be used in medicine and the biological sciences. Our grasp of soliton dynamics is improved by this research, which also opens up new directions for biomedical investigation and medical developments. A few 3D and contour profiles have also been created for new solutions, and interaction behaviors have also been shown.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. RESULTS: In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth Algorithm. Finally, the optimal feature space from the training dataset was fed to the deep neural network to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. AVAILABILITY AND IMPLEMENTATION: https://github.com/MateeullahKhan/DeepAVP-TPPred.

Transformation of heavy metals from contaminated water to soil, fodder and animals.

A serious environmental problem that threatens soil quality, agricultural productivity, and food safety is heavy metal pollution in water sources. Heavy metal pollution is the main problem in tehsil Pasrur, Sialkot, Pakistan. Present study was arranged to notice the heavy metals in water, soil, forages and buffalo milk. There are seven sites that were used for this experiment. Highest malondialdehyde (MDA) contents (3.00 ± 0.01) were noticed in barseem roots at site 7. Ascorbate Peroxidase (APX) was reached at its peak (1.93 ± 0.01) at site 7 in the fresh barseem. Maximum protein contents (0.36 ± 0.01) were observed in fresh plant samples at site 2. Site 3's buffalo milk samples had the highest Ni content (7.22 ± 0.33 ppm), while Site 3's soil samples had the lowest Cr content (8.89 ± 0.56 ppm), Site 1's plant shoots had the lowest Cr content (27.75 ± 1.98 ppm), and Site 3's water had the highest Cr content (40.07 ± 0.49 ppm). The maximum fat content (5.38 ± 2.32%) was found in the milk of the animals at site 7. The highest density (31.88 ± 6.501%), protein content (3.64 ± 0.33%), lactose content (5.54 ± 0.320%), salt content (0.66 ± 0.1673%), and freezing point (- 0.5814 ± 0.1827 °C) were also observed in the milk from animals at site 7, whereas site 5 displayed the highest water content (0.66 ± 0.1673%) and peak pH value (11.64 ± 0.09). In selected samples, the pollution load index for Ni (which ranged from 0.01 to 1.03 mg/kg) was greater than 1. Site 7 has the highest conductivity value (5.48 ± 0.48). Values for the health risk index varied from 0.000151 to 1.00010 mg/kg, suggesting that eating tainted animal feed may pose health concerns. Significant health concerns arise from metal deposition in the food chain from soil to feed, with nickel having the highest health risk index.

An Interesting Case of Alpha-Fetoprotein (AFP)-Producing Pancreaticoduodenal Tumor.

Alpha-fetoprotein (AFP) is considered one of the best-known predictive serum markers, playing a crucial role in cancer investigation and subsequent treatment. In most adult cells, the production of this marker is suppressed after embryogenesis. However, its increased level raises concerns about underlying malignant conditions, which provide a valuable diagnostic tool for medical professionals in oncology. The existing AFP-producing adenocarcinomas exhibit unique clinical characteristics, including high malignancy and early metastatic potential, which result in poorer outcomes. To illustrate these characteristics, we decided to describe a case report of a 70-year-old African American female with a significantly elevated level of AFP. Further pathology results confirmed a duodenal adenocarcinoma versus adenocarcinoma from the pancreas. While AFP-producing adenocarcinoma has multiple underlying molecular mechanisms that correlate with poor prognosis, definitive treatment based on molecular pathways has yet to be defined. Therefore, further research is needed for new therapeutic modalities.

Unveiling the Uncommon: Cushing's Syndrome (CS) Masquerading as Severe Hypokalemia.

Cushing's syndrome (CS) arises from an excess of endogenous or exogenous cortisol, with Cushing's disease specifically implicating a pituitary adenoma and exaggerated adrenocorticotropic hormone (ACTH) production. Typically, Cushing's disease presents with characteristic symptoms such as weight gain, central obesity, moon face, and buffalo hump. This case report presents an unusual manifestation of CS in a 48-year-old male with a history of hypertension, where severe hypokalemia was the primary presentation. Initial complaints included bilateral leg swelling, muscle weakness, occasional shortness of breath, and a general feeling of not feeling well. Subsequent investigations revealed hypokalemia, metabolic alkalosis, and an abnormal response to dexamethasone suppression, raising concerns about hypercortisolism. Further tests, including 24-hour urinary free cortisol and ACTH testing, confirmed significant elevations. Brain magnetic resonance imaging (MRI) identified a pituitary macroadenoma, necessitating neurosurgical intervention. This case underscores the rarity of CS presenting with severe hypokalemia, highlighting the diagnostic challenges and the crucial role of a collaborative approach in managing such intricate cases.

Extracorporeal Shockwave for Intermittent Claudication and Quality of Life: A Randomized Clinical Trial.

Importance: Intermittent lower limb claudication limits function and quality of life. Supervised exercise programs are not readily available, and a noninvasive alternative is needed. Objective: To assess extracorporeal corporeal shockwave therapy in improving quality of life in patients with claudication. Design, Setting, and Participants: In this double-blind, placebo-controlled randomized clinical trial, patients in the outpatient setting at a single tertiary center for vascular surgery were randomized in a 1:1 ratio to extracorporeal shockwave therapy or placebo therapy with no shockwaves delivered. Recruitment was between June 2015 and January 2020, with 12-week follow-up ending in March 2020. A convenience sample of patients with claudication and conservative treatment requirements who refused or were unable to participate in supervised exercise were eligible. Patients receiving anticoagulation therapy or with an active cancer were excluded. Of 522 patients screened, 389 were eligible, 138 were enrolled, and 110 completed follow-up and were included in the primary analysis. Statistical analysis was completed by May 2021. Intervention: In the intervention group, patients received 100 impulses of 0.1mJ/mm/cm2 in an area of the gastrocnemius muscle 3 times weekly for 3 weeks. The steps for treatment were replicated for the control group without delivering the treatment. Main Outcomes and Measures: The primary outcome was the Physical Functioning domain of the 36-item Short-Form Quality of Life Questionnaire at 12-week follow-up. Secondary outcomes included walking distances, ankle brachial pressure index, and other quality-of-life measures. Results: Of 138 patients recruited and randomized, 92 (67%) were male, and the mean (SD) age of the study population was 67 (9.6) years. The intervention group had a significantly higher physical function score at 12 weeks (estimated median difference 3.8; 95% CI, 0.0-7.7; P = .03). However, this significance did not remain when adjusting for covariates. At 12 weeks, the intervention group had significantly longer pain-free and maximum walking distances (pain-free estimated median difference, 34.1, 95% CI, 11.4-56.8; P = .004; maximum estimated median difference, 51.4; 95% CI, 10.7-86.5; P = .01). Conclusions and Relevance: To our knowledge, this is the first double-blind, placebo-controlled, randomized clinical trial to consider extracorporeal shockwave therapy for the management of intermittent claudication. It demonstrated efficacy for walking distances, may have a positive effect on quality of life, and may provide a safe, noninvasive alternative therapy for patients with intermittent claudication. Trial Registration: ClinicalTrials.gov Identifier: NCT02652078.

Molecular functions of microRNAs in colorectal cancer: recent roles in proliferation, angiogenesis, apoptosis, and chemoresistance.

MiRNAs (microRNAs) constitute a group of diminutive molecules of non-coding RNA intricately involved in regulating gene expression. This regulation is primarily accomplished through the binding of miRNAs to complementary sequences situated in the 3'-UTR of the messenger RNA (mRNA) target; as a result, they are degraded or repressed. The multifaceted biogenesis of miRNAs is characterized by a meticulously orchestrated sequence of events encompassing transcription, processing, transportation, and decay. Colorectal cancer stands as a pervasive and formidable ailment, afflicting millions across the globe. Colorectal cancer is not well diagnosed early, and metastasis rates are high, which results in low survival rates in advanced stages. The genesis and progression of colorectal cancer are subject to the influence of genetic and epigenetic factors, among which miRNAs play a pivotal role. When it comes to colorectal cancer, miRNAs have a dual character, depending on the genes they target, functioning as either tumor suppressors or oncogenes and the prevailing cellular milieu. Their impact extends to modulating critical facets of colorectal cancer pathogenesis, including proliferation, angiogenesis, apoptosis, chemoresistance, and radiotherapy response. The discernible potential of miRNAs which are used as biomarkers to diagnose colorectal cancer, prognosis, and treatment response has come to the forefront. Notably, miRNAs are easily found and detected readily in a variety of biological fluids, including saliva, blood, urine, and feces. This prominence is attributed to the inherent advantages of miRNAs over conventional biomarkers, including heightened stability, specificity, sensitivity, and accessibility. Various investigations have pinpointed miRNA signatures or panels capable of differentiating colorectal cancer patients from their healthy counterparts, predicting colorectal cancer stage and survival, and monitoring colorectal cancer recurrence and therapy response. Although there has been research on miRNAs in various diseases, there has been less research on miRNAs in cancer. Moreover, updated results of preclinical and clinical studies on miRNA biomarkers and drugs are required. Nevertheless, the integration of miRNAs as biomarkers for colorectal cancer is not devoid of challenges and limitations. These encompass the heterogeneity prevalent among colorectal cancer subtypes and stages, the variability in miRNA expression across different tissues and individuals, the absence of standardized methodologies for miRNA detection and quantification, and the imperative for validation through extensive clinical trials. Consequently, further research is imperative to conclusively establish the clinical utility and reliability of miRNAs as colorectal cancer biomarkers. MiR-21 demonstrates carcinogenic characteristics by targeting several tumor suppressor genes, which encourages cell division, invasion, and metastasis. On the other hand, by controlling the Wnt/ß-catenin pathway, the tumor suppressor miRNA miR-34a prevents CRC cell proliferation, migration, and invasion. Furthermore, in colorectal cancer, the miR-200 family increases chemotherapy sensitivity while suppressing epithelial-mesenchymal transition (EMT). As an oncogene, the miR-17-92 cluster targets elements of the TGF-ß signaling pathway to encourage the growth of CRC cells. Finally, miR-143/145, which is downregulated in CRC, influences apoptosis and the progression of the cell cycle. These miRNAs affect pathways like Wnt, TGF-ß, PI3K-AKT, MAPK, and EMT, making them potential clinical biomarkers and therapeutic targets. This review summarizes recent research related to miRNAs, their role in tumor progression and metastasis, and their potential as biomarkers and therapeutic targets in colorectal cancer. In addition, we combined miRNAs' roles in tumorigenesis and development with the therapy of CRC patients, leading to novel perspectives on colorectal cancer diagnosis and treatment.