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Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources, such as patient records, medical imaging, wearable devices, and clinical research surveys. This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare. It evaluates the effectiveness of federated learning strategies in the field of healthcare. It offers a systematic analysis of federated learning in the healthcare domain, encompassing the evaluation metrics employed. In addition, this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies. [ABSTRACT FROM AUTHOR]

Introduction: Chronic hepatitis C-therapy is limited to the combined use of PEG-ylated interferons and ribavirin. Side effects of this therapy include retinal changes that manifest with bleeding, cotton wool spots and/or thrombosis., Patient: A 51-year-old man presented with a sudden decrease of visual acuity. Chronic hepatitis C was known for the duration of 6 months and treated for 3 months with a combined therapy of PEG-interferon alpha2B and ribavirin. A sudden visual loss occurred in the right eye and bilateral visual field defects were detected. Ophthalmoscopic examination revealed bilateral papilloedema. After withdrawal of PEG-interferon alpha2B and ribavirin, a standard haemodilution therapy was started without functional improvement. After papilloedema regression, a beginning secondary atrophy of the optic nerve was diagnosed bilaterally. Vision did not improve., Conclusion: The ocular side effects of combined hepatitis C therapy with PEG-interferon alpha2B and ribavirin range from mild retinal changes to severely impaired vision. Treatment should be carried out in co-operation with an ophthalmologist. With respect to the uncertain effectiveness of hepatitis C therapy with interferon alpha2B and ribavirin, therapy should be stopped as soon as severe ophthalmological complications occur.

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., (© 2022 The Authors. Journal of Biophotonics published by Wiley-VCH GmbH.)

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The extensive and improper use of antibiotics has led to a dramatic increase in the frequency of antibiotic resistance among human pathogens, complicating infectious disease treatments. In this work, a method for rapid antimicrobial susceptibility testing (AST) is presented using microstructured silicon diffraction gratings integrated into prototype devices, which enhance bacteria‐surface interactions and promote bacterial colonization. The silicon microstructures act also as optical sensors for monitoring bacterial growth upon exposure to antibiotics in a real‐time and label‐free manner via intensity‐based phase‐shift reflectometric interference spectroscopic measurements (iPRISM). Rapid AST using clinical isolates of Escherichia coli (E. coli) from urine is established and the assay is applied directly on unprocessed urine samples from urinary tract infection patients. When coupled with a machine learning algorithm trained on clinical samples, the iPRISM AST is able to predict the resistance or susceptibility of a new clinical sample with an Area Under the Receiver Operating Characteristic curve (AUC) of ∼ 0.85 in 1 h, and AUC > 0.9 in 90 min, when compared to state‐of‐the‐art automated AST methods used in the clinic while being an order of magnitude faster. [ABSTRACT FROM AUTHOR]

In a pandemic era, rapid infectious disease diagnosis is essential. Surface-enhanced Raman spectroscopy (SERS) promises sensitive and specific diagnosis including rapid point-of-care detection and drug susceptibility testing. SERS utilizes inelastic light scattering arising from the interaction of incident photons with molecular vibrations, enhanced by orders of magnitude with resonant metallic or dielectric nanostructures. While SERS provides a spectral fingerprint of the sample, clinical translation is lagged due to challenges in consistency of spectral enhancement, complexity in spectral interpretation, insufficient specificity and sensitivity, and inefficient workflow from patient sample collection to spectral acquisition. Here, we highlight the recent, complementary advances that address these shortcomings, including (1) design of label-free SERS substrates and data processing algorithms that improve spectral signal and interpretability, essential for broad pathogen screening assays; (2) development of new capture and affinity agents, such as aptamers and polymers, critical for determining the presence or absence of particular pathogens; and (3) microfluidic and bioprinting platforms for efficient clinical sample processing. We also describe the development of low-cost, point-of-care, optical SERS hardware. Our paper focuses on SERS for viral and bacterial detection, in hopes of accelerating infectious disease diagnosis, monitoring, and vaccine development. With advances in SERS substrates, machine learning, and microfluidics and bioprinting, the specificity, sensitivity, and speed of SERS can be readily translated from laboratory bench to patient bedside, accelerating point-of-care diagnosis, personalized medicine, and precision health. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Physik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis – linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample. [ABSTRACT FROM AUTHOR]

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags., (© 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A Raman-based, strain-independent, semi-automated method is presented that allows the rapid (<3 hours) determination of antibiotic susceptibility of bacterial pathogens isolated from clinical samples. Applying a priori knowledge about the mode of action of the respective antibiotic, we identified characteristic Raman marker bands in the spectrum and calculated batch-wise weighted sum scores from standardized Raman intensity differences between spectra of antibiotic exposed and nonexposed samples of the same strains. The lead substances for three relevant antibiotic classes (fluoroquinolone ciprofloxacin, third-generation cephalosporin cefotaxime, ureidopenicillin piperacillin) against multidrug-resistant Gram-negative bacteria (MRGN) revealed a high sensitivity and specificity for the susceptibility testing of two Escherichia coli laboratory strains and 12 clinical isolates. The method benefits from the parallel incubation of control and treated samples, which reduces the variance due to alterations in cultivation conditions and the standardization of differences between batches leading to long-term comparability of Raman measurements., (© 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A microfluidic device enabling the isolation and concentration of bacteria for analysis by confocal Raman spectroscopy is presented. The glass-on-silicon device employs a tapered chamber surrounded by a 500 nm gap that serves to concentrate cells at the chamber apex during sample perfusion. The sub-micrometer gap retains bacteria by size exclusion while allowing smaller contaminants to pass unimpeded. Concentrating bacteria within the fixed volume enables the use of single-point confocal Raman detection for the rapid acquisition of spectral signatures for bacteria identification. The technology is evaluated for the analysis of E. cloacae, K. pneumoniae, and C. diphtheriae, with automated peak extraction yielding distinct spectral fingerprints for each pathogen at a concentration of 103 CFU/ml that compare favorably with spectra obtained from significantly higher concentration reference samples evaluated by conventional confocal Raman analysis. The nanogap technology offers a simple, robust, and passive approach to concentrating bacteria from dilute samples into well-defined optical detection volumes, enabling rapid and sensitive confocal Raman detection for label-free identification of focused cells. [ABSTRACT FROM AUTHOR]

In this study, Raman spectroscopy is used to characterize different formulations of active pharmaceutical ingredient (API) of Lornoxicam, a nonsteroidal anti‐inflammatory drug for qualitative and quantitative analyses. The Raman spectral characteristic peaks associated with API and excipients are identified, which increase or decrease as their concentrations change in different formulations. The spectral response was analyzed qualitatively and quantitatively by using chemometrics tools such as principal component analysis (PCA) model and partial least squares regression (PLSR) analysis model, respectively. The characteristic Raman spectral features associated with the different formulations of API of Lornoxicam were used to differentiate different groups of Raman spectral data using PCA. By using PLSR model, root mean square error of cross validation (RMSECV) was found to be 0.76 mg during the development of prediction model and according to the (R2) value, the goodness of the model was found to be 0.99. Furthermore, the concentration of API in the blind sample was measured. In comparison to the 15/35 mg (w/w), this concentration was projected to be 14.13/35 mg (w/w). Raman spectroscopy along with PCA and PLSR analyses is a useful tool for determining the concentration of Lornoxicam in pharmaceutical samples, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

The hospital environment is a field with unique microbiological characteristics. Pathogens evolve and spread in different areas of the hospital affecting patients and staff. In addition, a constant circulation of pathogens between the hospital and the outer environment is ongoing. In this context, an extensive management is required in order to minimize the harmful effect of hospital flora on humans as well as the natural environment. Raman spectroscopy has been shown to be an effective tool for this purpose since it is applicable in a variety of biological samples ranging from the patient samples to the hospital's wastewater. It enables the detection of infection, bacterial species identification, antimicrobial resistance determination, epidemiological typing as well as infection control, mandatory for hospital management. The biggest advantages of this analytical method are the limited time and minimal resources required in its workflow. In the current review the Raman‐based analytical methods that have been developed over years in the field of microbiology are presented and their applicability in the different areas of the hospital environment is discussed. [ABSTRACT FROM AUTHOR]

Mutations in β-amyloid (Aβ) precursor protein (APP) cause familial Alzheimer's disease (AD) probably by enhancing Aβ peptides production from APP. An antibody targeting Aβ (aducanumab) was approved as an AD treatment; however, some Aβ antibodies have been reported to accelerate, instead of ameliorating, cognitive decline in individuals with AD. Using conditional APP mutations in human neurons for perfect isogenic controls and translational relevance, we found that the APP-Swedish mutation in familial AD increased synapse numbers and synaptic transmission, whereas the APP deletion decreased synapse numbers and synaptic transmission. Inhibition of BACE1, the protease that initiates Aβ production from APP, lowered synapse numbers, suppressed synaptic transmission in wild-type neurons, and occluded the phenotype of APP-Swedish–mutant neurons. Modest elevations of Aβ, conversely, elevated synapse numbers and synaptic transmission. Thus, the familial AD-linked APP-Swedish mutation under physiologically relevant conditions increased synaptic connectivity in human neurons via a modestly enhanced production of Aβ. These data are consistent with the relative inefficacy of BACE1 and anti-Aβ treatments in AD and the chronic nature of AD pathogenesis, suggesting that AD pathogenesis is not simply caused by overproduction of toxic Aβ but rather by a long-term effect of elevated Aβ concentrations. Synapses from Sweden: The so-called Swedish mutation (K595N/M596L) located in exon 16 of the β-amyloid (Aβ) precursor protein (APP) gene causes familial Alzheimer's disease. The mutation has been associated with increased Aβ production due to abnormal APP cleavage. To better understand the role of the mutation in neuronal functions, here, Zhou et al. generated human neurons bearing the Swedish mutation. The mutation caused an increase in the number of functional synapses that was abrogated by inhibition of the APP cleaving enzyme BACE1. Furthermore, whereas APP deletion reduced synapse number, restoring physiological Aβ amounts in APP-deleted neurons elevated synapse number and synaptic transmission, suggesting that at physiological level, Aβ might be synaptogenic. [ABSTRACT FROM AUTHOR]

Enterobacteriaceae are the leading cause of urinary tract infections, and include pathogens such as E. coli, K. pneumoniae and P. mirabilis. Due to their similarity, the correct identification of these pathogens is difficult and time-consuming. Raman spectroscopy has been demonstrated extensively as a tool for rapid microbiological differentiation. However, for pathogenic Enterobacteriaceae the application of Raman spectroscopy has been particularly challenging. In this study, two promising methods for Raman-based microbiological diagnostics were compared for differentiating Enterobacteriaceae. Spectra were collected from single-cells with Raman microspectroscopy and from colonies on agar with an NIR Raman fiber-probe. A comprehensive dataset of spectra from 8 different, clinically relevant, genera was collected. Visually, the spectra obtained from both methods presented little difference between the genera. For classification, single cell analysis yielded limited results, while the fiber-probe spectra enabled perfect classification of all 16 isolates. Moreover, the model was validated on new replicates and 15/16 strains were correctly identified (94% overall accuracy). This is the first study to focus on the closely related Enterobacteriaceae, who have previously been avoided or differentiated poorly. It shows how, with the correct spectroscopic setup, even challenging questions in clinical microbiology can be resolved with Raman spectroscopy, highlighting the method's potential for improving patient care. [ABSTRACT FROM AUTHOR]

Sterility testing is a laborious and slow process to detect contaminants present in drug products. Raman spectroscopy is a promising label-free tool to detect microorganisms and thus gaining relevance as a future alternative culture-free method for sterility testing in the pharmaceutical industry. However, reaching detection limits similar to standard procedures while keeping a high accuracy remains challenging, due to weak bacterial Raman signals. In this work, we show a new non-invasive approach focusing on detection of different bacteria in concentrations below 100 CFU per ml within drug product containers using Raman spectroscopy and multivariate data analysis. Even though Raman spectra from drug product with and without bacteria are similar, a partial least squared discriminant analysis (PLS-DA) model shows great performance to distinguish samples with bacterial contaminants in concentrations down to 10 CFU per ml. We used spiked samples with bacterial spores for model validation achieving a detection accuracy of 99%. Our results indicate the great potential of this rapid, and cost-effective approach to be used in quality control in the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

The pyroelectric effect converts thermal energy into usable electrical energy. It is an ideal and effective strategy to enhance the surface-enhanced Raman scattering (SERS) detection sensitivity by tuning the electromagnetic fields. In this work, a plasmonic metal/ferroelectric hybrid SERS substrate was fabricated by integrating a pyroelectric functional layer composed of BiFeO3 with an electron/heat conduction layer of carbon nanofibers (CNFs) using the electrospinning technique. The initial layer was followed by a plasmonic layer of Ag nanowires, which were uniformly spin-coated on BiFeO3/CNFs. By introducing periodically switched near-infrared (NIR) light, the BiFeO3/CNFs could effectively convert the thermal energy induced by light into pyroelectric charge, which could adjust the electron densities of Ag, and in turn boost the electromagnetic fields at the "hot spots". The hybrid Ag–BiFeO3/CNFs were used to test the Raman signals of methylene blue (MB), and a 5.6-fold enhancement of the SERS signal was achieved compared with that noted without the pyroelectric effect and the following parameters were obtained: an ultra-low detection of 10−13 M, a high enhancement factor of 8.3 × 109, and excellent reproducibility with an RSD of 4.08%. The theoretical calculations revealed that the excellent sensitivity was mainly ascribed to the synergistic light-induced pyroelectric and plasmon effects. In addition, this substrate was also used for the detection of ciprofloxacin (CIP) in milk with a limit of detection as low as 10−10 M. Furthermore, the Ag–BiFeO3/CNFs were used to successfully in situ monitor the photocatalytic reduction of p-nitrophenol (p-NTP) to 4,4′-dimercaptoazobenzene (DMAB) by SERS in real time. This work provides an efficient strategy to develop highly sensitive SERS substrates through pyroelectric-plasmonic hybrid structures, and opens a new avenue for the development of energy harvesters that can be used for extensive applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Emerging Economies & Policy is the property of JOEEP: Journal of Emerging Economies & Policy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV‐Resonance Raman spectroscopy (UVRR) and single‐cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine‐learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications. [ABSTRACT FROM AUTHOR]

While Raman spectroscopy can provide label‐free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain‐specific (and function‐specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model. [ABSTRACT FROM AUTHOR]

Antimicrobial susceptibility testing (AST) is a key measure in clinical microbiology laboratories to enable appropriate antimicrobial administration. During an AST, the determination of the minimum inhibitory concentration (MIC) is an important step in which the bacterial responses to an antibiotic at a series of concentrations obtained in separate bacterial growth chambers or sites are compared. However, the preparation of different antibiotic concentrations is time-consuming and labor-intensive. In this paper, we present a microfluidic device that generates a concentration gradient for antibiotics that is produced by diffusion in the laminar flow regime along a series of lateral microwells to encapsulate bacteria for antibiotic treatment. All the AST preparation steps (including bacterium loading, antibiotic concentration generation, buffer washing, and isolated bacterial growth with an antibiotic) can be performed in a single chip. The viable bacterial cells in each microwell after the antibiotic treatment are then quantified by their surface-enhanced Raman scattering (SERS) signals that are acquired after placing a uniform SERS-active substrate in contact with all the microwells. For proof-of-concept, we demonstrated the AST performance of this system on ampicillin (AMP)-susceptible and -resistant E. coli strains. Compared with the parameters for conventional AST methods, the AST procedure based on this chip requires only 20 μL of bacteria solution and 5 h of operation time. This result indicates that this integrated system can greatly shorten and simplify the tedious and labor-intensive procedures required for current standard AST methods. [ABSTRACT FROM AUTHOR]

Biological materials found at a crime scene are crucially important evidence for forensic investigation because they provide contextual information about a crime and can be linked to the donor-individuals through combination with DNA analysis. Applications of vibrational spectroscopy to forensic biological analysis have been emerging because of its advantageous characteristics such as the non-destructivity, rapid measurement, and quantitative evaluation, compared to most current methods based on histological observation or biochemical techniques. This review presents an overview of recent developments in vibrational spectroscopy for forensic biological analysis. We also emphasize chemometric techniques, which can elicit reliable and advanced analytical outputs from highly complex spectral data from forensic biological materials. The analytical subjects addressed herein include body fluids, hair, soft tissue, bones, and bioagents. Promising applications for various analytical purposes in forensic biology are presented. Simultaneously, future avenues of study requiring further investigation are discussed. [ABSTRACT FROM AUTHOR]

Biofilm‐related infections occur quite frequently in hospital settings and require rapid diagnostic identification as they are recalcitrant to antibiotic therapy and make special treatment necessary. One of the standard microbiological in vitro tests is the crystal violet test. It indirectly determines the amount of biofilm by measuring the optical density (OD) of the crystal violet‐stained biofilm matrix and cells. However, this test is quite time‐consuming, as it requires bacterial cultivation up to several days. In this study, we correlate fast Raman spectroscopic read‐out of clinical Staphylococcus aureus isolates from 47 patients with different disease background with their biofilm‐forming characteristics. Included were low (OD < 10), medium (OD ≥ 10 and ≤20), and high (OD > 20) biofilm performers as determined by the crystal violet test. Raman spectroscopic analysis of the bacteria revealed most spectral differences between high and low biofilm performers in the fingerprint region between 750 and 1150 cm−1. Using partial least square regression (PLSR) analysis on the Raman spectra involving the three categories of biofilm formation, it was possible to obtain a slight linear correlation of the Raman spectra with the biofilm OD values. The PLSR loading coefficient highlighted spectral differences between high and low biofilm performers for Raman bands that represent nucleic acids, carbohydrates, and proteins. Our results point to a possible application of Raman spectroscopy as a fast prediction tool for biofilm formation of bacterial strains directly after isolation from the infected patient. This could help clinicians make timely and adapted therapeutic decision in future. [ABSTRACT FROM AUTHOR]