Over the past few decades, the synthesis and potential applications of nanocatalysts have received great attention from the scientific community. Many well-established methods are extensively utilized for the synthesis of nanocatalysts. However, most conventional physical and chemical methods have some drawbacks, such as the toxicity of precursor materials, the requirement of high-temperature environments, and the high cost of synthesis, which ultimately hinder their fruitful applications in various fields. Bioinspired synthesis is eco-friendly, cost-effective, and requires a low energy/temperature ambient. Various microorganisms such as bacteria, fungi, and algae are used as nano-factories and can provide a novel method for the synthesis of different types of nanocatalysts. The synthesized nanocatalysts can be further utilized in various applications such as the removal of heavy metals, treatment of industrial effluents, fabrication of materials with unique properties, biomedical, and biosensors. This review focuses on the biogenic synthesis of nanocatalysts from various green sources that have been adopted in the past two decades, and their potential applications in different areas. This review is expected to provide a valuable guideline for the biogenic synthesis of nanocatalysts and their concomitant applications in various fields.

Several coronaviruses (CoVs) have been associated with serious health hazards in recent decades, resulting in the deaths of thousands around the globe. The recent coronavirus pandemic has emphasized the importance of discovering novel and effective antiviral medicines as quickly as possible to prevent more loss of human lives. Positive-sense RNA viruses with group spikes protruding from their surfaces and an abnormally large RNA genome enclose CoVs. CoVs have already been related to a range of respiratory infectious diseases possibly fatal to humans, such as MERS, SARS, and the current COVID-19 outbreak. As a result, effective prevention, treatment, and medications against human coronavirus (HCoV) is urgently needed. In recent years, many natural substances have been discovered with a variety of biological significance, including antiviral properties. Throughout this work, we reviewed a wide range of natural substances that interrupt the life cycles for MERS and SARS, as well as their potential application in the treatment of COVID-19.

The novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide, and it has become a greater risk for human beings. To curb the community transmission of this virus, rapid detection and identification of the affected people via a quick diagnostic process are necessary. Media studies have shown that most COVID-19 victims endure lung disease. For rapid identification of the affected patient, chest CT scans and X-ray images have been reported to be suitable techniques. However, chest X-ray (CXR) shows more convenience than the CT imaging techniques because it has faster imaging times than CT and is also simple and cost-effective. Literature shows that transfer learning is one of the most successful techniques to analyze chest X-ray images and correctly identify various types of pneumonia. Since SVM has a remarkable aspect that tremendously provides good results using a small data set thus in this study we have used SVM machine learning algorithm to diagnose COVID-19 from chest X-ray images. The image processing tool called RGB and SqueezeNet models were used to get more images to diagnose the available data set. Our adopted model shows an accuracy of 98.8% to detect the COVID-19 affected patient from CXR images. It is expected that our proposed computer-aided detection tool (CAT) will play a key role in reducing the spread of infectious diseases in society through a faster patient screening process.

Introduction: Colorectal cancer (CRC) is the most common cancer that accounts for nearly 10% of the cancers, with 1.36 million people worldwide. Nodal status (N-stage) evaluation was inferior between observers, which considered CT scanning a good N-stage. We hypothesized that CRC patients’ preoperative CT scan imaging predicts the nodal and metastatic stage. Methods: This noninterventional retrospective study was carried out using patients’ medical records, including medical history and results of diagnostic tests, and preoperative clinical and pathological stages. All direct identifiers have been removed from all patient data. This study included 96 patients who underwent resection curative surgery for CRC at the Tertiary Hospital, Sudan, between March 2009 and December 2020. Results: The median age was 69 years (47–74 years), and 49 (51.04%) were female. The tumor stage of the patients was 4, 11, 74, and 7 as T1, T2, T3, and T4, respectively. A total of 38 patients with a malignant spread in lymph nodes were observed, and the median lymph node count was 11 (range 4–52). Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value (PPV), negative predictive value (NPV), and accuracy calculated for the malignant lymph nodes were 75.56% (95% CI: 60.46–87.12%), 45.10% (95% CI: 31.13–59.66%), 1.38 (95% CI: 1.02–1.86), 0.54 (95% CI: 0.30–0.98), 54.84% (95% CI: 47.38–62.09%), 67.65% (95% CI: 53.53–79.15%), and 59.38% (95% CI: 48.87–69.29%), respectively. Conclusion: The preoperative CT scan images were used to predict lymph nodes with a diagnostic accuracy of 59.38% for N-stage in patients with CRC.

Background: MRI is a widely used modality for diagnosing patients with knee complaints, and it has increasingly replaced diagnostic arthroscopy in this regard. The current research aimed to study the assessment of traumatic knee injuries using MRI, to identify any correlations between the type of tissue damage and age, to determine the ligaments most frequently involved, and to compare the frequency of knee injuries in athletes to those in the general population. Methods and Results: This cross-sectional study included 150 patients with a history of knee injuries who were referred for knee MRI. The study was carried out in the radiology departments of two hospitals (King Faisal Hospital and King Abdulaziz Specialist Hospital, Taif, Saudi Arabia). Medical reports were collected from March 2021 to April 2022. The largest number of affected males were in the age group of 20-30 years; the largest number of affected females were in the age groups of 51-60 years and >60 years. Most athletic patients were in the age group of 20-30 years of age. The anterior cruciate ligament (51.9%) and meniscus (43.1%) were the most affected parts of the knee joint in patients of all ages. Joint effusions and cysts were found in 74.4% and 21.6%, respectively, among all age groups. A torn disc was found in 4 cases, only in male patients aged <20 to 40 years. Conclusion: A non-invasive, radiation-free procedure, MRI facilitates accurate “lesion mapping,” which is important in the diagnosis and follow-up of traumatic knee injuries.