Prevalence and Clinical Implications of Nontuberculous Mycobacteria Isolation and Infection among Patients with Bronchiectasis in the Jerusalem Area.

BACKGROUND: Bronchiectasis is an obstructive chronic lung disease characterized by structural changes in large and small airways, namely permanent widening of bronchial lumen resulting in chronic inflammation and infection. Nontuberculous mycobacteria (NTM) are environmental mycobacteria that may cause human infection or colonization with over 150 species identified to date. Bronchiectasis with NTM colonization or infection is often encountered but with varying prevalence and unknown clinical or prognostic significance. OBJECTIVES: To find the prevalence of NTM among patients with bronchiectasis in the Jerusalem district. To assess whether there were clinical differences between patients with bronchiectasis who were isolated with NTM and those without. METHODS: In this retrospective observational research study, we reviewed all computerized medical charts of patients over 18 years of age, who were diagnosed with bronchiectasis at Hadassah Medical Centers in Jerusalem between 2012 and 2017. We assessed the prevalence of NTM pulmonary disease. To compare patients with and without NTM, we reviewed and analyzed clinical, radiological, and microbiological data of all NTM patients and a group of controls in a 4:1 ratio. RESULTS: Prevalence of NTM among bronchiectasis patients was 5.1%, slightly lower than previously reported in Israel. We did not find clinically or radiological significant differences in patients with NTM disease compared to controls. This result included a similar number of exacerbations, hospitalization rates, number of lobes involved, and pulmonary function tests. CONCLUSIONS: Bronchiectasis patients with isolation of Pseudomonas aeruginosa experienced more exacerbations than patients with other isolates, consistent with previous studies.

Low-Dose Colchicine Attenuates Sepsis-Induced Liver Injury: A Novel Method for Alleviating Systemic Inflammation.

Sepsis is a significant public health challenge. The immune system underlies the pathogenesis of the disease. The liver is both an active player and a target organ in sepsis. Targeting the gut immune system using low-dose colchicine is an attractive method for alleviating systemic inflammation in sepsis without inducing immunosuppression. The present study aimed to determine the use of low-dose colchicine in LPS-induced sepsis in mice. C67B mice were injected intraperitoneal with LPS to induce sepsis. The treatment group received 0.02 mg/kg colchicine daily by gavage. Short and extended models were performed, lasting 3 and 5 days, respectively. We followed the mice for biochemical markers of end-organ injury, blood counts, cytokine levels, and liver pathology and conducted proteomic studies on liver samples. Targeting the gut immune system using low-dose colchicine improved mice's well-being measured by the murine sepsis score. Treatment alleviated the liver injury in septic mice, manifested by a significant decrease in their liver enzyme levels, including ALT, AST, and LDH. Treatment exerted a trend to reduce creatinine levels. Low-dose colchicine improved liver pathology, reduced inflammation, and reduced the pro-inflammatory cytokine TNFα and IL1-ß levels. A liver proteomic analysis revealed low-dose colchicine down-regulated sepsis-related proteins, alpha-1 antitrypsin, and serine dehydratase. Targeting the gut immune system using low-dose colchicine attenuated liver injury in LPS-induced sepsis, reducing the pro-inflammatory cytokine levels. Low-dose colchicine provides a safe method for immunomodulation for multiple inflammatory disorders.

Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections.

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients' adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them.IMPORTANCE SECTIONThe paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.Key messagesAntimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents.We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.

Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects.

Opioids remain an essential part of the treatment of chronic pain. However, their use and increasing rates of misuse are associated with high morbidity and mortality. The development of tolerance to opioids and analgesics further complicates dosing and the need to reduce side effects. First-generation digital systems were developed to improve analgesics but are not always capable of making clinically relevant associations and do not necessarily lead to better clinical efficacy. A lack of improved clinical outcomes makes these systems less applicable for adoption by clinicians and patients. There is a need to enhance the therapeutic regimens of opioids. In the present paper, we present the use of a digital analgesic that consists of an analgesic administered under the control of a second-generation artificial intelligence system. Second-generation systems focus on improved patient outcomes measured based on clinical response and reduced side effects in a single subject. The algorithm regulates the administration of analgesics in a personalized manner. The digital analgesic provides advantages for both users and providers. The system enables dose optimization, improving effectiveness, and minimizing side effects while increasing adherence to beneficial therapeutic regimens. The algorithm improves the clinicians' experience and assists them in managing chronic pain. The system reduces the financial burden on healthcare providers by lowering opioid-related morbidity and provides a market disruptor for pharma companies.

Right-sided endocarditis caused by polyclonal Staphylococcus aureus infection.

We present a case of bacterial endocarditis with both methicillin-sensitive and methicillin-resistant Staphylococcus aureus, which based on typing, originated from two distinct clones. Such a case may be misinterpreted by microbiology lab automation to be a monoclonal multi-drug resistant Staphylococcus aureus, while simple microbiology techniques will instantly reveal distinct clonality.

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases.

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

Establishment of an Individualized Chronotherapy, Autonomic Nervous System, and Variability-Based Dynamic Platform for Overcoming the Loss of Response to Analgesics.

BACKGROUND: Control of chronic pain and mainly the partial or complete loss of response to analgesics is a major unmet need. Multiple mechanisms underline the development of tolerance to analgesics in general and specifically to opioids. The autonomic nervous system (ANS) plays a role in the development of analgesic tolerance and chronobiology. OBJECTIVES: To review the mechanisms associated with the development of nonresponsiveness to analgesics. STUDY DESIGN: Literature review. SETTING: The review is followed by a description of a new method for overcoming resistance and improving the response to analgesics. METHODS: Conducted a detailed review of the relevant studies describing the mechanisms that underlie tolerance to pain medications, and the potential roles of the ANS and chronobiology in the development of drug resistance. RESULTS: The autonomic balance is reflected by heart rate variability, an example of a fundamental variability that characterizes biological systems. Chronotherapy, which is based on the circadian rhythm, can improve the efficacy and reduce the toxicity of chronic medications. In this article, we present the establishment of an individualized variability- and chronobiology-based therapy for overcoming the compensatory mechanisms associated with a loss of response to analgesics. We describe the premise of implementing personalized signatures associated with the ANS, and chronobiology, as well as with the pathophysiology of pain for establishing an adaptive model that could improve the efficacy of opioids, in a highly dynamic system. LIMITATIONS: The studies presented were selected based on their relevance to the subject. CONCLUSIONS: The described variability-based system may ensure prolonged effects of analgesics while reducing the toxicity associated with increasing dosages.

Predictors of responses to clinic-based childhood obesity care.

BACKGROUND/OBJECTIVE: Lifestyle modification is the therapy of choice for childhood obesity, yet the response rate is variable and may be affected by genetic factors. We aimed to investigate predictors of poor response to lifestyle modification obesity treatment in children. METHODS: A prospective cohort study of 434 youths (64.5% females) between 4 and 20 years of age undergoing a standard care of lifestyle modification obesity management for 35.9 ± 20.8 months at Yale Childhood Obesity Clinic, USA. The primary outcome was a "poor response," defined as the quintile with the largest increase in BMI Z-score over time. The secondary outcome was the endpoint BMI Z-score. Covariates investigated were sex, baseline pubertal status and degree of obesity, race, biochemical profile, and family history of overweight. A subsample (n = 214) had FTO genotyping (SNP rs8050136) tested. RESULTS: Males (hazard ratio [HR] = 5.35, 95% confidence interval [CI] [3.32-8.61], P < 0.0001) and pubertal adolescents (HR = 2.78, [1.40-5.50], P = 0.003) compared to prepubertal children were more prone to respond poorly. Baseline degree of obesity was associated with relative protection from responding poorly (HR per BMI Z-score unit = 0.32, [0.17-0.61], P = 0.0006). Carriers of the FTO obesity-predisposing allele (AA genotype) were protected from responding poorly compared to non-carriers (CC genotype) (HR = 0.33, [0.12-0.88], P = 0.028). CONCLUSIONS: Boys and pubertal adolescents are more prone to respond poorly to standard obesity care while those with greater baseline degree of obesity and carriers of the FTO obesity-predisposing allele are not.