Soft rot bacteria of the Pectobacterium and Dickeya genera are Gram-negative phytopathogens that produce and secrete plant cell wall-degrading enzymes (PCWDE), the actions of which lead to rotting and decay of their hosts in the field and in storage. Host chemical signals are among the factors that induce the bacteria into extracellular enzyme production and virulence. A class of compounds (Class I) made up of intermediate products of cell wall (pectin) degradation induce exoenzyme synthesis through KdgR, a global negative regulator of exoenzyme production. While the KdgR⁻ mutant of P. carotovorum is no longer inducible by Class I inducers, we demonstrated that exoenzyme production is induced in this strain in the presence of extracts from hosts including celery, potato, carrot, and tomato, suggesting that host plants contain another class of compounds (Class II inducers) different from the plant cell wall-degradative products that work through KdgR. The Class II inducers are thermostable, water-soluble, diffusible, and dialysable through 1 kDa molecular weight cut off pore size membranes, and could be a target for soft rot disease management strategies.

The current study investigated Listeria monocytogenes inactivation using mild heat with elevated hydrostatic pressure and nisin under buffered condition. A four-strain pathogen mixture was exposed to 0 (control) and up to 9 min of (1) 4 °C elevated pressure; (2) 4 °C elevated pressure and nisin; (3) 4 °C nisin; (4) heat at 40 °C; (5) 40 °C elevated pressure; (6) 40 °C elevated pressure and nisin; and (7) 40 °C nisin. Elevated hydrostatic pressure at 400 MPa (Hub880 Explorer, Pressure BioScience Inc., Easton, MA, USA) and nisin concentration of 5000 IU/mL were used in the trials. Analyses of variance were conducted, followed by Dunnett’s- and Tukey-adjusted means separations. Under conditions of these experiments, nisin augmented (p < 0.05) decontamination efficacy of 40 °C heat and elevated hydrostatic pressure treatments, particularly at treatment interval of 3 min. This synergism with nisin faded away (p ≥ 0.05) as the treatment time for thermal, high-pressure, and thermal-assisted pressure processing increased. The results of our study, thus, exhibit that practitioners and stakeholders of pressure-based technologies could benefit from synergism of mild heat and nisin for short-term, high-pressure pasteurization treatments to achieve microbial safety and economic feasibility comparable to traditional heat-treated products.

The current study investigated synergism of elevated hydrostatic pressure, habituation, mild heat, and antimicrobials for inactivation of O157 and non-O157 serogroups of Shiga toxin-producing Escherichia coli. Various times at a pressure intensity level of 450 MPa were investigated at 4 and 45 °C with and without carvacrol, and caprylic acid before and after three-day aerobic habituation in blueberry juice. Experiments were conducted in three biologically independent repetitions each consist of two replications and were statistically analyzed as a randomized complete block design study using ANOVA followed by Tukey- and Dunnett’s-adjusted mean separations. Under the condition of this experiment, habituation of the microbial pathogen played an influential (p < 0.05) role on inactivation rate of the pathogen. As an example, O157 and non-O157 serogroups were reduced (p < 0.05) by 1.4 and 1.6 Log CFU/mL after a 450 MPa treatment at 4 °C for seven min, respectively, before habituation. The corresponding log reductions (p < 0.05) after three-day aerobic habituation were: 2.6, and 3.3, respectively at 4 °C. Carvacrol and caprylic acid addition both augmented the pressure-based decontamination efficacy. As an example, Escherichia coli O157 were reduced (p < 0.05) by 2.6 and 4.2 log CFU/mL after a seven-min treatment at 450 MPa without, and with presence of 0.5% carvacrol, respectively, at 4 °C.

Pectobacterium and Dickeya species, usually referred to as soft rot Enterobacteriaceae, are phytopathogenic genera of bacteria that cause soft rot and blackleg diseases and are responsible for significant yield losses in many crops across the globe. Diagnosis of soft rot disease is difficult through visual disease symptoms. Pathogen detection and identification methods based on cultural and morphological identification are time-consuming and not always reliable. A polymerase chain reaction (PCR)-based detection method with the species-specific primers is fast and reliable for detecting soft rot pathogens. We have developed a specific and sensitive detection system for some species of soft rot Pectobacteriaceae pathogens in the Pectobacterium and Dickeya genera based on the use of species-specific primers to amplify unique genomic segments. The specificities of primers were verified by PCR analysis of genomic DNA from 14 strains of Pectobacterium, 8 strains of Dickeya, and 6 strains of non-soft rot bacteria. This PCR assay provides a quick, simple, powerful, and reliable method for detection of soft rot bacteria.

Since the historic outbreak near Broad Street in London, which serves as cornerstone of modern epidemiology, infectious diseases spread in surface and sub-surface water has been a persisting public health challenge. The current study investigated persistence of wild-type and pressure-stressed Listeria monocytogenes, Escherichia coli O157:H7, and non-typhoidal Salmonella enterica serovars in surface water stored aerobically for up to 28 days at 5, 25, and 37 °C. Additionally, biofilm formation of wild-type and pressure-stressed non-typhoidal Salmonella serovars were monitored on surface of stainless steel and rubber coupons for 28 days at 25 and 37 °C. While L. monocytogenes exhibited a lower (p < 0.05) survival rate at 5 °C, relative to the two Gram-negative pathogens, at higher temperatures of 25 and 37 °C, all three pathogens exhibited similar (p ≥ 0.05) trends for survival in surface water. Both wild-type and pressure-stressed Salmonella serovars in the vast majority of tested times, temperatures, and surfaces exhibited comparable (p ≥ 0.05) persistence and biofilm formation capability. Our study thus indicates the occurrence of contamination could lead to prolonged survival of these microorganisms in low-nutrient environments and highlights the need for preventive measures such as those articulated under Produce Safety Rule of the U.S. Food Safety Modernization Act.

Objectives: Early identification of sepsis is critical, as delayed diagnosis significantly increases morbidity and mortality. We aimed to develop and validate AI/ML models for early sepsis prediction using structured electronic health record (EHR) data, waveform data, and a combination of both. Methods: We conducted a retrospective observational study using the AIM-AHEAD60 subset of the CHoRUS dataset. Adult patients (≥18 years) with a final diagnosis of sepsis were included. Structured EHR data (demographics, initial vital signs, laboratory results) and waveform data (continuous vital signs) from the first hour of hospital arrival were extracted. Three algorithms (i.e., XGBoost, LightGBM, and HistGB) were developed with a focus on maximizing the performance metric of recall. Other performance metrics were also assessed, including accuracy, precision, F1 score, and the area under the receiver operating characteristic curve (AUROC). Results: A total of 11,312 unique patients met the inclusion criteria, among whom 2245 individuals (19.85%) were diagnosed with sepsis at least once. Using structured EHR data alone, laboratory variables such as lactate and leukocyte count were most predictive. Waveform models identified respiratory rate, systolic blood pressure, and temperature trends in the first hour as key predictors. Combined models highlighted mean temperature and mean systolic blood pressure as top features. XGBoost achieved the highest AUROC (0.922) across all data configurations, with a recall above 80%, demonstrating robust performance despite substantial missing data. Conclusions: High-performing AI/ML models for early sepsis prediction can be developed from publicly available datasets using only first-hour clinical information. XGBoost models demonstrate strong potential for real-time clinical screening.