Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis.

Purpose: We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021. Methods: Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis. Results: A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", "survival", "classification", and "management" are the research hotspots in this field. Conclusion: The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.

Salicylaldehyde derivative of nano-chitosan as an efficient adsorbent for lead(II), copper(II), and cadmium(II) ions.

In this study, a biodegradable natural polymer chitosan was modified with salicylaldehyde to prepare salicylaldehyde functionalized chitosan nanoparticles (N-Ch-Sal). The N-Ch-Sal was characterized by atomic force microscopy (AFM), scanning electron microscopy-energy-dispersive X-ray (SEM-EDX), Fourier transform infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA). The salicylaldehyde functionalized chitosan nanoparticles (N-Ch-Sal) (~80 nm) were then used for the adsorption of three heavy metals viz., Cu(II), Cd(II) and Pb(II) ions. The above-mentioned techniques were also employed for evaluation of changes in N-Ch-Sal after metal adsorption. The parameters affecting the adsorption of metal ions including pH, contact time, amount of adsorbent, initial metal ion concentration and the effect of interfering ions, were studied thoroughly and optimized. The concentration of metal ions remaining in the aqueous system after adsorption experiments was analyzed by ICP-MS. At optimal conditions, sorption capacity of Pb(II) ion was found to be highest i.e., 123.67 followed by Cu(II) (84.60) and Cd(II) (63.71 mg/g). The adsorption process followed the pseudo-second-order kinetic model and fitted well with the Langmuir adsorption isotherm. The adsorption method was applied to a real tap water sample for the quantification and removal of Pb(II) ions. The concentration of Pb(II) ions in the tested sample was 4.88 ppb.

Metagenomic Characterization of Bacterial Communities in Drinking Water Supply System of a Mega City.

Supplying safe water to consumers is vital for protection of public health. With population of > 15 million, Karachi is the main economical hub of Pakistan. Lake Keenjhar serves as the main source of fresh water while Hub dam is the secondary water reservoir for Karachi. In this study, bacterial community of the drinking water supply system (DWSS) of Karachi was studied from source to tap using metagenomics approach. For this purpose, we collected 41 water samples from different areas of the city (n = 38) and water reservoirs (n = 3). 16S rDNA metagenomic sequencing of water samples revealed that 88% sequences were associated with Proteobacteria (52%), Planctomycetes (15%), Becteroidetes (12%), and Verrucomicrobia (6%). On the class level, α-proteobacteria (6-56%) were found to be the most abundant followed by ß- (8-41%) and γ-proteobacteria (6-52%). On the genus level, substantial diversity was observed among the samples. Bacterial communities in water from Hub dam was found to be distantly related while among the residential towns, Lyari was highly distant from the others. Twenty-four bacterial genera were found to be exclusively present in residential area samples in comparison to the source waters which is suggestive of their resistance against treatment procedures and/or contamination. Metagenomic analysis revealed abundance of Pseudomonas, Legionella, Neisseria, Acinetobacter, Bosea, and Microcystis genera in residential areas water samples. The present metagenomic analysis of DWSS of Karachi has allowed the evaluation of bacterial communities in source water and the water being supplied to the city. Moreover, measurement of heavy metals in water samples from Karachi revealed arsenic concentration according to WHO standards which is in contrast of recent study which reported extensive arsenic contamination in aquifers in the Indus valley plain. To the best of our knowledge, this is the first metagenomic study of DWSS of Karachi.