Molecular imaging modalities show valuable non-invasive techniques capable of precisely and selectively addressing molecular markers associated with prostate cancer (PCa). This systematic review provides an overview of imaging markers utilized in positron emission tomography (PET) methods, specifically focusing on the pathways and mediators involved in PCa. This systematic review aims to evaluate and analyse existing literature on the diagnostic accuracy of molecular imaging techniques for detecting PCa. The PubMed, EBSCO, ScienceDirect, and Web of Science databases were searched, identifying 32 studies that reported molecular imaging modalities for detecting PCa. Numerous imaging modalities and radiotracers were used to detect PCa, including 68Ga-prostate-specific membrane antigen (PSMA) PET/computed tomography (CT), 68Ga-PSMA-11 PET/magnetic resonance imaging (MRI), 18F-PSMA-1007 PET/CT, 18F-DCFPyL PET/MRI, 18F-choline PET/MRI, and 18F-fluoroethylcholine PET/MRI. Across 11 studies, radiolabelled 68Ga-PSMA PET/CT imaging had a pooled sensitivity of 80 (95% confidence interval [CI]: 35-93), specificity of 90 (95% CI: 71-98), and accuracy of 86 (95% CI: 64-96). The PSMA-ligand 68Ga-PET/CT showed good diagnostic performance and appears promising for detecting and staging PCa.

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

Polycyclic aromatic hydrocarbons (PAHs) represent a category of persistent organic pollutants that pose a global concern in the realm of food safety due to their recognized carcinogenic properties in humans. Food can be contaminated with PAHs that are present in water, air, or soil, or during food processing and cooking. The wide and varied sources of PAHs contribute to their persistent contamination of food, leading to their accumulation within these products. As a result, monitoring of the levels of PAHs in food is necessary to guarantee the safety of food products as well as the public health. This review paper attempts to give its readers an overview of the impact of PAHs on crops, their occurrence and sources, and the methodologies employed for the sample preparation and detection of PAHs in food. In addition, possible directions for future research are proposed. The objective is to provide references for the monitoring, prevention, and in-depth exploration of PAHs in food.

Extracellular vesicles (EVs) are enclosed by a nanoscale phospholipid bilayer membrane and typically range in size from 30 to 200 nm. They contain a high concentration of specific proteins, nucleic acids, and lipids, reflecting but not identical to the composition of the parent cell. The inherent characteristics and variety of EVs give them extensive and unique advantages in the field of cancer identification and treatment. Recently, EVs have been recognized as potential tumor markers for the detection of cancer. Aptamers, which are molecules of single-stranded DNA or RNA, demonstrate remarkable specificity and affinity for their targets by adopting distinct tertiary structures. Aptamers offer various advantages over their protein counterparts, such as reduced immunogenicity, the ability for convenient large-scale synthesis, and straightforward chemical modification. In this review, we summarized EVs biogenesis, sample collection, isolation, storage and characterization, and finally provided a comprehensive survey of analysis techniques for EVs detection that are based on aptamers.

537 million people worldwide suffer from diabetes mellitus, a problem of glucose management that is related to a number of major health risks, including cardiovascular diseases. There is a need for new, efficient formulations of diabetic medications to address this condition and its related consequences because existing treatments have a number of drawbacks and limits. This encouraged the development of treatment plans to get around some of these restrictions, like low therapeutic drug bioavailability or patients\' disobedience to existing therapies. Approaches based on nanotechnology have a lot of promise to enhance the treatment of diabetic patients. In order to manage blood glucose, this review article highlights recent developments and explores the potential applications of different materials (polymeric, ceramic, dendrimers, etc.) as nanocarriers for the delivery of insulin and other antidiabetic medications. Using an injectable and acid-degradable polymeric network produced by the electrostatic interaction of oppositely charged dextran nanoparticles loaded with insulin and glucose-specific enzymes, we reviewed a glucose-mediated release approach for the self-regulated delivery of insulin, in which, after a degradable nano-network was subcutaneously injected into type 1 diabetic mice,in vivoexperiments confirmed that these formulations improved glucose management. In addition, a discussion of silica-based nanocarriers, their potential for treating diabetes and controlling blood glucose levels, and an explanation of the role of dendrimers in diabetes treatment have been covered. This is done by utilizing the properties of silica nanoparticles, such as their tuneable particle and pore size, surface chemistry, and biocompatibility. The article summarized the significance of nanomaterials and their uses in the diagnosis and treatment of diabetes overall, illuminating the field\'s potential and outlining its prospects for the future.

UNASSIGNED: Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs.
UNASSIGNED: In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients\' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing.
UNASSIGNED: The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a p -value of 0.0054 for precision and a p -value of 0.00034 for specificity.
UNASSIGNED: In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.

Cryptosporidium infection is a common occurrence in rodents worldwide. In this study, 435 wild brown rats were captured from an animal feedlot in Xinjiang, China, with a fecal sample obtained directly from the rectal contents of each rat. The DNA extracted from these fecal samples was analyzed for Cryptosporidium spp. using PCR targeting the SSU rRNA gene. The prevalence of Cryptosporidium infection in brown rats was found to be 5.5% (24 out of 435). Interestingly, the infection rates varied among different animal enclosures, with rates of 0% in the chicken coop (0/51), cowshed (0/3), and varying rates in other areas including the sheepfold (6.1%, 6/98), the pigsty (7.6%, 10/132), the dovecote (7.0%, 5/71), and outdoor environments (3.8%, 3/80). The study identified three species and one genotype of Cryptosporidium, namely C. occultus (n = 10), C. parvum (n = 4), C. ditrichi (n = 1), and Cryptosporidium rat genotype IV (n = 9). Additionally, two of the C. parvum isolates were successfully subtyped as IIdA19G1 (n = 2) at the gp60 gene. These results offer valuable insights into the prevalence and genetic diversity of Cryptosporidium in brown rats within the region.

The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering.

A survey of Diaporthe/Phomopsis Complex (DPC) species was carried out on 479 asymptomatic soybean (Glycine max (L.) Merrill) seed samples collected from commercial soybean fields in the states of Santa Catarina (20 counties) and Rio Grande do Sul (41 counties), in the 2020/21 (n=186), 2021/22 (n=138) and 2022/23 (n=155) seasons from 120 cultivars. The seeds were provided by seed producers who collected according to the sampling standard of the Ministry of Agriculture, Livestock and Food Supply. From each sample received, 200 symptomless seeds were randomly sorted out. The seeds were surface disinfected by immersion in a sodium hypochlorite solution (1%) for two minutes and placed on Potato Dextrose Agar (PDA). The plates were incubated for 7 days at 23°C with a photoperiod of 12-h. The average prevalence of 73.7% of DPC-infected seeds. Colonies were isolated by transferring mycelial tips to PDA and incubating for 14 days at 25ºC in a 12-h photoperiod. One colony (isolate MEMR0500) had morphological characteristics similar to those reported in Lopez-Cardona (2021). This isolate had a floccose, dense colony ranging from grayish beige to brown with greenish regions and black globose pycnidia (3 to 4 pycnidia/cm²). Alpha-conidia, 5.1 to 7.0 µm x 1.5 to 2.8 µm, were observed after 30 days and were hyaline, aseptate and fusiform (Figure S1). No beta-conidia were observed. Soybean plants of cultivars BMX Cromo IPRO, BMX Zeus IPRO, BRS 5804 RR, FPS 1867 IPRO and NEO 750 IPRO were tested for pathogenicity using the toothpick inoculation method (Siviero and Menten 1995). Non-colonized toothpicks served as a negative control. Plants were incubated for four days at 25°C and 90% relative humidity. Elongated 1.0 to 2.5 cm x 0.5 to 0.9 cm lesions gray-brown/reddish-brown with a depressed center were observed in all inoculated cultivars. The fungus was reisolated and the characteristics of the colonies were identical to those previously isolated. For molecular characterization, DNA was extracted from the mycelia using the CTAB method (Doyle and Doyle 1990). End-point PCR was performed using GoTaq® Flexi DNA Polymerase (Promega, USA) and primer pairs, ITS-4F/ITS-5, T2/Bt2b and EF1-728F/EF1-986R to amplify the internal transcribed spacer (ITS) (Costamilan et al. 2008), β-tubulin (TUB2) (Glass and Donaldson 1995), and translation elongation factor 1-α (TEF1) (Carbone and Kohn 1999) genes, respectively. The amplified fragments were sequenced and submitted to blast search (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with the sequences available in GenBank. The fragment from ITS (accession number OR912979) showed 99.8% (549/582 bp) identity with Diaporthe ueckeri Udayanga & Castl. [as \'ueckerae\'] [syn. D. miriciae R.G. Shivas, S.M. Thomps. & Y.P. Tan] isolate FAU656 (Ac. N. KJ590726). The sequence of TEF (Ac. N. PP372869) showed 99.7% (339/355 bp) identity with D. ueckeri FAU656 (Ac. N. KJ590747), and of TUB (Ac. N. PP372870) showed 98.9% (436/536 bp) identity with D. ueckeri FAU656 (Ac. N. KJ610881). A phylogenetic tree with amplified sequences of each gene and the corresponding representative sequences from the DPC was constructed in MEGA X (Kumar et al. 2018). The MEMR0500 isolate was clustered only with the D. ueckeri clade, confirming the identity of the fungus (Figure S2). In Brazil, this is the first report of the association of this pathogen with soybean seeds. In other countries, this pathogen has been identified as the causal agent of stem canker (Mena et al. 2020; Lopez-Cardona et al. 2021). Further research is needed to analyze the risk of this seed-associated pathogen.

BACKGROUND: There is an increasing disease trend for SARS-COV-2, so need a quick and affordable diagnostic method. It should be highly accurate and save costs compared to other methods. The purpose of this research is to achieve these goals.
METHODS: This study analyzed 342 samples using TaqMan One-Step RT-qPCR and fast One-Step RT-LAMP (Reverse Transcriptase Loop-Mediated Isothermal Amplification). The One-Step LAMP assay was conducted to assess the sensitivity and specificity.
RESULTS: The research reported positive samples using two different methods. In the RT-LAMP method, saliva had 92 positive samples (26.9%) and 250 negative samples (73.09%) and nasopharynx had 94 positive samples (27.4%) and 248 negative samples (72.51%). In the RT-qPCR method, saliva had 86 positive samples (25.1%) and 256 negative samples (74.8%) and nasopharynx had 93 positive samples (27.1%) and 249 negative samples (72.8%). The agreement between the two tests in saliva and nasopharynx samples was 93% and 94% respectively, based on Cohen\'s kappa coefficient (κ) (P < 0.001). The rate of sensitivity in this technique was reported at a dilution of 1 × 101 and 100% specificity.
CONCLUSIONS: Based on the results of the study the One-Step LAMP assay has multiple advantages. These include simplicity, cost-effectiveness, high sensitivity, and specificity. The One-Step LAMP assay shows promise as a diagnostic tool. It can help manage disease outbreaks, ensure prompt treatment, and safeguard public health by providing rapid, easy-to-use testing.