Allelic variations in the chpG effector gene within Clavibacter michiganensis populations determine pathogen host range.

Plant pathogenic bacteria often have a narrow host range, which can vary among different isolates within a population. Here, we investigated the host range of the tomato pathogen Clavibacter michiganensis (Cm). We determined the genome sequences of 40 tomato Cm isolates and screened them for pathogenicity on tomato and eggplant. Our screen revealed that out of the tested isolates, five were unable to cause disease on any of the hosts, 33 were exclusively pathogenic on tomato, and two were capable of infecting both tomato and eggplant. Through comparative genomic analyses, we identified that the five non-pathogenic isolates lacked the chp/tomA pathogenicity island, which has previously been associated with virulence in tomato. In addition, we found that the two eggplant-pathogenic isolates encode a unique allelic variant of the putative serine hydrolase chpG (chpGC), an effector that is recognized in eggplant. Introduction of chpGC into a chpG inactivation mutant in the eggplant-non-pathogenic strain Cm101, failed to complement the mutant, which retained its ability to cause disease in eggplant and failed to elicit hypersensitive response (HR). Conversely, introduction of the chpG variant from Cm101 into an eggplant pathogenic Cm isolate (C48), eliminated its pathogenicity on eggplant, and enabled C48 to elicit HR. Our study demonstrates that allelic variation in the chpG effector gene is a key determinant of host range plasticity within Cm populations.

The Global Transcription Regulator XooClp Governs Type IV Pili System-Mediated Bacterial Virulence by Directly Binding to TFP-Chp Promoters to Coordinate Virulence Associated Functions.

Type IV pili (TFP) play a crucial role in the sensing of the external environment for several bacteria. This surface sensing is essential for the lifestyle transitions of several bacteria and involvement in pathogenesis. However, the precise mechanisms underlying TFP's integration of environmental cues, particularly in regulating the TFP-Chp system and its effects on Xanthomonas physiology, social behavior, and virulence, remain poorly understood. In this study, we focused on investigating Clp, a global transcriptional regulator similar to CRP-like proteins, in Xanthomonas oryzae pv. oryzae, a plant pathogen. Our findings reveal that Clp integrates environmental cues detected through diffusible signaling factor (DSF) quorum sensing into the TFP-Chp regulatory system. It accomplishes this by directly binding to TFP-Chp promoters in conjunction with intracellular levels of cyclic-di-GMP, a ubiquitous bacterial second messenger, thereby controlling TFP expression. Moreover, Clp-mediated regulation is involved in regulating several cellular processes, including the production of virulence-associated functions. Collectively, these processes contribute to host colonization and disease initiation. Our study elucidates the intricate regulatory network encompassing Clp, environmental cues, and the TFP-Chp system, providing insights into the molecular mechanisms that drive bacterial virulence in Xanthomonas spp. These findings offer valuable knowledge regarding Xanthomonas pathogenicity and present new avenues for innovative strategies aimed at combating plant diseases caused by these bacteria. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

Wellness and burnout in cardiac surgery: not black and white.

PURPOSE OF REVIEW: Despite a growing emphasis on burnout in medicine, there remains a paucity of data in cardiac surgery. Herein, we summarize recent data on cardiac surgeon well being and identify factors for consideration in future burnout inquiries and management. RECENT FINDINGS: Overall, 70-90% of cardiothoracic surgeons report job satisfaction in the United States. However, 35-60% still endorse burnout symptoms, and the specialty reports some of the highest rates of depression (35-40%) and suicidal ideation (7%). Such negative experiences are greater among early-stage and female surgeons and may be addressed through targeted, program-specific wellness policies. Canada's single-payer healthcare system might exacerbate surgeon burnout due to lower financial compensation and job autonomy. SUMMARY: Cardiothoracic surgeons appear simultaneously burnt out and professionally fulfilled. They report a high incidence of depression and clock in the most hours, yet the majority would choose this specialty again. These findings reveal a more nuanced state of well being than previously appreciated and speak to ambiguities in how burnout is conceived and measured. A broader examination across surgical and social contexts highlights the hierarchical nature of burnout factors and potential ways forward. Collectively, these insights can inform assessments of burnout in Canadian cardiac surgery that remain absent to date.

Obesity/cardiometabolic phenotype of heart failure with preserved ejection fraction: mechanisms to recent trials.

PURPOSE OF REVIEW: Heart failure with preserved ejection fraction (HFpEF) is a leading and growing cause of morbidity and mortality globally. Of the various phenotypes identified, the obesity (or cardiometabolic) phenotype appears to be most common. The purpose of this review is to provide the clinician with an abridged understanding of recent developments that have elucidated obesity/visceral adiposity as a central mechanism linking inflammation/immune dysregulation to the development of the HFpEF syndrome. Recent clinical trials examining the efficacy of pharmacological treatments that target obesity in HFpEF will also be discussed. RECENT FINDINGS: Recent data indicate that visceral adiposity and insulin resistance in HFpEF serve as key mechanisms driving inflammation and immune dysregulation, which play a critical role in the development of cardiac stiffness, diastolic dysfunction and fibrosis in HFpEF. In obesity, alterations in macrophage polarization, changes in innate and adaptive immune systems and altered myocardial energetics promote metabolic inflammation in HFpEF. Finally, emerging data suggest that inflammatory biomarkers, specifically, IL-6, may provide useful information about HFpEF severity and symptom burden in obesity. SUMMARY: The obesity phenotype of HFpEF is seen in upward of 80% with HFpEF. Obesity is not just a bystander, but plays an essential role in the pathobiology and clinical course of HFpEF. Targeting overweight/obesity in HFpEF with GLP-1 receptor agonists holds promise in these patients.

2023 Update on equity, diversity, and inclusion in Canadian cardiac surgery.

PURPOSE OF REVIEW: Despite efforts to diversify the medical field, cardiac surgery remains amongst the least diverse specialties. Specifically, the percentage of women and racial minorities has remained low in past few decades. This may impact prospective trainee recruitment and surgical care. This paper highlights recent efforts that aim to promote diversity and inclusion of the Canadian cardiac surgical workforce. RECENT FINDINGS: Formal programs have been established to support students at different stages of training. In 2022, the Canadian Society for Cardiac Surgery has released an equity, diversity, and inclusion statement to summarize the current state and the strategic goals to accomplish a more just working environment. At the local level, the University of Toronto Next Surgeon high school pilot program, provided low-income, women, and racial minority students mentorship and experiential exposure to our field. Also, the University of Toronto, scholarships funded summer research with cardiac surgeons for women, as well as Black and Indigenous medical students. SUMMARY: Tangible efforts that target high school, undergraduate, and medical students are underway to promote equity and diversity of cardiac surgeons in Canada. Future studies that evaluate the gaps and identify bottlenecks could better guide interventions at institutions across the country.

"The impact of orthognathic surgery on articulation proficiency and speech intelligibility in skeletal Class III malocclusion: 18 months follow up".

Introduction: Orthognathic surgery results in the positional change of the maxilla and mandible that may affect speech. The present study evaluated the effect of combined maxillary advancement and mandibular setback surgery on articulation proficiency and speech intelligibility in patients with non-syndromic skeletal Class III malocclusion. Methods: In this prospective study, twenty-five patients with skeletal class III malocclusion and consecutively treated with Lefort-1 maxillary advancement and mandibular setback (BSSO) orthognathic surgery were included in this study. The speech sample was recorded with a digital audio tape recorder one day before surgery and at 3, 6, 9, 12 and 18 months after surgery. Three qualified and experienced speech and language pathologists evaluated articulation errors and intelligibility of speech samples. Repeated One-way analysis of variance was used to compare articulation proficiency and speech intelligibility at different time intervals. Results: The substitution, omission, distortion and addition errors showed no significant changes at 3 months and 6 months. The total articulation errors decreased to zero at 9 months and no significant increase was observed till 18 months (P < 0.05). Speech intelligibility showed statistically non-significant improvement at any time interval. Cephalometric skeletal parameters SNA and N l A°. were significantly correlated with addition and total articulation errors at 18 months follow up. Conclusions: The ortho-surgical treatment improves speech (decreases. articulation errors) in most of the patients usually 6-9 months post-surgery. Speech intelligibility is not affected by bimaxillary orthognathic surgery in skeletal class III patients. The articulation errors were correlated to changes in position of maxilla.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Vascular Regenerative Cell Deficiencies in South Asian Adults.

BACKGROUND: South Asian individuals shoulder a disproportionate burden of cardiometabolic diseases. OBJECTIVES: The purpose of this study was to determine if vascular regenerative cell content varies significantly between South Asian and White European people. METHODS: Between January 2022 and January 2023, 60 South Asian and 60 White European adults with either documented cardiovascular disease or established diabetes with ≥1 other cardiovascular risk factor were prospectively enrolled. Vascular regenerative cell content in venous blood was enumerated using a flow cytometry assay that is based on high aldehyde dehydrogenase (ALDHhi) activity and cell surface marker phenotyping. The primary outcome was the difference in frequency of circulating ALDHhi progenitor cells, monocytes, and granulocytes between the 2 groups. RESULTS: Compared with White European participants, those of South Asian ethnicity were younger (69 ± 10 years vs 66 ± 9 years; P < 0.05), had lower weight (88 ± 19 kg vs 75 ± 13 kg; P < 0.001), and exhibited a greater prevalence of type 2 diabetes (62% vs 92%). South Asian individuals had markedly lower circulating frequencies of pro-angiogenic ALDHhiSSClowCD133+ progenitor cells (P < 0.001) and ALDHhiSSCmidCD14+CD163+ monocytes with vessel-reparative capacity (P < 0.001), as well as proportionally more ALDHhi progenitor cells with high reactive oxygen species content (P < 0.05). After correction for sex, age, body mass index, and glycated hemoglobin, South Asian ethnicity was independently associated with lower ALDHhiSSClowCD133+ cell count. CONCLUSIONS: South Asian people with cardiometabolic disease had less vascular regenerative and reparative cells suggesting compromised vessel repair capabilities that may contribute to the excess vascular risk in this population. (The Role of South Asian vs European Origins on Circulating Regenerative Cell Exhaustion [ORIGINS-RCE]; NCT05253521).