In microbiological research, traditional methods for bacterial screening and antibiotic susceptibility testing are resource-intensive. Microfluidics offers an efficient alternative with rapid results and minimal sample consumption, but the demand for cost-effective, user-friendly platforms persists in communities and hospitals. Inspired by the Magdeburg hemispheres, the strategy adapts to local conditions, leveraging omnipresent atmospheric pressure for self-sealing of Rotation-SlipChip (RSC) equipped with a 3D circular Christmas tree-like microfluidic concentration gradient generator. This innovative approach provides an accessible and adaptable platform for microbiological research and testing in diverse settings. The RSC can avoid leakage concerns during multiple concentration gradient generation, chip-rotating, and final long-term incubation reaction (≥24 h). Furtherly, RSC subtypes adapted to different reactions can be fabricated in less than 15 min with cost less than $1, the result can be read through designated observational windows by naked-eye. Moreover, the RSC demonstrates its capability for evaluating bacterial biomarker activity, enabling the rapid assessment of β-galactosidase concentration and enzyme activity within 30 min, and the limit of detection can be reduced by 10-fold. It also rapidly determines the minimum antibiotic inhibitory concentration and antibiotic combined medications results within 4 h. Overall, these low-cost and user-friendly RSC make them invaluable tools in determinations at previously impractical environment.

OBJECTIVE: This study aims to assess the prevalence and antimicrobial susceptibility patterns of bacterial infections associated with both early-onset sepsis (EOS) and late-onset sepsis (LOS).
METHODS: This descriptive retrospective surveillance research was conducted on all neonates admitted to the neonatal ICU with bacterial sepsis, where positive cultures were isolated from sterile sites (either cerebrospinal fluid or blood) at Tawam Hospital, Al Ain, Emirate of Abu Dhabi, UAE, from January 2012 and December 2021. Antimicrobial susceptibility analysis was performed.
RESULTS: The incidence of LOS (94.43%) was higher compared to EOS (5.56%). The most prevalent isolates (59.2%) were gram-positive bacteria, with gram-negative bacteria accounting for 40.8%. The leading isolates included coagulase-negative Staphylococci (CONS, 40.98%), Klebsiella (16.04%), Staphylococcus aureus (8.46%), Escherichia coli (8.24%), Pseudomonas (7.57%), and Group B Streptococcus (GBS, 5.12%). CONS were predominant in LOS cases (42.9%), while GBS was the main pathogen in EOS cases (44%).
CONCLUSIONS: We observed reduced resistance levels of CONS against ampicillin, benzylpenicillin, clindamycin, erythromycin, fusidic acid, gentamicin, oxacillin, rifampicin, and trimethoprim/sulfa. S. aureus exhibited increased resistance to erythromycin, fusidic acid, gentamicin, and levofloxacin, while E. coli demonstrated decreased resistance against cephalothin, gentamicin, and trimethoprim/sulfa. The antibiotics currently employed empirically appear to provide adequate coverage against the most prevalent bacteria causing early- and late-onset neonatal infections.

When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident\'s location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim\'s age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.

Microorganisms present on the surface of tobacco leaves play a significant role in shaping the composition of the tobacco microbial ecosystem, which undergoes continuous changes throughout the curing process. In the present study, a total of four distinct tobacco curing periods were selected for sampling, namely the fresh, yellowing, leaf-drying, and stem-drying stages. The bacterial 16S rRNA gene sequences of the collected samples were subsequently analyzed to identify operational taxonomic units (OTUs). The findings indicated that the complete dataset of leaf microbial samples was clustered, resulting in the identification of 1,783 operational taxonomic units (OTUs). Furthermore, the analysis of diversity revealed a pattern of initially increasing and subsequently decreasing community diversity. Redundancy Analysis (RDA) and weighted gene correlation networks for analysis (WGCNA) were employed in conjunction with environmental factors to assign OTUs to 22 modules for functional analysis. Additionally, a classification model utilizing the random forest algorithm was utilized to identify seven marker microorganisms (Escherichia coli, Faecalibacterium prausnitzii, Faecalibacterium, Escherichia-Shigella, Peptostreptococcaceae, Peptostreptococcales-Tissierellales, and Proteobacteria) that exhibited discriminative characteristics across different time periods. This study aimed to investigate the dynamic changes in the bacterial community throughout the curing process and their impact on the community\'s function. Additionally, certain bacteria were identified as potential markers for detecting changes in the curing stage. These findings offer a novel opportunity to accurately regulate the curing environment, thereby enhancing the overall quality of tobacco leaf curing.

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.

OBJECTIVE: To characterize the microbiome composition in peri-implant pocket of peri-implantitis and peri-implant sulcus controls using 16S rRNA gene sequencing.
METHODS: In this controlled clinical cross-sectional study, 23 subjects with control implants (n = 14) and diseased implants (peri-implantitis, n = 21) were included. The peri-implant pocket/sulcus was sampled and used to extract DNA and amplify the 16S rRNA gene using universal primers targeting the V3-V4 regions. The resulting 16S PCR amplicons were sequenced on Illumina MiSeq, and the sequences were processed using DADA2 and the Human Oral Microbiome Database (HOMD) as references. Alpha and Beta diversity, as well as core microbiome and differential abundance analyses, were performed using the MicrobiomeAnalyst workflow.
RESULTS: There were no significant differences in microbial diversity between control implants and implants with peri-implantitis (Shannon p = 0.82). Overall bacterial community structure assessed through beta diversity analysis was also not significantly different between the two groups (p = 0.18). However, high levels of Gram-negative bacteria were detected in peri-implant pockets compared to the control sulcus. Abundant species in peri-implantitis were Capnocytophaga leadbetteri, Treponema maltophilum, Peptostreptococcus, Neisseria, P. gingivalis, and Porphyromonas endodontali, Lactococcus lactis and Filifactor alocis (p < 0.05). Gram-positive bacteria such as Streptococcus salivaris, Prevotella melaninogenica, L. wadei, and Actinomyces spp. serve were more abundant in peri-implant control sulcus.
CONCLUSIONS: Peri-implant sulcus in control implants harbors predominantly Gram-positive bacteria, whereas pockets of implants with peri-implantitis harbor predominantly Gram-negative bacteria.

UNASSIGNED: Access to an adequate amount of water is restricted because of the increase in the use of natural resources, which is caused by the rapid growing in world population and the climate change that global warming brings, and the development in the industry. Investigating the causes of water pollution, creating solutions for the problem, taking the control of the pollution, and maintaining monitorability are necessary.
UNASSIGNED: This study was carried out in order to determine microbiological and chemical characteristics of drinking water and their compatibility for human consumption with the aim of providing safety of drinking waters.
UNASSIGNED: Thirty-four drinking water samples obtained from different sampling points in Ankara, Turkey, in 2019 were subjected to microbiological analysis and chemical analysis in terms of anions (bromide-Br-, chloride-Cl-, fluoride-F-, nitrate-NO3-, nitrite-NO2-, sulfate-SO4-2) by ion chromatography. Microbiological analyses were applied according to the international standards.
UNASSIGNED: None of the samples contained coliform bacteria, Escherichia coli and intestinal enterococci. It was established that concentrations of the specified anions in tested waters were within the acceptable levels of with the Council Directive 98/83/EC.
UNASSIGNED: The determined chemical and microbiological qualities of these samples are suitable for drinking, and do not pose any threats to public health.

Milk is a complete and highly nutritious source of food for human beings. However, in many developing countries, including Ethiopia, the quality of milk products has become a major health concern for consumers, particularly for infants and children. Therefore, the aim of the present study was to assess the quality of raw and pasteurized milk marketed in Gondar city, Northwest Ethiopia. A laboratory-based cross-sectional study was conducted on 90 milk samples. The samples were chosen using a simple random sampling technique. For statistical analysis, ANOVA and the Pearson correlation coefficient were used. The specific gravity of pasteurized milk, farm milk, and milk vendors were found to be 1.021, 1.027, and 1.026, respectively. Farm milk, milk vendors, and pasteurized milk had fat contents of 3.38%, 3.22%, and 3.09%, respectively. The total bacterial count in pasteurized milk, farm milk, and milk vendors was found to be 7.08, 6.73, and 6.94 log10 CFU/mL, respectively. In raw milk, hydrogen peroxide (7.7%), formalin (7.7%), and water (3.8%) were found, whereas in pasteurized milk, hydrogen peroxide (50%), formalin (50%), and water (19.8%) were found. Based on the findings of this study, the quality of both raw and pasteurized milk was found to be poor as per the milk quality standards. This may cause significant public health-related problems. Therefore, an appropriate intervention should be conducted to improve the quality of milk.

Organochlorine pesticides (OCPs) were typical persistent organic pollutants that posed great hazards and high risks in soil. In this study, a peanut shell biochar-loaded nano zero-valent iron (BC/nZVI) material was prepared in combination with soil indigenous microorganisms to enhance the degradation of α-hexachlorocyclohexane(α-HCH) and γ-hexachlorocyclohexane(γ-HCH) in water and soil. The effects of BC/nZVI on indigenous microorganisms in soil were investigated based on the changes in redox potential and dehydrogenase activity in the soil. The results showed as follows: (1) The specific surface area of peanut shell biochar loaded with nano-zero-valent iron was large, and the nano-zero-valent iron particles were evenly distributed on the peanut shell biochar; (2) peanut shell BC/nZVI had a good degradation effect on α-HCH and γ-HCH in water, with degradation rates of 64.18% for α-HCH and 91.87% for γ-HCH in 24 h; (3) peanut shell BC/nZVI also had a good degradation effect on α-HCH and γ-HCH in soil, and the degradation rates of α-HCH and γ-HCH in the 1% BC/nZVI reached 55.2% and 85.4%, second only to 1% zero-valent iron. The degradation rate was the fastest from 0 to 7 days, while the soil oxidation-reduction potential (ORP) increased sharply. (4) The addition of BC/nZVI to the soil resulted in a significant increase in dehydrogenase activity, which further promoted the degradation of HCHs; the amount of HCHs degradation was significantly negatively correlated with dehydrogenase activity. This study provides a remediation strategy for HCH-contaminated sites, reducing the human health risk of HCHs in the soil while helping to improve the soil and increase the activity of soil microorganisms.

Burn is one of the leading causes of death and disability in children worldwide, and wound infection is an excellent challenge in burn treatment. We performed a retrospective review of pediatric burn patients with wound infections to reveal their clinical data and investigate pathogens\' distribution and drug resistance patterns to provide references for treatment. As a result, 330 pediatric burn patients with wound infections were identified; 65.8% (217/330) were < 2 years old. Most of the injuries were scalded and involved <10% total body surface area in size (TBSA), mainly causing II-degree burn and II + III-degree burn. Three hundred and fifty nine strains of pathogens were isolated, the primary pathogens were Staphylococcus aureus (45.4%) and Pseudomonas aeruginosa (18.7%). Both S. aureus and P. aeruginosa isolated from 2012 to 2016 were more likely to be multi-resistant than those isolated from 2017 to 2021, as they were significantly associated with resistance to ≥4 Clinical and Laboratory Standard Institute (CLSI) classes (p = 0.040 and 0.006, respectively). In conclusion, children aged <2 years old were the main pediatric burn patients with wound infections. The primary bacteria isolated from the wound were S. aureus and P. aeruginosa, with a decreasing tendency of multi-resistance.