Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood.

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

When can we reconstruct the ancestral state? Beyond Brownian motion.

Reconstructing the ancestral state of a group of species helps answer many important questions in evolutionary biology. Therefore, it is crucial to understand when we can estimate the ancestral state accurately. Previous works provide a necessary and sufficient condition, called the big bang condition, for the existence of an accurate reconstruction method under discrete trait evolution models and the Brownian motion model. In this paper, we extend this result to a wide range of continuous trait evolution models. In particular, we consider a general setting where continuous traits evolve along the tree according to stochastic processes that satisfy some regularity conditions. We verify these conditions for popular continuous trait evolution models including Ornstein-Uhlenbeck, reflected Brownian Motion, bounded Brownian Motion, and Cox-Ingersoll-Ross.

Prevalence, species distribution and antifungal susceptibility of Candida albicans causing vaginal discharge among symptomatic non-pregnant women of reproductive age at a tertiary care hospital, Vietnam.

BACKGROUND: Vaginal candidiasis is frequent in women of reproductive age. Accurate identification Candida provides helpful information for successful therapy and epidemiology study; however, there are very limited data from the Vietnam have been reported. This study was performed to determine the prevalence, species distribution of yeast causing vaginal discharge and antifungal susceptibility patterns of Candida albicans among symptomatic non-pregnant women of reproductive age. METHODS: Vaginal discharge samples were collected from 462 women of reproductive age in Hanoi, Vietnam between Sep 2019 and Oct 2020. Vaginal swabs from these patients were examined by direct microscopic examination (10% KOH). CHROMagar™ Candida medium and Sabouraud dextrose agar supplemented with chloramphenicol (0.5 g/l) were used to isolate yeast, and species identification was performed using morphological tests and molecular tools (PCR and sequencing). Antifungal susceptibility testing was determined according to the Clinical and Laboratory Standards Institute guidelines (M27-A3 and M27-S4). RESULTS: The prevalence of vaginal yeast colonization in non-pregnant women was 51.3% of 462 participants. Nine different yeast species were identified. Among these isolates, C. albicans (51.37%) was the most frequent, followed by C. parapsilosis (25.88%), C. glabrata (11.37%), C. tropicalis (4.31%), C. krusei (3.92%), C. africana (1.57%), Saccharomyces cerevisiae (0.78%), C. nivariensis (1 isolates, 0.39%), and C. lusitaniae (1 isolates, 0.39%), respectively. Among C. albicans, all 46 isolates were 100% susceptible to micafungin, caspofungin, and miconazole. The susceptibility rates to amphotericine B, 5-flucytosine, fluconazole, itraconazole and voriconazole were 95.65, 91.30, 91.30, 82.61 and 86.95%, respectively. CONCLUSIONS: The prevalence of VVC among symptomatic non-pregnant women of reproductive age in Vietnam was higher than many parts of the world. The high frequency of non-albicans Candida species, which were often more resistant to antifungal agents, was a notable feature. Resistance rates of vaginal C. albicans isolates to antifungal agents was low. Our findings suggest that continued surveillance of changes in species distribution and susceptibility to antifungals should be routinely screened and treated.

Theoretical Study of the Binding of the Thiol-Containing Cysteine Amino Acid to the Silver Surface Using a Cluster Model.

Computational approaches within the framework of density functional theory (DFT) were employed to elucidate the binding mechanism of the cysteine amino acid on silver nanoparticles using several small silver clusters Agn with n = 2-10 as surface models. The long-range corrected LC-BLYP functional and correlation consistent basis sets cc-pVTZ-PP and cc-pVTZ were used to determine the structural features, energetics, and spectroscopic and electronic properties of the resulting complexes. In vacuum and highly acidic conditions, cysteine molecules prefer to adsorb on silver clusters via their amine group. In aqueous solution, the thiolate head turns out to be the most energetically favorable binding site. The cysteine affinity of silver clusters is greatly altered in different conditions, i.e., acidic solution < vacuum < aqueous solution, and is strongly dependent on the cluster size. As compared to free clusters, the frontier orbital energy gap of the ones capped by cysteine is significantly improved, which corresponds to stronger stability, especially in aqueous solution. The analysis of frontier orbitals also reveals that both forward and backward electron donations exhibit comparable contributions to the enhancement of stabilizing interactions. As for an application, a chemical enhancement mechanism of the surface-enhanced Raman scattering (SERS) procedure of cysteine by silver clusters was also analyzed.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths annually and 10 million new cases reported each year. The causative organism, Mycobacterium tuberculosis (Mtb) can take nearly 40 days to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the MtB complex strain Bacillus Calmette Guerin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin and amikacin, as well as a pan susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and in patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all 5 BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Porphyrin derivatives inhibit tumor necrosis factor α-induced gene expression and reduce the expression and increase the cross-linked forms of cellular components of the nuclear factor κB signaling pathway.

The transcription factor nuclear factor κB (NF-κB) is activated by proinflammatory cytokines, such as tumor necrosis factor α (TNF-α) and Toll-like receptor (TLR) ligands. Screening of NPDepo chemical libraries identified porphyrin derivatives as anti-inflammatory compounds that strongly inhibited the up-regulation of intercellular adhesion molecule-1 (ICAM-1) expression induced by TNF-α, interleukin-1α, the TLR3 ligand, and TLR4 ligand in human umbilical vein endothelial cells. In the present study, the mechanisms of action of porphyrin derivatives were further elucidated using human lung adenocarcinoma A549 cells. Porphyrin derivatives, i.e., dimethyl-2,7,12,18-tetramethyl-3,8-di(1-methoxyethyl)-21H,23H-porphine-13,17-dipropionate (1) and pheophorbide a (2), inhibited TNF-α-induced ICAM-1 expression and decreased the TNF-α-induced transcription of ICAM-1, vascular cell adhesion molecule-1, and E-selectin genes. 1 and 2 reduced the expression of the NF-κB subunit RelA protein for 1 h, which was not rescued by the inhibition of proteasome- and lysosome-dependent protein degradation. In addition, 1 and 2 decreased the expression of multiple components of the TNF receptor 1 complex, and this was accompanied by the appearance of their cross-linked forms. As common components of the NF-κB signaling pathway, 1 and 2 also cross-linked the α, ß, and γ subunits of the inhibitor of NF-κB kinase complex and the NF-κB subunits RelA and p50. Cellular protein synthesis was prevented by 2, but not by 1. Therefore, the present results indicate that porphyrin derivative 1 reduced the expression and increased the cross-linked forms of cellular components required for the NF-κB signaling pathway without affecting global protein synthesis.

Optoelectronic Properties of Nitrogen-Doped Hexagonal Graphene Quantum Dots: A First-Principles Study.

Graphene quantum dots have been widely studied owing to their unique optical, electrical, and optoelectrical properties for various applications in solar devices. Here, we investigate the optoelectronic properties of hexagonal and nitrogen-doped graphene quantum dots using the first-principles method. We find that doping nitrogen atoms to hexagonal graphene quantum dots results in a significant red shift toward the visible light range as compared to that of the pristine graphene quantum dots, and the doped nitrogen atoms also induce a clear signature of anisotropy of the frontier orbitals induced by the electron correlation between the doped nitrogen atoms and their adjacent carbon atoms. Moreover, time-dependent density functional theory calculations with the M06-2X functional and 6-311++G(d,p) basis set reproduce well the experimental absorption spectra reported recently. These results provide us with a novel approach for more systematic investigations on next-generation solar devices with assembled quantum dots to improve their light selectivity as well as efficiency.

Atosiban improves implantation and pregnancy rates in patients with repeated implantation failure.

This prospective cohort study examined the effects of atosiban on uterine contraction, implantation rate (IR) and clinical pregnancy rate (CPR) in women undergoing IVF/embryo transfer. The study enrolled 71 women with repeated implantation failure (RIF; no pregnancies from an average of 4.8 previous embryo transfers with a mean of 12 top-quality embryos) undergoing IVF/embryo transfer using cryopreserved embryos. The total atosiban dose was 36.75 mg. The IR per transfer and CPR per cycle were 13.9% and 43.7%, respectively. Before atosiban, 14% of subjects had a high frequency of uterine contractions (≥ 16 in 4 min). The frequency of uterine contractions was reduced after atosiban. This reduction of uterine contractions in all cycles was significant overall (from 6.0 to 2.6/4 min; P<0.01), in cycles with ≥ 16 uterine contractions/4 min at baseline (from 18.8 to 5.1; P<0.01) and in cycles with <16 uterine contractions/4 min (from 3.9 to 2.2; P<0.01). IR and CPR improved in all subjects, irrespective of baseline uterine contraction frequency. This is the first prospective study showing that atosiban may benefit subjects with RIF undergoing IVF/embryo transfer with cryopreserved embryos. One potential mechanism is the reduction in uterine contractility, but others may also contribute. Many women undergoing IVF/embryo transfer do not achieve the outcome that they wish for. In fact, IVF/embryo transfer repeatedly fails for a subgroup of patients. There are limited options available to help these patients with repeat implantation failure (RIF) to become pregnant. This study looks at one potential new treatment option for women who experience RIF. A drug called atosiban is already being used to delay premature labour by inhibiting contractions of the uterus. In this study, atosiban was given at the time of embryo transfer to women undergoing IVF/embryo transfer. Atosiban reduced the number of uterine contractions in these patients and also increased the implantation and pregnancy rates. The pregnancy rate went from zero to 43.7%. The beneficial effects of atosiban were observed not only in patients who had a high frequency of uterine contractions at baseline but also in those who had a low frequency. These findings suggest that atosiban may have other benefits in addition to its effect on contractions of the uterus. More studies are required to find out exactly how atosiban works and to increase the knowledge of its use in patients with RIF undergoing IVF/embryo transfer.

Cafestol analogues from Coffea canephora: in vitro inhibition and molecular docking to α-glucosidase.

A new cafestol-type diterpenoid, 5ß-hydroxy-2-oxocafestol named coffecanepholide C (1) along with three known diterpenoids including cafestol (2), tricalysiolide A (3) and atractyligenin (4) were identified from the Coffea canephora trunks collected at Lam Dong province, Vietnam. Their structures were elucidated by HRESIMS and NMR spectroscopic analysis (1H, 13C, COSY, HSQC, HMBC, and NOESY NMR) as well as compared with data in the literature. Upon evaluation of the α-glucosidase inhibitory activity, compound 1 (IC50 = 142.0 ± 0.2 µM) and compound 3 (IC50 = 286.2 ± 1.2 µM) exhibited activity against α-glucosidase, while structures 2 and 4 showed no activity. Furthermore, the docking simulations revealed that the carbonyl groups of compounds 1 and 3 formed hydrogen bonds with Lys506 residue at the enzyme pocket, which may induce the α-glucosidase inhibitory activity.

Serotype distribution and antibiotic resistance of Streptococcus pneumoniae isolates collected from unvaccinated children with pneumonia at a province in central Vietnam.

Background and Objectives: Identification of pnemococcal serotypes and antimicrobial resistance provides helpful information for the use of suitable vaccines and antibiotics; however, very limited data is available on these issues in Vietnam. The present study aimed to find the serotype distribution and drug resistance patterns of Streptococcus pneumoniae isolated from unvaccinated children less than 5 years of age with pneumonia at a province in centre Vietnam. Materials and Methods: A total of 126 clinical pnemococcal strains isolated from unvaccinated children less than 5 years of age with pneumonia at the Nghe An province, Vietnam between Nov 2019 and Mar 2021. All strains were identified using conventional microbiological method, VITEK® 2 Compact system, specific PCR and sequencing. The serotypes and antimicrobial resistance patterns of pnemococcal strains were determined using the multiplex PCR assays and VITEK® 2 Compact system. Results: The results showed that, eight different pneumococcal serotypes were identified. The most common serotypes were 19F (67.46%), followed by 23F (10.32%), 19A (9.52%), 6A/B (3.17%), 15A (2.38%), 9V (3.17%), 11A (1.59%) and 14 (0.80%), respectively. More than half of the pneumococcal strains were non-susceptible to penicillin. The resistance rate to ceftriaxone and cefotaxime were 41.3% and 50.8%. The percentage of pneumococci strains resistant to clarithromycin, azithromycin, erythromycin, cotrimoxazole, tetracyclin, and clindamycin were more than 93% of all strains. All pneumococcal serotypes were highly resistant to clarithromycin, azithromycin, erythromycin, cotrimoxazole, and clindamycin. Conclusion: Our findings showed high antibiotic resistance rates of the strains causing pneumococcal pneumonia, mostly macrolide resistance, among unvaccinated children.

Successful Pregnancy Following Preimplantation Genetic Diagnosis of Adrenoleukodystrophy by Detection of Mutation on the ABCD1 Gene.

BACKGROUND: Adrenoleukodystrophy (ALD) is a rare sex-linked recessive disorder that disrupts adrenal gland function and the white matter of the nervous system. According to recent epidemiological statistics, up to this moment, the disease is the most recorded peroxisomal disorder. ABCD1 is a gene related to ALD, with more than 850 unique mutations have been reported. Early diagnosis of the disease would help to consult families with ALD to plan for interventions to prevent passing along the pathogenic mutations to their children. MATERIAL AND METHODS: A heterozygous ABCD1 gene mutation related to ALD found in a Vietnamese woman was used to design primers for the polymerase chain reaction (PCR) to amplify the segment spanning the mutation. Then, combining sequencing methods for the PCR products, especially Sanger sequencing and next-generation sequencing (NGS), a protocol was developed to detect mutations on the ABCD1 gene to apply for the DNA samples of in-vitro fertilization (IVF) embryos biopsied at the blastocyst stage to screen for pathogenic alleles. RESULTS: The established protocol for PGD of ALD detected mutant alleles in 5/8 embryos (62.5%), while the remaining 3 embryos (37.5%) did not carry any mutation. One of the 3 embryos was transferred, and a healthy female baby was born after a full-term pregnancy. CONCLUSION: The developed protocol was helpful for the preimplantation genetic diagnosis process to help families with the monogenic disease of ALD but wish to have healthy children.

Structure and stability of aluminium doped lithium clusters (Li(n)Al(0/+), n = 1-8): a case of the phenomenological shell model.

Quantum chemical calculations are performed on the aluminium doped lithium clusters Li(n)Al at both neutral and cationic states using the DFT/B3LYP and CCSD(T) methods in conjugation with the aug-cc-pVaZ (a = D,T,Q) basis sets. The global minima are located and the growth mechanism is established. The electronic structure, geometrical parameters and energetic properties, such as average binding energy E(b), second difference of energy Δ(2)E, adiabatic and vertical ionization energy, and dissociated enthalpy, are evaluated using the coupled-cluster CCSD(T) method, whose energies are extrapolated to the complete basis set limit (CBS). The high stability of Li(5)Al, Li(7)Al, Li(6)Al(+) and Li(8)Al(+) that have the "magic numbers" of valence electrons, can be understood using the phenomenological shell model.

Multiple structure tracing in 3D electron micrographs.

Automatic interpretation of Transmission Electron Micrograph (TEM) volumes is central to advancing current understanding of neural circuitry. In the context of TEM image analysis, tracing 3D neuronal structures is a significant problem. This work proposes a new model using the conditional random field (CRF) framework with higher order potentials for tracing multiple neuronal structures in 3D. The model consists of two key features. First, the higher order CRF cost is designed to enforce label smoothness in 3D and capture rich textures inherent in the data. Second, a technique based on semi-supervised edge learning is used to propagate high confidence structural edges during the tracing process. In contrast to predominantly edge based methods in the TEM tracing literature, this work simultaneously combines regional texture and learnt edge features into a single framework. Experimental results show that the proposed method outperforms more traditional models in tracing neuronal structures from TEM stacks.