Rapid Detection of Single Bacteria Using Filter-Array-Based Hyperspectral Imaging Technology.

Rapid and accurate detection of bacterial pathogens is crucial for preventing widespread public health crises, particularly in the food industry. Traditional methods are often slow and require extensive labeling, which hampers timely responses to potential threats. In response, we introduce a groundbreaking approach using filter-array-based hyperspectral imaging technology, enhanced by a super-resolution demosaicking technique. This innovative technology streamlines the detection process and significantly enhances the resolution of mosaic hyperspectral imaging. By utilizing a snapshot hyperspectral camera with a 15 ms integration time, it facilitates the identification of bacteria at the single-cell level without requiring chemical labels. The integration of a 3D convolutional neural network optimizes the recognition of pathogenic bacteria, achieving an impressive accuracy of 91.7%. Our approach dramatically improves the efficiency and effectiveness of bacterial detection, providing a promising solution for critical applications in public health and the food industry.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks.

IMPORTANCE: The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

Infrared spectra of the SARS-CoV-2 spike receptor-binding domain: Molecular dynamics simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread around the world rapidly, which seriously threatens to human health and safety. The rapid detection of the virus in the early stage is very important to prevent the cross infection and transmission. It is also a key link in the post-treatment examination. This paper has explored the infrared (IR) spectra of spike protein receptor-binding domain (RBD) for SARS-CoV-2 using molecular dynamics simulations, and the absorption bands are assigned. The calculated IR spectra of water and insulin are compared with that measured in the related literatures. The results showed that O-H stretching vibration generated a strong absorption band located around 3591 cm-1, the oscillator strength of 310 K is slightly higher than that at 298 K. The absorption peaks have a small red shift or blue shift with the change of temperature. As a theoretical basis for the optical detection of SARS-CoV-2 virus, this work will play a positive role in promoting the development of new virus detection technology.

Conditional Generative Adversarial Network for Spectral Recovery to Accelerate Single-Cell Raman Spectroscopic Analysis.

Raman spectroscopy is a powerful tool to investigate cellular heterogeneity. However, Raman spectra for single-cell analysis are hindered by a low signal-to-noise ratio (SNR). Here, we demonstrate a simple and reliable spectral recovery conditional generative adversarial network (SRGAN). SRGAN reduced the data acquisition time by 1 order of magnitude (i.e., 30 vs 3 s) by improving the SNR by a factor of ∼6. We classified five major foodborne bacteria based on single-cell Raman spectra to further evaluate the performance of SRGAN. Spectra processed using SRGAN achieved an identification accuracy of 94.9%, compared to 60.5% using unprocessed Raman spectra. SRGAN can accelerate spectral collection to improve the throughput of Raman spectroscopy and enable real-time monitoring of single living cells.

Insights into the conformation changes of SARS-CoV-2 spike receptor-binding domain on graphene.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely spread in the world, causing more than two million deaths and seriously threatening human life. Effective protection measures are important to prevent the infection and spreading of the virus. To explore the effects of graphene on the virus adsorption and its biological properties, the adsorption process of the receptor binding domain (RBD) of SARS-CoV-2 on graphene has been investigated by molecular dynamics simulations in this paper. The results show that RBD can be quickly adsorbed onto the surface of graphene due to π - π stacking and hydrophobic interactions. Residue PHE486 with benzene ring has stronger adsorption force and the maximum contact area with graphene. Graphene significantly affects the secondary structure of RBD area, especially on the three key sites of binding with human ACE2, GLY476, PHE486 and ASN487. The binding free energy of RBD and graphene shows that the adsorption is irreversible. Undoubtedly, these changes will inevitably affect the pathogenicity of the virus. Therefore, this study provides a theoretical basis for the application of graphene in the protection of SARS-CoV-2, and also provides a reference for the potential application of graphene in the biomedical field.

On-chip monolithic Fourier transform spectrometers assisted by cGAN spectral prediction.

Silicon photonic spatial heterodyne Fourier transform spectrometers (SH-FTSs) are attractive with chip-scale monolithic arrays of imbalanced Mach-Zehnder interferometers; however, there exist optical path difference (OPD) errors from the inevitable fabrication imperfection, which will severely distort the retrieved spectra. In this Letter, we propose that a predictive model can be created for rapid and accurate spectral recovery based on the conditional generative adversarial network (cGAN) featuring strong input-on-output supervision, instead of both complicated physical OPD modification and time-consuming iterative spectral calculation. As a demonstration, cGAN spectral prediction was performed for our previously presented dual-polarized SH-FTS with large OPD errors [Opt. Lett.44, 2923 (2019)OPLEDP0146-959210.1364/OL.44.002923]. Due to the strong noise-resistant capability, the cGAN-predicted spectra can stay reliable, even though the signal-to-noise ratio of acquired interferograms dramatically drops from 1000 to 100, implying a lower limit of detection.

Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks.

Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Speeding up Raman spectral imaging by the three-dimensional low rank estimation method.

Raman spectral imaging has been widely used as a very important analytical tool in various fields. For obtaining the high spectral signal-to-noise ratio Raman images, the long integration time is necessary, which is placing a limit on the application of Raman spectral imaging. We introduce a simple and feasible numerical method of the Three-dimensional Low Rank Estimation (3D-LRE), which can speed up the data acquisition process of the Raman spectral imaging. The spectral signal-to-noise ratio of the Raman images can be increased by over 75 times and the speed of the data acquisition can be improved by over 30 times. By combining with line-scan or multifocus-scan techniques, the Raman images can be obtained in a few seconds.

High-resolution broadband sum frequency generation vibrational spectroscopy using intrapulse interference.

We theoretically propose a new approach to obtain high-spectral-resolution sum frequency generation (SFG) vibrational spectra using intrapulse interference. By introducing a π-step phase modulation to the broadband 800 nm pulse, the broadband 800 nm laser pulse splits into two distinguishable pulses in the time domain with a fixed time delay. The resolution of the intrapulse interference SFG can be better than 1 cm-1 and is limited only by the spectral resolution of the spectrometer. This approach can accurately retrieve the amplitude and the relative phase of vibrational peaks. Additionally, the sensitivity of SFG is enhanced by adopting femtosecond IR and femtosecond visible pulses.

Enhancing the signal-to-noise ratio of FTIR spectrometers by a digital J-Stop.

Fourier transform infrared (FTIR) spectrometers have been widely used as very important analytical tools in various fields. Owing to the Jacquinot Stop (J-Stop), high throughput is a widely recognized advantage inherent to Fourier transform interferometers. However, there is a fundamental trade-off between the throughput and spectral resolution, which is primarily affected by the size of the J-Stop. So far, no effective optimization methods have been provided to break the trade-off. In this paper, we introduce a numeric technique of the digital J-Stop, which has been experimentally validated using the FTIR spectra collected from a commercial spectrometer. The result shows that the throughput can be increased by ~12 times, while the spectral resolution is also improved. In this way, the signal-to-noise ratio (SNR) gets improved by ~3 times.

Improving the resolution and the throughput of spectrometers by a digital projection slit.

The contradiction between spectral resolution and throughput for the optical spectrometers is still a problem that needs to be solved. We introduce a simple and feasible method of the digital projection slit (DPS), which can improve both the spectral resolution and throughput of the optical spectrometer. The DPS spectrum is accurate and reliable without using the optical transfer function (OTF) of the optical spectrometer. The method has been successfully applied in the fiber spectrometer and the Raman spectrometer. The resolution of the recovered spectra can be increased by ~3 times and the throughput can be increased by ~5 times.

Improvement of Orbitofrontal Cortex Function Associated with Blephrospasm Symptom Remission.

OBJECTIVE: To determine whether orbitofrontal cortex (OFC) function improves with blepharospasm (BSP) symptom remission using a verbal fluency task and near-infrared spectroscopy (NIRS). METHODS: Nineteen BSP patients and 9 healthy controls (HCs) matched by gender and education were examined using NIRS. The BSP patients were divided into 2 groups based on the onset or remission of BSP symptoms. A covariance analysis was conducted to analyze the differences among the 3 groups to avoid the influence of different ages. The least significant difference was used to process the post hoc test. RESULTS: The hemoglobin concentration and cerebral blood flow of the bilateral orbitofrontal area (channels 27, 31, 34, 37, and 39) were not significantly different between the BSP remission and HC groups (p > 0.05); however, both groups were significantly increased compared with the BSP onset group (BSP remission group vs. BSP onset group: p = 0.003, p = 0.018, p = 0.013, p = 0.001, and p = 0.011, respectively; BSP remission group vs. BSP onset group: p = 0.037, p = 0.044, p = 0.023, p = 0.016, and p = 0.025, respectively). CONCLUSION: This is the first investigation to control for symptom stages in BSP patients examined via NIRS. Cognitive ability and OFC function improve with BSP symptom remission. Thus, the OFC may be inter-connected with motor and cognitive symptoms in BSP.

[Investigation on under-reported deaths in Xuanwei Yunnan province, during 2011-2013].

OBJECTIVE: To evaluate the completeness of the death registration system, so as to understand the death patterns in Xuanwei. METHODS: The investigation on under-reported deaths was conducted in 30 villages selected with a multi-stage random sampling strategy. Participants were asked about changes of their family members (family members born or dead) during past 3 years with door to door visit. Then, death cases obtained in our investigation were matched with those from routine death registration system and under-reported rate of deaths during 2011-2013 was calculated employing capture-recapture method. RESULTS: Total under-reported rate of deaths was 31.88%. For people aged between 0-14, 15-39, 40-69 and 60 above, under-reported rates of death were 33.35%, 34.93%, 29.10%, and 32.88%, respectively. And they were 31.72% and 32.02% for males and females, respectively. There was no significant difference shown in under-reported rates among deaths in different age groups (χ² = 7.24, P = 0.065) and genders (χ² = 0.06, P = 0.803). The under-reported rates in high-mortality, medium-mortality and low-mortality regions were 17.48%, 38.01%, and 36.22%, respectively with a significant difference (χ² = 213.25, P < 0.001). Death in local regions with mortality rate higher than 600.00/10(5), between 400.00/105 and 600.00/105 and lower than 400.00/105 were adjusted with under-reported rates in three regions above respectively. The total adjusted morality rate in Xuanwei during 2011-2013 was 776.47/105. For males and females, they were 918.73/105 and 617.46/105, respectively. CONCLUSION: Overall under-reported rate of death was high in death registration system in Xuanwei. It was necessary to adjust mortality data reported with under-reported rate of death to estimate death patterns in this area.

Long-term cognitive effects of electroconvulsive therapy in major depressive disorder: A systematic review and meta-analysis.

OBJECTIVE: Electroconvulsive therapy (ECT) is endorsed as a principal treatment approach for major depressive disorder (MDD) worldwide. Despite prior studies highlighting potential short-term cognitive deficits post-ECT, the debate regarding its long-term implications persists. This study endeavors to elucidate the reasons for this contention using an evidence-based approach. METHODS: This investigation, meticulously aligned with PRISMA guidelines, was prospectively enlisted on PROSPERO (CRD42023439259). A comprehensive search was performed across various databases, including PubMed, Cochrane Library, Web of Science, Embase, SCOPUS, PsycINFO, CINAHL Plus, and OpenGrey. This review, traversing the literature from inception until June 2023, encapsulated 10 studies (five RCTs and five quasi-experimental studies) involving a cohort of 868 individuals diagnosed with major depressive disorder. RESULTS: The meta-analysis revealed that the persistent discourse on ECT-induced long-term cognitive impairment chiefly emanates from the inadequacies in the specificity and sensitivity of conventional assessment instruments. Conversely, subgroup analyses showed that cognitive impairment in ECT, as gauged by the nascent assessment tool, Electroconvulsive Therapy Cognitive Assessment (ECCA) (SMD = -0.94, 95 % CI [-1.33, -0.54], p < 0.00001), exerted a detrimental influence on the long-term trajectory of individuals with MDD. Notably, there was an adverse effect of ECT on the subdomain of long-term learning cognitive abilities in patients with MDD (SMD = -0.37, 95 % CI [-0.55, -0.18], p < 0.0001). Contrarily, memory (SMD = 0.16, 95 % CI [-0.02, 0.34], p = 0.08), attention (SMD = 0.23, 95 % CI [-0.07, 0.54], p = 0.14), language (SMD = -0.10, 95 % CI [-0.25, 0.05], p = 0.19), spatial perception, and orientation (SMD = -0.04, 95 % CI [-0.28, 0.20], p = 0.75) exhibited no significant detriments. Intriguingly, ECT showed favorable effects on executive function and processing speed among patients with MDD (SMD = 0.52, 95 % CI [0.29, 0.74], p < 0.00001). CONCLUSION: This meta-analysis underscores ECCA's superior sensitivity of the ECCA compared to the MMSE or MoCA in detecting cognitive changes in patients with post-ECT MDD. Following Electroconvulsive Therapy (ECT), deterioration was observed in overall cognitive function and learning capabilities, while memory, attention, language, and spatial perception remained stable. Notably, enhancements were discerned in executive function and processing speed, which not only augmented academic perspectives but also steered the formulation of international clinical guidelines, accentuating the progressive role of ECT in the therapeutic approach to MDD.

Rare-earth-doped indium oxide nanosphere-based gas sensor for highly sensitive formaldehyde detection at a low temperature.

Formaldehyde (HCHO) is widely viewed as a carcinogenic volatile organic compound in indoor air pollution that can seriously threaten human health and life. Thus, there is a critical need to develop gas sensors with improved sensing performance, including outstanding selectivity, low operating temperature, high responsiveness, and short recovery time, for HCHO detection. Currently, doping is considered an effective strategy to raise the sensing performance of gas sensors. Herein, various rare earth elements-doped indium oxide (RE-In2O3) nanospheres were fabricated as gas sensors for improved HCHO detection via a facile and environmentally solvothermal method. Such RE-In2O3 nanosphere-based sensors exhibited remarkable gas-sensing performance, including a high selectivity and stability in air. Compared with pure, Yb-, Dy-doped In2O3 and different La ratios doped into In2O3, 6% La-doped In2O3 (La-In2O3) nanosphere-based sensors demonstrated a high response value of 210 to 100 ppm at 170 °C, which was around 16 times higher than that of the pure In2O3 sensor, and also exhibited a detection limit of 10.9 ppb, and a response time of 30 s to 100 ppm HCHO with a recovery time of 160 s. Finally, such superior sensing performance of the 6% La-In2O3 sensors was proposed to be attributed to the synergistic effect of the large specific surface area and enhanced surface oxygen vacancies on the surface of In2O3 nanospheres, which produced chemisorbed oxygen species to release electrons and provided abundant reaction sites for HCHO gas. This study sheds new light on designing nanomaterials to build gas sensors for HCHO detection.

Anti-Na+/K+-ATPase DR antibody attenuates UUO-induced renal fibrosis through inhibition of Na+/K+-ATPase α1-dependent HMGB1 release.

Reduced Na+/K+-ATPase (NKA) activity and NKAα1 expression are engaged in the pathologies of renal diseases. NKA-mediated Src activation is not the only reason for NKA-related renal fibrosis. In this study, we found that genetic reduction of NKAα1 exhibited exacerbated tubulointerstitial lesions and fibrosis in the UUO mice model. Activation of NKAα1 with an antibody against the extracellular DR region of the NKAα1 subunit (DRm217) prevented UUO-induced tubulointerstitial lesions, preserved kidney function, and decrease renal fibrosis. Further studies revealed that NKAα1 deficiency mice exhibited high inflammation factors expression when they suffered UUO surgery, compared with NKAα1+/+ (WT) mice. DRm217 alleviated inflammatory cell infiltration, suppress NF-κB phosphorylation, and decreased inflammatory factors expression in the UUO mice model. Released HMGB1 can trigger the inflammatory response and contribute to renal fibrosis. Knockdown of NKA in renal tubular cells or in NKAα1+/- mice was associated with more susceptibility to HMGB1 release in the UUO mice model. DRm217 exerted its antifibrotic effect via inhibiting HMGB1 release. Furthermore, AMPK activation participates in the effect of DRm217 on inhibiting HMGB1 release. Our findings suggest that NKAα1 is a regulator of renal fibrosis and its DR-region is a novel target on it.

Characteristics of psychomotor retardation distinguishes patients with depression using multichannel near-infrared spectroscopy and finger tapping task.

BACKGROUND: Psychomotor retardation (PMR) is frequently noted as a characteristic feature of major depressive disorder (MDD). In patients with depression, it is characterized by retardation of speech, emotion, thinking, and cognition. This study explored the activation pattern of the prefrontal cortex (PFC) during the finger-tapping task (FTT) in subjects with MDD, aiming to provide additional understanding on the connection between PMR and PFC activation pattern in depression through the use of near-Infrared Spectroscopy (NIRS). We hypothesized that, through use of NIRS during the FTT, motor retardation in depression would generate a distinct PFC activation pattern, allowing for differentiation between patients with MDD and healthy controls (HCs). METHODS: Thirty-five patients with MDD and thirty-nine HCs underwent NIRS evaluation during performance of the FTT. The FTT included both left-finger tapping and right-finger tapping performed by a computer screen. Each participant was assessed using a 45-channel NIRS and various clinical scales. FINDINGS: During the left-FTT, the left orbitofrontal cortex (OFC) showed higher oxy-hemoglobin (Oxy-Hb) activation in the MDD group when compared to the HCs. During the right-FTT, the right dorsolateral prefrontal cortex (DLPFC) demonstrated lower Oxy-Hb activation, and the dorsomedial prefrontal cortex (DMPFC) showed higher Oxy-Hb activation in the MDD group versus the HC group. CONCLUSION: Our results demonstrated different activation patterns of the PFC between the MDD and HC groups, using FTT as a motor performance task. In particular, the OFC, the DLPFC and the DMPFC areas hold promise as new useful sites for such differentiation in future investigations.

Multi-dentate chelation induces fluorescence enhancement of pyrene moiety for highly selective detection of Fe(III).

Fluorescence enhancement has great advantages and various promising applications for a fluorescent molecular probe, which shows high sensitivity and high selectivity. In this report, a novel pyrene-based fluorescent probe with multidentate ligand (PPD) was synthesized for highly selective detection of Fe(III), which exhibited great fluorescence enhancement response upon the addition of Fe(III) in aqueous solution of pH 3.5 ~ 7.5, with a detection limit of 115 nM. The probe also has good water solubility and photostability. Further fluorescence titration confirmed 1:1 stoichiometric ratio for the probe PPD-Fe(III), which can be applied for quantification of Fe(III). The probe was validated for ferric detection in real water samples by spike and recovery test.

Prefrontal cortex alterations in major depressive disorder, generalized anxiety disorder and their comorbidity during a verbal fluency task assessed by multi-channel near-infrared spectroscopy.

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are frequently comorbid with each other, and both associated with substantial cognitive impairments; however, it is still unclear whether their impairments are neurobiologically similar or distinct. This study aims to investigate the cognitive functions of the prefrontal cortex (PFC) in patients with MDD and GAD during the verbal fluency task (VFT) using functional near-infrared spectroscopy (fNIRS). Fifty-two patients with MDD, fifty-one patients with GAD, fifty-two patients with the comorbidity of MDD and GAD (CMG), and forty-seven healthy controls (HC) participated in the study. Significant hypoactivation in the left ventrolateral and the left dorsolateral PFC was common in all patient groups when compared to HCs, suggesting a shared etiology. Furthermore,  MDD patients showed significant hypoactivation at the right frontal pole cortex (FPoC) when compared to HCs and significant hypoactivation at the middle FPoC when compared to the CMG patients. Our work is the first fNIRS study to reveal the shared and unique neurobiological profiles of MDD, GAD and their comorbidity under the same standard experimentation condition, suggesting fNIRS holds promise as an adjutant to assist clinical diagnosis.

Differentiating between bipolar and unipolar depression using prefrontal activation patterns: Promising results from functional near infrared spectroscopy (fNIRS) findings.

BACKGROUND: Bipolar depression (BD) is a unique, severe and prevalent mental illness that shares many similarities in symptoms with unipolar depression (UD). Improving precision of their diagnoses would enhance treatment outcome and prognosis for both conditions. This study aims to provide evidence from functional Near-Infrared Spectroscopy (fNIRS) as a potential tool to differentiate UD and BD based on their differences in hemodynamic change in the prefrontal cortex during verbal fluency tasks (VFT). METHODS: We enrolled 179 participants with clinically confirmed diagnoses, including 69 UD patients, 68 BD patients and 42 healthy controls(HC). Every participant was assessed using a 45-channel fNIRS and various clinical scales. FINDINGS: Compared with HC, region-specific fNIR leads show UD patients had significant lower hemodynamic activation in 4 particular pre-frontal regions: 1) the left dorsolateral prefrontal cortex (DLPFC), 2) orbitofrontal cortex (OFC), 3) bilateral ventrolateral prefrontal cortex (VLPFC) and 4) left inferior frontal gyrus (IFG). In contrast, BD vs. HC comparisons showed only significant lower hemodynamic activation in the LIFG area. Furthermore, compared to BD patients, UD patients showed decreased hemodynamic activation changes in the VLPFC region. CONCLUSION: Our results show significant frontal lobe activation pattern differences between UD and BD groups. fNIRS can be a potential tool to increase diagnostic precision for these conditions. In particular, the VLPFC area holds promise to be a useful site for such differentiation for further investigations.