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This study analyzes the impact of physicochemical parameters on the microbiological and fatty acid profiles of cream, buttermilk, and anhydrous milk fat. Using gas chromatography coupled with mass spectrometry (GC-MS), the fatty acids present in these dairy products were qualitatively determined, highlighting the differences associated with the processing stages. Specifically, the distribution of short-chain, medium-chain, and long-chain fatty acids, such as butyric, caproic, caprylic, capric, lauric, myristic, palmitic, stearic, oleic, and linoleic acids, were analyzed, assessing their nutritional implications. The microbiological composition of the samples was also analyzed using MALDI-ToF MS. The presence of lipolytic bacteria, such as Serratia monocytogenes, which can negatively impact the oxidative stability of fats, was detected. The results show that both environmental and processing conditions significantly affect the quantity and quality of fatty acids, thereby influencing the overall dietary value of dairy products. These findings offer insight into developing improved dairy product formulations that may lead to enhancement of their health benefits. [ABSTRACT FROM AUTHOR]

Aeromonas hydrophila (Family: Aeromonadaceae) is a traditional aquatic animal pathogen. It has been widely prevalent in our country since the 1980s and 1990s, causing diseases in a series of freshwater fish, leading to serious economic losses. It has been confirmed to be one of the main pathogens in freshwater aquaculture worldwide. The American shad (Alosa sapidissima) is one of the biggest shad in the world and grows much faster than other shad. Because of its delicious taste as reeves shad (Tenualosa reevesii), American shad were introduced into China from the USA by the Shanghai Fisheries Research Institute for local farming in 1998 and is widely welcomed in Shanghai, Jiangsu, and Zhejiang Provinces. In recent years, with the rapid development of the American shad culturing, fish diseases have become a major threat to fish farming. However, because most of the American shad are cultured in extensive ponds, few diseases have been reported in China. In this study, we reported a case of A. hydrophila infection in American shad. In 2021, the disease outbreak was observed in American shad cultured in Linyi City, Shandong Province, with severe mortality. The daily mortality could be up to 2.5%. The fish were cultured in indoor ponds for breeding and outdoor ponds when fish reached 300 g. The American shad were cultured with underground water and water was changed 1–3 times daily. The water temperature was 18–20 ℃. The fish were fed largemouth bass (Micropterus salmoides) commercial feed, and the daily feeding rate was approximately 2%. The disease broke out in the indoor ponds first, and then in outdoor ponds. The cumulative mortality was approximately 90% in 2 months. Enrofloxacin was administered orally, but no effects were observed and the disease continued to progress. The typical disease symptoms in the American shad were furunculosis or ulceration, with shedding scales and surface bleeding, especially on the tail, sometimes swollen and pus-filled. In autopsies of the diseased fish, ascites were found in the fish abdomen, and dark red necrosis on the liver, with sepsis and enteritis. The liver, spleen, and kidney of American shad with typical symptoms were collected and cut into about 1 cm3 tissue blocks, immersed in Davidson's Fixative (Davidson's AFA) for 24 h, and preserved in 70% ethanol. The tissues were mounted onto glass slides with hematoxylin and eosin staining for histological analysis. Histopathological results showed swollen liver cells, vacuolar degeneration, basophilia, and diffuse necrosis; the spleen showed hemorrhagic anemic necrosis, nuclear rupture, and atrophy. Glomerular atrophy of the renal corpuscle, cells in the proximal and distal tubules cytoarchitectural loss, and necrosis and shedding of kidney lymphocytes was also observed. No parasite was found on the fish surface, fins, in the gills, or internal organs with the naked eye and a light microscope. Freshwater viruses, such as Cyprinus herpesvirus type 2, largemouth bass ranavirus, megalocytivirus, and rhabdovirus, were checked by polymerase chain reaction (PCR), and no viruses were detected. The liver, spleen, and kidney of the diseased fish were sampled and cultured in tryptic soy agar medium (TSA) and Luria-Bertani agar medium (LB) plate medium at 28 ℃ for 24 h. Several pure and dominant colonies with the same morphology were observed on all the plates. These colonies were purified and cultured. The 16S rRNA gene sequencing results of all the purified colonies showed that the dominant strains were of the same species. The typical isolate was purified and named AS-AH2101. The results of biochemical identification with Biolog GenⅢ showed that the isolate AS-AH2101 was negative to gentiobiose, stachyose, D-raffinose, α-D-lactose, D-melibiose, 3-methyl glucose, D-fucose, D-sorbitol, D-arabitol, and Myo-inositol, while positive to dextrin, D-maltose, D-trehalose, D-cellobiose, sucrose, D-turanose, and β-methyl-D-glucoside. According to the Biolog GenⅢ identification system database, the biochemical characterization of AS-AH2101 was similar to that of A. hydrophila, with a confidence of 0.999. The 16S rRNA gene sequence of AS-AH2101 was submitted to GenBank databases under the accession number OP787967 and blasted in GenBank and EzTaxon. Comparison of the 16S rRNA gene sequences showed 99%−100% identity with those of A. hydrophila. The phylogenetic tree was constructed using Mega 7 with the Aeromonas typical strains 16S rRNA gene sequences obtained from GenBank, and the phylogenetic analysis also clustered AS-AH2101 with A. hydrophila. Thus, the molecular analysis results identified the SC18032201 strains as A. hydrophila, and the phenotype also supported this result. Because of the strong stress response of American shad, it is difficult to perform the experimental culturing and infection in the laboratory. As a classic pathogenic infection model organism in aquatic animals, blue gourami (Trichogaster trichopterus) is a traditional model for fish pathogen study and has been widely used in the research of A. hydrophilia and E. piscicida. Therefore, in this study, blue gouramis were used as the model organism for virulence evaluation of AS-AH2101 in the experimental infection. The results of the challenge experiment showed that the death of the blue gourami infected via intramuscular injection was observed on the third day post infection. The infected fish showed redness, bleeding, and scale shedding at the injection site, congestion or bleeding at the base of the fin, abdominal ascites, and liver necrosis, which were similar to the naturally infected American shad. The isolate strain AS-AH2101 showed high virulence to blue gouramis, with the median lethal dose (LD50) of 3.23×104 CFU/fish. The virulence genes of A. hydrophilia were also detected by PCR, and results indicated that AS-AH2101 possessed six virulence genes, including aerolysin (aerA), hemolysin (hlyA), extracellular protease (ahpA), anti-metalloproteinases (ast), enterotoxin (altA), and quorum sensing gene (luxS). Antibiotic sensitivity studies showed that AS-AH2101 was resistant to cefradine, amoxicillin, ampicillin, and erythromycin. These results provided important information for disease control and A. hydrophila prevention and control of American shad culturing in China.

Aeromonas dhakensis is increasingly recognised to be an important pathogen responsible for disease losses in warm-water aquaculture and, similar to several other Aeromonas species, it can infect humans. Knowledge of A. dhakensis is accumulating, but this species remains relatively under-investigated compared to its close relative, Aeromonas hydrophila. The significance of A. dhakensis may have been overlooked in disease events of aquatic animals due to issues with reliable identification. Critical to appreciating the importance of this pathogen is the application of dependable molecular tools that enable accurate identification and discrimination from A. hydrophila and other motile aeromonads. This review aims to synthesise the key literature on A. dhakensis, particularly with relevance to aquaculture, including knowledge of the bacterium derived from disease case studies in aquatic hosts. Identification methods and strain phylogeny are discussed, with accurate detection important for prompt diagnosis and for distinguishing strains with heightened virulence. Increasing evidence suggests that A. dhakensis may be more virulent than A. hydrophila and correct identification is required to determine the zoonotic risks posed, which includes concerns for antibiotic-resistant strains. This review provides an impetus to improve species identification in the future and screen strain collections of presumptive Aeromonas spp. retrospectively to reveal the true prevalence and impact of A. dhakensis in aquaculture, the environment, and healthcare settings. [ABSTRACT FROM AUTHOR]

The genus Aeromonas includes well-known pathogenic species for fishes and humans that are widely distributed in the aquatic environment and foods. Nowadays, one of the main issues related to wild Aeromonas isolates is their identification at the species level, which is challenging using classical microbiological and biomolecular methods. This study aims to test MALDI-TOF MS technology in the identification of Aeromonas strains isolated from n. 60 retail sushi and sashimi boxes using an implemented version of the SARAMIS software V4.12. A total of 43 certified Aeromonas strains were used to implement the SARAMIS database by importing the spectra obtained from their identification. The original SARAMIS version (V4.12) failed to recognize 62.79% of the certified strains, while the herein-implemented version (V4.12plus) allowed the identification of all the certified strains at least to the genus level with a match of no less than 85%. Regarding the sushi and sashimi samples, Aeromonas spp. was detected in n. 18 (30%) boxes. A total of 127 colonies were identified at the species level, with A. salmonicida detected as the most prevalent species, followed by A. bestiarum and A. caviae. Based on the results of the present study, we could speculate that MALDI-TOF technology could be a useful tool both for the food industry to monitor product contamination and for clinical purposes to make diagnoses effectively and quickly. [ABSTRACT FROM AUTHOR]

Marine bacteria have been considered as important participants in revealing various carbon/sulfur/nitrogen cycles of marine ecosystem. Thus, how to accurately identify rare marine bacteria without a culture process is significant and valuable. In this work, we constructed a single‐cell Raman spectra dataset from five living bacteria spores and utilized convolutional neural network to rapidly, accurately, nondestructively identify bacteria spores. The optimal CNN architecture can provide a prediction accuracy of five bacteria spore as high as 94.93% ± 1.78%. To evaluate the classification weight of extracted spectra features, we proposed a novel algorithm by occluding fingerprint Raman bands. Based on the relative classification weight arranged from large to small, four Raman bands located at 1518, 1397, 1666, and 1017 cm−1 mostly contribute to producing such high prediction accuracy. It can be foreseen that, LTRS combined with CNN approach have great potential for identifying marine bacteria, which cannot be cultured under normal condition. [ABSTRACT FROM AUTHOR]

Bacterial infections have emerged as the leading causes of mortality and morbidity worldwide. Herein, we developed a dual‐channel fluorescence "turn‐on" sensor array, comprising six electrostatic complexes formed from one negatively charged poly(para‐aryleneethynylene) (PPE) and six positively charged aggregation‐induced emission (AIE) fluorophores. The 6‐element array enabled the simultaneous identification of 20 bacteria (OD600=0.005) within 30s (99.0 % accuracy), demonstrating significant advantages over the array constituted by the 7 separate elements that constitute the complexes. Meanwhile, the array realized different mixing ratios and quantitative detection of prevalent bacteria associated with urinary tract infection (UTI). It also excelled in distinguishing six simulated bacteria samples in artificial urine. Remarkably, the limit of detection for E. coli and E. faecalis was notably low, at 0.000295 and 0.000329 (OD600), respectively. Finally, optimized by diverse machine learning algorithms, the designed array achieved 96.7 % accuracy in differentiating UTI clinical samples from healthy individuals using a random forest model, demonstrating the great potential for medical diagnostic applications. [ABSTRACT FROM AUTHOR]

Matrix-Assisted Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in the last decade has revealed itself as a valid support in the workflow in the clinical microbiology laboratory for the identification of bacteria and fungi, demonstrating high reliability and effectiveness in this application. Its use has reduced, by 24 h, the time to obtain a microbiological diagnosis compared to conventional biochemical automatic systems. MALDI-TOF MS application to the detection of pathogens directly in clinical samples was proposed but requires a deeper investigation, whereas its application to positive blood cultures for the identification of microorganisms and the detection of antimicrobial resistance are now the most useful applications. Thanks to its rapidity, accuracy, and low price in reagents and consumables, MALDI-TOF MS has also been applied to different fields of clinical microbiology, such as the detection of antibiotic susceptibility/resistance biomarkers, the identification of aminoacidic sequences and the chemical structure of protein terminal groups, and as an emerging method in microbial typing. Some of these applications are waiting for an extensive evaluation before confirming a transfer to the routine. MALDI-TOF MS has not yet been used for the routine identification of parasites; nevertheless, studies have been reported in the last few years on its use in the identification of intestinal protozoa, Plasmodium falciparum, or ectoparasites. Innovative applications of MALDI-TOF MS to viruses' identification were also reported, seeking further studies before adapting this tool to the virus's diagnostic. This mini-review is focused on the MALDI-TOF MS application in the real life of the diagnostic microbiology laboratory. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of physicochemical parameters on the microbiological and fatty acid profiles of cream, buttermilk, and anhydrous milk fat. Using gas chromatography coupled with mass spectrometry (GC-MS), the fatty acids present in these dairy products were qualitatively determined, highlighting the differences associated with the processing stages. Specifically, the distribution of short-chain, medium-chain, and long-chain fatty acids, such as butyric, caproic, caprylic, capric, lauric, myristic, palmitic, stearic, oleic, and linoleic acids, were analyzed, assessing their nutritional implications. The microbiological composition of the samples was also analyzed using MALDI-ToF MS. The presence of lipolytic bacteria, such as Serratia monocytogenes, which can negatively impact the oxidative stability of fats, was detected. The results show that both environmental and processing conditions significantly affect the quantity and quality of fatty acids, thereby influencing the overall dietary value of dairy products. These findings offer insight into developing improved dairy product formulations that may lead to enhancement of their health benefits.

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions. [ABSTRACT FROM AUTHOR]

The genus Aeromonas includes well-known pathogenic species for fishes and humans that are widely distributed in the aquatic environment and foods. Nowadays, one of the main issues related to wild Aeromonas isolates is their identification at the species level, which is challenging using classical microbiological and biomolecular methods. This study aims to test MALDI-TOF MS technology in the identification of Aeromonas strains isolated from n. 60 retail sushi and sashimi boxes using an implemented version of the SARAMIS software V4.12. A total of 43 certified Aeromonas strains were used to implement the SARAMIS database by importing the spectra obtained from their identification. The original SARAMIS version (V4.12) failed to recognize 62.79% of the certified strains, while the herein-implemented version (V4.12plus) allowed the identification of all the certified strains at least to the genus level with a match of no less than 85%. Regarding the sushi and sashimi samples, Aeromonas spp. was detected in n. 18 (30%) boxes. A total of 127 colonies were identified at the species level, with A. salmonicida detected as the most prevalent species, followed by A. bestiarum and A. caviae. Based on the results of the present study, we could speculate that MALDI-TOF technology could be a useful tool both for the food industry to monitor product contamination and for clinical purposes to make diagnoses effectively and quickly.

Aeromonas dhakensis is increasingly recognised to be an important pathogen responsible for disease losses in warm-water aquaculture and, similar to several other Aeromonas species, it can infect humans. Knowledge of A. dhakensis is accumulating, but this species remains relatively under-investigated compared to its close relative, Aeromonas hydrophila. The significance of A. dhakensis may have been overlooked in disease events of aquatic animals due to issues with reliable identification. Critical to appreciating the importance of this pathogen is the application of dependable molecular tools that enable accurate identification and discrimination from A. hydrophila and other motile aeromonads. This review aims to synthesise the key literature on A. dhakensis, particularly with relevance to aquaculture, including knowledge of the bacterium derived from disease case studies in aquatic hosts. Identification methods and strain phylogeny are discussed, with accurate detection important for prompt diagnosis and for distinguishing strains with heightened virulence. Increasing evidence suggests that A. dhakensis may be more virulent than A. hydrophila and correct identification is required to determine the zoonotic risks posed, which includes concerns for antibiotic-resistant strains. This review provides an impetus to improve species identification in the future and screen strain collections of presumptive Aeromonas spp. retrospectively to reveal the true prevalence and impact of A. dhakensis in aquaculture, the environment, and healthcare settings.

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed. [ABSTRACT FROM AUTHOR]

The greenfin horse-faced filefish (Thamnaconus septentrionalis) and rockfish (Sebastes schlegeli) occupy important positions in the offshore net fishery in Shandong Province. Interest in their mariculture has been developing rapidly in recent years as candidates for submerged cage open-sea aquaculture. With the development of breeding techniques and the expansion of large- scale farming, fish disease may become a serious constraint that limits sustainable aquaculture and leads to great economic losses. Epidemiological investigation is the basis of disease control and should be carried out throughout the culture process. In this study, we describe the diseases of T. septentrionalis and S. schlegeli caused by Aeromonas salmonicida subsp. masoucida. In November 2018, an outbreak of T. septentrionalis disease was observed in a farm located in Penglai, Shandong Province, and an outbreak of S. schlegeli disease occurred in the same farm in April 2019, with daily mortalities of 0.4%~1% and about 1%, respectively. The main symptoms in the diseased fish were ulcers, redness, swelling, and bleeding in the mouth. Most diseased fish in the ponds showed "red mouth". No parasites were observed by the naked eye or light microscope. From the liver, spleen, and kidney of all the diseased fish, many homogeneous colonies were observed after three days incubation on TSA and 2216E agar plates. All strains had the same shape, color, and size, and the 16S rRNA genes of all strains were the same, with high identity with A. salmonicida. The virulence of the isolates was tested experimentally via injection with T. septentrionalis (infected by 2018TS-1) and S. schlegeli (infected by 2019SS-1) in the laboratory to calculate the median lethal dose (LD50). The results showed that the LD50 of 2018TS-1 to T. septentrionalis was 1.78×105 CFU/fish, and that of 2019SS-1 to S. schlegeli was 0.89×105 CFU/fish. The dead fish of the experimentally infected group showed ulcers and red mouth, the same symptoms as in naturally infected fish. Dominant colonies isolated from experimentally infected fish were all identified as A. salmonicida by 16S rRNA gene sequencing, which indicated that 2018TS-1 and 2019SS-1 were the pathogens of T. septentrionalis and S. schlegeli, respectively. Bacterial identification was carried out by 16S rRNA gene analysis and Biolog Gen Ⅲ characterization. The 16S rRNA gene sequences of 2018TS-1 and 2019SS-1 (Gene Bank: OK258319 and OK258320) isolated from T. septentrionalis and S. schlegeli were analyzed using MEGA5, and the phylogenetic tree derived from 16S rRNA gene sequences clustered the isolates with A. salmonicida. Among the Biolog Gen Ⅲ tests, 31 produced positive reactions or weak positive reactions for both strains (Dextrin, D-Maltose, D-Trehalose, D-Cellobiose, Sucrose, β-Methyl-D-Glucoside, D-Salicin, α-D-Glucose, D-Mannose, D-Fructose, D-Mannitol, Glycerol, Gelatin, Glycyl-L-Proline, L-Arginine, D-Gluconic Acid, Methyl Pyruvate, L-Malic Acid, Bromo-Succinic Acid, Tween 40, α-Keto-Butyric Acid, Acetoacetic Acid, Propionic Acid, Acetic Acid, pH 6, 1% NaCl, 1% Sodium Lactate, L-Aspartic Acid, L-Glutamic Acid, L-Histidine, and L-Serine), and two weak positive reactions for 2019SS-1, while the others were negative. According to the Biolog database, both strains were identified as A. salmonicida. Based on the molecular analysis of 16S rRNA genes and Biolog Gen Ⅲ phenotype results, the isolates were identified as A. salmonicida. The vapA gene, which encodes the outer membrane protein (A-layer protein) and causes the auto-aggregation of bacteria, is a conserved gene with some variation region in A. salmonicida. vapA gene typing is an effective and important method for classifying the molecular types and subspecies of this fish. vapA gene typing was also used in this study to identify subspecies of strains isolated from T. septentrionalis and S. schlegeli. The vapA gene sequences of 2018TS-1 and 2019SS-1 (Gene Bank: OK300094 and OK300095) were analyzed using MEGA5 with type strains obtained from Gene Bank. The phylogenetic tree derived from the vapA gene sequences clustered 2018TS-1 and 2019SS-1 with type strain ATCC 27013, indicating that the strains isolated from T. septentrionalis and S. schlegeli belonged to A. salmonicida subsp. masoucida, similar to the A-layer type Ⅶ strains, which are all from the northeast Asian and Canadian coasts in the Pacific Ocean. Based on the experimental infection, 16S rRNA sequence analysis, Biolog Gen Ⅲ characterization, and vapA gene typing, we confirmed that A. salmonicida subsp. masoucida is the pathogen of T. septentrionalis and S. schlegeli, and the cause of these two diseases on the farm. This is the first report of T. septentrionalis and S. schlegeli infected by A. salmonicida in industrial aquaculture, as well as the first report of a disease of T. septentrionalis in culture. It has been reported that A. salmonicida subsp. masoucida can infect Atlantic salmon (Salmo salar), turbot (Scophthalmus maximus), sablefish (Anoplopoma fimbria), and tongue sole (Cynoglossus semilaevis) cultured in Shandong Province. In this study, we expanded the host list of A. salmonicida subsp. masoucida to include two new species in aquaculture, T. septentrionalis and S. schlegeli, on the same farm, indicating that A. salmonicida subsp. masoucida may translate and adapt to a new host in a short period. Considering the increasing host and economic losses caused by A. salmonicida in fish culture, the prevention of A. salmonicida subsp. masoucida should be an important objective for mariculture in the future.

Accurate and rapid determination of bacterial disease agents of fish is an important step for sustainable and efficient aquaculture production. In general, biochemical and molecular methods are used for pathogen detection but they are usually time-consuming and required qualified personnel. Recently spectroscopic methods are preferred in clinical and food microbiology and declared as a promising alternative method for pathogens diagnosis with many advantages. In this study, the significant spectra of three important bacterial fish pathogens (Lactococcus garvieae, Vibrio anguillarum and Yersinia ruckeri) were determined by Raman spectroscopy. The first data of the pathogens were obtained and the distinctive differences in polysaccharides, nucleic acids, fatty acids and amino acids were identified. This preliminary study aimed to be pioneer for further studies in aquaculture and veterinary microbiology toward developing an alternative method for routine identification. [ABSTRACT FROM AUTHOR]

Invasive infections caused by methicillin‐resistant Staphylococcus aureus (MRSA) are associated with high mortality and morbidity. The sooner the pathogen is determined, the better it is beneficial to patient. However, routine laboratory inspections are time‐consuming and laborious. A thorough research was conducted in PubMed and Web of Science (until June 2021) to identify studies evaluating the accuracy of MRSA identification by matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS). STATA 15.0 software was used to analyze the pooled results of sensitivity, specificity, and 95% confidence intervals (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) were utilized to show the overall performance of MALDI‐TOF MS. Fifteen studies involving 2471 isolates were included in this study after the final selection in this meta‐analysis. Using the random effects model forest plot to summarize the overall statistics, the sensitivity of MALDI‐TOF MS for identifying MRSA was 92% (95% CI: 81%–97%), and the specificity was 97% (95% CI: 89%–99%). In the SROC curve, the AUC reached 0.99 (95% CI: 97%–99%). Deeks' test showed no significant publication bias in this meta‐analysis. Compared with clinical reference methods, MALDI‐TOF MS identification of MRSA shows a higher degree of sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Bloodstream infection (BSI) is a life-threatening infection that causes more than 80,000 deaths and more than 500,000 infections annually in North America. The rapid diagnosis of infection reduces BSI mortality. We proposed bacterial enrichment and separation approach in the current work that may reduce culturing time and accelerate the diagnosis of infection. Over the last two decades, multiple separation methods have been developed, and among these methods, insulator-based dielectrophoresis (iDEP) is considered a powerful technique for separating biological particles. Bacterial separation in the blood is challenging due to the presence of other blood cells, such as white blood cells, red blood cells, and platelets. In the present study, a model is presented which is capable of blood cells separation and directing each cell to a specific outlet using continuous flows of particles with sizes larger than 8 µm, 8–4 µm, and smaller than 4 µm. Compared to other methods, such as filtration, the main advantage of this model is that particles larger than 8 µm are separated from the flow before other particles, which prevents the accumulation of particles in the channel. The outcomes of simulations demonstrated that the factors such as applied voltage and channel dimensions significantly affect the separation efficiency. If these values are properly selected (for example voltage of 70 V that was causing an electric field of 200 V/cm), the proposed model can completely (100%) separate particles larger than 8 µm and smaller than 4 µm (8–4 µm particles separation efficiency is 95%). [ABSTRACT FROM AUTHOR]

Matrix-Assisted Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in the last decade has revealed itself as a valid support in the workflow in the clinical microbiology laboratory for the identification of bacteria and fungi, demonstrating high reliability and effectiveness in this application. Its use has reduced, by 24 h, the time to obtain a microbiological diagnosis compared to conventional biochemical automatic systems. MALDI-TOF MS application to the detection of pathogens directly in clinical samples was proposed but requires a deeper investigation, whereas its application to positive blood cultures for the identification of microorganisms and the detection of antimicrobial resistance are now the most useful applications. Thanks to its rapidity, accuracy, and low price in reagents and consumables, MALDI-TOF MS has also been applied to different fields of clinical microbiology, such as the detection of antibiotic susceptibility/resistance biomarkers, the identification of aminoacidic sequences and the chemical structure of protein terminal groups, and as an emerging method in microbial typing. Some of these applications are waiting for an extensive evaluation before confirming a transfer to the routine. MALDI-TOF MS has not yet been used for the routine identification of parasites; nevertheless, studies have been reported in the last few years on its use in the identification of intestinal protozoa, Plasmodium falciparum, or ectoparasites. Innovative applications of MALDI-TOF MS to viruses’ identification were also reported, seeking further studies before adapting this tool to the virus’s diagnostic. This mini-review is focused on the MALDI-TOF MS application in the real life of the diagnostic microbiology laboratory.

The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases. [ABSTRACT FROM AUTHOR]

The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism's growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R2 ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative–quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices. [ABSTRACT FROM AUTHOR]

We have developed a system for bacteria identification based on absorption spectroscopy in the mid‐infrared spectral range. The data collected are analyzed with a deep learning algorithm. It is based on a neural‐network model which takes one‐dimensional signal vectors and outputs a probability score of identification of a bacterium type by extracting micro and macro scale features, using convolutions and nonlinear operations. The results are achieved in real time and do not require any offline postprocessing. The study was done on 12 of the most common bacteria usually seen in clinical microbiology laboratories. The system sensitivity is 0.94 ± 0.04, with a specificity of 0.95 ± 0.02. The system can be extended to additional bacterium types and variants with no change to its hardware or software, but only updating the model's parameters. The system's accuracy, size, ease of operation and low cost make it suitable for use in any type of clinical setting. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health. Main steps of identifying bacteria utilizing the WPGA algorithm. The Raman spectra with features extracted by wavelet packet transform techniques, were transformed into two-dimensional spectrograms by Gramian angular field techniques. The deep learning model-enabled spectrogram analysis demonstrated excellent performances. Our study provides unprecedented opportunities for developing rapid and accurate bacteria identification. [Display omitted] • The algorithm speeds up bacteria identification by extracting frequency features. • The algorithm improves accuracy by analyzing 2D Raman spectrograms. • The algorithm is insensitive to the bacteria types and quantities. [ABSTRACT FROM AUTHOR]

The people can survive day, weak and month with eat food, but without water people cannot survive for a day. This contamination of drinking water due to (E coli, Salmonella, Shigella, and cholera), viruses (Hepatitis A, Polio Virus and Rota Virus). In this study, drinking water collected from different area of Allahabad District. We performed the microbiological, biochemical, Molecular biological, Molecular biological to confirm the presence of E. coli, Salmonella and Shigella etc. type of pathogenic bacteria in different area of drinking water. Molecular based method is evaluated by the 16S r DNA gene sequencing technique to characterized the pathogenic bacteria belonging to family Enterobacteriaceae. Design and synthesis a set of oligonucleotides (Primer) capable of amplifying nearly full length (1500bp) 16S r-DNA from many bacteria like Clastridium perfringer, pseudomonas, Neissiria, Gonorrhaeae, Proteus, Escherichia, Eracinia, antibiotics such as Ampicillin, streptomycin, Gentamycin Ciprofloxacine and observed high background of resistant and virulence bacteria in Tap water. [ABSTRACT FROM AUTHOR]

Bloodstream infections (BSI) result in significant morbidity and mortality rates, and delayed administration of appropriate antimicrobial treatment is a major predictor of poor outcomes. T2 magnetic resonance (T2MR®) (T2 Biosystems®, Lexington, MA, USA) is an innovative technology that can rapidly identify pathogens from a sample of whole blood in a remarkably short time frame of 3-5 h. We are evaluating if the T2Bacteria Panel (T2BP) contributes to the etiological diagnosis of bloodstream infections when combined with standard blood cultures (BC). The study was performed between December 2018 and March 2019, and a total of 28 patients with suspected BSI were included. The most notable finding of our study was that the addition of T2BP to BC in a diagnostic workflow led to a statistically significant higher rate of T2BP-targeted bacteria identification in patients with suspected BSI (46.4% versus 7.1%, P = 0.001) when compared to BC alone. Considering the measures of diagnostic accuracy, T2BP showed 100.00% sensitivity, 88.24% specificity, 100% negative predictive value (NPV), and 84.62% positive predictive value (PPV). Our findings give valuable insights for microbiologists and clinicians into this molecular method and its advantages in routine diagnostics of BSI.

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes - the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system--the Microbial Containment Device (MCD)--that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical. [ABSTRACT FROM AUTHOR]

The quick and accurate identification of microorganisms and the study of resistance to antibiotics is crucial in the economic and industrial fields along with medicine. One of the fastest-growing identification methods is the spectrometric approach consisting in the matrix-assisted laser ionization/desorption using a time-of-flight analyzer (MALDI-TOF MS), which has many advantages over conventional methods for the determination of microorganisms presented. Thanks to the use of a multiomic approach in the MALDI-TOF MS analysis, it is possible to obtain a broad spectrum of data allowing the identification of microorganisms, understanding their interactions and the analysis of antibiotic resistance mechanisms. In addition, the literature data indicate the possibility of a significant reduction in the time of the sample preparation and analysis time, which will enable a faster initiation of the treatment of patients. However, it is still necessary to improve the process of identifying and supplementing the existing databases along with creating new ones. This review summarizes the use of "-omics" approaches in the MALDI TOF MS analysis, including in bacterial identification and antibiotic resistance mechanisms analysis. [ABSTRACT FROM AUTHOR]

Infectious diseases have always been a major threat to the survival of humanity. Additionally, they bring an enormous economic burden to society. The conventional methods for bacteria identification are expensive, time-consuming and laborious. Therefore, it is of great importance to automatically rapidly identify pathogenic bacteria in a short time. Here, we constructed an AI-assisted system for automating rapid bacteria genus identification, combining the hyperspectral microscopic technology and a deep-learning-based algorithm Buffer Net. After being trained and validated in the self-built dataset, which consists of 11 genera with over 130,000 hyperspectral images, the accuracy of the algorithm could achieve 94.9%, which outperformed 1D-CNN, 2D-CNN and 3D-ResNet. The AI-assisted system we developed has great potential in assisting clinicians in identifying pathogenic bacteria at the single-cell level with high accuracy in a cheap, rapid and automatic way. Since the AI-assisted system can identify the pathogenic genus rapidly (about 30 s per hyperspectral microscopic image) at the single-cell level, it can shorten the time or even eliminate the demand for cultivating. Additionally, the system is user-friendly for novices. [ABSTRACT FROM AUTHOR]

Caulerpa racemosa is a green algae consumed by people in northern coastal areas. C. racemosa has a habitat attached to the shallow seabed. C. racemosa usualy consumed fresh without any cooking process so that the contamination of microorganisms can be eaten. Molecular identification using 16S rRNA is needed to determine the type of bacterial contaminants in C. racemosa. The isolates of C. racemosa were cultured in HIA, BAP, and BHI media. Bacteria from BHI media were isolated by DNA, PCR for 16S rRNA gene, and sequencing. Bacteria isolate C. racemosa was found to have the α-hemolytic ability in BAP media. The sequencing analysis showed that the three bacterial colonies of C. racemosa isolate had high similarity with V. parahemolyticus, Caldalkalibacillus mannanilyticus, and Exiguobacterium profundum.

The identification of bacteria plays an essential role in multiple areas of research. Those areas include experimental biology, food and water industries, pathology, microbiology, and evolutionary studies. Although there exist methodologies for identification, a transition to a whole-genome sequence-based taxonomy is already undergoing. Next-Generation Sequencing helps the transition by producing DNA sequence data efficiently. However, the rate of DNA sequence data generation and the high dimensionality of such data need faster computer methodologies. Machine learning, an area of artificial intelligence, has the ability to analyze high dimensional data in a systematic, fast, and efficient way. Therefore, we propose a sequential deep learning model for bacteria identification. The proposed neural network exploits the vast amounts of information generated by Next-Generation Sequencing, in order to extract an identification model for whole-genome bacteria sequences. After validating the identification model, the bidirectional recurrent neural network outperformed other classification approaches.

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Various bacterial diseases have caused great economic losses to the high-density and intensive aquaculture industry; however, the pathogenic mechanism underlying the large-scale challenged to caused by many bacteria remain unclear, making the prevention and treatment of these diseases difficult. In the present study, we isolated a bacterial strain from Cyprinus carpio having a typical bacterial disease and named it Cc2021. Through subsequent morphological observations, a regression challenge, biochemical identification, and 16S rRNA gene sequence analysis, we determined Cc2021 to be Plesiomonas shigelloides. Subsequently, we comprehensively investigated the pathogenicity of P. shigelloides in C. carpio through a regression challenge and assessed the underlying the pathogenic mechanism. Mortality results revealed that P. shigelloides is highly pathogenic and infects various tissues throughout the body, resulting in edema of the liver, spleen, and body and head kidneys. Histopathological analysis revealed obvious inflammation, bleeding, and necrosis in the intestine, spleen, and head kidney. The body's immune tissues actively produce complement C3, superoxide dismutase, and lysozyme after a challenge to resist bacterial invasion. With regard to the underlying pathogenesis of P. shigelloides , comparative transcriptome analysis revealed 876 upregulated genes and 828 downregulated genes in the intestine of C. carpio after the challenge. Analysis of differentially expressed unigenes revealed the involvement of major immune pathways, particularly the TNF signaling pathway, interleukin (IL)-17 signaling pathway, and Toll-like receptor signaling pathway. The present study provides new valuable information on the immune system and defense mechanisms of P. shigelloides. [ABSTRACT FROM AUTHOR]

The present work focused on the utilization of three local wastes, i.e., rambutan (Nephelium lappaceum), langsat (Lansium parasiticum), and mango (Mangifera indica) wastes, as organic substrates in a benthic microbial fuel cell (BMFC) to reduce the cadmium and lead concentrations from synthetic water. Out of the three wastes, the mango waste promoted a maximum current density (87.71 mA/m2) along with 78% and 80% removal efficiencies for Cd2+ and Pb2+, respectively. The bacterial identification proved that Klebsiella pneumoniae, Enterobacter, and Citrobacter were responsible for metal removal and energy generation. In the present work, the BMFC mechanism, current challenges, and future recommendations are also enclosed. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the presence of beneficial microorganisms in the micropropagation of Pyrus communis rootstocks through the isolation, identification, and selection of auxin-producing bacteria. The selected microorganisms were also inoculated in vitro to evaluate their effect on the plant rooting process and their behavior during the acclimatization phase. The results showed the presence of endophytic microorganisms in plant tissue cultures of the 'OH×F87′ and 'PDW' selections. A low diversity was observed in the population of microorganisms isolated from the in vitro culture of the 'OH×F87'and 'PDW' selections, with a predominance of the genera Acinetobacter, Bacillus, and Buttiauxella. The selection of promoting microorganisms was performed based on the auxin production test, in which 30.36% of the microorganisms tested positive. In the in vitro inoculation, it was possible to observe the promotion of growth and emission of roots in the pear rootstocks, from the inoculation with bacteria identified as capable of producing auxin. This process can be used to replace the inclusion of synthetic auxin in the productive chain of woody fruit trees propagated by vegetative means, such as pears. None of the bacterial isolates was notably more promising, but the general similarity of treatments containing the A. septicus and A. ursingii strains, with the synthetic auxin treatment, suggests the possibility of its use on a large scale allowing the adoption of the cheaper method. of rooting. This work opens the door for further research using new, more promising microbial isolates, and also for lower-cost microorganism cultivation techniques, such as low-cost media obtained from agro-industrial residues. [ABSTRACT FROM AUTHOR]

Various bacterial diseases have caused great economic losses to the high-density and intensive aquaculture industry; however, the pathogenic mechanism underlying the large-scale challenged to caused by many bacteria remain unclear, making the prevention and treatment of these diseases difficult. In the present study, we isolated a bacterial strain from Cyprinus carpio having a typical bacterial disease and named it Cc2021. Through subsequent morphological observations, a regression challenge, biochemical identification, and 16S rRNA gene sequence analysis, we determined Cc2021 to be Plesiomonas shigelloides. Subsequently, we comprehensively investigated the pathogenicity of P. shigelloides in C. carpio through a regression challenge and assessed the underlying the pathogenic mechanism. Mortality results revealed that P. shigelloides is highly pathogenic and infects various tissues throughout the body, resulting in edema of the liver, spleen, and body and head kidneys. Histopathological analysis revealed obvious inflammation, bleeding, and necrosis in the intestine, spleen, and head kidney. The body’s immune tissues actively produce complement C3, superoxide dismutase, and lysozyme after a challenge to resist bacterial invasion. With regard to the underlying pathogenesis of P. shigelloides, comparative transcriptome analysis revealed 876 upregulated genes and 828 downregulated genes in the intestine of C. carpio after the challenge. Analysis of differentially expressed unigenes revealed the involvement of major immune pathways, particularly the TNF signaling pathway, interleukin (IL)-17 signaling pathway, and Toll-like receptor signaling pathway. The present study provides new valuable information on the immune system and defense mechanisms of P. shigelloides.

In this study, eight types of bacteria were cultivated, including Staphylococcus aureus. The infrared absorption spectra of the gas surrounding cultured bacteria were recorded at a resolution of 0.5 cm−1 over the wavenumber range of 7500–500 cm−1. From these spectra, we searched for the infrared wavenumbers at which characteristic absorptions of the gas surrounding Staphylococcus aureus could be measured. This paper reports two wavenumber regions, 6516–6506 cm−1 and 2166–2158 cm−1. A decision tree–based machine learning algorithm was used to search for these wavenumber regions. The peak intensity or the absorbance difference was calculated for each region, and the ratio between them was obtained. When these ratios were used as training data, decision trees were created to classify the gas surrounding Staphylococcus aureus and the gas surrounding other bacteria into different groups. These decision trees show the potential effectiveness of using absorbance measurement at two wavenumber regions in finding Staphylococcus aureus. [ABSTRACT FROM AUTHOR]

The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because of the single and fixed scales of deep neural networks. We propose a deep neural network that can learn multi-scale features of Raman spectra by using the automatic combination of multi-receptive fields of convolutional layers. This model is based on the expert knowledge that the discrimination information of Raman spectra is composed of multi-scale spectral peaks. We enhance the interpretability of the model by visualizing the activated wavenumbers of the bacterial spectrum that can be used for reference in related work. Compared with existing state-of-the-art methods, the proposed method achieves higher accuracy and efficiency for bacterial identification on isolate-level, empiric-treatment-level, and antibiotic-resistance-level tasks. The clinical bacterial identification task requires significantly fewer patient samples to achieve similar accuracy. Therefore, this method has tremendous potential for the identification of clinical pathogenic bacteria, antibiotic susceptibility testing, and prescription guidance. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.

The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism’s growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R2 ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative–quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices.

The identification of bacteria plays an essential role in multiple areas of research. Those areas include experimental biology, food and water industries, pathology, microbiology, and evolutionary studies. Although there exist methodologies for identification, a transition to a whole-genome sequence-based taxonomy is already undergoing. Next-Generation Sequencing helps the transition by producing DNA sequence data efficiently. However, the rate of DNA sequence data generation and the high dimensionality of such data need faster computer methodologies. Machine learning, an area of artificial intelligence, has the ability to analyze high dimensional data in a systematic, fast, and efficient way. Therefore, we propose a sequential deep learning model for bacteria identification. The proposed neural network exploits the vast amounts of information generated by Next-Generation Sequencing, in order to extract an identification model for whole-genome bacteria sequences. After validating the identification model, the bidirectional recurrent neural network outperformed other classification approaches. [ABSTRACT FROM AUTHOR]

Abstract Background Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been rapidly developed and widely used as an analytical technique in clinical laboratories with high accuracy in microorganism identification. Objective To validate the efficacy of MALDI-TOF MS in identification of clinical pathogenic anaerobes. Methods Twenty-eight studies covering 6685 strains of anaerobic bacteria were included in this meta-analysis. Fixed-effects models based on the P-value and the I-squared were used for meta-analysis to consider the possibility of heterogeneity between studies. Statistical analyses were performed by using STATA 12.0. Results The identification accuracy of MALDI-TOF MS was 84% for species (I2 = 98.0%, P

Infectious diseases have always been a major threat to the survival of humanity. Additionally, they bring an enormous economic burden to society. The conventional methods for bacteria identification are expensive, time-consuming and laborious. Therefore, it is of great importance to automatically rapidly identify pathogenic bacteria in a short time. Here, we constructed an AI-assisted system for automating rapid bacteria genus identification, combining the hyperspectral microscopic technology and a deep-learning-based algorithm Buffer Net. After being trained and validated in the self-built dataset, which consists of 11 genera with over 130,000 hyperspectral images, the accuracy of the algorithm could achieve 94.9%, which outperformed 1D-CNN, 2D-CNN and 3D-ResNet. The AI-assisted system we developed has great potential in assisting clinicians in identifying pathogenic bacteria at the single-cell level with high accuracy in a cheap, rapid and automatic way. Since the AI-assisted system can identify the pathogenic genus rapidly (about 30 s per hyperspectral microscopic image) at the single-cell level, it can shorten the time or even eliminate the demand for cultivating. Additionally, the system is user-friendly for novices.

Rapid screening of infected people plays a crucial role in interrupting infection chains. However, the current methods for identification of bacteria are very tedious and labor intense. Fast on-site screening for pathogens based on volatile organic compounds (VOCs) by ion mobility spectrometry (IMS) could help to differentiate between healthy and potentially infected subjects. As a first step towards this, the feasibility of differentiating between seven different bacteria including resistant strains was assessed using IMS coupled to multicapillary columns (MCC-IMS). The headspace above bacterial cultures was directly drawn and analyzed by MCC-IMS after 90 min of incubation. A cluster analysis software and statistical methods were applied to select discriminative VOC clusters. As a result, 63 VOC clusters were identified, enabling the differentiation between all investigated bacterial strains using canonical discriminant analysis. These 63 clusters were reduced to 7 discriminative VOC clusters by constructing a hierarchical classification tree. Using this tree, all bacteria including resistant strains could be classified with an AUC of 1.0 by receiver-operating characteristic analysis. In conclusion, MCC-IMS is able to differentiate the tested bacterial species, even the non-resistant and their corresponding resistant strains, based on VOC patterns after 90 min of cultivation. Although this result is very promising, in vivo studies need to be performed to investigate if this technology is able to also classify clinical samples. With a short analysis time of 5 min, MCC-IMS is quite attractive for a rapid screening for possible infections in various locations from hospitals to airports. Key Points • Differentiation of bacteria by MCC-IMS is shown after 90-min cultivation. • Non-resistant and resistant strains can be distinguished. • Classification of bacteria is possible based on metabolic features. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the presence of beneficial microorganisms in the micropropagation of Pyrus communis rootstocks through the isolation, identification, and selection of auxin-producing bacteria. The selected microorganisms were also inoculated in vitro to evaluate their effect on the plant rooting process and their behavior during the acclimatization phase. The results showed the presence of endophytic microorganisms in plant tissue cultures of the ‘OH×F87′ and ‘PDW’ selections. A low diversity was observed in the population of microorganisms isolated from the in vitro culture of the ‘OH×F87’and ‘PDW’ selections, with a predominance of the genera Acinetobacter, Bacillus, and Buttiauxella. The selection of promoting microorganisms was performed based on the auxin production test, in which 30.36% of the microorganisms tested positive. In the in vitro inoculation, it was possible to observe the promotion of growth and emission of roots in the pear rootstocks, from the inoculation with bacteria identified as capable of producing auxin. This process can be used to replace the inclusion of synthetic auxin in the productive chain of woody fruit trees propagated by vegetative means, such as pears. None of the bacterial isolates was notably more promising, but the general similarity of treatments containing the A. septicus and A. ursingii strains, with the synthetic auxin treatment, suggests the possibility of its use on a large scale allowing the adoption of the cheaper method. of rooting. This work opens the door for further research using new, more promising microbial isolates, and also for lower-cost microorganism cultivation techniques, such as low-cost media obtained from agro-industrial residues.

Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial infections. Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The approach was evaluated and compared with several well-established tools across various datasets. Precision, recall, and F1-score were used to quantify the accuracy of species prediction. Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision and F1-score. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. Conclusion: With a novel approach to reducing false positives and the effective use of a modified Bloom filter to index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in the field. The study lays the groundwork for future improvements in computational efficiency and the expansion of microbial databases., Competing Interests: Both authors declared that there are no conflicts of interest., (© The Author(s) 2024.)