Bacteroides and related species: The keystone taxa of the human gut microbiota.

Microbial communities play a significant role in maintaining ecosystems in a healthy homeostasis. Presently, in the human gastrointestinal tract, there are certain taxonomic groups of importance, though there is no single species that plays a keystone role. Bacteroides spp. are known to be major players in the maintenance of eubiosis in the human gastrointestinal tract. Here we review the critical role that Bacteroides play in the human gut, their potential pathogenic role outside of the gut, and their various methods of adapting to the environment, with a focus on data for B. fragilis and B. thetaiotaomicron. Bacteroides are anaerobic non-sporing Gram negative organisms that are also resistant to bile acids, generally thriving in the gut and having a beneficial relationship with the host. While they are generally commensal organisms, some Bacteroides spp. can be opportunistic pathogens in scenarios of GI disease, trauma, cancer, or GI surgery, and cause infection, most commonly intra-abdominal infection. B. fragilis can develop antimicrobial resistance through multiple mechanisms in large part due to its plasticity and fluid genome. Bacteroidota (formerly, Bacteroidetes) have a very broad metabolic potential in the GI microbiota and can rapidly adapt their carbohydrate metabolism to the available nutrients. Gastrointestinal Bacteroidota species produce short-chain fatty acids such as succinate, acetate, butyrate, and occasionally propionate, as the major end-products, which have wide-ranging and many beneficial influences on the host. Bacteroidota, via bile acid metabolism, also play a role in in colonization-resistance of other organisms, including Clostridioides difficile, and maintenance of gut integrity.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

INTRODUCTION: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. METHODS AND RESULTS: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. CONCLUSION: The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Detection and classification of arrhythmia using an explainable deep learning model.

BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. METHODS: In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. RESULTS: During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12­lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991. CONCLUSION: Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

Prevention and treatment of recurrent Clostridioides difficile infection.

PURPOSE OF REVIEW: Clostridioides difficile infection (CDI) is a significant burden on the health system, especially due to high recurrence rates. Since the beginning of the CDI epidemic in early 2000s, many strategies for combatting recurrence have been explored, with moderate success so far. This review will focus on the most recent developments in recurrent CDI prevention and treatment. RECENT FINDINGS: There are two main mechanisms of CDI recurrence: alteration in microbiome and poor antibody response. Development of new antibiotics aims to minimize damage to the microbiome. Fecal transplant or other microbiome replacement therapies seek to replenish the missing elements in the microbiome. Fecal microbiota transplant is the most effective treatment for prevention of CDI recurrenceso far, but is difficult to standardize and regulate, leading to efforts to develop microbiome-derived therapeutics. A deficiency in developing antibodies to C. difficile toxins is another mechanism of recurrence. Active immunization using toxoid vaccines or passive immunization using mAbs address this aspect. SUMMARY: There are promising new treatments for recurrent CDI in development. Fecal microbiota transplant remains the most effective therapy for multiply recurrent CDI. New antibiotics, microbiome-derived therapeutics, and immunologic therapies are in development.

Innate Immune Response and Outcome of Clostridium difficile Infection Are Dependent on Fecal Bacterial Composition in the Aged Host.

Background: Clostridium difficile infection (CDI) is a serious threat for an aging population. Using an aged mouse model, we evaluated the effect of age and the roles of innate immunity and intestinal microbiota. Methods: Aged (18 months) and young (8 weeks) mice were infected with C difficile, and disease severity, immune response, and intestinal microbiome were compared. The same experiment was repeated with intestinal microbiota exchange between aged and young mice before infection. Results: Higher mortality was observed in aged mice with weaker neutrophilic mobilization in blood and intestinal tissue and depressed proinflammatory cytokines in early infection. Microbiota exchange improved survival and early immune response in aged mice. Microbiome analysis revealed that aged mice have significant deficiencies in Bacteroidetes phylum and, specifically, Bacteroides, Alistipes, and rc4-4 genera, which were replenished by cage switching. Conclusions: Microbiota-dependent alteration in innate immune response early on during infection may explain poor outcome in aged host with CDI.

Vancomycin Treatment Alters Humoral Immunity and Intestinal Microbiota in an Aged Mouse Model of Clostridium difficile Infection.

BACKGROUND: The elderly host is highly susceptible to severe disease and treatment failure in Clostridium difficile infection (CDI). We investigated how treatment with vancomycin in the aged host influences systemic and intestinal humoral responses and select intestinal microbiota. METHODS: Young (age, 2 months) and aged (age, 18 months) C57BL/6 mice were infected with VPI 10463 after exposure to broad-spectrum antibiotics. Vancomycin was given 24 hours after infection, and treatment was continued for 5 days. At select time points, specimens of serum and intestinal tissue and contents were collected for histopathologic analysis, to measure antibody levels and the pathogen burden, and to determine the presence and levels of select intestinal microbiota and C. difficile toxin. RESULTS: Levels of disease severity, relapse, and mortality were increased, and recovery from infection was slower in aged mice compared to young mice. Serum levels of immunoglobulin M, immunoglobulin A, and immunoglobulin G against C. difficile toxin A were depressed in aged mice, and vancomycin treatment reduced antibody responses in both age groups. While baseline levels of total bacterial load, Bacteroidetes, Firmicutes, and Enterobacteriaceae were mostly similar, aged mice had a significant change in the Firmicutes to Bacteroidetes ratio with vancomycin treatment. CONCLUSIONS: Vancomycin treatment decreases the systemic humoral response to CDI. Increased mortality from and recurrence of CDI in the aged host are associated with an impaired humoral response and a greater susceptibility to vancomycin-induced alteration of intestinal microbiota.

Treatment of Clostridium difficile infection using SQ641, a capuramycin analogue, increases post-treatment survival and improves clinical measures of disease in a murine model.

OBJECTIVES: Clostridium difficile infection (CDI) is a primary cause of antibiotic-associated diarrhoeal illness. Current therapies are insufficient as relapse rates following antibiotic treatment range from 25% for initial treatment to 60% for treatment of recurrence. In this study, we looked at the efficacy of SQ641 in a murine model of CDI. SQ641 is an analogue of capuramycin, a naturally occurring nucleoside-based compound produced by Streptomyces griseus. METHODS: In a series of experiments, C57BL/6 mice were treated with a cocktail of antibiotics and inoculated with C. difficile strain VPI10463. Animals were treated orally with SQ641 for 5 days at a dose range of 0.1-300 mg/kg/day, 20 mg/kg/day vancomycin or drug vehicle. Animals were monitored for disease severity, clostridial shedding and faecal toxin levels for 14 days post-infection. RESULTS: Five day treatment of CDI with SQ641 resulted in higher 14 day survival rates in mice compared with either vancomycin or vehicle alone. CDI survival rates were 100% (13 of 13) and 94% (32 of 34), respectively, in the 1 and 10 mg/kg/day SQ641 treatment groups, 37% (7 of 19) with vancomycin treatment at 20 mg/kg/day and 32% (14 of 44) in the vehicle-only control group. Secondary measures of efficacy, such as prevention of weight loss, decreased disease severity, decreased C. difficile shedding and decreased toxin in faeces, were observed with SQ641 and vancomycin treatment. CONCLUSIONS: SQ641 is effective for CDI treatment with prevention of relapse in the murine model of CDI.

Clinical impact of human coronaviruses 229E and OC43 infection in diverse adult populations.

BACKGROUND: The incidence and clinical impact of coronavirus (CoV) infection in elderly persons and those with underlying cardiopulmonary disease over a long duration is not well described. We determined the incidence and clinical impact of 229E and OC43 CoV in this population during 4 consecutive winters, and compared illnesses to influenza A, respiratory syncytial virus, and human metapneumovirus. METHODS: CoV 229E and OC43 were detected by reverse transcription polymerase chain reaction and serology in 4 adult populations under surveillance for acute respiratory illness during the winters of 1999-2003. Cohorts included healthy young adults, healthy elderly adults, high-risk adults with underlying cardiopulmonary disease, and a hospitalized group. RESULTS: Three hundred ninety-eight CoV infections were identified, with annual infection rates ranging from 2.8% to 26% in prospective cohorts, and prevalence ranging from 3.3% to 11.1% in the hospitalized cohort. The incidence of infections with each strain was similar, although asymptomatic infection and viral coinfection was significantly more common with 229E than OC43 infection. Although the incidence and clinical manifestations were similar for each strain, OC43-infected subjects tended to seek more medical care, as OC43 was twice as common as 229E among the hospitalized cohort. CONCLUSIONS: CoV infections in the elderly are frequent, likely causing substantial medical disease burden.

Fabrication of Substrate-Integrated Waveguide Using Micromachining of Photoetchable Glass Substrate for 5G Millimeter-Wave Applications.

A millimeter-wave substrate-integrated waveguide (SIW) was firstly demonstrated using the micromachining of photoetchable glass (PEG) for 5G applications. A PEG substrate was used as a dielectric material of the SIW, and its photoetchable properties were used to fabricate through glass via (TGV) holes. Instead of the conventional metallic through glass via (TGV) array structures that are typically used for the SIW, two continuous empty TGV holes with metallized sidewalls connecting the top metal layer to the bottom ground plane were used as waveguide walls. The proposed TGV walls were fabricated by using optical exposure, heat development and anisotropic HF (hydrofluoric acid) etching of the PEG substrate, followed by a metal sputtering technique. The SIW was fed by microstrip lines connected to the waveguide through tapered microstrip-to-waveguide transitions. The top metal layer, including these feedlines and transitions, was fabricated by selective metal sputtering through a silicon shadow mask, which was prefabricated by a silicon deep-reactive ion-etching (DRIE) technique. The developed PEG-based process provides a relatively simple, wafer-level manufacturing method to fabricate the SIW in a low-cost glass dielectric substrate, without the formation of individual of TGV holes, complex time-consuming TGV filling processes and repeated photolithographic steps. The fabricated SIW had a dimension of 6 × 10 × 0.42 mm3 and showed an average insertion loss of 2.53 ± 0.55 dB in the Ka-band frequency range from 26.5 GHz to 40 GHz, with a return loss better than 13.86 dB. The proposed process could be used not only for SIW-based devices, but also for various millimeter-wave applications where a glass substrate with TGV structures is required.

Alanyl-glutamine supplementation for Clostridioides difficile infection treatment (ACT): a double-blind randomised controlled trial study protocol.

INTRODUCTION: Clostridioides difficile is the leading cause of healthcare-associated infections in the USA, with an estimated 1 billion dollars in excess cost to the healthcare system annually. C. difficile infection (CDI) has high recurrence rate, up to 25% after first episode and up to 60% for succeeding episodes. Preliminary in vitro and in vivo studies indicate that alanyl-glutamine (AQ) may be beneficial in treating CDI by its effect on restoring intestinal integrity in the epithelial barrier, ameliorating inflammation and decreasing relapse. METHODS AND ANALYSIS: This study is a randomised, placebo-controlled, double-blind, phase II clinical trial. The trial is designed to determine optimal dose and safety of oral AQ at 4, 24 and 44 g doses administered daily for 10 days concurrent with standard treatment of non-severe or severe uncomplicated CDI in persons age 18 and older. The primary outcome of interest is CDI recurrence during 60 days post-treatment follow-up, with the secondary outcome of mortality during 60 days post-treatment follow-up. Exploratory analysis will be done to determine the impact of AQ supplementation on intestinal and systemic inflammation, as well as intestinal microbial and metabolic profiles. ETHICS AND DISSEMINATION: The study has received University of Virginia Institutional Review Board approval (HSR200046, Protocol v9, April 2023). Findings will be disseminated via conference presentations, lectures and peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT04305769.

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. METHODS: This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m2). RESULTS: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI. CONCLUSION: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.

Hospitalized Older Patients with Clostridioides difficile Infection Refractory to Conventional Antibiotic Therapy Benefit from Fecal Microbiota Transplant.

BACKGROUND: Options for Clostridioides difficile infection (CDI) refractory to conventional therapy are limited. Fecal microbiota transplant (FMT) is considered safe and effective treatment for recurrent CDI and could be a treatment option for refractory CDI. We investigated the efficacy and safety of FMT in hospitalized patients who were not responding to standard treatments for CDI. METHODS: Electronic medical records of patients who received FMT inpatient for refractory CDI were reviewed as part of quality improvement efforts to evaluate safety and efficacy of FMT in inpatient setting. RESULTS: Between July 2014 and December 2019, 9 patients (age 60-96) received FMT for CDI as inpatient for refractory or fulminant CDI. Most (7 of 9) of these patients had pseudomembranous colitis and underwent multiple FMTs (mean 2.15, range 1 to 3). Five patients had complete resolution and one patient had diarrhea that was C. difficile-negative. There was one recurrent CDI and two deaths, one of which may have been related to FMT or CDI. Compared to recurrent CDI at diagnosis, patients with refractory CDI had higher WBC and neutrophil counts, which decreased after FMT. The overall cure rate of FMT in refractory cases was 66.7%. CONCLUSIONS: This study shows moderate efficacy of FMT for treatment of refractory CDI although multiple FMT treatment may need to be administered in the presence of pseudomembranous colitis. Inpatient FMT may be an alternative strategy for managing refractory CDI in this population of patients who may not have any effective medical treatment available.

Teaching old mice new tricks: the utility of aged mouse models of C. difficile infection to study pathogenesis and rejuvenate immune response.

BACKGROUND: Clostridioides difficile is a serious problem for the aging population. Aged mouse model of C. difficile infection (CDI) has emerged as a valuable tool to evaluate the mechanism of aging in CDI. METHODS: We reviewed five published studies utilizing aged mice (7-28 months) for CDI model for findings that may advance our understanding of how aging influences outcome from CDI. RESULTS: Aged mouse models of CDI uniformly demonstrated more severe disease in the old compared to young mice. Diminished neutrophil recruitment to intestinal tissue in aged mice is the most consistent finding. Differences in innate and humoral immune responses were also observed. The effects of aging on the outcome of infection were reversed by pharmacologic or microbiota-targeted interventions. CONCLUSION: The aged mouse presents an important in vivo model to study CDI and elucidate the mechanisms underlying advanced age as an important risk factor for severe disease.

Deep-learning model for screening sepsis using electrocardiography.

BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). METHODS: This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. RESULTS: During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882-0.920) and 0.863 (0.846-0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877-0.936) and 0.899 (95% CI, 0.872-0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845-0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793-0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). CONCLUSIONS: The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm.

Aims: Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results: This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948-0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion: The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

Outcomes of a Multidisciplinary Clinic in Evaluating Recurrent Clostridioides difficile Infection Patients for Fecal Microbiota Transplant: A Retrospective Cohort Analysis.

Fecal microbiota transplantation (FMT) has been shown to be an effective treatment for recurrent Clostridioides difficile infections (rCDIs). We assessed the benefits of a multidisciplinary C. difficile clinic for screening FMT eligibility in patients with rCDI. Patients seen at the University of Virginia Complicated C. difficile Clinic (CCDC) underwent comprehensive evaluation for possible FMT. Patients were eligible for FMT if there was history of greater than two episodes of rCDI. Patients were evaluated for the outcome after evaluation in the clinic. A total of 113 patients were evaluated: 77 were eligible for FMT, of which 25 patients did not undergo FMT. The rate of recurrence at three months and all-cause mortality were 4.5% and 7% for patients who received FMT and 16.7% and 12.5% for eligible patients who did not receive FMT. There were 36 patients who were not eligible for FMT, with two or fewer recurrences and a recurrence rate of 8.8% and all-cause mortality of 6%. One in three patients screened for FMT had a nutritional deficiency diagnosed, with zinc deficiency being most common (20%). Additional diagnoses, including inflammatory bowel disease, were made during the evaluation. FMT is a highly effective treatment for rCDI, most notably in patients with multiple recurrences. A systematic approach for evaluating patients with rCDI helps identify patients who benefit most from FMT and those who have other conditions.

Collateral damage during antibiotic treatment of C. difficile infection in the aged host: Insights into why recurrent disease happens.

Clostridium difficile infection (CDI) is one of the most common causes of healthcare-associated infections but an even bigger problem for the aging population. Advanced age leads to higher incidence, higher mortality, and higher recurrences. In our study, recently published in the Journal of Infectious Diseases, we investigated the effect of aging on CDI using a mouse model. We were able to demonstrate that aging leads to worse clinical outcomes, as well as lead to changes in microbiota composition and lower antibody production against C. difficile toxin A, but not toxin B. An association between advanced age and lower antibody production against C. difficile is a new finding which would explain the effect of aging on CDI outcome. Vancomycin, an anti-C. difficile antibiotic, led to similar changes in antibody response, suggesting a connection between microbiome and antibody response in the context of aging, which would require a much more nuanced look at the treatment of CDI.

Older Is Not Wiser, Immunologically Speaking: Effect of Aging on Host Response to Clostridium difficile Infections.

Clostridium difficile infection (CDI) is the most common cause of antibiotic-associated diarrhea and a significant burden on the health care system. Aging has been identified in the literature as a risk factor for CDI as well as adverse outcome from CDI. Although this effect of advanced age on CDI could be partially explained by clinical factors associated with aging, biologic factors are important. Innate immune system, responsible for immediate response to acute infections, plays a major role in CDI pathogenesis. Impairment in function of innate immunity with aging, demonstrated in other infection models, may lead to worse outcome with CDI. C. difficile toxin-specific antibody response protects the host against initial and recurrent infections as shown in observational studies and clinical trial. Effect of aging on antibody response to CDI has not been demonstrated, but the results from vaccine studies in other infections suggest a negative effect on humoral immunity from aging. Although intestinal microbiota from healthy people confers resistance to CDI by preventing C. difficile colonization, changes in composition of microbiota with aging may affect that resistance and increase risk for CDI. There are also age-associated changes in physiology, especially of the gastrointestinal tract, that may play a role in CDI risk and outcomes. In this review, we will first discuss the epidemiology of CDI in the elderly people, then the alteration in innate immunity, humoral response, and microbiota that increases susceptibility to CDI and severe disease and lastly, the physiological and functional changes that may modify outcomes of infection.

Clostridium difficile Infection.

Clostridium difficile is an anaerobic, Gram-positive, spore-forming, toxin-secreting bacillus that has long been recognized to be the most common etiologic pathogen of antibiotic-associated diarrhea. C. difficile infection (CDI) is now the most common cause of health care-associated infections in the United States and accounts for 12% of these infections (Magill SS et al., N Engl J Med370:1198-1208, 2014). Among emerging pathogens of public health importance in the United States, CDI has the highest population-based incidence, estimated at 147 per 100,000 (Lessa FC et al., N Engl J Med372:825-834, 2015). In a report on antimicrobial resistance, C. difficile has been categorized by the Centers for Disease Control and Prevention as one of three "urgent" threats (http://www.cdc.gov/drugresistance/threat-report-2013/). Although C. difficile was first described in the late 1970s, the past decade has seen the emergence of hypertoxigenic strains that have caused increased morbidity and mortality worldwide. Pathogenic strains, host susceptibility, and other regional factors vary and may influence the clinical manifestation and approach to intervention. In this article, we describe the global epidemiology of CDI featuring the different strains in circulation outside of North America and Europe where strain NAP1/027/BI/III had originally gained prominence. The elderly population in health care settings has been disproportionately affected, but emergence of CDI in children and healthy young adults in community settings has, likewise, been reported. New approaches in management, including fecal microbiota transplantation, are discussed.