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.

Integrating Porous Silicon Nanoneedles within Medical Devices for Nucleic Acid Nanoinjection.

Porous silicon nanoneedles can interface with cells and tissues with minimal perturbation for high-throughput intracellular delivery and biosensing. Typically, nanoneedle devices are rigid, flat, and opaque, which limits their use for topical applications in the clinic. We have developed a robust, rapid, and precise substrate transfer approach to incorporate nanoneedles within diverse substrates of arbitrary composition, flexibility, curvature, transparency, and biodegradability. With this approach, we integrated nanoneedles on medically relevant elastomers, hydrogels, plastics, medical bandages, catheter tubes, and contact lenses. The integration retains the mechanical properties and transfection efficiency of the nanoneedles. Transparent devices enable the live monitoring of cell-nanoneedle interactions. Flexible devices interface with tissues for efficient, uniform, and sustained topical delivery of nucleic acids ex vivo and in vivo. The versatility of this approach highlights the opportunity to integrate nanoneedles within existing medical devices to develop advanced platforms for topical delivery and biosensing.

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.

The effects of the first wave of COVID-19 restrictions on physical activity: a longitudinal study from "step into health" program in Qatar.

Introduction: The COVID-19 pandemic led to restrictions that prevented physical activity in public places. This study sought to conduct a comprehensive longitudinal analysis of how lockdown policies in an Arabian Gulf country influenced the patterns of physical activity during first wave. Methods: In a longitudinal study design, members of the ongoing "Step into health" community-based health promotion program who provided valid pedometer data from January to August 2020, covering pre, during and post-covid first wave period met the inclusion criteria. Results: 420 (76.7% men, 13.8% ≤40 years) were included in the study. Overall, significant decline in daily step counts was recorded (-1,130 ± SE302) after the implementation of lockdown policies (p < 0.001). When the restrictions were removed, the steps per day were still lower compared to pre-covid for men (-910 ± SE610, p = 0.017) and among individuals with normal BMI (-1,304 ± SE409, p = 0.004). The lockdown in Qatar did not significantly affect women and individuals with obesity who already had lower daily steps pre-covid. Discussion: The present study confirms immediate decline in daily steps imposed indirectly through the COVID-19 lockdown measures. Participants with higher physical activity levels pre-covid experienced significant decline in step count during and even after restrictions were uplifted.

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.

Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning.

Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.

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%.

Examining the intervening effect of earning management in governance mechanism and financial misstatement with lens of SDG and ESG: a study on non-financial firms of Pakistan.

In today's corporate world, a company's long-term viability and prosperity depend on its corporate governance practices. The present study investigates the interplay between financial misrepresentation, earnings management, and corporate governance within the context of Pakistan. To estimate the financial data of enterprises obtained from non-financial organizations listed on the Pakistan Stock Exchange a panel regression analysis was conducted. The analysis covered the time from 2009 to 2020 and employed quantitative data. The findings of the study show that the different aspects of corporate governance mechanisms have varying levels of influence. Specifically, remuneration paid to directors had a significant impact on financial misstatement, while the size of the board strongly impacts the earning management. The financial misstatement was also found affected by the earning management. The M score (statistical model used to predict the probability of financial misstatement) positively influenced when board diligence was incorporated in the mediation of earning management. It is important to note that this study only considers the internal governance mechanisms of firms, suggesting that future research could benefit from the inclusion of external governance mechanisms for a more holistic model. This study is aligned with the ESG's governance aspects and SDG-17, providing valuable insights for specialists, financial backers, policymakers, and experts. The results of this study catalyze further research in this area and can aid in achieving SDG 17 by raising awareness of the significance of good governance practices, ethical reporting that leads to sustainable firm performance, and ensuring long-term economic growth and development.

CRISPR/Cas-Assisted Nanoneedle Sensor for Adenosine Triphosphate Detection in Living Cells.

The clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein (Cas) (CRISPR/Cas) systems have recently emerged as powerful molecular biosensing tools based on their collateral cleavage activity due to their simplicity, sensitivity, specificity, and broad applicability. However, the direct application of the collateral cleavage activity for in situ intracellular detection is still challenging. Here, we debut a CRISPR/Cas-assisted nanoneedle sensor (nanoCRISPR) for intracellular adenosine triphosphate (ATP), which avoids the challenges associated with intracellular collateral cleavage by introducing a two-step process of intracellular target recognition, followed by extracellular transduction and detection. ATP recognition occurs by first presenting in the cell cytosol an aptamer-locked Cas12a activator conjugated to nanoneedles; the recognition event unlocks the activator immobilized on the nanoneedles. The nanoneedles are then removed from the cells and exposed to the Cas12a/crRNA complex, where the activator triggers the cleavage of an ssDNA fluorophore-quencher pair, generating a detectable fluorescence signal. NanoCRISPR has an ATP detection limit of 246 nM and a dynamic range from 1.56 to 50 µM. Importantly, nanoCRISPR can detect intracellular ATP in 30 min in live cells without impacting cell viability. We anticipate that the nanoCRISPR approach will contribute to broadening the biomedical applications of CRISPR/Cas sensors for the detection of diverse intracellular molecules in living systems.

Fabrication of Temperature- and Humidity-Independent Silver Nanoparticle's Carbon Composite-Based Strain Sensor Through Additive Manufacturing Process.

A highly sensitive low-cost strain sensor was fabricated in this research study based on microdispensing direct write (MDDW) technique. MDDW is an additive manufacturing approach that involves direct deposition of functional material to the substrate. The devices were printed directly onto a polymeric substrate by optimizing the fabrication parameters. A composite of silver and carbon was used as active sensor material where both materials in the composite have opposite resistance temperature coefficients. The ratio of materials in the composite was selected so that the effect of temperature on the resistance of overall composite was canceled out. This resulted in achieving temperature compensation or inherent independence of the strain sensor resistance on temperature without requiring any additional sensors and components. The sensor was further encapsulated by electrospray deposition, which is also an additive manufacturing approach, to eliminate the effect of humidity as well. Electrical and morphological characterizations were performed to investigate the output response of the sensors and their physical and structural properties. An analog signal conditioning circuit was developed for seamless interfacing of the sensor with any electronic system. The sensor had an excellent gauge factor of 45 and a strain sensitivity of 45 Ω/µÉ› that is higher than most of the conventional strain sensors. The sensor's response showed excellent temperature and humidity compensation reducing the relative effect of temperature on the resistance by ∼99.5% and humidity by ∼99.8%.

Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning.

Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.

Updates on the Status of Carbapenem-Resistant Enterobacterales in Lebanon.

Carbapenem-resistant Enterobacterales (CRE) pathogens have been increasingly isolated and reported in Lebanon. Several studies have been published over the last two decades about the CRE situation in the country. However, compared to the worldwide data, those studies are scarce and mostly restricted to single center studies. In this review, we aim to present a comprehensive and reliable report illustrating the current situation regarding CRE in Lebanon. Variable studies have shown an increasing pattern of carbapenem resistance in Enterobacterales since the first reports of CRE isolates in 2007 and 2008. Escherichia coli and Klebsiella pneumoniae were the most detected ones. The OXA-48 class D carbapenemases were the most prevalent carbapenemases among CRE isolates. Moreover, the emergence of other carbapenemases like the NDM class B carbapenemase has been noticed. Strict infection control measures in hospitals, including the identification of CRE carriers, are needed in Lebanese hospitals since carriage is a potential risk for the spread of CRE in healthcare settings. The dissemination of CRE in the community is noticed and attributed to multiple causes, such as the refugee crisis, water contamination, and antimicrobial misuse. In conclusion, strict infection control measures in healthcare settings, in addition to accurate antimicrobial stewardship program implementation, are urgently needed.

Detection of extended-spectrum ß-lactamase-producing bacteria isolated directly from urine by infrared spectroscopy and machine learning.

Resistant bacteria have become one of the leading health threats in the last decades. Extended-spectrum ß-lactamase (ESBL) producing bacteria, including Escherichia (E.) coli and Klebsiella (K.) pneumoniae (the most frequent ones), are a significant class out of all resistant infecting bacteria. Due to the widespread and ongoing development of ESBL-producing (ESBL+) resistant bacteria, many routinely used antibiotics are no longer effective against them. However, an early and reliable ESBL+ bacteria detection method will improve the efficiency of treatment and limit their spread. In this work, we investigated the capability of infrared (IR) spectroscopy based machine learning tools [principal component analysis (PCA) and Random Forest (RF) classifier] for the rapid detection of ESBL+ bacteria isolated directly from patients' urine. For that, we examined 1881 E. coli samples (416 ESBL+ and 1465 ESBL-) and 609 K. pneumoniae samples (237 ESBL+ and 372 ESBL-). All samples were isolated directly from the urine of midstream patients. This study revealed that within 40 min of receiving the patient urine it is possible to determine the infecting bacterium as E. coli or K. pneumoniae with 95% success rate while it was possible to determine the ESBL+E. coli and ESBL+K. pneumoniae with 83% and 78% accuracy rates, respectively.

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.

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%.

Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest.

Bacterial infections cause serious illnesses that are treated with antibiotics. Currently used methods for detecting bacterial antibiotic susceptibility consume 48-72 h, leading to overuse of antibiotics. Thus, many bacterial species have acquired resistance to a broad range of available antibiotics. There is an urgent need to develop efficient methods for rapid determination of bacterial susceptibility to antibiotics. The combination of machine learning and Fourier-transform infrared (FTIR) spectroscopy has generated a promising diagnostic approach in medicine and biology. Our main goal is to examine the potential of FTIR spectroscopy to determine the susceptibility of urinary tract infection-Proteus mirabilis to a specific range of antibiotics, within about 20 min after 24 h culture and identification. We measured the infrared spectra of 489 different P. mirabilis isolates and used random forest to analyze this spectral database. A classification success rate of ~84% was achieved in differentiating between the resistant and sensitive isolates based on their susceptibility to ceftazidime, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, ciprofloxacin, gentamicin, and sulfamethoxazole antibiotics in a time span of 24 h instead of 48 h.

Fast identification and susceptibility determination of E. coli isolated directly from patients' urine using infrared-spectroscopy and machine learning.

For effective treatment, it is crucial to identify the infecting bacterium at the species level and to determine its antimicrobial susceptibility. This is especially true now, when numerous bacteria have developed multidrug resistance to most commonly used antibiotics. Currently used methods need âˆ¼ 48 h to identify a bacterium and determine its susceptibility to specific antibiotics. This study reports the potential of using infrared spectroscopy with machine learning algorithms to identify E. coli isolated directly from patients' urine while simultaneously determining its susceptibility to antibiotics within âˆ¼ 40 min after receiving the patient's urine sample. For this goal, 1,765 E. coli isolates purified directly from urine samples were collected from patients with urinary tract infections (UTIs). After collection, the samples were tested by infrared microscopy and analyzed by machine learning. We achieved success rates of âˆ¼ 96% in isolate level identification and âˆ¼ 84% in susceptibility determination.

Nanoneedles Induce Targeted siRNA Silencing of p16 in the Human Corneal Endothelium.

Nanoneedles can target nucleic acid transfection to primary cells at tissue interfaces with high efficiency and minimal perturbation. The corneal endothelium is an ideal target for nanoneedle-mediated RNA interference therapy aimed at enhancing its proliferative capacity, necessary for tissue regeneration. This work develops a strategy for siRNA nanoninjection to the human corneal endothelium. Nanoneedles can deliver p16-targeting siRNA to primary human corneal endothelial cells in vitro without toxicity. The nanoinjection of siRNA induces p16 silencing and increases cell proliferation, as monitored by ki67 expression. Furthermore, siRNA nanoinjection targeting the human corneal endothelium is nontoxic ex vivo, and silences p16 in transfected cells. These data indicate that nanoinjection can support targeted RNA interference therapy for the treatment of endothelial corneal dysfunction.

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%.