Bidirectional regulation between AP-1 and SUMOylation pathway genes modulates inflammatory signaling during Salmonella infection.

Post-translational modifications (PTMs), such as SUMOylation, are known to modulate fundamental processes of a cell. Infectious agents such as Salmonella Typhimurium (STm), which causes gastroenteritis, utilize the PTM mechanism SUMOylation to hijack the host cell. STm suppresses host SUMO pathway genes UBC9 (also known as UBE2I) and PIAS1 to perturb SUMOylation for an efficient infection. In the present study, the regulation of SUMO pathway genes during STm infection was investigated. A direct binding of c-Fos (encoded by FOS), a component of activator protein-1 (AP-1), to promoters of both UBC9 and PIAS1 was observed. Experimental perturbation of c-Fos led to changes in the expression of both UBC9 and PIAS1. STm infection of fibroblasts with SUMOylation-deficient c-Fos (c-FOS-KOSUMO-def-FOS) resulted in uncontrolled activation of target genes, leading to massive immune activation. Infection of c-FOS-KOSUMO-def-FOS cells favored STm replication, indicating misdirected immune mechanisms. Finally, chromatin immunoprecipitation assays confirmed a context-dependent differential binding and release of AP-1 to and from target genes due to its phosphorylation and SUMOylation, respectively. Overall, our data point towards the existence of a bidirectional cross-talk between c-Fos and the SUMO pathway and highlight their importance in AP-1 function in STm infection and beyond. This article has an associated First Person interview with the first author of the paper.

Instant detection of extended-spectrum ß-lactamase-producing bacteria from the urine of patients using infrared spectroscopy combined with machine learning.

Antibiotics are considered the most effective treatment against bacterial infections. However, most bacteria have already developed resistance to a broad spectrum of commonly used antibiotics, mainly due to their uncontrolled use. Extended-spectrum beta-lactamase (ESBL)-producing bacteria are an essential class of multidrug-resistant (MDR) bacteria. It is of extreme urgency to develop a method that can detect ESBL-producing bacteria rapidly for the effective treatment of patients with bacterial infectious diseases. Fourier transform infrared (FTIR) microscopy is a sensitive method that can rapidly detect cellular molecular changes. In this study, we examined the potential of FTIR spectroscopy-based machine learning algorithms for the rapid detection of ESBL-producing bacteria obtained directly from a patient's urine. Using 591 ESBL-producing and 1658 non-ESBL-producing samples of Escherichia coli (E. coli) and Klebsiella pneumoniae, our results show that the FTIR spectroscopy-based machine learning approach can identify ESBL-producing bacteria within 40 minutes from receiving a patient's urine sample, with a success rate of 80%.

Recent Developments in Colorimetric and Fluorometric Detection Methods of Trivalent Metal Cations (Al3+, Fe3+ and Cr3+) Using Schiff Base Probes: At a Glance.

This review basically concerned with the application of different Schiff bases (SB) based fluorimetric (turn-off and turn-on) and colorimetric chemosensors for the detection of heavy metal cations particularly Al(III), Fe(III), and Cr(III) ions. Chemosensors based on Schiff bases have exhibited outstanding performance in the detection of different metal cations due to their facile and in-expensive synthesis, and their excellent coordination ability with almost all metal cations and stabilize them in different oxidation states. Moreover, Schiff bases have also been used as antifungal, anticancer, analgesic, anti-inflammatory, antibacterial, antiviral, antioxidant, and antimalarial etc. The Schiff base also can be used as an intermediate for the formation of various heterocyclic compounds. In this review, we have focused on the research work performed on the development of chemosensors (colorimetric and fluorometric) for rapid detection of trivalent metal cations particularly Al(III), Fe(III), and Cr(III) ions using Schiff base as a ligand during 2020-2022.

Quick Detection of Proteus and Pseudomonas in Patients' Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning.

Bacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and determination of its antibiotic susceptibility are crucial for optimal treatment and can save lives in many cases. Classical methods for determining bacterial susceptibility take at least 48 h, leading physicians to resort to empirical antibiotic treatment based on their experience. This random and excessive use of antibiotics is one of the most significant drivers of the development of multidrug-resistant (MDR) bacteria, posing a severe threat to global healthcare. To address these challenges, considerable efforts are underway to reduce the testing time of taxonomic classification of the infecting bacterium at the species level and its antibiotic susceptibility determination. Infrared spectroscopy is considered a rapid and reliable method for detecting minor molecular changes in cells. Thus, the main goal of this study was the use of infrared spectroscopy to shorten the identification and the susceptibility testing time of Proteus mirabilis and Pseudomonas aeruginosa from 48 h to approximately 40 min, directly from patients' urine samples. It was possible to identify the Proteus mirabilis and Pseudomonas aeruginosa species with 99% accuracy and, simultaneously, to determine their susceptibility to different antibiotics with an accuracy exceeding 80%.

IoT-based disease prediction using machine learning.

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.

Culture-independent susceptibility determination of E. coli isolated directly from patients' urine using FTIR and machine-learning.

One of the most common human bacterial infections is the urinary tract infection (UTI). The main cause of UTI is Escherichia (E.) coli bacteria (∼75%). Because most of the bacteria are resistant to many antibiotics as a result of their indiscriminate overuse, it is extremely important, for effective treatment, to identify the infecting bacteria and to determine, as quickly as possible, their susceptibility to antibiotics. Classical methods require at least 48 hours for determining bacterial susceptibility. In this study, 1798 E. coli isolates from different UTIs were isolated directly from patients' urine, measured by Fourier transform infrared (FTIR) microscopy and analyzed by machine learning algorithms for simultaneous identification and susceptibility determination within 40 minutes since receiving the urine samples. Our results show that it is possible to identify the bacteria at the species level with an accuracy of ∼95% and to determine their susceptibility to different antibiotics with an accuracy ranging from 75% to 83%.

Understanding Multi-stakeholder Complexity & Developing a Causal Recipe (fsQCA) for achieving Sustainable Ecotourism.

Ecotourism offers several economic, environmental, and cultural benefits; however, even after all these years, achieving ecotourism sustainability is still complex because of multiple stakeholders with diversified interests and influence. This study focused on the multiple stakeholders' complexity and management for achieving sustainable ecotourism in Penang Hill in Malaysia. Understanding the existence of multiple stakeholders with varying interests and their respective power of influence is critical for a tourism destination to be sustainable. This study aimed to create a multi-stakeholder management framework and understand stakeholder management's mediating role toward ecotourism sustainability in Penang Hill. Data were collected from Penang Hill key stakeholders and analyzed using SmartPLS and fuzzy-set Qualitative Comparative Analysis (fsQCA). The findings revealed that stakeholder management plays a significant mediating role in achieving ecotourism sustainability. Stakeholders' interests and their level of influence should be understood to develop engagement, empowerment, and monitoring strategies for managing stakeholders. Thus, the study contributes by validating the results through symmetric and asymmetric techniques, offering solutions to the emerging issues during the Covid-19 pandemic, and recommending policy changes. Lastly, the study also extends prior literature by displaying the mediating role played by stakeholder management on ecotourism sustainability, comparing indirect and total effects on stakeholder management support for achieving sustainable ecotourism in Penang Hill.

Determination of Klebsiella pneumoniae Susceptibility to Antibiotics Using Infrared Microscopy.

Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria associated with human infections, resulting in high mortality and morbidity. We obtained 1190 K. pneumoniae isolates from different patients with urinary tract infections. The isolates were measured to determine their susceptibility regarding nine specific antibiotics. This study's primary goal is to evaluate the potential of infrared spectroscopy in tandem with machine learning to assess the susceptibility of K. pneumoniae within approximately 20 min following the first culture. Our results confirm that it was possible to classify the isolates into sensitive and resistant with a success rate higher than 80% for the tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful method for a K. pneumoniae susceptibility test.

Rapid detection of Klebsiella pneumoniae producing extended spectrum ß lactamase enzymes by infrared microspectroscopy and machine learning algorithms.

Antimicrobial drugs have played an indispensable role in decreasing morbidity and mortality associated with infectious diseases. However, the resistance of bacteria to a broad spectrum of commonly-used antibiotics has grown to the point of being a global health-care problem. One of the most important classes of multi-drug resistant bacteria is Extended Spectrum Beta-Lactamase-producing (ESBL+) bacteria. This increase in bacterial resistance to antibiotics is mainly due to the long time (about 48 h) that it takes to obtain lab results of detecting ESBL-producing bacteria. Thus, rapid detection of ESBL+ bacteria is highly important for efficient treatment of bacterial infections. In this study, we evaluated the potential of infrared microspectroscopy in tandem with machine learning algorithms for rapid detection of ESBL-producing Klebsiella pneumoniae (K. pneumoniae) obtained from samples of patients with urinary tract infections. 285 ESBL+ and 365 ESBL-K. pneumoniae samples, gathered from cultured colonies, were examined. Our results show that it is possible to determine that K. pneumoniae is ESBL+ with ∼89% accuracy, ∼88% sensitivity and ∼89% specificity, in a time span of ∼20 minutes following the initial culture.

Correction: Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms.

Correction for 'Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms' by Adam H. Agbaria et al., Analyst, 2020, DOI: 10.1039/D0AN00752H.

Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms.

Physicians diagnose subjectively the etiology of inaccessible infections where sampling is not feasible (such as, pneumonia, sinusitis, cholecystitis, peritonitis), as bacterial or viral. The diagnosis is based on their experience with some medical markers like blood counts and medical symptoms since it is harder to obtain swabs and reliable laboratory results for most cases. In this study, infrared spectroscopy with machine learning algorithms was used for the rapid and objective diagnosis of the etiology of inaccessible infections and enables an assessment of the error for the subjective diagnosis of the etiology of these infections by physicians. Our approach allows for diagnoses of the etiology of both accessible and inaccessible infections as based on an analysis of the innate immune system response through infrared spectroscopy measurements of white blood cell (WBC) samples. In the present study, we examined 343 individuals involving 113 controls, 89 inaccessible bacterial infections, 54 accessible bacterial infections, 60 inaccessible viral infections, and 27 accessible viral infections. Using our approach, the results show that it is possible to differentiate between controls and infections (combined bacterial and viral) with 95% accuracy, and enabling the diagnosis of the etiology of accessible infections as bacterial or viral with >94% sensitivity and > 90% specificity within one hour after the collection of the blood sample with error rate <6%. Based on our approach, the error rate of the physicians' subjective diagnosis of the etiology of inaccessible infections was found to be >23%.

Detection of Extended-Spectrum ß-Lactamase-Producing Escherichia coli Using Infrared Microscopy and Machine-Learning Algorithms.

The spread of multidrug resistant bacteria has become a global concern. One of the most important and emergent classes of multidrug-resistant bacteria is extended-spectrum ß-lactamase-producing bacteria (ESBL-positive = ESBL+). Due to widespread and continuous evolution of ESBL-producing bacteria, they become increasingly resistant to many of the commonly used antibiotics, leading to an increase in the mortality associated with resulting infections. Timely detection of ESBL-producing bacteria and rapid determination of their susceptibility to appropriate antibiotics can reduce the spread of these bacteria and the consequent complications. Routine methods used for the detection of ESBL-producing bacteria are time-consuming, requiring at least 48 h to obtain results. In this study, we evaluated the potential of infrared spectroscopic microscopy, combined with multivariate analysis for rapid detection of ESBL-producing Escherichia coli ( E. coli) isolated from urinary-tract infection (UTI) samples. Our measurements were conducted on 837 samples of uropathogenic E. coli (UPEC), including 268 ESBL+ and 569 ESBL-negative (ESBL-) samples. All samples were obtained from bacterial colonies after 24 h culture (first culture) from midstream patients' urine. Our results revealed that it is possible to detect ESBL-producing bacteria, with a 97% success rate, 99% sensitivity, and 94% specificity for the tested samples, in a time span of few minutes following the first culture.

To what extent is the variation in cardiac rehabilitation quality associated with patient characteristics?

BACKGROUND: Huge variability in quality of service delivery of cardiac rehabilitation (CR) in the UK. This study aimed to ascertain whether the variation in quality of CR delivery is associated with participants' characteristics. METHODS: Individual patient data from 1 April 2013 to 31 March 2014 were collected electronically from the UK's National Audit of Cardiac Rehabilitation database. Quality of CR delivery is categorised as low, middle, and high based on six service-level criteria. The study included a range of patient variables: patient demographics, cardiovascular risk factors, comorbidities, physical and psychosocial health measures, and index of multiple deprivation. RESULTS: The chance that a CR patient with more comorbidities attended a high-quality programme was 2.13 and 1.85 times higher than the chance that the same patient attended a low- or middle-quality programme, respectively. Patients who participated in high-quality CR programmes tended to be at high risk (e.g. increased waist size and high blood pressure); high BMI, low physical activity levels and high Hospital Anxiety and Depression Scale scores; and were more likely to be smokers, and be in more socially deprived groups than patients in low-quality programmes. CONCLUSIONS: These findings show that the quality of CR delivery can be improved and meet national standards by serving a more multi-morbid population which is important for patients, health providers and commissioners of healthcare. In order for low-quality programmes to meet clinical standards, CR services need to be more inclusive in respect of patients' characteristics identified in the study. Evaluation and dissemination of information about the populations served by CR programmes may help low-quality programmes to be more inclusive.

Differential Diagnosis of the Etiologies of Bacterial and Viral Infections Using Infrared Microscopy of Peripheral Human Blood Samples and Multivariate Analysis.

Human viral and bacterial infections are responsible for a variety of diseases that are still the main causes of death and economic burden for society across the globe. Despite the different responses of the immune system to these infections, some of them have similar symptoms, such as fever, sneezing, inflammation, vomiting, diarrhea, and fatigue. Thus, physicians usually encounter difficulties in distinguishing between viral and bacterial infections on the basis of these symptoms. Rapid identification of the etiology of infection is highly important for effective treatment and can save lives in some cases. The current methods used for the identification of the nature of the infection are mainly based on growing the infective agent in culture, which is a time-consuming (over 24 h) and usually expensive process. The main objective of this study was to evaluate the potential of the mid-infrared spectroscopic method for rapid and reliable identification of bacterial and viral infections based on simple peripheral blood samples. For this purpose, white blood cells (WBCs) and plasma were isolated from the peripheral blood samples of patients with confirmed viral or bacterial infections. The obtained spectra were analyzed by multivariate analysis: principle component analysis (PCA) followed by linear discriminant analysis (LDA), to identify the infectious agent type as bacterial or viral in a time span of about 1 h after the collection of the blood sample. Our preliminary results showed that it is possible to determine the infectious agent with high success rates of 82% for sensitivity and 80% for specificity, based on the WBC data.

Enabling profound hearing impaired children to articulate words using lip-reading through software application.

OBJECTIVE: To mitigate the communication barriers of profound hearing-impaired children by enabling their word articulation ability. METHODS: This pre-experimental pilot study was conducted from September 2016 to March 2017 at the National Special Education Centre for Hearing Impaired Children, Islamabad, Pakistan, and comprised deaf children of both genders aged 5-8 years. A specially designed software application for lip-reading was employed to help the subjects articulate words. Each participant received 125 lip-reading sessions using the application. Evaluation was performed in five steps after every 25 individual sessions by a sign-language teacher, a speech therapist and family members of the individual concerned. SPSS 23 was used for data analysis. RESULTS: Of the 20 children, 10(50%) each were boys and girls. All participants reported an increased performance in articulating words with every passing session. The median score on the performance of children increased from first assessment to the last (p<0.05). CONCLUSIONS: The articulation of words by the profound hearing-impaired children after experimentation was usually comprehensible for an inexperienced or a lay listener.

Using Infrared Spectroscopy and Multivariate Analysis to Detect Antibiotics' Resistant Escherichia coli Bacteria.

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings and, sometimes, can save lives. Currently classical procedures require at least 48 h for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility to specific antibiotics based on the IR spectra of the bacteria. IR spectroscopy was conducted on bacterial colonies, obtained after 24 h culture from patients' samples. An IR microscope was utilized, and a computational classification method was developed to analyze the IR spectra by novel pattern-recognition and statistical tools, to determine E. coli susceptibility within a few minutes to different antibiotics, gentamicin, ceftazidime, nitrofurantoin, nalidixic acid, ofloxacin. Our results show that it was possible to classify the tested bacteria into sensitive and resistant types, with success rates as high as 85% for a number of examined antibiotics. These promising results open the potential of this technique for faster determination of bacterial susceptibility to certain antibiotics.

Detection of antibiotic resistant Escherichia Coli bacteria using infrared microscopy and advanced multivariate analysis.

Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involved in many diseases, and antibiotics have been the most effective treatment for them. It is essential to treat an infection with an antibiotic to which the infecting bacteria is sensitive; otherwise, the treatment is not effective and may lead to life-threatening progression of disease. Classical microbiology methods that are used for determination of bacterial susceptibility to antibiotics are time consuming, accounting for problematic delays in the administration of appropriate drugs. Infrared-absorption microscopy is a sensitive and rapid method, enabling the acquisition of biochemical information from cells at the molecular level. The combination of Fourier transform infrared (FTIR) microscopy with new statistical classification methods for spectral analysis has become a powerful technique, with the ability to detect structural molecular changes associated with resistivity of bacteria to antibiotics. It was possible to differentiate between isolates of Escherichia (E.) coli that were sensitive or resistant to different antibiotics with good accuracy. The objective computational classifier, based on infrared absorption spectra, is highly sensitive to the subtle infrared spectral changes that correlate with molecular changes associated with resistivity. These changes enable differentiating between the resistant and sensitive E. coli isolates within a few minutes, following the initial culture. This study provides proof-of-concept evidence for the translational potential of this spectroscopic technique in the clinical management of bacterial infections, by characterizing and classifying antibiotic resistance in a much shorter time than possible with current standard laboratory methods.

Multi-modal LSTM network for anomaly prediction in piston engine aircraft.

An aircraft is a highly intricate system that features numerous subsystems, assemblies, and individual components for which regular maintenance is inevitable. The operational efficiency of an aircraft can be maximised, and its maintenance needs can be reduced using an effective yet automatic AI-based health monitoring systems which are more efficient as compared to designing and constructing expensive and harder to operate engine testbeds. It has been observed that aircraft engine anomalies such as undergoing flameouts can occur due to the rapid change in the temperature of the engine. Engine oil temperature and cylinder head temperature, two measures connected to this issue, might be affected differently depending on flight modes and operational conditions which in turn hamper AI-based algorithms to yield accurate prediction on engine failures. In general, previous studies lack comprehensive analysis on anomaly prediction in piston engine aircraft using modern machine learning solutions. Furthermore, abrupt variation in aircraft sensors' data and noise result in either overfitting or unfavourable performance by such techniques. This work aims at studying conventional machine learning and deep learning models to foretell the possibility of engine flameout using engine oil and cylinder head temperatures of a widely used Textron Lycoming IO-540 six-cylinder piston engine. This is achieved through pre-processing the data extracted from the aircraft's real-time flight data recorder followed by prediction using specially designed multi-modal regularised Long Short-Term Memory network to enhance generalisation and avoid overfitting on highly variable data. The proposed architecture yields improved results with root mean square error of 0.55 and 3.20 on cylinder head and engine oil temperatures respectively averaged over three case studies of five different flights. These scores are significantly better i.e., up to 84% as compared to other popular machine learning predictive approaches including Random Forest, Decision Tree Regression, Artificial Neural Networks and vanilla Long Short-Term Memory networks. Through performance evaluation, it can be established that the proposed system is capable of predicting engine flameout 2 minutes ahead and is suitable for integration with the software system of aircraft's engine control unit.

Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy.

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours. This remarkable reduction in turnaround time (from 48 hours to 10 hours), achieved by minimizing the culturing period, offers a game-changing advantage for clinical applications. Our study involves a dataset comprising 400 bacterial samples (200 E. coli, 100 Klebsiella pneumoniae, and 100 Pseudomonas aeruginosa) with an impressive 96% accuracy in the taxonomic classification at the species level and up to 82% accuracy in bacterial susceptibility to various antibiotics.

Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics.

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.